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Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China

Author

Listed:
  • Zhanhang Zhou

    (School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, China
    Research Center for Urbanization and New Rural Construction, Tianjin Chengjian University, Tianjin 300384, China)

  • Linjian Cao

    (School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, China
    Research Center for Urbanization and New Rural Construction, Tianjin Chengjian University, Tianjin 300384, China)

  • Kuokuo Zhao

    (School of Management, Guangzhou University, Guangzhou 510006, China)

  • Dongliang Li

    (School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, China
    Research Center for Urbanization and New Rural Construction, Tianjin Chengjian University, Tianjin 300384, China)

  • Ci Ding

    (School of Economics and Management, Tianjin Chengjian University, Tianjin 300384, China
    Research Center for Urbanization and New Rural Construction, Tianjin Chengjian University, Tianjin 300384, China)

Abstract

Under the influence of complex urbanization, improving the carbon emission efficiency (CEE) plays an important role in the construction of low-carbon cities in China. Based on the panel data of 283 prefectural-level cities in China from 2005 to 2017, this study evaluated the CEE by the US-SBM model, and explored the spatial agglomeration evolution characteristics of CEE from static and dynamic perspectives by integrating ESDA and Spatial Markov Chains. Then, the spatial heterogeneity of the impacts of multi-dimensional urbanization on CEE were analyzed by using the Geographically and Temporally Weighted Regression (GTWR). The results show that: (1) with the evolution of time, the CEE has a trend of gradual improvement, but the average is 0.4693; (2) from the perspective of spatial static agglomeration, the “hot spots” of CEE mainly concentrated in Shandong Peninsula, Pearl River Delta, and Chengdu-Chongqing urban agglomeration; The dynamic evolution of CEE gradually forms the phenomenon of “club convergence”; (3) urbanization of different dimensions shows spatial heterogeneity to CEE. The impact of economic urbanization in northern cities on CEE shows an inverted “U” shape, and the negative impact of spatial urbanization on CEE appears in the northwest and resource-based cities around Bohai Sea. Population and social urbanization have a positive promoting effect on CEE after 2010. These findings may help China to improve the level of CEE at the city level and provide a reference for low-carbon decision-making.

Suggested Citation

  • Zhanhang Zhou & Linjian Cao & Kuokuo Zhao & Dongliang Li & Ci Ding, 2021. "Spatio-Temporal Effects of Multi-Dimensional Urbanization on Carbon Emission Efficiency: Analysis Based on Panel Data of 283 Cities in China," IJERPH, MDPI, vol. 18(23), pages 1-20, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:23:p:12712-:d:693428
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    as
    1. Zhang, Pengyan & Yang, Dan & Qin, Mingzhou & Jing, Wenlong, 2020. "Spatial heterogeneity analysis and driving forces exploring of built-up land development intensity in Chinese prefecture-level cities and implications for future Urban Land intensive use," Land Use Policy, Elsevier, vol. 99(C).
    2. Ghali, Khalifa H. & El-Sakka, M. I. T., 2004. "Energy use and output growth in Canada: a multivariate cointegration analysis," Energy Economics, Elsevier, vol. 26(2), pages 225-238, March.
    3. Chudech Losiri & Masahiko Nagai & Sarawut Ninsawat & Rajendra P. Shrestha, 2016. "Modeling Urban Expansion in Bangkok Metropolitan Region Using Demographic–Economic Data through Cellular Automata-Markov Chain and Multi-Layer Perceptron-Markov Chain Models," Sustainability, MDPI, vol. 8(7), pages 1-23, July.
    4. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
    5. Huang, Junbing & Liu, Qiang & Cai, Xiaochen & Hao, Yu & Lei, Hongyan, 2018. "The effect of technological factors on China's carbon intensity: New evidence from a panel threshold model," Energy Policy, Elsevier, vol. 115(C), pages 32-42.
    6. Hu, Jin-Li & Wang, Shih-Chuan, 2006. "Total-factor energy efficiency of regions in China," Energy Policy, Elsevier, vol. 34(17), pages 3206-3217, November.
    7. Eric S. Coker & Laura Cavalli & Enrico Fabrizi & Gianni Guastella & Enrico Lippo & Maria Laura Parisi & Nicola Pontarollo & Massimiliano Rizzati & Alessandro Varacca & Sergio Vergalli, 2020. "The Effects of Air Pollution on COVID-19 Related Mortality in Northern Italy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(4), pages 611-634, August.
    8. Kuokuo Zhao & Xuezhu Cui & Zhanhang Zhou & Peixuan Huang & Dongliang Li, 2021. "Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development," IJERPH, MDPI, vol. 18(21), pages 1-20, October.
    9. Xie, Qiwei & Hu, Ping & Jiang, An & Li, Yongjun, 2019. "Carbon emissions allocation based on satisfaction perspective and data envelopment analysis," Energy Policy, Elsevier, vol. 132(C), pages 254-264.
    10. Wang, Shaojian & Wang, Jieyu & Fang, Chuanglin & Feng, Kuishuang, 2019. "Inequalities in carbon intensity in China: A multi-scalar and multi-mechanism analysis," Applied Energy, Elsevier, vol. 254(C).
    11. Wang, Shaojian & Zeng, Jingyuan & Huang, Yongyuan & Shi, Chenyi & Zhan, Peiyu, 2018. "The effects of urbanization on CO2 emissions in the Pearl River Delta: A comprehensive assessment and panel data analysis," Applied Energy, Elsevier, vol. 228(C), pages 1693-1706.
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