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.
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References
Al-Mulali U, Saboori B, Ozturk I (2015) Investigating the environmental Kuznets curve hypothesis in Vietnam. Energy Policy 76:123–131
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
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
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
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
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
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
Dagum C (1997a) A new approach to the decomposition of the Gini income inequality ratio. Empirical Econ 22:515–531
Dagum C (1997b) Decomposition and interpretation of Gini and the generalized entropy inequality measures. Statistica (Bologna) 57:295–308
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
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
Du K, Li J (2019) Towards a green world: how do green technology innovations affect total-factor carbon productivity. Energy Policy 131:240–250
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
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
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
Fiss PC (2011) Building better causal theories: a fuzzy set approach to typologies in organization research[J]. Acad Manag J 54(2):393–420
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
Glaser EL, Kahn ME (2010) The greenness of cites: carbon dioxide emissions and urban development. J Urban Econ 67(3):404–418
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
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
He AP, Xue QH, Zhao RJ et al (2021) Renewable energy technological innovation, market forces, and carbon emission efficiency. Sci Total Environ 796:148908
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
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
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
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
Li LS, Cai Y, Liu L (2020a) Research on the effect of urbanization on China’s carbon emission efficiency. Sustainability 12:163
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
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
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
Liu Y, Hao Y, Gao Y (2017) The environmental consequences of domestic and foreign investment: evidence from China. Energy Policy 108:271–280
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
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
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
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
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
Mielnik O, Goldemberg J (1999) The evolution of the ‘carbonization index’ in developing countries. Energy Policy 27:307–308
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
Papadopoulos T, Balta M (2022) Climate change and big data analytics: challenges and opportunities. Int J Inf Manag 63:102448
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
Ragin CC (2008) Redesigning social inquiry: fuzzy sets and beyond[M]. University of Chicago Press, Chicago, pp 56–78
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
Rihoux B (2013) Qualitative comparative analysis (QCA), anno 2013: reframing the comparative method’s seminal statements. Swiss Political Sci Rev 19:233–245
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
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
Sun JW (2005) The decrease of CO2 emission intensity is decarbonization at national and global levels. Energy Policy 33:975–978
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
Sun W, Huang C (2020) How does urbanization affect carbon emission efficiency? Evidence from China. J Clean Prod 272:122828
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
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
Tone K (2002) A slacks-based measure of super-efficiency in data envelopment analysis. Eur J Oper Res 143:32–41
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
Wang D, Li J (2022) Environmental regulation, technological progress and energy carbon emission efficiency. Technol Econ Manage Res 7:31–36
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
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
Wang S, Wang H, Zhang L, Dang J (2019b) Provincial carbon emissions efficiency and its influencing factors in China. Sustainability 11(8):2355
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
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
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
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
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
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
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
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
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
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
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
Zaim O, Taskin F (2000) Environmental efficiency in carbon dioxide emissions in the OECD: a non-parametric approach. J Environ Manag 58:95–107
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
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
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
Zhang CY, Lin J (2022) An empirical study of environmental regulation on carbon emission efficiency in China. Energy Sci Eng 10:4756–4767
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
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
Zhang N, Wang B, Liu Z (2016) Carbon emissions dynamics, efficiency gains, and technological innovation in China’s industrial sectors. Energy 99:10–19
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
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
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
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
Zheng S, Du R (2020) Agglomeration economies and urban public infrastructure. Cities 97:102529
Zheng D, Shi MJ (2017) Multiple environmental policies and pollution haven hypothesis: evidence from China’s polluting industries. J Clean Prod 141:295–304
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
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
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
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We would like to thank AJE (https://www.aje.cn/) for English language editing.
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This work was supported by the National Natural Science Foundation of China (grant number 41771162).
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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
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DOI: https://doi.org/10.1007/s11356-023-30674-7