亚开行-数字金融能否推动低碳转型？来自中华人民共和国的证据（英）-2023-WN7.pdf

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1、 ADBI Working Paper Series CAN DIGITAL FINANCE PROMOTE LOW-CARBON TRANSITION?EVIDENCE FROM THE PEOPLES REPUBLIC OF CHINA Xing Ge and Tomoki Fujii No.1399 July 2023 Asian Development Bank Institute The Working Paper series is a continuation of the formerly named Discussion Paper series;the numbering

2、of the papers continued without interruption or change.ADBIs working papers reflect initial ideas on a topic and are posted online for discussion.Some working papers may develop into other forms of publication.The Asian Development Bank refers to“China”as the Peoples Republic of China.Suggested cita

3、tion:Ge,X.and T.Fujii.2023.Can Digital Finance Promote Low-Carbon Transition?Evidence from the Peoples Republic of China.ADBI Working Paper 1399.Tokyo:Asian Development Bank Institute.Available:https:/doi.org/10.56506/FMXX6317 Please contact the authors for information about this paper.Email:,tfujii

4、smu.edu.sg Xing Ge is a joint training PhD student at Xian Jiaotong University and Singapore Management University.Tomoki Fujii is Associate Dean(Undergraduate Curriculum)and Associate Professor of Economics at the School of Economics,Singapore Management University.The views expressed in this paper

5、 are the views of the author and do not necessarily reflect the views or policies of ADBI,ADB,its Board of Directors,or the governments they represent.ADBI does not guarantee the accuracy of the data included in this paper and accepts no responsibility for any consequences of their use.Terminology u

6、sed may not necessarily be consistent with ADB official terms.Discussion papers are subject to formal revision and correction before they are finalized and considered published.Asian Development Bank Institute Kasumigaseki Building,8th Floor 3-2-5 Kasumigaseki,Chiyoda-ku Tokyo 100-6008,Japan Tel:+81

7、-3-3593-5500 Fax:+81-3-3593-5571 URL:www.adbi.org E-mail:infoadbi.org 2023 Asian Development Bank Institute ADBI Working Paper 1399 Ge and Fujii Abstract Using panel data of cities in the Peoples Republic of China from 2011 to 2019,this paper analyzes the impact of digital finance on low-carbon tran

8、sition derived from a super-efficiency slacks-based measure data envelopment analysis.We find that digital finance promotes low-carbon transition,and this finding is robust with respect to the choice of sample,potential presence of measurement issue,choice of study period,presence of other policies,

9、and potential endogeneity,among others.This impact,at least in part,is through increased green innovations.We also find evidence for impact heterogeneity across locations and by the level of low-carbon transition.This paper provides policy implications for the low-carbon transition of the region fro

10、m a digital finance perspective.Keywords:digital finance,low-carbon transition,green innovation,slacks-based measure data envelopment analysis JEL Classification:G20,Q54,Q55 ADBI Working Paper 1399 Ge and Fujii Contents 1.INTRODUCTION.1 2.LITERATURE REVIEW.3 3.DATA,METHODOLOGY,AND EMPIRICAL MODEL.4

11、3.1 Data Sources.4 3.2 Measurement of Low-Carbon Transition.4 3.3 Measurment of Digital Finance and Control Variables.7 3.4 Spatial Distribution of Key Variables.8 3.5 Empirical Model.9 4.EMPIRICAL RESULTS.9 4.1 Baseline Results.9 4.2 Robustness Checks.10 4.3 Green Innovation as a Channel of Impact.

12、15 5.IMPACT HETEROGENEITY.17 6.CONCLUSIONS AND POLICY IMPLICATIONS.18 REFERENCES.19 APPENDIX.23 ADBI Working Paper 1399 Ge and Fujii 1 1.INTRODUCTION With the development of information technology,digital financewhich refers to the use of digital technologies in the provision of or access to financi

13、al serviceshas grown rapidly in recent years.Digital finance is an important factor influencing the economy,finance,and energy(Zhang,Jin,and Wang 2015)and may enable a higher level of consumption and promote inclusive development,for example,through increased availability of loans for small and medi

14、um-sized enterprises and vulnerable groups.Digital finance has also contributed to green innovation and reduced pollution(Meng and Zhang 2022;Zhang and Ling 2022).Digital finance can be expected to play an important role in low-carbon transition,or a shift towards lower emissions of pollutants(Chen

15、2012).This is because the key driver of low-carbon transition is green innovation,which requires substantial financial support from the financial sector.Nevertheless,the impact of digital finance on low-carbon transition has been underexplored in the existing literature.This study fills this researc

16、h gap.Digital finance may affect low-carbon transition by contributing to green innovation through the provision of funding for green and clean projects.This is possible,since digital finance may absorb funds from long-tail groups,1 thereby reducing borrowing costs for firms and individuals and faci

17、litating green innovation projects with potentially high risks and long payback cycles,which are typically excluded from traditional finance.Our findings are indeed consistent with the relevance of green innovation.There are at least three additional theoretical channels through which digital financ

18、e can affect low-carbon transition.First,digital finance includes some ecological restoration projects(such as Alipays Ant Forest),which aim to encourage the public to reduce carbon emissions.Second,digital finance facilitates the green consumption of disadvantaged groups by providing them with fund

19、s that contribute to low-carbon transition.Finally,digital finance breaks through time and space constraints and reduces transaction costs for consumption.While these three channels are potentially important,the analysis of these channels is beyond the scope of this paper due to the lack of availabl

20、e data.The discussion above merely suggests the possible causal channel running from digital finance to low-carbon transition,and whether digital finance indeed influences low-carbon transition is an empirical question.Thus,we explore this question using panel data from 283 cities in the Peoples Rep

21、ublic of China(PRC)between 2011 and 2019.There are three important reasons why we study cities in the PRC.First,the PRC is the second largest economy and the largest developing country in the world.Further,the PRC is already highly urbanized with 63%of the population living in urban areas in 2020.Gi

22、ven the number of large cities in the PRC and the continuing trend of urbanization,cities in the PRC are of interest to study.Second,the PRC is the largest carbon emitter in the world,accounting for more than 30%of the worlds carbon emissions from fossil fuels and industry but without accounting for

23、 land use change,according to the Global Carbon Atlas.Finally,cities are the basic unit for policy implementation in the PRC and play a vital role in reaching peak carbon emissions.With 70%of global carbon emissions coming from cities,cities are also relevant to the analysis of green transition both

24、 inside and outside of the PRC.1 The long-tail group refers to individuals or small businesses with relatively small financial assets but large numbers.ADBI Working Paper 1399 Ge and Fujii 2 Measuring digital finance and low-carbon transition is critical in this study.For the measurement of the form

25、er,this paper employs the Peking University Digital Financial Inclusion Index of China(PKU_DFIIC),which provides an overall index for digital finance as well as its subindices for coverage breadth,usage depth,and digitization level.To measure low-carbon transition,we use the technical efficiency mea

26、sure derived from unoriented slacks-based measure data envelopment analysis(SBM-DEA)and its super-efficiency counterpart with undesired outputs.The technical efficiency measure tends to be higher when a city uses fewer inputs and produces more desired outputs and fewer undesired outputs compare to o

27、ther cities.Using these measures,we regress the low-carbon transition on the digital finance index and other control variables.The baseline regression results indicate that digital finance significantly accelerates low-carbon transition.This conclusion is robust with respect to the exclusion of the

28、four direct-administered municipalities,exclusion of certain outliers,changes in the study period,and inclusion of potentially confounding policies.Further,addressing the potential endogeneity of digital finance by a type of shift-share instrument variable(SSIV)also does not change the results.We ar

29、gue that this is a plausibly valid instrument,because the inverse of the spherical distance between a city and Hangzhou is positively correlated with digital finance on the one hand and the inverse of the spherical distance between a city and Hangzhou is largely irrelevant to the low-carbon transiti

30、on on the other.In addition,based on the approach proposed by Conley,Hansen,and Rossi(2012),this paper finds that the positive effect of digital finance on the low-carbon transition is robust with respect to a modest violation of the exclusion restriction.We then analyze the mechanisms through which

31、 digital finance influences low-carbon transition.The results indicate that digital finance drives low-carbon transition at least in part by promoting green innovation,which includes all types of innovations that enable the production of goods and services while reducing or removing undesirable impa

32、cts on the environment and natural resources.We also analyze the impact heterogeneity with respect to various city characteristics.This analysis suggests that digital finance in cities to the east of the HeiheTengchong linea hypothetical line that extends from the city of Heihe in the northeast to t

33、he city of Tengchong in the southwestpromoted low-carbon transition,but this is not the case for cities to the west of this line.We also find that digital finance only facilitates low-carbon transition in cities above the median low-carbon transition.There are three innovations in this paper.First,p

34、revious studies typically ignored the presence of potential endogeneity concerns.We propose a new type of SSIV for digital finance defined as the product between the inverse of the spherical distance between the city and Hangzhou multiplied by the PRC digital finance index for each year.As elaborate

35、d subsequently,this IV is plausibly exogenous and our results are robust to a modest violation of the exogeneity of the SSIV.Second,this paper analyzes whether the impact of digital finance on low-carbon transition is heterogeneous across cities with different low-carbon transition levels,a point th

36、at has also been previously ignored.Third,unlike previous studies,this paper offers a granular analysis of green innovation as a channel through which digital finance affects low-carbon transition by dividing it into low-level and high-level innovations.The paper is structured as follows.Section 2 r

37、eviews the related literature.Section 3 presents the data,methods,and model.Section 4 shows the empirical results and analysis.Section 5 presents the heterogeneity analysis.Finally,Section 6 offers conclusions and policy implications.ADBI Working Paper 1399 Ge and Fujii 3 2.LITERATURE REVIEW This pa

38、per is related to the body of literature on digital finance.In the early literature,scholars assessed the impacts of digital finance on economic outcomes,such as entrepreneurship(Xie et al.2018),economic growth(Qian et al.2020),and income disparity(Ji et al.2021).More recently,studies have examined

39、the environmental effects of digital finance.For example,Wan,Pu,and Tavera(2023)find a significant negative relationship between digital finance and pollutant emissions.Fu et al.(2023)used PRC provincial data to find an inverted U-shaped effect of digital finance on energy efficiency.In particular,t

40、his paper adds to the literature on the analysis of the impact of digital finance on carbon emissions and green economy efficiency.Digital finance has been found to reduce carbon emissions in the PRC based on provincial data by Zhao et al.(2021)and city-level data by Wang and Guo(2022).Wang et al.(2

41、022)identified that digital finance improves green economy efficiency by strengthening credit constraints on highly polluting firms.This study also contributes to the literature on the factors influencing low-carbon transition.Existing studies have examined various factors affecting low-carbon trans

42、ition,such as industrial structure(Wang et al.2019),industrial agglomeration(Zhang et al.2019),technological innovation(Liu and Zhang 2021),green innovation(Zhang and Liu 2022),green bonds(Sartzetakis 2021),and green credit(Liu et al.2022b).We complement this literature by examining green innovation

43、 as one of the key channels through which digital finance promotes low-carbon transition.This study also carefully constructs a measure of low-carbon transition by adopting the(super-efficiency)SBM-DEA.This is an important point because the measurement can potentially affect our results.We employ th

44、e(super-efficiency)SBM-DEA with undesired outputs,since it allows us to compute the total factor efficiency,taking into consideration not only the inputs and desired outputs but also emissions(undesired outputs).This is in contrast to single-factor efficiency measures,such as per capita carbon emiss

45、ions(Zheng et al.2019),per capita energy consumption(Truong,Wiktor,and Boxall 2015),and carbon emissions per unit GDP(Liu et al.2019).Since single-factor efficiency cannot fully reflect the multiple outcomes we are interested in,we argue that the total-factor efficiency in the DEA approach is more s

46、uitable.The DEA approach also has an advantage over parametric approaches,such as the stochastic frontier analysis,because we do not need to assume a particular form of production function.Some other studies use the index system method to measure low-carbon transition.Tan et al.(2017)used the entrop

47、y weight method to construct a low-carbon economic index that reflects seven dimensions of(i)economic development,(ii)energy pattern,(iii)society and life,(iv)carbon and environment,(v)urban transportation,(vi)solid waste,and(viii)water.Deng and Yang(2019)applied the entropy weight method to constru

48、ct an industrial low-carbon transition index from five dimensions of(i)resource saving,(ii)pollution reduction,(iii)industrial upgrading,(iv)productivity improvement,and(v)development sustainability.Sun et al.(2020)built a sustainable development indicator from the three dimensions of(i)environment,

49、(ii)energy,and(iii)economy,and evaluated the sustainable development performance of South Asia.Huang et al.(2022)adopted entropy-weighted TOPSIS to comprehensively evaluate the level of green and low-carbon development from the three dimensions of(i)green benefits,(ii)low-carbon benefits,and(iii)eco

50、nomic and social benefits.We also consider a similar entropy-weighted index as an alternative measure of low-carbon transition,ADBI Working Paper 1399 Ge and Fujii 4 even though our preferred measure of low-carbon transition is based on the(super-efficiency)SBM-DEA.As shown subsequently,the current