Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities
Abstract
:1. Introduction
2. Literature Review and Model Building
2.1. Literature Review
2.2. MLP Framework
2.3. Urban Low-Carbon Transformation Framework
3. Methods and Data
3.1. FsQCA Method
3.2. Sample Selection and Data Source
3.3. Measurement and Calibration
3.3.1. Outcome Measurement (Urban Low-Carbon Transformation)
3.3.2. Conditioned Measurements
3.3.3. Data Calibration
4. Results
4.1. The Role of Individual Conditions in Urban Low-Carbon Transformation
4.2. Multiple Paths of Urban Low-Carbon Transformation
4.2.1. The First Stage Paths (2010–2012)
4.2.2. The Second Stage Paths (2012–2017)
4.2.3. The Third Stage Paths (2017–2019)
4.3. Comparative Analysis
4.4. Robustness Test
5. Conclusions and Discussion
5.1. Conclusions
5.2. Theoretical Contributions
5.3. Practical Inspiration
5.4. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar] [CrossRef]
- World Health Organization (WHO). Ambient air pollution: Health impacts. Lancet Planet. Health 2018, 2, e23–e24. [Google Scholar]
- Romero-Lankao, P. Governing carbon and climate in the cities: An overview of policy and planning challenges and options. Eur. Plan. Stud. 2012, 20, 26–27. [Google Scholar] [CrossRef]
- UN-Habitat. World Cities Report 2020: The Value of Sustainable Urbanization. United Nations Human Settlements Programme (UN-Habitat) 2020. Available online: https://unhabitat.org/wcr/ (accessed on 1 December 2023).
- Tan, S.; Yang, J.; Yan, J.; Lee, C.; Hashim, H.; Chen, B. A holistic low carbon city indicator framework for sustainable development. Appl. Energy 2017, 185, 1919–1930. [Google Scholar] [CrossRef]
- Wang, Y.; Wei, S.; He, X.; Gu, H. Environmental regulation and entrepreneurial activity: Evidence from the low-carbon city pilot policy in China. Sustain. Cities Soc. 2023, 98, 104829. [Google Scholar] [CrossRef]
- Gehrsitz, M. The Effect of Low Emission Zones on Air Pollution and Infant Health. J. Environ. Econ. Manag. 2017, 83, 121–144. [Google Scholar] [CrossRef]
- Shen, L.; Wu, Y.; Lou, Y.; Zeng, D.; Shuai, C.; Song, X. What drives the carbon emission in the Chinese cities?—A case of pilot low carbon city of Beijing. J. Clean. Prod. 2018, 174, 343–354. [Google Scholar] [CrossRef]
- Qu, F.; Xu, L.; He, C. Leverage Effect or Crowding out Effect? Evidence from Low-Carbon City Pilot and Energy Technology Innovation in China. Sustain. Cities Soc. 2023, 91, 104423. [Google Scholar] [CrossRef]
- Lei, M.; Yin, Z.; Yu, X.; Deng, S. Carbon-weighted economic development performance and driving force analysis: Evidence from China. Energy Policy 2017, 111, 179–192. [Google Scholar] [CrossRef]
- Chen, S.; Mao, H.; Sun, J. Low-carbon city construction and corporate carbon reduction performance: Evidence from a quasi-natural experiment in China. J. Bus. Ethics 2021, 180, 125–143. [Google Scholar] [CrossRef]
- Xu, P.; Wang, Y.; Yao, H.; Hou, H. (Cynthia) An exploratory analysis of low-carbon transitions in china’s construction industry based on multi-level perspective. Sustain. Cities Soc. 2023, 92, 104460. [Google Scholar] [CrossRef]
- McPhearson, T.; Haase, D.; Kabisch, N.; Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 2016, 70, 566–573. [Google Scholar] [CrossRef]
- Khanna, N.; Fridley, D.; Hong, L. China’s pilot low-carbon city initiative: A comparative assessment of national goals and local plans. Sustain. Cities Soc. 2014, 12, 110–121. [Google Scholar] [CrossRef]
- Wang, C.; Engels, A.; Wang, Z. Overview of research on China’s transition to low-carbon development: The role of cities, technologies, industries and the energy system. Renew. Sustain. Energy Rev. 2018, 81, 1350–1364. [Google Scholar] [CrossRef]
- Freeman, R.; Yearworth, M. Climate change and cities: Problem structuring methods and critical perspectives on low-carbon districts. Energy Res. Soc. Sci. 2017, 25, 48–64. [Google Scholar] [CrossRef]
- Williams, J. Can low carbon city experiments transform the development regime? Futures 2016, 77, 80–96. [Google Scholar] [CrossRef]
- Jeswani, H.; Chilvers, A.; Azapagic, A. Environmental sustainability of biofuels: A review. Proc. R. Soc. A Math. Phys. Eng. Sci. 2020, 476, 20200351. [Google Scholar] [CrossRef]
- Yao, M.; Liu, H.; Feng, X. The development of low-carbon vehicles in China. Energy Policy 2011, 39, 5457–5464. [Google Scholar] [CrossRef]
- Wu, X.; Xu, Y.; Lou, Y.; Chen, Y. Low Carbon Transition in a Distributed Energy System Regulated by Localized Energy Markets. Energy Policy 2018, 122, 474–485. [Google Scholar] [CrossRef]
- Hu, F.; Qiu, L.; Xiang, Y.; Wei, S.; Sun, H.; Hu, H.; Weng, X.; Mao, L.; Zeng, M. Spatial network and driving factors of low-carbon patent applications in China from a public health perspective. Front. Public Health 2023, 11, 1121860. [Google Scholar] [CrossRef]
- Gao, P.; Wang, Y.; Zou, Y.; Su, X.; Che, X.; Yang, X. Green technology innovation and carbon emissions nexus in China: Does industrial structure upgrading matter? Front. Psychol. 2022, 13, 951172. [Google Scholar] [CrossRef]
- Li, W.; Wang, J.; Chen, R.; Xi, Y.; Liu, S.Q.; Wu, F.; Masoud, M. Innovation-driven industrial green development: The moderating role of regional factors. J. Clean. Prod. 2019, 220, 55–63. [Google Scholar] [CrossRef]
- Zhang, F.; Xiangzheng, D.; Phillips, F.; Fang, C.; Wang, C. Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China. Technol. Forecast. Soc. Change 2020, 154, 119949. [Google Scholar] [CrossRef]
- Zheng, J.; Shao, X.; Liu, W.; Kong, J.; Zuo, G. The impact of the pilot program on industrial structure upgrading in low-carbon cities. J. Clean. Prod. 2021, 290, 125868. [Google Scholar] [CrossRef]
- He, J. Global low-carbon transition and China’s response strategies. Adv. Clim. Change Res. 2016, 7, 204–212. [Google Scholar] [CrossRef]
- Wu, Z.; Tang, J.; Wang, D. Low Carbon Urban Transitioning in Shenzhen: A Multi-Level Environmental Governance Perspective. Sustainability 2016, 8, 720. [Google Scholar] [CrossRef]
- Cheng, J.; Yi, J.; Dai, S.; Xiong, Y. Can low-carbon city construction facilitate green growth? Evidence from China’s pilot low-carbon city initiative. J. Clean. Prod. 2019, 231, 1158–1170. [Google Scholar] [CrossRef]
- Rahman, H.A. Malaysia commitment towards low carbon cities. Int. J. Acad. Res. Bus. Soc. Sci. 2020, 10, 253–266. [Google Scholar] [CrossRef]
- Geels, F.W. A socio-technical analysis of low-carbon transitions: Introducing the multi-level perspective into transport studies. J. Transp. Geogr. 2018, 24, 471–482. [Google Scholar] [CrossRef]
- Raven, R.; Schot, J.; Berkhout, F. Space and scale in socio-technical transitions. Environ. Innov. Soc. Transit. 2012, 4, 63–78. [Google Scholar] [CrossRef]
- Geels, F.W. Technological transitions as evolutionary reconfiguration processes: A multi-level perspective and a case-study. Res. Policy 2002, 31, 1257–1274. [Google Scholar] [CrossRef]
- Raven, R.; Kern, F.; Verhees, B.; Smith, A. Niche construction and empowerment through socio-political work: A meta-analysis of six low-carbon technology cases. Environ. Innov. Soc. Transit. 2016, 18, 164–180. [Google Scholar] [CrossRef]
- Schot, J.; Geels, F.W. Strategic niche management and sustainable innovation journeys: Theory, findings, research agenda, and policy. Technol. Anal. Strateg. Manag. 2008, 20, 537–554. [Google Scholar] [CrossRef]
- Verbong, G.; Geels, F.W. The ongoing energy transition: Lessons from a socio-technical, multi-level analysis of the Dutch electricity system (1960–2004). Energy Policy 2007, 35, 1025–1037. [Google Scholar] [CrossRef]
- Ma, X.; Jiang, H.; Tong, L.; Zhang, J.; Dong, M. Sustainability of the new energy automobile industry: Examining the relationship among government subsidies, R&D intensity, and innovation performance. Sustainability 2023, 15, 14794. [Google Scholar] [CrossRef]
- Fernández, Y.; Fernández López, M.A.; Olmedillas Blanco, B. Innovation for sustainability: The impact of R&D spending on CO2 emissions. J. Clean. Prod. 2018, 172, 3459–3467. [Google Scholar] [CrossRef]
- Zhang, J.; Jiang, H.; Liu, G.; Zeng, W. A study on the contribution of industrial restructuring to reduction of carbon emissions in China during the five five-year plan periods. J. Clean. Prod. 2018, 176, 629–635. [Google Scholar] [CrossRef]
- Caragliu, A.; Del Bo, C.F. Smart innovative cities: The impact of smart city policies on urban innovation. Technol. Forecast. Soc. Change 2019, 142, 373–383. [Google Scholar] [CrossRef]
- Norouzi, N.; Fani, M.; Talebi, S. Green tax as a path to greener economy: A game theory approach on energy and final goods in Iran. Renew. Sustain. Energy Rev. 2022, 156, 111968. [Google Scholar] [CrossRef]
- Douglas, E.J.; Shepherd, D.A.; Prentice, C. Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship. J. Bus. Ventur. 2020, 35, 105970. [Google Scholar] [CrossRef]
- Zhao, M.; Sun, T.; Feng, Q. Capital allocation efficiency, technological innovation and vehicle carbon emissions: Evidence from a panel threshold model of Chinese new energy vehicles enterprises. Sci. Total Environ. 2021, 784, 147104. [Google Scholar] [CrossRef]
- Shen, X.; Lin, B. Policy incentives, R&D investment, and the energy intensity of China’s manufacturing sector. J. Clean. Prod. 2020, 255, 120208. [Google Scholar] [CrossRef]
- Ragin, C.C. Redesigning Social Inquiry: Fuzzy Sets and Beyond; University of Chicago Press: Chicago, IL, USA, 2008. [Google Scholar]
- Xu, Q.; Xu, P.; Bao, C. Multifaceted pathways of carbon emission reduction policies: A study based on the decoupling effect and fsQCA method in the Yangtze River Delta urban agglomeration. Sustainability 2023, 15, 13745. [Google Scholar] [CrossRef]
- Rihoux, B. Qualitative Comparative Analysis: Discovering core combinations of conditions in political decision making. In Oxford Research Encyclopedia of Politics; Oxford University Press: Oxford, UK, 2020. [Google Scholar] [CrossRef]
- Dul, J. Identifying single necessary conditions with NCA and fsQCA. J. Bus. Res. 2016, 69, 1516–1523. [Google Scholar] [CrossRef]
- Wu, J.; Guo, Z. Analysis of carbon emission convergence in China based on the continuous dynamic distribution method. Stat. Res. 2016, 33, 54–60. (In Chinese) [Google Scholar]
- Liu, Z.; Huang, M.; Shen, H. China’s carbon trading pilot policy and green technology progress bias: An investigation based on city-level data. Ind. Econ. Rev. 2022, 1, 201–219. (In Chinese) [Google Scholar]
- Zhou, E.; Liu, G. An empirical study on the factors influencing the innovation performance of new R&D institutions in China: A case study of Guangdong Province. Sci. Technol. Prog. Policy 2018, 35, 42–47. (In Chinese) [Google Scholar]
- Wang, Q.; Wang, S.; Dong, N. Factors influencing regional carbon emission intensity and spatial spillover effects under the carbon peak background: A case study of Chongqing. Soft Sci. 2022, 36, 97–103. (In Chinese) [Google Scholar]
- Wang, K.; Wang, Y.; Zhao, B. Local government competition, FDI, and pollution emission efficiency. J. Beijing Inst. Technol. (Soc. Sci. Ed.) 2021, 23, 1–15. (In Chinese) [Google Scholar]
- Tan, J.; Zhao, Z. Fiscal expenditure structure, new-type urbanization, and carbon reduction effects. Contemp. Financ. Econ. 2021, 8, 28–40. (In Chinese) [Google Scholar]
- Lu, J.; Wang, X.; Liu, L. The industrial structure upgrading effect of low-carbon city policies: A quasi-natural experiment based on low-carbon city pilots. J. Xi’an Jiaotong Univ. (Soc. Sci. Ed.) 2020, 40, 104–115. (In Chinese) [Google Scholar]
- Du, Y.; Kim, P.H. One size does not fit all: Strategy configurations, complex environments, and new venture performance in emerging economies. J. Bus. Res. 2021, 124, 272–285. [Google Scholar] [CrossRef]
- Li, Y.D.; Yan, C.L.; Zhao, Y.H.; Bai, J.Q. Analysing multiple paths of urban low-carbon governance: A fuzzy-set qualitative comparative analysis method based on 35 key cities in China. Sustainability 2023, 15, 7613. [Google Scholar] [CrossRef]
- Fan, X.; Li, J.; Wang, Y. The driving factors of innovation quality of agricultural enterprises—A study based on NCA and FSQCA methods. Sustainability 2023, 15, 1809. [Google Scholar] [CrossRef]
- Schneider, C.Q.; Wagemann, C. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis; Cambridge University Press: Cambridge, UK, 2021. [Google Scholar] [CrossRef]
- Wang, C.; Zhan, J.; Xin, Z. Comparative analysis of urban ecological management models incorporating low-carbon transformation. Technol. Forecast. Soc. Change 2020, 159, 120190. [Google Scholar] [CrossRef]
- De Flander, K. Operationalizing holistic urban concepts. J. Environ. Stud. Sci. 2014, 7, 141–144. [Google Scholar] [CrossRef]
- Rogov, M.; Rozenblat, C. Urban Resilience discourse analysis: Towards a multi-level approach to cities. Sustainability 2018, 10, 4431. [Google Scholar] [CrossRef]
- Webb, R.; Bai, X.; Smith, M.; Costanza, R.; Griggs, D.; Moglia, M.; Neuman, M.; Newman, P.; Newton, P.; Norman, B.; et al. Sustainable urban systems: Co-design and framing for transformation. Ambio 2017, 47, 57–77. [Google Scholar] [CrossRef]
Period | Batch | Pilot Cities |
---|---|---|
2010–2012 | First Batch | Guangzhou, Shenyang, Wuhan, Xi’an, Kunming, Tianjin, Chongqing, Shenzhen, Xiamen, Hangzhou, Nanchang, Guiyang, and Baoding |
2012–2017 | Second Batch | Beijing, Shanghai, Haikou, Shijiazhuang, Qinhuangdao, Jincheng, Hulunbuir, Jilin, Suzhou, Huai’an, Zhenjiang, Ningbo, Wenzhou, Chizhou, Nanping, Jingdezhen, Qianzhou, Qingdao, Guilin, Guangyuan, Zunyi, Yan’an, Jinchang, and Urumqi |
2017–2019 | Third Batch | Wuhai, Dalian, Chaoyang, Nanjing, Changzhou, Jiaxing, Jinhua, Quzhou, Hefei, Huaibei, Huangshan, Lu’an, Xuancheng, Sanming, Ji’an, Fuzhou, Jinan, Yantai, Weifang, Changsha, Zhuzhou, Xiangtan, Chenzhou, Zhongshan, Liuzhou, Sanya, Chengdu, Yuxi, Ankang, Lanzhou, Xining, Yinchuan, and Wuzhong |
Conditions and Result | Measurement Standard | Data Sources |
---|---|---|
Urban carbon intensity (UCI) | (Carbon emissions from natural gas + coal gas + liquefied petroleum gas + electricity + heat energy)/Regional GDP | China Urban Statistical Yearbook China Energy Statistical Yearbook |
R&D investment intensity (R&D II) | (R&D expenditure + education expenditure)/Regional GDP | China Urban Statistical Yearbook |
R&D human capital (R&D HC) | R&D personnel full-time equivalent | China Science and Technology Statistical Yearbook |
Regional industrial structure (RIS) | Rate of change in the ratio of value added in the secondary industry to regional gross domestic product | China Urban Statistical Yearbook |
Green innovation level (GIL) | Number of green patent authorizations of listed companies in each city | National Intellectual Property Administration |
Low-carbon consumption awareness (LCCA) | Public trams available at the end of the year/(Public trams available at the end of the year + taxis available at the end of the year) | China Urban Statistical Yearbook |
Economic development level (EDL) | Per capital regional gross domestic product in the whole city | China Urban Statistical Yearbook |
Conditions and Outcome | Mean | Variance | Minimum | Maximum | Sample Size |
---|---|---|---|---|---|
R&D II | 0.191 | 0.037 | 0.120 | 0.289 | 70 |
R&D HC | 21.880 | 22.093 | 0.5476 | 80.321 | 70 |
RIS | 51.029 | 9.765 | 22.140 | 78.370 | 70 |
GIL | 48.243 | 35.578 | 1.000 | 134.000 | 70 |
LCCA | 0.379 | 0.128 | 0.126 | 0.781 | 70 |
EDL | 8.679 | 4.066 | 2.876 | 20.349 | 70 |
UCI | 0.592 | 0.522 | 0.214 | 3.115 | 70 |
Conditions and Results | 2010–2012 | 2012–2017 | 2017–2019 | ||||||
---|---|---|---|---|---|---|---|---|---|
Full Membership | Crossover Point | Non-membership | Full Membership | Crossover Point | Non-membership | Full Membership | Crossover Point | Non-membership | |
R&D II | 0.211 | 0.181 | 0.133 | 0.248 | 0.190 | 0.112 | 0.264 | 0.190 | 0.136 |
R&D HC | 37.938 | 7.429 | 2.758 | 51.565 | 9.476 | 2.511 | 63.528 | 15.728 | 2.490 |
RIS | 29.950 | 35.230 | 43.928 | 34.460 | 47.630 | 67.016 | 37.902 | 51.210 | 65.944 |
GIL | 25.600 | 9.000 | 2.200 | 120.2 | 48.000 | 8.000 | 108.55 | 46.000 | 2.000 |
LCCA | 0.530 | 0.376 | 0.172 | 0.493 | 0.373 | 0.204 | 0.558 | 0.381 | 0.205 |
EDL | 10.272 | 6.832 | 2.775 | 14.266 | 6.416 | 2.915 | 16.110 | 7.816 | 3.475 |
UCI | 0.281 | 0.447 | 0.691 | 0.251 | 0.405 | 1.103 | 0.245 | 0.397 | 1.623 |
Antecedent Condition | 2010–2012 | 2012–2017 | 2017–2019 | |||
---|---|---|---|---|---|---|
Consistency | Coverage | Consistency | Coverage | Consistency | Coverage | |
R&D II | 0.777 | 0.724 | 0.669 | 0.706 | 0.525 | 0.660 |
Non-R&D II | 0.698 | 0.617 | 0.647 | 0.721 | 0.653 | 0.731 |
R&D HC | 0.703 | 0.679 | 0.652 | 0.789 | 0.454 | 0.623 |
Non-R&D HC | 0.794 | 0.678 | 0.630 | 0.618 | 0.682 | 0.709 |
RIS | 0.674 | 0.627 | 0.699 | 0.781 | 0.562 | 0.654 |
Non-RIS | 0.765 | 0.677 | 0.635 | 0.668 | 0.616 | 0.743 |
GIL | 0.720 | 0.674 | 0.623 | 0.671 | 0.527 | 0.660 |
Non-GIL | 0.737 | 0.648 | 0.668 | 0.729 | 0.576 | 0.647 |
LCCA | 0.715 | 0.677 | 0.682 | 0.806 | 0.453 | 0.553 |
Non-LCCA | 0.776 | 0.676 | 0.603 | 0.604 | 0.670 | 0.769 |
EDL | 0.660 | 0.674 | 0.678 | 0.722 | 0.503 | 0.613 |
Non-EDL | 0.759 | 0.619 | 0.582 | 0.642 | 0.643 | 0.738 |
Dimensions | Time (Number) | 2010–2012 | 2012–2017 | ||||||
---|---|---|---|---|---|---|---|---|---|
Configuration | 1a | 1b | 1c | 2a | 2b | 2c | 2d | 2e | |
Micro-level niche | R&D II | ||||||||
R&D HC | |||||||||
Meso-level system layer | RIS | ||||||||
GIL | |||||||||
Macro-level landscape | LCCA | ||||||||
EDL | |||||||||
Consistency | 0.860 | 0.795 | 0.993 | 0.947 | 0.939 | 0.976 | 0.984 | 0.968 | |
Original coverage | 0.305 | 0.220 | 0.193 | 0.287 | 0.291 | 0.327 | 0.245 | 0.212 | |
Unique coverage | 0.158 | 0.088 | 0.101 | 0.050 | 0.012 | 0.070 | 0.073 | 0.046 | |
Overall coverage | 0.494 | 0.548 | |||||||
Overall consistency | 0.870 | 0.934 |
Dimensions | Time (Number) | 2017–2019 | ||||||
---|---|---|---|---|---|---|---|---|
Configuration | 3a | 3b | 3c | 3d | 3e | 3f | 3g | |
Micro-level niche | R&D II | |||||||
R&D HC | ||||||||
Meso-level system layer | RIS | |||||||
GIL | ||||||||
Macro-level landscape | LCCA | |||||||
EDL | ||||||||
Consistency | 0.990 | 0.968 | 0.993 | 0.978 | 0.983 | 0.989 | 0.972 | |
Original coverage | 0.242 | 0.245 | 0.255 | 0.228 | 0.308 | 0.187 | 0.364 | |
Unique coverage | 0.021 | 0.010 | 0.030 | 0.015 | 0.004 | 0.012 | 0.026 | |
Overall coverage | 0.533 | |||||||
Overall consistency | 0.978 |
Type | Configuration 1 | Multi-Factor Combination Paths |
---|---|---|
Low-carbon industry-driven | 1a | RIS * + GIL |
1b | RIS * + R&D II | |
1c | RIS * + R&D HC + LCCA + EDL |
Types | Configuration 2 | Multi-Factor Combination Paths |
---|---|---|
Landscape-driven | 2a | LCCA * + EDL * |
2b | LCCA * + EDL * + R&D HC | |
Low-carbon industry and R&D human capital collaboration | 2c | R&D HC * + RIS * + R&D II + LCCA + EDL |
2d | R&D HC * + RIS * + EDL | |
2e | R&D HC * + RIS * + GIL + LCCA |
Types | Configuration 3 | Multi-Factor Combination Paths |
---|---|---|
Base and landscape cooperation | 3a | R&D HC * + LCCA * |
3b | R&D II * + LCCA * + RIS + EDL | |
3c | R&D HC * + EDL * + RIS | |
Multilayer collaboration | 3d | RIS * + LCCA * + EDL * |
3e | RIS * + GIL * + LCCA * + R&D II * + EDL | |
3f | RIS * + GIL * + LCCA * + R&D II * + R&D HC | |
3g | RIS * + LCCA * + R&D II * |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Li, Y.-D.; Yan, C.-L. Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities. Sustainability 2024, 16, 7630. https://doi.org/10.3390/su16177630
Li Y-D, Yan C-L. Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities. Sustainability. 2024; 16(17):7630. https://doi.org/10.3390/su16177630
Chicago/Turabian StyleLi, You-Dong, and Chen-Li Yan. 2024. "Driving Paths and Evolution Trends of Urban Low-Carbon Transformation: Configuration Analysis Based on Three Batches of Low-Carbon Pilot Cities" Sustainability 16, no. 17: 7630. https://doi.org/10.3390/su16177630