Abstract
Digital inclusive finance has an essential impact on improving the urban green economy efficiency by demonstrating environmental friendliness in agglomerating factors and promoting the flow of factors. Based on the panel data of 284 cities in China from 2011 to 2020, this paper uses the super-efficiency SBM model with undesirable outputs to measure the urban green economy efficiency. Then, the fixed effect model and spatial econometric model of panel data are used to empirically test the impact of digital inclusive finance on urban green economic efficiency and its spatial spillover effect, and the heterogeneity analysis is carried out. This paper draws the following conclusions. (1) The average value of urban green economic efficiency of 284 Chinese cities from 2011 to 2020 is 0.5916, showing a “high in the east and low in the west.” In terms of time, it showed a rising trend year by year. (2) Digital financial inclusion and urban green economy efficiency have a high spatial correlation, both showing “high-high” and “low-low” agglomeration characteristics. (3) Digital inclusive finance significantly impacts urban green economic efficiency, especially in the eastern region. (4) The impact of digital inclusive finance on urban green economic efficiency has a spatial spillover effect. In the eastern and central regions, digital inclusive finance will inhibit the improvement of urban green economic efficiency in adjacent cities. In contrast, it will promote urban green economy efficiency in the western regions in adjacent cities. (5) The coverage and depth of digital inclusive finance significantly affect the urban green economy efficiency, while the level of digitization has yet to show a significant effect. This paper puts forward some suggestions and references for promoting the coordinated development of digital inclusive finance in various regions and improving urban green economic efficiency.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The data that support the findings of this study are available from www.cnki.net.
Change history
17 April 2023
Minor formatting correction in Table 4, 6 and 8.
References
Anselin L, Getis A (1992) Spatial statistical analysis and geographic information systems. Ann Reg Sci 26(1):19–33
Bai Y, Hua C, Jiao J, Yang M, Li F (2018) Green efficiency and environmental subsidy: evidence from thermal power firms in China. J Clean Prod 188:49–61. https://doi.org/10.1016/j.jclepro.2018.03.312
Beck T, Demirgüç-Kunt A, Levine R (2007) Finance, inequality and the poor. J Econ Growth 12(1):27–49. https://doi.org/10.1007/s10887-007-9010-6
Bollaert H, Lopez-de-Silanes F, Schwienbacher A (2021) Fintech and access to finance. J Corp Financ 68. https://doi.org/10.1016/j.jcorpfin.2021.101941
Boukhatem J (2016) Assessing the direct effect of financial development on poverty reduction in a panel of low- and middle-income countries. Res Int Bus Financ 37:214–230. https://doi.org/10.1016/j.ribaf.2015.11.008
Brito VTF, Ferreira FAF, Pérez-Gladish B, Govindan K, Meidutė-Kavaliauskienė I (2019) Developing a green city assessment system using cognitive maps and the Choquet Integral. J Clean Prod 218:486–497. https://doi.org/10.1016/j.jclepro.2019.01.060
Chai J, Hao Y, Wu H, Yang Y (2021) Do constraints created by economic growth targets benefit sustainable development? Evidence from China. Bus Strateg Environ 30(8):4188–4205. https://doi.org/10.1002/bse.2864
Chen Y, Xu JT (2019) An assessment of energy efficiency based on environmental constraints and its influencing factors in China. Environ Sci Pollut Res 26:16887–16900. https://doi.org/10.1007/s11356-018-1912-7
Cheng M-Y, Wang S, Xia L, Zhang X (2022) Testing specification of distribution in stochastic frontier analysis. J Econom. https://doi.org/10.1016/j.jeconom.2022.03.002
Cohen G, Jalles JT, Loungani P, Marto R, Wang G (2019) Decoupling of emissions and GDP: evidence from aggregate and provincial Chinese data. Energy Econ 77:105–118. https://doi.org/10.1016/j.eneco.2018.03.030
Corrado G, Corrado L (2017) Inclusive finance for inclusive growth and development. Curr Opin Environ Sustain 24:19–23. https://doi.org/10.1016/j.cosust.2017.01.013
Ding L, Yang Y, Wang L, Calin AC (2020) Cross efficiency assessment of China’s marine economy under environmental governance. Ocean Coastal Manag 193. https://doi.org/10.1016/j.ocecoaman.2020.105245
Ding R, Shi F, Hao S (2022) Digital inclusive finance, environmental regulation, and regional economic growth: an empirical study based on spatial spillover effect and panel threshold effect. Sustainability 14(7). https://doi.org/10.3390/su14074340
Du L, Tian M, Cheng J, Chen W, Zhao Z (2022a) Environmental regulation and green energy efficiency: an analysis of spatial Durbin model from 30 provinces in China. Environ Sci Pollut Res Int 29(44):67046–67062. https://doi.org/10.1007/s11356-022-20551-0
Du L, Wang F, Tian M (2022b) Environmental information disclosure and green energy efficiency: a spatial econometric analysis of 113 prefecture-level cities in China. Front Environ Sci 10. https://doi.org/10.3389/fenvs.2022.966580
Elhorst JP (2010) Spatial panel data models. Springer, Berlin Heidelberg
Elhorst JP (2014) Spatial econometrics: from cross-sectional data to spatial panels, vol 479. Springer, Heidelberg, pp 480. https://doi.org/10.1007/978-3-642-40340-8_3
Feng S, Chong Y, Li G, Zhang S (2022) Digital finance and innovation inequality: evidence from green technological innovation in China. Environ Sci Pollut Res Int. https://doi.org/10.1007/s11356-022-21826-2
Gao D, Li G, Yu J (2022) Does digitization improve green total factor energy efficiency? Evidence from Chinese 213 cities. Energy 247. https://doi.org/10.1016/j.energy.2022.123395
Gao K, Yuan Y (2022) Effects of industrial green total factor energy efficiency on haze abatement: a spatial econometric analysis based on China’s 272 cities. J Environ Manag 317:115399. https://doi.org/10.1016/j.jenvman.2022.115399
Ge T, Hao X, Li J (2021) Effects of public participation on environmental governance in China: a spatial Durbin econometric analysis. J Clean Prod 321. https://doi.org/10.1016/j.jclepro.2021.129042
Guo F, Wang J, Wang F, Cheng Z, Kong T, Zhang X (2020) Measuring the development of China’s digital financial inclusion: index compilation and spatial characteristics. Economics (quarterly) 19:1401–1418 (In Chinese)
Guo H, Gu F, Peng Y, Deng X, Guo L (2022) Does digital inclusive finance effectively promote agricultural green development?-A case study of China. Int J Environ Res Public Health 19(12). https://doi.org/10.3390/ijerph19126982
Hao X, Li Y, Ren S, Wu H, Hao Y (2022) The role of digitalization on green economic growth: does industrial structure optimization and green innovation matter? J Environ Manag 325(Pt A):116504. https://doi.org/10.1016/j.jenvman.2022.116504
He X, Shi J, Xu H, Cai C, Hu Q (2022) Tourism development, carbon emission intensity and urban green economic efficiency from the perspective of spatial effects. Energies 15(20). https://doi.org/10.3390/en15207729
Hong Y, Lyu X, Chen Y, Li W (2020) Industrial agglomeration externalities, local governments’ competition and environmental pollution: evidence from Chinese prefecture-level cities. J Clean Prod 277. https://doi.org/10.1016/j.jclepro.2020.123455
Hou S, Song L, Wang J, Ali S (2021) How land finance affects green economic growth in Chinese cities. Land 10(8). https://doi.org/10.3390/land10080819
Hu J, Liu Y, Fang J, Jing Y, Liu Y, Liu Y (2019) Characterizing pollution-intensive industry transfers in China from 2007 to 2016 using land use data. J Clean Prod 223:424–435. https://doi.org/10.1016/j.jclepro.2019.03.139
Huang L, Yang P, Zhang B, Hu W (2021) Spatio-temporal coupling characteristics and the driving mechanism of population-land-industry urbanization in the Yangtze River Economic Belt. Land 10(4). https://doi.org/10.3390/land10040400
Kihombo S, Saud S, Ahmed Z, Chen S (2021) The effects of research and development and financial development on CO2 emissions: evidence from selected WAME economies. Environ Sci Pollut Res Int 28(37):51149–51159. https://doi.org/10.1007/s11356-021-14288-5
Kuang Y, Lin B (2022) Natural gas resource utilization, environmental policy and green economic development: empirical evidence from China. Resour Policy 79. https://doi.org/10.1016/j.resourpol.2022.102992
Lampe HW, Hilgers D (2015) Trajectories of efficiency measurement: a bibliometric analysis of DEA and SFA. Eur J Oper Res 240(1):1–21. https://doi.org/10.1016/j.ejor.2014.04.041
Lee C-C, Wang F (2022) How does digital inclusive finance affect carbon intensity? Econ Anal Policy 75:174–190. https://doi.org/10.1016/j.eap.2022.05.010
Lee C-C, Chen M-P, Wu W, Xing W (2021) The impacts of ICTs on tourism development: International evidence based on a panel quantile approach. Inf Technol Tour 23(4):509–547. https://doi.org/10.1007/s40558-021-00215-4
Lee C-C, Yuan Z, Wang Q (2022) How does information and communication technology affect energy security? International evidence. Energy Econ 109. https://doi.org/10.1016/j.eneco.2022.105969
LeSage JP, Pace RK (2010) Spatial econometric models. In Handbook of Applied Spatial Analysis. Springer, Berlin, Heidelberg
Li J, Li B (2021) Digital inclusive finance and urban innovation: evidence from China. Rev Dev Econ 26(2):1010–1034. https://doi.org/10.1111/rode.12846
Li X, Shao X, Chang T, Albu LL (2022) Does digital finance promote the green innovation of China’s listed companies? Energy Econ 114. https://doi.org/10.1016/j.eneco.2022.106254
Li Z, Wang J (2022) The dynamic impact of digital economy on carbon emission reduction: evidence city-level empirical data in China. J Clean Prod 351. https://doi.org/10.1016/j.jclepro.2022.131570
Lin B, Ma R (2022) How does digital finance influence green technology innovation in China? Evidence from the financing constraints perspective. J Environ Manag 320:115833. https://doi.org/10.1016/j.jenvman.2022.115833
Lin B, Zhu J (2019) Fiscal spending and green economic growth: evidence from China. Energy Econ 83:264–271. https://doi.org/10.1016/j.eneco.2019.07.010
Liu H, Tang YM, Iqbal W, Raza H (2022a) Assessing the role of energy finance, green policies, and investment towards green economic recovery. Environ Sci Pollut Res Int 29(15):21275–21288. https://doi.org/10.1007/s11356-021-17160-8
Liu Y, Dong F (2021) How technological innovation impacts urban green economy efficiency in emerging economies: a case study of 278 Chinese cities. Resour Conserv Recycl 169. https://doi.org/10.1016/j.resconrec.2021.105534
Liu Y, Lei J, Zhang Y (2021) A study on the sustainable relationship among the green finance, environment regulation and green-total-factor productivity in China. Sustainability 13(21). https://doi.org/10.3390/su132111926
Liu Z, Vu TL, Phan TTH, Ngo TQ, Anh NHV, Putra ARS (2022b) Financial inclusion and green economic performance for energy efficiency finance. Econ Chang Restruct 55(4):2359–2389. https://doi.org/10.1007/s10644-022-09393-5
Lv C, Song J, Lee C-C (2022) Can digital finance narrow the regional disparities in the quality of economic growth? Evidence from China. Econ Anal Policy 76:502–521. https://doi.org/10.1016/j.eap.2022.08.022
Manta AG, Florea NM, Bădîrcea RM, Popescu J, Cîrciumaru D, Doran MD (2020) The nexus between carbon emissions, energy use, economic growth and financial development: evidence from central and eastern European countries. Sustainability 12(18). https://doi.org/10.3390/su12187747
Peng X (2020) Strategic interaction of environmental regulation and green productivity growth in China: green innovation or pollution refuge? Sci Total Environ 732:139200. https://doi.org/10.1016/j.scitotenv.2020.139200
Pu Z, Fei J (2022) The impact of digital finance on residential carbon emissions: evidence from China. Struct Chang Econ Dyn. https://doi.org/10.1016/j.strueco.2022.07.006
Qiu Y, Wang H, Wu J (2022) Impact of industrial structure upgrading on green innovation: evidence from Chinese cities. Environ Sci Pollut Res Int. https://doi.org/10.1007/s11356-022-22162-1
Raddatz C (2006) Liquidity needs and vulnerability to financial underdevelopment. J Financ Econ 80(3):677–722. https://doi.org/10.1016/j.jfineco.2005.03.012
Saeed Meo M, Karim MZA (2022) The role of green finance in reducing CO2 emissions: an empirical analysis. Borsa Istanbul Rev 22(1):169–178. https://doi.org/10.1016/j.bir.2021.03.002
Sethi P, Chakrabarti D, Bhattacharjee S (2020) Globalization, financial development and economic growth: perils on the environmental sustainability of an emerging economy. J Policy Model 42(3):520–535. https://doi.org/10.1016/j.jpolmod.2020.01.007
Sohag K, Taşkın FD, Malik MN (2019) Green economic growth, cleaner energy and militarization: evidence from Turkey. Resour Policy 63. https://doi.org/10.1016/j.resourpol.2019.101407
Song X, Zhou Y, Jia W (2019) How do economic openness and R&D investment affect green economic growth?—Evidence from China. Resour Conserv Recycl 146:405–415. https://doi.org/10.1016/j.resconrec.2019.03.050
Sun, Y, Tang X (2022) The impact of digital inclusive finance on sustainable economic growth in China. Financ Res Lett 50. https://doi.org/10.1016/j.frl.2022.103234
Tan Y, Xu H, Zhang X (2016) Sustainable urbanization in China: a comprehensive literature review. Cities 55:82–93. https://doi.org/10.1016/j.cities.2016.04.002
Tao X, Wang P, Zhu B (2016) Provincial green economic efficiency of China: a non-separable input–output SBM approach. Appl Energy 171:58–66. https://doi.org/10.1016/j.apenergy.2016.02.133
Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143(1):32–41. https://doi.org/10.1016/S0377-2217(01)00324-1
Tone K (2003) Dealing with undesirable outputs in DEA: a slacks-based measure (SBM) approach. GRIPS research report series 2003
Tone K (2004) Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. Toronto, 44–45
Wang H, Guo J (2022) Impacts of digital inclusive finance on CO2 emissions from a spatial perspective: evidence from 272 cities in China. J Clean Prod 355. https://doi.org/10.1016/j.jclepro.2022.131618
Wang H, Li X, Tian X, Ma L, Wang G, Wang X, Wang Z, Wang J, Yue Q (2022a) Socioeconomic drivers of China’s resource efficiency improvement: a structural decomposition analysis for 1997–2017. Resour Conserv Recycl 178. https://doi.org/10.1016/j.resconrec.2021.106028
Wang L, Wang Y, Sun Y, Han K, Chen Y (2021) Financial inclusion and green economic efficiency: evidence from China. J Environ Planning Manage 65(2):240–271. https://doi.org/10.1080/09640568.2021.1881459
Wang Q, Su M, Li R (2018) Toward to economic growth without emission growth: the role of urbanization and industrialization in China and India. J Clean Prod 205:499–511. https://doi.org/10.1016/j.jclepro.2018.09.034
Wang Y, Liu D, Sui X, Li F (2022b) Does logistics efficiency matter? Evidence from green economic efficiency side. Res Int Bus Financ 61. https://doi.org/10.1016/j.ribaf.2022.101650
Wellalage NH, Fernandez V (2019) Innovation and SME finance: evidence from developing countries. Int Rev Financ Anal 66. https://doi.org/10.1016/j.irfa.2019.06.009
Wu YQ, Lu HX, Liao XL, Liu JB, Zhu JM (2021) Foreign direct investment, regional innovation, and green economic efficiency: an empirical test based on the investigation of intermediary effect and threshold effect. Comput Intell Neurosci 2021:7348599. https://doi.org/10.1155/2021/7348599
Xie R, Fu W, Yao S, Zhang Q (2021) Effects of financial agglomeration on green total factor productivity in Chinese cities: insights from an empirical spatial Durbin model. Energy Econ 101. https://doi.org/10.1016/j.eneco.2021.105449
Yang Y, Guo H, Chen L, Liu X, Gu M, Ke X (2019) Regional analysis of the green development level differences in Chinese mineral resource-based cities. Resour Policy 61:261–272. https://doi.org/10.1016/j.resourpol.2019.02.003
Yang Z, Wei X (2019) The measurement and influences of China’s urban total factor energy efficiency under environmental pollution: based on the game cross-efficiency DEA. J Clean Prod 209:439–450. https://doi.org/10.1016/j.jclepro.2018.10.271
Yu B (2021) Urban spatial structure and total-factor energy efficiency in Chinese provinces. Ecol Indic 126. https://doi.org/10.1016/j.ecolind.2021.107662
Yuan H, Feng Y, Lee C-C, Cen Y (2020) How does manufacturing agglomeration affect green economic efficiency? Energy Econ 92. https://doi.org/10.1016/j.eneco.2020.104944
Yuan H, Zhang T, Feng Y, Liu Y, Ye X (2019) Does financial agglomeration promote the green development in China? A spatial spillover perspective. J Clean Prod 237. https://doi.org/10.1016/j.jclepro.2019.117808
Yue P, Korkmaz AG, Yin Z, Zhou H (2022) The rise of digital finance: financial inclusion or debt trap? Financ Res Lett 47. https://doi.org/10.1016/j.frl.2021.102604
Zhang D, Mohsin M, Rasheed AK, Chang Y, Taghizadeh-Hesary F (2021) Public spending and green economic growth in BRI region: mediating role of green finance. Energy Policy 153. https://doi.org/10.1016/j.enpol.2021.112256
Zhang R, Fu W, Kuang Y (2022) Can digital economy promote energy conservation and emission reduction in heavily polluting enterprises? Empirical evidence from China. Int J Environ Res Public Health 19(16). https://doi.org/10.3390/ijerph19169812
Zhang R, Wu K, Cao Y, Sun H (2023) Digital inclusive finance and consumption-based embodied carbon emissions: a dual perspective of consumption and industry upgrading. J Environ Manag 325. https://doi.org/10.1016/j.jenvman.2022.116632
Zhao P, Gao Y, Sun X (2022) How does artificial intelligence affect green economic growth?-Evidence from China. Sci Total Environ 834:155306. https://doi.org/10.1016/j.scitotenv.2022.155306
Zhao PJ, Zeng LE, Lu HY, Zhou Y, Hu HY, Wei XY (2020) Green economic efficiency and its influencing factors in China from 2008 to 2017: based on the super-SBM model with undesirable outputs and spatial Dubin model. Sci Total Environ 741:140026. https://doi.org/10.1016/j.scitotenv.2020.140026
Zhou L, Zhou C, Che L, Wang B (2020) Spatio-temporal evolution and influencing factors of urban green development efficiency in China. J Geog Sci 30(5):724–742. https://doi.org/10.1007/s11442-020-1752-5
Zhu Y, Yang S, Lin J, Yin S (2022) Spatial and temporal evolutionary characteristics and its influencing factors of economic spatial polarization in the Yangtze River Delta Region. Int J Environ Res Public Health 19(12). https://doi.org/10.3390/ijerph19126997
Funding
The authors acknowledge the support from the MUST FACULTY RESEARCH GRANTS (FRG-22-106-MSB).
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Kunyan Zhu, Rufei Ma, and Lei Du. The first draft of the manuscript was written by Kunyan Zhu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
This study involves the macro data of the human economy and society. All the data are from the official statistical yearbook. The data collection process is in line with ethical and moral standards. The research method of this study is DEA and econometrics, and there is no need for ethical approval and animal experiment content. The author guarantees that this study’s process, content, and conclusion do not violate the theory and moral principles.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Nicholas Apergis
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhu, K., Ma, R. & Du, L. Does digital inclusive finance affect the urban green economic efficiency? New evidence from the spatial econometric analysis of 284 cities in China. Environ Sci Pollut Res 30, 63435–63452 (2023). https://doi.org/10.1007/s11356-023-26619-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-023-26619-9