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Shared Savings Programs in Europe LESSONS FOR THE UNITED STATESDECEMBER202 2Acceptance and use of biosimilars has been rising in the U.S.Biosimilars can enhance the sustainability of the overall healthcare system through savings and increased access.Currently,there are policies under discussion in the U.S.to improve access to biosimilars,and some of these policies include shared savings programs(also known as benefit sharing or gain sharing in other countries).Shared savings programs have been implemented in a number of countries in Europe.While contexts across countries vary,the experience of such programs in other countries can provide lessons for the U.S.In this report,five case studies of shared savings/benefit sharing programs from across Europe are examined using secondary research,expert interviews and IQVIA data.The background to setting up a program is provided for each case study along with the structure of the program and results as stated in secondary literature and as per IQVIA data.Lessons for the U.S.are summarized based on these case studies.This report has been developed independently by the IQVIA Institute for Human Data Science,drawing on IQVIA proprietary data and published literature across selected European countries.Funding for this research and report has been provided by the Biosimilars Forum.Find Out MoreIf you wish to receive future reports from the IQVIA Institute for Human Data Science or join our mailing list,visit iqviainstitute.org.MURRAY AITKENExecutive Director IQVIA Institute for Human Data Science2022 IQVIA and its affiliates.All reproduction rights,quotations,broadcasting,publications reserved.No part of this publication may be reproduced or transmitted in any form or by any means,electronic or mechanical,including photocopy,recording,or any information storage and retrieval system,without express written consent of IQVIA and the IQVIA Institute.IntroductionShared Savings Programs in Europe:Lessons for the United StatesTable of ContentsOverview 2Role of biosimilars in healthcare sustainability 4Biosimilar policies and use of benefit sharing programs 5Benefit sharing(shared savings)programs:Case studies from Europe 9Ireland 10France 13United Kingdom 16Germany 20Italy 24Lessons for the U.S.29Appendix 31Methodologies 36References 37About the authors 39About the Institute 40Biologic drugs have revolutionized treatment of patients over the past two decades,particularly in the fields of serious inflammatory auto-immune diseases.These drugs are products derived from living organisms or their components that treat a number of conditions such as diabetes,cancer,and immune disorders,and they constitute some of the most expensive drugs on the market.Nearly half of all medicine spending(48%)in the U.S.is on biologics,for a total of$259 billion gross expenditure in 2021.Biosimilars offer the potential to optimize sustainability of healthcare systems by reducing costs while ensuring the same quality of care.A biosimilar is a biological medicine highly similar to another already approved biological medicine(the reference medicine)and can compete with original biologic products after the originators period of exclusivity is completed.Biosimilar entry into the market can drive and stimulate competition,resulting in reduction of prices,savings for broader access to existing biologics,additional services,and innovative drugs,and potentially allowing for earlier treatment of more patients due to reduced costs.Europe has adopted a number of policies to increase the use of biosimilars,leading to broader availability and uptake compared to the U.S.Acceptance and use of biosimilars in the U.S.have risen over the past couple of years,however,availability of biosimilars remains lower than in European countries in some cases.While the context in these countries is different from the U.S.,some of the policies adopted in Europe may hold lessons for the U.S.and contribute to optimizing biosimilar use.One of the policies to incentivize physician use of biosimilars that has been commonly used across European countries is benefit sharing programs,also referred to as shared savings programs.Such programs have also been proposed in the U.S.and therefore,understanding the structure and impact of these programs may be helpful to assessing their viability in the U.S.context.While no consensus definition of benefit sharing programs exists,these generally refer to selective contracts at the national/regional/provider level that incorporate elements of sharing of benefits/savings to incentivize the use of off-patent biologics and biosimilars.Based on existing research,such programs have been used in at least 10 countries at a national or regional level.Based on selected case studies,benefit sharing programs have been implemented at a national level in Ireland and France and at a regional level in Germany,Italy,and England(selected case studies,not an exhaustive list).While it is hard to directly correlate the use of biosimilars to any one program or policy due to the presence of several policies and activities,in general,benefit sharing programs which have been part of a comprehensive set of policies for biosimilars-have been associated with increasing use of lower cost biosimilars,leading to savings for the health system.For example,in Ireland,biosimilar use was very low prior to the implementation of the program and has since risen to more than 50%(for molecules included in the program),Biosimilars offer the potential to optimize sustainability of healthcare systems by reducing costs while ensuring the same quality of care.2|Shared Savings Programs in Europe:Lessons for the United StatesOverviewresulting in savings of 22.7 million in the first year of implementation(i.e.,by July 2020).The program also generated 3.6 million as savings that were shared with the specialties to invest back into patient care.Subsequently,biosimilar use relative to total molecule use has increased past 60%in Ireland,leading to$47 million in savings by end of 2021(based on IQVIA data and analysis).Similarly,increases in biosimilar share and associated savings can also be seen in France,England,Germany,and Italy.In Germany,the program was piloted in the Westphalia-Lippe region before being extended to other regions,and Westphalia-Lippe saw faster biosimilar uptake in the initial period compared to other regions.In thCampania region of Italy,a benefit sharing program was implemented along with other biosimilar policies,which has been associated with an increase in biosimilar use;however,comparisons with other regions in Italy are mixed as biosimilar uptake is high across regions,and many regions have targeted biosimilar uptake policies.In the UK,many regional benefit sharing programs have been implemented,resulting in fast uptake/switching to biosimilars.In the case of the U.S.,innovative models which promote cost efficient behavior such as the Oncology Care Model have led to slight increases in biosimilar use(compared to those not taking part in these programs),however the difference for biosimilars launched more recently is not substantial,which suggests that a more biosimilar focused approach may be needed.The experiences of these countries offer lessons for the U.S.as it considers shared savings programs for Medicare Part B.As these examples show,benefit sharing programs can be an approach to increasing biosimilar uptake and subsequently,increasing savings.Greater biosimilar use can reduce overall costs and may increase overall patient access.Pilot studies can be useful in understanding the best approaches to benefit sharing and can ensure that appropriate incentives are provided while physician and patient autonomy in decision-making is maintained.Physician and patient education to increase comfort with biosimilar use and regular communication of the impact of such a policy are important.If the key stakeholders are not comfortable with biosimilar use,such a program may not be successful.Government health ministries and departments and insurers have generally been the central driving forces behind benefit sharing programs.Finally,a shared savings program is one of many other policies that must be considered to optimize an appropriate system for biosimilar use.As an increasing number of lower-cost biosimilars enter the U.S.market,there is a unique opportunity to reduce overall healthcare expenditure while maintaining the sustainability of the overall market.Shared savings models hold the potential to align physician incentives with cost-saving efforts without having an impact on overall healthcare quality.Developing such a model will likely require leadership from CMS and partnership with providers,physicians,and patient advocacy groups.While other policies to encourage biosimilar use will be needed,developing and accessing pilot shared savings models at this stage may help with ensuring that benefits from biosimilars are optimized moving forward.Benefit sharing programs can be an approach to increasing biosimilar uptake and subsequently,increasing savingsiqviainstitute.org|34|Shared Savings Programs in Europe:Lessons for the United StatesRole of biosimilars in healthcare sustainability Biologic drugs constitute some of the most expensive drugs on the market and have been a growing contributor to the overall drug spending in U.S.and Europe,with more than 48%of all drug spending in the U.S.coming from biologics Biosimilars can play a crucial role in maintaining sustainability of healthcare systems by providing savings without compromising on the overall quality of care In general,Europe has witnessed broader and faster uptake of biosimilars compared to the U.S.and may hold lessons for the U.S.on optimizing biosimilar useBiologic drugs have revolutionized treatment of patients over the past two decades,particularly in the fields of serious inflammatory auto-immune diseases.1 Biologic drugs,which are products derived from living organisms or their components,can treat a number of conditions such as diabetes,cancer,and immune disorders.These drugs also constitute some of the most expensive drugs on the market and have been a growing contributor to the overall drug spending in U.S.and Europe.Nearly half of all medicine spending(48%)in the U.S.is on biologics,for a total of$259 billion gross expenditure in 2021(See Exhibit 1).Biologics constitute a large share of Medicare spending as some of Medicare Part Ds highest-expenditure drugs,and all 10 of the highest-expenditure drugs in Medicares Part B program are biologics.2,3Interest in the role of biosimilars in maintaining economic sustainability of the healthcare system has been growing.A biosimilar is a biological medicine highly similar to another already approved biological medicine(the reference medicine)and can compete with original biologic products after the originators period of exclusivity is completed.4 Biosimilar entry in the market can drive and stimulate competition,resulting in reduction of prices,savings for increased access to existing biologics,additional services,and innovative drugs,and potentially allowing for earlier treatment of more patients due to reduced costs.Exhibit 1:Biologic share of overall medicine spending(U.S.,EU)Source:IQVIA MIDAS,Jun 2022.Biologic medicinesOther medicinesEU4 UKQuarterly medicine spending in EU4 UK and U.S.by product type,US$Bn,Q1 2011Q2 2022U.S.50454035302520151050Q1 2017Q1 2011Q1 2012Q1 2013Q1 2014Q1 2022Q1 2015Q1 2016Q1 2018Q1 2019Q1 2020Q1 2021160140120100806040200Q1 2017Q1 2011Q1 2012Q1 2013Q1 2014Q1 2022Q1 2015Q1 2016Q1 2018Q1 2019Q1 2020Q1 2021iqviainstitute.org|5The U.S.developed and adopted legislation for regulating and approving biosimilars in 2009 and expectations at that time were that these biosimilars would lead to billions of dollars of savings annually.5 In reality,biosimilar pathways have taken some time to mature as they faced a few issues(such as regulatory uncertainties,intellectual property issues,coverage and reimbursement,patient/provider knowledge,etc.)that have impacted their uptake and usage.In contrast,many countries across Europe have adopted various policies to enhance the uptake of biosimilars,subsequently leading to savings.Overall,Europe has seen broader approval,launch and uptake of biosimilars compared to the U.S.As of September 2022,86 biosimilar medicines have been approved in Europe(EMA)since 20066 compared to 44 in the U.S.(as of October 2022 based on IQVIA data).The first product approved as a biosimilar in the U.S.was in 2015,almost nine years after the first approval in EMA,contributing to the lag of uptake in biosimilars in the U.S.(Exhibit 2).Major European countries have also seen faster uptake of some biosimilars compared to the U.S.(Exhibit 3),especially those that launched prior to 2019.Acceptance and use of biosimilars in the U.S.has witnessed a rise over the past couple of years(Exhibit 2,3).With many additional biologics losing exclusivity over the next five to ten,it is important that the sustainability of the healthcare system is considered and approaches to optimizing the use of biosimilars are explored.While biosimilar policies in Europe are not directly transferable,they can hold lessons for the U.S.and could help it move toward a more optimal ecosystem for biologics and biosimilar use.7 In particular,policies such as benefit sharing programs(also known as gain sharing agreements or shared savings programs)can align incentives across stakeholders and further encourage market participation and biosimilar uptake.Exhibit 2:Timeline of biosimilar launches Source:IQVIA Institute,Oct 2022.Notes:Biosimilars approved but not yet launched are placed based on expected launch announced by company.Marketer and manufacturer relationships are based on information publicly disclosed in the initial FDA approval letter,product label,or company announcements.Approved etanercept(Enbrel)biosimilars not included as they are not expected to launch until 2029.Launched biosimilarsApproved biosimilars not yet launchedLaunch notannouncedBiosimilars approved and launched in the U.S.somatropinfilgrastiminfliximabinsulin glargineinsulin lispropegfilgrastimepoetin alfabevacizumabtrastuzumabrituximabteriparatideranibizumabadalimumabJan-07Jan-13Jan-14Jan-15Jan-16Jan-17Jan-18Jan-19Jan-20Jan-21Jan-22Jan-236|Shared Savings Programs in Europe:Lessons for the United StatesExhibit 3:Uptake of selected biosimilars(France,Germany,Italy,UK,U.S.)Biosimilar share of volumeSource:IQVIA MIDAS,Jun 2022;IQVIA Institute,Nov 2022.filgrastimFrance,Germany,Italy,UK,U.S.biosimilar uptake curves,defined daily doses,quarters since launchpegfilgrastim100 %069 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54Biosimilar share of volume100 %046810121413579111215Biosimilar share of volumeinfliximabbevacizumab100 %069 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54Biosimilar share of volume100 %046810121413579111215Biosimilar share of volumeFrancerituximabtrastuzumab100 %0468101214161820Biosimilar share of volume100 %0468101214161820U.S.UKGermanyItalyQuarters since launchQuarters since launchQuarters since launchQuarters since launchQuarters since launchQuarters since launchiqviainstitute.org|7Biosimilar policies and use of benefit sharing programs Europe has adopted a number of supply(e.g.,tendering,reference pricing)and demand side(physician incentives,benefit sharing)policies to encourage the use of biosimilars Benefit sharing programs have been implemented in several European countries to incentivize prescribing of biosimilars While no consensus definition of benefit sharing programs exists,these generally refer to selective contracts at the national/regional/provider level that incorporate elements of the sharing of benefits/savings to incentivize the use of off-patent biologics and biosimilars Discussions around a Medicare part B shared savings program(which is another term for benefit sharing)for biosimilars have been gaining increasing attention in the U.S.with legislative bills proposing potential pilot modelsSUPPLY SIDE POLICIES8,9Price link Many countries have chosen to set the price of the biosimilar in relation to the reference medicine.In some cases,the price of a biosimilar medicine has to be set at a certain percentage lower than the reference medicine price.In some other countries,prices are fixed upon negotiation based on several factors(such as any improvements over reference medicine,price across Europe,sales-volume forecast in France)but are generally 10-20%lower than the reference medicine price.While in some countries,prices are freely set.Tendering and reference pricingTendering is commonly used for procurement of biosimilars in an inpatient setting.Tendering can also take place in an outpatient setting in a few countries(e.g.,Denmark,the Netherlands).DEMAND SIDE POLICIESPhysician incentives and substitutionPhysician incentives to prescribe biosimilars have been implemented in various forms across Europe.These incentives include quotas for share of biosimilars on overall prescribing.For example,in Germany,quotas exist for some biosimilars within the context of regionally negotiated economic targets.There can also be quotas for low-cost medicine use,which can include biosimilars,as is the case in Belgium.Pharmacy level substitution for biosimilars remains rare in Europe.In general,physicians are expected to prescribe rationally and to retain final decision-making power on therapeutic choices.Benefit-sharing programsAnother approach to incentivizing rational prescribing of biosimilars by physicians and providers is through the use of benefit sharing programs(also called gain sharing or shared savings programs).These benefit sharing programs can be set up in several different forms and there is no clear consensus on their definition.These programs are a part of a broader biosimilar set of policies.NHS in England refers to them as“Financial arrangements to incentivize the provider to implement processes that can maximize the early adoption and prescribing of biosimilars.”10 Other countries do not provide a clear definition,however,these programs generally refer to selective contracts at the national/regional/provider level that incorporate elements of the sharing of benefits(i.e.,savings or other benefits such as lack of limits to biologic use)to incentivize the use of off-patent biologics and biosimilars.Based on research published in 2022,benefit sharing agreements have generally been used to“(i)set prescription objectives for Best Value Biologics i.e.the most cost effective option out of the set of biologic and biosimilars;(ii)engage prescribers in being compliant with the set objectives;(iii)generate and reinvest savings according to the needs of the stakeholders who produced them;and(iv)establish pathways for savings 8|Report Title:Strapline All In Title Casereinvestment that would fund additional health services and quality-of-care improvements.”11Such agreements have been used extensively in Europe.The UK,Germany and Italy have seen examples of benefit sharing agreements since 2016.In total,based on existing literature,there are more than 10 benefit sharing agreements in Europe at national and regional/provider levels(See Exhibit 4).Experts interviewed in prior research have also suggested such programs as being an effective approach to enhancing the use of biosimilars.9 Discussions around a Medicare part B shared savings program for biosimilars(which would be similar to the benefit sharing programs)have been gathering increasing attention in the U.S.In 2021,Senators Cornyn and Bennet introduced a bill(increasing access to biosimilars act)which includes a pilot program that encourages physicians to prescribe less expensive biosimilars through shared savings.12 Prior articles have discussed possible structures for shared savings program for Medicare Part B.13 Given this background,understanding the experience of European countries with respect to benefit sharing programs and assessing learnings for the U.S.context is critical.The next section covers a set of case studies of benefit sharing programs.The existing literature provides an overview of the structure and impact of these programs,and this background is supplemented with proprietary IQVIA data(where available)and country expert interviews to understand the impact of these programs in further detail.Exhibit 4:Summary table of benefit sharing agreements COUNTRYLEVELTARGET MOLECULESFRANCENational(for selected hospitals)etanercept,adalimumab,insulin glargineIRELANDNationaletanercept,adalimumabPORTUGALNationalall hospital-use molecules exposed to biosimilar competitionENGLANDNational Guideline;Regional implementation(multiple programs)TNFi(etanercept,adalimumab,infliximab),rituximabSCOTLANDNational Guideline;Regional implementation(Lothian,Gramnian)infliximab,adalimumab(IBD patients)WALESCardiff hospitalrituximabGERMANYRegional-Westfalen-Lippe and sick fund Barmer(Subsequently expanded to other regions)TNFi(etanercept,adalimumab,infliximab)ITALYRegional-Campania Regional Health Serviceall hospital-use molecules exposed to biosimilar competitionSWEDENRegional-Skanesomatropin,infliximabNETHERLANDSInsurers and individual hospitalsUnknown Source:Lacosta,Vulto et al,Qualitative Analysis of the Design and Implementation of Benefit-Sharing Programs for Biologics Across Europe,Mar 2022;available at:https:/sharing(shared savings)programs:Case studies from Europe Benefit sharing programs,which are part of a holistic set of biosimilar policies,have been implemented at a national level in Ireland and France and at a regional level in Germany,Italy and England(selected case studies,not an exhaustive list). In Ireland,biosimilar use was very low prior to the implementation of the program and for biosimilars included in the program,use has since risen to more than 50%,resulting in savings of 22.7 million in the first year of implementation In Germany,the program was piloted in Westphalia-Lippe region before being extended to other regions;Westphalia-Lippe saw faster biosimilar uptake in the initial period compared to other regions In the Campania region of Italy,a benefit sharing program was implemented which resulted in an increase in biosimilar use,however,comparisons with other regions are mixed as biosimilar uptake is high across regions,and many regions have targeted biosimilar uptake policies A number of regional benefit sharing programs have been implemented in the UK,resulting in fast uptake/switching to biosimilarsAcross Europe,multiple examples of benefit sharing programs have been identified.These examples vary in terms of the level of implementation(national vs regional),structure of program(level of benefits shared,voluntary pulsory,etc.)and overall uptake objectives.This section summarizes the key available details regarding a few selected programs.These case studies were selected because they represent some of the larger countries where benefit sharing programs have been implemented as well as countries where the most information was accessible(a 2022 article by Lacosta,Vulta et al.provides a comprehensive list of benefit sharing programs).11 While it is hard to directly link increases in biosimilar use to any one policy,an increase in biosimilar uptake was seen in each of these case studies.This section aims to provide data from published literature,government documents,and from IQVIA MIDAS to better understand the potential impact of these programs(see appendix for IQVIA Institute approach to calculating savings).10|Shared Savings Programs in Europe:Lessons for the United StatesCase study:Ireland14 BACKGROUNDBiosimilar uptake had been historically low in Ireland in 2018,a fact that has been acknowledged and discussed by the health ministry.14,15 For example,the biosimilar for etanercept(Benepali)had been available since September 2016,but in its first two years,had only 2%market share.Concerns around the sub-optimal use of biosimilars led the Health Service Executive(i.e.,the public health system in Ireland)to develop a Medicines Management Programme(MMP)roadmap for the prescribing of best-value biological(BVB)medicines in December 2018.The BVB program assessed the biologic/biosimilars available for etanercept and adalimumab based on a number of criteria,including costs,and in May 2019,the MMP published its recommendations indicating that Imraldi and Amgevita(should clinicians wish to prescribe a citrate-free formulation)were the BVBs for adalimumab and Benepali for etanercept.STRUCTURE OF BENEFIT SHARING PROGRAMBased on these BVBs,a benefit sharing program was started in June 2019.This program offered the relevant clinical service at a cost of 500 for each patient who initiated or switched to a BVB medicine.This funding can be invested to further enhance service delivery for patients.The program was led by government bodies and the key stakeholders involved were HSE-MMP and HSE-Primary Care Reimbursement Service(hospital management and clinical departments).The program was implemented at a national level.IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEPublished literature The impact of the first year of the program has been assessed through prior published literature.In the case of adalimumab,8,163 patients were on the reference medicine and 166 were on biosimilars in May 2019(i.e.,prior to the start of the program).By May 2020,more than 3,400 patients were on the BVB for adalimumab.Similarly,in the case of etanercept,4,208 patients were on the reference medicine and 104 were on biosimilars in May 2019.By May 2020,more than 1,800 patients were on the BVB for adalimumab.By July 2020,the total patients on BVB across both molecules was more than 8,500 and biosimilars had a 50%market share by Q3 2020.The total estimated savings due to the use of BVBs in this time frame(June 2019 to July 2020)is 22.7 million,and 3.6 million was provided to specialties as part of the benefit sharing to be invested back.Based on statements by experts,reinvestments have focused on patient care,such as additional equipment(e.g.,ultrasound machines,polar machines),IT development(e.g.,online biologic registry)and telemedicine.Gain-sharing has also enabled extra clinics and extra clinic time to allow for more infusions.Additionally,funding has also been used for hiring local consultants,education programs and infrastructure.These steps can enhance patient care and lead to more prescribers being available for patients,thereby allowing more patients to be seen and cared for.BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE IRELANDiqviainstitute.org|11IQVIA DATASince July 2020,there has been further increase in biosimilar share of the overall adalimumab and etanercept market(Exhibits 5 and 6),with the biosimilar share based on volume reaching 70%and 63%,respectively.Adalimumab has seen a volume increase of 50%since introduction of the program while costs have increased at a slower rate by 19%(at list price level)(Exhibit 5).Quarterly adalimumab defined daily doses in Ireland by product,20152021Exhibit 5:Uptake of adalimumab biosimilars over time and spending on adalimumab biologics and biosimilars over timeYuflymaTOTALIdacioHulioImraldiAmgevitaHumiraDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q12021Start of the program0.5 0.5 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8 0.7 0.8 0.8 0.8 0.8 0.9 0.9 0.9 0.9 0.9 1.0 1.0 1.1 1.2 1.1 1.2 100000000000000000ygYPB7410%4(48ACF%3%8%1%2%2%2%3%4%7%9%1%1%1%1%2NEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE IRELANDSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Quarterly adalimumab spending in Ireland by product,US$Mn,2015202133 21 22 23 24 24 26 26 28 26 29 31 32 33 33 33 32 28 28 28 28 28 26 29 31 32 34 33 YuflymaTOTALIdacioHulioImraldiAmgevitaHumiraUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q12021Start of the program100000000000000000tfWPEA87%3%8$047B%2%6%1%2%2%2%2%4%6%8%9%9%1%1%1%1%1|Shared Savings Programs in Europe:Lessons for the United StatesCumulative biosimilar savings in Ireland,US$Mn,Q2 2019Q4 2021Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Note:In Ireland,the total savings are shown from the start of the Benefit Sharing Program.These savings may not be directly attributable to the benesfit sharing program alone and are driven by a number of biosimilar policies and levers.TOTALetanerceptadalimumabUS$MnQ22019Q32019Q42019Q12020Q22020Q32020Q42020Q12021Q22021Q32021Q420210.8 2.9 6.5 11.8 17.6 21.5 25.0 28.4 32.7 38.6 47.0 0.1 0.51.12.02.93.43.74.04.34.85.70.4 1.74.07.511.515.018.822.727.533.240.2BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE IRELANDExhibit 6:Uptake of etanercept biosimilars over time and spending on etanercept biologics and biosimilars over timeExhibit 7:Cumulative savings due to increase in adalimumab and etanercept biosimilars uptake after program start 0.34 0.37 0.38 0.41 0.37 0.41 0.40 0.40 0.37 0.39 0.40 0.42 0.38 0.40 0.40 0.41 0.37 0.37 0.37 0.38 0.40 0.38 0.40 0.42 0.41 0.42 0.41 0.42 BenepalTOTALEnbrelDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q12021Start of the program100000000000wcUFB9777%1%1%1%2%2%2%2%3#7ETXaccc%Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Sep 2022.Quarterly etanercept defined daily doses in Ireland by product,2015202114 14 15 14 15 15 12 13 12 12 12 12 13 12 11 11 11 11 11 11 11 10 11 12 12 12 12 12 BenepalTOTALEnbrelUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q12021Start of the program1%1%1%1%1%1%2%3!4BQTXYXIFBA0000000000yf%Etanercepts volume has increased by 14%while costs have increased by 7%(at list price level)(Exhibit 6).Utilizing the IQVIA data to estimate savings between Q2 2019 and Q4 2021,the total savings due to biosimilar use(at a list price level)for adalimumab and etanercept are estimated at$47 million(Exhibit 7).Although the savings and usage may not be directly attributable to any one policy,the benefit sharing program has been viewed as a success in Ireland,especially given the low uptake of biosimilars prior to 2019.Quarterly etanercept spending in Ireland by product,US$Mn,20152021iqviainstitute.org|13Case study:France BACKGROUNDSimilar to Ireland,France also experienced slow biosimilar uptake initially.In the case of etanercept,a biosimilar(Benepali)has been available since Q4 2016,but the overall market share of the biosimilar was 10%by Q1 2018(Exhibit 9).To improve the use of biosimilars in 2018 and again in 2021,a national benefit sharing program was launched by the French health insurance system in“Plan dappui la transformation du systme de sant“for etanercept,adalimumab and insulin glargine,and an 80%biosimilar uptake target was set for 202216 for biosimilars that have been in the market for past three years.The stakeholders involved in the benefit sharing program are the regional health agency,the National Health Service Financial Division,and selected hospitals/clinics across France.The national health insurance(Caisse nationale dassurance maladie(CNAM),and ministry of health(Direction de la scurit sociale(DSS)and the Direction gnrale de loffre de soins(DGOS),and regional health agencies(ARS)across France are also leading a pilot model(See below).STRUCTURE OF BENEFIT SHARING PROGRAMThere are currently two programs with benefit sharing arrangements17 Main Program(Started 2018):This program includes all hospitals that have a compulsory contract to improve the quality and efficiency of care(CAQES).Hospital prescribers can get 20%of the difference between the reference drug price and the biosimilar price.This 20%goes directly to the hospital.Experimental Program(Started:October 2018;up to Jan 1,2023):59 NHS hospitals and clinics are voluntarily participating in this pilot program.Hospital prescribers get 30%of the difference between reference drug price and the biosimilar price.The difference is given to the specific clinical department and not the hospital as a whole.The target molecules in both these programs are etanercept,insulin glargine,and adalimumab.More recently,a new program targeting community-based practitioners was also started.Prescribers can share up to 30%in 2022 and then 20%in 2023 of savings on use of biosimilar(for initiation or switching).The molecules included in this program are etanercept,adalimumab,follitropin alpha,enoxaparin,and insulin aspart.IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEPublished LiteraturePublished literature has assessed the initial impact of the programs on uptake of etanercept and adalimumab.In the case of etanercept,in the first 10 months,18 the general program(i.e.,main program)saw an increase of 10.4 percentage points between October 2018 and July 2019(21.3%to 31.7%).The experimental program saw an increase of 19.2 percentage points between October 2018 and July 2019(24.7%to 43.8%).Savings for the experimental program were estimated at 650,000 after 10 months.For adalimumab,after 19 months(March 2019 to Oct 2020),19 the general program saw an increase in biosimilar share from 8.3%to 29.8%while the experimental program saw an increase from 10.3%to 29.8%.IQVIA dataAt a national level,there have been further increases in biosimilar use through 2020 and 2021,with etanercept biosimilar use reaching 43%and adalimumab biosimilar use reaching 35%by end of 2021(Exhibits 8 and 9).While the volume of etanercept use has remained relatively steady,adalimumab use increased by 35tween Q1 2019 and Q4 2021.This increase in biosimilar use is at a faster rate compared to the rate in time period preceding biosimilar entry.BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE FRANCE14|Shared Savings Programs in Europe:Lessons for the United StatesSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Sep 2022.BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE FRANCEQuarterly adalimumab spending in France by product,20152021Exhibit 8:Uptake of adalimumab biosimilars over time and spending on adalimumab biologics and biosimilars over timeYuflymaAmsparityTOTALHyrimozIdacioHulioImraldiAmgevitaHumiraUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q12021Start of the program110 115 119 120 116 118 119 117 113 116 131 134 134 130 131 122 114 116 118 119 121 113 130 138 139 137 127 125 10000000000000000ywuspi%1%2%5%7%8%1%2%3%3%4%4%4%4%4%4%5%5%1%1%2%3%3%3%4%5%5%6%7%1%1%1%1%1%1%2%2%2%2%YuflymaAmsparityTOTALHyrimozIdacioImraldiHulioAmgevitaHumiraUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202110000000000000000 xvtrihge%1%3%6%9%1%2%3%3%4%4%5%6%6%7%7%2%3%3%4%4%5%5%5%5%5%5%5%1%1%1%2%2%3%3%3%1%1%1%2%2%2%2%2%2%2%3.23.43.53.63.63.83.94.03.94.14.34.44.44.54.74.94.95.15.35.45.65.35.76.06.06.26.46.6Start of the programAdalimumab has seen a volume increase of 35%since introduction of program while costs have remained stable(at list price level).Quarterly adalimumab defined daily doses in France by product,20152021iqviainstitute.org|15Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Sep 2022.Note:The data shown here is national level data.The Benefit Sharing Program may not apply to all prescribers at a national level.There may also be additional biosimilar policies being utilized across the country or in specific regions.Hence,a direct correlation of the impact of the policy is challenging,the data shown above should be considered directional.Quarterly etanercept spending in France by product,20152021TOTALNepextoErelziBenepaliEnbrelDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q12021Start of the program2.0 2.1 2.2 2.2 2.1 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.0 2.1 2.1 2.1 2.1 1.9 2.0 2.1 2.0 2.0 2.1 2.0 100000000wsphfedcbYW%1%2%4%5%7$&()0123%1%2%3%4%5%6%6%7%7%7%8%8%9%9%TOTALNepextoErelziBenepaliEnbrelUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202168.2 69.7 67.8 59.9 58.1 61.5 60.6 55.8 53.6 56.8 60.7 59.9 56.2 54.4 52.9 52.0 49.0 49.4 48.8 47.8 44.1 39.0 44.0 45.5 44.4 44.6 44.3 42.4 100000000 xusihgfedba%1%2%3%5%6%8 %&()0%1%1%2%3%4%5%5%6%6%7%7%8%8%9%9%Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Exhibit 10:Cumulative savings due to increase in adalimumab and etanercept biosimilars uptake after program startCumulative biosimilar savings in France,US$Mn,Q4 2018Q4 2021TOTALetanerceptadalimumabUS$MnQ22019Q12019Q42018Q32019Q42019Q12020Q22020Q32020Q42020Q12021Q22021Q32021Q420212.8 8.5 16.1 25.5 35.1 38.7 40.5 41.7 49.7 73.1 101.2 1.4 3.5 9.4 16.8 26.7 42.6 60.2 69.8 76.8 82.7 95.5 124.9 160.3 1.4 3.5 6.5 8.4 10.6 17.0 25.0 31.1 36.3 41.1 45.9 51.8 59.1 BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE FRANCEEtanercepts volume has remained steady while costs have decreased by 30%(at list price level)(Exhibit 9).Utilizing the IQVIA data to estimate savings since the start of the program(i.e.,from Q4 2018 to Q4 2021),the total savings due to biosimilar use(at a list price level)for adalimumab and etanercept are estimated to be$160 million.Biosimilar use may be driven by a number of policies,therefore directly attributing the use to any one policy is challenging,especially since not all hospitals may be a part of the program.However,the benefit sharing program is viewed as a contributor the increase in biosimilar use.(Exhibit 10)Exhibit 9:Uptake of etanercept biosimilars over time and spending on etanercept biologics and biosimilars over timeQuarterly etanercept defined daily doses in France by product,2015202116|Shared Savings Programs in Europe:Lessons for the United StatesBENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE UNITED KINGDOMCase study:United Kingdom BACKGROUNDBenefit sharing programs have been commonly used at a regional(Clinical Commissioning Group)level across England with the NHS supporting their development.A 2018 NHS document reports that that out of the incentive schemes allowed for providers,benefit sharing was used in 75%of the cases.The NHS commissioning framework mentions that these programs should be set up for a short timeframe and utilized to achieve best value biologic targets of 80-90%(90%uptake for treatment-nave patients within three months of biosimilar market entry;80%uptake for established patients within 12 months of biosimilar entry).20A number of these benefit sharing programs have been reported publicly.In this case study,we cover the details of one such program as an example.STRUCTURE OF BENEFIT SHARING PROGRAMIn general,the benefit sharing programs have utilized a 50:50 split of the savings between the CCG and providers.As an example,North Bristol NHS Trust,set up a benefit sharing program with the North Somerset and South Gloucestershire CCG in July 2015 to manage the switch of originator infliximab to biosimilar.21 IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEPublished literature and IQVIA data In the case of North Bristol NHS Trust,a total of 64/65 patients on originator Inflixmab were identified for switching.Fifty-two patients were switched to the biosimilar resulting in savings of GBP 200,000 over three months.In a post switch survey,97%of patients were satisfied with the switch process.The share of savings provided to the trust were reinvested into gastroenterology services and an additional pharmacist was funded for closer monitoring and funding of biologic treatments.iqviainstitute.org|17Exhibit 11:Uptake of adalimumab biosimilars over time and spending on adalimumab biologics and biosimilars over timeHulioTOTALHyrimozIdacioImraldiAmgevitaHumiraDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120213.9 4.5 4.6 4.6 4.6 4.5 4.6 5.0 5.2 5.4 5.3 5.5 5.7 5.7 5.8 5.9 5.6 5.8 6.4 6.5 7.0 7.5 7.4 6.9 8.1 7.9 8.4 8.7 10000000000000000uF0$#!#14567698A%142122210)%2%1%4%6%7%2%5%7%8%9%9%8%8%7%7NEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE UNITED KINGDOMSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Quarterly adalimumab spending in UK by product,20152021IdacioTOTALHulioHyrimozImraldiAmgevitaHumiraDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q12021134 153 161 155 147 144 134 138 144 155 154 164 176 174 170 169 158 159 165 172 186 193 197 189 232 229 239 241 10000000000000000wI3&%#$! 034565889$03102110)(%2%5%2%7%8%9%9%9%8%8%7%7%1%2%4%6%7%Quarterly adalimumab defined daily doses in UK by product,20152021In general,and across the UK as a whole,there has been a high uptake of biosimilars,with more than 80%uptake for each of the Anti-TNF(Tumor Necrosis Factor)biologics(adalimumab,infliximab and etanercept).Adalimumab has seen a volume increase of 55%since the introduction of biosimilars,while costs have increased at a slower rate of 42%(at list price level)(Exhibit 11).18|Shared Savings Programs in Europe:Lessons for the United StatesSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Exhibit 12:Uptake of etanercept biosimilars over time and spending on etanercept biologics and biosimilars over timeQuarterly etanercept spending in UK by product,2015-2021TOTALErelziBenepaliEnbrelDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120212.6 2.5 2.7 2.7 2.4 2.3 2.5 2.3 2.6 2.7 2.6 2.8 2.9 2.9 3.0 3.1 3.0 3.0 3.1 3.0 3.0 3.1 3.1 2.8 3.2 3.0 3.1 3.0 1000000rXPD5(%#%4(BPVdipprsrsttvutxwww%1%3%5%7%8%8%9%9%9%9%9%9%8%9%8%9%TOTALErelziBenepaliEnbrelUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202185 83 91 88 76 73 70 61 67 73 71 77 83 80 79 82 79 79 77 78 77 80 81 76 91 86 87 82 1000000tRF70%! !%4&HTbghiqrqrssutswvvv%1%3%5%7%8%8%9%9%9%8%8%9%9%8%9%8%8NEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE UNITED KINGDOMEtanercept has seen a volume increase of 25%while costs have remained stable(at list price level)(Exhibit 12).Quarterly etanercept defined daily doses in UK by product,2015-2021iqviainstitute.org|19Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Exhibit 13:Uptake of infliximab biosimilars over time and spending on infliximab biologics and biosimilars over timeTOTALFlixabiZesslyInflectraRemsimaRemicadeDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120213.1 3.3 3.4 3.5 3.5 3.7 3.8 3.8 3.9 4.1 4.2 4.3 4.3 4.4 4.5 4.7 4.7 4.8 5.0 5.2 5.2 5.1 5.3 5.7 5.7 5.9 6.2 6.3 100pYF9)#%9%8%7%7%7%6%6%5%5%4%4%4%4%4%8#6ACEFGGFCBB9650%1%4%7%9%9%8%8%9%7%1%1%2%3%3%4%5%7%7%8%9%8%8%8%7%7%7%2%6#6EVaXIGEECBA99779DFQU%3%TOTALZesslyFlixabiRemsimaInflectraRemicadeUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202164 68 72 72 67 70 64 61 62 66 69 71 75 75 73 75 76 76 76 83 82 78 86 93 97 103 106 106 100raIA2%!%9%8%8%7%7%6%5%5%5%5%5%4%4%3%859ACEFFGECBB9640%2%6!5CTWHFEDCBA99769CFPU%1%1%2%3%3%4%5%7%7%8%9%8%8%8%7%7%7%1%4%7%9%9%8%8%9%7%Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Note:In UK,the total savings are shown from biosimilar entry as benefit sharing program dates varied by region and were generally started around the tsime of biosimilar entry.These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and levers.Exhibit 14:Cumulative savings due to entry of biosimilars(anti-TNF)etanerceptinfliximabTOTALadalimumabUS$MnQ12015Q32015Q12016Q32016Q12017Q32017Q12018Q32018Q12019Q32019Q12020Q32020Q12021Q320214.6 8.9 20.5 35.5 53.4 70.7 85.9 102.0 115.3 130.4 149.7 171.4 191.3 212.2 236.9 257.6 278.8 303.8 325.3 343.1 359.7 374.1 390.1 407.3 2.9 5.4 7.7 12.3 21.6 32.0 49.4 71.1 94.0 116.0 137.4 158.5 176.5 196.8 220.9 247.0 272.4 299.1 330.4 359.3 388.8 420.3 450.4 480.3 505.9 531.3 559.5 590.9 3 5 8 12 26 41 70 107 147 187 223 261 292 327 371 418 464 511 567 617 668 724 776 823 866 905 950 998 Quarterly infliximab spending in UK by product,20152021Quarterly infliximab defined daily doses in UK by product,20152021BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE UNITED KINGDOMInfliximabs volume of use has doubled while costs have increased at a slower rate of 65%(at list price level)(Exhibit 13).Utilizing IQVIA data to estimate savings since the entry of biosimilars,the total savings due to biosimilar use(at a list price level)for infliximab,adalimumab and etanercept are estimated to be$998 million.The biosimilar use may be driven by a number of policies,therefore directly attributing the use to any one policy is challenging,however the benefit sharing program is viewed as a contributor to the increase in biosimilar use.(Exhibit 14)Cumulative biosimilar savings in UK,US$Mn,Q1 2015Q4 202120|Shared Savings Programs in Europe:Lessons for the United StatesCase study:Germany22,23 BACKGROUNDHealth insurance companies(Krankenkassen;KKs)and regional associations of health insurance accredited companies(Kassenrztliche Vereinigungen;KVs)represent payers and providers in Germany and many biosimilar related decisions take place at a regional level.For example,different regions and insurance companies can establish varying biosimilar quota levels.In 2015,around the time of the launch of infliximab biosimilar,a pilot benefit sharing program was started for the region of Westphalia-Lippe by BARMER,a health insurance company in Germany.STRUCTURE OF BENEFIT SHARING PROGRAMThis program was set up as voluntary with providers opting in.Prescription objectives were set in consultation between insurance companies and providers.The Anti-TNF biologics/biosimilars were included in the program.If providers were able to meet the prescription objectives,they received financial remuneration(amount not specified)and were exempted from adhering to budget caps concerning prescription of biologics,which is likely to increase patient access to biologics in general.IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEPublished literatureWhile the results of the program have not been publicly released,the impact can be assessed by analyzing biosimilar uptake data.The pilot program started in Westphalia-Lippe in 2015 for infliximab and as Exhibit 15 shows,this region saw faster uptake of the biosimilar compared to other regions between 2015 and 2018.The pilot program was also viewed as a success,leading to it being replicated with other KVs and BARMER;however,the details of these programs vary in terms of the prescription objectives,remuneration etc.The details of the programs are not publicly available,making comparisons across regions challenging.IQVIA dataBased on IQVIA data at a national level in Germany,the Anti-TNFs have witnessed strong biosimilar uptake with biosimilars comprising 7080%of the market.This level of uptake is likely driven by a mix of policies such as prescription quotas,benefit sharing,physician education,etc.BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPESource:Moorkens et al.,Learnings from Regional Market Dynamics of Originator and Biosimilar Infliximab and Etanercept in Germany,2020.Notes:Other regions may have seen benefit sharing programs as well.However,the details may vary and are not publicly available.Additionally,Westphalia-Lippe was the pilot model and provides the earliest example of such a program.Exhibit 15:Regional uptake of biosimilars over time(infliximab and etanercept)100pP0 %0%Q1 2015Q2 2015Q3 2015Q4 2015Q1 2016Q2 2016Q3 2016Q4 2016Q1 2017Q2 2017Q3 2017Q4 2017Q1 2018Q2 2018Q3 2018Q4 2018Westphalia-Lippe90pP0 %0%Q1 2015Q2 2015Q3 2015Q4 2015Q1 2016Q2 2016Q3 2016Q4 2016Q1 2017Q2 2017Q3 2017Q4 2017Q1 2018Q2 2018Q3 2018Q4 2018Westphalia-Lippeinfliximabetanerceptiqviainstitute.org|21BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE GERMANYAdalimumab has seen a volume increase of 47%since the introduction of biosimilars,while costs have decreased by 20%(at list price level)(Exhibit 16).Exhibit 16:Uptake of adalimumab biosimilars over time and spending on adalimumab biologics and biosimilars over timeSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.YuflymaTOTALImraldi IdacioHyrimozHulioAmgevitaHumiraUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q12021216 217 227 236 223 246 248 252 240 258 279 292 294 297 299 301 270 271 276 268 254 226 260 279 261 221 229 239 10000000000000000 xpdWSPFA(%1%5%8 %2%4%5%8%8%2%5%7%8%9%9%4%9%1%1%1%2%2%3%3%4%Quarterly adalimumab spending in Germany by product,20152021Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:In Germany,the total savings are shown from biosimilar entry as benefit sharing program dates varied by region and were generally started around the time of biosimilar entry.These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and levers.Benefit-sharing program start date varied across regions.YuflymaTOTALImraldi IdacioHyrimozHulioAmgevitaHumiraDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120213.7 3.8 4.0 4.2 3.9 4.3 4.3 4.6 4.4 4.6 4.7 4.8 4.7 4.9 5.0 5.3 5.3 5.5 5.9 6.1 6.3 5.8 6.4 7.0 6.7 6.8 7.2 7.8 10000000000000000iYRFA741(&%2%8 %1%3%5%7%9%3%7%9%7%1%1%1%2%2%2%3%3%4%1%Quarterly adalimumab defined daily doses in Germany by product,2015202122|Shared Savings Programs in Europe:Lessons for the United StatesBENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE GERMANYSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Exhibit 17:Uptake of etanercept biosimilars over time and spending on etanercept biologics and biosimilars over timeQuarterly etanercept spending in Germany by product,20152021TOTALNepextoErelziBenepaliEnbrelDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120212.3 2.3 2.4 2.5 2.4 2.5 2.6 2.7 2.7 2.9 3.0 3.1 3.0 3.1 3.2 3.3 3.0 3.1 3.2 3.3 3.2 3.0 3.2 3.4 3.3 3.3 3.4 3.6 1000000thdYSPGDA742)%#! %5&257CDEGPQRSTUTTTST%1%4%6%7%9 #$%1%1%1%2%2%TOTALNepextoErelziBenepaliEnbrelUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q12021126 126 130 135 128 138 139 138 134 146 160 163 162 163 162 164 148 147 145 121 119 110 122 131 124 103 105 108 1000000 xrieYVSPGCA7520)!%4%9(0268ACFHHIPQSSTTT%1%3%5%6%8%9#%1%1%1%2%2%Etanercept has seen a volume increase of 50%while costs have been reduced by 16%(at list price level)(Exhibit 17).Quarterly etanercept defined daily doses in Germany by product,20152021iqviainstitute.org|23Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Sep 2022.Notes:In Germany,the total savings are shown from biosimilar entry as benefit sharing program dates varied by region and were generally started around the time of biosimilar entry.These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and levers.Exhibit 18:Uptake of infliximab biosimilars over time and spending on infliximab biologics and biosimilars over timeTOTALZesslyFlixabiInflectraRemsimaRemicadeDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120213.3 3.4 3.5 3.6 3.6 3.8 4.0 4.0 4.2 4.2 4.3 4.5 4.5 4.6 4.7 4.7 4.8 4.7 5.0 4.8 4.9 4.9 5.2 5.6 5.8 5.9 6.2 6.3 98xurbUUQIGEDFFDBA9865200%6%8%9#(47%1%4%1%4%6%8 #$%$%# %4%7%6%8%8%2%3%4%4%4%5%5%6%6%6%6%6%TOTALZesslyFlixabiInflectraRemsimaRemicadeUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202191 88 91 92 92 98 102 94 95 95 99 98 102 90 88 84 83 79 83 79 79 77 87 95 100 101 105 103 98ywseYUQHB786420&%#! %4%6%8%9 #%06BEI%1%1%4%6%7%9$%)00111353)&# %2%4%3%5%5%7%9%2%2%1%2%2%3%3%4%3%3%3%3%etanerceptinfliximabTOTALadalimumabUS$MnQ12015Q32015Q12016Q32016Q12017Q32017Q12018Q32018Q12019Q32019Q12020Q32020Q12021Q3202121.3 67.6 130.3 209.6 311.0 435.2 556.8 682.5 823.5 969.2 1,159.1 1,365.4 1,594.5 2.9 13.8 23.4 24.3 26.3 23.8 28.2 37.2 50.5 65.2 84.0 109.0 162.4 217.2 268.8 318.2 372.2 424.8 497.1 575.0 660.7 11.9 27.9 46.2 67.5 88.0 108.0 131.4 160.4 195.6 231.9 266.9 306.5 343.8 395.8 452.8 513.2 577.9 643.7 715.9 785.8 858.8 932.8 1,007.3 1,085.3 1,164.8 1,246.5 1,333.6 1,426.6 163 12 28 46 67 87 104 128 209 255 291 333 368 424 490 585 711 858 1,035 1,259 1,511 1,758 2,008 2,281 2,559 2,903 3,274 3,682 BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE GERMANYInfliximabs volume of use has doubled while costs have remained relatively stable(at list price level)(Exhibit 18).Quarterly infliximab defined daily doses in Germany by product,20152021Quarterly infliximab spending in Germany by product,20152021Exhibit 19:Cumulative savings due to entry of biosimilars(anti-TNF)Utilizing IQVIA data to estimate savings since the entry of biosimilars,the total savings due to biosimilar use(at a list price level)for infliximab,adalimumab and etanercept are estimated to be$3.6 billion.Given the variation in biosimilar policies across regions in Germany(different quota levels,different incentives),it is challenging to attribute this saving to any one specific policy,but discussions around benefit sharing policies suggest that they have played a role in the overall use(Exhibit 19).Cumulative biosimilar savings in Germany,US$Mn,Q1 2015Q4 202124|Shared Savings Programs in Europe:Lessons for the United StatesCase study:Italy BACKGROUNDIn Italy,policies related to biosimilars are often made at a regional level.In general,there has been a push to increase biosimilar uptake through the use of various affordability measures such as prescribing targets,purchasing framework agreements,and related.There is a degree of regional variability in the choice of policies that are adopted.Campania is the only region in Italy that has adopted a benefit sharing program to encourage use of biosimilars.24STRUCTURE OF BENEFIT SHARING PROGRAMFrom 2016 onward,a benefit sharing program was established by the regional health ministry in Campania that covered all hospital use molecules that have biosimilar competition.Fifty percent of the savings from biosimilars is retained by the hospital administration with 5%meant for clinical departments.The aim is to utilize these savings to fund innovative drugs.The regional health ministry retains the remaining 50%.Other regions in Italy may have financial incentives for use of biosimilars as well,however,these have not been captured in publicly available literature.IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEPublished literature In general,Italy has witnessed high uptake of biosimilars across most regions.It is challenging to estimate the impact of the benefit sharing program in Campania given the prevalence of a number of other policies to promote biosimilar uptake in Campania and across regions.On the whole,Campania has witnessed higher biosimilar uptake than the national average in some cases(bevacizumab,rituximab and infliximab)while it has been lower than the national average in other cases(adalimumab,etanercept and somatropin)(Exhibit 20).25 The reinvestment of savings in innovative therapies would be important to consider as well,but data to assess this is limited.BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE ITALYSource:AIFA Regional Biosimilar Data May 2022(accessed Oct 2022).Notse:Benefit sharing program start date varied across regions.Exhibit 20:Level of biosimilar uptake Campania vs average across Italy CampaniaItaly84y$w00 0alimumab bevacizumab etanercept filgrastim infliximab rituximab somatropin trastuzumab iqviainstitute.org|25BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE ITALYIQVIA data Adalimumab has seen a volume increase of 25%since the introduction of biosimilars while costs have remained stable(at list price level)(Exhibit 21).Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Note:Benefit-sharing program start date varied across regions.Exhibit 21:Uptake of adalimumab biosimilars over time and spending on adalimumab biologics and biosimilars over timeQuarterly adalimumab spending in Italy by product,20152021TOTALIdacioAmgevitaHyrimozImraldiHumiraDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120211.9 1.9 1.9 1.9 2.0 2.1 2.1 2.1 2.0 2.3 2.3 2.4 2.4 2.5 2.4 2.4 2.4 2.6 2.5 2.6 2.7 2.6 2.6 2.8 2.9 2.9 3.0 3.0 10000000000000000wfYRFC962(%2%7 $1330&%1%2%5%8$%2%)331! %1%2%2%3%4%5%TOTALIdacioHyrimozAmgevitaImraldiHumiraUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202170 70 71 72 70 75 74 72 69 82 89 90 93 94 91 87 83 88 85 84 87 83 88 94 95 96 95 92 10000000000000000pcVQHDA7310%1%6%8!00$%2#120! %2%5%7%9%1%2%2%3%4%5%Quarterly adalimumab defined daily doses in Italy by product,2015202126|Shared Savings Programs in Europe:Lessons for the United StatesBENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE ITALYSource:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Exhibit 22:Uptake of etanercept biosimilars over time and spending on etanercept biologics and biosimilars over timeQuarterly etanercept spending in Italy by product,20152021TOTALErelziBenepaliEnbrelDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120211.6 1.6 1.6 1.5 1.6 1.6 1.6 1.5 1.6 1.6 1.6 1.7 1.7 1.7 1.6 1.6 1.5 1.5 1.5 1.4 1.5 1.4 1.5 1.5 1.5 1.5 1.5 1.4 100000000vhWTHDB976410)(%1%4%8 $2BBCFFIPQSTUSS%1%4%9%TOTALErelziBenepaliEnbrelUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202160 59 58 55 55 57 56 53 52 55 58 58 60 57 53 50 47 45 43 41 42 41 44 46 45 44 43 40 100000000uhfcXTQHFEC986%1%3%6%234689BCDFHIHH%1%3%7%8%9%Etanercept has seen a marginal volume decrease while costs have been reduced by 29%(at list price level)(Exhibit 22).Quarterly etanercept defined daily doses in Italy by product,20152021iqviainstitute.org|27Exhibit 23:Uptake of etanercept biosimilars over time and spending on etanercept biologics and biosimilars over timeQuarterly infliximab defined daily doses in Italy by product,2015-2021TOTALInflectraRemsimaZesslyFlixabiRemicadeDefined daily doses(Millions)Q12015Q12016Q12017Q12018Q12019Q12020Q120211.7 1.6 1.7 1.7 1.7 1.7 1.8 1.7 1.8 1.9 1.7 1.8 1.9 1.9 1.9 1.8 1.9 1.9 1.9 1.8 1.9 1.8 1.9 2.0 2.0 1.9 2.1 2.1 100ytfaUIFE95)#%9%8%8%7%7%6%6%2%7$0347BDDTUWUR%1%1%3%7%8%4%8#0356754)(&%$#$%9%2%5%7%9#0&# !%Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:Benefit-sharing program start date varied across regions.Source:IQVIA MIDAS,Dec 2021;IQVIA Institute,Nov 2022.Notes:In Italy,the total savings are shown from biosimilar entry as benefit sharing program dates varied by region and were generally started around the time of biosimilar entry.These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and levers.Quarterly infliximab spending in Italy by product,20152021Cumulative biosimilar savings in Italy,US$Mn,Q1 2015Q4 2021TOTALInflectraRemsimaZesslyFlixabiRemicadeUS$MnQ12015Q12016Q12017Q12018Q12019Q12020Q1202131 29 30 29 30 31 31 29 30 32 31 32 35 34 33 32 31 31 31 29 30 29 32 34 35 33 35 35 100vidXRIIC82&$!%9%9%8%7%8%6%6%2%5%6%8 &)%&# !%3%7!%(134532(%$#$%9%2%7%9$)346BDDTTVUR%1%1%3%7%8%etanerceptinfliximabTOTALadalimumabUS$MnQ12015Q32015Q12016Q32016Q12017Q32017Q12018Q32018Q12019Q32019Q12020Q32020Q12021Q320212.3 7.4 15.4 25.0 36.5 50.7 65.0 75.9 87.1 99.4 111.1 126.9 145.9 1.8 3.3 2.4 2.3 0.5 2.1 6.5 12.3 18.6 26.3 34.7 43.9 54.1 64.4 72.5 80.5 88.3 95.9 104.9 115.1 3.3 7.3 11.7 16.7 21.8 26.8 32.3 39.0 46.5 53.4 57.8 62.7 66.5 71.6 78.0 84.8 92.0 100.2 108.9 117.4 126.5 135.3 142.5 149.3 156.0 161.6 169.3 178.3 3 7 12 17 22 27 32 39 48 57 60 65 67 74 85 99 118 142 169 198 231 265 291 317 344 369 401 439 BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDIES FROM EUROPE ITALYInfliximabs volume of use has increased marginally while costs have remained relatively stable(at list price level)(Exhibit 23).Utilizing the IQVIA data to estimate savings since the entry of biosimilars,the total savings due to biosimilar use(at a list price level)for infliximab,adalimumab and etanercept are estimated to be$439 million.Given the variation in biosimilar policies across regions(different quota levels,different incentives),it is challenging to attribute this saving to any one specific policy,but discussions around benefit sharing policies suggest that they have played a role in the overall use in Campagnia.Exhibit 24:Cumulative savings due to entry of biosimilars(anti-TNF)28|Shared Savings Programs in Europe:Lessons for the United StatesOncology Care Model in United StatesExhibit 25:Comparison of oncology biosimilar share trend by provider OCM status%of new-to-brand patients13)12(0%1000BCDP%5%8%9%5(148CCE%Source:IQVIA LAAD Medical Procedure Claims,US Market Access Strategy Consulting analysis.%of new-to-brand patientsPre 2019 launches Avg(n=3)Pre 2019 launches Avg(n=3)While a shared savings or benefit sharing program for biosimilars has not been implemented in the U.S.,the Oncology Care Model(OCM)can offer insights into the impact of programs that are based on incentivizing affordability.OCM ran from 2016 to 2021 and participants covered one-fourth of Medicare FFS chemotherapy-related cancer care practices.The goal of OCM was to utilize appropriately aligned financial incentives to enable improved care coordination,appropriateness of care,and access to care for beneficiaries undergoing chemotherapy.OCM encouraged participating practices to improve care and lower costs through an episode-based payment model that financially incentivized high-quality,coordinated care.Performance-based payment are used to incentivize practices to lower the total cost of care and improve care for beneficiaries during treatment episodes.As such,OCM providers may have greater financial incentive to administer biosimilar products compared to providers not taking part in the OCM.26 However,there were no direct incentives for incentivizing the use of biosimilars.Based on a 2021 IQVIA study,27 OCM participants had a slightly higher level of uptake for biosimilars launched in 2019 compared to those that did not take part in OCM(Exhibit 30).This difference may be driven by the pressure to reduce costs,however,given the affordability benefits that the use of biosimilars can provide,the difference is not large for biosimilars launched in 2019.This suggests that a greater focus on incentivizing biosimilars may be required to actively enhance biosimilar uptake.Overall across Europe,benefit sharing programs have been used extensively.While data on the impact of these programs is not always readily available,in countries where such data is provided(e.g.,Ireland,England,France),an increase in biosimilar use leading to savings for the health system can be observed.Cases such as the one in Ireland suggest that a benefit sharing program had a large impact.Prior research on these programs has also highlighted the importance of educating physicians and patients on biosimilars,and establishing a regular line of communication across patients,physicians and health bodies leading the program are important for its successful implementation.Communicating the savings achieved and the impact of reinvestment have also been identified as useful for generating greater engagement from physicians.These aspects will be important to reflect on in the U.S.context as shared savings programs are considered.OCM participant(2%)Other(98%)iqviainstitute.org|29Lessons for the U.S. As European examples show,benefit sharing programs,as part of an overall set of biosimilar policies,can be an approach to increasing biosimilar uptake and subsequently,increasing savings Shared savings programs can also allow for enhanced patient care through reinvestment of savings by specialties/clinical teams Increased biosimilar use can reduce overall costs and may increase overall patient access Government health ministries and insurers have generally been the central driving forces behind benefit sharing programs Pilot studies can be useful in understanding the best approaches to benefit sharing and can ensure that appropriate incentives are provided while physician and patient autonomy in decision-making is maintained Physician and patient education to increase comfort with biosimilar use and regular communication of the impact of such a policy can be importantHealth policies applied in Europe have a different context from the U.S.;however,a number of lessons can be taken from the experiences of biosimilars in European countries.With many biosimilars expected to launch in the U.S.in the next five to ten years,it is important that the use of biosimilars is optimized.The case studies researched for this report offer insights that could potentially be considered as the U.S.evaluates legislation to improve access to biosimilars,which include proposals for shared savings programs for Medicare part B.BENEFIT SHARING PROGRAMS CAN HELP INCREASE BIOSIMILAR UPTAKEBenefit sharing programs can be an approach to increasing biosimilar uptake and subsequently,increasing savings.They can also allow for enhanced patient care through reinvestments.Across the case studies,the use of benefit sharing has been associated with an increase in use of biosimilars.While other policies may impact biosimilar uptake,the Ireland example is of particular interest as the use of biosimilars was substantially low prior to the program and biosimilars saw substantial increase in uptake once the program was implemented.Ireland has also seen the savings that were shared with the specialties be used to improve patient care.France also witnessed increases in biosimilar use after the implementation of benefit sharing programs.Regional examples in Germany and the UK point to a similar dynamic.INCREASED BIOSIMILAR USE CAN REDUCE OVERALL COSTSIncreasing use of biosimilars which are generally lower in cost has led to savings in each of the case studies discussed earlier.While policies related to incentivizing affordability overall are in place in a number of European countries/regions,a specific policy targeted at biosimilars may provide more impetus to increase use of biosimilars.In the U.S.,the Oncology Care Model has led to some preference for biosimilars due to their cost advantages,however,a more targeted approach toward incentivizing biosimilars could prove more effective.In a number of countries,the increasing use of biosimilars has not only led to reduced costs but also to an increase in overall use.This increase in use suggests that lower cost of biosimilars may have allowed for greater use due to overall affordability.However,it is important to note that this increase in overall use of the molecule is not seen in all cases.For example,in most countries,the use of etanercept molecule remained steady or declined slightly upon biosimilar entry.30|Shared Savings Programs in Europe:Lessons for the United StatesGOVERNMENT HEALTH MINISTRIES HAVE DRIVEN DEVELOPMENT OF BENEFIT SHARING PROGRAMSGovernment health ministries and departments and insurers have generally been central driving forces behind benefit sharing programs.The benefit sharing programs have been led by government bodies in most countries;for example,in Ireland,the Health Services Executive developed the program,while in France,both the general program and the pilot study were led by the health ministry and the National Health Insurance Fund.Similarly,regional health ministries and departments in Italy and the UK have led benefit sharing programs.In the U.S.,given the large potential for savings for Medicare,CMS Innovation Center may be well suited to test potential innovative approaches to enhancing biosimilar use.PILOT STUDIES CAN HELP IN UNDERSTANDING BEST APPROACHESPilot studies can be useful in understanding the best approaches to benefit sharing and can ensure that appropriate incentives are provided while physician and patient autonomy in decision-making is maintained.Several countries(such as France,Germany,UK)initially implemented benefit sharing programs at a pilot level before expanding it more broadly.These pilot models allowed the countries to experiment with different models and approaches.A similar approach may be suited for the U.S.,as a pilot study would allow for experimentation on aspects such as level of incentive,voluntary vs compulsory,targets,etc.,while ensuring physician and patient comfort with such policies.This will also help ensure that the policy is effective before broader use.It is crucial that patients and physicians feel comfortable with such policies and that these pilot models ensure that prescribing decisions are being made with clinical factors as the driving criteria.PHYSICIAN EDUCATION AND COMMUNICATION MATTERSPhysician education to increase comfort with biosimilar use and regular communication of the impact of such a policy can be important.Optimal biosimilar use will require participation and alignment of multiple stakeholders and factors across the healthcare system.As part of the benefit sharing programs,most countries had programs to increase the understanding of biosimilars among physicians so that they had increased comfort with their use.While there is growing acceptance of biosimilars globally(e.g.,the recent statement on interchangeability of reference medicines and biologics by the European Medicines Association),education efforts can help overcome any concerns from patients or physicians.CONCLUSIONAs an increasing number of lower-cost biosimilars enter the U.S.market,there is an important opportunity to capitalize on potential savings they could provide,thereby reducing overall healthcare expenditure while maintaining the sustainability of the overall market.Shared savings models hold the potential to align physician incentives with cost saving efforts without having an impact on overall healthcare quality.Examples from Europe suggest that such models,along with other biosimilar policies,can increase savings due to greater biosimilar use.As such models are considered,it will be important to ensure that physicians and patients feel comfortable with biosimilars and have autonomy to make decisions.Developing such a model will likely require leadership from CMS and partnership with providers,physicians,and patient advocacy groups.While other policies to encourage biosimilar use will also be needed,developing,and assessing pilot shared savings models at this stage can help with ensuring that benefits from biosimilars are optimized moving forward.iqviainstitute.org|31APPENDIX:BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDY SUMMARIES FROM EUROPECase study summary:IrelandSource:Duggan et al,Uptake of biosimilars for TNF-inhibitors adalimumab and etanercept following the best-value biological medicine initiative in Ireland,Oct 2021,IQVIA MIDAS and IQVIA Institute Analysis.Notes:*In Ireland,the total savings are shown from the start of the Benefit Sharing Program.These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and levers.BACKGROUND Biosimilar uptake was low(2%)for etanercept despite two years on the market Health Services Executive designed a Best Value Biologic(BVB)program to promote use of BVBs(selected based on cost and other factors)in 201819 Time frame:June 2019 Stakeholders:Health Services Executive(public healthcare system),local clinics/hospitals departments Molecules:adalimumab and etanercept Note:Benefit Sharing is a component of biosimilar related policiesSTRUCTURE OF PROGRAM EUR 500 provided to clinical department for every patient initiated or switched to best value biologic Savings must be reinvested into the department(monitored by HSE-Primary Care Reimbursement)IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEUptake Adalimumab:patients on biosimilars increased from 166 in May 2019 to over 3400 on BVB in May 2020 Etanercept:patients on biosimilars increased from 104 in May 2019 to over 1800 by May 2020 By July 2020,the total patients on BVB across both molecules was over 8500 and biosimilars had 50%market share by Q3 2020 and over 60%by Q4 2021Change in volume of use and cost since start of the program Adalimumab:Volume increased by 50%since introduction of program while costs have increased by 19%(at list price level)Etanercept:Volume has increased by 14%while costs have increased by 7%(at list price level)Savings Total estimated savings due to the use of BVBs between Jun 2019 to July 2020 is EUR 22.7 million(based on available literature)Estimated Savings from Q2 2019 and Q4 2021(on list price level)due to increase in biosimilar use across all of Ireland(For adalimumab and etanercept)$47 Mn(based on IQVIA Institute calculations)*Reinvestment EUR 3.6 million was provided to specialties as part of the benefit sharing to be invested back between July 2019 and July 2020Reinvestments have focused on patient care such as additional equipment(e.g.ultrasound machines,polar machines),IT development(e.g.online biologic registry);gain-sharing has also enabled extra clinics and extra clinic time to allow for more infusions;funding has also been used for hiring local consultants,education programs and infrastructure.32|Shared Savings Programs in Europe:Lessons for the United StatesCase study summary:FranceSource:Incentives promoting use of biosimilar medicines in France,Oct 2022;Paubel et al.,Impact of French experiment for incentivizing Etanercept biosimilar use after 10 months,Mar 2020;Tano et al.,Assessment of the French National Experimentation for Incentivising Hospital Prescriptions of Adalimumab Biosimilars Delivered in Retail Pharmacies,July 2022;IQVIA MIDAS and IQVIA Institute Analysis.Notes:*In France,the total savings are shown from the start of the Benefit Sharing Program.These savings may not be directly attributable to the benefit sharing program alone as not all hospitals may have taken part in the program and are driven by a number of biosimilar policies and levers.BACKGROUND Biosimilar uptake was low(80%switch to biosimilarsIMPACT OF THE PROGRAM AND OF BIOSIMILAR USEUptake North Bristol Example Infliximab:52 patients were switched to the biosimilar out of 64/65(80%).97%patients satisfied with switching process.National Overall,based on IQVIA data at a national level,the anti-TNFs have witnessed strong biosimilar uptake with biosimilars comprising of 80%of the market Change in volume of use and costs since biosimilar entry National Adalimumab:Volume increased by 55%while costs have increased by 42%(at list price level)Etanercept:Volume has increased by 25%while costs have remained stable(at list price level)Infliximab:Volume has doubled while costs have increased by 65%(at list price level)Savings North Bristol Example GBP 200,000 in savings from switching to biosimilarsNational Total estimated savings due to the use of anti-tnf biosimilars at a national level(at a list price level based on IQVIA Institute calculations)=$998 Mn Note:These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and leversReinvestment North Bristol Example The share of savings provided to the trust were reinvested into gastroenterology services and an additional pharmacist was funded for closer monitoring and funding of biologic treatments34|Shared Savings Programs in Europe:Lessons for the United StatesCase study summary:GermanySource:Lacosta,Vulto et al,Qualitative Analysis of the Design and Implementation of Benefit-Sharing Programs for Biologics Across Europe,Mar 2022;available at:https:/et al.,Learnings from Regional Market Dynamics of Originator and Biosimilar Infliximab and Etanercept in Germany,2020;IQVIA MIDAS and IQVIA Institute Analysis.APPENDIX:BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDY SUMMARIES FROM EUROPEBACKGROUND Biosimilar policies are developed by Health insurance companies(Krankenkassen;KKs)and regional associations of health insurance accredited companies(Kassenrztliche Vereinigungen;KVs)Different regions and insurance companies can establish varying biosimilar quota levels In 2015,a pilot benefit sharing program was started for the region of Westphalia-Lippe by BARMER,a health insurance company Health Services Executive designed a Best Value Biologic(BVB)program to promote use of BVBs(selected based on cost and other factors)in 201819 Time frame:2015 onwards Stakeholders:BARMER,Westphalia-Lippe providers Molecules:Anti-TNFs(infliximab,etanercept,adalimumab)Note:Benefit Sharing is a component of biosimilar related policiesSTRUCTURE OF PROGRAM Voluntary program that providers can opt-in Prescription objectives set in consultation between BARMER and providers Providers meeting objectives given financial remuneration(amount not specified)and were exempted from adhering to budget capsIMPACT OF THE PROGRAM AND OF BIOSIMILAR USEUptake Westphalia-Lippe Infliximab:Westphalia-Lippe witnessed faster uptake of biosimilar compared to other regions reaching over 80%by 2018 Etanercept:Westphalia-Lippe witnessed faster uptake of biosimilar compared to other regions reaching over 70%by 2018 The benefit sharing program has been extended to other regions since the initial pilot but details of the program vary and are not publicly availableNational Overall,based on IQVIA data at a national level,the anti-TNFs have witnessed strong biosimilar uptake with biosimilars comprising of 70-80%of the market likely driven by a mix of biosimilar policiesChange in volume of use and costs since biosimilar entry National Adalimumab:Volume increased by 47%while costs have decreased by 20%(at list price level)Etanercept:Volume has increased by 50%while costs have reduced by 16%(at list price level)Infliximab:Volume has doubled while costs have remained relatively stable(at list price level)Savings National While it is challenging to estimate savings at a regional level,total estimated savings due to the use of anti-tnf biosimilars at a national level(at a list price level based on IQVIA Institute calculations)=$3,682 Mn Note:These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and leversReinvestment Details on reinvestment are not available,exemption form budget caps likely to increase access to biologicsiqviainstitute.org|35Case study summary:ItalyAPPENDIX:BENEFIT SHARING(SHARED SAVINGS)PROGRAMS:CASE STUDY SUMMARIES FROM EUROPEBACKGROUND Biosimilar policies are made at regional level by regional health ministries in consultation with providers Campania region established a benefit sharing program;Other regions in Italy may have financial incentives for use of biosimilars,however,these have not been captured in publicly available literature Time frame:2016 onwards Stakeholders:Campania Ministry of Health,Regional providers(Hospitals/Clinics)Molecules:all hospital use molecules that have biosimilar competition Note:Benefit Sharing is a component of biosimilar related policiesSTRUCTURE OF PROGRAM 50%of the savings from biosimilars is retained by the hospital administration with aim to fund innovative drugs with 5%meant for clinical departments The regional health ministry retains the remaining 50%IMPACT OF THE PROGRAM AND OF BIOSIMILAR USEUptake Campania Campania has witnessed higher biosimilar uptake than the national average in some cases(bevacizumab,rituximab and infliximab)However,it has been lower than the national average in other cases(adalimumab,etanercept and somatropin)National In general,biosimilar uptake in Italy has been high nationally with each of the anti tnfs(adalimumab,etanercept and inflixmab)achieving over 70%uptake A variety of regional biosimilar policies are being used making an assessment of relative impact of benefit sharing program difficultChange in volume of use and costs since biosimilar entry National Adalimumab:Volume increased by 25%since biosimilar entry while costs have remained stable(at list price level)Etanercept:Volume has seen a marginal decline while costs have reduced by 29%(at list price level)Infliximab:Volume has seen a marginal increase while costs have remained relatively stable(at list price level)Savings National While it is challenging to estimate savings at a regional level,total estimated savings due to the use of anti-tnf biosimilars at a national level(at a list price level based on IQVIA Institute calculations)=$439 Mn Note:These savings may not be directly attributable to the benefit sharing program alone and are driven by a number of biosimilar policies and leversReinvestment Details on reinvestment are not availableSource:Lacosta,Vulto et al,Qualitative Analysis of the Design and Implementation of Benefit-Sharing Programs for Biologics Across Europe,Mar 2022;available at:https:/Regional Biosimilar Data May 2022(accessed Oct 2022);IQVIA MIDAS and IQVIA Institute Analysis.36|Shared Savings Programs in Europe:Lessons for the United StatesMETHODOLOGY FOR CALCULATING SAVINGSTo estimate the savings associated with the entry of the biosimilar,we compare the actual sales that took place with the hypothetical scenario where the biosimilar is not available in the market(or the program was not started)and therefore,the molecule price per DDD stays constant at pre-expiry(or pre-start of program)prices across the time period.This approach to calculate the savings has been previously adopted in studies which assessed savings from biosimilars in individual countries.28 This approach allows U.S.to account for savings due to the lower price of the biosimilar as well as the potentially lower price of the other somatropin products due to the increased price competition from the biosimilar.The following dataset was utilized to conduct this analysisIQVIA MIDAS is a unique platform for assessing worldwide healthcare markets.It integrates IQVIAs national audits into a globally consistent view of the pharmaceutical market,tracking virtually every product in hundreds of therapeutic classes,and provides estimated product volumes,trends and market share through retail and non-retail channels.MIDAS data are updated quarterly and retain 12 years of data.Historic archives of MIDAS were used to extend analyses to the full time period analyzed,2005 to 2021.Methodologiesiqviainstitute.org|37References 1.Andrews L,Ralston S,Blomme E,Barnhart K.A snapshot of biologic drug development:Challenges and opportunities.Hum Exp Toxicol.2015;34(12):1279-85.2.Nguyen NX,Sheingold S.Medicare Part B Drugs:Trends in 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Cornyn.Cornyn,Bennet Introduce Bipartisan Bill to Increase Access to Generic Drugs,Accessed October 30,2022.Available from:https:/www.cornyn.senate.gov/content/news/cornyn-bennet-introduce-bipartisan-bill-increase-access-generic-drugs.13.Brill A.Shared Savings Demonstration for Biosimilars in Medicare:An Opportunity to Promote Biologic Drug Competition:Matrix Global Advisors.Available from:https:/B,Smith A,Barry M.Uptake of biosimilars for TNF-inhibitors adalimumab and etanercept following the best-value biological medicine initiative in Ireland.International Journal of Clinical Pharmacy.2021;43(5):1251-6.15.Irish Government Economic and Evaluation Service(IGEES).Spending Review 2021:Review of High-Tech Drug Expenditure.Available from:https:/igees.gov.ie/wp-content/uploads/2021/10/Review-of-High-Tech-Drug-Expenditure-Spending-Review-2021.pdf.16.Plan dappui la transformation du systme de sant.Available from:https:/and Biosimilars Initiative(GaBI).Incentives promoting use of biosimilar medicines in FranceAccessed October 30,2022.Available from:https:/P,Degrassat-Theas A,Bocquet F.Impact of French Experiment for Incentivising Etanercept Biosimilar Use Afte 10 MonthsAccessed October 30,2022.Available from:https:/www.eahp.eu/sites/default/files/2spd-008_0.pdf.19.Tano M,Degrassat Theas A,Ribault M,Paubel P.HPR12 Assessment of the French National Experimentation for Incentivising Hospital Prescriptions of Adalimumab Biosimilars Delivered in Retail Pharmacies.Value in Health.2022;25(7,Supplement):S468-S9.20.NHS England.CCG Biosimilars National Questionnaire Report.NHS England;2018.21.Chung L,Arnold B,Johnson R,Lockett M.OC-038 Making The Change:Switching to Infliximab Biosimilars for IBD at North Bristol NHS Trust.Gut.2016;65(Suppl 1):A22-A3.38|Shared Savings Programs in Europe:Lessons for the United States 22.Moorkens E,Barcina Lacosta T,Vulto AG,Schulz M,Gradl G,Enners S,et al.Learnings from Regional Market Dynamics of Originator and Biosimilar Infliximab and Etanercept in Germany.Pharmaceuticals.2020;13(10):324.23.Arbeitsgemeinschaft Pro Biosimilars.Handbuch Biosimilars.Available from:https:/probiosimilars.de/app/uploads/2021/04/Handbuch-Biosimilars_Oktober-2019.pdf.24.Lobo F,Ro-lvarez I.Barriers to Biosimilar Prescribing Incentives in the Context of Clinical Governance in Spain.Pharmaceuticals(Basel).2021;14(3).25.Agenzia Italiana del Farmaco(AIFA).Biosimilari:Analisi della variabilit regionale.Accessed Octoer 30,2022.Available from:https:/www.aifa.gov.it/en/monitoraggio-consumi-e-spesa-biosimilari.26.U.S.Centers for Medicare&Medicaid Services(CMS).Oncology Care Model updated 2022 Aug 4.Available from:https:/innovation.cms.gov/innovation-models/oncology-care.27.The IQVIA Institute.Biosimilars in the U.S.:Reimbursement and Impacts to Uptake.Available from:https:/M,Ro-lvarez I,Carcedo D,Villacampa A.Budget Impact Analysis of Biosimilar Products in Spain in the Period 2009-2019.Pharmaceuticals(Basel).2021;14(4).iqviainstitute.org|39About the authorsMURRAY AITKENExecutive Director,IQVIA Institute for Human Data Science Murray Aitken is Executive Director,IQVIA Institute for Human Data Science,which provides policy setters and decisionmakers in the global health sector with objective insights into healthcare dynamics.He led the IMS Institute for Healthcare Informatics,now the IQVIA Institute,since its inception in January 2011.Murray previously was Senior Vice President,Healthcare Insight,leading IMS Healths thought leadership initiatives worldwide.Before that,he served as Senior Vice President,Corporate Strategy,from 2004 to 2007.Murray joined IMS Health in 2001 with responsibility for developing the companys consulting and services businesses.Prior to IMS Health,Murray had a 14-year career with McKinsey&Company,where he was a leader in the Pharmaceutical and Medical Products practice from 1997 to 2001.Murray writes and speaks regularly on the challenges facing the healthcare industry.He is editor of Health IQ,a publication focused on the value of information in advancing evidence-based healthcare,and also serves on the editorial advisory board of Pharmaceutical Executive.Murray holds a Master of Commerce degree from the University of Auckland in New Zealand,and received an M.B.A.degree with distinction from Harvard University.VIBHU TEWARY Project Director,IQVIA Institute for Human Data ScienceVibhu Tewary is a Project Director at the IQVIA Institute for Human Data Science and is based out of New York,NY.His key areas of interest include healthcare policy,global market access,and economic modeling.Vibhu has authored multiple reports on global healthcare policy and market access.Prior to joining IQVIA,he worked as a researcher in a policy think tank in India.Vibhu did his undergraduate studies at the Indian Institute of Technology,Madras,and holds an MBA from Duke University.40|Shared Savings Programs in Europe:Lessons for the United StatesThe IQVIA Institute for Human Data Science contributes to the advancement of human health globally through timely research,insightful analysis and scientific expertise applied to granular non-identified patient-level data.Fulfilling an essential need within healthcare,the Institute delivers objective,relevant insights and research that accelerate understanding and innovation critical to sound decision making and improved human outcomes.With access to IQVIAs institutional knowledge,advanced analytics,technology and unparalleled data the Institute works in tandem with a broad set of healthcare stakeholders to drive a research agenda focused on Human Data Science including government agencies,academic institutions,the life sciences industry,and payers.Research agendaThe research agenda for the Institute centers on five areas considered vital to contributing to the advancement of human health globally:Improving decision-making across health systems through the effective use of advanced analytics and methodologies applied to timely,relevant data.Addressing opportunities to improve clinical development productivity focused on innovative treatments that advance healthcare globally.Optimizing the performance of health systems by focusing on patient centricity,precision medicine and better understanding disease causes,treatment consequences and measures to improve quality and cost of healthcare delivered to patients.Understanding the future role for biopharmaceuticals in human health,market dynamics,and implications for manufacturers,public and private payers,providers,patients,pharmacists and distributors.Researching the role of technology in health system products,processes and delivery systems and the business and policy systems that drive innovation.Guiding principlesThe Institute operates from a set of guiding principles:Healthcare solutions of the future require fact based scientific evidence,expert analysis of information,technology,ingenuity and a focus on individuals.Rigorous analysis must be applied to vast amounts of timely,high quality and relevant data to provide value and move healthcare forward.Collaboration across all stakeholders in the public and private sectors is critical to advancing healthcare solutions.Insights gained from information and analysis should be made widely available to healthcare stakeholders.Protecting individual privacy is essential,so research will be based on the use of non-identified patient information and provider information will be aggregated.Information will be used responsibly to advance research,inform discourse,achieve better healthcare and improve the health of all people.About the Instituteiqviainstitute.org|41CONTACT US100 IMS Drive Parsippany,NJ 07054 United Statesinfoiqviainstitute.org iqviainstitute.orgCopyright 2022 IQVIA.All rights reserved.12.2022.ENTThe IQVIA Institute for Human Data Science is committed to using human data science to provide timely,fact-based perspectives on the dynamics of health systems and human health around the world.The cover artwork is a visual representation of this mission.Using algorithms and data from the report itself,the final image presents a new perspective on the complexity,beauty and mathematics of human data science and the insights within the pages.The algorithmic art on this report was generated from data that captures the shifts in biosimilar use in selected case studies before and after the introduction of the benefit sharing program.

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CourseCorrectingCorrectingCorrectingCourseCorrectingCourseCorrecting 2022 International Bank for Reconstruction and Development/The World Bank1818 H Street NW,Washington,DC 20433Telephone:202-473-1000;Internet:www.worldbank.orgSome rights reserved1 2 3 4 25 24 23 22This work is a product of the staff of The World Bank with external contributions.The findings,interpre-tations,and conclusions expressed in this work do not necessarily reflect the views of The World Bank,its Board of Executive Directors,or the governments they represent.The World Bank does not guarantee the accuracy,completeness,or currency of the data included in this work and does not assume responsibility for any errors,omissions,or discrepancies in the information,or liability with respect to the use of or failure to use the information,methods,processes,or conclusions set forth.The boundaries,colors,denominations,and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.Nothing herein shall constitute or be construed or considered to be a limitation upon or waiver of the priv-ileges and immunities of The World Bank,all of which are specifically reserved.Rights and PermissionsThis work is available under the Creative Commons Attribution 3.0 IGO license(CC BY 3.0 IGO)http:/creativecommons.org/licenses/by/3.0/igo.Under the Creative Commons Attribution license,you are free to copy,distribute,transmit,and adapt this work,including for commercial purposes,under the following conditions:AttributionPlease cite the work as follows:World Bank.Poverty and Shared Prosperity 2022:Correcting Course.Washington,DC:World Bank.doi:10.1596/978-1-4648-1893-6.License:Creative Commons Attribution CC BY 3.0 IGOTranslationsIf you create a translation of this work,please add the following disclaimer along with the attribution:This translation was not created by The World Bank and should not be considered an official World Bank translation.The World Bank shall not be liable for any content or error in this translation.AdaptationsIf you create an adaptation of this work,please add the following disclaimer along with the attribution:This is an adaptation of an original work by The World Bank.Views and opinions expressed in theadaptation are the sole responsibility of the author or authors of the adaptation and are not endorsed byTheWorld Bank.Third-party contentThe World Bank does not necessarily own each component of the content contained within the work.The World Bank therefore does not warrant that the use of any third-party-owned indi-vidual component or part contained in the work will not infringe on the rights of those third parties.The risk of claims resulting from such infringement rests solely with you.If you wish to re-use a component of the work,it is your responsibility to determine whether permission is needed for that re-use and to obtain permission from the copyright owner.Examples of components can include,but are not limited to,tables,figures,or images.All queries on rights and licenses should be addressed to World Bank Publications,The World Bank Group,1818 H Street NW,Washington,DC 20433,USA;e-mail:pubrightsworldbank.org.ISBN(paper):978-1-4648-1893-6ISBN(electronic):978-1-4648-1894-3DOI:10.1596/978-1-4648-1893-6Cover design:Bill Pragluski,Critical Stages,LLCInterior design:Ricardo Echecopar,Beyond SACLibrary of Congress Control Number:2022947109.vContents Foreword xiii Acknowledgments xv About the Team xvii Main Messages xxi Abbreviations xxv Overview 1Introduction 1Part 1.Progress on poverty and shared prosperity 2Part 2.Fiscal policy for an inclusive recovery 9Notes 22References 23Part 1.Progress on Poverty and Shared Prosperity 271 Global Poverty:The Biggest Setback in Decades 29Summary 29Setting the scene:Poverty on the eve of the pandemic 30Poverty over the pandemic period:The nowcast 46Implications for reaching the 3 percent global poverty target by 2030 56Notes 58References 602 Shared Prosperity and Inequality:Uneven Losses and an Uneven Recovery 63Summary 63Introduction 64Shared prosperity and inequality,201419 66Shared prosperity and inequality during COVID-19 74Global inequality 83Notes 88References 88viPOVERTY AND SHARED PROSPERITY 20223 Beyond the Monetary Impacts of the Pandemic:A Lasting Legacy 91Summary 91Introduction 92Multidimensional poverty on the eve of the pandemic 94Pandemic impacts from a multidimensional perspective 98Notes 108References 109Part 2.Fiscal Policy for an Inclusive Recovery 111Why focus on fiscal policy?114What is in part 2?114Note 116References 1164 Protecting Households with Fiscal Policy:Learning from COVID-19 117Summary 117The nature of the fiscal response to the COVID-19 crisis 118The impact of the fiscal response on household welfare 121Factors that influenced the impact of fiscal policy 137Conclusion 142Notes 142References 1445 Taxes,Transfers,and Subsidies:Improving Progressivity and Reducing the Cost to the Poor 151Summary 151Introduction 152The impact of taxes and transfers on short-term poverty andinequality 155Taxation and distribution 162Transfers and distribution 171Economies of all income levels and capacities can achieve progressive fiscal policy 177Conclusion 180Notes 182References 1846 Fiscal Policy for Growth:Identifying High-Value FiscalPolicies 187Summary 187Introduction 187viiContentsMeasuring the value of fiscal policies 188Using information on the value of policies to informpolicychoices 191High-value policies that support growth 195Constraints on investing in high-value policies 201Increasing the value of policies through increased efficiency of spending 202Conclusion 204Notes 205References 2057 Putting It All Together:Better Fiscal Policy for Reducing Poverty and Increasing Shared Prosperity 211Summary 211Accelerating progress with better fiscal policy:Different options for different countries 211Spending for faster growth 213Positioning fiscal policy to protect households against future crises 215Raising revenue 221Data and evidence for better fiscal decision-making 229Can better fiscal policy put progress back on track?The need for global action 231Notes 235References 235Boxes O.1 Introducing the new 2017 PPP-based poverty lines 3 O.2 Measuring poverty in India 5 O.3 Tools that help to prioritize fiscal policies 17 1.1 How the new international poverty lineswere derived 31 1.2 New data now available to measure poverty in India 35 1.3 Predicting changes in poverty with nowcasts 46 1.4 The impacts of rising global food and energy prices on poverty 54 2.1 Data coverage:A growing challenge for measuring shared prosperity,particularly for poorer countries 64 2.2 Inequality and top incomes 71 2.3 Experiences on the ground with sharedprosperity 73 2.4 High-frequency phone surveys 77 3.1 Poverty-adjusted life expectancy:An index aggregating poverty and mortality 99 3.2 Lifecycle foundations for multidimensional comparisons interms of years of life 103 4.1 COVID-19 cash transfers in Togo 131 5.1 The CEQ framework:An integrated approach to fiscal incidence analysis 153 5.2 Different types of tax instruments 163 5.3 Incidence curves,concentration shares,and fiscal progressivity 165 5.4 Chile:The distributional impact of commonly missing CEQ fiscal instruments 170 5.5 Uruguay:The impact of indirect taxes and direct transfers 178 5.6 Bolivia and Ethiopia:Fiscal system impact on poverty and inequality 179 6.1 Calculating the value of a policy using the MVPF 190 6.2 The progressivity of spending on education and health 196viiiPOVERTY AND SHARED PROSPERITY 2022 7.1 Digitalization can improve the efficiency of fiscal administration,but not without challenges 227 7.2 Nudging tax compliance:How behavioral science tools can improve compliance at low financial and politicalcosts 228 7.3 Using evidence and data to expand COVID-19 social protection in South Africa 229Figures O.1 The COVID-19 pandemic triggered a historic shock to global poverty 2 O.2 Recent global inequality trends were reversed in 2020 4 O.3 Poverty reduction resumed slowly in 2021but may stall in 2022 7 O.4 A widespread reduction in poverty acrosscountries in 2020,followed by anascent and uneven recovery 8 O.5 Progress in poverty reduction has been altered in lasting ways 9 O.6 The interplay of shocks,policy,and poverty affects workplace mobility 11 O.7 Fiscal policy reduced the impact of the COVID-19 crisis on poverty but less so in poorer economies 12 O.8 Delivering support on time and to those in most need was challenging 14 O.9 In poorer economies,poorer householdsare more likely to be left withless money after taxes have been paidand transfers received 15 O.10 Poorer economies rely more on indirect taxes,which are less progressive 16 O.11 Poorer economies spend less on transfersthan on subsidies,which benefitthe poor less 16 1.1 Global extreme poverty has continued tofall but at a slower rate in recent years 30 B1.1.1 Poverty lines expressed in constant 2017 US$32 1.2 The global extreme poor are concentrated in Sub-Saharan Africa 36 1.3 From 1990 to 2019,poverty fell in all regions except the Middle East and North Africa 37 1.4 Poverty rates are higher among children in every region 39 1.5 The extreme poor were less connected online going into the pandemic 40 1.6 Global poverty at higher poverty lines continued to fall,slowly 41 1.7 At the higher poverty lines,the regional distribution of the global poor changes 42 1.8 The cost of basic needs increases as countries grow 43 1.9 Progress has been made in reducing the societal poverty rate in recent years 44 B1.3.1 Cross-checking poverty derived using various methods,change in poverty,201920 48 1.10 The COVID-19 pandemic was a historic shock to global poverty 50 1.11 Poverty increased across income groups in 2020 and displayed an uneven recovery in 202122 51 1.12 Poverty reduction stalled at all poverty lines in 2022 53 1.13 Poorer households spend more on food 56 1.14 Progress in poverty reduction has been altered in lasting ways 57 2.1 From 2014 to 2019,the vast majority ofeconomies made substantial progress insharedprosperity 67 2.2 Significant differences occured in sharedprosperity across regions and country income groups 68 2.3 Median income growth and shared prosperity are highly correlated 70 2.4 Within-country inequality was as likely to fall as to increase before the pandemic,but reductions in inequality were likely to be larger than increases 72 2.5 The pandemic led to large income losses among the bottom 40 75 2.6 Employment and income losses arising from the pandemic were severe,with certain groupsbeing hit harder 78 2.7 The pandemic likely harmed the quality of jobs among those who continued to work 80 2.8 In selected countries,the probability of income loss was greater for the bottom 40 than the top60,especially in urban areas 81 2.9 Projected changes in the Gini index showno clear pattern across countries with different income levels,with increasesand decreases equally likely 82ixContents 2.10 The decline in global inequality before the pandemic reflects the strong income growthof the global middle class,whereas those in the bottom and the middle lost the most during the pandemic 84 2.11 The pandemic caused the largest increase in global inequality since World War II,after a steady decline over the past two decades 85 2.12 An increase in between-country inequality was mainly responsible for the reversal in global inequality 86 2.13 The increase in between country inequality was driven by larger countries with large income shocks 87 2.14 The bottom 40 suffered a larger shock from the pandemic and is recovering more slowly than the top 60 88 3.1 Widespread learning losses were reported,especially among low-income countries during the COVID-19 crisis 93 3.2 Meals skipped were highest at the start of the COVID-19 crisis and in lower-income countries 94 3.3 Almost 40 percent of the multidimensionally poor are not monetarily poor 97 B3.1.1 Lower-income economies have experienced larger reductions in poverty-adjusted lifeexpectancy 101 B3.1.2 Reduction in poverty-adjusted life expectancy was driven by learning loss in lower-income countries and by increased mortality in higher-income countries 102 3.4 The pandemics impact on well-being through additional current and future poverty and excess mortality varies substantially across economies 107 4.1 COVID-19 elicited an unprecedented,but highly unequal,fiscal response 119 4.2 Health spending increased,but the shareofspending on education fell in many countries 119 4.3 Nearly all countries provided support to households and firms,but the type ofsupport varied by income group 120 4.4 Household and firm outcomes are strongly correlated in low-and middle-income countries 121 4.5 Fiscal support received by households and firms was lower in poorer economies 122 4.6 Support provided to households had significant impact 125 4.7 Households quickly employed coping strategies in response to lower labor incomes 127 4.8 Countries announced fiscal support quickly at the outset of pandemic 127 4.9 Fiscal support often arrived after needs emerged 128 4.10 In 2022,the fiscal response to rising foodand energy prices was much smallerand focused on subsidies 130 4.11 Countries implemented more broad-based support than targeted support during COVID-19 131 4.12 A breakdown by country income group reveals it was challenging to direct support to need 134 4.13 In simulations,fiscal policy reduced the impact of the COVID-19 crisis on poverty but less so in poorer economies 136 4.14 Fiscal policy reduced poverty more when more was spent 138 4.15 A higher credit rating was correlated with a larger fiscal response and increased externalborrowing 138 4.16 Support reached more households in formal economies and in countries with high prepandemic rates of social assistance 140 B5.1.1 CEQ framework:Fiscal policy impacts on household income through taxes and transfers 153 5.1 Taxes,transfers,and subsidies reduce inequality in all economies,butindifferent ways,from different starting positions,and to different degrees 157 5.2 Taxes,transfers,and subsidies increase short-term poverty in a majority of non-HICs 160 5.3 Taxes,transfers,and subsidies increase consumable income for the poorest households at all income levels except LICs 161xPOVERTY AND SHARED PROSPERITY 2022 5.4 Developing economies rely on indirect taxes for a majority of revenues;as economies get richer,they collect more through direct taxes,the main source ofOECD revenues 162 5.5 Direct taxes collect a higher percentage of income from richer households,but indirect taxes collect more relative to incomes from poorer households 164 B5.3.1 Progressive,neutral,and regressive incidence curves 166 B5.3.2 Inequality-reducing and inequality-increasing concentration shares 167 5.6 Poorer households buy more from informal vendors,reducing their effective indirect tax rates 168 5.7 Richer economies spend more on education,health,and social protection 171 5.8 Subsidies are expensive in many developing economies,often exceeding social protectionbudgets 173 5.9 Most transfers go to poorer households and provide strong income support;most subsidies go to richer households and provide little support to poorer ones 175 5.10 The indirect tax burden usually exceeds the benefit of direct transfers for the poor in all but HICs 177 B5.5.1 Net incidence of transfers and indirect taxes in Uruguay 178 B5.6.1 Net incidence of transfers and indirect taxes in Bolivia 179 B5.6.2 Net incidence of transfers and indirect taxes in Ethiopia 180 6.1 Fiscal policy trade-offs 192 B6.2.1 Education and health concentration shares,by income category and decile 196 6.2 Average MVPF of policies in the United States,and of two policies targeted to children in low-and middle-income countries 197 7.1 Some countries are facing the dual challenge of stimulating recovery and coping with limited access to external finance 213 7.2 Improving fiscal policy can help recoverthe losses of 2020,but it requires historic efforts and does not result in ending extreme poverty by 2030 233 7.3 Many countries cannot recover the losses of 2020 by 2030,despite historic fiscal efforts 234Map 1.1 In 2019,countries with the highest poverty rate at the US$2.15-a-day poverty line were mostly in Sub-Saharan Africa 37Tables B1.1.1 Derivation of global poverty lines,2011 PPPs versus 2017 PPPs 32 B1.3.1 Method used for nowcasting global poverty in 2020 47 B2.1.1 Data coverage summary,shared prosperity,circa 201419 65 2.1 Summary,shared prosperity and shared prosperity premium,78 economies 69 2.2 Within-country inequality tended to decrease but with variations across world regions,201419 73 3.1 Deprivations in education and infrastructure raise the multidimensional poverty measure above monetary poverty 96 3.2 Multidimensional poverty declined in recent years,along with monetary poverty 98 3.3 Declines across all dimensions of the multidimensional poverty measure are apparent even when restricting comparison to a consistent set of economies over time 98 3.4 Years lost to premature mortality exceed increase in years lived in poverty in about half of economies 105 3.5 Years lost to premature mortality and the increase in years of future poverty exceed the increase in years of current poverty in most economies 108xiContents 4.1 Cross-country correlations highlight the importance of access to external borrowing 139 5.1 Number of fiscal incidence studies,by region and income category 155 6.1 Cash transfers are higher value and better targeted than subsidies 195 7.1 Comparison of risk financing instruments 217 7.2 Progressive fiscal policy strategies areavailable to all countries 222xiiiForewordCOVID-19 marked the end of a phase of global progress in poverty reduction.During the three decades that preceded its arrival,more than 1 billion people escaped extreme poverty.The incomes of the poorest nations gained ground.By 2015,the global extreme-poverty rate had been cut by more than half.Since then,poverty reduction has slowed in tandem with subdued global economic growth.The economic upheavals brought on by COVID-19 and later the war in Ukraine produced an outright reversal in progress.It became clear that the global goal of ending extreme poverty by 2030 would not be achieved.Given current trends,574 million peoplenearly 7 percent of the worlds populationwill still be living on less than US$2.15 a day in 2030,with most in Africa.In 2020 alone,the number of people living below the extreme poverty line rose by over 70 million.That is the largest one-year increase since global poverty monitoring began in 1990.Looking at poverty more broadly,nearly half the worldover 3 billion peoplelives on less than US$6.85 per day,which is the average of the national poverty lines of upper-middle-income countries.Using that measure,poverty persists well beyond Africa.The prevalence and persistence of poverty darken the outlook for billions of people living around the world.The data confirm that the income losses of the poorest 40 percent of worlds population were twice as high as those of the richest 20 percent.Global median income declined by 4 percent in 2020the first decline since our measurements of median income began in 1990.This decline represents a major setback for the goal of shared prosperity.The poorest also suffered disproportionate setbacks in education and health,with massive learning losses and shortened lifespans.These setbacks,if left unaddressed by policy action,will have lasting consequences for peoples lifetime income prospects and for development more broadly.This latest Poverty and Shared Prosperity report offers the first comprehensive look at the global landscape of poverty in the aftermath of COVID-19 and the war in Ukraine.It outlines the limits of current fiscal policies for poverty reduction in low-and lower-middle-income economies,and points to the importance of reviving economic growth.It also shows the potential of fiscal-policy reforms to help reduce poverty and support broad-based growth and development.Strong fiscal policy measures made a notable difference in reducing COVID-19s impact on poverty.In fact,the average poverty rate in developing economies would have been 2.4 percentage points higher without a fiscal response.Yet government spending proved far more beneficial to poverty reduction in the wealthiest countries,which generally managed to fully offset COVID-19s impact on poverty through fiscal policy and other emergency support measures.Developing economies had fewer resources and therefore spent less and achieved xivPOVERTY AND SHARED PROSPERITY 2022less:upper-middle-income economies offset just 50 percent of the poverty impact,and low-and lower-middle-income economies offset barely a quarter of the impact.The rise in poverty in poorer countries reflects economies that are more informal,social protection systems that are weaker,and financial systems that are less developed.Yet several developing economies achieved notable successes during COVID-19.Helped by digital cash transfers,India managed to provide food or cash support to a remarkable 85 percent of rural households and 69 percent of urban households.South Africa initiated its biggest expansion of the social safety net in a generation,spending US$6 billion on poverty relief that benefited nearly 29 million people.And Brazil managed to reduce extreme poverty in 2020 despite an economic contraction,primarily using a family-based digital cash-transfer system.In short,fiscal policyprudently used and considering the initial country conditions in terms of fiscal spacedoes offer opportunities for policy makers in developing economies to step up the fight against poverty and inequality.To realize the potential of fiscal measures,the report calls for action on three fronts:Choose targeted cash transfers instead of broad subsidies.Half of all spending on energy subsidies in low-and middle-income economies went to the richest 20 percent of the population,who also happen to consume more energy.Targeted cash transfers are a far more effective mechanism for supporting poor and vulnerable groups:more than 60 percent of spending on cash transfers goes to the bottom 40 percent.Cash transfers also have a larger impact on income growth than subsidies.Prioritize public spending for long-term growth.COVID-19 has underlined how progress achieved over decades can vanish suddenly.High-return investments in education,research and development,and infrastructure projects should be made now.Governments need to improve their preparation for the next crisis.They also should improve the efficiency of their spending.Better procurement processes and incentives for public sector managers can boost both the quality and efficiency of government spending.Mobilize tax revenues without hurting the poor.This can be done by introducing property taxes,broadening the base of personal and corporate income taxes,and reducing regressive tax exemptions.If indirect taxes need to be raised,their design should minimize economic distortions and negative distributional impacts,and they should be accompanied with targeted cash transfers protecting the incomes of the most vulnerable households.Restoring progress in poverty reduction is possible when helped by strong and broad-based economic growthnot only in the poorest economies but in middle-income economies as well.The policy reforms outlined in this report can help in achieving the necessary course corrections,recognizing that it will likely require stronger global growth and focused policy adjustments.David MalpassPresidentWorld Bank GroupxvThe preparation of this report was co-led by Jed Friedman and Ruth Hill.The core team included Jessica Adler,Pierre Bachas,Katy Bergstrom,Ben Brunckhorst,Benoit Decerf,Uche Ekhator-Mobayode,Yeon Soo Kim,Christoph Lakner,Daniel Gerszon Mahler,Marta Schoch,Mahvish Shaukat,Mariano Sosa,Samuel Kofi Tetteh-Baah,Matthew Wai-Poi,and Nishant Yonzan.The extended team included,Evie Calcutt,Andres Castaneda,Mark Conlon,Leif Jensen,Jose Ernesto Lopez-Cordova,Arthur Galego Mendes,Rose Mungai,Minh Cong Nguyen,Stephen Michael Pennings,Tatiana Skalon,Veronica Montalva Talledo,Marika Verulashvili,Martha Viveros,and Kushan Sanuka Weerakoon,all of whom provided key inputs.Jessica Adler was project coordinator,and Anna Regina Rillo Bonfield,Karem Edwards,and Claudia Gutierrez provided general support to the team.The authors are especially appreciative of the Poverty and Inequality Data Team;the Data for Goals(D4G)Team,in particular Carolina Diaz-Bonilla,Minh Cong Nguyen,and Rose Mungai;and the regional statistical teams for their tireless work to ensure consistency and accuracy in global poverty monitoring and projections.The authors benefitted from discussions with the staff of the International Comparison Program Global Office at the World Bank,particularly Maurice Nsabimana,Marko Olavi Rissanen,and Mizuki Yamanaka.This work was conducted under the general direction of Deon Filmer,Haishan Fu,and Carolina Snchez-Pramo,with additional input from Benu Bidani,Luis Felipe Lopez-Calva,Berk Ozler,and Umar Serajuddin.The team is also grateful for the overall guidance received from Indermit Gill,Aart Kraay,and Carmen Reinhart.The report would not have been possible without the communications,editorial,and publishing teams.Elizabeth Howton,Anugraha Palan,and Joe Rebello led the communications strategy and engagement,with support from Paul Blake,Paul Gallagher,Nicholas Nam,Inae Riveras,Shane Kimo Romig,Torie Smith,and Nina Vucenik.The report was edited by Gwenda Larsen,Catherine Lips,Sabra Ledent,Honora Mara,and Sara Proehl,and designed by Ricardo Echecopar and Bill Pragluski.Alberto Cairo and Divyanshi Wadhwa provided data visualization services.Mary Fisk,Amy Lynn Grossman,Patricia Katayama,and Yaneisy Martinez from the World Bank Groups Publishing Program managed the editing,design,typesetting,translation,and printing of the report.The team gratefully acknowledges the advice from peer reviewers and external advisers.Peer reviewers for this report included Paloma Anos Casero,Dean Jolliffe,Ambar Narayan,Norbert Schady,and Celine Thevenot.External advisers included Stefan Dercon,Nathan Hendren,and Nora Lustig.Patrick Heuveline also provided expert guidance.In addition,the team would like tothank the many World Bank colleagues who provided comments during the preparation of AcknowledgmentsxviPOVERTY AND SHARED PROSPERITY 2022this report.In particular,the team is grateful for comments from Alan Fuchs,Ugo Gentilini,Alvaro Gonzalez,Chadi Bou Habib,Alaka Holla,Gabriela Inchauste,Maria Ana Lugo,Johan Mistiaen,Yuko Okamura,Pierella Paci,and Rinku Murgai.The team also benefited from many helpful discussions with teams across the World Bank Group,including the Office of the Chief Economist of the Human Development Global Practice.The report is a joint project of the Development Data and Development Research Groups in the Development Economics Vice Presidency,and the Poverty and Equity Global Practice in the Equitable Growth,Finance and Institutions Vice Presidency of the World Bank.Financing from the government of the United Kingdom helped support analytical work through the Data and Evidence for Tackling Extreme Poverty Research Programme.xviiCo-Leads of the ReportJed Friedman is a lead economist in the Development Research Group(Poverty and Inequality Team)at the World Bank.His research interests include the measurement of well-being and poverty as well as the evaluation of health and social policies.His current work involves investigating the effectiveness of health financing reforms,assessing the nutritional and development gains from early life investment programs,and incorporating new approaches to survey-based well-being measurement.Jeds previous work has appeared in the Journal of the European Economic Association,the Review of Economics and Statistics,the Journal of Development Economics,the Journal of Human Resources,The Lancet,and other outlets.Jed holds a BA in philosophy from Stanford University and a PhD in economics from the University of Michigan.Ruth Hill is a lead economist in the Global Unit of the Poverty and Equity Global Practice at the World Bank.Previously,she worked in the Sub-Saharan Africa and South Asia units on rural income diagnostics,poverty assessments,systematic country diagnostics,and an urban safety net project.From 2019 to 2021,Ruth was on external service as the chief economist at the UKgovernments Centre for Disaster Protection.Before joining the World Bank in 2013,she was a senior research fellow at the International Food Policy Research Institute,where she conducted impact evaluations on insurance,credit,and market interventions.Ruth has published in the Journal of Development Economics,World Bank Economic Review,Economic Development and Cultural Change,Experimental Economics,the American Journal of Agricultural Economics,and World Development.She has a DPhil in economics from the University of Oxford.Core TeamJessica Adler is a senior operations officer in the World Banks Global Unit of the Poverty and Equity Global Practice.She supports the delivery of the Poverty and Equity work program,including strategyand program design,operational advice,quality assurance,portfolio management,and results monitoring.Jessica also serves as the program manager for the Umbrella Facility for the Poverty and Equity trust fund.She holds a BA in international economics from George Washington University and an MPP from George Mason University.Pierre Bachas is an economist in the Development Research Group(Macroeconomics and Growth Team)at the World Bank.His research focuses on public finance in developing countries,About the TeamxviiiPOVERTY AND SHARED PROSPERITY 2022in particular,on optimal tax design and challenges to tax collection faced by low-and middle-income countries as a result of tax evasion,informality,and differences in economic structure.Prior to joining the World Bank,Pierre was a postdoctoral researcher at Princeton University.He holds a PhD in economics from the University of California,Berkeley.Katy Bergstrom is an economist in the World Banks Development Research Group(Poverty and Inequality Team).Her research interests lie at the intersection of public and development economics,specifically in optimal taxation and redistribution in developing countries,the determinants of income inequality,and investment differentials among children.Katy holds a BS in economics and mathematics from the University of Canterbury,New Zealand,and a PhD in economics from Stanford University.Ben Brunckhorst is a consultant in the Global Unit of the Poverty and Equity Global Practice at the World Bank.His research interests include climate change and poverty,disaster risk finance,and public infrastructure investment.Before joining the World Bank,he was a research assistant at the University of Oxford and the UK governments Centre for Disaster Protection.Ben holds bachelor degrees in engineering and economics from the University of Queensland,and an MSc in economics for development from the University of Oxford.Benoit Decerfis a research economist in the Development Research Group at the World Bank.He is an applied micro-theorist whose research interests include poverty measurement,welfare economics,and mechanism design.His current research on poverty measurement focuses on the design of poverty indicators aggregating different dimensions of deprivation,for example,combining subsistence and social participation,or combining poverty and mortality.Benoit holds an MS from the Katholieke Universiteit Leuven and a PhD from the Universit Catholique de Louvain,both in Belgium.Uche Ekhator-Mobayode is a World Bank Young Professional in the Global Unit of the Poverty and Equity Global Practice.She was previously an assistant professor of economics at the University of Pittsburgh at Bradford.Her previous World Bank experience includes one year with the pioneer cohort of the Forced Displacement Research Fellowship in 2018,and as a consultant on the Gender Dimensions of Forced Displacement project with the Global Gender Unit.Uche completed her PhD in economics at Northern Illinois University.Yeon Soo Kim is a senior economist in the Global Unit of the Poverty and Equity Global Practice,where she co-leads the global program on the distributional impact of the COVID-19 crisis.Shepreviously worked in the Europe and Central Asia and South Asia regions and was based in the Sri Lanka country office from 2018 to 2021.Yeon Soo has led and contributed to reports on a wide range of topics,including poverty,inequality,fiscal incidence,informality,and spatial disparities.Before joining the World Bank,she was an associate research fellow at the Korea Development Institute,where she worked on labor and health issues.She holds a PhD in economics from the University of Maryland,College Park.Christoph Lakneris a senior economist in the Development Data Group at the World Bank.His research interests include inequality,poverty,and labor markets in developing countries.In particular,he has been working on global inequality,the relationship between inequality of opportunity and growth,the implications of regional price differences for inequality,and the xixAbout the teAmincome composition of top incomes.He is also involved in the World Banks global poverty monitoring.Christoph leads the Poverty and Inequality Data Team,which publishes the Poverty and Inequality Platform,the home of the World Banks global poverty numbers.He holds a BA in economics,an MPhil,and a DPhil from the University of Oxford.Daniel Gerszon Mahleris an economist in the Development Data Group,where he is part of the Sustainable Development Statistics Team and the team behind the Poverty and Inequality Platform.Prior to joining the World Bank,he was a visiting fellow at Harvard Universitys Department of Government and worked for the Danish Ministry of Foreign Affairs.He holds a PhD in economics from the University of Copenhagen.Daniel conducts research related to the measurement of poverty,inequality,and well-being.Marta Schoch is a consultant in the Development Data Group at the World Bank,contributing to the groups work on global poverty and inequality measurement.Her research interests are in political economy,inequality,and poverty,with a focus on the formation of political preferences and its link with inequality.Since she joined the World Bank in 2020,she worked on the Poverty and Shared Prosperity Report 2020 and contributed to the Nigeria Poverty Assessment 2022.Previously,she worked for the University of Sussex,the Migrating out of Poverty Research consortium,and the Imperial College London.Marta holds a PhD in economics from the University of Sussex.Mahvish Shaukat is an economist in the World Banks Development Research Group(Macroeconomics and Growth Team).Her research studies issues in governance,political economy,and public finance,with the goal of understanding how institutions and incentives shape state efficacy and citizen welfare.Mahvish holds a PhD in economics from the Massachusetts Institute of Technology.Mariano Sosa is a consultant for the Global Unit of the Poverty and Equity Global Practice at the World Bank.His research interests include public finance and fiscal policy.His areas of expertise are fiscal incidence analysis,social policy,and the redistributive impact of fiscal policy in developing countries.Before joining the World Bank,Mariano was a research fellow for the Research Department of the Inter-American Development Bank.He holds an MPA in international development from Harvard Kennedy School.Samuel Kofi Tetteh-Baah is a consultant in the Development Data Group at the World Bank,Washington,DC.He generally works on the empirical analysis of poverty and inequality.He has primarily been assessing the impact of purchasing power parity data on global poverty estimates.He holds a PhD in development economics from the Swiss Federal Institute of Technology,Zrich,Switzerland.Matthew Wai-Poi is a lead economist in the World Banks Poverty and Equity Global Practice,where he supports the regional work program in East Asia and Pacific on understanding and addressing poverty and inequality,as well as on topics such as the middle class,top incomes,female labor force participation,and the distributional impacts of climate change.He is also global lead for the Distributional Impacts of Fiscal and Social Policies.Previously,also at the World Bank,he worked on poverty and inequality issues in the Middle East and North Africa,including the role of gender and displacement,and was based in Jakarta for eight years.He was co-editor of xxPOVERTY AND SHARED PROSPERITY 2022the recent flagship report on Targeting in Social Assistance and has published in the Journal of Political Economy and American Economic Association Papers and Proceedings,among others.Matthew has a PhD in economics from Columbia University and degrees in law and business.He worked in management consulting before joining the World Bank.Nishant Yonzan is a consultant for the Development Data Group(Poverty and Inequality Data Team)at the World Bank,contributing to the groups global agenda on measuring poverty and inequality.His research interests include the measurement and the causes and consequences of economic poverty and inequality.Some of his work has highlighted the role of institutions in shaping economic distributions and civil conflict,the impact of COVID-19 on poverty and inequality,the effect of cash transfers on fertility,and the differences in top incomes captured in survey and tax data.Nishant holds a PhD in economics from the Graduate Center of the City University of New York.xxiThe World Banks latest Poverty and Shared Prosperity report provides the first comprehensive look at global poverty in the aftermath of an extraordinary series of shocks to the global economy.The COVID-19 pandemic dealt the biggest setback to global poverty in decades.The pandemic increased the global extreme poverty rate to an estimated 9.3 percent in 2020up from 8.4 percent in 2019.That indicates that more than 70 million people were pushed into extreme poverty by the end of 2020,increasing the global total to over 700 million.2020 marked a historic turning pointan era of global income convergence gave way to global divergence.The worlds poorest people bore the steepest costs of the pandemic.Incomes in the poorest countries fell much more than incomes in rich countries.As a result,the income losses of the worlds poorest were twice as high as the worlds richest,and global inequality rose for the first time in decades.The poorest also suffered disproportionately in many other areas that directly affect their well-being.For example,they faced large setbacks in health and education,with devastating consequences,including premature mortality and pronounced learning losses.These setbacks,if left unaddressed by policy action,will have lasting consequences for peoples lifetime income prospects.The economic recovery from the COVID-19 pandemic has been uneven.The richest economies have recovered from the pandemic at a much faster pace than low-and middle-income economies.Rising food and energy pricesfueled by climate shocks and conflict among the worlds biggest food producershave hindered a swift recovery.By the end of 2022,as many as 685 million people could still be living in extreme poverty.This would make 2022 the second-worst year for poverty reduction in the past two decades(after 2020).These setbacks occurred when the speed of progress toward poverty reduction was already slowing.In the five years leading up to the pandemic,poverty reduction had slowed to 0.6 percentage point per year.Before 2020,the world was already significantly off course on the global goal of ending extreme poverty by 2030.This report projects that 7 percent of the worlds populationroughly 574 million peoplewill still struggle in extreme poverty in 2030.That is far short of the global goal of 3 percent in 2030.Further,the report shows that in 2019 nearly half of the worlds population(47 percent)lives in poverty when this is measured as living on less than US$6.85 a day.Main MessagesxxiiPOVERTY AND SHARED PROSPERITY 2022These setbacks call for a major course correction.Despite difficult global and domestic circumstances,policy makers must redouble their efforts to grow their economies in the coming yearswhile paying careful attention to who benefits from that growth.The need for growth that boosts the incomes of the poorest could not be greater than it is today.Resilient recovery will depend on a wide range of policies.This report focuses on fiscal policya key area at the center of pandemic and postpandemic responses.Fiscal policy concerns how governments raise revenue and spend public resources.This report offers new analysis on how fiscal policy was used during the first year of the pandemic.It also sheds light on the impact of taxes,transfers,and subsidies on poverty and inequality in 94 countries before 2020,providing important new insights into the impacts of fiscal policynot only during crises but also during normal conditions.Fiscal policy made a noticeable difference in reducing the pandemics impact on poverty.Without it,the average poverty rate in developing economies,assessed at national poverty lines,would have been 2.4 percentage points higher than it was.Yet fiscal policy was much less protective in poorer economies than in richer ones.Most high-income economies fully offset the pandemics impacts on poverty through the use of fiscal policy,and upper-middle-income economies offset one-half of the impact.However,low-and lower-middle-income economies offset only one-quarter of the impact.The differences in effectiveness reflected more limited access to finance,weaker delivery systems,and higher levels of informality,which made protecting jobs much more challenging.In general,low-and middle-income economies tend to be less successful than high-income ones in ensuring that the combination of taxes,transfers,and subsidies benefit the poor.Taxes finance spending on core services and investments,and transfers and subsidies can offset their impact on household incomes.But in two-thirds of low-and middle-income economies,the income of poor households falls by the time taxes have been paid and trans fers and subsidies have been received.This divergence is due in part to the larger share of the informal sector in those economies.As a consequence,taxes are predominantly collected indirectlythrough sales and excise taxesand income transfers are often too low to compensate.Given these structural challenges,this report identifies three key priority actions for fiscal policy in the coming years,as countries work to correct course:1.Reorient spending away from subsidies toward support targeted to poor and vulnerable groups.Subsidies are often poorly targeted.For example,one-half of all spending on energy subsidies in low-and middle-income economies goes to the richest 20 percent of the population,who consume more energy.In contrast,programs like targeted cash transfers are far more likely to reach poor and vulnerable groups.More than 60 percent of spending on cash transfers goes to the bottom 40 percent.Cash transfers also tend to have a larger impact on income growth than subsidies.2.Increase public investment that sup ports long-run development.Some of the highest-value public spendingsuch as investments in the human capital of young people or investments in infrastructure and research and developmentcan have a beneficial impact on growth,inequality,or poverty decades later.Amid crises,it is difficult to protect such xxiiimAin messAgesinvestments,but it is essential to do so.The COVID-19 pandemic has shown that hard-won progress achieved over decades can suddenly vanish.Designing forward-looking fiscal policies today can help countries to be better prepared and protected against future crises.3.Mobilize revenue without hurting the poor.This can be accomplished through property and carbon taxes and by making personal and corporate income taxes more progressive.Ifindirect taxes need to be raised,cash transfers can be simultaneously used to offset their effects on the most vulnerable households.Reforming fiscal policy will be an essential element of correcting course,but we must be realistic about how much we can expect it to do.Despite the promise of fiscal reforms,simulations suggest it will take heroic efforts toward more effective fiscal policy choices to restore the pandemic-related losses in the next four to five years.Successful fiscal reform will require the support of sufficiently powerful domestic coalitions interested in pursuing these types of policy goals,as well as stepped-up global cooperation.Accelerating global poverty reduction,and progress toward the Sustainable Development Goals,will require more comprehensive policy action.This will involve a broader set of policies to stimulate the kind of growth that can benefit people across all income levelsbut particularly those at the bottom.Correcting course is both urgent and difficult.Even if the course correction proves insufficient to end extreme poverty by 2030,it must begin nowfor the sake of a lasting recovery from the overlapping crises of today.xxvAbbreviationsBPS business pulse surveysCCT conditional cash transferCEA cost-effectiveness analysisCEQ Commitment to EquityCIT corporate income taxCPHS Consumer Pyramids Household SurveyGDP gross domestic productGDSP Global Database of Shared ProsperityGIC growth incidence curveGMD Global Monitoring DatabaseHFPS high-frequency phone surveysHIC high-income countryIMF International Monetary FundLIC low-income countryLMIC lower-middle-income countryMCPF marginal cost of public fundsMEB marginal excess burdenMIC middle-income countryMPM multidimensional poverty measureMSME micro,small,and medium enterprisesMVPF marginal value of public fundsNCD noncommunicable diseaseNSS National Sample SurveyOECD Organisation for Economic Co-operation and DevelopmentPALE poverty-adjusted life expectancyPDI pensions as deferred incomePFP pay-for-performance(program)PGT pensions as government transferPIP Poverty and Inequality PlatformPIT personal income taxPPP purchasing power paritiesR&D research and developmentSPP shared prosperity premiumxxviPOVERTY AND SHARED PROSPERITY 2022SPL societal poverty lineTCC tax compliance costsUCT unconditional cash transferUMIC upper-middle-income countryVAT value added taxWHO World Health Organization1IntroductionThe COVID-19 pandemic triggered a pronounced setback in the fight against global povertylikely the largest setback since World War II.Many low-and middle-income countries have yet to see a full recovery.High indebtedness in many countries has hindered a swift recovery,while rising food and energy pricesfueled in part by the Russian Federations invasion of Ukraine and climate shocks among the worlds biggest food producershave made areturn to progress on poverty reduction more challenging than ever.These setbacks have altered the trajectory of poverty reduction in large and lasting ways,sending the world even further off course on the goal of ending extreme poverty by 2030.The year 2020 marked a historic turning pointan era of global income convergence gave way to global divergence as the worlds poorest people were hardest hit.The richest people have recovered from the pandemic at a faster pace,further exacerbating differences.These diverging fortunes between the global rich and poor ushered in the first rise in global inequality in decades.COVID-19,along with surging relative hikes in food and energy prices,have affected every economy around the world.Yet the impact has not been uniform across countries.In fact,it has been a function of the policy choices made during the crisis.Similarly,a range of policies and actions today will be critical to a resilient recovery tomorrow.This report focuses on fiscal policy:how governments raise revenue and spend public resources.Fiscal policy is a main instrument used by governments to address immediate needs and pro-mote long-term growth,with wide-ranging impacts on poverty and inequality.For many coun-tries,fiscal policy is currently under considerable pressure.The fiscal demands of responding to a sustained crisis have left little fiscal space for additional spending,given that many countries already had little fiscal space at the onset of the pandemic(a result of years of lower growth and high debt).History shows that the fiscal choices that governments make in these moments can act as a lifeline for poor and vulnerable householdsor they can further impoverish and increase inequality.This report offers new analysis of how fiscal policy was used during the first year of the pandemic.It also sheds light on the impact of taxes,transfers,and subsidies on poverty and inequality in 94 countries before 2020,providing important new insights into the impacts of fiscal policy not only during crises but also under normal conditions.The analysis shows that the ability of fiscal policy to protect welfare during crises is limited in poorer countries.Fiscal policies fully offset the impact of COVID-19 on poverty in high-income countries(HICs),but they offset barely a quarter of the impact in low-income countries(LICs)and lower-middle-income countries(LMICs).Improving support to households as crises continue will require reorienting protective spending away from generally regressive and inefficient subsidies and Overview2POVERTY AND SHARED PROSPERITY 2022toward a direct transfer support systema first key priority.Reorienting fiscal spending toward sup-porting growth should be a second key priority.Some of the highest-value public spendingsuch as investments in the human capital of young citizens or investments in infrastructure and research and development(R&D)often pays out decades later.Amid crises,it is difficult to protect such investments,but it is essential to do so.Finally,it is not enough just to spend wiselywhen addi-tional revenue does need to be mobilized,it must be done in a way that minimizes reductions in poor peoples incomes.Exploring underused forms of progressive taxation(such as property,health,or carbon taxes)and increasing the efficiency of tax collection can help in this regard.What follows is a description of the two parts of this report,the first part painting in broad strokes the poverty and inequality trends since 2020,and the second part describing the possible role of fiscal policy in addressing the current crisis and putting poverty reduction back on track.Part 1.Progress on poverty and shared prosperityThree decades of successful global poverty and inequality reductionhit the pandemic wall in 2020The onset of the COVID-19 pandemic in 2020 marked a turning point in the 30-year pursuit of successful poverty reduction.Global poverty had declined from more than one in three persons(38 percent of the global population)in 1990 to less than one in 10 persons(8.4 percent)by 2019.1 The pandemic,delivering a broad-based shock to the global economy,triggered the first increase in extreme poverty in more than two decades(figure O.1).Because of a lack of official FIGURE O.1The COVID-19 pandemic triggered a historic shock to global povertySources:World Bank estimates based on Mahler,Yonzan,and Lakner,forthcoming;World Bank,Poverty and Inequality Platform,https:/pip.worldbank.org;World Bank,Global Economic Prospects database,https:/databank.worldbank.org/source/global-economic-prospects.Note:Panel a shows the global poverty headcount rate at the US$2.15 a day poverty line for 19502020.“Historical data”for the period 19902019 are from the Poverty and Inequality Platform.“Backcast”estimates are extrapolated backward from the 1990 lineup using growth in national accounts.National accounts data before 1990 are from World Bank,World Development Indicators database,https:/databank.worldbank.org/source/world-development-indicators;International Monetary Fund,World Economic Outlook,https:/www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases;Bolt and van Zanden 2020.“Current projection”uses the nowcast methodology outlined in chapter 1 and a variety of data sources to project the latest 2019 lined-up estimate to 2020.“Pre-COVID-19 projection”extrapolates the 2019 lineup to 2020 using per capita gross domestic product(GDP)growth forecasts from the January 2020 Global Economic Prospects database.Panel b shows the annual percentage point change in the global poverty headcount rate.Pre-COVID-19 projectionCurrent projectiona.Poverty rate(at the US$2.15 a day poverty line)BackcastHistorical dataBackcastHistorical datab.Annual changePercentage pointsPercent19501960197019801990200020102020200406019501960197019801990200020102020210178910112015201620172018201920208.49.38.1Percent3oVeRVieWsurvey data in many countries,uncertainty does exist around the poverty estimates for2020,and they will continue to be updated as more information becomes available.The survey work on which poverty numbers rely was halted or conducted by phone(rather than via the usual in-person interviews)during the peak of the crisis in the second quarter of 2020.Nevertheless,survey-informed assessments are now possible for an increasing number of countries.Taken together,they point to an increase in poverty that is large by historic standards.The incomes of the poorest 40 percent of the worlds population likely fell by 4 percent in 2020.As a result,the number of people living in extreme poverty likely increased by 11 percent in 2020from 648 million to 719 million.This increase pushed the extreme poverty rate 1.2 percentage points higher than projections going into the year(extreme poverty had been expected to fall).This is a historic setback in the fight against global poverty.Although data prior to 1990 are largely imputed based on national growth rates,and thus are more uncertain,the global scale of the pandemic shock likely renders the current shock the largest since 1945.Typically,past shocks(such as the 1997 Asian financial crisis,which resulted in a 0.2 percentage point increase in global poverty)tended to affect particular countries or regions.The current economic shock has led to widespread losses in employment and income as people stopped working and reduced consump-tion in every region of the world.Data collected by the World Bank using high-frequency phone surveys during the COVID-19 crisis found that,on average,23 percent of respondents in the coun-tries surveyed reported that they stopped working from April to June 2020,and 60 percent reported losing income.This report documents these trends using new poverty lines based on the 2017 round of International Comparison Program(ICP)price data collected to generate estimates of purchasing power parity(PPP)(see box O.1).All poverty estimates in this report use the 2017 PPP-based BOX O.1Introducing the new 2017 PPP-based poverty linesThe 2019 poverty numbers are the first to adopt the new estimates of global prices from the 2017 round of purchasing power parities(PPPs)that enable international comparisons of living standards across countries.With the new PPPs,the international poverty lines were revised.International poverty lines are calculated as the median of national poverty lines in low-income countries(LICs),lower-middle-income countries(LMICs),and upper-middle-income countries(UMICs),converted to US dollars using PPP exchange rates.The extreme poverty line of US$1.90(2011 PPP)increased to US$2.15(2017 PPP).The higher poverty line typically used to measure poverty in LMICs was updated from US$3.20(2011 PPP)to US$3.65(2017 PPP)and in UMICs from US$5.50(2011 PPP)to US$6.85(2017 PPP).This change,however,does not mean the new extreme poverty line is now higher,and therefore more people will be counted as living in extreme poverty.The increase in the international poverty line from US$1.90 to US$2.15 primarily reflects the difference between 2017 and 2011 nominal dollar values.The change in the global poverty rate due to these updated poverty lines is thus negligible.As a result,the new extreme poverty line does not dramatically change the number of people living in extreme poverty in 2019.Extreme poverty decreases slightly,by 0.3 percentage point,to 8.4 percent,reducing the global count of the extreme poor by 20 million.This is also true of the increase from US$3.20 to US$3.65 for LMICs,and thus the poverty rate also increases slightly at the global level by 0.5 percentage point.In UMICs,the national poverty lines have increased in real terms,on average,so the change in the international poverty line from US$5.50 to US$6.85 represents an increase in real as well as nominal terms.The global poverty rate at this line increased from 43 percent to 47 percent.4POVERTY AND SHARED PROSPERITY 2022FIGURE O.2Recent global inequality trends were reversed in 2020Sources:World Bank estimates based on Mahler,Yonzan,and Lakner,forthcoming;World Bank,Poverty and Inequality Platform,https:/pip.worldbank.org;World Bank,Global Economic Prospects database,https:/databank.worldbank.org/source/global-economic-prospects.Note:Panel a shows the global Gini index for 1950 to 2020.“Historical data”for the period 19902019 are from the Poverty and Inequality Platform.“Backcast”estimates are extrapolated backward from the 1990 lineup using growth in national accounts.National accounts data before 1990 are from World Bank,World Development Indicators database,https:/databank.worldbank.org/source/world-development-indicators;International Monetary Fund,World Economic Outlook,https:/www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases;Bolt and van Zanden 2020.“Current projection”uses the nowcast methodology outlined in chapter 1 and a variety of data sources to project the latest 2019 lined-up estimate to 2020.“Pre-COVID-19 projection”extrapolates the 2019 lineup to 2020 using per capita gross domestic product(GDP)growth forecasts from the January 2020 Global Economic Prospects database.Panel b shows the annual change in the global Gini Index,in Gini points.Pre-COVID-19 projectionCurrent projection6360666972a.Global inequality(Gini index)BackcastHistorical dataBackcastHistorical datab.Annual percentage change in global inequality19501960197019801990200020102020Gini indexAnnual change(Gini points)1.00.50.501.01950196019701980199020002010202063626120152016201720182019202062.062.661.9Gini indexpoverty lines.This updated approach changes the specification of the extreme-poverty line from US$1.90(2011 PPP)to US$2.15(2017 PPP),as well as the specification of other international poverty lines.The rise in global poverty is not limited to extreme poverty measured at the international pov-erty line.At the US$3.65 poverty line,the line for the typical LMIC,global poverty increased by about 1.3 percentage pointsfrom 23.5 percent in 2019 to 24.8 percent in 2020.At the US$6.85 poverty line,the line for the typical upper-middle-income country(UMIC),the poverty head-count rate also increased by 1.2 percentage points in 2020(equivalent to 134 million more poor people).The pandemic also increased global inequality.In terms of lost income,the worlds poor paidthe highest price for the pandemic:the percentage income losses of the poorest were estimated to be double those of the richest.The global Gini coefficient increased by a little over 0.5 points during the pandemic,from 62 points in 2019 to an estimated 62.6 points in 2020(figure O.2).By contrast,earlier years had seen a shrinking gap between the global poor and others.For example,the global Gini coefficient dropped by around 0.5 points every year between 2003 and 2013.The Asian financial crisis previously brought a cumulatively large increase in global inequality in the late 1990s.It is yet to be seen what the full impact of the current crisis will be on global inequality,but diverging recoveries since 2020 suggest the impact may be large.5OverviewBOX O.2Measuring poverty in IndiaThis report publishes global and regional estimates based on new data for India available for 201519.The source of the data is the Consumer Pyramids Household Survey(CPHS),conducted by the Centre for Monitoring Indian Economy,a private data company.India has not published official survey data on poverty since 2011.Given the countrys size and importance for global and regional poverty estimates,the CPHS data help fill an important gap.The household consumption data used for poverty monitoring is based on an analysis by SinhaRoy and van der Weide(2022)in which the CPHS sample is re-weighted to more closely resemble a nationally representative survey and the consumption aggregate is adjusted to more closely matchthe consumption aggregate used in the official series.Other methods have been used to estimate the evolution of poverty in India since 2011.The methodological differences between the national accountsbased approach of Bhalla,Bhasin,and Virmani(2022)and SinhaRoy and van der Weide(2022)have been outlined elsewhere(Ravallion 2022;Sandefur 2022).Given widespread agreement that microdata from household surveys are necessary for credibly measuring poverty,this report uses the CPHS.The CPHS was also conducted during 2020.Although the full analysis required to ensure consistency between this survey and previous surveys has not been completed,initial analysis indicates that the CPHS serves as a useful source of data on the trends in consumption in 2020.Most countries experienced increases in poverty,but not always higher inequalityThe estimated increase in global poverty of 71 million people is heavily influenced by the most populous countries because each individual in the world is weighted equally.Although large,China is home to a small share of the global extreme poor and had a moderate economic shock in 2020;as a result,China does not contribute to the global increase in extreme poverty in 2020.The global and regional poverty estimates presented in this report include new data for India for 201519(seebox O.2).This represents an improvement over the previous edition of this report,in which the absence of recent data for India severely limited the measurement of poverty in South Asia.In 2020,India experienced a pronounced economic contraction.Because 2020 pov-erty estimates from household survey data for India are still being finalized,there is considerable uncertainty over the estimates of poverty increases in India in 2020.A national accountsbased projection implies an increase of 23 million,whereas initial estimates using the data described in box O.2 suggest an increase of 56millionthis latter number is used for the global estimate.While the final number could be higher or lower,all indications suggest the global shock to poverty reduction as a result of the pandemic was historically large.Although smaller in popula-tion,Nigeria and the Democratic Republic of Congo are still relativelylarge countries and home to a large share of the global extreme poor;however,they had relatively mild economic shocks in 2020 and so contribute less to the global increase in extreme poverty,about three million and half a million,respectively.Another way to explore the global scope of this crisis period is to note the number of coun-tries that experienced substantive changes in poverty and inequality.Poverty increases were widespread across regions and income groups(figure O.4).With the exception of 19 countries that reduced poverty through generous transfers,nearly all countries saw poverty increases,often quite large,at the poverty line relevant to their income group.The effects were much larger in some countries than in others,highlighting the fact that a countrys economic structure and policy response mediated the welfare effects of the common global crisis.In aggregate,in terms 6POVERTY AND SHARED PROSPERITY 2022of extreme poverty,the largest increases were observed in LICs and LMICs.In UMICs,poverty actually fell in 2020,driven in part by fiscal support in large UMICs,such as Brazil and South Africa,that mitigated the impact of the crisis on poverty(and even reduced poverty in some cases).Although global inequality rose,this rise did not stem from widespread within-country increases in inequality.In fact,within-country inequality actually fell in many countries,thereby somewhat mitigating the increase in global inequality.The increase in global inequality would be 37 percent higher if within-country inequality changes are not taken into account.Because the change in inequality at the national level is mixed as well as small in most cases,the increase in country poverty rates is largely driven by declines in average income at the country level.The nonmonetary dimensions of the pandemic and its impacts may ultimately prove to be more costly than the monetary dimensionsThe costs of the pandemic go beyond monetary impacts.These broader costs principally include the lost learning of students unable to attend school and significantly higher global mortality rates.In fact,the world experienced the first decline in global life expectancy since the end of World War II:life expectancy fell by almost two full years(Heuveline 2022).Significant increases in pandemic-related mortality,both directly from COVID-19 infections and indirectly from factors such as declines in health care use,have afflicted every region of the world.The countries with the largest mortality burdens were middle-income countries(MICs)that confronted large economic shocks and social disruptions,but also had a relatively high share of older adults in the population who were more vulnerable to COVID-19(WHO 2022).As for the learning of young students today,many countries enforced social distancing mea-sures to curtail the spread of the illness.Measures included closing schools for extended periods.From the onset of the pandemic until October 2021,the formal school system was closed for an entire school year,on average,across all countries,and even for a longer period in MICs.As a result,multidimensional poverty,which includes an educational dimension,increased in the short run.Perhaps more important,the learning loss will have significant long-term con-sequences for todays students and even the wider society if students are unable to make up their losses.This is because the growth potential of economies over the long term will be lower.Poverty will be prolonged for millions of people,especially the students of today who have borne the brunt of extended school closures and are now likely to earn less over their lifetime.A comparison of the poverty shock in 2020 and 2021 with the impact of the current learn-ing losses on long-run poverty simulations suggests that the persistence of poverty from learn-ing losses will exceed the contemporaneous crisis-induced poverty shock for many countries.2 The reason is that the drag on growth could persist for decades if unaddressedeven though the implications of learning loss for aggregate growth may appear modest within any one year.In LICs,the crisis increased the number of years spent in poverty by 6.1 per 100 persons for the two-year period 202021,whereas the loss in learning may lead to an additional total of 13.3years in poverty per 100 persons,distributed over the longer 202250 period.The same metrics for LMICs and UMICs are 6.6 and 4.5 years in poverty now(202021)and 11.8 and 12.9 years in poverty over the future(202250),respectively.In 80 percent of the countries stud-ied,the simulated future poverty increase due to learning loss exceeds the measured short-run increase in poverty.These simulations are a point-in-time comparison that projects current conditions into the future.To the extent that the losses of 2020 can be reversed through policies addressing learning loss,the projected declines can be corrected and a comeback could even be quite rapid.But such outcomes will depend in part on the policy choices of today,including those discussed in this report and in the World Banks forthcoming report Collapse and Recovery:How the COVID-19 Pandemic Eroded Human Capital and What to Do About It(World Bank,forthcoming b).7oVeRVieW202122:The great divergence and a stalled recoverySince 2020,progress in poverty reduction has been slow.Poverty estimates for 2021 and 2022 have been“nowcasted”that is,gross domestic product(GDP)growth rates have been used to forecast household incomes,assuming all households experience equal growth.Nowcast esti-mates suggest that poverty reduction resumed in 2021,but only at the slow rate of progress seen prior to the crisis(figure O.3).Projections for 2022 are that the pace of poverty reduction will further stall as global growth prospects dim with the war in Ukraine,a growth slowdown in China,and higher food and energy prices.High food price inflation can in the short run have especially detrimental impacts on poorer households,which spend a larger share of their income on food.To highlight the additional nega-tive impact of food prices in the short run,poverty simulations are also presented for a downside scenario that assumes maximum impact,as reflected by the price data and consumption choices of poorer households.3 In the long run,households may adapt to higher prices by changing their con-sumption patterns to at least partly lessen the impacts,and wages in certain sectors can adjust.For many poor rural households engaged in agriculture,higher food prices can be a source of income growth.World Bank poverty assessments conducted after the 2008 and 2011 food price crises in Bangladesh,Cambodia,Ethiopia,India,and Uganda showed the important role that higher food prices,which led to higher agricultural income growth and higher agricultural wages,played in raising the incomes of poor households.However,increases in food prices will hurt some poorsuch as poor urban householdsmuch more than others.The impacts on the urban poor can lead to unrest in cities(as in earlier food price crises)and require a strong policy response.At least 667 million people were expected to be in extreme poverty by 2022.That is 70 million more than the forecast would have been without the lingering effects of COVID-19 and the Russian invasion of Ukraine.In a worst-case scenario,up to 685 million people could be inextreme poverty89 million more than would have otherwise been the case.At these levels,thenumber of people forecasted to move out of poverty in 2022 could be as low as 5 million.FIGURE O.3Poverty reduction resumed slowly in 2021 but may stall in 2022Sources:World bank estimates based on mahler,Yonzan,and Lakner,forthcoming;World bank,Poverty and inequality Platform,https:/pip.worldbank.org;World bank,global economic Prospects database,https:/databank.worldbank.org/source/global-economic-prospects.Note:the figure shows the number of poor at the us$2.15 a day poverty line.For 2022,nowcasts are reported for the“Current projection”and for the“Current projection(allowing for food price impacts on poor).”56061066071076081020152016201720182019202020212022Historical dataNumber of poor(millions)Pre-COVID-19 projectionCurrent projectionCurrent projection(allowing for food price impacts on poor)6486296125967196906676858POVERTY AND SHARED PROSPERITY 2022This finding places 2022 on track to be the second-worst year for poverty reduction in the last 22years(after 2020).Global poverty rates are projected to be just as high in 2022 as they were in 2019,indicating several years of lost progress.The pathways countries have followed since the pandemic have exacerbated global inequality,with richer countries recovering faster than poor countries.Figure O.4 shows the change in the number of extreme poor,by year,for three income groups.Recovery is estimated to have been lower in LICs,with 11 of 27 countries still estimated to have poverty increases in 2021 and full recovery expected in only six.Although recovery was more widespread in LMICs in 2021,it is estimated that most countries had not reversed the substantial increase in poverty seen in 2020.In UMICs,recovery was stronger but not bymuch.From 2020 to 2022,because of differences in between-country growth rates,the incomes of the worlds richest 20 percent likely grew by 3.3 percent.By contrast,the rate for the bottom four quintiles was 2.1 percent to 2.5 percent.Taken together,the threats to poverty reduction noted in this report have set back progress by at least four years(figure O.5).By 2030,the global extreme poverty rate will be 7 percent.The goal of reducing global poverty to 3 percent by 2030 was difficult enough to achieve before the current crises.The recent setbacks have put this target nearly out of reachand there is an urgent need to correct course.These projections mask substantial differences in projections between regions.Extreme poverty is projected to become increasingly concentrated in Sub-Saharan Africa.Other regions will likely reach the 2030 target of less than 3 percent extreme poverty by 2030,but poverty is projected to remain well above target in Sub-Saharan Africa.Achieving the 3 percent goal by 2030 would require Sub-Saharan Africa to achieve growth rates about eight times higher than historical rates between 2010 and 2019.The compounding pressure of the overlapping crises experienced over the past two years has created an elevated risk profile for the world.Government policies play a critical role in shielding societies from the worst outcomes of crises.Fiscal policy is a key instrument of such policies.Unfortunately,many countries,especially LICs,entered the pandemic with fiscal systems unable to fully confront or deal with the challenges they faced.The coming years present new opportu-nities and challenges.The second part of this report discusses how fiscal policy can be employed to promote a robust and inclusive recovery.FIGURE O.4A widespread reduction in poverty across countries in 2020,followed by a nascent and uneven recoverySources:World bank estimates based on mahler,Yonzan,and Lakner,forthcoming;World bank,Poverty and inequality Platform,https:/pip.worldbank.org;World bank,global economic Prospects database,https:/databank.worldbank.org/source/global-economic-prospects.Note:the figure shows the share of economies where the poverty rate has decreased or increased relative to the prior year and relative to 2019,by income group.economies where poverty increased include those where poverty did not change.LiCs=low-income countries;LmiCs=lower-middle-income countries;umiCs=upper-middle-income countries.020406080100Share of economies(%)Share of economies(%)Share of economies(%)a.LICs(US$2.15 a day)202020212022 b.LMICs(US$3.65 a day)020406080100202020212022c.UMICs(US$6.85 a day)020406080100202020212022Poverty lower than 2019Poverty fell,but still higher than 2019Poverty increased9oVeRVieWPart 2.Fiscal policy for an inclusive recoveryDuring the COVID-19 crisis,various public health policiessuch as stay-at-home directives as well as new and existing monetary,financial,and fiscal policiesaffected the dynamics of disease transmission and altered growth,poverty,and learning outcomes.These outcomes were also shaped by the economic and social conditions of the country and the particular mix FIGURE O.5Progress in poverty reduction has been altered in lasting ways Sources:World bank estimates based on mahler,Yonzan,and Lakner,forthcoming;World bank,Poverty and inequality Platform,https:/pip.worldbank.org;World bank,global economic Prospects database,https:/databank.worldbank.org/source/global-economic-prospects.Note:two growth scenarios are considered:the“Current projection”uses growth rates from the June 2022 global economic Prospects(geP)database to project poverty up to 2024.the“Pre-CoViD-19 projection”uses the January 2020 geP growth rate to project poverty to 2022.both scenarios use the country-level average annual historical(201019)growth rate to project poverty in the remaining years.the”3percent target”line in panel b is based on the estimate of the number of poor in 2030255 million.Pre-COVID-19 projectionCurrent projection0246810122015201620172018201920202021202220232024202520262027202820292030Historical data3 percent target3 percent target0100200300400500600700800MillionsPercent9002015201620172018201920202021202220232024202520262027202820292030a.Poverty rateb.Number of poorHistorical data6.58.49.38.88.46.855164871969066757410POVERTY AND SHARED PROSPERITY 2022ofpolicies chosen.Many of these policies were adopted in an environment of economic stress,with great uncertainty about the ultimate impacts they might have.The effects of some of those policy choices are now on view amid the current food and energy price crisis.Today,food-export bans risk further exacerbating food price volatility,as they did during the 200608 food price crisis(Martin and Anderson 2011).Monetary,trade,and fis-cal policies(such as lower food tariffs and protective cash transfers)tailored to specific coun-try conditions offer the potential to soften the impacts.However,the dominant policy choice has been subsidies,implemented by 93 percent of the countries that took early fiscal action in response to the food and energy price crisis,even though such subsidies are often not well tar-geted to need and can be detrimental in the long run.The second part of this report starts with the recognition that the same policy can have very different effects in different countries.Higher-income economies are more resilient in the face of shocks(World Bank 2013)because their households and firms are endowed with wealth and superior health and education systems and thus are able to adapt to changing circumstances.Governments in LICs and MICs face policy options with more limited effectiveness during a crisis than richer countries because of the structure of their economies(Loayza 2020).A stay-at-home order will be largely futile if people are compelled by necessity to work outside the home.Financial sector policy is less effective when it cannot reach a large informal sector.And fiscal policy cannot achieve much if fiscal space is constrained and the structure of an economy limits the reach of standard fiscal policy instruments.Various features of an economy can amplify the impact of any shock and limit the impact of policies to address it.This interplay of shocks,policy impact,and poverty is well illustrated in figure O.6(Aminjonov,Bargain,and Bernard 2021).The figure depicts average workplace mobility(based on smartphone data)across 43 low-and middle-income countries.Stay-at-home directives and private decisions to avoid exposure to COVID-19 drove a dramatic reduction in mobility in March 2020.Reductions in mobility were large in both high-as well as low-poverty regions in countries.The reductions in mobility,however,were larger in the regions with lower poverty and in those that received income support.Mobility fell further in those areas that were better able to accommodate a stay-at-home order by virtue of the prevailing nature of work and the relative ability of the well-off to stay home.The difference in mobility in places with and without income support exceeds the difference in mobility in places with low and high poverty rates.As a result,income support policies also had a larger impact on mobility in higher-poverty areas than in lower-poverty areas.This finding underscores the fact that policies that promote development enable more resilience in the face of crises.The focus on fiscal policyFiscal policy consists of the decisions governments make on how to raise revenue and spend public resources.Part 2 of this report focuses on how fiscal policy affects poverty and inequality.Fiscal choices affect growth,employment,and wages,as well as the services available,the prices people pay,and the income people have left after taxes are paid and transfers are received.In many countries,fiscal policy is currently under considerable pressure.Even as govern-ments decide which fiscal policies are the most suitable for achieving an inclusive recovery and long-run growth,they must deal with rising fiscal deficits and debt burdens and with little space for fiscal policy to support the recovery and prepare for ongoing and future crises.LICs and MICs are significantly more indebted today than two years ago.In 2020,more emerging econ-omies experienced credit rating downgrades than over the entire 201019 period(Kose et al.2022).Even as countries saw their revenues drop because of the COVID-19 crisis,they had to pursue expansionary fiscal policy if they wanted to mitigate the worst impacts of the downturn.Many countries now need to raise revenue,reduce spending,or both to escape debt distress.Historically,such fiscal policy decisions have often hurt the poornot only in the immediate 11oVeRVieWterm,but also limiting the longer-term opportunities available to them.Policy makers must nav-igate the current challenges in ways that do not further impoverish the poor today or reduce the opportunities they might enjoy tomorrow.4 Fiscal policy,poverty,and inequality:Three findings1.In low-and middle-income countries,fiscal policy can protect peoples welfare in a crisisbut with limitsDuring the early stages of the pandemic,fiscal policy effectively prevented some vulnerable house-holds from slipping into poverty.Microsimulations in LICs and MICs suggest poverty would have been,on average,2.4 percentage points higher without a fiscal response(figure O.7).However,even though fiscal policy nearly fully offset the impact of the pandemic on poverty in HICs,it offset only half of the impact in UMICs and just over a quarter of the impact in LICs and LMICs.There are some lessons to learn from this global experiencenot only how to improve fiscal policy in the years to come but also how to be clear-eyed about the limits of protecting poor FIGURE O.6The interplay of shocks,policy,and poverty affects workplace mobilitySource:based on data from Aminjonov,bargain,and bernard 2021.Note:the figure depicts workplace mobility in 2020(based on smartphone data)across subnational regions with high and low poverty rates and with and without income support in 43 low-and middle-income countries.the data points reflect the calculations by Aminjonov,bargain,and bernard(2021)based on google mobility data(change in visits to workplaces relative to the daily median from January 3 to February 6,2020);poverty data from national statistical offices and estimates by Aminjonov,bargain,and bernard(2021)using household surveys;and oxfordCoViD-19 government Response tracker data on CoViD-19 income support.the figure shows the local polynomial fit with a 95 percent confidence interval of daily mobility across regions,weighted by 1 divided by the number of regions in the corresponding country.Poverty is measured as the share of people living below national or international poverty lines in a subnational region.Poverty is defined as lower(higher)if a regions poverty rate is below(above)the countrys median regional poverty rate.CoViD-19 income support shows the daily status of whether the government provides any income support to those who cannot work or who have lost their jobs because of the CoViD-19 pandemic(country-day variation in income support).First COVID-19 income support5060708090100Workplace mobility indexFeb 17Mar 16Apr 13May 112020Jun 8Jul 6Aug 3Aug 31Higher poverty and no income supportLower poverty and no income supportHigher poverty and income supportLower poverty and income support12POVERTY AND SHARED PROSPERITY 2022FIGURE O.7Fiscal policy reduced the impact of the COVID-19 crisis on poverty but less so in poorer economiesSources:Estimates collected from published and World Bank microsimulation studies.See chapter 4 of the report for a full list.Note:The figure shows the results of two simulations from each economy study:one showing the increase in poverty that would have occurred had no fiscal response been present(no mitigation),and one showing the increase in poverty taking into account the fiscal response(mitigation).The increase in poverty is measured against the national poverty line or the global poverty line appropriate to the economy income category.For some economies,more than one study is available,as indicated by the use of“1”or“2”after the economy name in the figure.Full details of the data used are in chapter 4 online annex,annex 4A,table 4A.1,available at http:/mitigationMitigation5.33.91.312.812.610.510.18.88.78.57.57.26.76.36.35.45.35.14.64.23.02.82.61.97.67.27.17.06.95.43.73.32.92.82.72.52.42.21.71.20.30.31.61.41.00.142024681012High-incomeChile-2High-income averageChile-1UruguayUpper-middle-incomeArmeniaSouth AfricaPeruGeorgiaColombia-1Dominican RepublicPanamaArgentina-2GuatemalaUpper-middle-income averageArgentina-1TrkiyeCosta RicaEcuadorParaguayColombia-2MexicoBrazil-1Brazil-2MoldovaRussian FederationLower-middle-incomePhilippinesTunisiaBoliviaKenyaHondurasEl SalvadorLower-middle-income averageNicaraguaHaitiWest Bank and GazaEgypt,Arab Rep.Sri LankaZambia-2Zambia-1IndonesiaGhanaTanzaniaVietnamLow-incomeMozambiqueUgandaLow-income averageMalawiPercentage point change in poverty13oVeRVieWand vulnerable households through fiscal policy.High borrowing costs limited the scale of the COVID-19 fiscal response in many low-and lower-middle-income countries.In survey results reported in World Development Report 2022:Finance for an Equitable Recovery(World Bank 2022b),83 percent of LIC policy makers indicated they were concerned about debt sustainabil-ity or access to external borrowing for financing their fiscal response to the crisis.ManyLIC and LMIC policy makers were even more concerned about access to international financial support.LICs relied almost entirely(95 percent)on international support to finance a fiscal response.Such financing was also a major source of support for LMICs(73 percent)and for UMICs(50 percent).Going into the crisis,more than half of International Development Association(IDA)countries were in debt distress,so could not borrow much.Their main source of external finance was highly-concessional flows from multilateral development banks.This highlights the importance of access to finance in a crisis response.The structure of the economy also limited the type and impact of fiscal policy tools that could be used.Providing firms with the support needed to save jobs was almost impossible in countries with large informal sectors.The share of workers at firms receiving wage subsidy support was larger in countries with a greater share of formal workers in the economy prior to the crisiseven when controlling for the overall level of spending and GDP per capita.This finding is trou-bling because emerging evidence suggests that spending to protect jobs may have been more impactful in hastening economic recovery,increasing employment,and reducing poverty than income support measures(World Bank,forthcoming a).Faced with widespread uncertainty about the impact of the crisis on household incomes and the widespread losses across poor,vulnerable,and middle-class households,most countries were under considerable political pressure to quickly provide broad income support.HICs and UMICs were more likely to provide this support through universal transfers,whereas LMICs and LICs were more likely to implement subsidies alongside targeted transfers.Although subsi-dies were similarly universal and often rapidly introduced,they came with several disadvantages.A greater share of subsidy support went to the better-off,and subsidies distorted the prices that households faced.On average,almost three months passed after lockdowns began before transfers reached recipients in LICs and MICs,even though income losses and rising food insecurity took hold immediatelysee Beazley,Marzi,and Steller(2021)and figure O.8.Delivery was much quicker when digital payment systems were present.Transfers did target poorer households in general.However,reaching vulnerable households with income losses who were not the usual benefi-ciaries of social protection systems proved more challenging,especially in LICs and LMICs.Nevertheless,there are standout examples of innovation in reaching well-targeted beneficia-ries during challenging times,such as South Africa and Togo(discussed in further detail in the report).In summary,the experience of delivering support during the pandemic highlights the importance of investing in delivery systems for transfers thatwhen neededcan deliver timely support beyond a narrowly targeted group.2.In poorer countries,poor households often have less income after taxes are paid and transfers are receivedThe lack of fiscal space in many poorer countries going into the COVID-19 crisis and the lim-ited delivery systems available to deliver direct transfers to poor and vulnerable households reflected fiscal choices made in the run-up to the crisis.This report brings together for the first time analysis of the impact of taxes,transfers,and subsidies on household income in 94 LICs and MICs(including 55 LICs and LMICs).This analysis assesses the degree to which taxes are raised equitably and transfers and subsidies reach poor and vulnerable households.Taken together,taxes,transfers,and subsidies reduce inequality in all countries while financing spending on security,health,education,and investments for growth and poverty reduction.HICs are effective at ensuring that taxes,transfers,and subsidies do not reduce the disposable income of 14POVERTY AND SHARED PROSPERITY 2022poor households.However,this is not the case for LICs and MICs.In two-thirds of those countries,the income of poor households falls by the time taxes have been paid and transfers and subsidies have been received(figure O.9).In LICs the income of all households is lower after taxes,transfers and subsidies.The informal sector accounts for a large share of the economy in LICs and MICs.As a consequence,taxes are predominantly collected indirectly,and income transfers are often too low to compensate for the offsetting impact of indirect taxes on poor and vulnerable households.Encouragingly,though,across all income levels some countries are able to reduce both inequality and poverty.The highest performers in each category tend to reduce poverty by 6 to 8 percentage points at the poverty line relevant to their income category.On average,however,reducing poverty becomes much less likely for countries in lower income categories.All HICs reduce poverty by FIGURE O.8Delivering support on time and to those in most need was challenging Source:World bank estimates based on data from World bank CoViD-19 high-frequency phone surveys.Note:Panel a shows the share of households in each income group that lost income and the share of households that received support across three periods during the pandemic(averaging across economies in each income category).Panel b shows the difference between the share of households that received support and lost income or a job and the share of households that received support but did not lose income or a job(each dot represents an economy).economies are weighted equally.hiCs=high-income countries;LiCs=low-income countries;LmiCs=lower-middle-income countries;umiCs=upper-middle-income countries.a.Income losses from the pandemic and support received,202021b.Availability of public assistance for households with job or income losses versusthose with no losses Countries where householdsWITH job or income losseswere more likely to receive supportCountries where householdsWITHOUT job or income losseswere more likely to receive supportWith losses more likelyWithout losses more likelyEqual likelihoodTwice as likelyTwice as likelyHICsUMICsLMICsLICs020406080Share of households(%)Share of households(%)Share of households(%)AprJun 20JulDec 20JanAug 21020406080AprJun 20JulDec 20JanAug 21020406080AprJun 20JulDec 20JanAug 21UMICsLMICsLICsReceived supportLost incomeReceived supportLost incomeReceived supportLost income15oVeRVieWFIGURE O.9In poorer economies,poorer households are more likely to be left with less money after taxes have been paid and transfers received Sources:original estimates based on data from CeQ institute,CeQ Data Center on Fiscal Redistribution,https:/commitmentoequity.org/datacenter;organisation for economic Co-operation and Development data;World bank data.Note:the figure shows consumable income(income after direct and indirect taxes have been paid and cash transfers and subsidies have been received)as a percentage of market income(income before any taxes have been paid or transfers or subsidies received),by market income decile,aggregated by income group using the median.the sample includes 5 hiCs,19 umiCs,16 LmiCs,and 3 LiCs.hiCs=high-income countries;LiCs=low-income countries;LmiCs=lower-middle-income countries;umiCs=upper-middle-income countries.809010011012013014015016017012345678910Consumable income as a share of market income(%)Market income decileHICsUMICsLMICsLICsmore than 1 percentage point,compared with only six of the 23 UMICs and only one of the 24 LICs and LMICs.It is thus a challenge to raise revenue without increasing poverty in a country with a large informal sector and limited safety net coverage.Poorer countries collect less tax revenue and primarily collect taxes in the least progres-sive way64 percent of taxes are from indirect taxes(value added,excise,and trade taxes).By contrast,just 28 percent of tax revenue in Organisation for Economic Co-operation and Development(OECD)member countries is derived from these sources(figure O.10).In richer countries,more taxes are collected from direct taxes:personal income tax and other taxes on income such as social security contributions.Direct taxes are typically more progressive because they can be designed to increase with income,unlike taxes on goods that everyone must pur-chase regardless of income level.In informal economies where income is not easily observed,recorded,and taxed,there is a greater reliance on indirect taxes.Because of this reliance,a signif-icant share of revenue is collected from the poor.5 In LICs and LMICs,spending on direct transfers is low on average,and it is dwarfed by spending on subsidies.Figure O.11 compares spending on energy and agricultural subsidies with all social protection spending.In HICs,spending on social protection far exceeds spend-ing on subsidies.In UMICs,spending on energy and agricultural subsidies is equal to spend-ing on social protection,whereas in LMICs and LICs social protection spending is less than one-half and one-tenth of spending on energy and agricultural subsidies,respectively.Only 20 percent of spending on subsidies reaches the bottom 40 percent in each country,and this,combined with low spending on transfers,means there is little compensation for the reduction in income and consumption brought about by indirect taxes.Subsidies are widespread,in part,because they are popular,providing support to many interest groups on whom governments 16POVERTY AND SHARED PROSPERITY 2022FIGURE O.10Poorer economies rely more on indirect taxes,which are less progressiveSources:international Centre for tax and Development,https:/www.ictd.ac/;CeQ institute,CeQ Data Center on Fiscal Redistribution,https:/commitmentoequity.org/datacenter;oeCD data;World bank data.Note:Panel a shows each type of government revenue as a percentage of gross domestic product(gDP),aggregated by income group.Panelb shows direct and indirect taxes as a percentage of total market income by market income decile,aggregated by income group.indirect tax incidence is not available for oeCD countries.Cit=corporate income tax;gDP=gross domestic product;hiCs=high-income countries;LiCs=low-income countries;LmiCs=lower-middle-income countries;oeCD=organisation for economic Co-operation and Development;Pit=personal income tax;ssC=social security contribution;umiCs=upper-middle-income countries;VAt=value added tax.a.Tax revenue as a share of GDP Indirect taxesDirect taxes010203040LICsLMICsUMICsNon-OECDHICsOECDShare of GDP(%)Nontax revenuePropertyCITPayroll and SSCPITExciseTradeVAT051015202530 Share of market income(%)15101510Income decileb.Direct and indirect taxes as a share of market income,by income decile OECDLMICs1510HICs1510UMICs1510LICsFIGURE O.11Poorer economies spend less on transfers than on subsidies,which benefit the poor less Sources:Agricultural subsidies:international organisations Consortium for measuring the Policy environment for Agriculture database,http:/www.ag-incentives.org/;energy subsidies:international institute for sustainable Development,https:/www.iisd.org/;social protection:World bank,boost open budget Portal,https:/www.worldbank.org/en/programs/boost-portal,and international monetary Fund,government Finance statistics database,https:/data.imf.org/gfs;cash transfers and subsidies as a share of total benefits:CeQinstitute,CeQ Data Center on Fiscal Redistribution,https:/commitmentoequity.org/datacenter;oeCD data;World bank data.Note:Panel a compares spending on energy and agricultural subsidies with spending on social protection(excluding pensions)as a share of gross domestic product(gDP),aggregated by income group.Panel b shows transfers and subsidies as a share of total benefits by market income decile,aggregated by income group.subsidy incidence is not available for oeCD countries.gDP=gross domestic product;hiCs=high-income countries;LiCs=low-income countries;LmiCs=lower-middle-income countries;oeCD=organisation for economic Co-operation and Development;umiCs=upper-middle-income countries.b.Cash transfers and subsidies as a share oftotal benefits,by income decile SubsidiesCash transfersIncome decileLMICs1510HICs1510Share of total benefits(%)OECD0102030401510UMICs1510LICs151001234567HICsUMICsLMICsLICsShare of GDP(%)Energy and agricultural subsidiesSocial protection(excluding pensions)a.Subsidy and social protectionspending as a share of GDP 17oVeRVieWBOX O.3Tools that help to prioritize fiscal policiesAnyone assessing the impact of any given fiscal policyboth tax and spendingon poverty and equity must seek answers to two key questions:1.Who is benefiting from or paying for a given fiscal policy and to what degree?Answering this question is an essential first step in assessing the distributional implications of fiscal policy.In this report,the results from the Commitment to Equity(CEQ)methodology used to conduct fiscal incidence analysis are collated and analyzed for 94 countries.2.What is the value of this spending in terms of its long-term benefits for beneficiaries,nonbeneficiaries,and government revenue?The concept of the marginal value of public funds(MVPF),a systematic way of determining this value,has resurfaced in recent years and is being applied to a vast range of policies in the United States.It is now also being used more broadly,and in this report it is applied to selected interventions in low-and middle-income settings.Often,a discussion of the impacts of fiscal policies on poverty and inequality focuses only on answering the first question,but answers to both questions are needed to properly assess the full set of welfare impacts.This information helps governments choose policies.A welfare judgment is needed as well:how much does a society value an additional dollar in the hands of the beneficiaries of one policy versus the beneficiaries of another?In some cases,the trade-off appears quite straightforward:it is between a policy with a high MVPF appropriately targeting the poor versus a policy with a low MVPF targeting the rich.The choice is not always this clear,but even when it is the high-MVPF policy is not always chosen,perhaps reflecting the challenge of incorpor

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第 1 页 共 26 页2022 年 9 月北京总报告北京师范大学法学院&澄观治库目录引引言言.1一、共享经济行业发展历程及现状一、共享经济行业发展历程及现状.2二、共享经济规制历程及现状二、共享经济规制历程及现状.4（一）共享经济规制历程.41.2015-2018 年：初步探索 摸着石头过河.42.2018-2019 年：风雨兼程 破釜沉舟强规制.73.2020 年至今：疫情之下 辗转腾挪稳大局.8（二）共享经济规制总体情况.81.共享经济规制环境进一步完善.82.共享经济规制体系进一步完备.113.既有规制措施之偏差得到相应调整.16三、共享经济未来发展及规制趋势三、共享经济未来发展及规制趋势.18（一）共享经济发展趋势.181.共享经济业态持续丰富.182.共享经济开始步入盈利时代.193.更加关注社会公平，平衡新旧业态权益。.19（二）共享经济规制趋势.191.延续既有规制思路，继续完善配套制度.192.升级规制理念，搭建联合规制体系.213.持续优化营商环境.214.强化平台企业合规建设.22北京师范大学法学院&澄观治库第 1 页 共 26 页引引言言1978 年，美国得克萨斯州立大学的社会学教授马科斯费尔逊（MarcusFelson）和伊利诺伊大学社会学教授琼斯潘思（Joe L.Spaeth）共同发表了名为群落结构和协同消费：基于日常生活方式（Community Structure and CollaborativeConsumption:A Routine Activity Approach）一文。这篇文章中首次提出了“协同消费”这一概念，这一概念可以被理解为是“共享经济”概念的前身。1984 年，美国麻省理工学院的马丁威茨曼（Martin L.Weitzman）率先提出了“共享经济”（Sharing Economy）的概念。2011 年，美国自由职业联盟的创始人萨拉霍洛维茨（SaraHorowitz）撰写了 共享经济：一场静悄悄的革命（Occupy Big Business:The Sharing Economys Quiet Revolution）一文；同年，美国时代周刊将“共享经济”列为改变世界的十大想法之一。未几，“共享经济”一词也开始在中国被频繁热议。2015 年 10 月，中国共产党第十八届中央委员会第五次全体会议指出“坚持创新发展，发展分享经济”；同年 12 月，习近平主席在第二届世界互联网大会开幕式讲话中表示“中国正在发展分享经济，支持基于互联网的各类创新”。2016年起，“共享经济”连续被写入政府工作报告。2017 年，“共享”一词入选中国国家语言资源监测与研究中心“2017 年度中国媒体十大流行语”。共享经济的大规模兴起和普及是社会经济发展、科技发展和文化发展多重因素合力作用的结果。工业革命使人类的物质生活得到了极大的满足，同时也带来了产能过剩的问题。产能过剩是共享经济得以发展的前提，没有资源的“过剩”，共享行为就无从谈起。智能手机、移动互联网、大数据等新科技的力量让“共享经济”演变成了一场深刻的信息技术革命。平台经济顺应而生，在线平台的力量丰富了分享主体和客体，激活了分享主体的积极性，让“共享”行为更加简单便捷，使共享经济步入“万物皆可共享、人人皆可参与共享”的新纪元。“分享”“共享”行为古来有之。共享经济大规模兴起以前，也存在着“以租代买”行业、知识共享平台等经济形态。可以说，“共享经济”不但是一场对传统经济模式的革新，也是一场资源革命和信息技术革命。除此之外，资本在共享经济发展道路上扮演的角色也不可忽略。资本凭借其敏锐的嗅觉，积极投身参与了共享经济的发展。共享经济聚合用户和吸引流量的巨大威力必定会吸引以趋利为本的资本的目光。反之，资本的投入也有力地推动了共享经济的进一步发展。北京师范大学法学院&澄观治库第 2 页 共 26 页那么，“共享经济”究竟是怎样一种经济模式？经过近些年的发展与沉淀，“共享经济”的概念已较为固定和统一。国家信息中心将“共享经济”定义为“利用互联网平台将分散资源进行优化配置，通过推动资产权属、组织形态、就业模式和消费方式的创新，提高资源利用效率、便利群众生活的新业态型模式”。“共享经济”的特点主要为强调所有权与使用权的相对分离和强调供给侧与需求侧的弹性匹配。共享经济横跨众多行业、涵盖了巨大的公司列阵和众多工作岗位，其种类包括产品共享、空间共享、知识技能共享、劳务共享、资金共享、生产能力共享等，涵盖了衣食住行、学习、旅游等领域。共享经济能够在中国发展壮大有着其内在逻辑。首先，中国经济处于转型发展期，亟需发展新动能。共享经济的优势之一是实现闲散资源和供需配置方式的最优化。借助互联网、大数据等技术手段，共享经济能够快速且精准地实现供需双方匹配，满足市场主体的多样化需求。其次，中国享有网民大国红利。2016年 8 月，中国互联网络信息中心（CNNIC）发布第 38 次中国互联网络发展状况统计报告，截至 2016 年 6 月，中国网民规模达 7.1 亿，手机网民规模达 6.6亿，互联网普及率达 51.7%；2022 年 8 月，中国互联网络信息中心发布第 50 次中国互联网络发展状况统计报告，截至 2022 年 6 月，我国网民规模达到 10.5亿，互联网普及率达到 74.4%。再次，勤俭节约是中华民族的传统美德，加之我国资源约束趋紧、环境保护形势严峻，共享经济很好地顺应了中央绿色发展理念。一、共享经济行业发展历程及现状一、共享经济行业发展历程及现状从 2015 年“元年”，到 2017 年“丰盛之年”，到 2019 年“深度调整之年”，再到 2022 年“沉淀之年”，不管是业态模式，还是法律规制，中国的共享经济正在逐步探索一条适合中国国情、具有中国特色的发展之路。在我国，共享经济的发展主要经历了两个阶段。第一阶段第一阶段，引领创新发展引领创新发展，持续高速增长阶段持续高速增长阶段。“十三五”期间，中国经济发展进入新常态阶段，中国经济面临创新能力不强和部分行业常能过剩严重等众多难题。为此，党中央和政府推行创新驱动发展战略，将创新摆在国家发展全局的国家信息中心：中国共享经济发展报告（2022），最后访问时间：2022 年 9 月 1 日。国家发展和改革委员会：“十四五”规划名词解释之 85|共享经济，https:/年 9 月 1 日。北京师范大学法学院&澄观治库第 3 页 共 26 页核心位置，同时深入推进大众创业万众创新，打造发展新引擎。共享经济的热浪从海外传导至中国后，不但国外共享经济企业陆续打入中国市场，而且国内也涌现出大量共享经济企业。美国的 Uber（优步）、Airbnb（爱彼迎）率先打入中国市场，本土的滴滴出行、摩拜单车、小猪短租也陆续成立并迅速成长。自共享经济兴起至 2018 年，国内共享经济市场交易规模、融资规模、参与人数均持续高速增长。据统计，2018 年共享经济市场交易额为人民币 29,420 亿元，比上年增长 41.6%；平台员工数为 598 万，比上年增长 7.5%；共享经济参与者人数约 7.6亿人，其中提供服务者人数约 7500 万人，同比增长 7.1%。截至 2018 年底，全球 305 家独角兽企业中有中国企业 83 家，其中具有典型共享经济属性的中国企业 34 家，占中国独角兽企业总数的 41%。经过几年的迅速发展，网约车、共享单车、共享民宿等已经成为共享经济领域中新业态新模式的代表行业，成为推动国内经济结构优化、促进消费方式转型的新动能。同时，该阶段也是我国共享经济规制的探索阶段，共享经济规制体系经历了从无到有的过程。共享经济在对社会经济发展产生深远影响的同时，由于其异于传统商业形态的特点，对我国既有法律规制体系带来了挑战与冲击。具体而言，立法具有滞后性，我国规制体系的形成亦滞后于规制对象的发展速度。由于规制者难以预见共享经济之新业态的出现，进而可能将针对传统商业模式形成的规制体系简单套用到共享经济上，由此对共享经济之规制面临“规制不足”与“过度规制”的双重风险。因此，如何创新共享经济规制体系，是共享经济在我国得以健康发展的重要保障，也是摆在我国规制者面前的一大课题。第二阶段，深度调整，逐步成熟，继续发展。第二阶段，深度调整，逐步成熟，继续发展。2018 年是共享经济规制历程中具有标志性意义的一年。这一年，共享经济部分领域问题集中爆发，尤其是共享单车领域的重大变化引起了社会对共享经济发展走向的讨论和反思，这也导致了规制成为 2018 年共享经济行业的重中之重。2019 年是共享经济深度调整的一年，共享经济市场、企业经营和资本陆续开始回归理性，行业开始注重可持续发展。国家对于共享经济规范发展的力度进一步加大，积极完善规制环境，优化营商环境，制定反不正当竞争和反垄断、信用体系建设等方面的政策。2020 年突发新冠肺炎疫情以来年突发新冠肺炎疫情以来，共享经济总体来讲保持着稳中有进的态势共享经济总体来讲保持着稳中有进的态势。国家信息中心：中国共享经济发展年度报告（2019），https:/年 9 月 1 日。同上。北京师范大学法学院&澄观治库第 4 页 共 26 页虽然疫情的不确定性为共享经济带来一定的风险，但危中藏机，共享经济凭借自身的韧性和优势，在有效激发市场需求，纾困市场主体，保障就业民生，稳住经济大盘等方面起到了十分积极的作用。为确保疫情期间共享经济能最大化发挥其韧性和潜力，政府面向中小企业实施了包括降低企业资金压力、提供融资支持、减免税收、延迟缴纳社保等多项支持政策，多方面帮助中小企业渡过疫情难关。在此阶段，共享经济不但已经完成了在此阶段，共享经济不但已经完成了“中国化中国化”，成功发展出，成功发展出了了一批经营稳一批经营稳定的本土共享经济企业，凭借互联网技术等科技的创新优势，成为了社会经济定的本土共享经济企业，凭借互联网技术等科技的创新优势，成为了社会经济发展的新动能；而且在疫情反复之下，在保主体促就业稳民生方面也发挥了重发展的新动能；而且在疫情反复之下，在保主体促就业稳民生方面也发挥了重要作用。要作用。二、共享经济规制历程及现状二、共享经济规制历程及现状（一）共享经济规制历程（一）共享经济规制历程1.2015-2018 年：初步探索年：初步探索 摸着石头过河摸着石头过河共享经济作为一种新的经济形态，“新”不但体现在理念模式和技术手段方面，也体现在制度规制方面。2018 年之前，我国对共享经济的规制只是一个开端，是一个初步探索的过程。在这一阶段，规制的重点在于表明中央对发展共享经济的肯定态度、逐步厘清“共享经济”的定义和范围，并对如何规制共享经济提出总原则和基本要求，为日后进一步完善规制体系、细化规制措施奠定良好基础。但是，因“摸着石头过河”而不可避免在规制策略选择上存在一些偏差。2018 年之前，我国对共享经济之规制经历了从无到有的过程，这一阶段共享经济之规制主要呈现出以下两大特点：一方面，共享经济在国家顶层战略中的定位逐步确立，共享经济在政策上一方面，共享经济在国家顶层战略中的定位逐步确立，共享经济在政策上得到认可，共享经济之规制理念逐渐明晰。得到认可，共享经济之规制理念逐渐明晰。2015 年世界互联网大会上，习近平总书记强调将共享经济作为国家经济战略，表明了对共享经济的重视。2016 年，共享经济首次被写入政府工作报告，并被纳入国民经济和社会发展第十三个五年规划纲要，由此拉开了我国共享经济规制的序幕，国家层面鼓励共享经济的规制政策相继出台。其中最具代表性的是 2017 年 7 月国家发展和改革委员会等八部门联合发布的关于促进分享经济发展的指导性意见（发改高技北京师范大学法学院&澄观治库第 5 页 共 26 页20171245 号），其对共享经济的重要意义进行了阐释，即“分享经济作为全球新一轮科技革命和产业变革下涌现的新业态新模式，正在加快驱动资产权属、组织形态、就业模式和消费方式的革新。推动分享经济发展，将有效提高社会资源利用效率，便利人民群众生活，对推进供给侧结构性改革，落实创新驱动发展战略，进一步促进大众创业万众创新，培育经济发展新动能，具有重要意义”。作为我国共享经济规制发展过程中的里程碑文件，关于促进分享经济发展的指导性意见 围绕市场准入、行业规制、营造发展环境等进行了全面部署，并确立了以“鼓励创新，包容审慎”为核心的经济发展共享原则和政策取向，为推动共享经济健康、有序发展提供了顶层设计和制度安排。随后，地方层面如重庆、浙江、天津、江苏、甘肃等地区纷纷紧随中央的步伐，出台了鼓励共享经济发展的指导性意见。另一方面，针对不同行业领域的共享经济业态，配套政策初步形成。另一方面，针对不同行业领域的共享经济业态，配套政策初步形成。我国共享经济的配套政策，以对网约车这一典型共享经济业态的规制为开端。2016年 7 月，国务院办公厅印发关于深化改革推进出租汽车行业健康发展的指导意见（国办发201658 号），给予网约车合法地位，并对网约车提出规范发展、规范经营的要求；同月，交通运输部等七部门联合发布网络预约出租汽车经营服务管理暂行办法，即所谓的“网约车新政”，正式赋予网约车合法地位，使得我国成为了全球范围内第一个实现网约车合法化的国家，为网约车的规制搭建了制度架构。随后，地方层面如北京、上海、天津等各地政府相继以规范性文件，甚至是地方政府规章的形式出台了网约车规制细则，就驾驶员的条件、车辆标准和营运要求、市场定价以及网络预约出租汽车运输证的发放条件这几个方面作了细化规定。在“网约车新政”之后，我国针对共享经济的规制体系不断完善，共享单车、民宿等共享经济业态相继被纳入规制体系，共享经济开始逐步摆脱野蛮生长之状态，而步入到规范发展阶段。随着共享单车的迅猛发展，实践中出现了共享单车过度投放、乱停乱放之现象，大大超出了城市非机动车可停放区域承载之能力，加快暴露出城市道路规划、交通管理方面的问题。共享单车的出现为城市治理提出了新要求。这一背景下，2017 年 8 月交通运输部等 10 部门联合发布关于鼓励和规范互联网租赁自行车发展的指导意见（交运发2017109 号），成为继“网约车政策”后在交通出行领该暂行办法于 2019 年被修订，删去第六条第一款第三项中的“外商投资企业还应当提供外商投资企业批准证书”，并对个别文字作相应调整。刘奕：以监管体系优化促进网约车行业健康发展，载中国观察2019 年第 19 期，第 56-59 页。北京师范大学法学院&澄观治库第 6 页 共 26 页域推出的又一项规制政策，其不仅明确互联网租赁自行车（俗称“共享单车”）是移动互联网和租赁自行车融合发展的新型服务模式，而且针对共享单车的车辆停放等实践中较为突出的问题进行了规范。这一指导意见充分体现了“鼓励创新、包容审慎”的规制思路和原则，不仅获得了共享单车企业的支持，也获得社会各界较多的赞同。此后，自 2017 年 4 月成都市颁布全国首部地方性管理规范成都市交通运输委员会成都市公安局成都市城市管理委员会关于鼓励共享单车发展的试行意见以来，北京、上海、天津、深圳、杭州、南京、济南、武汉、成都、昆明、海口等十余个城市分别出台了针对共享单车的管理规则。共享民宿在我国的兴起与发展在很长一段时间内是无章可循的。对此，2017年国家旅游局出台了旅游民宿基本要求与评价，其作为我国首部对共享民宿行业标准作出规范要求的国家标准，对相关定义、评价原则、基本要求、安全管理、环境和设施、卫生和服务、等级划分七个方面作了具体规定，在共享民宿行业的发展有一定指导意义。与此同时，相配套的法律规范性文件也在进一步修订中，如 2017 年发布的旅馆业治安管理条例（征求意见稿）将“民宿”纳入管理范围。又如 2017 年住房和城乡建设部、公安部、国家旅游局联合印发了农家乐（民宿）建筑防火导则（试行），切实加强农家乐（民宿）建筑防火安全，保护人民群众生命和财产安全，推动农家乐（民宿）健康发展。在此基础上，由于旅游法授权省一级政府就居民从事的旅游经营的住房管理问题制定细则，地方层面陆续出台的旅游产业相关服务配套制度，对民宿产业发展相关问题进行了明确规定。如浙江省 2017 年 1 月 5 日正式实施关于确定民宿范围和条件的指导意见对民宿的范围、条件和经营管理等事项作了详细规定。与此相配套的还有浙江省民宿（农家乐）治安消防管理暂行规定 浙江省农家乐经营户（点）旅游服务质量星级评定办法 等规定，共同为民宿行业在浙江省的发展保驾护航。总体上，2018 年之前我国共享经济的规制只是一个开端，即经历了一个从无到有的过程，由于是“摸着石头过河”，难免在规制策略选择上出现一些偏差。具体而言，作为共享经济规制的纲领性文件，关于促进分享经济发展的指导性意见规定由各地区、各部门制定规制共享经济的实体性规则，由此必然导致规制的“碎片化”，而这与共享经济之跨行业、跨地区和网络性特征相悖。以上规制策略在网约车这一共享经济业态上体现得尤为明显。具体而言，从“网约车新政”的实质内容来看，其在很大程度上是将规制网约车的权力下放到地北京师范大学法学院&澄观治库第 7 页 共 26 页方政府网络预约出租汽车经营服务管理暂行办法不仅明确网约车行业采用经营许可证、网络预约出租汽车运输证、网络预约出租汽车驾驶员证“三证”管理的许可模式，还规定“城市人民政府对网约车发放网络预约出租汽车运输证另有规定的，从其规定”；从事网约车服务的驾驶员需符合“城市人民政府规定的其他条件”。而地方政府在规制网约车时，或是为维护出租车行业之既有利益，或是在受传统规制策略之惯性作用下，而对网约车之运行在驾驶员条件、车辆标准等方面设置了诸多限制性条件，前者如地域限制、户籍限制，后者如车轴间距、车型、车长、车宽、车高以及发动机功率甚至车辆的排放量等方面的限制。高准入门槛客观上降低了网约车行业的供给能力，推动一部分从业者重新转回地下。此外，对共享经济规制之难题还体现在新商业模式与旧法律制度不适应性上。共享经济是利用互联网等现代信息技术，以使用权分享为主要特征，整合海量、分散化资源，满足多样化需求的经济活动总和。这表明共享经济涵盖了大数据、云计算、人工智能等多种新一代信息技术应用，并催化了平台经济的进一步发展。因此，用户信息安全和隐私保护、押金规制、平台责任界定等难题日益凸显；新业态与传统属地管理模式之间的矛盾也日益显现。2.2018-2019 年：风雨兼程年：风雨兼程 破釜沉舟强规制破釜沉舟强规制2018 年之后，我国对共享经济之规制有了新的发展，逐步形成一条有中国特色的共享经济法律规制之路。这一年，共享经济部分领域问题集中爆发，甚至一度被公众舆论热议，社会、市场和公众对规制制度的呼声更为高涨和坚定。也是在这一年，民宿领域出台了首个行业自律标准。行业开始主动运用自身的力量优化行业服务环境，推动行业高质量发展。例如，共享经济交通出行领域的发展在 2018 年速度明显放缓，增速从 2017 年的 57%下降至 23%，其中共享单车领域出现的重大变化引发了公众对于共享经济的反思和热议。在资本短时间大量涌入的背景下，短短两年时间，共享单车领域先后经历了“彩虹大战”和“两强争霸”，暴露出了共享经济商业模式不清晰不成熟、城市公共管理不足、平台规制欠缺、法律规制滞后等痛点问题。痛点问题如何解决对规制部门是不小的挑战，解决不当则可能会打击公众、市场和资本对整个共享经济发展前景的信心。国家信息中心：中国共享经济发展报告（2019），http:/年 9 月 1 日。北京师范大学法学院&澄观治库第 8 页 共 26 页2019 年，共享经济进入深度调整阶段，市场交易规模和直接融资规模都出现了大幅下降。共享经济的经营行为和资本都逐步回归理性，从追求规模和速度发展转向高质量可持续发展。民宿的增长速度开始后来居上，成为增长速度最快的领域。共享经济发展情况良好亦有利于法律规制的完善更新。2019 年，国家层面推进了共享经济标准化建设，并针对平台经济发展制定了相关政策。进入2019 年，对于共享经济的规制也从“摸着石头过河”过渡到“完善细化补充”的阶段。2018 年，共享经济在稳就业方面作用初显，在助力解决过剩产业工人再就业问题的同时，也为贫困地区劳动力就业作出贡献。2019 年，在共享经济和直接融资规模均出现下降趋势的情况下，共享经济领域的就业仍保持了较高水平的增长，由此也带来了劳动关系认定难、劳动者合法权益保障难、传统社会保险体系与灵活就业不相容的问题。3.2020 年至今：疫情之下年至今：疫情之下 辗转腾挪稳大局辗转腾挪稳大局2020 年突发的新冠肺炎疫情对共享经济来说可谓是喜忧参半。交通出行、共享民宿、共享办公等依托线下活动完成整个交易的领域交易规模出现下降；仅依托线上完成服务交易的互联网医疗、网络餐饮等领域的交易规模则出现增长。在国内发展改革稳定要求和疫情冲击的双重压力下，共享经济表现出强健的韧性，不但在创新方面继续有所突破，还在稳民生保就业、推动复工复产和经济社会数字化转型方面表现优异。因此，我国继续采取了“坚持发展共享经济、加快出台共享经济领域相关配套规章制度”的政策。（二）共享经济规制总体情况（二）共享经济规制总体情况在此背景下和在总结经验与反思此前的教训之基础上，我国对共享经济之规制有了新的发展，逐步形成一条有中国特色的共享经济法律规制之路完善共享经济规制环境，完备规制体系，落地规制举措。对此，可从以下几个方面展开论述：1.共享经济规制环境进一步完善共享经济规制环境进一步完善行业的可持续正向发展离不开良好的政策环境。共享经济的发展涵盖了大数据、云计算、人工智能等多种新一代信息技术应用，并且加速了平台经济的进一步发展。“平台”，简而言之，是利用互联网等科技手段，搭建在供需双方之间的一座桥梁。共享经济的良性发展首先需要推进平台的合规化。北京师范大学法学院&澄观治库第 9 页 共 26 页（1）电子商务法出台电子商务法出台2018 年以来，我国共享经济规制的一大发展是电子商务法的制定并正式实施，电子商务法通过将共享经济平台作为调整对象，为共享经济平台的合规化提供了基本依循，为我国共享经济的规制提供了更为坚实的法律依据。具体而言，共享经济在诞生之初被广泛认为是平台经济的延伸。共享经济的要义在于闲散资源使用权之暂时性转移，在最大程度上发挥市场中既有资源之利用效益，以创造更多的社会价值。至于闲散资源使用权之暂时性转移则又是藉由互联网第三方平台实现的，即共享经济之下是由互联网第三方平台在闲置资源之所有者和使用者之间建立起联系，提供一个供需双方之间交易闲散资源的“场所”。从这一意义上说，共享经济亦属平台经济，即依托第三方平台进行资源交易的商业模式。显然，无论是 Airbnb 还是 Uber 等共享经济企业，都具有典型的平台经济之特征，共享经济平台本身并不生产产品以供交易，而仅促成供求双方或多方之间的交易。概言之，共享经济发轫于互联网第三方平台，互联网第三方平台是共享经济不可或缺的要素。电子商务法以电子商务活动、电子商务经营者为调整对象，进而有助于实现共享经济平台的规范化。具体而言，根据电子商务法第 2条规定，电子商务是指通过互联网等信息网络销售商品或者提供服务的经营活动；仅金融类产品和服务，利用信息网络提供新闻信息、音视频节目、出版以及文化产品等内容方面的服务，不受电子商务法之调整。“由此表明，起草组专家前瞻性地考虑到电子商务是一个宽泛的领域，例如，电子商务、搜索引擎、操作系统、通讯社交、共享经济、智能服务、游戏直播等平台，都可以被涵盖在电子商务的概念之中，均可以纳入电子商务法规制范畴”。“电子商务”之概念在外延上涵盖共享经济平台，由此共享经济平台需遵守电子商务法为电子商务活动所设定的一系列行为规则。那么，作为立法的调整对象，共享经济平台在电子商务法中又是如何定位的呢？电子商务法第 9 条规定，电子商务经营者，是指通过互联网等信息网络从事销售商品或者提供服务的经营活动的自然人、法人和非法人组织，包括电子商务平台经营者、平台内经营者以及通过自建网站、其他网络服务销售商品或者提供服务的电子商务经营者。其中的电子商务平台经营者，则是指在电子商务中为交易双方或者多方提供网络经营场所、交易撮合、信息发布等服务，供交杨东：也是平台经济竞争法，载法制日报2019 年 9 月 4 日，第 11 版。北京师范大学法学院&澄观治库第 10 页 共 26 页易双方或者多方独立开展交易活动的法人或者非法人组织，其核心要义是不直接介入用户之间的交易，但创设和决定用户之间的交易模式和规则，并撮合用户之间的交易，而这与共享经济平台等新业态之特点具有相当的契合性。从电子商务法的规定看，其“对电子商务平台经营者的义务和责任作了细致规定，包括市场准入、网络安全、交易信息留存、消费者权益保护、信誉评价、竞价排名广告、知识产权通知-删除规则等”，由此对共享经济平台的规范发展提供了依循。可以预见，共享经济平台之合规化管理将成为我国共享经济规制之重点发展方向。（2）优化营商环境、规范发展平台经济优化营商环境、规范发展平台经济新业态发展的好坏与营商环境的优劣紧密相关。2015 年 10 月 2 日，国务院发布了关于实行市场准入负面清单制度的意见（国发201555 号），决定建立市场准入负面清单制度，市场准入负面清单以外的行业、领域、业务等，各类市场主体皆可依法平等进入，并严格落实“全国一张清单”的管理要求。该制度的建立，旨在赋予市场主体更多的主动权，落实市场主体自主权和激发市场活力，形成各类市场主体依法平等使用生产要素、公开公平公正参与竞争的市场环境，形成统一开放、竞争有序的现代市场体系，为发挥市场在资源配置中的决定性作用提供更大空间。2022 年 4 月，为加快建立全国统一的市场制度规则，打破地方保护和市场分割，国务院发布中共中央 国务院关于加快建设全国统大市场的意见，其中要求“加快营造稳定公平透明可预期的营商环境”。2019 年 10 月，为持续优化营商环境，不断解放和发展社会生产力，加快建设现代化经济体系，推动高质量发展，国务院出台了优化营商环境条例。该条例确定了对新技术、新产业、新业态、新模式实行包容审慎的规制政策，并应当针对其性质、特点分类制定和实施相应的规制规则和标准。优化营商环境不但包括建立清晰统一的市场准入制度，还包括营造公平竞争的市场环境，确保市场主体拥有公平竞争的权利。随着共享经济行业的发展壮大，共享经济依托的平台也同时开始扩张起来，产生了一批大型平台企业。由于立法的滞后性，2008 年起实施的反垄断法中并未针对互联网领域的反垄断法进行规制；随着社会经济的发展和需要，2020 年 1 月公布的修订草许多奇：在线短租平台的性质与法律责任，载互联网金融法律评论2018 年第 1 辑，第 154-156 页。齐爱民、张哲：共享经济发展中的法律问题研究，载求是学刊2018 年第 2 期，第 97-108 页。北京师范大学法学院&澄观治库第 11 页 共 26 页案（征求意见稿）首次增设了互联网经营者市场支配地位认定依据的相关规定；2022 年正式实行的反垄断法也保留了征求意见稿中的相关规定。上文提到，共享经济在诞生之初被广泛认为是平台经济的延伸，因此规范发展平台经济可以说是规制共享经济的基石。规范发展平台经济，具体包括创新规制模式、规制平台算法、提高平台企业合规发展水平、降低平台企业运营成本等众多面向。2019 年 8 月，国务院办公厅发布实施了关于促进平台经济规范健康发展的指导意见，旨在聚焦平台经济发展面临的突出问题，加大政策引导、支持和保障力度，创新规制理念和方式，落实和完善包容审慎的规制要求，努力营造公平竞争的市场环境。该指导意见主要包含以下几个方面：为降低企业合规成本，提出各部门要合理设置行业准入规定和许可，优化审批流程；实行包容审慎的规制政策，明确各方责任，更好地保护各方权益；推动完善社会信用体系建设，优化平台经济发展环境；强化平台经济发展的法治保障，完善平台经济相关的法律法规建设，加快破除制约平台经济发展的体制机制障碍。（3）加强个人信息保护加强个人信息保护共享经济逐渐渗透至生活服务的方方面面，大型平台企业中汇聚了大量用户的个人信息数据，包含了用户的身份证号、家庭地址、单位地址、手机号码、银行卡账号等隐私信息，同时也包含了用户的交易信息数据。企业在收集数据环节可能存在未经用户同意收集、过度收集的情况，在数据存储环节可能存在数据泄露的安全隐患，在运用数据时可能存在不恰当使用的行为。例如，企业利用并对用户数据行为进行分析，进而构建用户画像，从而进行广告精准投放进行“杀熟”。2021 年 11 月，个人信息保护法正式实施。个人信息保护法对个人信息保护做了全面规定，包括个人信息处理的基本原则、与政府信息公开条例的关系、对政府机关与其他个人信息处理者的不同规制方式及其效果、协调个人信息保护与促进信息自由流动的关系、个人信息保护法在特定行业的适用问题、关于敏感个人信息问题、法律的执行机构、行业自律机制、信息主体权利、跨境信息交流问题、法律责任问题等。个人信息保护法聚焦于民事权利保护，为个人信息处理者设定了大量法律义务，更容易触发政府主管部门的规制。2.共享经济规制体系进一步完备共享经济规制体系进一步完备共享经济作为一种新业态，发展异常活跃，这就决定了共享经济发展过程中北京师范大学法学院&澄观治库第 12 页 共 26 页的新问题可能层出不穷，由此需要针对这些新问题不断出台新的规制政策。2018年以来，我国共享经济规制体系进一步发展，既体现在宏观层面规制理念的明晰，还表现在微观层面对不同共享经济业态规制策略的丰富以及规制网络之织密上。首先，我国共享经济规制理念进一步明晰。首先，我国共享经济规制理念进一步明晰。2017 年国家发改委等八部门联合发布的关于促进分享经济发展的指导性意见围绕市场准入、行业规制、营造发展环境等进行了全面部署，并确立了以“鼓励创新，包容审慎”为核心的经济发展共享原则和政策取向。为加快实施关于促进分享经济发展的指导性意见，有效应对共享经济发展过程中出现的新情况新问题，推动共享经济健康良性发展，2018 年 5 月，国家发改委会同有关部门发出关于做好引导和规范共享经济健康良性发展有关工作的通知（发改办高技2018586 号），进一步提出了构建综合治理机制、推进实施分类治理、压实企业主体责任、规范市场准入限制、加强技术手段建设、推动完善信用体系、合理利用公共资源、保障个人信息安全、规范市场竞争秩序、加强正面宣传引导、完善应急处置保障等 11 个方面之要求，为我国对共享经济之规制指明了具体方向。2019 年政府工作报告中进一步明确要“坚持包容审慎规制，支持新业态新模式发展，促进平台经济、共享经济健康成长”。2020 年 9 月，国务院办公厅发布国务院办公厅关于以新业态新模式引领新型消费加快发展的意见（国办发202032 号），提出“顺应新型消费发展规律，加快出台电子商务、共享经济等领域相关配套规章制度，研究制定分行业分领域的管理办法，有序做好与其他相关政策法规的衔接”。其次，为回应实践中层出不穷的共享经济新业态，我国共享经济规制体系其次，为回应实践中层出不穷的共享经济新业态，我国共享经济规制体系不断开辟新的领域。不断开辟新的领域。具体而言，2018 年以来，我国共享经济新业态不断涌现。我国共享经济逐步溢出交通出行、共享民宿领域，而延伸至生活服务、知识技能、医疗诊断等多个细分领域，并渐成规模。为回应实践中出现并壮大的丰富多彩的共享经济新业态，我国出台了一系列新的规制政策，使得共享经济规制体系涵盖的对象由之前的网约车、共享单车、民宿拓展到互联网医疗、网络餐饮等新业态。如在互联网医疗领域，2018 年 4 月，国务院办公厅出台关于促进“互联网 医疗健康”发展的意见（国办发201826 号）；2018 年 7 月，为落实上述意见文件，国家卫健委和国家中医药管理局发布互联网诊疗管理办法（试行）互联网医院管理办法（试行）远程医疗服务管理规范（试行）。2020 年开始持续至今的新冠肺炎疫情，让共享医疗行业的价值得以凸显。在共享出行、民宿和北京师范大学法学院&澄观治库第 13 页 共 26 页办公三大传统领域交易规模均出现下降的情况下，共享医疗市场规模大幅增长。在疫情背景下，互联网医疗为公众医疗健康提供了便捷化、多元化的选择。为进一步保障患者权益，方便患者更好地享受医保及互联网医疗的服务，2020 年 10月，国家医疗保障局发布了医保局关于积极推进“互联网 ”医疗服务医保支付工作的指导意见（医保发202045 号）。该指导意见在稳步拓展医保支付范围、完善“互联网 ”医疗服务医保支付政策、强化“互联网 ”医疗服务规制措施等方面作出了重要指示。2022 年 2 月，国家卫健委和国家中医药局联合制定并发布了互联网诊疗规制细则（试行），这是互联网医疗行业第一个全国范围的规制细则，标志着互联网医疗从高速发展转向高质量发展。该细则首先确立了互联网诊疗规制的基本原则，并对开展互联网治疗活动的医疗机构提出了从医疗人员到业务活动方面的规制要求。如在线外卖领域，2018 年 1 月，国家食品药品监督管理总局出台网络餐饮服务食品安全监督管理办法；2018 年 7 月，国家市场监督管理总局出台餐饮服务食品安全操作规范；2019 年 5 月，中共中央和国务院印发中共中央、国务院关于深化改革加强食品安全工作的意见，要求所有提供网上订餐服务的餐饮单位必须有实体店经营资格，并要求严格落实网络订餐平台责任，保证线上线下餐饮同质同标。由此共享经济规制体系将共享医疗、共享餐饮等共享经济新业态纳入到法治化轨道上来。最后，针对网约车、共享单车、民宿等传统共享经济业态，进一步织密了最后，针对网约车、共享单车、民宿等传统共享经济业态，进一步织密了规制之网络规制之网络。共享经济想要步入健康良性的可持续发展道路，就不能够仅仅依赖集中式专项整治行动，长远而言，还需要建立长效化的规制机制。就网约车而言就网约车而言，为细化和落实网络预约出租汽车经营服务管理暂行办法中有关驾驶员资格的要求，2018 年 3 月，交通运输部会同公安部印发了关于切实做好出租汽车驾驶员背景核查与规制等有关工作的通知（交办运201832 号），明确了驾驶员背景核查的内容、流程以及部门分工，公安机关与交通运输部门的协作。2018 年 5月和 8 月，河南郑州和浙江温州连续出现两起顺风车司机杀人案件，引发社会广泛关注。同年 9 月 10 日，交通运输部办公厅和公安部办公厅联合发布关于进一步加强网络预约出租汽车和私人小客车合乘安全管理的紧急通知（交办运2018119 号），立即开展行业安全大检查、加强网约车和顺风车平台驾驶员背景核查等一系列措施。为加强网约车规制信息交互平台的运行管理工作，规范数据传输，提高网约车行业规制效能，交通运输部办公厅网络预约出租汽车规制信北京师范大学法学院&澄观治库第 14 页 共 26 页息交互平台运行管理办法；为加强对网约车行业的服务考核，交通运输部发布了出租车服务质量信誉考核办法；为强化网约车行业的事中事后规制，维护市场公平竞争秩序，保障乘客合法权益，交通运输部先后关于加强网络预约出租车汽车行业事中事后联合规制有关工作的通知 关于加强和规范事中事后规制的指导意见 等规定，分别就平台运营数据传输、驾驶员背景核查、合乘安全、服务质量考核、事中事后联合规制工作流程进行了规范，由此回应了网约车运行实践中的突出问题，填补了网约车规制的漏洞。就共享民宿而言就共享民宿而言，2018 年 10 月，国务院办公厅印发完善促进消费体制机制实施方案（2018-2020 年），方案指出进一步放宽服务消费领域市场准入，鼓励发展租赁式公寓、民宿客栈等旅游短租服务。2018 年底，我国共享民宿领域首个行业自律性标准共享住宿服务规范正式发布，其“首次对共享住宿、平台企业、房东、房客等行业术语进行了明确界定，并对平台企业、房东和房客三方主体进行了相应约束和规范；该服务规范不仅适用于乡村民宿，还包括分散于城市社区中的民宿。同时，针对目前行业发展过程中存在的和社会公众关注的热点问题，如城市民宿社区关系、入住身份核实登记、房源信息审核机制、卫生服务标准、用户信息保护体系、黑名单共享机制、智能安全设备的使用等，也提出了具体的内容规范，并结合智能安全硬件设施的使用等技术创新和未来发展趋势展开前瞻性引导”。2019 年，文化和旅游部发布旅游民宿行业标准旅游民宿基本要求与评价（LB/T 065-2019），分别从民宿的定义、评价原则、基本要求、安全管理、等级划分条件等方面对民宿行业发展提供了指导性意见，对我国民宿行业健康发展具有重要意义。2020 年 7 月，国家发改委、中央网信办等 13 部门印发的关于支持新业态新模式健康发展激活消费市场带动扩大就业的意见（发改高技20201157 号）明确提出“鼓励共享住宿、文化旅游等领域产品智能化升级和商业模式创新，发展生活消费新方式，培育线上高端品牌”，这是“鼓励发展共享住宿”首次被写入我国政府文件。2020 年 9 月，为整体提升全国乡村民宿服务质量水平，中国标准化研究院制定了乡村民宿服务质量规范。2022 年 7 月，国家标准旅游民宿基本要求与等级划分正式颁布（GB/T 41648-2022），为旅游民宿管理部门和经营者提供了规范的、可参照的依据，对规范行业发展具有重要意义。促进旅游彭晓菲：正式发布，载燕赵都市报2018 年 11 月 20 日，第 23 版。北京师范大学法学院&澄观治库第 15 页 共 26 页民宿行业的高质量发展。上述标准的发布提升了共享民宿服务的标准化和品质化，规范行业发展秩序。我国虽尚未对共享民宿在国家层面进行立法，但多个地方政府已经依据我国旅游法第 46 条规定，通过在相关立法中采用专门条款或单独立法的模式对民宿的定义、开办要求和程序、经营规范等做出了相应的规范，实际上也走出了一条国家原则上承认民宿、地方先行探索民宿管理办法的合法化路径。各地方政府尤其是一、二线城市以及旅游资源丰富的城市陆续出台鼓励和支持民宿发展的政策文件，浙江省早在 2016 年颁布实施的浙江省旅游条例就明确提出了有关民宿的概念；广东省则出台了省级层面规范民宿的政府规章广东省民宿管理暂行办法；上海、北京、重庆、海南、苏州、成都、济南等地也纷纷出台了有关民宿的规范性文件。就共享单车而言就共享单车而言，为加强生态文明建设、推进绿色发展，国家发改委会同有关部门研究制定了绿色产业指导目录（2019 年版），提出将互联网租赁自行车以及互联网租赁电动自行车纳入共享交通设施建设和运营之中，同时规定完善包括步行交通系统建设、自行车交通系统建设、非机动车停车设施建设、公共自行车租赁点建设、都市绿道建设、道路交叉口路灯优化、路段过街设施建设、慢行系统优化等城市慢行系统建设和运营。这也表明中央延续了支持共享单车发展的顶层设计。为了促进交通运输新业态健康发展，加强用户押金和预付资金管理，有效防范用户资金风险，2019 年 5 月，交通运输部等六部门联合印发了交通运输新业态用户资金管理办法（试行），确立了共享单车运营企业原则上不收取用户押金，同时规定了对确有必要收取押金的押金管存方式、收取押金的额度以及退回押金的时限。在共享单车发展得如火如荼之时在共享单车发展得如火如荼之时，共享电动车也在悄然兴起共享电动车也在悄然兴起。但与共享单车行业的发展相比，共享电动车行业的发展道路则略显曲折。不同主管部门对于共享电动车的发展策略呈现较为矛盾的态度。2017 年发布的关于鼓励和规范互联网租赁自行车发展的指导意见中有关部门表示“不鼓励发展互联网租赁电动自行车”；然而在关于政协十二届全国委员会第五次会议第 4141 号（工交邮电类 381 号）提案答复的函中，交通运输部充分肯定了共享电动车的优势，并且对相关行业的发展持鼓励的态度。2019 年 2 月，国家发展改革委等七部门联合出台绿色产业指导目录（2019 年版）（发改环资2019293 号），有关部门提北京师范大学法学院&澄观治库第 16 页 共 26 页出要将互联网租赁电动自行车纳入共享交通设施建设和运营之中。2019 年 3 月，在市场规制总局等三部门联合出台的 关于加强电动自行车国家标准实施监督的意见（国市监标创201953 号）中，有关部门又强调要“按照国家有关政策要求，清理共享电动自行车。”2020 年在关于政协十三届全国委员会第三次会议第1441 号（工交邮电类 154 号）提案答复的函中，公安部则认为，目前应当保持不鼓励发展共享电动车政策的连续性。中央关于共享电单车发展的政策和方针的不确定性为行业发展带来了一定程度的不确定性，也导致了全国各地对共享电单车态度及政策制定的不统一。例如北京、上海、郑州、宁波、成都等城市在其发布的地方规范性文件中对共享电单 车 均持否定的不发展态度，昆明和长 沙表示共享电单车必须符合GB177612018电动自行车安全技术规范有关技术要求，并遵守当地电动自行车管理规范。3.既有规制措施之偏差得到相应调整既有规制措施之偏差得到相应调整前文谈到，我国共享经济规制在初始阶段由于缺乏经验可循，难免在规制策略选择上出现一些偏差。随着规制经验的不断累积和“试错”，2018 年以来，既有规制策略逐步被重新审视，其中的偏差亦得到相应调整。例如，前文所提到的地方网约车规制细则对网约车之过度规制，究其根源在于规制者将针对出租车的旧的规制措施套用到网约车这一全新的经济形式上，由此不仅背离了共享经济发展的内在规律和要求，亦与国家鼓励共享经济的政策导向相悖。由于地方网约车规制细则，沿袭对传统出租车的规制手段对网约车进行了诸多限制，使“打车难、打车贵”现象再度出现，甚至迫使一些网约车司机转入黑车市场。这一情境下，地方政府逐渐意识到，有必要针对网约车规制细则中科学性有所欠缺之条款进行修正，而这与地方网约车规制细则“暂行办法”之定位恰相契合。以洛阳市的实践为例，2017 年发布的洛阳市网约车经营服务管理试行办法在施行两年之后得到了修订，主要目的是给网约车的运行“松绑”，以回归网约车所体现的共享经济本质与特性。修订之后洛阳市网络预约出租汽车经营服务管理试行办法从平台、车辆、司机等维度全方位降低网约车从业准入门槛。如不再要求裸车价为出租车均价 1.5 倍以上，车辆轴距高于市区主流出租车即可，车龄也由不超 3 年卞亚璇：共享经济的规制策略，载河南司法警官职业学院学报2019 年第 3 期，第 90-94 页。北京师范大学法学院&澄观治库第 17 页 共 26 页修改为不超过 5 年、行驶里程不超过 10 万公里。洛阳市为网约车“松绑”的做法在实践中并不鲜见。截至 2019 年 12 月 31 日，全国共有 254 个城市（含 4 个直辖市和 250 个地级市）发布网约车规制细则。其中有 87 个城市没有对轴距作出限制或者规定车价达标则不限制轴距，有 47 个城市在网约车规制细则中没有规定驾驶员需要具备户籍、居住证、社保等条件，由此给网约车行业的发展提供了更大的空间。近两年还有多地对网约车司机的户籍或者居住证条件做了改变，对车辆条件进行了调整。2018 年共享单车经历了一次行业大洗牌，洗牌开端是因“两强争霸”主角之一 ofo 单车相继被曝出存在资金链紧张、供应商债务、私自挪用用户押金等一系列严重问题，并被多家企业以合同纠纷为由告上法庭。长期以来，我国共享经济行业大多采用收取使用押金的运营模式，押金从几十到几千元不等，一物多押的方式使押金规模巨大，形成“资金池”。由于大部分共享经济企业未将押金未交由第三方存管，且缺乏风险准备金制度，如果出现资金链断裂、平台跑路等情况，消费者押金无处索要，或出现押金兑付危机。加之在共享单车兴起之时，由于立法客观存在的滞后性，国家尚未制定关于押金规制方面的法律法规，不少共享单车企业肆意使用押金。实践中，这种“退押金难”的情况并不鲜见，“ofo 押金难退”的新闻就将共享单车推到了风口浪尖之上，引发社会的广泛关注。事实上，规制部门对于共享经济企业收取押金之运营模式在早先采取的是模拟两可之态度，如关于鼓励和规范互联网租赁自行车发展的指导意见中规定，“鼓励互联网租赁自行车运营企业采用免押金方式提供租赁服务”。换言之，共享单车企业既可收取押金，也可不收取押金。但 2018 年以来，中央对待共享经济企业收取押金之运营模式持原则上不支持的态度。为了将共享经济的发展引入规范、科学的轨道，同时保护消费者的合法权益，2019 年 5 月，交通运输部、人民银行等六部门联合印发的交通运输新业态用户资金管理办法(试行)规定运营企业原则上不收取用户押金，确有必要收取的，应当为用户提供运营企业专用存款账户和用户个人银行结算账户两种存管方式，供用户选择。同时，为减少个人资金损失，对用户的押金和预付资金收取规定了限额。此外，2019 年 11 月，北京市市场监督管理局发布关于加强预付式消费市场管理的意见（征求意见稿）等康二聪：洛阳修改网约车细则，很多标准都降低了，https:/年 9 月 1 日。搜狐网：浅析共享经济押金问题，https:/年9 月 1 日。北京师范大学法学院&澄观治库第 18 页 共 26 页7 份文件，旨在针对预付式消费市场加强规范，其中涉及共享单车、网约车等在内的交通运输新业态。具体到其他地方层面的规范性文件，各地普遍要求，各共享单车企业应当将用户押金与企业自有资金严格区分开来，在当地银行开设专门的押金账户并且接受有关部门的监督，严禁各共享单车企业擅自挪用用户押金。至此，业界也形成了共享单车用车免收押金的共识。三、共享经济未来发展及规制趋势三、共享经济未来发展及规制趋势尽管我国在共享经济领域的发展，无论是业态模式，还是法律规制，都取得了一定的成果，但是共享经济的发展仍然面临着新的机遇和挑战。首先，根据“十四五”期间“高质量发展”的主体思路，共享经济不但将继续承担创新发展、绿色发展的重任，还必须实现高质量的发展。共享经济新业态的萌生，也是对规制创新能力的再考验。其次，共享经济发展面临严峻复杂的外部环境。全球经济下行，加之疫情尚未结束，共享经济发展面临更大的不确定性。要实现“加快形成以国内大循环为主体、国内国际双循环相互促进的新发展格局”的目标，有必要对现有共享经济之业态模式加以完善巩固。因此，更有必要对现行共享经济之规制进行补充完善。（一）共享经济发展趋势（一）共享经济发展趋势1.共享经济业态持续丰富共享经济业态持续丰富共享经济横跨了众多行业、涵盖了巨大的公司列阵和众多工作岗位，是一个具有高度弹性的经济业态，涵盖了生活服务的方方面面。同时“共享”行为本身也会激发创新能力。长此以往，消费者的消费理念被逐渐重塑，根据消费者的消费趋势，未来将有更多行业转变经营模式加入共享经济的行列。例如共享衣橱，美国二手租衣平台 Rent the Runway 已于 2021 年在美国纳斯达克挂牌上市，该平台主要为消费者提供舞会、排队礼服和日常服装的租借服务；国内也出现了共享衣橱模式的企业衣二三。中新网：制度、监管、趋严 2019 年共享经济作别野蛮生长，http:/年 9 月 1 日。例如厦门市 2020 年 6 月 1 日起施行的厦门市互联网租赁自行车管理办法第四条（四）款规定“地方金融监督管理部门负责协调辖区内开户银行监测运营企业开立用户押金、预付资金专用存款账户情况以及提供用户资金风险警示”；第十三条第（一）款规定“运营企业原则上不得收取用户押金。确有必要收取的，应当提供运营企业专用存款账户和用户个人银行结算账户两种资金存管方式，供用户选择。运营企业不得挪用用户押金”。北京师范大学法学院&澄观治库第 19 页 共 26 页2.共享经济开始步入盈利时代共享经济开始步入盈利时代共享经济发展前期，因市场竞争激烈，各企业之间需快速抢占市场份额，部分企业采取大量投放、低价竞争、高额补贴等方式吸引大量用户群体，给公众造成了“共享经济是免费经济”的错误印象。以共享单车行业为例，ofo 单车率先推出了“百万单车免费骑”活动，之后摩拜单车和 ofo 单车先后进行了“红包车”“免费骑”“免押金”等“价格大战”。共享单车行业发展至“三足鼎立”时代后，拉开了整个行业涨价的序幕，摩拜单车、青桔单车、哈啰单车先后宣布涨价，在最近一次涨价后，青桔单车 7 天、30 天和 90 天骑行卡的无折扣价分别上涨至 10 元、25 元和 75 元，而哈啰单车与美团单车上述三种骑行卡的无折扣价则分别达到了 15元、35 元和 90 元。解决通勤“最后一公里”难题的单车价格要跟通勤本身费用一样多，让广大单车用户直呼“骑不起了”。在经过“烧钱”的价格战阶段，共享经济企业的运营开始回归理性，逐渐步入盈利时代。低价战略虽然能在短期内获得大量用户，但同时也会使企业背上沉重的经营负担，长此以往该经营模式必定是难以为继。共享经济行业是市场经济参与主体，虽然具备一定的公共服务属性，但最终是以盈利目的。加之，政府规制的日益完善也增加了企业的运营成本，行业涨价是必然之理。3.更加关注社会公平，平衡新旧业态权益。更加关注社会公平，平衡新旧业态权益。有观点认为共享经济将会造成新的社会不公平问题，例如平台用工劳动关系和社会保障的困境、新业态传统行业份额的挤占和对其发展空间的冲击、平台经济背后导致的算法歧视等。不可否认的是，这是经济、技术创新过程中的必然现象，是经济升级转型的必经之路，也是因立法滞后性带来的阶段性现象。这些社会不公平问题，以及新旧业态权益冲突问题，主要依靠立法规制加以解决，行业自律加以辅助。随着立法规制的跟进与完善、行业发展日趋成熟，缓解和弥合由共享经济引发的社会不公平问题将会成为共享经济发展新趋势。（二）共享经济规制趋势（二）共享经济规制趋势1.延续既有规制思路，继续完善配套制度延续既有规制思路，继续完善配套制度2021 年 3 月，中华人民共和国国民经济和社会发展第十四个五年规划和2035 年远景目标纲要提出“培育壮大人工智能、大数据.促进共享经济、平台经济健康发展”。2022 年，国家发改委发布“十四五”市场规制现代化规划北京师范大学法学院&澄观治库第 20 页 共 26 页提出“探索符合平台经济共享经济等新经济特点的规制模式”“完善网约车、共享单车等交通运输新业态规制规则和标准”；“十四五”数字经济发展规划提出“推动平台经济健康发展.深化共享经济在生活服务领域的应用，拓展创新、生产、供应链等资源共享新空间”“深入发展共享经济，鼓励共享出行等商业模式创新”。以上文件表明中央对共享经济，不但延续了旧有以上文件表明中央对共享经济，不但延续了旧有“支持发展支持发展”的顶层的顶层设计设计，同时也更加注重发展对共享经济的同时也更加注重发展对共享经济的“创新发展创新发展”“健康成长健康成长”和和“新型规制模新型规制模式式”。对于公共交通出行、共享民宿等传统共享经济行业，中央也表现出继续支对于公共交通出行、共享民宿等传统共享经济行业，中央也表现出继续支持的态度。持的态度。2021 年 2 月，国务院发布国务院关于加快建立健全绿色低碳循环发展经济体系的指导意见（国发20214 号），提出“提高服务业绿色发展水平有序发展出行、住宿等领域共享经济，规范发展闲置资源交易”。共享经济规制在保障新业态劳动者权益上仍有发展空间共享经济规制在保障新业态劳动者权益上仍有发展空间。上文提到，共享经济在稳民生保就业方面表现十分抢眼，但平台用工相关的劳动保障制度体现尚未建立。劳动保障制度应根据社会经济的变化进行差异化改革，应根据平台用工的就业特点改变旧有劳动关系认定的方法。2021 年 7 月，人力资源和社会保障部等部门联合发布关于维护新就业形态劳动者劳动保障权益的指导意见（人社部发202156 号），确立了“有劳动关系”“无劳动关系”外不完全符合确立劳动关系情形的第三类劳动者，这保证了无论平台经营企业采用哪种用工模式，都应该对新就业形态劳动者承担一定的雇主责任。2021 年，中央网络安全和信息化委员会印发的“十四五”信息化规划也中提出“健全适应共享平台灵活就业的政策体系等配套措施和要求”。共享民宿行业的法律规制探索仍有较大空间。共享民宿业目前没有高位阶的立法用以指导。相比网约车行业的网络预约出租汽车经营服务管理暂行办法，国家层面仅提供了有关容许共享民宿行业发展的政策性文件，尚未出台共享民宿行业规制的专门性法规文件，无法为地方政府制定相关的管制规范提供了有效的指导。共享民宿业则缺乏具有可操作性的全国性立法或法律解释。规制依据缺失将造成全国各个地方制度政策及执法不统一的现象。共享经济承担着“十四五”期间社会经济高质量发展的重任，同时也是提升社会经济数字化转型的重要抓手。对于共享经济可能产生的新业态新模式，规制者应具备一定的敏感度，做好随时规制新业态的准备，在总结以往规制经验教训的北京师范大学法学院&澄观治库第 21 页 共 26 页基础上，及时对新出现的业态和模式进行合理规制，在满足对新业态新模式的合理规制下，促进共享经济健康有序发展。共享经济在生活服务领域的渗透，对共享经济规制的出台速度、全面性及共享经济在生活服务领域的渗透，对共享经济规制的出台速度、全面性及细化程度提出了更高的要求细化程度提出了更高的要求。“十四五”期间，我国将开始全面建设社会主义现代化国家的新征程，并将加快发展健康、养老、托育、文化、旅游、体育、物业等服务业。这意味着共享经济极有可能渗透至生活服务领域的方方面面，互联网平台将更大地发挥其优化配置闲散资源的优势。对于新业态的规制，需要吸取早先顶层政策过于粗放、配套政策迟于跟进的经验教训，进一步深入研究。顶层政策过于原则化且配套政策过于滞后将导致地方政府自由裁量权过大，导致企业疲于应对，经营压力和成本高升，进而不利于共享经济全行业的正向发展。2.升级规制理念，搭建联合规制体系升级规制理念，搭建联合规制体系就目前情况而言，对于共享经济的规制通常涉及多个部门。就网约车、共享两轮车而言，规制部门涉及交通运输、住房和城乡建设、市场规制、公安、人力资源等，容易出现“多头规制”“九龙治水”的规制现象。各规制部门之间缺乏有效协同，既不利于政策制定的一致性，也不利于政策的有效施行，严重影响治理效能。政府部门之间应建立合作规制机制，防止政出多门，确保政策制定和实施政府部门之间应建立合作规制机制，防止政出多门，确保政策制定和实施的一致性，保证共享经济健康有序发展。的一致性，保证共享经济健康有序发展。同时，政府也应该善用行业自治的力量，鼓励行业出台行业规范，或与行同时，政府也应该善用行业自治的力量，鼓励行业出台行业规范，或与行业协会开展合作规制业协会开展合作规制。行业协会对营造共享经济有序健康发展的环境也应承担这重要责任。国务院办公厅关于促进平台经济规范健康发展的指导意见中明确强调要“鼓励行业协会商会等社会组织出台行业服务规范和自律公约，开展纠纷处理和信用评价，构建多元共治的规制格局”。行业协会具备大量实践经验，对行业发展规律和机制都较规制者更为熟悉，政府也可采取联合行业协会及企业，实现共同规制。政府提高行业在规制中参与感也能够督促企业更好地履行社会责任，促进行业良性发展。实践中，存在不少一味追求企业盈利而忽略履行社会责任的情况，肆意侵犯消费者合法权益，侵犯消费者隐私权，扰乱破坏市场公平竞争秩序，引发劣币驱逐良币的现象。3.持续优化营商环境持续优化营商环境北京师范大学法学院&澄观治库第 22 页 共 26 页近年来，中央为持续优化营商环境、推动形成全面开放新格局作出了一系列决策部署2015 年国家决定建立市场准入负面清单制度，国家发改委和商务部发布会不定期更新发布市场准入负面清单。市场准入负面清单制度实施“对市场准入负面清单以外的行业、领域、业务等，各类市场主体皆可依法平等进入”政策，严格落实“全国一张清单”，各地区各部门不得自行发布市场准入性质的负面清单。然而在共享两轮车领域，国内部分城市为共享单车企业进入当地市场设置了较高的门槛。一些地方政府甚至通过公开招标的方式，将本地今后某一时期内共享单车或者共享电动车的经营权进行打包出售，或简单粗暴的以“价高者得”的拍卖方式来确定中标企业。希望进入当地市场的共享单车企业必须参加政府或者相关职能部门举行招标活动或拍卖会，通过公开竞争的方式获得该经营权。虽然此举在一定程度上增加了地方政府的收入，缓解了地方政府在经济下行背景下所面临的压力，但是却给共享两轮车企业带来了沉重的经济负担，使得整个行业的经营成本陡增，正常经营难以为继。更重要的是，这种通过拍卖经营权为共享两轮车进入市场设定门槛的行为其实是在实质上设定了行政许可，明显违反了行政许可法和反垄断法的有关规定。4.强化平台企业合规建设强化平台企业合规建设2021 年 7 月 4 日，因滴滴出行 APP 存在严重违法违规收集使用个人信息问题，国家互联网信息办公室依据中华人民共和国网络安全法相关规定，通知应用商店下架滴滴出行 APP。4 天后，网信办等 7 部门联合进驻滴滴，继续进行网络安全审查。2021 年 12 月 3 日，滴滴在美国退市。滴滴网络安全审查持续了整整一年，2022 年 7 月 21 日，国家互联网信息办公室依据网络安全法 数据安全法 个人信息保护法 行政处罚法等法律法规，对滴滴全球股份有限公司处人民币 80.26 亿元罚款。加强平台企业的合规建设是完善共享经济规制体系必不可少的环节。引导共享经济行业健康有序发展不能仅依靠强规制的力量。平台企业不仅是用户数据的汇聚地，也是共享经济运作行为的汇聚地，市场准入、主体资格、竞争行为、数据管理、平台用工等都需要被纳入合规化管理的范畴。新京报：严重违法违规收集使用个人信息，“滴滴出行”App 被下架，https:/年 9 月 1 日。北京师范大学法学院&澄观治库第 23 页 共 26 页作者杨启哲 澄观治库研究员李烁 澄观治库研究员王静 北京师范大学法学院副教授张卿 中国政法大学法与经济研究院教授、澄观治库高级研究员张红 北京师范大学法务办公室主任、教授王轩 广州大学数字法治研究中心副主任、研究员，澄观治库执行主任、高级研究员吴小亮 澄观治库主任、高级研究员北京师范大学法学院&澄观治库第 24 页 共 26 页北师大法学院，前身为 1995 年哲学系设立的法学专业，2002 年成立的法律系，2005 年成立刑事法律科学研究院，2006 年正式成立法学院。在全球权威的 QS 大学排行榜 2020 年法学专业排名位列中国大陆地区第 9位、全亚洲第 21 位。同时还是全国首批卓越法律人才教育培养基地。法学院是一级学科博士学位授予单位，专业齐全，已形成从本科生、硕士生到博士生的三级人才培养格局，培养所有层次的专业人才。下设 7 个教研中心、15 个研究中心和 4 个刑事法交流中心。处于跨越式发展阶段的法学院依托北京师范大学这一享誉海内外的著名学府，秉承“德育英才，法行天下”的院训精神，将继续坚持高起点、研究型、国际化的发展特色，着力培养一流的法律实务人才和法学高端研究人才，致力于把学院建设成为国内一流、国际知名的法学教学科研机构。澄观治库（CG Think Tank）由一群致力于经济、法律、科技交叉研究的中青年学者组成，重点研究证券金融、生物医药、高端制造、文化媒体、网络平台、数据算法等领域的政府规制、企业合规以及未来成长。治库通过举行沙龙、论坛、圆桌会议、会展、公益活动、访谈、专业调研、培训等多种形式的活动，为政府、企业、科研机构和社会组织等搭建沟通交流的平台。治库定期发布研究规划、政策评估、风险预判、以及证券金融规制、自媒体规制、大数据治理、人工智能与算法、生物安全法律规制等研究报告，为国家治理、政府治理、企业治理提供具有独立性和前瞻性的研究成果，为各界提供政策法律综合咨询建议与参考。

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此外，消费者似乎更关心那些良好的企业公民品牌。消费者回应说，他们愿意向致力于保护环境的商家支付更多的钱。消费者的忠诚建立在信任的基础上。获得消费者信任的品牌将在这个假日季从愿意倾听它们的消费者那里获得销售优势。当品牌开始以顾客至上的理念进行营销时，忠诚和信任就会建立起来。假日促销始于人际关系，并建立在人际关系之上。

47人已浏览 2022-10-12 44页 5星级

中国主要城市共享单车/电单车骑行报告2022年度住房和城乡建设部城市交通基础设施监测与治理实验室中 国 城 市 规 划 设 计 研 究 院|美 团ANNUAL REPORT ON SHARING BIKES AND SHARING ELECTRIC BIKES RIDING IN MAJOR CHINESE CITIES我们力争通过大数据分析，全面系统、真实客观的呈现不同规模城市的共享单车/电单车出行特征。但因数据时空覆盖、计算方法设定等原因，相关指标值可能存在偏差，所载全部内容仅供参考。地方管理、企业运营等方面调整对部分共享骑行特征规律影响显著，依靠指标值观测不一定完全呈现客观事实，还需要全方位、多角度持续深入的细致研究。未来期待与更多的合作伙伴一道，共同挖掘共享单车/电单车的数据价值，产出更多有影响力的学术观点与政策建议，持续提高我国治理的科学化、精细化、智能化水平，为建设人民满意的共享骑行环境贡献积极力量。声明活力骑行1.活跃用户单次骑行距离2.活跃用户单次骑行时长3.活跃用户夜间骑行占比目录研究基础1.研究背景2.城市选取3.指标定义4.数据说明234791315轨道骑行1.轨道周边相对骑行强度2.轨道周边平均骑行距离3.轨道站点骑行服务占比182022壹贰叁目录效率骑行1.高峰时段平均骑行速度2.骑行服务通勤人口占比3032共享电单车专题研究1.数据分析篇2.行业价值篇3543减碳骑行1.活跃用户人均年减碳量2.活跃车辆车均年减碳量2628肆伍陆研究基础1壹研究背景12共享骑行成为民生“刚需”，以需求为导向的服务提升是关注重点现阶段，共享骑行已成为民生“刚需”，极大便利了老百姓日常出行，也为促进绿色出行做出了积极贡献。进入新发展阶段进入新发展阶段，共享骑行需要从关注共享骑行需要从关注“有没有有没有”到到“好不好好不好”，切实以百姓需要为导向切实以百姓需要为导向，提升服务提升服务质量质量，尤其在服务疫情常态化管控尤其在服务疫情常态化管控、助力生活圈建设助力生活圈建设、促进夜经济发展等适配度高的场景发力促进夜经济发展等适配度高的场景发力。因此，需持续加强对共享骑行服务提升方面的指标观测。2021年中国主要城市共享单车/电单车骑行报告发布以来，引发了社会各界高度关注与积极讨论。20222022年年，在接续上一年研究内容的基础上在接续上一年研究内容的基础上，结合外部环境变化结合外部环境变化、行业发展趋势行业发展趋势、国家政策导国家政策导向等向等，对部分观测指标进行了优化调整对部分观测指标进行了优化调整，更更突出突出了了对共享骑行服务对共享骑行服务水平水平的关注的关注，以期为城市治理、政策制定、行业发展、学术研究提供更为丰富的实证与参考。结合共享电单车加速布局、快速普及的行业发展态势，本次报告新增了电单车研究专题本次报告新增了电单车研究专题，从数从数据视角据视角全方位全方位挖掘电单车的特征规律挖掘电单车的特征规律，为应对电单车快速发展背景下为应对电单车快速发展背景下的的城市治理提供支撑城市治理提供支撑。共享电单车加速布局，提升治理能力、推动行业健康有序发展成为迫切需要据有关企业统计，截至2021年底，共享电单车已在全国1000多个城镇、区县投放运营，总投放量约1000万辆。行业管理方面，至今共有70多个城市出台了共享电单车的管理办法，地方政府将共享电单车纳入管理已成发展趋势。共享电单车的快速普及已成为客观事实，也是今后政府治理中不可回避的一环。从数据视角全从数据视角全方位挖掘电单车的特征规律方位挖掘电单车的特征规律，将对电单车快速发展背景下将对电单车快速发展背景下的的城市治理提供支撑城市治理提供支撑，也为推动行业健康也为推动行业健康有序发展提供启发有序发展提供启发。轨道发展如火如荼，做优“轨道 骑行”是促进轨道高质量发展重要工作截止2021年底，中国大陆已开通轨道交通运营城市50个，线路总里程9207km，在建线路长度6096km，相比2020年，新增轨道运营线路1237公里。轨道发展从轨道发展从“重规模重规模”走向走向“重效益重效益”阶段阶段，通过通过“轨道轨道 骑行骑行”出行模式出行模式，可以有效拓展轨道服务覆可以有效拓展轨道服务覆盖盖、促进轨道客流提升促进轨道客流提升。因此，重点挖掘其规律与特征，对做优“轨道 骑行”有重要现实意义，也对轨道交通提质增效、TOD发展有积极促进作用。城市选取2本报告以36个全国主要城市作为研究对象，包括直辖市、省会城市及计划单列市，在此基础上，进一步筛选出32个数据样本全、置信度高的城市进行具体分析。其中，共享单车样本城市共享单车样本城市2626个个，包含超大城市4个、特大城市8个、I型大城市8个、II型大城市6个；共享电单车样本城市共享电单车样本城市1818个个，包含特大城市5个、I型大城市6个、II型大城市7个。相比较2021年样本城市，共享单车城市增加了共享单车城市增加了2 2个个，分别是长春市、南宁市。共享电单车城市共享电单车城市增加了增加了4 4个个，分别是合肥市、哈尔滨市、厦门市、宁波市。图1-1 共享单车/电单车订单数据选取城市重庆市重庆市天津市天津市武汉市武汉市成都市成都市西安市西安市南京市南京市杭州市杭州市沈阳市沈阳市青岛市青岛市郑州市郑州市特大城市济南市济南市石家庄市石家庄市长沙市长沙市昆明市昆明市太原市太原市合肥市合肥市厦门市厦门市长春市长春市哈尔滨市哈尔滨市型大城市南宁市南宁市南昌市南昌市福州市福州市贵阳市贵阳市兰州市兰州市海口市海口市呼和浩特市呼和浩特市银川市银川市宁波市宁波市型大城市北京市北京市上海市上海市广州市广州市深圳市深圳市超大城市48865670共享单车城市数量共享电单车城市数量注：黑色为仅有共享单车的样本城市（14个）绿色为仅有共享电单车的样本城市（6个）紫色为同时拥有单车与电单车的样本城市（12个）城市规模分类延续2021年报告，便于年度数据比较3指标定义32022年中国主要城市共享单车/电单车骑行报告从活力骑行从活力骑行、轨道骑行轨道骑行、减碳骑行和效率骑减碳骑行和效率骑行四个方面行四个方面，建立了建立了10项骑行指标项骑行指标，并通过城市间横向比较、时间轴追踪对比、与其它指标交叉分析等方式，揭示出2021年中国主要城市共享单车、电单车出行特征与规律。相比较2021年骑行报告，在轨道骑行方面进行指标优化调整，更加突出“轨道 骑行”服务效能间差异；新增了效率骑行内容，体现对骑行便捷性的关注。图1-2 2022年城市共享单车/电单车骑行指标（说明：为2022年报告新增指标）轨道骑行5 轨道周边平均骑行距离4 轨道周边相对骑行强度 效率骑行9 高峰时段平均骑行车速 10 骑行服务通勤人口占比 1 活跃用户单次骑行距离活力骑行2 活跃用户单次骑行时长3 活跃用户夜间骑行占比减碳骑行7 活跃用户人均年减碳量8 活跃车辆车均年减碳量（1）中国主要城市共享单车/电单车骑行指标列表6 轨道站点骑行服务占比 4共享单车共享单车/电单车骑行活跃用户电单车骑行活跃用户，单次骑行距离：单次骑行距离：侧面反映出共享单车/电单车的日常使用目的，对共享骑行的功能定位有重要的参考意义。活跃用户单次骑行距离共享单车共享单车/电单车骑行活跃用户电单车骑行活跃用户，单次骑行时长：单次骑行时长：侧面反映出共享单车/电单车的日常使用目的，对共享骑行的功能定位有重要的参考意义。活跃用户单次骑行时长共享单车共享单车/电单车电单车活跃用户活跃用户，在轨道出入口周边在轨道出入口周边100米周累计订单强度与运营范围内订单强度的米周累计订单强度与运营范围内订单强度的比值：比值：衡量各城市“轨道 共享骑行”出行模式使用强度、紧密程度的测度指标，对指导共享骑行服务提升、促进轨道TOD发展均有参考意义。轨道周边相对骑行强度共享单车共享单车/电单车活跃用户电单车活跃用户，在轨道出入口周边在轨道出入口周边100米周累计订单量的平均骑行距离：米周累计订单量的平均骑行距离：识别各城市“轨道 共享骑行”出行模式的重点服务圈层，对扩展轨道交通覆盖，改善轨道周边地区慢行设施具有重要的参考意义。轨道周边平均骑行距离活力骑行轨道骑行共享单车共享单车/共享电单车骑行订单中共享电单车骑行订单中，22:00至次日至次日6:00的骑行订单量所占比重的骑行订单量所占比重：侧面反映出共享单车/电单车在填补夜间公共交通服务空档和丰富城市夜间经济活力的作用。活跃用户夜间骑行占比轨道站点骑行服务占比共享单车共享单车/电单车活跃用户电单车活跃用户，在轨道出入口周边在轨道出入口周边100米周累计每日均有订单量的轨道站点数量与米周累计每日均有订单量的轨道站点数量与全部轨道站点的比例：全部轨道站点的比例：衡量各城市“轨道 共享骑行”的服务覆盖指标，对指导共享骑行服务提升，完善轨道交通慢行接驳有参考价值。指标定义3（2）中国主要城市共享单车/电单车骑行指标详解5共享单车共享单车/电单车骑行活跃用户电单车骑行活跃用户，人均每年碳减排量：人均每年碳减排量：从使用者角度出发，考虑共享骑行替代机动化出行方式所带来的碳减排量。人均碳减排可以是衡量城市绿色交通发展水平的测度指标，居民采用共享骑行的比例越高，累计骑行距离越长，相应人均碳减排就越多。活跃用户人均年减碳量效率骑行共享单车共享单车/共享电单车活跃用户共享电单车活跃用户，周累计早高峰期间平均骑行车速：周累计早高峰期间平均骑行车速：反映共享单车/电单车高峰期间的骑行效率与便捷程度，对改进城市骑行基础设施具有重要的参考意义。高峰时段平均骑行速度共享单车共享单车/电单车在运营边界内所服务通勤居住人口与城市建设用地通勤居住总人口之比：电单车在运营边界内所服务通勤居住人口与城市建设用地通勤居住总人口之比：评价共享骑行对城市通勤人口服务覆盖耦合关系的测度指标，对改进企业运营服务效率，帮助政府划定运营管理边界具有重要的参考意义。骑行服务通勤人口占比活跃车辆车均年减碳量共享单车活跃车辆共享单车活跃车辆，车均每年碳减排量：车均每年碳减排量：从车辆角度出发，考虑共享骑行车辆替代机动化出行工具所带来的碳减排量。车辆被使用次数越多，累计被骑行距离越长，相应车均碳减排就越多，该指标对推动城市达成“双碳目标”，调控车辆投放有一定参考意义。减碳骑行指标定义3（2）中国主要城市共享单车/电单车骑行指标详解6数据说明4（1）数据来源（2）数据处理本报告所用数据为美团单车提供的共享单车和共享电单车订单记录数据，订单时间为2021年7月和8月每月第一周，累计2周。7夜间骑行时段定义为22:00至次日6:00。夜间骑行时段轨道站点周边单车出行轨道站点周边单车出行指单次订单出行起讫点一端在轨道出入口周边100米范围内的出行记录。高峰骑行时段定义为每日7:30至8:30。高峰骑行时段活跃车辆活跃车辆指在数据时间范围内有订单记录的车辆。单次订单出行时间单次订单出行时间指在订单记录中所标记的借车点时间和还车点时间的时间差。单次订单出行距离单次订单出行距离指根据订单记录中起点坐标和终点坐标所计算的曼哈顿距离。曼哈顿距离在本研究中指两点之间的线段在平面投影坐标轴上的长度之和。活跃用户活跃用户指在数据时间范围内有出行记录的用户。活力骑行8贰1活跃用户单次骑行距离图2-1 主要城市共享单车活跃用户单次骑行距离图2-2 主要城市共享电单车活跃用户单次骑行距离0.00.51.01.52.02.53.03.5沈阳市青岛市天津市成都市重庆市石家庄市昆明市合肥市哈尔滨市长沙市厦门市呼和浩特市银川市南宁市南昌市兰州市宁波市贵阳市特大城市型大城市型大城市单次骑行距离/公里2.342.432.372021、2020平均值均为2.49共享单车骑行距离全面增长，电单车骑行距离增减不一0.00.51.01.52.0北京市上海市深圳市广州市西安市沈阳市郑州市杭州市南京市天津市成都市武汉市长春市昆明市太原市石家庄市济南市合肥市厦门市长沙市兰州市呼和浩特市海口市南昌市南宁市福州市超大城市特大城市型大城市型大城市单次骑行距离/公里1.371.491.531.502021平均值1.52020平均值1.3 单车单次骑行平均距离单车单次骑行平均距离1.5公里，对比公里，对比2020年增加年增加140米，米，II型大城市增幅更为明显型大城市增幅更为明显II型大城市骑行距离平均增加160米，约是超大、特大、I型大城市平均增幅1.3倍；增幅超过200米的城市包括兰州、沈阳、郑州，分别为230米、220米、200米；天津、武汉骑行距离基本保持不变。电单车单次骑行平均距离电单车单次骑行平均距离2.4公里，对比公里，对比2020年部分城市增减变化突出年部分城市增减变化突出青岛、重庆、南宁、南昌等4个城市呈现上升趋势，分别增加为430米、220米、110米、130米；贵阳、昆明、长沙、兰州等4个城市呈现下降趋势，分别减少为290米、190米、190米、150米；电单车平均骑行距离2.4km，相较2020年，单车骑行距离差异从1.8倍小幅缩短至约1.6倍。1活跃用户单次骑行距离表2-1 主要城市共享单车活跃用户单次骑行距离变化表2-2 主要城市共享电单车活跃用户单次骑行距离变化城市分类城市名称单次骑行距离/公里（2021年）单次骑行距离/公里（2020年）单次骑行距离变化北京市1.51.40.11上海市1.41.30.12深圳市1.31.20.16广州市1.21.10.11西安市1.61.50.13沈阳市1.61.40.22郑州市1.51.30.20杭州市1.51.40.14南京市1.51.30.15天津市1.41.40.07成都市1.41.30.12武汉市1.31.30.03昆明市1.81.60.16太原市1.61.50.08石家庄市1.61.50.04济南市1.51.40.13合肥市1.41.30.09厦门市1.31.10.19长沙市1.31.10.18兰州市1.71.50.23呼和浩特市1.71.50.14海口市1.51.30.16南昌市1.41.30.14福州市1.41.20.15超大城市特大城市型大城市型大城市城市分类城市名称单次骑行距离/公里（2021年）单次骑行距离/公里（2020年）单次骑行距离变化沈阳市2.82.80.04青岛市2.52.00.43天津市2.22.20.03成都市2.22.3-0.10重庆市2.01.70.22石家庄市3.13.00.05昆明市2.52.7-0.19长沙市2.02.2-0.19呼和浩特市2.93.0-0.07银川市2.72.7-0.02南宁市2.32.20.11南昌市2.32.20.13兰州市2.22.4-0.15贵阳市2.02.3-0.29特大城市型大城市型大城市10代表指标幅度变化较大的城市1活跃用户单次骑行距离51QFFEECCHHHHFFDD99888888IIFFDDBBAA3311HHFFEEEE993344885577441144555599667777994433666666335577332288%9%9%9%9%5%5%6%6%9%9%8%8%7%7%9%9%9%9%9%9%5%5%6%6%6%6%9%9%7%7%7%7%0 0Pp0%广州市深圳市北京市上海市武汉市天津市成都市南京市沈阳市杭州市郑州市西安市长沙市厦门市合肥市济南市太原市石家庄市昆明市长春市南昌市福州市南宁市海口市呼和浩特市兰州市超大城市特大城市型大城市型大城市单次骑行距离分布/km/km/#km/%3km及以上/ (&$!7744(44%4444111122$6611442244(% ! ! ! $44%!333311EE$(%66AA%0 0Pp0%重庆市成都市青岛市天津市沈阳市长沙市厦门市合肥市昆明市哈尔滨市石家庄市贵阳市南昌市宁波市南宁市兰州市银川市呼和浩特市特大城市型大城市型大城市单次骑行距离分布/km/km/#km/%3km及以上/%图2-3 主要城市共享单车单次骑行距离分布图2-4 主要城市共享电单车单次骑行距离分布11距离分布方面，共享单车中长距离骑行需求显著增长，共享电单车保持稳定 对比对比2020年年，共享单车短途骑行需求降低共享单车短途骑行需求降低，2公里以上中长距离显著增长公里以上中长距离显著增长单车大于2公里的出行占比由18%增长至22%，其中上升幅度最大的为沈阳、昆明、兰州，分别上升了7、6、6个百分点；单车1公里内出行占比由50%下降至43%，其中下降幅度最大的为兰州、厦门、沈阳，分别下降了12、11、12个百分点。对比对比2020年年，共享电单车共享电单车3公里以上出行在特大城市公里以上出行在特大城市、大城市需求一增一减大城市需求一增一减特大城市3公里以上出行占比从25%增长至28%，青岛3公里以上骑行绝对比例增长13%；I、II型大城市3公里以上比例从34%下降至31%。1活跃用户单次骑行距离14!$( %(44221166$1144%4444(77 ! ! AA66%(EE33!%$44% %# $33$00)334422%22445577 # ! !CC880011%&FF88&99%&!%0 0Pp0%呼和浩特市银川市兰州市南宁市南昌市贵阳市石家庄市昆明市长沙市沈阳市天津市成都市青岛市重庆市型大城市型大城市特大城市33399EEFFHH33AABBDDFFII88888899DDFFHHHHCCEEFFQQ8822335533666633449977779966555544441177558844%9%9%9%9%7%7%9%9%6%6%6%6%5%5%9%9%9%9%9%9%7%7%8%8%9%9%6%6%5%5EEDDQQTTQQ77DDCCIIPPWWWWEEEEHHQQQQRRQQRRIIPPUUYY11112211336622330055113366335500331122)443333)%9%9%9%9%9%9%8%8%7%7%9%9%9%9%8%8%7%7%7%7%5%5%6%6%9%9%6%6%4%4%3%3%8%8%7%7%9%9%7%7%7%7%7%7%9%9%7%7%8%8%4%4%5%5%0 0Pp0%兰州市呼和浩特市海口市福州市南昌市昆明市石家庄市太原市济南市合肥市厦门市长沙市杭州市西安市郑州市沈阳市南京市成都市武汉市天津市上海市北京市深圳市广州市型大城市型大城市特大城市超大城市12图2-5 主要城市共享单车活跃用户单次骑行距离分布变化图2-6 主要城市共享电单车活跃用户单次骑行距离分布变化2活跃用户单次骑行时长图2-7 主要城市共享单车活跃用户单次骑行时长图2-8 主要城市共享电单车活跃用户单次骑行时长 受骑行距离增长影响，单车骑行时间出现不同程度增长受骑行距离增长影响，单车骑行时间出现不同程度增长单车骑行时间增长超过2分钟的城市包括了沈阳市、郑州市、厦门市和兰州市。超大城市保持了相比其它规模城市更短的骑行时长超大城市保持了相比其它规模城市更短的骑行时长超大城市单次骑行时长为9.9分钟，低于特大城市10.7分钟和大城市11.0分钟。电单车骑行时间变化在电单车骑行时间变化在1.5分钟以内分钟以内13骑行时长变化特征与距离保持同步，骑行效率相对稳定02468101214深圳市北京市上海市广州市沈阳市郑州市西安市天津市杭州市南京市成都市武汉市昆明市石家庄市济南市太原市厦门市合肥市长沙市兰州市呼和浩特市海口市南昌市福州市南宁市超大城市特大城市型大城市型大城市单次骑行时长/分钟2021年平均值10.79.910.710.611.22020年平均值 9.2 024681012141618沈阳市青岛市天津市成都市重庆市石家庄市厦门市哈尔滨市昆明市合肥市长沙市呼和浩特市银川市兰州市南宁市贵阳市宁波市南昌市特大城市型大城市型大城市单次骑行时长/分钟2021年平均值13.413.014.113.22020年平均值13.7 单车单次骑行时长单车单次骑行时长10.7分钟分钟，对比对比2020年出现不同程度增长年出现不同程度增长，超大城市时长保持最短超大城市时长保持最短各类型城市单车平均时长增长为1.5分钟，其中增长超过2.0分钟的城市包括了郑州市、厦门市、沈阳市、兰州市和深圳市；单车在超大城市平均骑行时长同比增长1.4分钟，达到9.9分钟，仍低于特大城市10.7分钟和大城市11.0分钟。电单车单次骑行时长电单车单次骑行时长13.4分钟分钟，对比对比2020年保持相对稳定年保持相对稳定，仅个别城市呈现小幅变化仅个别城市呈现小幅变化青岛增长了1.0分钟；昆明、呼和浩特、石家庄、银川分别下降1.4、1.3、1.0、1.0分钟。2活跃用户单次骑行时长表2-3 主要城市共享单车活跃用户单次骑行时长变化表2-4 主要城市共享电单车活跃用户单次骑行时长变化城市分类城市名称单次骑行时长/分钟（2021年）单次骑行时长/分钟（2020年）单次骑行时长变化深圳市10.48.42.0北京市9.88.61.2上海市9.78.71.1广州市9.58.31.2沈阳市12.510.52.0郑州市11.59.02.5西安市11.19.71.4天津市10.99.91.0杭州市10.59.01.4南京市9.98.41.5成都市9.88.61.1武汉市9.59.10.4昆明市12.010.51.5石家庄市11.010.10.9济南市11.09.61.4太原市10.910.00.9厦门市10.58.02.5合肥市9.58.51.1长沙市9.58.11.4兰州市12.410.42.0呼和浩特市12.310.91.4海口市11.49.91.5南昌市10.68.91.7福州市10.48.51.9超大城市特大城市型大城市型大城市城市分类城市名称单次骑行时长/分钟（2021年）单次骑行时长/分钟（2020年）单次骑行时长变化沈阳市16.817.1-0.3青岛市13.012.01.0天津市12.212.4-0.2成都市11.912.5-0.6重庆市11.110.50.6石家庄市15.516.5-1.0昆明市13.915.4-1.4长沙市12.512.40.1呼和浩特市14.916.2-1.3银川市14.515.5-1.0兰州市13.213.4-0.2南宁市13.013.00.0贵阳市12.312.8-0.5南昌市12.211.90.3特大城市型大城市型大城市14代表指标幅度变化较大的城市3活跃用户夜间骑行占比图2-9 主要城市共享单车活跃用户夜间骑行占比图2-10 主要城市共享电单车活跃用户夜间骑行占比0.0%2.0%4.0%6.0%8.0.0.0.0.0%南宁市海口市兰州市厦门市长春市福州市深圳市长沙市杭州市广州市上海市合肥市郑州市南京市武汉市济南市西安市北京市成都市太原市石家庄市沈阳市南昌市昆明市天津市呼和浩特市夜间骑行占比/%北方城市南方城市平均值8.5%平均值6.6%0.0%2.0%4.0%6.0%8.0.0.0.0.0.0%厦门市南宁市兰州市宁波市贵阳市长沙市哈尔滨市银川市合肥市重庆市南昌市昆明市成都市沈阳市石家庄市呼和浩特市天津市青岛市夜间骑行占比/%北方城市南方城市平均值8.8%平均值11.7夜间骑行比例上涨，南方城市更为突出 单车夜间骑行占比单车夜间骑行占比7.7%，对比对比2020年上涨年上涨1.6个百分点个百分点南、北方城市分别由6.5%、4.8%，上升至8.5%、6.6%，南方上升幅度略高于北方；夜间骑行比例增加2个百分点的主要为南方城市；除昆明市小幅下降以外，其余城市均有不同程度上涨，兰州、厦门增幅相对较高，上升幅度为6.8、3.2个百分点。电单车夜间骑行占比电单车夜间骑行占比10.4%，对比对比2020年上涨年上涨2个百分点个百分点南、北方城市分别由8.7%、7.1%，上升至11.7%、8.8%，上升幅度分别为3.0、1.7个百分点；夜间骑行比例增加2.0个百分点的仅有兰州市为北方城市；兰州、贵阳电单车夜骑行占比上涨幅度最高，均超过5个百分点，分别为7.7、5.7。3活跃用户夜间骑行占比表2-5 主要城市共享单车活跃用户夜间骑行占比变化表2-6 主要城市共享电单车活跃用户夜间骑行占比变化城市名称夜间订单量占比%（2021年）夜间订单量占比%（2020年）夜间订单量占比变化兰州市16.0%8.3%7.7%贵阳市12.0%6.3%5.7%长沙市12.0%8.3%3.7%南昌市9.5%6.3%3.2%南宁市16.0.0%3.0%银川市10.0%8.3%1.7%成都市9.0%7.7%1.3%沈阳市8.0%6.7%1.3%昆明市9.5%9.0%0.5%呼和浩特市6.5%6.0%0.5%天津市6.5%6.0%0.5%青岛市6.0%6.0%0.0%重庆市9.5.0%-0.5%石家庄市7.5%8.7%-1.2%城市名称夜间订单量占比%（2021年）夜间订单量占比%（2020年）夜间订单量占比变化兰州市11.5%4.7%6.8%厦门市10.5%7.3%3.2%福州市9.5%7.3%2.2%杭州市8.5%6.3%2.2%长沙市9.0%7.0%2.0%上海市8.0%6.0%2.0%郑州市7.5%5.7%1.8%海口市11.5%9.7%1.8%北京市6.0%4.3%1.7%南京市7.0%5.3%1.7%济南市7.0%5.3%1.7%广州市8.0%6.3%1.7%武汉市7.0%5.7%1.3%成都市6.0%4.7%1.3%太原市6.0%4.7%1.3%西安市6.5%5.3%1.2%深圳市9.5%8.3%1.2%石家庄市5.5%4.3%1.2%合肥市8.0%7.3%0.7%沈阳市5.0%4.3%0.7%南昌市5.0%4.3%0.7%天津市4.0%3.3%0.7%呼和浩特市3.5%3.3%0.2%昆明市5.0%5.7%-0.7北方城市南方城市代表指标幅度变化较大的城市轨道骑行17叁图3-2 主要城市共享电单车轨道周边相对骑行强度图3-1 主要城市共享单车轨道周边相对骑行强度01234567成都市广州市北京市上海市深圳市武汉市南京市杭州市西安市天津市沈阳市南宁市合肥市长沙市郑州市昆明市南昌市长春市石家庄市济南市福州市厦门市兰州市太原市呼和浩特市500公里以上300500公里100300公里50100公里50公里以下相对骑行强度6.04.12.12.62.5平均值3.5012345678成都市重庆市青岛市天津市南昌市合肥市昆明市南宁市沈阳市长沙市兰州市厦门市石家庄市哈尔滨市贵阳市呼和浩特市500公里以上300500公里100300公里50100公里50公里以下相对骑行强度7.65.73.22.62.6平均值3.5181轨道周边相对骑行强度 轨道里程越高的城市轨道里程越高的城市，周边共享单车相对骑行强度越高周边共享单车相对骑行强度越高轨道里程300-500公里城市，除杭州外，轨道周边共享单车骑行发生在出入口100米范围内相对骑行强度基本都在4.0以上（代表同类轨网规模城市平均强度水平的4倍）。500km以上轨道里程城市显著高于其它城市，相对强度达到6.0；成都市轨道周边相对骑行强度最高，达到6.8，其次为广州、北京，分别为6.4、5.9。共享电单车趋势类似共享电单车趋势类似，但相对骑行强度显著低于共享单车但相对骑行强度显著低于共享单车轨道100公里以上城市相对骑行强度平均为2.9，显著低于同类城市共享单车骑行强度平均值3.8；成都市轨道周边相对骑行强度最高，达到7.6，其次为重庆，达到5.7。300公里以上轨道城市，共享骑行接驳功能更显著，相对骑行强度明显高于其它城市1轨道周边相对骑行强度典型案例：成都市典型案例：合肥市19成都市共享单车骑行与轨道接驳联系紧密。轨道周边相对骑行强度为6.8，全国横比最高。骑行强度高的地区主要集中在中环路（轨道7号线）以内的城市中心区域以及城市南部天府新区。外围相对强度高的地区，基本与轨道放射线路相吻合。合肥市共享电单车轨道周边相对骑行强度为2.8，在100300公里规模轨道城市中排名居中，低于全国均值3.3。高强度地区主要集中在轨道2号线、3号线周边。四牌楼、三里庵、洪岗地铁站周边骑行强度全市前三。图 3-3 轨道周边共享单车骑行周订单量强度分布图3-4 轨道周边共享电单车骑行周订单量强度分布2轨道周边平均骑行距离图3-5 主要城市共享单车轨道周边平均骑行距离图3-6 主要城市共享电单车轨道周边平均骑行距离0.00.20.40.60.81.01.21.41.61.82.02.22.42.62.83.0成都市重庆市沈阳市昆明市合肥市青岛市南宁市南昌市天津市长沙市石家庄市哈尔滨市兰州市厦门市贵阳市呼和浩特市500公里以上300500公里100300公里50100公里50公里以下平均骑行距离/公里2.141.682.272.342.782021、2020平均值均为2.320 单车轨道周边单车轨道周边骑行平均距离骑行平均距离1.4公里公里，对比对比2020年增加年增加200米米100-300公里轨道里程城市增幅最明显，轨道周边平均骑行距离增加了12.6%;骑行距离增加在200米以上的城市包括郑州、厦门、沈阳、兰州，分别为290米、230米、220米、220米。电单车轨道周边电单车轨道周边骑行平均距离骑行平均距离2.3公里公里，对比对比2020年近半城市同比下降年近半城市同比下降，100-300公里轨道公里轨道里程城市降幅更大里程城市降幅更大100-300公里轨道里程城市骑行距离平均下降120米，下降幅度5.1%;下降幅度超过200米的城市分别为昆明（270米）、贵阳（200米）。共享单车轨道周边平均骑行距离增幅明显，电单车增减变化不一0.00.20.40.60.81.01.21.41.61.8北京市上海市成都市广州市南京市杭州市武汉市深圳市长春市昆明市郑州市沈阳市西安市南昌市合肥市南宁市天津市长沙市兰州市济南市石家庄市厦门市福州市呼和浩特市太原市500公里以上300500公里100300公里50100公里50公里以下平均骑行距离/公里1.251.251.301.421.372021平均值1.42020平均值1.22轨道周边平均骑行距离表3-1 主要城市共享用户轨道站点周边平均骑行距离变化表3-2 主要城市共享电单车轨道站点周边平均骑行距离变化轨道里程分类城市名称站点接驳平均出行距离/公里（2021年）站点接驳平均出行距离/公里（2020年）站点接驳平均出行距离变化北京市1.31.20.12上海市1.21.20.08成都市1.21.20.08广州市1.11.00.10南京市1.31.20.13杭州市1.31.20.13武汉市1.21.20.03深圳市1.21.10.11昆明市1.61.50.13郑州市1.51.20.29沈阳市1.51.30.22西安市1.51.40.09南昌市1.41.30.12合肥市1.31.20.14天津市1.31.20.10长沙市1.21.10.16兰州市1.61.40.22济南市1.51.50.01石家庄市1.51.50.01厦门市1.31.10.23福州市1.21.10.15呼和浩特市1.51.50.02太原市1.41.5-0.09500公里以上300500公里100300公里50100公里50公里以下轨道里程分类城市名称站点接驳平均出行距离/公里（2021年）站点接驳平均出行距离/公里（2020年）站点接驳平均出行距离变化300500公里重庆市1.71.60.09沈阳市2.72.60.08昆明市2.42.6-0.27南宁市2.32.20.06南昌市2.12.2-0.05天津市2.11.90.15长沙市1.92.0-0.11石家庄市3.03.0-0.03兰州市2.22.3-0.10贵阳市1.92.1-0.2050公里以下呼和浩特市2.82.9-0.16100300公里50100公里21代表指标幅度变化较大的城市 轨道站点共享单车骑行服务占比与城市轨道里程轨道站点共享单车骑行服务占比与城市轨道里程、人口规模有一定正相关性人口规模有一定正相关性北上广深中，除广州仅为58%以外，北京、上海、深圳均超过85%，分别为88%、91%、95%；成都、上海、深圳、太原、沈阳、兰州等6个城市站点共享单车服务站点占比超过90%，其中成都、太原超过95%，分别为96%、98%。共享电单车处于发展培育期共享电单车处于发展培育期，部分城市指标表现突出部分城市指标表现突出电单车轨道站点骑行服务占比平均值为55%，低于单车的77%；部分城市指标表现突出，接驳功能凸显，沈阳、昆明、呼和浩特、南昌等6座城市轨道站点骑行服务占比超过70%，其中，沈阳、昆明最高，达到80%。轨道 共享骑行全面普及，近80%轨道站点已有共享单车服务，电单车城市间服务差异大图3-7 主要城市共享单车轨道站点骑行服务占比图3-8 主要城市共享电单车轨道站点骑行服务占比0 0Pp0%成都市上海市北京市广州市深圳市武汉市南京市杭州市沈阳市合肥市天津市西安市昆明市郑州市长沙市南昌市南宁市长春市兰州市石家庄市济南市福州市厦门市太原市呼和浩特市500公里以上300500公里100300公里50100公里50公里以下骑行服务占比/%平均值77vtu%0 0Pp%成都市重庆市沈阳市昆明市南昌市南宁市合肥市长沙市天津市哈尔滨市石家庄市兰州市贵阳市呼和浩特市500公里以上300500公里100300公里50100公里50公里以下骑行服务占比/%平均值55%7abx3轨道站点骑行服务占比典型案例：成都市典型案例：合肥市233轨道站点骑行服务占比成都市轨道周边单车骑行服务覆盖比例达到96%，远高于全国均值77%。三环以内轨道站点均可提供共享单车服务。城市外围无骑行覆盖的站点，主要是因为其周边仍是待开发地区。合肥市轨道周边电单车骑行服务覆盖比例为56%，略高于全国均值55%。核心地区骑行服务完善，二环线内站点骑行服务占比接近100%。二环线以外缺乏骑行服务覆盖的站点，主要集中在滨湖新区、蜀山西区、瑶海区等。图3-9 共享单车轨道站点骑行服务覆盖情况图3-10 共享电单车轨道站点骑行服务覆盖情况减碳骑行24肆计算方法：共享骑行出行替代小汽车、地面公交车出行的年减碳量活跃用户人均年减碳活跃用户人均年减碳量量=T1(P1-P0) T2(P2-P0)年订单量年订单量 单次订单骑行距离单次订单骑行距离 年骑行用户数年骑行用户数活跃车辆车均年减碳量活跃车辆车均年减碳量=T1(P1-P0) T2(P2-P0)年订单量年订单量 单次订单骑行距离单次订单骑行距离 年骑行车辆数年骑行车辆数式中：T1为共享单车/电单车替代小汽车的出行比例，T2 为共享单车/电单车替代公交车的出行比例；P0为共享单车/电单车碳排放因子，P1为小汽车碳排放因子，P2为公交车辆碳排放因子；订单骑行距离（公里）根据共享单车/电单车的轨迹数据计算得到。小汽车小汽车公交车公交车共享单车共享单车共享电单车共享电单车0.2500.05400.012表4-1 不同交通方式的碳排放因子（kgCO2/PKM）计算说明：计算说明：1.不同交通方式的碳排放引子，来自北京市研究报告北京市低碳出行碳减排方法学（试行版）。2.共享骑行中为替代小汽车、地面公交车出行的比例，为研究团队通过咨询共享单车运营企业，并结合部分样本城市问卷调查数据以及共享出行行业专家意见征求综合预测得出，为统一比较，不同规模城市假设相同替代比例，指标值可能存在偏差指标值可能存在偏差，相关计算结果仅供比对参考相关计算结果仅供比对参考，不能作为依据不能作为依据。25碳 减 排 计 算 方 法1活跃用户人均年减碳量图4-1 主要城市共享单车活跃用户人均年减碳量图4-2 主要城市共享电单车活跃用户人均年减碳量26共享单车人均年减碳量增幅明显，电单车城市间变化差异大 单车用户人均年减碳单车用户人均年减碳43.3kg，对比对比2020增加增加8.2kg型大城市活跃用户人均年减碳量平均增幅最小，仅有7.3kg，其中石家庄市、合肥市和厦门市增幅均小于5kg；型大城市涨幅最为明显，活跃用户人均年减碳量平均增加9.7kg，其中呼和浩特市、南昌市和海口市增幅均高于10kg，分别为14.2kg、12.8kg和10.9kg。电单车用户人均年减碳电单车用户人均年减碳52.1kg，对比对比2020各城市同比变化差异大各城市同比变化差异大各城市间共享电单车人均年减碳量变化值的标准差高达6.6kg，城市间变化差异大；呼和浩特市人均年减碳量增幅最大，高达11.6kg；长沙市、兰州市和贵阳市呈现下降趋势，人均年减碳量分别降低6.2kg、8.2kg和7.8kg。0102030405060北京市上海市深圳市广州市天津市西安市沈阳市武汉市成都市南京市杭州市郑州市太原市昆明市济南市石家庄市长春市长沙市合肥市厦门市呼和浩特市南昌市兰州市海口市福州市南宁市超大城市特大城市型大城市型大城市人均年减碳量/千克41.444.542.643.82021平均值43.32021平均值35.101020304050607080沈阳市青岛市天津市成都市重庆市石家庄市合肥市昆明市哈尔滨市长沙市厦门市呼和浩特市银川市南昌市宁波市南宁市兰州市贵阳市特大城市型大城市型大城市人均年减碳量/千克51.049.547.52021平均值52.12020平均值48.31活跃用户人均年减碳量表4-2 共享单车活跃用户人均年减碳量变化表4-3 共享电单车活跃用户人均年减碳量变化城市分类城市名称人均年减碳量/千克（2021年）人均年减碳量/千克（2020年）人均年减碳量/千克变化沈阳市56.456.20.2青岛市52.543.68.9天津市51.844.87.0成都市50.148.12.0重庆市44.035.58.5石家庄市66.658.48.2昆明市56.251.74.4长沙市39.545.7-6.2呼和浩特市69.858.211.6银川市60.755.35.3南昌市54.344.69.7南宁市48.445.13.3兰州市41.349.5-8.2贵阳市39.647.3-7.8特大城市型大城市型大城市城市分类城市名称人均年减碳量/千克（2021年）人均年减碳量/千克（2020年）人均年减碳量/千克变化北京市45.236.58.7上海市44.034.39.6深圳市38.931.07.9广州市37.730.67.1天津市50.438.811.6西安市48.340.77.6沈阳市44.737.07.7武汉市44.435.39.1成都市44.036.37.6南京市43.134.19.0杭州市41.334.27.1郑州市40.035.05.0太原市53.241.311.9昆明市50.840.710.1济南市46.036.69.4石家庄市43.038.54.4长沙市37.029.97.1合肥市35.431.83.6厦门市34.129.84.3呼和浩特市54.340.114.2南昌市47.534.712.8兰州市46.142.23.9海口市45.034.010.9福州市38.732.26.5超大城市特大城市型大城市型大城市27代表指标幅度变化较大的城市2活跃车辆车均年减碳量共享单车车均年减碳量普遍增长，3座城市上涨超20kg图4-3 主要城市共享单车活跃车辆车均年减碳量28 对比对比2020年年，共享单车车均年减碳量同比增加共享单车车均年减碳量同比增加13.1kg型大城市涨幅最为明显，车均年减碳量平均增加17.3kg；呼和浩特市、太原市、南昌市增长超过20.0kg，分别达到33.8kg、23.2kg、20.8kg。020406080100120北京市深圳市广州市上海市沈阳市郑州市天津市武汉市成都市南京市西安市杭州市太原市昆明市济南市厦门市石家庄市长春市长沙市合肥市呼和浩特市南昌市海口市福州市南宁市兰州市超大城市特大城市型大城市型大城市车均年减碳量/千克56.754.861.566.02021平均值60.32020平均值47.2图4-3 主要城市共享单车活跃车辆车均年减碳量城市分类城市名称车均年减碳量/千克（2021年）车均年减碳量/千克（2020年）车均年减碳量/千克变化北京市77.762.315.4深圳市68.552.216.3广州市44.534.510.0上海市35.927.08.9沈阳市78.364.314.0郑州市61.752.09.7天津市52.437.714.7武汉市51.038.212.7成都市49.639.310.2南京市49.638.011.7西安市48.840.78.1杭州市46.737.59.2太原市100.977.723.2昆明市77.059.417.6济南市67.750.816.9厦门市56.147.09.1石家庄市52.144.57.6长沙市46.937.39.6合肥市39.835.44.4呼和浩特市109.575.733.8南昌市73.552.720.8海口市60.043.316.6福州市56.545.511.0兰州市43.238.84.4超大城市特大城市型大城市型大城市代表指标幅度变化较大的城市表4-4 主要城市共享单车活跃车辆车均年减碳量变化效率骑行29伍1高峰时段平均骑行速度图5-2 主要城市共享电单车高峰时段平均骑行速度图5-1 主要城市共享单车高峰时段平均骑行速度电单车早高峰平均骑行速度是单车的1.3倍，超特大城市骑行更便捷02468101214成都市天津市青岛市重庆市沈阳市合肥市石家庄市昆明市哈尔滨市长沙市厦门市呼和浩特市银川市南宁市南昌市宁波市贵阳市兰州市特大城市型大城市型大城市高峰时段平均骑行速度(公里/小时)13.112.612.7平均值12.8024681012北京市上海市广州市深圳市南京市成都市西安市杭州市武汉市郑州市天津市沈阳市昆明市太原市合肥市长沙市石家庄市济南市厦门市长春市兰州市南宁市呼和浩特市南昌市福州市海口市超大城市特大城市型大城市型大城市高峰时段平均骑行速度(公里/小时)9.79.89.79.4平均值9.730 电单车高峰时段平均骑行速度电单车高峰时段平均骑行速度（12.8km/h）约是单车约是单车（9.7km/h）的的1.3倍倍单车方面，北京、南京、成都、昆明、太原等5个城市高峰时段单车平均骑行速度高于10.0km/h，昆明骑行速度最快，达到10.5km/h，长春市骑行速度较低，仅为8.3km/h；电单车方面，成都市和合肥市的高峰时段电单车平均骑行速度最快，高达13.8km/h，12.0km/h以下的城市有兰州、长沙和厦门，分别为11.9km/h、11.8km/h、11.3km/h。超特大城市骑行速度相比大城市超特大城市骑行速度相比大城市（I、II型型）更快更快超特大城市单车速度为9.8km/h，略高于大城市（I、II型）速度9.5km/h。特大城市整体的电单车高峰时段平均骑行速度最高，为13.1km/h，高于大城市12.6km/h。1高峰时段平均骑行速度典型案例：西安市典型案例：哈尔滨市31西安市高峰时段单车平均骑行速度随城市空间范围向外围延展而提高。一环线内为9km/h，二环线内为9.7km/h，二环外达到10.3km/h。二环线内高峰时段平均骑行效率有待提升。骑行速度低于全国均值9.7km/h和西安市均值10.2km/h。哈尔滨市高峰时段电单车平均骑行速度呈现明显的圈层差别，核心区内环线内平均速度为9.5km/h，内环线外为11.9km/h，相比较内环线内增加了25%。图5-3 共享单车高峰时段分区域速度分布图5-4 共享电单车高峰时段分区域速度分布2骑行服务通勤人口占比图5-5 主要城市共享单车骑行服务通勤人口占比图5-6 主要城市共享电单车骑行服务通勤人口占比0 0Pp0%沈阳市天津市成都市重庆市青岛市昆明市哈尔滨市石家庄市合肥市长沙市厦门市南宁市银川市呼和浩特市南昌市兰州市贵阳市宁波市特大城市型大城市型大城市骑行服务通勤人口占比/%平均值50aU%0 0Pp0%深圳市广州市上海市北京市西安市成都市郑州市武汉市南京市沈阳市杭州市天津市昆明市长春市太原市济南市石家庄市合肥市长沙市厦门市海口市福州市呼和浩特市南宁市兰州市南昌市超大城市特大城市型大城市型大城市骑行服务通勤人口占比/%平均值78us2 人口规模越大的城市人口规模越大的城市，共享单车服务通勤人口占比越高共享单车服务通勤人口占比越高特大、超大城市服务通勤人口占比均为82%，高于I型大城市（75%）和II型大城市（73%）；西安、成都、深圳通勤服务人口占比均超过90%，分别为98%、95%、90%；厦门、南昌不足50%，仅为48%、44%。电电单车服务通勤单车服务通勤人口占比平均水平不高人口占比平均水平不高，但部分城市表现突出但部分城市表现突出由于电单车受到更严格的区域运营限制，平均服务通勤人口占比仅为50%，远低于单车的78%；南宁、银川、昆明、呼和浩特、哈尔滨、石家庄、合肥等7座城市共享电单车已经形成较高服务规模，服务占比均超过70%，南宁比例最高，达到98%。共享单车全面普及，各城市平均服务通勤人口占比接近80%，显著高于共享电单车50%2骑行服务通勤人口占比33典型案例：沈阳市典型案例：南宁市沈阳市共享单车骑行服务通勤人口占比为77%，与全国均值齐平。无服务区域主要在城市南部，包括沈阳中关村、中德园、沈水科技城、航空城部分地区。南宁市共享电单车运营范围与通勤人口分布高度重合，可满足通勤人口的出行需求。骑行服务通勤人口占比达到了98%，全国最高。无服务的地区主要在城市东北部的五塘组团和东部产业新城的部分地区。图5-7 共享单车骑行服务通勤人口分布图5-8 共享电单车骑行服务通勤人口分布共享电单车专题研究34陆 共享电单车服务通勤高峰出行超共享电单车服务通勤高峰出行超30%，订单强度与职住分布耦合性强订单强度与职住分布耦合性强共享电单车早晚高峰时段骑行强度显著高于其他时刻：以昆明、合肥、成都、哈尔滨、南昌、南宁为研究对象，六个城市高峰时段平均出行量占比为32.9%，合肥市最高，达到41.1%；共享电单车订单强度与职住分布耦合性强：以南宁市工作日早高峰为例，在广西大学附近，共享电单车高强度出行的起点与居住人口密集区域重合；在国贸购物中心、金湖广场和三祺广场附近，骑行终点与就业人口密集区域重合。图6-2 南宁共享电单车出行热力与职住分布图6-1 典型城市共享电单车订单强度按时辰分布0.0%2.0%4.0%6.0%8.0.0.0.0234567891011121314151617181920212223昆明合肥成都哈尔滨南昌南宁广西大学城广西大学城国贸购物中心金湖广场三祺广场国贸购物中心金湖广场三祺广场居住人口就业人口早高峰订单起点热力图早高峰订单起点热力图1数 据 分 析 篇（1）共享电单车便捷市民通勤，塑造“骑行 ”生活圈35南昌大院街道南昌大院街道公园社区公园社区步行15分钟可达范围自行车15分钟可达范围电单车15分钟可达范围医疗保健类POI体育休闲类POI生活服务类POI1.1km1.1km2.1km2.1km3.7km3.7km0100020003000400050006000医疗保健类POI数量休闲体育类POI数量生活服务类POI数量步行自行车电单车 共享电单车弥补老旧社区配套设施不足共享电单车弥补老旧社区配套设施不足，提高生活服务便捷触达提高生活服务便捷触达以南昌大院街道公园社区为例，电单车15分钟出行可触达的生活服务类POI数量远高于步行和自行车出行，分别是自行车的4倍，步行的39倍。图6-3 不同交通方式15分钟出行覆盖POI情况图6-5 南昌大院街道公园社区按步行、自行车、电单车15分钟出行触达POI对比05101520253035步行自行车电单车2727倍倍 4 4倍倍4 4倍倍3333倍倍3939倍倍 4 4倍倍2020倍倍4 4倍倍骑行 服务图6-4 不同交通方式15分钟出行覆盖范围1数 据 分 析 篇（1）共享电单车便捷市民通勤，塑造“骑行 ”生活圈36（单位：平方公里）大院街道公园社区为老旧小区，位于南昌市人民公园南门，存在生活圈内服务配套不足的问题 共享电单车改善社区医院服务短板共享电单车改善社区医院服务短板，提升生活圈就医便利性提升生活圈就医便利性以昆明市主城区为例，社区医院的密度为0.81个/平方公里，通过步行15分钟可达社区医院人口占比为75%，近1/4人口无法服务；若选择电单车出行，15分钟可达社区医院的人口覆盖接近100%，为24%的人口改善了便利就医条件。骑行 健康图6-6 昆明社区医院按步行、电单车15分钟出行所覆盖人口对比24$%电单车步行昆明计算方法计算方法:将研究区域划分为栅格，计算各栅格至最近的社区医院的出行时间，最终分别得到步行与电单车骑行的15分钟可达社区医院人口分布。注：居住人口数据来源于2022年度中国主要城市通勤监测报告（1）共享电单车便捷市民通勤，塑造“骑行 ”生活圈1数 据 分 析 篇37骑行 餐饮 共享电单车促进城市夜间经济繁荣共享电单车促进城市夜间经济繁荣，拓展餐饮业辐射范围拓展餐饮业辐射范围以成都著名犀浦夜市为例，夜市周边的骑行强度呈现“早低晚高”趋势，在晚间17时至23时达到峰值，期间订单量占全天的52.2%；犀浦夜市周边订单的平均出行距离达到2.5公里，较步行、自行车大大拓展了其辐射范围。0.0%1.0%2.0%3.0%4.0%5.0%6.0%7.0%8.0%9.0.0234567891011121314151617181920212223订单占比订单起始时间（小时）图6-7 犀浦夜市周边订单强度按时辰分布犀浦夜市犀浦夜市平均出行距离：平均出行距离：2.5km2.5km 犀浦夜市是成都最火爆的夜市之一，紧邻西南交通大学，一直深受外来游客、附近学生和居民的喜爱。注：图片来源于互联网（1）共享电单车便捷市民通勤，塑造“骑行 ”生活圈1数 据 分 析 篇38图6-8 犀浦夜市周边订单OD分布1数 据 分 析 篇案例城市：南昌市391.51.71.92.12.32.5一环线二环线二环以外共享电单车轨道站点平均接驳距离（公里）上涨上涨26&%1.91.92.12.12.42.4上涨上涨14%（2）共享电单车推动绿色出行，是公共交通的有益补充 共享电单车可以有效拓展轨道服务范围共享电单车可以有效拓展轨道服务范围，促进轨道客流提升促进轨道客流提升多数城市轨道线网呈现“外疏内密”的结构，外围轨网密度低，覆盖不足，通过“轨道 共享电单车”出行可以大幅提升轨道服务覆盖人口，吸引更多客流。以南昌市为例，呈现出中心到外围轨网密度越低，共享电单车接驳距离越高的趋势，说明共享电单车有效补充了外围轨道覆盖不足；其中，共享电单车一环内平均接驳距离为1.9公里，二环内升至2.1公里，二环外接驳距离最长，高达2.4公里。图6-10 南昌市共享电单车轨道站点平均接驳距离图6-9 分圈层共享电单车轨道站点平均接驳距离效率提升：效率提升：对24公里的出行区间，共享电单车完成公交相同出行，距离仅需公交出行距离的74%，减少了1.4公里；时间仅需公交出行时间的48%，减少了18分钟。便捷性提升：便捷性提升：对24公里的出行区间，公交出行的步行距离占公交全程距离的16%，达到约0.9公里；公交车外出行时间占公交全程时间的31%，达到了约11.9分钟。1数 据 分 析 篇（2）共享电单车推动绿色出行，是公共交通的有益补充案例城市：哈尔滨4001234567822.533.54距离（公里）起终点直线距离（公里）公交全程距离电单车全程距离05101520253035404522.533.54时间（分钟）起终点直线距离（公里）公交全程时间电单车全程时间01234567822.533.54距离（公里）起终点直线距离（公里）公交全程距离公交步行距离05101520253035404522.533.54时间（分钟）起终点直线距离（公里）公交全程时间公交车外时间图6-11 公交与共享电单车出行距离与时间对比 共享电单车填补部分地区中短距离公交出行服务短板共享电单车填补部分地区中短距离公交出行服务短板现阶段，各城市提供中短途出行服务的社区公交、支线公交、接驳公交发展滞后，同时，由于客流规模较小，大规模布线运营的经济效益不高；共享电单车灵活便捷，在2-4公里间出行有一定优势，可在部分地区填补中短距离公交服务不足的问题。共享电单车促进城市包容性发展共享电单车促进城市包容性发展，为不同收入水平群体提供机会均等的出行服务为不同收入水平群体提供机会均等的出行服务以合肥为例，分析房租水平与电单车出行强度的空间相关性。实证发现，共享电单车总体上对房租高、中、低水平地区均有较好的服务，订单产生空间分别占总出行空间的20%、40%、40%；此外，电单车高强度出行相对集中在中、低房租的地区，占总出行空间的31%。案例城市：合肥图6-12 合肥市房租水平与电单车订单强度的空间相关性分析（3）共享电单车促进社会公平，提升城市交通韧性1数 据 分 析 篇41说明：房租数据来自互联网，房租数据时间与2021年所用共享电单车数据时间一致。0.0%5.0.0.0 .0%.00.05.0%0-11-22-33-44-55-66-77疫情前疫情中 共享电单车成为疫情中公众重要的替代出行选择共享电单车成为疫情中公众重要的替代出行选择，长距离长距离出行大幅度提升出行大幅度提升以呼和浩特市为例，疫情期间次均骑行距离、次均骑行时耗相较于疫情前分别增加19.5%和20.8%，疫情期间长距离骑行订单（4km）增加10.7%；对通勤出行方式选择影响显著，以典型就业吸引点鄂尔多斯大街为例，疫情前电单车平均通勤距离为2.5km，疫情中提升至3.4km，约是疫情前的1.4倍。1.51.71.92.12.32.52.72.9疫情前疫情中骑行距离（公里）9101112131415疫情前 疫情中骑行时耗（分钟）图6-13 单次骑行距离图6-14 单次骑行时耗上涨上涨19.5.5%上涨上涨20.8 .8%图6-15 骑行距离分布对比呼和浩特2022年2月底至3月初爆发了疫情，当地采取了公交线路调整，公交、地铁甩站运行等应急措施，公共出行服务受到较大影响。共享电单车以其独立安全、快速便捷，成为公众在疫情期间重要的替代出行选择，提升了城市交通韧性。图 6-17 鄂尔多斯大街通勤距离对比新华广场新华广场如意和如意和鄂尔多斯大街鄂尔多斯大街新华广场新华广场如意和如意和鄂尔多斯大街鄂尔多斯大街疫情前疫情中2.52.23.43.301234平均值中位值疫情前疫情中2.92.92.42.411.911.914.414.4（3）共享电单车促进社会公平，提升城市交通韧性呼和浩特市典型就业吸引点特征1数 据 分 析 篇42图 6-16 典型就业点疫情前后起终点连线对比（单位：公里）数据分析背景（单位：公里）（1）提升用户骑行安全，降低火灾隐患闭环管理保障电动车充电安全闭环管理保障电动车充电安全。美团从场站选址、改造、运营三个重要环节系统布置，主动预防，通过安装具备独立消防设备且符合室外安全与消防要求的充换电柜，实现充换电安全闭环管理，大幅度降低火灾安全隐患。2017年12月31日，公安部紧急下发关于规范电动车停放充电加强火灾防范的通告关于规范电动车停放充电加强火灾防范的通告，严禁在建筑内的共用走道、楼梯间、安全出口处等公共区域停放电动车或者为电动车充电。2行 业 价 值 篇43锁扣车篮锁驱动机构头盔锁钩车篮盖RFID Tag身份芯片RFID Reader 身份识别电路美团智能头盔美团智能头盔采用头盔与车无绳连接采用头盔与车无绳连接，保证骑行安全保证骑行安全内置传感器和电子锁内置传感器和电子锁，方方便取用和归还便取用和归还头盔通风防水头盔通风防水，干净卫生干净卫生电单车企业采取多维措施提高用户骑行安全电单车企业采取多维措施提高用户骑行安全。美团通过限制骑行速度、提供安全头盔、落实用户实名认证、严防违规超载、强化骑行安全教育、组建专业的维保团队、建立非机动车骑行安全监管系统等措施，保持良好的车辆状态，提升用户骑行安全。（2）推动新型基础设施应用，赋能城市治理提升2行 业 价 值 篇44共享电单车平台化管理推进城市现代化共享电单车平台化管理推进城市现代化治理进程治理进程共享电单车采用平台化管理，通过构建统一的政府监管平台，借助搭载的智能终端，实现车辆、电池、骑行等实时线上监管，提升管理质量和执法效率，赋能城市治理现代化。共享电单车是新型基础设施共享电单车是新型基础设施应用的应用的重要载体重要载体依托人工智能、物联网、大数据、北斗定位等新技术应用，共享电单车可推动智慧道路感知基础、分布式锂电能源网络、专用停车站点等基础设施更新建设，助力基础设施数字化与智慧城市建设。三、促进就业稳定，助力乡村振兴发展2行 业 价 值 篇45共享电单车企业充分发挥平台优势共享电单车企业充分发挥平台优势，积极参与到乡村振兴工作中积极参与到乡村振兴工作中以美团为例，通过将废旧单车和电单车的轮胎100%回收再生，制成符合标准的塑胶颗粒，并在乡村地区捐建运动场地，助力乡村教育、体育振兴。截止2022年8月，通过美团单车、电单车用户低碳骑行 美团配捐的方式，捐建了环保操场23座，覆盖11个省区，1万多名乡村儿童受益。共享电单车共享电单车带动上下游产业发展带动上下游产业发展，将直接或间接创造运维将直接或间接创造运维、研发研发、制造等近百万就业机会制造等近百万就业机会。共享电单车可在解决困难群体就业、促进就业结构调整中发挥积极作用。电单车运营、维修部门为社会提供了大量学历和技能要求相对低的岗位，员工以农业户口为主，22.22%的从业者家庭为零就业家庭。

795人已浏览 2022-09-23 49页 5星级

事实上，到2025年，中国市场预计将达到269亿美元，年复合增长率为12.8%。预计美国市场仍将是最大的市场，2025年预计收入为342亿美元。包括大型轿车和suv、高级轿车和跑车*在内的共享汽车市场的高级细分市场，预计从大流行低迷中恢复得更慢，比整体市场更晚恢复到大流行前的收入。不过，中国在这方面是个例外。

60人已浏览 2022-09-08 31页 5星级

消费消费报告报告9090后分享经济后分享经济分享经济利用互联网整合配置海量资源，不断满足人们的多样化需求，正悄然改变着每个人的生活。90后群体，作为互联网原住民，开放、自信，更愿意信任他人。在分享经济中活跃成长的他们不断大开脑洞、迸发创意，在分享经济的平台上制造了无穷无尽的惊喜。2017年3月，第一财经商业数据中心（CBNData）联合闲鱼发布了90后分享经济消费报告，通过闲置交易平台闲鱼上的用户行为洞察还原90后群体的分享消费现象与特性。研究发现，90后用户在兴趣、租房、拍卖、奇葩技能、生活方式以及公益等六个方面呈现出独有的特性。同时互动也成为了闲置交易平台的热门功能。90后用户在闲鱼上用的最多的词语是面交、手刀以及走闲鱼，平均每人在平台上累计发布17件。从互动到购买的过程中，90后用户平均要花费53分钟，互动次数高达15次。90后的分享经济消费现状究竟是如何的？让我们一起揭开神秘面纱！我 们 想 说兴趣鱼塘个性&反传统奇葩技能“新”租屋方式挖宝公益精神分享经济下的高频互动46.64Q.41Q.22后90后95后闲鱼各年龄段用户性别占比女性男性“分享”成为年轻一代的新消费方式分享经济受到年轻一代的欢迎，成为他们的新消费方式之一；90后用户在闲鱼上占据半壁江山，相较其他年龄的用户，其中女性用户居多。33.52.11%闲鱼各年龄段用户占比80后90后其他90后52.37.9612.7713.1480后90后95后闲鱼年轻人单日互动次数80后90后95后90后是闲鱼上高频互动的典型用户在年轻用户中，90后用户在闲鱼上的互动更频繁，互动频次比所有用户平均互动高出20%，是闲鱼互动主力军。其中95后的表现尤为突出。90后80后70后90后80后70后90后80后70后闲鱼平台各年龄层用户发布含“交换”、“求赞”、“求约”关键词的商品数对比90后在闲鱼平台互动流行词汇：交换、求赞、求约分享经济进入年轻人的生活后，除了闲置物品的交易，闲鱼平台还承载了用户之间交流的社区功能；从实际考虑的90后用户对于“交换”的需求远远大于“求赞”和“求约”两大需求。交换求赞求约3033405360后70后80后90后闲鱼各年龄段用户从互动到购买的平均时长（分钟）60后70后80后90后12.6141570后80后90后闲鱼各年龄段用户从互动到购买之间的互动次数70后80后90后90后的闲置交易特征：互动到购买耗时最长，频次最高90后在闲置交易中会花更长的时间互动沟通宝贝相关信息，“货比三家”消费意识仍十分常见；数据显示，90后用户从互动到购买平均花费了53分钟时长，互动次数高达15次，均为年轻用户群体的NO.1。年轻人每月见面交易次数3.63.6次90后的闲置交易新社交方式：面交在闲鱼平台上，90后用户线上互动显著，线下的当面交易闲置物品也成为了一种互动交友新方式。圈群化社交闲鱼平台提供了90后兴趣交流的平台注：鱼塘：闲鱼里的互动分享社群90后用户平均加入4个鱼塘90后鱼塘塘主占总塘主数的43.03%高于全量人群平均加入鱼塘数量 3.29 个后在闲鱼平台上，个性的年轻用户能够找到各自兴趣圈子，互动交流并有机会接触更多的限量和孤品，满足自己的收藏爱好。闲鱼平台上的鱼塘已覆盖全国95%的高校，成为校园内旧物再利用的新渠道。236102361023106231062224622246深圳大学北京大学北京理工大学闲鱼高校鱼塘用户人数 TOP 390后的鱼塘生活：满足自身兴趣，构成圈群化社交二手相机俱乐部可乐收藏二手摄影器材交易中心动漫手办收藏改装车CLUB 25812（人）二手牛仔商店24573（人）无人机与航模21350（人）橡皮章12014（人）注：90后用户数的统计截止至2017年3月中旬9090后用户量 28995289959090后用户量 33469334699090后用户量 45651456519090后用户量 58759587599090后用户量 62765627659090后用户量65557655579090后用户量 71542715429090后用户量 78814788149090后用户量91092910929090后用户量 214830214830机械键盘玩镜头不败家摩托车古风手工母婴用品闲置群一起Cosplay汉服告别前任囧一刻一支口红的诱惑闲鱼平台90后用户最喜爱鱼塘 TOP10（人）90后的年轻一代在鱼塘中关注对自身相关的内容，其中美妆、娱乐、情感以及爱好成为他们主要的偏好鱼塘，此外，一些关于改装车、二手牛仔、无人机相关的新颖兴趣也成为他们关注的潜力方向；由兴趣集聚的鱼塘文化会产生相关“共振”效应，带来了更多交易的可能。90后关注的新颖个性鱼塘圈群化社交的特征推动更多90后加入鱼塘90后用户量个性、反传统的Lifestyle闲鱼平台上90后的绿色生活与个性品位走闲鱼注：面交：当面交易;手刀：砍价;走闲鱼：在闲鱼上的交易流程，主要指拍下发货确认付款。闲鱼平台90后用户三大最常用词汇：90后人群人均累计发布商品面交手刀17件家装/建材五金工具闲鱼平台90后用户30天内家装建材的成交数量（件）151512350490后的绿色生活：闲置家装建材物尽其用在闲鱼平台上，闲置分享成为年轻人热爱的生活方式之一；数据显示，五金工具成为90后在装修时可循环使用的闲置物品NO.1。90后的个性品味：小众耳机与香水成为闲置市场的新宠儿闲鱼上的小众品牌的销量与去年相比涨幅可观，其中耳机与香水类目尤其突出。B&OMARSHALLLE LABOAMOUAGE闲鱼平台90后用户小众品牌近30天成交量与去年同期涨幅对比去年同期近30天皮具制作工具套装羊毛毡礼物定做Cosplay服装转卖汉服转卖90后的个性品味：非品牌化的DIY商品成为新趋势多样化的鱼塘满足了年轻人的兴趣爱好，用户个性化的特征促进了原创、定制的新市场趋势；非品牌化的DIY商品从工具、订做到成品，提供给90后用户多种个性选择。奇葩技能大开发闲鱼平台上专属90后的技能型服务奇葩技能代叫起床塔罗牌占卜情话服务点歌代养宠物跑腿代办游戏代练Get新的技能点:专属90后的“小慵懒”作为技能发布者，90后用户利用自己的碎片化时间来产出更多价值，同时作为购买技能点的买家，平台也提供了技能型服务，满足专属90后的“小慵懒”。60%闲鱼平台技能发布者年龄段占比90后其他租屋不走套路闲鱼平台满足90后“简单生活”的需求32.04R.05.91%闲鱼平台房东人群占比80后90后其他24.53W.39.08%闲鱼平台租客人群占比80后90后其他90后的租房新想法：思维灵活，不愿墨守成规90后对房屋共享抱有热情，参与度和投入度均高于其他年龄层用户；在闲鱼租房版块90后用户最活跃，发布房源当日互动率达30.45%，3日互动率达49.5%。1984198420112011整租闲鱼平台90后用户租房均价与大盘对比（元）90后大盘均价1169116911571157单间442%2%闲鱼租房户型类别占比两室一室三室三室以上90后的租房新想法：向往简单生活，性价比是王道小户型和低廉价格是90后心目中完美租房的两大标配，其中两室户的小户型受到近半数用户的青睐。521521657657日租最年轻房东16岁最年轻租客16岁案例分享出租江苏徐州一单间月租100元租住湖南张家界某宿舍月租900元还有小伙伴在闲鱼平台上出租了半张床位“挖宝”新体验闲鱼平台激发90后的猎奇收藏与粉丝经济姚明限量版慈善赛小金球拍卖两天内有将近500万用户在淘宝网对姚明进行了情感互动，观看，点赞，评论，留言，互动超过4万条。林志玲T台步教学本拍品为技能拍卖，最终成交价为27000元。高考状元笔记拍卖学霸的笔记在学渣眼里是神秘而珍贵的。“笔记拍卖”发布之后广受媒体关注。三生三世十里桃花白浅塔服拍卖有近12万人围观此次拍卖，最终拍卖金额为8501元。杨洋郑爽骑过的自行车总共有36次出价，最后以2101元成交。闲鱼平台上的各种奇葩拍品激发了闲鱼平台上的各种奇葩拍品激发了9090后的猎奇收藏后的猎奇收藏功夫熊猫3大师4张手稿原图拍卖总共有59次出价，最终成交价为7179元。手工茶盘拍卖90后木头手艺人，住在市郊的毛坯房，安静地追求艺术，其作品深有禅意。漆艺制作木头包拍卖90后女孩用福州传统大漆工艺进行再创造，做成了木头包包。金绘梅花和田白玉戒指拍卖90后玉雕手艺人有创造性地在和田玉上雕金，作品精致又极具东方韵味。银饰：封印的微型昆虫90后设计师作品，将昆虫标本与银饰结合，再赋予蒸汽朋克的概念。9090后的匠心拍卖：后的匠心拍卖：创新型手工创新型手工艺人手工作品参与闲鱼拍卖艺人手工作品参与闲鱼拍卖诚意满满的公益精神闲鱼平台提升年轻人的社会责任感90后希望通过正规的途径获得明确的物资需求，从而将闲置物品给到真正需要它们的人。90后的分享经济观：不止于变现，更要奉献发布在闲鱼上社会责任感闲鱼公益平台快速匹配需求90后用户公益机构需求信息捐赠 义卖用闲置换钱仁德基金会通过闲鱼公益为四川达州某残障人士徐某筹集了4978元，用于购买电动轮椅。暖流计划通过闲鱼公益为云南加日村小学募集了体育用品、教学用品等。近7.57.5万件捐赠义卖商品

266人已浏览 2022-07-07 30页 5星级

基于51个县城的共享电单车出行大数据显示（图 3），共享电单车用户年龄集中在18-35岁之间，平均年龄31.4岁，最大64岁，最小16 岁。其中 18-35 岁用户占比均接近 4%，而大城23和24岁用户比例显著高于其他用户，说明县城共享电单车的服务人群的年龄结构更均衡。

211人已浏览 2022-06-21 27页 5星级

点击查看更多中兴新云：2022年中国共享服务领域调研报告——迈向世界一流（58页）.pdf精彩内容。

376人已浏览 2022-06-01 58页 5星级

几十年来，私营部门通过实施共享服务方法加速了转型。公司不仅能够降低成本，还能够改进流程、更好地管理风险并增强客户和员工的体验。

48人已浏览 2022-03-04 12页 5星级

中国共享经济发展报告（2022）目录一、发展概况.2（一）共享经济市场交易规模同比增长 9.2%.2（二）共享型服务和消费继续发挥稳增长的重要作用

1744人已浏览 2022-02-25 44页 5星级

区位优势明显，社群生态良好，入驻率高选址区位优势明显在选址上，氪空间的办公空间多位于核心城市核心区域，交通便利，配套设施也较为齐全。截止目前，氪空间覆盖北京、上海、杭州、苏州、南京、武汉、成都、天津、广州等9座城市，共有30个联合办公空间，其中成熟空间的入驻率在95%1以上。聚焦创新人群与注重互联网社群运营，入驻企业构成自成生态重度聚焦互联网创新人群与注重互联网社群运营，是氪空间空间入驻率相对较高的主要原因。氪空间的入驻企业既包括自由职业者、小型创业团队，也有中小企业以及大企业分支等。其中，氪空间自带的创投基因能够吸引很多创业团队入驻，这些创业团队又能够吸引提供第三方企业服务的中小公司以及一些传统企业的创新业务团队。因此，氪空间的联合办公区域中能够构成完整的互联网生态，能够吸引更多的互联网企业及其相关企业入驻。盈利方式多样化：工位服务费&非工位收入除了工位服务费之外，氪空间还有约20%的收入来自于非工位收入，其中既包括与第三方企业合作提供的支持服务的收入，如财会、法务、云计算、人力资源等，还有注册地址收入、基础设施服务以及各类活动支付的场地费用等。这样多样化的收入结构有利于联合办公空间的平衡发展，一定程度上能够降低运营风险。

354人已浏览 2021-09-26 31页 5星级

无桩共享单车(DBS)是近年来影响人们生活的最受欢迎的共享交通系统之一。自2016年在全球城市街道上迅速普及以来，星展银行的公司经历了一段混乱的旅程，一方面是需求的爆炸式增长，另一方面是巨大的监管障碍和负面看法。随着许多公司进入第五个年头，它们的全球足迹有所减少，但它们在亚洲城市的影响力有所增强。截至2月19日，星展系统支持了中国36个城市的数百万人次短途出行和公共交通连接。转向低排放模式和增加DBS用户的体力活动是该系统最显著的共同好处。然而，如果这些系统不是基于对其影响的评估，如果它们没有很好地融入交通生态系统，这些系统也会对公共空间的管理构成挑战。本报告通过对中国12个城市的星展系统的研究，为星展系统如何改变人们的日常生活提供了具体证据，并指出了一种新兴的城市管理理念，以及公共和私营部门在通过交通服务确保可持续和公平结果方面的作用。作者调查了该系统如何改变人们的出行行为，评估了其对公共健康、碳排放和道路安全的益处或风险，并展示了良好的管理实践。作者还建议，城市应该通过为运营商设立关键绩效指标、明确停车管理规则、提供更安全的自行车设施和鼓励标准化技术来改善edbs管理。在本报告发表时，严重急性呼吸系统综合征冠状病毒2(SARS-CoV-2)继续在全球传播，导致超过1163万例病例和538529例死亡”(JHU 7月7日，2020)。诚然，sars-cov-2永远改变了全球格局，它对城市生活的影响将是长期的。在危机最严重的时候，自行车是为数不多的有弹性、安全的出行方式之一，市民可以通过自行车来满足基本需求。随着城市重新开放和社会联系的恢复，交通文化可能会从集中的机动模式转向分布式的定制解决方案。虽然北京的共享单车使用量增长了15%，但骑车和步行是否会成为日常出行的新常态，还是仅仅是对疫情的临时应对，目前还不清楚。答案将在于公众观念的转变、良好的建设环境和城市管理，以及负责任和财务健康的经营者。对于那些希望重建经济、确保公共健康和减少排放的城市来说，星展系统作为一种有效的机制必须受到欢迎。虽然情况可能不同，但中国城市的故事和教训可以为其他城市提供如何建立自行车文化和鼓励共享单车的见解和良好实践。用创新的解决方案扩大共享单车文化也需要全球和当地社区网络之间的合作。这就是世界资源研究所(WRI)和新城市流动性(NUMO)联盟现在正在为世界各地的城市做的事情。自2016年以来，无桩共享单车(DBS)系统在中国迅速扩张。截至2019年底，他们已经服务了中国360多个城市。作为连接公共交通的短途出行的替代方式，该系统带来了便利，满足了市民的出行需求。本报告旨在调查DBS如何改变人们的出行行为，并评估其对中国12个城市的公共健康、碳排放、道路安全和城市管理的影响。影响评估结果表明，相比机动出行，人们可以通过使用DBS获得更多的健康益处，排放更少的碳排放。然而，与使用机动车的人相比，DBS用户和其他骑自行车的人一样容易受到攻击。本报告还回顾了星展银行的政策，并展示了12个研究城市的良好实践，可为其他有意采用星展银行系统的城市提供有益的参考。为了更好地规范星展车作为一种短途出行方式，城市可以通过引入创新政策和措施来改善星展车管理，如为运营商设立关键绩效指标(KPls)，明确停车管理规则，提供更安全的自行车设施，以及鼓励标准化技术。

99人已浏览 2021-09-15 82页 5星级

智研咨询中国领先产业研究机构2021年中国共享经济市场全景研究及预测分析报告01共享经济定义02共享经济运行特征03共享经济发展机遇04共享经济发展因素经济、政策、技术、社会05共享经济参与人数06共享经济市场规模07共享经济融资情况08共享经济主要细分市场发展状况与模式分析目录09共享经济未来发展趋势预测共享经济定义共享经济定义共享经济是指利用互联网等现代信息技术，以使用权分享为主要特征，整合海量、分散化资源，满足多样化需求的经济活动总和。共享经济是信息革命发展到一定阶段后出现的新型经济形态，是整合各类分散资源、准确发现多样化需求、实现供需双方快速匹配的最优化资源配置方式，是信息社会发展趋势下强调以人为本和可持续发展、崇尚最佳体验与物尽其用的新的消费观和发展观。资料来源：智研咨询整理01供给方平台需求方分享产品获取报酬满足需要付出成本共享经济运行特征共享经济运行特征近些年来，智能手机的普及，第三方支付的崛起以及成本的降低使得我国的共享经济能够在短时间内迅速发展壮大。从发展现状和演化态势看，我国共享经济的发展呈现以下三方面特征：01共享经济运行特征资料来源：智研咨询整理共享平台是一种再中介化组织，它是由硬件(信息网络)和软件（信任）构成的并由第三方创建的市场平台。每个人可以以个人的身份加入到一个平台，在这个平台里把剩余劳动力、剩余生产工具的产能全部释放出来。共享经济是整合线下的闲散物品或服务者，并基于陌生人且存在物品使用权暂时转移的一种商业模式，把闲置的资源提供给真正需要的人，创造新的价值，将成为未来世界经济增长的新动力。共享经济分享的规模和使用频次大，并且产权边界相对清晰。共享经济是“一次购买，多次出租”的商业(模式)思维在互联网条件下的体现，不断出现的共享平台将建立一个完整的商业生态系统。共享经济通过重复交易和高效利用大大减少了人类对资源的占用和环境的破坏。运行特征通过信息网络搭建共享平台暂时转移闲置资源的使用权以闲置资源的重复交易和高效利用为表现形式共享经济发展机遇共享经济发展机遇共享经济模式形成于2008年金融危机时期，失业率上升带来了大量闲置劳动力，收入锐减使基层民众生活变得拮据。为了节约成本，增加收入，许多人将空置的房屋、车辆通过互联网向陌生人出租使用权。其次工业革命使人类使用的物质产品得到了极大地丰富，但建立在私人所有制基础上的消费方式也带来了较大的闲置和浪费，尤其是对资源的过度使用和环境的破坏，而共享经济可以使闲置资源充分利用又可以缓解环境污染。01资料来源：智研咨询整理2008年，经济危机席卷全球，失业率上升带来了大量闲置劳动力，收入锐减使基层民众生活变得拮据。为了节约成本，增加收入，许多人将空置的房屋、车辆通过互联网向陌生人出租使用权。经济危机推动共享经济的产生工业革命使人类使用的物质产品极大地丰富，但建立在私人所有制基础上的消费方式也带来了较大的闲置和浪费，尤其是对资源的过度使用和环境的破坏；而共享经济可以使闲置资源充分利用又可以缓解环境污染。产能过剩促进共享经济的发展随着互联网技术的不断进步，移动终端的高度普及，人类社会的组织形式得到改变，人们可以通过互联网轻松找到自己所需要的商品，还可以通过互联网将自己闲置的资源分享给他人，从而赚取一些收入。互联网技术的进步使共享经济更加便捷共享经济发展因素04经济、政策、技术、社会经济因素中国经济的快速发展及人均可支配收入的增加促进共享经济的发展，而共享经济又在一定程度上反作用于经济发展，共享经济通过无限的壮大存量供给，扩大消费者可选择空间，实现供需匹配，从而扩大社会总消费需求，成为拉动经济的消费增长点。2020年中国生产总值首次超过100万亿元，达到101.60万亿元，居民人均可支配收入也不断增加，达到32189元。01资料来源：国家统计局、智研咨询整理59.3064.3668.8974.6483.2091.9399.09101.608.53%7.04%8.35.47.49%7.79%2.54%0.00%2.00%4.00%6.00%8.00.00.00.00%0.0020.0040.0060.0080.00100.00120.00201320142015201620172018201920202013-2020年中国生产总值及增速生产总值(万亿元)增速183112016721966238212597428228307333218905000100001500020000250003000035000201320142015201620172018201920202013-2020年中国居民人均可支配收入居民人均可支配收入(元)政策因素02近年来，国家出台了一系列政策鼓励发展共享经济，规范化、制度化和法治化的监管框架开始建立，平台企业合规化水平明显提高，多方协同的安全保障和应急管理体系建设取得积极进展，为共享经济长期更快更好发展奠定了坚实基础。中国共享经济相关政策时间颁发部门政策名称相关内容2016年2月国家发展改革委、中宣部、科技部等十部门关于促进绿色消费的指导意见支持发展共享经济，鼓励个人闲置资源有效利用，有序发展网络预约拼车、自有车辆租赁、民宿出租、旧物交换利用等，创新监管方式，完善信用体系。在中小学校试点校服、课本循环利用。2016年07月中共中央办公厅国务院办公厅国家信息化发展战略纲要牢固树立创新、协调、绿色、开放、共享的发展理念2017年1月26日国务院“十三五”促进就业规划通过放宽市场准入、创新监管手段、引导多方治理等优化环境，完善消费者权益保护等相关政策，促进共享经济健康发展。健全就业、劳动保障等相关制度，支持发展就业新形态。2017年7月3日国家发展改革委、中央网信办、工业和信息化部关于促进分享经济发展的指导性意见促进分享经济更好更快发展，要坚持以推进供给侧结构性改革为主线，以满足经济社会发展需求为目标，以支持创新创业为核心，以满足消费需求和消费意愿为导向，深入推进简政放权、放管结合、优化服务改革，按照“鼓励创新、包容审慎”的原则，发展与监管并重，积极探索推进，加强分类指导，创新监管模式，推进协同治理，健全法律法规，维护公平竞争，强化发展保障，充分发挥地方和部门的积极性、主动性，支持和引导各类市场主体积极探索分享经济新业态新模式。政策因素02资料来源：智研咨询整理中国共享经济相关政策时间颁发部门政策名称相关内容2018年5月22日国家发展改革委办公厅、中央网信办秘书局、工业和信息化部办公厅关于做好引导和规范共享经济健康良性发展有关工作的通知合理利用公共资源。对于使用公共资源的共享经济业态，统筹考虑市场需求、城市承载能力等因素，科学设定总量规模，加快制定公平、公正、公开的服务评价体系，定期向社会公布平台企业的服务质量，并将评价结果与企业投放规模挂钩，运用灵活多样的手段动态调控供给规模和结构。2019年10月22日工业和信息化部关于加快培育共享制造新模式新业态促进制造业高质量发展的指导意见创新资源共享机制。鼓励大型企业创新机制，释放闲置资源，推动研发设计、制造能力、物流仓储、专业人才等重点领域开放共享，增加有效供给。推动高等院校、科研院所构建科学有效的利益分配机制与资源调配机制，推动科研仪器设备与实验能力开放共享。创新激励机制，引导利益相关方积极开放生产设备的数据接口，推进数据共享。完善资源共享过程中的知识产权保护机制。2020年4月7日国家发展改革委中央网信办关于推进“上云用数赋智”行动培育新经济发展实施方案的通知大力发展共享经济、数字贸易、零工经济，支持新零售、在线消费、无接触配送、互联网医疗、线上教育、一站式出行、共享员工、远程办公、“宅经济”等新业态，疏通政策障碍和难点堵点。引导云服务拓展至生产制造领域和中小微企业。鼓励发展共享员工等灵活就业新模式，充分发挥数字经济蓄水池作用。2020年07月14日发展改革委、网信办、工业和信息化部等13个部门关于支持新业态新模式健康发展激活消费市场带动扩大就业的意见拓展共享生活新空间。推动形成高质量的生活服务要素供给新体系。鼓励共享出行、餐饮外卖、团购、在线购药、共享住宿、文化旅游等领域产品智能化升级和商业模式创新，发展生活消费新方式，培育线上高端品牌。推动旅游景区建设数字化体验产品，丰富游客体验内容。扩大电子商务进农村覆盖面，促进农产品进城和工业品下乡。鼓励康养服务范围向农村延伸，培育农村消费新业态。完善具有公共服务属性的共享产品相关标准，优化布局，规范行业发展。技术因素03资料来源：智研咨询整理中国互联网信息技术高速发展，数据挖掘、大数据分析、数据库存储等技术有较大的突破，5G时代的到来，加速了人们对互联网新事物的了解与互动。开放数据，移动互联，为共享经济资源优化配制提供了技术条件。2019年中国互联网数据中市场规模为1562.5亿元，2020年中国互联网数据中心市场规模约为1958.2亿元。102.2170.8210.8262.5372.2518.6714.5946.112281562.51958.205001000150020002500201020112012201320142015201620172018201920202010-2020年中国互联网数据中心市场规模互联网数据中心市场规模（亿元）技术因素03资料来源：工信部、智研咨询整理智能手机等移动设备的普及为人们的生活带来了便利，促进了人们生活方式的变革创新，人们对共享产品的需求也越来越高。2020年中国智能手机出货量为2.96亿部，较2019年的2.72亿部同比增长8.82%。3.894.575.224.613.92.722.9617.48.22%-11.69%-15.40%-30.26%8.82%-40.00%-30.00%-20.00%-10.00%0.00.00 .002345620142015201620172018201920202014-2020年中国智能手机出货量智能手机出货量（亿部）增速社会因素04支付宝、微信等网络支付的兴起为共享经济平台的各类应用提供了极大的支付便利，中国网络支付用户规模不断增加，截止2020年12月，中国网络支付用户规模为8.54亿人，占互联网总用户的86.4%。资料来源：CNNIC、智研咨询整理6.496.887.317.728.299.049.893.044.164.755.316.007.688.540.002.004.006.008.0010.0012.002014.122015.122016.122017.122018.122020.032020.122014-2020年中国互联网及网络支付用户规模互联网用户规模（亿人）网络支付用户规模（亿人）共享经济参与人数共享经济参与人数01新冠疫情使得线下活动受限的情况下，直播短视频、知识分享等领域强劲增长，这些领域的用工需求也随之大幅提升。2020年中国共享经济参与人数达8.3亿人，其中服务提供者8400万人，平台企业员工数量631万人。资料来源：国家信息中心、智研咨询整理60007000750078008400585556598623631877.688.366.577.588.50100020003000400050006000700080009000201620172018201920202016-2020年中国共享经济参与人数服务提供者（万人）共享经济平台企业员工数量（万人）参与人数（亿人）共享经济市场规模共享经济市场规模01资料来源：国家信息中心、智研咨询整理2018年以来中国共享经济市场规模增速有所放缓，主要受经济下行、互联网人口红利消减及新冠疫情的影响，2020年中国共享经济市场规模为3.38万亿元，较2019年的3.28万亿元同比增长2.96%，增速较2019年的11.58%下降8.62%。从2020年共享经济市场结构看，生活服务、生产能力、知识技能三个领域市场规模位居前三，分别为16175亿元、10848亿元和4010亿元，分别占共享经济总规模的47.9%、32.1%、11.9%。1.96 3.45 2.08 2.94 3.28 3.38 76.48%-39.83A.63.58%2.96%-60.00%-40.00%-20.00%0.00 .00.00.00.000.00%0.000.501.001.502.002.503.003.504.002015201620172018201920202015-2020年中国共享经济市场规模市场规模（万亿元）增速161751084840102276168158138生活服务生产能力知识技能交通出行共享办公共享住宿共享医疗市场规模（亿元）2020年中国共享经济各领域发展规模及占比（亿元）47.92.1.9%6.7%0.5%0.5%0.4%占比共享经济融资情况共享经济融资情况01资料来源：国家信息中心、智研咨询整理共享经济满足市场优化资源配置需求，深入衣食住行各领域，随着监管政策落地，共享经济行业洗牌结束，市场逐渐步入有序增长期，投资向共享经济行业头部企业集中的趋势更加明显。经过一轮又一轮的市场竞争和“洗牌”，一些企业脱颖而出，成为行业领军企业，吸引了更多投资者的关注。2020年中国共享经济融资规模为1185亿元，较2019年的714亿元同比增长66.0%。129119401490714118505001000150020002500201620172018201920202016-2020年中国共享经济融资规模融资规模（亿元）共享经济融资情况01资料来源：国家信息中心、智研咨询整理2020年中国共享经济融资规模增幅最大的领域为共享办公领域，共享办公领域直接融资规模为88亿元，增幅达566.67%；其次为生产能力领域，直接融资规模为186亿元，增幅为385.89%。知识技能生活服务交通出行生产能力共享医疗共享办公共享住宿2019-2020年中国共享经济各领域直接融资规模（亿元）08共享经济主要细分市场发展状况与模式分析共享出行市场发展分析01共享出行是指人们无需拥有车辆所有权，以共享和合乘方式与其他人共享车辆，按照自己的出行要求付出相应的使用费的一种新兴交通方式，包括网约车、共享单车、共享汽车等出行方式。共享出行的模式一方面满足了消费者“求而不得”的自驾需求；另一方面，避免了车辆闲置，资源无法被有效利用带来的浪费，共享出行逐渐成为公众的出行选择。资料来源：智研咨询整理车辆供应互联网技术供应上游中游下游电子设备配套设施供应共享出行平台消费者共享出行市场发展分析012019年中国人均出行消费支出为2764.4元，其中共享型人均消费支出为316.4元，占人均出行消费支出的11.4%。2020年中国人均出行消费支出为2311元，其中共享型人均消费支出为261.7元，占人均出行消费支出的11.3%。资料来源：国家信息中心、智研咨询整理2019年人均出行消费支出：2764.4元共享出行人均消费支出：316.4元2020年人均出行消费支出：2311元共享出行人均消费支出：261.7元占比：11.4%占比：11.3 19-2020年中国共享出行领域服务支出占比共享出行市场发展分析01资料来源：国家信息中心、智研咨询整理2020年受新冠肺炎疫情影响，人们出行活动减少，导致共享出行市场规模首次出现下降。2020年中国共享出行市场规模为2276亿元，较2019年的2700同比下降15.70%，占共享经济总市场规模的6.73%。20102478270022769.68%8.42%8.22%6.73%0.00%2.00%4.00%6.00%8.00.00.00001000150020002500300020172018201920202017-2020年中国共享出行市场规模及占比市场规模（亿元）占共享经济市场规模比重共享出行市场发展分析01资料来源：国家信息中心、智研咨询整理中国共享出行在经过前几年快速扩张阶段后，由于未与城市规划、监管政策等协调发展，遭遇诸多难题，发展步伐减缓，2020年融资事件减少，直接融资规模仅为115亿元。虽然中国共享出行行业发展速度放缓，但共享出行契合中国城市建设及居民出行需求，能有效解决交通拥堵等城市发展问题，共享出行的发展前景依然明朗。700107241978.7115020040060080010001200201620172018201920202016-2020年中国共享出行直接融资规模融资规模（亿元）共享出行市场发展分析01资料来源：中国汽车流通与后市场政策研究、智研咨询整理网约车市场现状1440861001101301702102400501001502002503002017201820192020E2021E2022E2023E2024E2025E2017-2025年中国合规网约车保有量规模走势及预测（万辆）网约车合规化，大批车企成立出行公司，合规车辆规模加速增加行业进入调整期，加之疫情影响，合规车辆增速放缓在总量管控的调控下，到2025年预计合规车辆规模为240万辆网约车是指以互联网技术为依托构建服务平台，接入符合条件的车辆和驾驶员，通过整合供需信息，提供非巡游的预约出租汽车服务的经营活动。滴滴打车，嘀嗒出行等网约车软件的涌现，很大程度上改变了人们的传统出行观念。随着巨大的市场需求，网约车车辆规模不断增长。2019年中国合规网约车保有量为86万辆，预计至2025年中国合规网约车保有量规模高达240万辆。网约车保有量共享出行市场发展分析01网约车市场现状受新冠肺炎疫情引发的诸多不确定因素影响，如许多公司要求员工在家办公、公众出行减少、及消费者对网约车安全防护信心不足等，网约车用户规模明显下降。截止2020年12月，中国网约车用户规模为3.65亿人，较2018年12月减少了0.24亿人。网约车用户规模00.511.522.533.542016.122017.122018.122020.032020.122016-2020年中国网约车用户规模用户规模（亿人）资料来源：CNNIC、智研咨询整理共享出行市场发展分析01资料来源：IT桔子、智研咨询整理网约车市场现状近两年来，网约车行业巨头“遭”整改，BAT等各路资本进入市场，加上传统车企纷纷布局网约车，中国网约车进入白热化竞争阶段。网约车投融资分析时间公司名称轮次金额投资方2018/1/17曹操出行A轮10亿人民币天堂硅谷、隆启投资2018/3/22微租汽车A轮未透露沣邦租赁2018/8/6小桔车服战略投资10亿美元滴滴出行2018/10/23斑马快跑D 轮3亿人民币千佳圆资本2018/11/16顺道出行天使轮未透露睿鼎资本2019/1/2帮邦行B 轮未透露昊翔资本2019/4/24飞的出行Pre-A轮千万级人民币滨海金控（领投）、康佳创投2019/9/17丰桔出行战略投资未透露丰田汽车、Destiny Mobility Investments Limited2019/10/10新变量科技天使轮未透露滴滴出行2019/12/18帮邦行B 轮1亿人民币浙华投资、浙江省丽水市莲都区国资公司、富浙资本2019/12/27昕动出行战略投资1500万人民币阿里巴巴2020/8/11海汽新能源战略投资未透露海汽集团2020/9/8新变量科技Pre-A轮1亿人民币一嗨租车（领投）2020/10/16首汽约车C轮数亿美元未透露2021/2/26量子出行战略投资未透露庞大集团2018年以来网约车部分投融资共享出行市场发展分析01网约车市场现状在“互联网 ”的影响下，出租车行业发生了巨大的变革，由传统出租车服务衍生出了网约出租车的服务模式。网约出租车的出现在一定程度上给传统出租车带来了巨大的挑战，双方形成了激烈的竞争格局。根据交通运输部数据显示，中国巡游出租车客运量不断下降，2019年巡游出租车客运量为347.89亿人次，网约出租车客运量为200亿人次。网约出租车客运量资料来源：交通运输部、国家信息中心、智研咨询整理2015-2019年中国巡游出租车及网约出租车客运量共享出行市场发展分析01网约车市场现状2016-2019年网约出租车客运量占出租车客运量比重不断上升，2019年占比达36.5%；2020年占比为36.2%，较2019年下降0.3%。网约出租车客运量资料来源：交通运输部、国家信息中心、智研咨询整理16.60.16.36.56.2.4i.9c.7c.5c.8%0 0Pp0 1620172018201920202016-2020年中国网约与巡游出租车客运量占比网约出租车客运量占比巡游出租车客运量占比共享出行市场发展分析01网约车市场现状近年来，车企加快向出行服务商战略转型，网约车出行市场成布局重点。2020年12月，全国网约车监管信息交互平台共收到订单信息8.1亿单。目前，中国共享出行主流平台主要有滴滴出行、嘀嗒出行、首汽约车、曹操出行等。主要平台资料来源：智研咨询整理平台简介滴滴出行滴滴出行成立于2012年，是领先的一站式移动出行和本地生活服务平台，在亚太、拉美、俄罗斯和南非提供出租车召车、网约车、顺风车、公交、共享单车、共享电单车、代驾、汽车服务、配送及货运、社区团购和金融等多元化服务。嘀嗒出行嘀嗒出行（前身嘀嗒拼车）成立于2014年，隶属于北京畅行信息技术有限公司，中国顺风车平台排名第一，出租车线上网约业务排名中国第二，截至2020年6月30日，在全国366个城市提供顺风车平台，拥有19.2百万名注册私家车主，包括9.8百万名认证私家车主。自成立至2020年6月30日为止，为36.7百万名顺风车乘客提供了服务。首汽约车首汽约车2015年9月上线，首汽约车平台在用车服务方面，包括了即时用车、预约用车、多日接送、包车业务、接送机、国际用车、城际拼车、深港通等用车服务场景，提供出租、畅享、舒适、商务、豪华、巴士等丰富车型。首汽约车还通过数据整合和智能科技陆续推出了宝妈车、学生车，以及服务于国家级大型会议“会议宝”等产品来满足不同人群、不同场景的企业和个人商旅出行需求，形成了独有的智能出行生态。曹操出行曹操出行是吉利控股集团布局“新能源汽车共享生态”的战略性投资业务，秉持“低碳致尚、服务至上”的核心价值观，将全球领先的互联网、车联网、自动驾驶技术以及新能源科技，创新应用于共享出行领域，2019年2月14日，曹操专车宣布“曹操专车”升级为“曹操出行”。神州专车神州专车是国内领先的租车连锁企业神州租车联合第三方公司优车科技推出的互联网出行品牌。2015年1月28日，神州专车在全国60大城市同步上线，利用移动互联网及大数据技术为客户提供“随时随地，专人专车”的全新专车体验。如祺出行如祺出行是广汽集团旗下移动出行品牌，由广汽集团与腾讯、广州公交集团及其他投资者共同投资开展，投资总额逾10亿元人民币。2019年6月份，如祺出行于广州正式推出市场，以粤港澳大湾区为核心，稳扎稳打再推向全国。享道出行享道出行是上汽集团投资的一家专注于出行服务的专业品牌，是上汽集团实现汽车产业“新四化”（即“电动化、智能网联化、共享化、国际化”）的重要组成部分。作为上汽集团移动出行战略品牌，享道出行充分利用全产业链竞争优势，从消费者对安全及品质的需求出发，通过为消费者提供安全、高效、舒适、便捷的品质体验，打造品质出行服务平台。共享出行市场发展分析01嘀嗒出行隶属于北京畅行信息技术有限公司，成立于2014年，主营业务为顺风车，2017年嘀嗒出行推出智慧出租车服务，并于2019年在西安试点，2020年开始在全国范围内推广，嘀嗒出行在中国顺风车平台排名第一，出租车线上网约业务排名中国第二。发展历程资料来源：招股说明书、智研咨询整理嘀嗒出行发展历程注册成立京畅行信息技术有限公司，推出“嘀嗒拼车”顺风车平台2014年2015年2016年2017年2018年2019年2020年完成由IDG及易车等著名机构及企业投资者牵头的B轮融资在既有顺风车业务的基础上开发多个变现渠道，乘客达到10百万人推出智慧出租车服务，完成了由蔚来资本牵头的D轮融资推出企业客户的出租车服务，建立了覆盖超过80个城市的全国出租车打车网络；把品牌由“嘀嗒拼车”升级为“嘀嗒出行”在西安推出智慧出租车试点项目开始在全国范围推广智慧出租车经营情况共享出行市场发展分析01嘀嗒出行营业收入大幅增长，2019年嘀嗒出行营业收入为5.81亿元，较2018年的1.18亿元同比增长493.8%；2020年上半年嘀嗒出行营业收入3.10亿元。2017-2020年上半年嘀嗒出行分别实现净利润1.94 亿元、-16.77亿元、-7.56 亿元、-7.22 亿元，将以股份为基础的付款开支、优先股及相关金融负债公允价值变动调整后，2017-2020年上半年，经调整净利润为-0.97 亿元、-10.68 亿元、1.72 亿元、1.51亿元。资料来源：招股说明书、智研咨询整理0.491.185.813.1-0.97-10.681.721.51-12-10-8-6-4-2024682017201820192020H12017-2020年上半年嘀嗒出行营业收入及净利润营业收入（亿元）净利润（亿元）经营情况共享出行市场发展分析01嘀嗒出行营收主要来自顺风车服务，较小程度上来自网约出租车服务、广告及其他服务（主要包括汽车增值服务）。2020年上半年嘀嗒出行顺风车业务营业收入为2.72亿元，占总营业收入的87.7%，网约出租车营业收入为0.16亿元，占总营业收入的5.2%。资料来源：招股说明书、智研咨询整理顺风车,5.33,91.9%网约车,0.06,1.0%广告及其他业务,0.41,7.1%顺风车,2.72,87.7%网约车,0.16,5.2%广告及其他业务,0.22,7.1 19-2020年上半年嘀嗒出行营业收入分布（亿元）20192020年H共享出行市场发展分析01共享单车市场现状共享单车是新科技的产物，共享单车作为共享经济时代下最典型的产物，并作为中国新世纪“新四大发明”之一走向世界，它切实解决了“最后一公里”的问题，为人们的出行提供了很大的便利。共享单车SWOT分析资料来源：招股说明书、智研咨询整理真正做到随时随地、一扫即用；低碳环保，减少对空气的污染使用秩序混乱；投放范围狭窄；易受天气影响共享单车的快速发展，加快了单车在市场上的竞争，各大公司为了抢占市场，不断扩大生产订单，加速市场覆盖，却忽视了用户的使用体验以及单车的使用性能。而且，共享单车需要的科学技术，软硬件技术得不到创新。企业只有在制度、文化和技术创新都不断发展的情况下，才能避免昙花一现。共享单车在保证单车质量的同时，要不断创新，才能在这个竞争激烈的互联网时代越走越远。目前，中国对自行车的需求还是非常大的，共享单车的出现，进一步增多了使用自行车的人数，在短距离范围内，使用共享单车价格实惠。所以，共享单车有着极大的市场。而且，共享单车解决了人们的两个问题。首先，骑完付钱就好，方便快捷，不用操心；其次，不用自己维修，随时可以骑行，方便用户出行，减少了很多麻烦。TS0WT共享出行市场发展分析01共享单车市场现状随着人们环保意识的不断增加，自行车在我国逐渐从代步工具转变为集健身和休闲为一体的工具。2020年中国自行车产量为7402.9万辆，其中电动自行车产量为2966.1万辆，占自行车总产量的40.1%。自行车产量-产量为7402.9万辆资料来源：国家统计局、智研咨询整理9753.378789.728518.268996.677315.646500.27402.93551.03257.03215.03097.93277.63609.32966.10.02000.04000.06000.08000.010000.012000.002000400060008000100001200020142015201620172018201920202014-2020年中国自行车及电动自行车产量自行车产量(万辆）电动自行车（万辆）共享出行市场发展分析01共享单车市场现状共享单车主要分为共享人力单车和共享助力单车，共享人力单车的驱动方式为人力驱动，车速较小，适合短距离骑行；共享助力单车是以电机、电池作为辅助动力，搭载智能传感器系统，根据骑行者脚踏力的大小，给予动力辅助，实现人力骑行、电动助力一体化的新型交通工具，适合中短距离骑行。共享单车产品分类资料来源：国家统计局、智研咨询整理产品竞争分析驱动方式最大车速平均制造成本出行范围人力 电动助力规定不高于25km/h2000-3000元中短距离短距离500-1000元12-20km/h全人力共享人力单车共享人力单车共享助力单车共享助力单车12.3102.8178.2236.8294735.77s.352.88$.24%0.000.00 0.0000.000.00P0.000.00p0.000.000100150200250300350201620172018201920202016-2020年中国共享单车市场规模市场规模（亿元）增速共享出行市场发展分析01共享单车市场现状共享单车在我国发展极为迅猛，2017年以来中国共享单车用户规模大幅度增加，达到2.05亿人；2019年中国共享单车用户规模为2.60亿人，较2018年的2.35亿人同比增长10.6%。共享单车用户规模资料来源：中国电子商务研究中心、智研咨询整理2015-2019年中国共享单车用户规模2016-2019年中国共享单车市场规模不断扩大，2019年中国共享单车市场规模为236.8亿元，较2018年增加了58.6亿元；2020年中国共享单车市场规模约为294亿元，较2019年同比增长24.24%。共享单车市场规模共享出行市场发展分析01共享单车市场现状随着互联网的快速发展，越来越多的企业进入到共享单车领域，以青桔、哈啰和摩拜为代表的共享单车企业将智能锁和物联网技术应用于传统单车上，实现了从有桩单车向无桩单车的转变。共享单车投融资情况资料来源：智研咨询整理时间企业轮次投资金额投资方2018/1/5永久出行天使轮1亿人民币乾川资本（领投）2018/1/23松果出行天使轮未透露险峰长青2018/1/25美团单车战略投资10亿美元未透露2018/1/29猛狮出行天使轮未透露宝驾租车2018/3/4ofo小黄车战略投资17.7亿人民币阿里巴巴2018/3/13ofo小黄车战略投资8.66亿美元阿里巴巴（领投）、君理资本、蚂蚁集团2018/3/22骑电单车A轮数千万美元IDG资本（领投）、君联资本、盈动资本2018/4/13哈啰出行HellobikeE轮7亿美元蚂蚁集团、复星集团2018/6/1哈啰出行HellobikeF轮20亿人民币蚂蚁集团2018/7/31骑电单车A 轮1000万美元金库资本（领投）、君联资本、IDG资本2018/9/3哈啰出行Hellobike战略投资40亿人民币春华资本Primavera（领投）、蚂蚁集团（领投）2019/7/16哈啰出行Hellobike战略投资4亿美元蚂蚁集团（领投）2020/4/17青桔单车A轮10亿美元君联资本（领投）2020/4/21青桔单车B轮1.5亿美元软银中国、君联资本2021/2/19青桔单车B 轮6亿美元未透露2018年以来部分共享单车品牌融资情况共享出行市场发展分析01共享单车市场现状从2020年10月共享两轮车市场小程序端月度活跃用户规模来看，青桔排名第一，月度活跃用户规模达到3491.6万人，其次为哈啰，月度活跃用户规模为3153.2万人；摩拜（美团）月度活跃用户规模为2262.4万人。共享单车主要平台资料来源：易观、智研咨询整理2020年10月共享两轮车市场小程序端月度活跃用户规模(万人)共享办公市场发展分析02资料来源：智研咨询整理共享办公空间又名联合办公空间，指的是受雇于不同机构、从事不同行业的人共同使用的物理办公场所及社群平台。共享办公并不是简单的分租，共享办公强调的是线上平台与公共设施使用率的提升。通过为使用者提供开放式办公空间，共享办公实现了企业间空间及物理位置的共享，与此同时，通过提供工具、设施及社会交往场所，促进了服务和资源的共享。创业企业共享办公企业房地产商房屋服务、办公空间报酬服务机构服务政府机构政策报酬报酬共享办公商业模式共享办公市场发展分析02资料来源：智研咨询整理共享经济理念在商业地产领域的渗透造就了以工位出租和社区构建为特征的共享办公空间在全球的崛起。互联网 时代技术创新、非标准劳动兴起和关系型生产的外部环境，加之政府推动、用户需求升级和城市中心商业地产去库存等内在动力，共同促成了共享办公行业的形成和发展。0102030405共享办公非标准型工作模式的兴起从空间生产到关系生产的嬗变千禧世代创业热潮企业对灵活办公需求的增长城市中心写字楼去库存压力发展机遇分析共享办公市场发展分析02资料来源：智研咨询整理共享办公迅速发展成为为共享经济产业领域的又一大亮点，共享办公空间数量不断攀升，2019年中国共享办公空间数量为4587家，2020年中国共享办公空间数量达到4851家，较2019年增加了264家。共享办公市场发展分析02资料来源：国家信息中心、智研咨询整理过去的两年里，在激烈的存亡淘汰机制下，我国共享办公产业逐渐从粗放扩张向深度整合和产业升级方向迈进，通过战略合作、合并、合资、互换股权和品牌加盟/整合等方式，由单一的“数桌子”向提升连锁运营能力和网络覆盖能力转型，由当二房东的收租模式向云端、生态、硬件设施开放方向转变。2020年上半年受疫情影响，许多企业选择在家办公，导致2020年中国共享办公市场规模有所下降，市场规模为168亿元，较2019年减少了59亿元，占共享经济总规模的0.5%。1102062271680.53%0.70%0.69%0.50%0.00%0.10%0.20%0.30%0.40%0.50%0.60%0.70%0.80010015020025020172018201920202017-2020年中国共享办公市场规模市场规模（亿元）占共享经济市场规模比重共享办公市场发展分析02资料来源：国家信息中心、智研咨询整理从市场区域竞争来看，中国共享办公主要集中于东部地区，以北上广为主要聚集区域，而西部地区共享办公行业发展较落后，但随着共享办公企业经营版图的不断扩大，西部地区的共享办公也将逐渐发展起来。中国共享办公市场规模分布新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛发达较发达较不发达共享办公市场发展分析02资料来源：国家信息中心、智研咨询整理在中国大众创业、万众创新的热潮下，共享办公从2016年开始在我国一二线城市的中央商务区逐渐兴起，推动传统办公空间由集约经营到共享经济、由空间硬件供应到软性服务变革的同时，也将引领传统办公模式向资源共享、价值共创、主体共赢的方向发展。2020年中国共享办公领域融资规模大幅增长，融资规模达到68亿元，主要是受领先企业优客工场上市和WeWork中国获得新融资的影响，两家企业的融资额占该领域融资额的九成以上。411268010203040506070802018201920202018-2020年中国共享办公融资规模融资规模（亿元）共享办公市场发展分析02资料来源：智研咨询整理目前，越来越多的企业看到了共享办公空间的优势，纷纷进入共享办公领域，共享办公行业迎来发展热潮期。共享办公平台主要有We Work、优客工场、氪空间、F-Space等。平台简介地区-价格We WorkWe Work是总部位于美国纽约的众创空间。We Work最早于2011年4月向纽约市的创业人士提供服务。截至2019年6月，We Work在全球的111座城市拥有528个共享办公场所，分布在美国的纽约、波士顿、费城、华盛顿特区、迈阿密、芝加哥、奥斯汀、伯克利、旧金山、洛杉矶、波特兰和西雅图等城市，以及英国伦敦、荷兰阿姆斯特丹、以色列特拉维夫等。北京We Work东煌大厦-2000元/人月优客工场优客工场是中国的联合办公空间运营商，成立于2015年4月。由红杉资本中国基金、真格基金、诺亚财富、歌斐资产、亿润投资等多家国内投资机构共同投资。截至2020年9月30日，共覆盖包括中国一线和新一线城市以及新加坡在内的51个城市。北京金源时代商务中心优客工场-开放工位-1600元/人月起氪空间氪空间是以联合办公为载体的企业服务平台。2016年1月，氪空间从36氪母公司拆分独立运营。氪空间已经打造出的联合办公空间产品，专为中小团队办公场景定制。产品体系包括能容纳不同人数的独立办公间、移动办公桌、开放工作区等。截至2018年4月，氪空间在全国10座城市拥有40个联合办公场所，分布在北京、上海、广州、杭州、南京、武汉、天津、苏州、成都、厦门等城市。氪空间的管理面积已接近20万平方米，提供超过30000个工位，有2000多家中小微企业在氪空间办公。北京金融街海峡国际社区-独立办公室-2600起/工位月F-SpaceF-Space联合办公社区是振丰集团旗下子公司丰动力重点发展业务之一，针对中小型企业量身打造的联合办公产品。上海市F-Space康桥中心-散位-1000元起/人/月LIMO零秒空间LIMO零秒空间隶属于北京京东方物业发展有限公司，LIMO零秒空间作为创新空间深度运营商，依托规范的业务流程和服务体系，提供整体解决方案，输出【策划定位、空间开发、市场营销、空间运营】四项标准化服务，现已形成【LIMO Space零秒办公空间】、【LIMO Hub零秒生态社区】两大核心产品。北京市东城区零秒空间天恒中心-独立办公室-2500元/月/工位共享办公市场发展分析02We work公司创立于2010年，是全球共享办公空间的鼻祖，自创立以来，We work经营范围及会员人数不断增加，截至2019年6月，We Work在全球的111座城市拥有528个共享办公场所，会员人数达到527000人。经营分布资料来源：招股说明书、智研咨询整理2010年2012年2014年2016年2018年2019H1年城市（个）13834100111地点（个）2723111425528会员数量（人）45040001500087000401000527000We work共享空间经营情况共享办公市场发展分析02根据招股说明书数据显示：2019年上半年we work营业收入为15.35亿美元，其中会员及服务收入为13.49亿美元，占总营业收入的87.8%。资料来源：招股说明书、智研咨询整理4.368.8618.2215.354.348.6716.9713.4999.6.8.2.8.0.0.0.0.0.0.0.0.0.00.02.0%0.002.004.006.008.0010.0012.0014.0016.0018.0020.002016201720182019H12016-2019年上半年we work营业收入营业收入（亿美元）会员及服务收入会员及服务收入占营业收入比重2016-2018年we work净亏损不断增加，2018年wework净亏损为19.27亿美元，较2017年同比增长106.5%；2019年上半年we work净亏损为9.05亿美元。4.309.3319.279.050.005.0010.0015.0020.0025.002016201720182019H12016-2019年上半年we work净亏损净亏损（亿美元）经营情况共享办公市场发展分析02发展历程资料来源：招股说明书、智研咨询整理优客工场是中国领先的联合办公空间运营商，成立于2015年4月，截至2020年6月30日，共覆盖包括中国一线和新一线城市以及新加坡在内的47个城市。2020年优客工场和特殊目的收购公司（SPAC）OrisunAcquisition Corp.完成合并，以SPAC模式登陆美股。优客工场成立；第一个联合办公空间北京阳光100社区于同年9月在北京开业运营。2015年优鲜集App正式上线；战略投资知识产权服务平台知呱呱，开始企业生态圈布局。2016年优客工场拥有空间超过90家。2017年，进入新加坡开始布局全球市场；启动运营管理输出U Brand模式。2017年收购位于联合办公、广告及品牌推广或建筑设计与施工行业的6家企业，包括大观建筑、省广众烁；进入香港和美国市场，进一步拓展海外布局。2018年收购火箭办公，巩固在联合办公行业的地位，巩固向“To B”领域纵向发展。2019年，上线社区电商平台和广告营销平台；启动U Partner资产托管模式。2019年优客工场和特殊目的收购公司（SPAC）OrisunAcquisitionCorp.完成合并，以SPAC模式登陆美股市场2020年发展历程共享办公市场发展分析02资料来源：招股说明书、智研咨询整理上海天津石家庄北京南海诸岛呼伦贝尔长春沈阳承德大连张家口保定青岛济南泰安郑州开封扬州杭州武汉重庆长沙成都拉萨昆明福州深圳东莞香港广州珠海珠海宁波苏州常州襄樊西安太原南京无锡厦门潍坊枣庄潜江南昌惠州大同廊坊雄安新区嘉兴乌鲁木齐时间空间数量自营模式下空间数量轻资产模式下空间数量2018年191160312019年204157472020年前三季度222116106优客工场共享空间数量（个）时间管理面积自营模式下管理面积轻资产模式下管理面积2018年57.1946.5810.612019年64.3147.1917.122020年前三季度59.7132.4227.29优客工场共享空间管理面积（万平方米）截止2020年9月优客工场共享空间分布共享办公市场发展分析02资料来源：招股说明书、智研咨询整理优客工场的线下产品包括自营模式下的标准化空间U Space，小型办公空间U Studio，定制化空间U Design；以及轻资产模式下的以运营、设计施工服务输出为主的UBrand模式，以管理、系统输出为主的U Partner模式。除此之外，优客工场还提供包括广告与品牌服务、孵化和企业投资服务、税务与金融服务、人力资源服务、法律服务、设计与施工服务、IT支持在内的全方位服务，全面赋能企业与个人会员。优客工场商业模式标准化空间小型办公空间定制化空间自营模式自营模式轻资产模式以运营、设计施工服务输出为主U Brand模式以管理、系统输出为主的U Partner模式操作系统UDA应用程序UBazaar数据管理系统Udata智能办公系统U Plus服务客户数据运行数据一般企业服务孵化和企业投资服务设计与施工服务广告与品牌服务新业务精准营销服务电子商务其他服务运行模式提供服务运行系统商业模式共享办公市场发展分析02资料来源：招股说明书、智研咨询整理自2015年9月启动首个合作空间以来，优客工场凭借强大的管理和连锁经营能力不断扩大经营版图，除自营模式外，还开发了轻资产模式，成为公司主要增长动力之一，2020年前三季度优客工场营业收入为5.98亿元，其中共享空间营收3.46亿元，占营业总收入的57.9%。1.674.4911.675.981.543.945.583.460.255.352.070.130.300.750.450.005.0010.0015.0020.0025.002017201820192020年前三季度2017-2020年前三季度优客工场营业收入情况（亿元）总营业收入共享空间营业收入营销和品 牌服务其他服务2019年优客工场营业成本为13.69亿元，其中共享空间营业成本为8.14亿元；2020年前三季度优客工场营业成本为6.83亿元，其中共享空间营业成本为4.47亿元。3.106.6413.696.833.096.258.144.470.000.224.851.890.020.160.700.470.005.0010.0015.0020.0025.0030.002017201820192020年前三季度2017-2020年前三季度优客工场营业成本情况（亿元）营业成本共享空间营销和品牌服务其他服务经营情况共享办公市场发展分析02资料来源：招股说明书、智研咨询整理2019年优客工场净亏损8.07亿元，2020年前三季度优客工场净亏损3.59亿元，优客工场的损失主要来自为发展业务而进行的投资，包括开辟更多空间，重建现有空间以及收购其他业务。优客工场会员人数不断增长，个人会员占比较大。2019年优客工场会员人数为71.56万人，其中个人会员人数为68.89万人；2020前三季度优客工场会员人数为86.04万人，其中个人会员人数为83.05万人。3.73 4.45 8.07 3.59 0.001.002.003.004.005.006.007.008.009.002017201820192020年前三季度2017-2020年前三季度优客工场净亏损净亏损（亿元）25.271.5686.0423.9768.8983.050102030405060708090100201820192020年前三季度2018-2020年前三季度优客工场会员人数（万人）会员人数个人会员人数经营情况共享医疗市场发展分析03资料来源：国家统计局、智研咨询整理共享医疗就是将共享经济这种商业模式引入到医疗服务供给领域，对原有的医疗资源供给方式进行创新。主要是指以互联网平台为载体，整合海量的、分散的专业化医疗资源，包括医生、护士、医疗设备等，以更为便捷和高效的方式满足消费者多样化医疗服务需求的一类经济活动。随着经济的不断发展，中国居民人均可支配收入及健康观念不断提升，医疗卫生消费迎来长期稳步的发展阶段。2019年中国居民人均医疗保健消费支出为1902元，2020年中国居民人均医疗保健消费支出1843元，同比下降3.1%。912104511651307145116851902184314.58.48.19.02.13.88%-3.10%-5.00%0.00%5.00.00.00 .0000400600800100012001400160018002000201320142015201620172018201920202013-2020年中国居民人均医疗保健消费支出居民人均医疗保健消费支出（元）增速共享医疗市场发展分析03资料来源：国家信息中心、智研咨询整理共享医疗在西方国家发展起步较早，目前已经出现多种形式的共享医疗商业模式。我国的共享医疗出现时间较晚，近两年才颇具规模，2020年中国共享医疗市场规模为138亿元，较2019年同比增长27.8%，占共享经济总市场规模的0.41%。56881081380.27%0.30%0.33%0.41%0.00%0.05%0.10%0.15%0.20%0.25%0.30%0.35%0.40%0.45040608010012014016020172018201920202017-2020年中国共享医疗市场规模及占比市场规模（亿元）占共享经济市场规模比重共享医疗市场发展分析03资料来源：国家信息中心、智研咨询整理作为一种资源分配方式，共享并非新鲜事物，在人类历史上共享行为早已有之。在共享医疗出现之前，医疗资源共享活动就一直存在，例如，医护人员会利用闲暇时间供职于不同的医疗单位，一方面可以获取额外报酬，另一方面能够增加医疗服务供给总量，缓解医疗资源供给不足压力；此外，不同医疗机构之间互相使用较为昂贵的设备、手术室等。但是，由于信息不对称以及传统的信息搜集方式成本高昂，这种医疗资源分享行为难以扩大，很多优质的医疗服务资源无法得到充分而有效地利用，限制了传统形式的医疗分享活动的发展。而共享医疗实际上是通过互联网构建一个双边市场平台，该平台汇集了大量的医疗资源供求信息，有效地将原本分散的医疗信息进行整合，大大降低了医疗资源供需双方的信息搜寻成本和交易成本，能够有效地进行供需匹配，从而进一步吸引更多的医疗资源供给方和需求方加入。2020年突如其来的新冠疫情让许多企业看到了共享医疗的发展前景，行业融资规模有所增长，直接融资规模为88亿元，较2019年增加了49.9亿元，同比增长131.0%。441914738.188020406080100120140160201620172018201920202016-2020年中国共享医疗直接融资规模融资规模（亿元）分类平台简介医疗知识和技能的分享春雨医生春雨医生（原名：春雨掌上医生）创立于2011年7月，是全球最大的移动医患交流App。春雨医生致力于利用移动互联网的科技手段帮助人们掌握健康、延缓衰老、治疗疾病。并且，春雨医生正努力给整个医疗体制建立一个更自由的生态，让老百姓的“看病难、药价高、保险亏”等问题得到有效的解决。就医160就医160成立于2005年2月，是国内优秀的预约挂号及导医、咨询和点评服务平台，同时也是深圳市卫生局、东莞市卫生局的官方预约挂号网站。就医160平台具有丰富的HIS系统对接经验，已开发出个人端、医生端、医院端的产品，用产品链连接患者、医生和医院，提供诊前、诊中、诊后全流程服务。平安好医生平安好医生是全球领先的互联网医疗健康服务平台，秉承“信任、专业、便捷”三大理念，致力于构建专业医患沟通桥梁，成为中国规模最大、模式最领先、竞争壁垒最坚实的互联网医疗平台。护理服务的分享e陪诊e陪诊作为移动医疗O2O平台，为广大患者提供预约挂号、诊前提醒、专车接送、就诊陪护、预约检查、取送报告、诊后健康管理等服务。贴心小护贴心小护为就医160旗下产品，是一款基于移动互联网技术的专业医疗服务平台，为用户提供分诊导诊、咨询、预约挂号、诊前提醒、院内陪诊、取号、缴费、取药、代取检查检验报告单、健康管理、就医心理疏导、专车接送等专业护理服务。医疗设备的分享卓健科技杭州卓健信息科技有限公司成立于2011年2月，专注于为医疗机构提供医疗 互联网产品和服务。自创立之初，卓健科技顺应医改大方向，抓住医疗核心业务，持续为医疗医药行业生态链各环节提供高质量高效率的互联网化技术产品及优质服务，打造智慧医疗生态闭环，致力成为国内有温度有影响力的医疗互联网企业。共享医疗市场发展分析03资料来源：智研咨询整理目前，中国共享医疗商业模式的类型尚没有统一的划分标准，根据国家信息中心分享经济研究中心的研究，可将我国的医疗分享平台按照分享内容进行划分。按照分享的内容划分，我国共享医疗可以分为三类，分别是：医疗知识和技能的分享，主要代表性平台有“春雨医生”“就医160”“平安好医生”等；护理服务的分享，主要代表性平台有“e陪诊”“贴心小护”等；医疗设备的分享，主要代表性平台有“卓健科技”等。新冠疫情发生后，越来越多的人逐渐接受在线医疗，并逐步习惯用其代替部分线下门诊。此外，监管机构和各地政府纷纷出台政策，支持和鼓励互联网医疗发展，推广线上分级诊疗，加速推进互联网医疗纳入医保支付系统。在需求增加和政策利好的促进下，从业者加快业务扩张步伐，行业竞争也越来越激烈。共享医疗市场发展分析03资料来源：招股说明书、智研咨询整理商业模式平安好医生于2015年上线，以在线医疗健康服务为抓手，致力构建专业医患沟通桥梁，公司主要业务包括在线医疗、消费型医疗、健康商城及健康管理和互动。公司经营自建的一站式医疗健康平台，利用自有医疗团队、人工智能辅助系统、外部合作医生及合作的第三方医疗健康服务供应商（包括医院、体检中心、药房等）资源为平台用户提供包括快速问诊、在线开药、闪电购药等在内的可覆盖就医前到就医后的一站式在线医疗及健康商城服务，并于平台提供健康资讯等健康管理和互动服务。此外，公司通过名医挂号、体检预约、提供包括销售体检套餐等在内的消费型医疗服务。公司也正在加速自建/共建互联网医院，已在全国布局超30家互联网医院，上线数量达14家。医院诊所药店快递公司平台医疗团队人工智能助理健康信息团队自有资源第三方资源在线医疗消费型医疗健康商城共建互联网医院健康管理和互动拓展企业战略合作保险公司 用户药店经营情况共享医疗市场发展分析03资料来源：招股说明书、智研咨询整理平安好医生营业收入快速增加，2019年平安好医生营业收入为50.65亿元，毛利润为11.71亿元，毛利率为23.1%；2020年平安好医生营业收入为68.66亿元，毛利润为18.64亿元，毛利润为27.2%。2.796.0218.6833.3850.6568.661.112.546.129.1211.7118.6439.8B.22.8.3#.1.2%0.0%5.0.0.0 .0%.00.05.0.0E.0%0.0010.0020.0030.0040.0050.0060.0070.0080.002015201620172018201920202015-2020年平安好医生营业收入及毛利润（亿元）营业收入毛利润毛利率健康商城是平安好医生营业收入最主要来源，但在线医疗业务毛利润贡献最大，2020年健康商城营业收入为37.1亿元，毛利润为2.55亿元；在线医疗业务营业收入为15.7亿元，毛利润为8.79亿元。共享医疗市场发展分析03资料来源：招股说明书、智研咨询整理2020年平安好医生注册用户数量达3.73亿人，较2019年增加了0.58亿人；累计咨询量超10亿人，达到10.04亿人，较2019年增加了3.3亿人。2.653.153.734.076.7410.040.002.004.006.008.0010.0012.002018201920202018-2020年平安好医生用户注册量及累计咨询量注册用户数（亿人）累计咨询量（亿人）经营情况共享住宿市场发展分析04资料来源：文化旅游部、智研咨询整理共享住宿是共享经济发展新出现的交易模式，是依托互联网诞生的一个新业态，近年来发展迅速，创新日益活跃，发展潜力巨大。共享经济与旅游的融合为人们出行提供了更多选择，住宿为旅行必不可少的重要环节。旅游人数的增加不断带动整个共享住宿领域交易规模的增长。2011-2019年中国旅游人次不断增加，2019年达到60.06亿人次；2020年受疫情影响，旅游人次仅为28.79亿人次。29.5732.6236.1139.944.3550.0155.3960.0628.790102030405060702012201320142015201620172018201920202012-2020年中国国内旅游人次旅游人次（亿人次）共享住宿市场发展分析04资料来源：智研咨询整理共享住宿形成了对传统酒店的降维攻击，可以提供更多产品选择，打破了房东和租户之间信息不对称的问题，而且轻资产的扩张模式可以让平台更专注产品本身，并可通过社交属性提高用户体验、降低运营成本。传统酒店共享住宿商品种类较单一，仅仅能提供经济房，标准房，豪华套房等品类丰富，提供的房屋类型包括：度假公寓、别墅、小木屋、城堡、房车、圆顶小屋、灯塔、蒙古包、洞穴、树屋、游艇、飞机和岛屿等主要成本租赁地产、管理和推广酒店品牌以及工作人员的雇佣成本人力成本和研发运营平台成本扩张模式重资产扩张，输出的是土地、人员、品牌和管理轻资产扩张，可以完全专注于管理输出，提高用户体验社交属性基本无社交属性结合社交网站整合租客住宿后留下的评价，借助过亿的社区力量和多种认证、增值服务，降低服务的不确定性，同时可帮助用户快速找到附近住宿地点，极大降低平台认证所需成本共享住宿市场发展分析04资料来源：智研咨询整理共享住宿平台是整个共享住宿产业链的核心环节，共享住宿运营模式主要有分为C2C模式（以Airbnb爱彼迎为代表）及B2C模式（以途家为代表）两种，其中C2C模式是中国共享住宿行业主要商业模式。C2C模式 共享住宿平台只是提供房东和房客对接渠道，只从中收取一定比例的服务费，共享经济平台不参与房源的管理和运行，由房东负责。B2C模式 共享住宿平台企业通过从个人房东、房地产开发商、房屋中介等批量获取房源，对房源进行统一配置，同时负责房源的日常维护和经营管理，并通过平台将房源对外租赁，从中收取一定比例佣金。共享住宿平台运营模式共享住宿市场发展分析04资料来源：国家信息中心、智研咨询整理2019年中国居民人均住宿消费支出为216.8元，其中共享型住宿服务支出为16元，占人均住宿消费支出的7.4%；2020年中国居民人均住宿消费支出为229.8元，其中共享型住宿服务支出为11.2元，占人均住宿消费支出的4.9%。2019-2020年中国共享住宿服务支出占比2019年人均住宿消费支出：216.8元共享住宿人均消费支出：16元2020年人均住宿消费支出：229.8元共享住宿人均消费支出：11.2元占比：7.4%占比：4.9 19年中国共享住宿收入占全国住宿业客房收入的6.9%，2020年中国共享住宿收入占全国住宿业客房收入的6.7%，较2019年下跌0.2%。3.5%4.4%5.8%6.9%6.7.5.6.2.1.3%0.0 .0.0.0.00.00.0 1620172018201920202016-2020年中国共享住宿收入占全国住宿业客房收入比重共享住宿传统住宿共享住宿市场发展分析04资料来源：国家信息中心、智研咨询整理随着中国经济的发展和国家相关政策的支持，2017-2019年中国共享住宿参与人数逐年增加，2019年参与人数达到20000万人，其中服务提供者618万人，客房住宿人数19382万人。220400618758012600193827800130002000005000100001500020000250002017201820192017-2019年中国共享住宿参与人数服务提供者人数（万人）客房人数（万人）参与人数（万人）共享住宿市场发展分析04资料来源：国家信息中心、智研咨询整理2020年住宿服务业是受疫情冲击最大的领域之一，共享住宿新业态也不例外；2020年中国共享住宿市场规模为158亿元，较2019年的225亿元同比下降29.78%，占共享经济总规模的0.47%。随着国内疫情防控取得显著成效，共享住宿市场规模将逐渐回升。53851201652251580.27%0.25%0.58%0.56%0.69%0.47%0.00%0.10%0.20%0.30%0.40%0.50%0.60%0.70%0.8001001502002502015201620172018201920202015-2020年中国共享住宿市场规模及占比市场规模（亿元）占共享经济市场规模比重共享住宿市场发展分析04资料来源：国家信息中心、智研咨询整理2019年房源量排名前十位的城市分别是：北京、上海、成都、广州、三亚、重庆、厦门、杭州、西安、青岛。2019年间夜量排名前十位的城市分别是：北京、成都、重庆、上海、厦门、三亚、广州、西安、丽江、杭州。2019年订单量排名前十位的城市分别是：北京、成都、广州、杭州、上海、丽江、三亚、西安、重庆、厦门。2019年订单量TOP10城市在2020年前五个月订单量均出现不同程度下降，其中降幅最大的为北京，同比下降为85%；其次为丽江，同比下降72%；厦门同比下降68%，上海同比下降67%，西安同比下降64%。-85%-72%-68%-67%-64%-59%-58%-57%-53%-40%-90%-80%-70%-60%-50%-40%-30%-20%-10%0%北京丽江厦门上海西安杭州广州重庆成都三亚2019年订单量TOP10城市在2020年前五个月订单总量降幅对比2019年中国共享住宿房源量、间夜量、订单量排名前十位的城市房源量订单量间夜量共享住宿市场发展分析04共享住宿行业在经过2018年的发展热潮之后，2019年整个共享住宿市场发展趋于平稳，投资者回归理性。2020年受疫情影响，共享住宿融资情况进入寒冬。2019年以来中国部分共享住宿投融资情况时间企业轮次金额投资方2019/1/14嘉兴孔雀城战略投资未透露华夏幸福(知合控股)2019/3/1蛋壳公寓C轮5亿美元Tiger老虎基金-中国（领投）、蚂蚁集团（领投）、春华资本Primavera2019/5/8木鸟民宿B 轮数千万人民币达晨创投、梅花创投、华冠资本（领投）2019/6/15自如网B轮5亿美元General Atlantic泛大西洋投资（领投）、红杉资本中国、腾讯投资2019/7/18贝壳集团D轮12亿美元腾讯投资（领投）、基汇资本、碧桂园创投2019/9/9城家公寓A轮3亿美元博裕资本（领投）、云锋基金、华住酒店集团2019/10/29蛋壳公寓D轮1.9亿美元CMC资本（华人文化产业投资基金）、春华资本Primavera2019/11/1贝壳集团D 轮24亿美元软银愿景基金（领投）、腾讯投资、高瓴资本2020/3/4自如网战略投资10亿美元软银愿景基金共享住宿市场发展分析04平台简介业务分布Airbnb爱彼迎Airbnb爱彼迎成立于2008年8月，总部设在美国加州旧金山市。Airbnb是一个旅行房屋租赁社区，用户可通过网络或手机应用程序发布、搜索度假房屋租赁信息并完成在线预定程序。在全球业务覆盖超过220个国家和地区的10万城市贝壳找房贝壳找房起于链家，但不同于链家网的垂直自营模式，其使命是缔造平台。它以共享真实房源信息与链家管理模式为号召，吸引经纪人与经纪公司入驻。2020年8月13日，贝壳找房在纽交所正式挂牌上市，成为中国居住服务平台第一股。途家民宿途家民宿是全球领先的民宿短租预定平台，于2011年12月1日正式上线，致力于为客户提供丰富、优质、更个性的出行住宿体验，同时也为房东提供高收益且有保障的闲置房屋分享平台。凭借旗下途家网、蚂蚁短租、携程民宿、去哪儿民宿、大鱼自助游五大平台的海量用户入口，目前，途家已经覆盖国内400多个城市和海外1037个目的地，在线房源超过230万套，包含民宿、公寓、别墅等住宿产品及延展服务，可满足以“多人、多天、个性化、高覆盖”为特征的出行住宿需求。新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛共享住宿行业整合步伐加快，领先企业的头部效应更加凸显，提质升级成为发展重点，小企业进一步寻求与行业领先企业合作，以期借助于大平台资金和流量等优势助力自身发展，目前国内领先的共享住宿平台有Airbnb爱彼迎、途家、小猪民宿等。共享住宿市场发展分析04平台简介业务分布小猪民宿小猪民宿于2012年8月正式上线，是国内依托于分享经济，为用户提供特色住宿服务的互联网平台，是中国房屋分享经济领域的代表企业。截止2019年5月，小猪民宿已覆盖国内400多座城市以及海外252个目的地，拥有超过5000万活跃用户，在全国超过20座城市设有运营中心。城家公寓城家公寓是城家旗下专注于打造实用型独立居住空间的租房品牌，当前已覆盖北京、上海、广州、深圳、苏州、杭州等多个城市。木鸟民宿木鸟民宿是分享经济模式下的C2C在线民宿短租预订平台，由北京爱游易科技有限公司旗下独立运营，于2012年5月正式上线。木鸟短租房源除城市民居外涵盖别墅、海景房、四合院、木屋、客栈、窑洞等，覆盖全国396个城市。趣墅趣墅是广州玩么网络科技有限公司旗下的别墅度假品牌。趣墅提供兼具旅游度假、聚会娱乐功能的短租别墅。用户可以通过电脑、手机、微信、客服电话等多种渠道预订，趣墅的度假别墅均为自营产品。新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛新疆西藏青海甘肃内蒙古宁夏四川云南海南广西贵州重庆陕西山西黑龙江吉林辽宁河北山东河南湖北湖南广东江西福建安徽江苏浙江香港台湾澳门上海天津钓鱼岛北京南海诸岛资料来源：智研咨询整理Airbnb爱彼迎资料来源：招股说明书、智研咨询整理商业模式Airbnb作为服务型平台、通过连接供需方促进多主体共同创造价值。每当有新房源被上传至平台，公司借助算法与反馈机制进行信息甄别，解决租客和房东间存在的信息不对称问题，使得短期内提供大量房源可能性增加，并且用技术手段去除酒店业占比较高的租赁成本和雇佣成本，通过规模效应将每笔中介费累积为巨额利润。Airbnb于2020年年底在纳斯达克交易所上市。共享住宿市场发展分析04潜在租客Airbnb用户Airbnb房东潜在房东房源数口碑效应转化率渗透率Airbnb爱彼迎商业模式Airbnb爱彼迎资料来源：公司财报、智研咨询整理2015-2019年Airbnb营业收入高速增长，但增速有所放缓，2020年全球受疫情影响，出游人数下降，导致Airbnb营业收入呈负增长，营业收入仅为33.8亿美元，较2019年减少了14.3亿美元，净亏损达到45.8亿美元，其中第四季度净亏损为39亿美元，Airbnb将大部分亏损归因于与IPO有关的费用。共享住宿市场发展分析049.216.625.636.548.133.81.41.50.70.26.745.80.010.020.030.040.050.060.02015201620172018201920202015-2020年Airbnb营业收入及净亏损收入（亿美元）净亏损（亿美元）由于Airbnb房间的平均预定价值低于许多地区酒店的平均每日房价，因此Airbnb成为许多旅游人士的重要选择，2019年Airbnb房间预订数为3.27亿晚，总价值为380亿美元；2020年Airbnb房间预订数为1.93亿晚，总价值为239亿美元。0.721.261.862.503.271.938.1 13.9 21.0 29.4 38.0 23.9 0.005.0010.0015.0020.0025.0030.0035.0040.002015201620172018201920202015-2020年Airbnb房间预订数及价值Airbnb 预订数（亿晚）总价值（十亿美元）经营情况贝壳找房资料来源：招股说明书、智研咨询整理商业模式贝壳找房作为中国最大的房屋交易服务平台，开拓经纪行业基础架构，成为交易服务数字化、标准化先驱，致力于搭建以ACN为核心的一体化开放平台。贝壳找房的业务模型可概括为“双网双核”，即“数据与技术驱动的线上运营网络”和“以社区为中心的线下门店网络”两张网，三大主营业务分别为二手房交易、新房交易和其他新兴业务。共享住宿市场发展分析04贝壳找房商业模式贝壳找房资料来源：招股说明书、智研咨询整理贝壳找房营业收入及总利润不断增加，2019年贝壳找房营业收入为460.1亿元，实现总利润112.7亿元；2020年前三季度贝壳找房营业收入为478.1亿元，总利润为114.4亿元，无论是营收还是总利润都已超过2019年全年，新房业务成为公司业绩在疫情影响下保持韧性的重要支柱。共享住宿市场发展分析04255.1286.5460.1478.147.768.7112.7114.40.0100.0200.0300.0400.0500.0600.02017201820192020前三季度2017-2020年前三季度贝壳找房营业收入及总利润（亿元）营业收入总利润经营情况贝壳找房资料来源：招股说明书、智研咨询整理2020年尽管受到新冠疫情影响，但贝壳找房交易额仍呈增长态势，2019年贝壳找房交易额为21276.9亿元，2020年前三季度贝壳找房交易额达到23790.8亿元。共享住宿市场发展分析042020年新房交易营业收入超越二手房交易营业收入，新房交易成为贝壳找房最主要的业务，但二手房交易额仍大于新房交易额，2020年前三季度贝壳找房新房交易营业收入为250.5亿元，交易额为91387.9亿元；二手房交易营业收入为214亿元，交易额为13552.8亿元。10144.111531.121276.923790.80.05000.010000.015000.020000.025000.02017201820192020前三季度2017-2020年前三季度贝壳找房交易额总交易额（亿元）经营情况0.02000.04000.06000.08000.010000.012000.014000.016000.0二手房交易 新房交易其他服务 二手房交易 新房交易其他服务营业收入交易额2017-2020年前三季度贝壳找房营业收入及交易额（亿元）2017201820192020前三季度共享充电宝市场发展现状05共享充电宝是指运营商在特定的场景中提供的租赁充电设备，消费者通过缴纳一定的押金或凭借芝麻信用分免押金成为注册用户，再扫描设备屏幕上的二维码后，即可租借一个充电宝获得充电服务，多以小时计费，充电宝成功归还后，押金可随时提现并退回账户。目前共享充电宝有桌面固定式、小机柜移动式和大机柜移动式三大产品形态，分别应用于不同的场景，以满足用户应急充电和移动充电的需求。产品类型桌面固定式小机柜移动式大机柜移动式图片数量可供多个设备同时充电可容纳6-12个充电宝可容纳20-80个充电宝使用场景同小机柜有交叉，在小型场景中的每一台布局，如餐厅、咖啡厅、酒吧、KTV等固定型的场景下，在餐厅、咖啡馆、酒吧、健身房、KTV等小型场景中布局流动型的场景，在大型场景中布局，如机场、火车站、景区、医院、大型商场等渠道拓展渠道的进入门槛低，进入成本低渠道的进入门槛低，进入成本低渠道的进入门槛高，渠道费用高线下运营线下运营压力较大，需专门人员运营线下运营压力小线下运营压力小资料来源：智研咨询整理共享充电宝市场发展现状05国内共享充电宝产业投放产能主要集中在怪兽充电、小电科技、街电、来电科技四大企业。2019年四家企业共投放共享充电宝1350万台。其中，怪兽充电500万台，小电科技400万台，街电300万台，来电科技150万台。5009001350020040060080010001200140016002017201820192017-2019年中国主流四家企业充电宝累积投放量四家企业充电宝累积投放量（万台）资料来源：智研咨询整理共享充电宝市场发展现状05共享充电宝正在占领商场、餐厅等场所，用户扫码，就可以租借一个充电宝，或者实现付费快速充电。2017年我国共享充电宝用户数量0.8亿人，到2019年增长到了2.5亿人，2020年随着出行人员减少及共享充电宝的涨价，用户规模将有所减少，约为2.3亿人。资料来源：智研咨询整理随着行业内竞争格局趋于固定，一二线城市的投放量已经趋于饱和，共享充电宝企业开始积极开拓三线城市以及四、五线城市市场。2019年，共享充电宝的使用用户中多集中于二线城市。18( 19年中国共享充电宝用户区域分布一线城市二线城市三线城市其他城市及地区共享充电宝市场发展现状05从行业市场价格来看，随着行业的市场竞争逐渐放缓，部分中小规模企业退出市场，头部企业建立了较为明显的优势，且用户的使用习惯逐渐形成，行业也逐步进入提价周期，单次租赁价格显著上升。同时行业内企业对各点位的共享充电宝租赁价格也设置了不同的价格区间，以覆盖更多差异化的需求。2017年中国共享充电宝行业交易规模为9.9亿元，2019年增长到了78.8亿元，2020年将达到92.2亿元。资料来源：智研咨询整理9.9 32.8 78.8 92.20.010.020.030.040.050.060.070.080.090.0100.020172018201920202017-2020年中国共享充电宝行业交易规模市场规模（亿元）共享充电宝市场发展现状05在市场初期发展阶段，共享充电宝平台有效利用餐饮、娱乐等场景需求，实现对餐饮、KTV、影院等场所的覆盖。餐厅充电宝渗透率已经超过40%，休闲娱乐场所、酒店、商城场景渗透率也超过10%。未来随着平台运营能力的进一步提升，包括在景区、交通枢纽、公共服务场所等场景将成为市场后续布局开拓的重要渠道。资料来源：Trust Data大数据平台、智研咨询整理44%9%6 20年中国共享充电宝线下场景分布餐厅休闲娱乐场所酒店商城交通枢纽其他共享充电宝市场发展现状05目前国内共享充电宝市场发展迅猛，市场集中度较高，经历三年多的探索，共享充电宝的商业模式也逐渐清晰起来。资料来源：智研咨询整理共享充电宝行业代表厂商竞争格局怪兽充电小电科技街电来电科技成立时间2017年5月2016年12月2015年11月2014年8月融资进度B轮3000万美元B 轮数亿元人民币2017年8月，聚美优品完成对深圳街电科技的收购A轮2000万美元业务覆盖1000 城市1000 城市300 城市（截至2018年12月）/主要产品小型柜机、大型柜机、组合充电柜小型柜机、大屏柜机小型柜机、大型柜机、组合充电柜、大屏充电终端大中小机柜、大屏机产品认证移动电源新国标、CCC、SRRC、Qi、RoHs认证CCC、CQC、CE认证CQC、UN38.3认证CQC、CCC、UN38.3等认证用户2亿 2亿 1亿 （截至2018年12月）1.2亿 投放情况约500万充电宝约400万充电宝约300万充电宝约150万充电宝场景布局商务、出行、旅游、社区、文娱、餐饮、医疗等场景商圈、餐饮、影院、酒店等高频消费场景泛娱乐、出行、餐饮、公共场所等餐饮、娱乐、商场、机场、景点、医院等供应链合作小米、紫米/飞毛腿技术应用与创新自主研发物联网底层通信协议；物联网大数据应用智能微电脑控制技术创新PoweriQ智速充科技发明、实用新型、外观、海外及行业底层专利共计99项资料来源：招股说明书、智研咨询整理04怪兽充电通过在人流量较大的地方开设网点来向用户提供移动设备充电服务，充电宝被存储在安装于网点中的柜机中。用户可以通过小程序来找到公司的网点，并在柜机上租借充电宝。用户可以在任何网点借用充电宝，并在全国范围内的任何柜机上归还。同时，用户也可以通过小程序直接购买充电宝。共享充电宝市场发展现状05商业模式资料来源：招股说明书、智研咨询整理04怪兽充电的收入主要是移动设备充电业务、移动电源销售以及其他收入三部分，2019-2020年怪兽充电移动设备充电业务的收入为19.24亿元、27.12亿元，分别占总收入的比例为95.15%、96.52%。2020年第一季度受疫情影响，致使充电订单大幅减少，但在2020年第二季度开始好转。共享充电宝市场发展现状0520.2228.0919.24 27.12 95.15.52.00.50.00.50.00.50.00%0.005.0010.0015.0020.0025.0030.00201920202019-2020年怪兽充电营业收入及移动设备充电业务营业收入（亿元）总营业收入移动设备充电业务占比经营情况资料来源：招股说明书、智研咨询整理04在保持快速扩张的同时，怪兽充电也实现盈利，但并不稳定。怪兽充电净利润由2019年的16660.6万元下降至2020年的7542.7万元，研发开支仅为7093.8万元。共享充电宝市场发展现状0516660.67542.76547.17093.805000100001500020000201920202019-2020年怪兽充电净利润及研发开支净利润（万元）研发开支（万元）截至2020年12月31日，怪兽充电在中国1500多个地区拥有超过66.4万个业务点。2019-2020年，怪兽充电累计注册用户分别为1.491亿人和2.194亿人，增加了0.703亿人。2019年2020年截至2019年12月31日，累计注册用户约为1.491亿截至2020年12月31日，累计注册用户约为2.194亿注册用户增加0.703亿2019-2020年怪兽充电累积注册用户经营情况共享雨伞市场发展分析06共享雨伞是指企业在地铁站点、商业区、居民区、校园、酒店等提供雨伞的共享服务，是共享经济的一种新形态。共享雨伞是社会公共服务进步的体现，于2016年开始兴起，经历了市场扩张期，市场冷静期，互联网巨头纷纷布局共享雨伞领域。资料来源：智研咨询整理共享雨伞发展历程第一阶段（2016-2017年）共享雨伞行业兴起，备受资本金关注第二阶段（2018-2019年）市场规模进一步扩展，用户规模快速增长第三阶段（2020-至今）行业进入冷静期，市场集中度进一步提高共享雨伞市场发展分析06目前消费者对共享雨伞的商业模式持消极态度的主要原因在于消费者观念中雨伞产品本身价值和耐用程度偏低，基本属于低值易耗品，这导致产品无论在使用还是付费环节都未达到消费预期，而消费者更偏好安全性和价值更高的有桩式雨伞也反映了这一点。中国共享雨伞用户规模不断增加，2019-2020年增速有所放缓，2019年中国共享雨伞用户规模为7.26百万人，2020年中国共享雨伞用户规模约为8.35百万人。资料来源：智研咨询整理共享雨伞市场发展分析06较之共享单车、共享充电宝，共享雨伞市场规模较小，但近年来中国共享雨伞市场规模不断增加。2019年中国共享雨伞市场规模为350.1亿元，2020年中国共享雨伞市场规模为386.2亿元，同比增长10.31%。资料来源：智研咨询整理236.8249.6282.6308.8350.1386.25.41.22%9.27.37.31%0.00%2.00%4.00%6.00%8.00.00.00.00.0001001502002503003504004502015201620172018201920202015-2020年中国共享雨伞市场规模及增速市场规模（亿元）增速共享雨伞市场发展分析06在竞争残酷，雄起得快也没落得快的共享经济领域，共享雨伞渐渐在人们的日常生活中稳稳占据了一席之地。相对于其他的共享产品和共享模式，共享雨伞要低调很多，经过一系列的厮杀，目前中国共享雨伞市场主要有漂流伞、魔力伞、摩伞等品牌。资料来源：智研咨询整理平台漂流伞魔力伞摩伞图片经营公司深圳市漂流伞科技有限公司上海艺升信息科技有限公司上海天伞网络科技有限公司简介是一家为用户解决晴雨天城市出行用伞问题，为商户提供共享雨伞综合解决方案的服务商。目前，漂流伞借还伞终端网点已超过4万个，已成为国内共享雨伞第一平台。魔力伞是一家提供雨伞的互联网智能租借平台，主要打造共享雨伞租赁服务。摩伞主要有备避雨、遮阳2种功能，2017年8月，摩伞共享雨伞在上海地铁2号线开始启用。投放城市杭州、深圳、上海、广州、海口、佛山、成都、东莞、惠州、珠海、福州、武汉、合肥、昆明、南昌、南京、南宁、宁波、苏州、温州、长沙、重庆目前已投放10个城市，主要以广深地区为核心。入驻了广州地铁，福州地铁，厦门地铁。上海价格1元/0.5小时，8元/24小时（芝麻信用分550分以上可免28元押金）1元/12小时（芝麻信用分600分以上可免30元押金）2元/24小时（押金39元）共享知识技能市场发展分析07知识技能分享是指个人或机构把其分散、盈余的知识技能等智力资源通过互联网平台，以免费或付费的形式分享给特定的个人或机构，在这个过程中，分享的特点体现为最大限度地利用全社会的智力资源，从而以更高的效率、更低的成本满足生产、工作及日常生活的服务需求。知识技能分享的种类形式多样，从业务领域看，主要有研发创意、知识内容、生活服务等方面的知识技能分享；从分享业务的模式看，主要有悬赏制、招标制、雇佣制和计件制等。本质上，知识技能的分享是通过互联网技术平台，把知识技能的需求方和提供者有效地对接起来的网络交易模式，互联网平台将闲置资源信息进行整合和加工，并将信息有针对性地与需求方进行对接；第三方支付平台同时连接了供给方、需求方和互联网平台进行金融服务从而获取服务报酬。资料来源：智研咨询整理知识技能分享经济交易模式共享知识技能市场发展分析07目前我国知识技能分享市场初具规模，2016年几乎每个月都有新的平台出现，因此被称为“知识付费元年”。继2016年知识技能分享平台异军突起后，2017年-2020年继续保持快速发展的态势。2020年共享知识技能市场规模达到4010亿元，较2019年的3063亿元同比增长30.9%，占共享经济总规模的11.9%。资料来源：国家信息中心、智研咨询整理13822353306340106.7%8.0%9.3.9%0.0%2.0%4.0%6.0%8.0.0.0.0001000150020002500300035004000450020172018201920202017-2020年中国共享知识技能行业市场规模共享知识技能市场规模（亿元）占共享经济市场规模比重共享知识技能市场发展分析07知识技能分享发展十分迅速，行业规模不断扩张，业务内容不断丰富，参与主体更加壮大，总体呈现出欣欣向荣的积极态势。2019年中国共享知识技能融资规模为314亿元，2020年中国共享知识技能融资规模为467亿元。资料来源：国家信息中心、智研咨询整理199266464314467050100150200250300350400450500201620172018201920202016-2020年中国共享知识技能融资规模融资规模（亿元）共享知识技能市场发展分析07互联网 与传统行业的结合在不同领域产生了一批垂直型分享平台。目前国内领先的知识技能共享平台主要有知乎、在行、小鹅通、自客等。资料来源：智研咨询整理平台简介知乎知乎是中文互联网高质量的问答社区和创作者聚集的原创内容平台，以问答业务为基础，经过近十年的发展，已经承载为综合性内容平台，覆盖“问答”社区、全新会员服务体系“盐选会员”、机构号、热榜等一系列产品和服务，并建立了包括图文、音频、视频在内的多元媒介形式。截至2020年，已有超过4000万名答主在知乎创作，全站问题总数超过4400万，回答总数超过2.4亿。在行在行是国内领先的知识技能共享平台，平台2015年由果壳网孵化，后获得腾讯、红杉、元璟等知名风险资本投资。平台目前已入驻万余名精选行家，覆盖行业经验、个人成长、职场规划、投资理财、市场公关、健康管理、生活方式等70余类话题。小鹅通小鹅通是一款集品牌营销、知识产品交付、用户管理和商业变现为一体的数字化工具，为有线上经营需求的企业提供一站式技术服务，助力企业完成数字化转型，至今已服务逾百万家客户，其中不乏吴晓波频道、十点读书、新东方、好未来等一线知名品牌。自客自客是国内领先的垂直于职场领域的知识技能共享平台，总部位于广州。在自客平台上，职场人可以畅享学习与分享的乐趣，通过覆盖技术、设计、运营、产品、市场、职能等领域的知识技能学习，提升个人的职场竞争力。自客成立不到一年已获得国内知名机构高榕资本、火山石资本投资，BOSS直聘战略投资。圈乎圈乎是由武汉悠然一指网络有限公司开发的一款资源社群平台，圈乎依托社群经济模式，吸引相同兴趣爱好的用户，汇聚几十个行业的优秀达人来实现资源知识共享与变现，构建出一个跨行业、多元化、创新型的资源分享平台。资料来源：招股说明书、智研咨询整理04知乎成立于2010年，当时为邀请制问答，并不向大众开放，直至2013年，才开始向大众开放，2016年开展广告业务，注册用户也不断攀升，2018年推出付费内容并于2019年引入“盐选”会员计划，2021年3月知乎向美国证券交易委员会(SEC)提交了IPO(首次公开发行)申请，并成功上市。发展历程共享知识技能市场发展分析072010年知乎成立，为邀请制问答社区2013年面向公众开放，注册用户攀升2015年注册用户超2000万人2016年开展广告业务2017年注册用户达1.2亿人2018年推出付费内容2019年引入“盐选”会员计划2020年推出内容商务解决方案2021年纽交所上市成功资料来源：招股说明书、智研咨询整理042020年知乎营业收入为13.52亿元，较2019年同比增长101.7%；毛利润为7.58亿元，较2019年同比增长142.7%；净亏损为10.04亿元，较2019年同比增长94.0%。经营情况共享知识技能市场发展分析076.7113.523.127.585.1810.040.002.004.006.008.0010.0012.0014.0016.00201920202019-2020年知乎营业收入、毛利润及净亏损情况（亿元）营业收入毛利润净亏损知乎收入主要由广告、付费会员和商务解决方案构成，2020年知乎广告业务营业收入为84328.4万元，占总营业收入的62.36%；会员付费营业收入为32047.1万元，占总营业收入的23.70%。广告,57742.4付费会员,8799.7内容商务解决方案,64.1其他,444.9广告,84328.4付费会员,32047.1内容商务解决方案,13581.3其他,5262.82019-2020年知乎营业收入来源分布（万元）2019年2020年资料来源：招股说明书、智研咨询整理04知乎目前保持了MAU的高速增长，2019年知乎平均月活跃用户数量为48百万人，平均每月付费会员数量为0.57百万人；2020年知乎平均月活跃用户数量为68.5百万人，平均每月付费会员数量为2.36百万人。经营情况共享知识技能市场发展分析0748.068.50.572.360.010.020.030.040.050.060.070.080.0201920202019-2020年知乎平均月活跃用户及平均每月付费会员数量平均月活跃用户数量（百万人）平均月付费会员（百万人）资料来源：智研咨询整理04随着互联网与餐饮业的深度融合，在线外卖行业迎来发展的高峰。2016-2020年中国在线外卖交易规模不断扩大，虽增速不断放缓，但仍有一定上升空间，2019年中国在线外卖交易规模达到5980.2亿元，较2018年同比增长28.44%；2020年中国在线外卖交易规模约为6561.5亿元。在线外卖市场发展分析081646.83012.94656.05980.26561.582.96T.54(.44%9.72%0.00.00 .000.00.00P.00.00p.00.00.00%0.01000.02000.03000.04000.05000.06000.07000.0201620172018201920202016-2020年中国在线外卖交易规模在线外卖交易规模（亿元）增速外卖产业的持续快速增长，推动了线上线下融合发展，拓宽了消费应用场景，为餐饮行业发展注入了新动能，在线外卖收入占全国餐饮业收入比重不断加重，2020年占比为16.6%，较2019年增加了3.8%。4.6%7.6.9.8.6%0.0%2.0%4.0%6.0%8.0.0.0.0.0.0 1620172018201920202016-2020年中国在线外卖收入占全国餐饮业收入比重占比资料来源：国家信息中心、智研咨询整理042020年3月，主要受新冠疫情影响，外卖用户规模有所下降，但随着新冠疫情的有效控制以及美团、饿了么加平台速向下沉市场扩张，截止2020年12月中国在线外卖用户规模达到4.19万人，较2020年3月增加了0.21亿人。在线外卖市场发展分析08资料来源：CNNIC、智研咨询整理04巨大的市场增长潜力吸引了众多电商企业踏入外卖行业，数据显示，2019年，我国餐饮外卖行业共发生14起融资事件，北京市、上海市、杭州市等一线城市和新一线城市餐饮外卖的资本吸引力更强。在线外卖市场发展分析08资料来源：智研咨询整理时间公司名称轮次金额投资方2018/9/30有得食天使轮未透露何伯权2018/10/28隐食动力天使轮数千万人民币未透露2018/12/31黄小递Pre-A轮数千万人民币未透露2019/4/15隐食动力Pre-A轮数百万美元SIG海纳亚洲（领投）、凌波资本（财务顾问）2019/4/16餐道A轮1亿人民币海阔天空创投（领投）、基汇资本（领投）、MFund魔量基金2019/7/15黄小递A轮近亿人民币锐盛投资AresManagement2019/9/12酒小二Pre-A轮数千万人民币厚润德基金2020/7/3酒小二战略投资未透露腾讯投资2020/11/2七点七十Pre-A轮1000万人民币喜普科技2020/12/1酒小二A轮未透露腾讯投资、红杉资本中国04在线餐饮外卖市场经历了从百花齐放到美团、饿了么、百度外卖的“三足鼎立”，再到美团、饿了么“双雄争霸”的局面。餐饮外卖市场的融资也主要集中在头部平台。在线外卖市场发展分析08资料来源：智研咨询整理平台简介营业收入美团外卖美团外卖是美团旗下网上订餐平台，于2013年11月正式上线，总部位于北京。合作商户数超过200万家，活跃配送骑手超过50万名，覆盖城市超过1300个，日完成订单2100万单。2020年，美团外卖营业收入为662.7亿元。美团外卖营业收入（亿元）饿了么饿了么是2008年创立的本地生活平台，主营在线外卖、新零售、即时配送和餐饮供应链等业务。饿了么在线外卖平台覆盖全国670个城市和逾千个县，在线餐厅340万家，旗下“蜂鸟”即时配送平台的注册配送员达300万。2018年4月，阿里巴巴联合蚂蚁金服对饿了么完成全资收购，饿了么全面汇入阿里巴巴推进的新零售战略，拓展本地生活服务新零售的全新升级。饿了么口碑营业收入（亿元）1.753.0210.3381.4548.4662.70.0200.0400.0600.0800.02015 2016 2017 2018 2019 资料来源：招股说明书、智研咨询整理美团餐饮外卖主要提供平台配送和商家自行配送两种配送模式，前者占主要地位，其订单量约为60%-70%。在平台配送模式下，美团的佣金率约为15%-25%，而商家自行配送模式下，美团的佣金率约为5%-10%。商业模式04在线外卖市场发展分析08美团餐饮外卖商业模式资料来源：公司财报、智研咨询整理餐饮外卖于2020年新冠肺炎疫情期间逐渐成为了一项不可或缺的服务，美团外卖在消费端、商家端及配送网络方面的优势仍然保持强劲，并于2020年继续产生庞大的网络效益，促使其业务实现稳健增长。2020年美团餐饮外卖业务交易笔数为101.47亿笔，交易金额为4889亿元。经营情况04在线外卖市场发展分析086.3715.8540.963.9387.22101.47156587171128283927488901000200030004000500060000204060801001202015201620172018201920202015-2020年美团外卖交易数量及金额餐饮外卖交易笔数（亿笔）交易金额（亿元）2019年美团实现营业收入975.3亿元，其中餐饮外卖营业收入为548.4亿元，占总营业收入的56.23%；2020年美团实现营业收入1147.9亿元，其中餐饮外卖营业收入为662.7亿元，占总营业收入的57.73%。40.2129.9339.3652.3975.31147.91.753.0210.3381.4548.4662.74.35.81a.99X.48V.23W.73%0.00.00 .000.00.00P.00.00p.00%0.0200.0400.0600.0800.01,000.01,200.01,400.02015201620172018201920202015-2020年美团餐饮外卖营业收入及占比收入（亿元）餐饮外卖营业收入（亿元）占比资料来源：公司财报、智研咨询整理美团餐饮外卖的收入来源包括佣金、在线营销服务以及其他服务和销售，尽管佣金占外卖收入的比例逐年降低，但短期来看，佣金仍将是其主要收入来源。2020年餐饮外卖佣金收入为585.9亿元，占公司总营业收入的51.04%。经营情况04在线外卖市场发展分析08其他485.342.3%佣金585.951.0%在线营销服务75.76.6%其他服务及销售1.10.1%餐饮外卖662.757.7 20年美团餐饮外卖营业收入分布（亿元）共享经济未来发展趋势预测共享经济市场规模预测01疫情冲击下，5G、人工智能、物联网等技术得到更广泛应用，推动线上线下加速融合，共享型服务和消费新业态、新模式发展加速，成为提升经济韧性和活力的重要力量，预计2021年中国共享经济市场规模增速将有较大回升，速度有望达到10%-15%；未来五年，我国共享经济的年均增速将保持在10%以上。资料来源：智研咨询整理3.84.094.464.935.486.24012345672021202220232024202520262021-2026年中国共享经济市场规模预测市场规模（亿元）共享经济市场整体发展趋势02数字经济时代，共享经济商业模式较之传统商业模式具有更高的适应性，目前在我国，共享经济的商业模式已广泛渗透到从消费到生产的各产业环节，正有力地推进着产业创新与转型升级。随着互联网 时代，移动终端、物联网和云计算的发展，为新商业模式创新与广泛应用提供了更多机遇。未来几年，在理念创新、技术创新、模式创新和制度创新的共同作用下，我国共享经济将呈现出一些新的发展趋势。资料来源：智研咨询整理1共享型消费新业态新模式将在构建双循环新发展格局发挥重要作用2对平台经济进行科学有效的反垄断监管成为大势所趋3国内外环境的深刻变化将使得平台企业国际化扩张面临更大挑战和风险整体发展趋势

37人已浏览 2021-09-15 71页 5星级

总体来看，我国公共数据开放工作有摩推避，建立公共数据开放平台只是刚起步，后续如何开放更多、更高价值的数据，让公共数据活起来、用起来才是重申之重。政企数据共享的特点是由政府主导型向政府合作型转变。第一类是搭建政企数据共享平台，政企优势互补、各取所需。第二类是政府数据授权运营。辽宁省市场监督管理局与美团点评集团签署食品安全战略合作协议，开展数据对接项目。南京市雨花台区与苏宁物流联手打造政企数据交互共享平台。重庆市成立了数字重庆大数据应用发展有限公司。国家卫健委、山东省政府、济南市政府和浪潮签署合作协议，将山东全省健康数据授权给浪潮运营。北京市开设金融公共数据专区，市经信局与北京金控集团签署北京市金融公共数据专区授权运营协议，通过市场手段推动政府公共数据在金融烫域的社会化应用。以互联网平台企业为例，以平台为纽带形成了数据开发利用生态，平台与平台上的商户，以及产业链上下游企业之间的数据共享往往进行内部约定或通过签订协议方式进行点对点提供，平台企业的数据定制化服务也仅对生态内企业进行提供，很少对外公布其数据开发利用方式。因此，企业间数据共享存在隐蔽、不透明等特点，亟待规范发展。将掌握的数据变现是企业数据开放追求的目标，包括提供金融数据的万得、提供企业信用数据的天眼查、企查查，以及提供股市投资数据的同花顺等等，这些企业均在细分行业领域内以其特色化的数据服务获得了大批用户，这些企业的数据开放已经完全是市场化行为。建立健全网络数据安全管理体系，针对不同重要程度信息的企业进行分级分类管理。加快推进数据安全法个人信息保护法等数据安全法律法规“以案释法工作。研究数据垄断监管机制，规范平台数据垄断竞争行为，引导市场合理有序竞争。

285人已浏览 2021-09-13 26页 5星级

说到共享经济，所有人的目光都应该放在拉丁美洲。该地区的数字化滞后，加上对政府机构的普遍低信任和发展中经济体固有的问题，如工资低和腐败，使其特别容易接受创新解决方案。同时慢慢产生自己的科技初创公司(例如 Rappi，一个目前正在发展为多种服务的哥伦比亚交付平台，或 Loggi，巴西的交付服务)，拉丁美洲也一直在以对消费者友好的方式对现有平台进行监管。根据 Americas Market Intelligence(AMI)2019 年进行的一项研究，56%的墨西哥千禧一代更喜欢 Airbnb 公寓和住宅而不是酒店。到 2020 年，三个拉丁美洲城市-瓜达拉哈拉(墨西哥)、卡利(哥伦比亚)和乌巴图巴(巴西)分别-进入了 Airbnb 的年度前 20 名目的地名单。Uber 等拼车应用程序也一直在充分利用这个机会来扩展其服务并为拉丁美洲消费者带来经济繁荣。Uber 认识到某些社会群体没有信用卡和/或无法访问互联网允许使用现金支付并开发了 Uber Lite，这是一款为欠发达地区的用户量身定制的应用程序版本。作为一个全球消费者权益倡导组织，我们消费者选择中心认为消费者的选择至关重要，为消费者带来价值的服务无论是优步、AirBnB、ShareNow 还是电动滑板车都应该得到认可和鼓励。受我们第一个共享经济指数和拉美最旅客友好机场指数的启发，我们检查了 44 个拉丁美洲城市，看看哪些城市对共享经济最友好。2021 年拉丁美洲共享经济指数旨在对拉丁美洲 44 个最大和最具活力的城市进行排名，为消费者提供可用的共享经济服务的宝贵指南。对于大多数国家，排名包括首都和第二大城市。但由于阿根廷、墨西哥、巴西和哥斯达黎加经济发展和政治制度的特殊性，我们也纳入了更多的城市。为了在拉丁美洲完成这项任务，消费者选择中心与两个区域网络合作：Somos Innovacion(Sl)是由一群个人和机构组成的网络，从墨西哥到阿根廷和智利，他们坚信创新解决方案是激励人们共同努力解决复杂问题的最佳方式，Sl 希望成为充满活力的公民的声音通过创新、采用新技术和人类创造力而进步的社会。

42人已浏览 2021-08-16 13页 5星级

渗透率仍有进一步提升空间，总市场空间可达数百亿行业快速增长趋势延续，单价上涨、单量扩增、下沉市场渗透成主要驱动力。2017 年至2020 年，共享充电宝场景渗透率大幅度提升，处于行业营业收入规模快速增长的阶段。租赁单价上涨，总体单量扩增，推动市场持续扩张。2017 年-2020 年，共享充电宝用户规模呈逐年扩张趋势，由此带来整个市场总体单量上升。对现有市场规模按城市发达程度进行拆分，可以发现下沉市场渗透率较低，潜力较大。三、四线城市中心地区(如商业区、交通枢纽)已占据一定市场，但机柜密度不够。底线城市大量小品牌占据市场份额，底线城市渗透率增长有望带动总体订单增量。我们在现有市场规模下，参考当下市场格局和运营状况，对未来的市场空间进行测算。随着疫情的负面影响逐步消失，行业在 2021 年预计会恢复快速增长趋势。未来推动行业市场规模增长的主要因素在于对下沉市场的渗透、一二线城市精细化运营形成对市场的扩容。以共享充电宝的营业收入来计算市场规模，预计 2021 年至 2025 年，年均复合增长率为20.8%。竞争演变探讨：重资产 弱场景掌控能力，决定了行业 2B 属性更强，寡头博弈之下求盈利成为行业共识，怪兽充电有望占据一席之地用户数增长放缓之时，扩张与盈利二者需居其一用户数增长放缓，行业逐渐进入存量竞争阶段。刚性需求和潜在用户是行业发展的基石，对充电宝的低频应急需求，使得用户较少计较租赁价格，因此共享充电宝的触发条件很高。但根据艾瑞咨询数据，共享充电宝用户规模增长率逐年下降，至 2020 年，预测增长率仅为 15.6%，获取新用户的难度变大。同时，收费标准不一等现象会影响用户体验，影响行业口碑，导致用户流失。行业整体增速放缓，逐渐从增量竞争开始进入存量竞争的阶段。19 年下半年行业提价后，租借时长缩短，但是整体客单价进一步提升，培养了用户习惯，赋予了运营商一定的议价能力，由于盈利成为寡头博弈下的共识，头部企业陆续涨价，从2019 年 9 月的 2 元/小时涨至 3-4 元/小时。由资本主导的运营主体，扩张和盈利二者需居其一。行业目前的商业模式以直营和服务商为主，直营模式是行业的主流模式，具有重资产特性，因此资本实力是行业发展期的厂商核心竞争力。由于技术门槛相对较低，在行业内部同质化竞争下，为了尽可能多且快地抢占市场份额，运营商不得不尽可能地满足商家对分成的要求，怪兽充电的商家“入场费”从 2019 年的 1.06 亿元增加至 2020 年的 3.8 亿元，大涨 260%;支付给合作伙伴的佣金也从 2019 年的 8.22 亿元增加至 2020 年的 11.96 亿元，增加 45.5%，运营成本大幅上升，利润缩水，试图挤出竞争失败的企业，直至市场形成寡头垄断或者垄断，获得最终定价权。当前平台的主要盈利方式是围绕产品自身租赁费用以及广告收入，未来平台将进一步加强共享充电宝服务入口能力，实现对商家价值的赋能，同时提升企业营收与价值。在行业的高速增长中，厂商对于优质点位和核心场景的争夺也一再升级，以深化多元场景布局为目标，进一步提升业务覆盖广度和密度。

445人已浏览 2021-06-24 19页 5星级

业务增长飞轮：哈啰增长飞轮属于典型的高频业务带低频业务，即共享两轮车业务作为高频入口，带动拼车服务、电动车销售等移动出行业务发展，进一步再拓展至酒店预订等本地生活服务。以拼车为例，2019年才起步的拼车业务，2020毛利率达81%，贡献总毛利的53%。用户转化：共享单车作为流量入口业务，为其他业务提供大量增量用户，比如电单车新用户中60.5%由共享单车车用户转化而来。同时，其他业务也为共享单车提供新用户，8.4%共享单车新用户由其他业务用户转化而来。34%用户平均使用两项及以上哈啰服务，使得哈啰用户粘性、留存高，一年留存率64%，两年留存率60%。从移动出行到本地服务市场。2020年移动出行市场规模3万亿，本地服务市场规模达19.5万亿元。移动出行服务为本地服务市场中消费频率最高的细分行业，相关APP每周使用6.8次，具有极高的入口价值。哈啰充分利用两轮车流量入口作用，目前业务已从出行市场拓展至酒旅预定等本地服务市场。

207人已浏览 2021-06-16 26页 5星级