Skip to main content
Log in

Spatiotemporal differences in and influencing factors of urban carbon emission efficiency in China’s Yangtze River Economic Belt

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

Abstract

Improving urban carbon emission efficiency (CEE) is vital to achieving the goal of urban carbon neutrality. However, the synergistic configurational effect of multiple influencing factors on CEE is not clear. Taking the Yangtze River Economic Belt (YREB) as an example, this paper adopts the standard deviation ellipse and Dagum Gini coefficient method to investigate the spatiotemporal differences in urban CEE in the YREB, and using the fuzzy-set qualitative comparative analysis (fsQCA) method, it explores the configurational effect of CEE influencing factors from the system perspective. The main conclusions are as follows: first, the overall level of urban CEE in the YREB is low, with a certain polarization phenomenon. Second, the relative differences in urban CEE in the YREB show a fluctuating upward trend, and the regional differences mainly originate from the overlapping part between regions. Finally, the main CEE influencing factors do not act in isolation, they constitute a complex process of synergistic interaction, with complementary substitution and causal asymmetry.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Data Availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  • Al-Mulali U, Saboori B, Ozturk I (2015) Investigating the environmental Kuznets curve hypothesis in Vietnam. Energy Policy 76:123–131

    Google Scholar 

  • Behera SR, Dash DP (2017) The effect of urbanization, energy consumption, and foreign direct investment on the carbon dioxide emission in the SSEA (south and south-east Asian) region. Renew Sustain Energy Rev 70:96–106

    CAS  Google Scholar 

  • Cai B, Guo H, Ma Z et al (2019) Benchmarking carbon emissions efficiency in Chinese cities: a comparative study based on high-resolution gridded data. Appl Energy 242:994–1009

    Google Scholar 

  • Campbell JT, Sirmon DG, Schijven M (2016) Fuzzy logic and the market: a configurational approach to investor perceptions of acquisition announcements[J]. Acad Manag J 59(1):163–187

    Google Scholar 

  • Cao P, Li XX, Cheng Y et al (2022) Temporal-spatial evolution and driving factors of global carbon emission efficiency. Int J Environ Res Public Health 19:14849

    Google Scholar 

  • Chen L, Liu YN, Gao Y et al (2021) Carbon emission trading policy and carbon emission efficiency: an empirical analysis of China’s prefecture-level cities. Front Energy Res 9:793601

    Google Scholar 

  • Cheng Y, Sun YX, Wang XJ, Yin JZ (2019) Research on the impact of global scientific and technological innovation on carbon productivity and countermeasures. China Popul Res Environ 29:30–40

    Google Scholar 

  • Dagum C (1997a) A new approach to the decomposition of the Gini income inequality ratio. Empirical Econ 22:515–531

    Google Scholar 

  • Dagum C (1997b) Decomposition and interpretation of Gini and the generalized entropy inequality measures. Statistica (Bologna) 57:295–308

    Google Scholar 

  • Deng RR, Zhang AX (2021) The impact of urban digital finance development on carbon emission performance in China and mechanism. Resour Sci 43:2316–2330

    Google Scholar 

  • Dong F, Zhu J, Li Y et al (2022) How green technology innovation affects carbon emission efficiency: evidence from developed countries proposing carbon neutrality targets. Environ Sci Pollut Res 29:35780–35799

    Google Scholar 

  • Du K, Li J (2019) Towards a green world: how do green technology innovations affect total-factor carbon productivity. Energy Policy 131:240–250

    CAS  Google Scholar 

  • Du YZ, Liu QC, Chen KW et al (2022) Ecosystem of doing business, total factor productivity and multiple patterns of high-quality development of Chinese cities: a configuration analysis based on complex systems view. J Manag World 38:127–145

    Google Scholar 

  • Fang GC, Gao ZY, Tian LX et al (2022a) What drives urban carbon emission efficiency? Spatial analysis based on nighttime light data. Appl Energy 312:118772

    Google Scholar 

  • Fang GH, Gao ZY, Wang L et al (2022b) How does green innovation drive urban carbon emission efficiency? —Evidence from the Yangtze River Economic Belt. J Clean Prod 375:134196

    CAS  Google Scholar 

  • Fiss PC (2011) Building better causal theories: a fuzzy set approach to typologies in organization research[J]. Acad Manag J 54(2):393–420

    Google Scholar 

  • Gai M, Zhu JM, Sun CZ et al (2018) Spatio-temporal evolution and influencing factors of marine economic efficiency in coastal areas of China. Resour Sci 40(10):1966–1979

    Google Scholar 

  • Glaser EL, Kahn ME (2010) The greenness of cites: carbon dioxide emissions and urban development. J Urban Econ 67(3):404–418

    Google Scholar 

  • Gu G, Zheng H, Tong L, Dai Y (2022) Does carbon financial market as an environmental regulation policy tool promote regional energy conservation and emission reduction? Empirical evidence from China. Energy Policy 163:112826

    Google Scholar 

  • Guo P, Liang D (2022) Does the low-carbon pilot policy improve the efficiency of urban carbon emissions: quasi-natural experimental research based on low-carbon pilot cities. J Nat Resour 37:1876–1892

    Google Scholar 

  • He AP, Xue QH, Zhao RJ et al (2021) Renewable energy technological innovation, market forces, and carbon emission efficiency. Sci Total Environ 796:148908

    CAS  Google Scholar 

  • IPCC (2023) AR6 synthesis report: climate change 2023. Panel’s 58th Session, Switzerland. https://www.ipcc.ch/report/sixth-assessment-report-cycle/. Accessed 19 Mar 2023

  • Jiang P, Li M, Zhao Y et al (2022a) Does environmental regulation improve carbon emission efficiency? Inspection of panel data from inter-provincial provinces in China. Sustainability 14:10448

    CAS  Google Scholar 

  • Jiang M, An H, Gao X (2022b) Adjusting the global industrial structure for minimizing global carbon emissions: a network-based multi-objective optimization approach. Sci Total Environ 829:154653

    CAS  Google Scholar 

  • Jin PZ, Zhang YB, Peng X (2014) The double-edged effect of technological progress in carbon dioxide emissions reduction: empirical evidence from 35 sub-industrial sectors in China. Stud Sci Sci 32:706–716

    Google Scholar 

  • Li X, Li D, Xu H et al (2017) Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria’s major human settlement during Syrian Civil War. Int J Remote Sens 38(21):5934–5951

    Google Scholar 

  • Li LS, Cai Y, Liu L (2020a) Research on the effect of urbanization on China’s carbon emission efficiency. Sustainability 12:163

    CAS  Google Scholar 

  • Li JB, Huang XJ, Chuai XW et al (2020b) Spatio-temporal characteristics and influencing factors of carbon emissions efficiency in the Yangtze River Delta Region. Resour Environ Yangtze Basin 29:1486–1496

    Google Scholar 

  • Li S, Diao H, Wang L, Li L (2022) A complete total-factor CO2 emissions efficiency measure and “2030•60 CO2 emissions targets” for Shandong Province, China. J Clean Prod 360:132230

    CAS  Google Scholar 

  • Liu Z, Geng Y, Xue B et al (2011) Relationships between economic growth and CO2 emissions for low-carbon pilot provinces in China. Resour Sci 33:620–625

    Google Scholar 

  • Liu Y, Hao Y, Gao Y (2017) The environmental consequences of domestic and foreign investment: evidence from China. Energy Policy 108:271–280

    Google Scholar 

  • Liu BQ, Chen T, Li YQ et al (2018) Research on the effects of urbanization on carbon emissions efficiency of urban agglomerations in China. J Clean Prod 197:1374–1381

    Google Scholar 

  • Liu HJ, Tian Z, Shi Y (2023a) Spatial difference of carbon dioxide emissions and its bi-dimensional internal structural decomposition and analysis from 2000 to 2019 in China. Geogr Res 42:857–877

    Google Scholar 

  • Liu CG, Sun W, Li PX et al (2023b) Differential characteristics of carbon emission efficiency and coordinated emission reduction pathways under different stages of economic development: evidence from the Yangtze River Delta, China. J Environ Manag 330:117018

    CAS  Google Scholar 

  • Luo LW, Li SS (2013) Technical effects of FDI and international trade and provincial carbon emission performance in China. J Int Trade 8:142–150

    Google Scholar 

  • Ma ZG, Jiang YX (2023) Influencing factors and path selection of industrial upgrading of national independent innovation demonstration zones: an empirical study based on the fsQCA method. Sci Technol Progress Policy 40(02):50–59

    Google Scholar 

  • Mielnik O, Goldemberg J (1999) The evolution of the ‘carbonization index’ in developing countries. Energy Policy 27:307–308

    Google Scholar 

  • Murshed M, Apergis N, Alam MS et al (2022) The impacts of renewable energy financial inclusivity globalization economic growth and urbanization on carbon productivity: Evidence from net moderation and mediation effects of energy efficiency gains. Renew Energy 196:824–838

    CAS  Google Scholar 

  • Papadopoulos T, Balta M (2022) Climate change and big data analytics: challenges and opportunities. Int J Inf Manag 63:102448

    Google Scholar 

  • Pappas IO, Mikalef P, Giannakos MN et al (2017) Value co-creation and trust in social commerce: an fsQCA approach[C]. In Proceedings of the 25th European Conference on Information Systems (ECIS), Portugal, pp 2153–2168

  • Pappas IO, Woodside AG (2021) Fuzzy-set qualitative comparative analysis (fsQCA): guidelines for research practice in information systems and marketing[J]. Int J Inf Manage 58:102310

    Google Scholar 

  • Ragin CC (2008) Redesigning social inquiry: fuzzy sets and beyond[M]. University of Chicago Press, Chicago, pp 56–78

    Google Scholar 

  • Ragin CC, Drasska, Daveys (2006) Fuzzy-set/qualitative comparative analysis 2.0. University of Arizona, Arizona

  • Rajpurohit SS, Sharma R (2021) Impact of economic and financial development on carbon emissions: evidence from emerging Asian economies. Manag Environ Qual 32(2):145–159

    Google Scholar 

  • Rihoux B (2013) Qualitative comparative analysis (QCA), anno 2013: reframing the comparative method’s seminal statements. Swiss Political Sci Rev 19:233–245

    Google Scholar 

  • Schneider C Q, Wagemann C (2012) Set-theoretic methods for the social science: a guide to qualitative comparative analysis[M]. Cambridge University Press, Cambridge, pp 101–148

  • Schneider CQ, Wagemann C (2010) Standards of good practice in qualitative comparative analysis (QCA) and fuzzy-sets[J]. Comp Sociol 9(3):397–418

    Google Scholar 

  • Shao S, Yang L (2014) Natural resource dependence, human capital accumulation, and economic growth: a combined explanation for the resource curse and the resource blessing. Energy Policy 74:632–642

    Google Scholar 

  • Sun JW (2005) The decrease of CO2 emission intensity is decarbonization at national and global levels. Energy Policy 33:975–978

    Google Scholar 

  • Sun YY (2016) Decomposition of tourism greenhouse gas emissions: revealing the dynamics between tourism economic growth, technological efficiency, and carbon emissions. Tour Manage 55:326–336

    Google Scholar 

  • Sun W, Huang C (2020) How does urbanization affect carbon emission efficiency? Evidence from China. J Clean Prod 272:122828

    CAS  Google Scholar 

  • Sun C, Li Z, Ma T et al (2019) Carbon efficiency and international specialization position: evidence from global value chain position index of manufacture. Energy Policy 128:235–242

    CAS  Google Scholar 

  • Tao Y, Li F, Wang R, Zhao D (2015) Effects of land use and cover change on terrestrial carbon stocks in urbanized areas: a study from Changzhou, China. J Clean Prod 103:651–657

    Google Scholar 

  • Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143:32–41

    Google Scholar 

  • Wang SJ, Huang YY (2019) Spatial spillover effect and driving forces of carbon emission intensity at city level in China. Acta Geogr Sin 74(06):1131–1148

    Google Scholar 

  • Wang D, Li J (2022) Environmental regulation, technological progress and energy carbon emission efficiency. Technol Econ Manage Res 7:31–36

    Google Scholar 

  • Wang G, Li SJ, Ma QF (2018) Spatial equilibrium and pattern evolution of ecological civilization construction efficiency in China. Acta Geogr Sin 73(11):2198–2209

    Google Scholar 

  • Wang CM, Chu J, Wang CH (2019a) Study on the inverted n relation and the greenhouse effect impact mechanism between foreign direct investment and carbon emissions. Therm Sci 23:2775–2782

    Google Scholar 

  • Wang S, Wang H, Zhang L, Dang J (2019b) Provincial carbon emissions efficiency and its influencing factors in China. Sustainability 11(8):2355

    CAS  Google Scholar 

  • Wang SJ, Gao S, Huang YY et al (2020) Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model. Acta Geogr Sin 75:1316–1330

    Google Scholar 

  • Wang Q, Zhang C, Li RR (2022a) Towards carbon neutrality by improving carbon efficiency-a system-GMM dynamic panel analysis for 131 countries? carbon efficiency. Energy 258:124880

    CAS  Google Scholar 

  • Wang SJ, Wang ZH, Fang CL (2022b) Evolutionary characteristics and driving factors of carbon emission performance at the city level in China. Sci Sin (Terrae) 52:1613–1626

    Google Scholar 

  • Wen SY, Jia ZJ, Chen XQ (2022) Can low-carbon city pilot policies significantly improve carbon emission efficiency? Empirical evidence from China. J Clean Prod 346:131131

    CAS  Google Scholar 

  • Wu C, Yang SW, Tang PC et al (2018) Construction of the efficiency promotion model of green innovation in China’s heavy polluted industries. China Popul Resour Environ 28(05):40–48

    Google Scholar 

  • Wu J, Zhao R, Sun J (2023) State transition of carbon emission efficiency in China: empirical analysis based on three-stage SBM and Markov chain models. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-022-24885-7

    Article  Google Scholar 

  • Xie Z, Wu R, Wang S (2021) How technological progress affects the carbon emission efficiency? Evidence from national panel quantile regression. J Clean Prod 307:127133

    CAS  Google Scholar 

  • Xu Q, Yang R, Dong Y-X et al (2016) The influence of rapid urbanization and land use changes on terrestrial carbon sources/sinks in Guangzhou, China. Ecol Indic 70:304–316

    Google Scholar 

  • Xu YQ, Cheng Y, Wang JJ et al (2022a) Spatio-temporal evolution and influencing factors of carbon emission efficiency in low carbon city of China. J Nat Resour 37:1261–1276

    Google Scholar 

  • Xu Y, Cheng Y, Zheng R, Wang Y (2022b) Spatiotemporal evolution and influencing factors of carbon emission efficiency in the Yellow River Basin of China: comparative analysis of resource and non-resource-based cities. Int J Environ Res Public Health 19(18):11625

    Google Scholar 

  • Yu ZX, Liu S, Zhu ZC (2022) Has the digital economy reduced carbon emissions? analysis based on panel data of 278 cities in China. Int J Environ Res Public Health 19:11814

    CAS  Google Scholar 

  • Zaim O, Taskin F (2000) Environmental efficiency in carbon dioxide emissions in the OECD: a non-parametric approach. J Environ Manag 58:95–107

    Google Scholar 

  • Zeng L, Lu H, Liu Y et al (2019) Analysis of regional differences and influencing factors on China’s carbon emission efficiency in 2005–2015. Energies 12(16):3081

    CAS  Google Scholar 

  • Zhang CQ, Chen PY (2021) Industrialization, urbanization, and carbon emission efficiency of Yangtze River Economic Belt—empirical analysis based on stochastic frontier model. Environ Sci Pollut Res 28:66914–66929

    CAS  Google Scholar 

  • Zhang M, Du YZ (2019) Qualitative comparative analysis (QCA) in management and organization research: position, tactics, and directions. Chin J Manag 16(09):1312–1323

    Google Scholar 

  • Zhang CY, Lin J (2022) An empirical study of environmental regulation on carbon emission efficiency in China. Energy Sci Eng 10:4756–4767

    Google Scholar 

  • Zhang ML, Liu Y (2022) Influence of digital finance and green technology innovation on China’s carbon emission efficiency: empirical analysis based on spatial metrology. Sci Total Environ 838:156463

    CAS  Google Scholar 

  • Zhang H, Wei X (2014) Green paradox or forced emission reduction—the dual effect of environmental regulation on carbon emission impact. China Popul Resour Environ 24(9):21–29

    CAS  Google Scholar 

  • Zhang N, Wang B, Liu Z (2016) Carbon emissions dynamics, efficiency gains, and technological innovation in China’s industrial sectors. Energy 99:10–19

    Google Scholar 

  • Zhang F, Deng X, Phillips F et al (2020) Impacts of industrial structure and technical progress on carbon emission intensity: evidence from 281 cities in China. Technol Forecast Soc Chang 154:119949

    Google Scholar 

  • Zhang ZQ, Zhang T, Feng DF (2022a) Study on regional differences, dynamic evolution and convergence of carbon emission tensity in China. J Quant Technol Econ 39:67–87

    Google Scholar 

  • Zhang RJ, Tai HW, Cheng KT et al (2022b) Analysis on evolution characteristics and dynamic mechanism of urban green innovation network: a case study of Yangtze River Economic Belt. Sustainability 14:297

    Google Scholar 

  • Zhang RJ, Tai HW, Cheng KT et al (2022c) Carbon emission efficiency network formation mechanism and spatial correlation complexity analysis: taking the Yangtze River Economic Belt as an example. Sci Total Environ 841:156719

    CAS  Google Scholar 

  • Zheng S, Du R (2020) Agglomeration economies and urban public infrastructure. Cities 97:102529

    Google Scholar 

  • Zheng D, Shi MJ (2017) Multiple environmental policies and pollution haven hypothesis: evidence from China’s polluting industries. J Clean Prod 141:295–304

    Google Scholar 

  • Zheng R, Cheng Y, Liu H et al (2022) The spatiotemporal distribution and drivers of urban carbon emission efficiency: the role of technological innovation. Int J Environ Res Public Health 19:9111

    CAS  Google Scholar 

  • Zhou ZH, Cao LJ, Zhao KK et al (2021) Spatio-temporal effects of multi-dimensional urbanization on carbon emission efficiency: analysis based on panel data of 283 cities in China. Int J Environ Res Public Health 18(23):12712

    Google Scholar 

  • Zhou X, Yu J, Li JF et al (2022) Spatial correlation among cultivated land intensive use and carbon emission efficiency: a case study in the Yellow River Basin, China. Environ Sci Pollut Res 29:43341–43360

    Google Scholar 

Download references

Acknowledgements

We would like to thank AJE (https://www.aje.cn/) for English language editing.

Funding

This work was supported by the National Natural Science Foundation of China (grant number 41771162).

Author information

Authors and Affiliations

Authors

Contributions

All authors contributed to the study conception and design. Methodology, conceptualization, supervision, and funding acquisition were handled by ZW. The first draft of the manuscript was written by HS. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Haiqin Shao.

Ethics declarations

Ethical approval

Ethical approval and informed consent are not applicable for this study.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Responsible Editor: V.V.S.S. Sarma

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

We affirm that this manuscript has not been published previously, and it is not under consideration for publication elsewhere.

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Shao, H. Spatiotemporal differences in and influencing factors of urban carbon emission efficiency in China’s Yangtze River Economic Belt. Environ Sci Pollut Res 30, 121713–121733 (2023). https://doi.org/10.1007/s11356-023-30674-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-023-30674-7

Keywords

Navigation