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County Economy, Population, Construction Land, and Carbon Intensity in a Shrinkage Scenario

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  • Tianyi Zeng

    (Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology, Harbin 150006, China)

  • Hong Jin

    (Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology, Harbin 150006, China)

  • Xu Gang

    (Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology, Harbin 150006, China)

  • Zihang Kang

    (Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology, Harbin 150006, China)

  • Jiayi Luan

    (Key Laboratory of Cold Region Urban and Rural Human Settlement Environment Science and Technology, Ministry of Industry and Information Technology, School of Architecture, Harbin Institute of Technology, Harbin 150006, China)

Abstract

As the largest ecological background system and basic economic unit in China, counties are of great significance to China’s carbon emission reduction targets. This article conducts theoretical model construction and empirical test research from a contraction perspective, using population and built-up area change as variables and combining indicators of county scale structure in an attempt to find key scale structure elements and representative indicators that affect the carbon emission intensity of counties. By using data from 140 counties in Northeast China during the period of 2015–2020, an empirical study was conducted on population shrinkage clustering, county size structure, and carbon emission intensity. The results show that: (1) population shrinkage significantly increases the carbon intensity of counties, but the contribution of population shrinkage to carbon intensity is scale-heterogeneous, the contribution effect decreases with population size, and the effect on large counties is minimal; (2) population size and industrial structure are the main factors influencing carbon intensity in counties, both have a negative linear elasticity relationship, and GDP per capita is not included in the overall model and is only significant in large counties; (3) the relationship between total construction land and carbon intensity is an inverted U-shaped Kuznets curve, with a critical value of 30 km 2 , and the total construction land in most counties is below or close to the critical value.

Suggested Citation

  • Tianyi Zeng & Hong Jin & Xu Gang & Zihang Kang & Jiayi Luan, 2022. "County Economy, Population, Construction Land, and Carbon Intensity in a Shrinkage Scenario," Sustainability, MDPI, vol. 14(17), pages 1-16, August.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:10523-:d:895852
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    1. Zhang, Fan & Deng, Xiangzheng & Phillips, Fred & Fang, Chuanglin & Wang, Chao, 2020. "Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China," Technological Forecasting and Social Change, Elsevier, vol. 154(C).
    2. Frank Jotzo & John Pezzey, 2007. "Optimal intensity targets for greenhouse gas emissions trading under uncertainty," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 38(2), pages 259-284, October.
    3. Al-mulali, Usama, 2012. "Factors affecting CO2 emission in the Middle East: A panel data analysis," Energy, Elsevier, vol. 44(1), pages 564-569.
    4. Greening, Lorna A. & Davis, William B. & Schipper, Lee, 1998. "Decomposition of aggregate carbon intensity for the manufacturing sector: comparison of declining trends from 10 OECD countries for the period 1971-1991," Energy Economics, Elsevier, vol. 20(1), pages 43-65, February.
    5. Fan, Ying & Liu, Lan-Cui & Wu, Gang & Tsai, Hsien-Tang & Wei, Yi-Ming, 2007. "Changes in carbon intensity in China: Empirical findings from 1980-2003," Ecological Economics, Elsevier, vol. 62(3-4), pages 683-691, May.
    6. Erik Dietzenbacher & Bart Los, 1998. "Structural Decomposition Techniques: Sense and Sensitivity," Economic Systems Research, Taylor & Francis Journals, vol. 10(4), pages 307-324.
    7. Wei, Yi-Ming & Liu, Lan-Cui & Fan, Ying & Wu, Gang, 2007. "The impact of lifestyle on energy use and CO2 emission: An empirical analysis of China's residents," Energy Policy, Elsevier, vol. 35(1), pages 247-257, January.
    8. Liu, Xiaoyu & Duan, Zhiyuan & Shan, Yuli & Duan, Haiyan & Wang, Shuo & Song, Junnian & Wang, Xian'en, 2019. "Low-carbon developments in Northeast China: Evidence from cities," Applied Energy, Elsevier, vol. 236(C), pages 1019-1033.
    9. Li, Ke & Lin, Boqiang, 2015. "Impacts of urbanization and industrialization on energy consumption/CO2 emissions: Does the level of development matter?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1107-1122.
    10. Glaeser, Edward L. & Kahn, Matthew E., 2010. "The greenness of cities: Carbon dioxide emissions and urban development," Journal of Urban Economics, Elsevier, vol. 67(3), pages 404-418, May.
    11. Moomaw, William R. & Unruh, Gregory C., 1997. "Are environmental Kuznets curves misleading us? The case of CO2 emissions," Environment and Development Economics, Cambridge University Press, vol. 2(4), pages 451-463, November.
    12. Liu, Xingjian & Wang, Mingshu & Qiang, Wei & Wu, Kang & Wang, Xiaomi, 2020. "Urban form, shrinking cities, and residential carbon emissions: Evidence from Chinese city-regions," Applied Energy, Elsevier, vol. 261(C).
    13. Tianyi Zeng & Hong Jin & Zhifei Geng & Zihang Kang & Zichen Zhang, 2022. "The Effect of Urban Shrinkage on Carbon Dioxide Emissions Efficiency in Northeast China," IJERPH, MDPI, vol. 19(9), pages 1-18, May.
    14. Zhenjun Gao & Shujie Li & Xiufeng Cao & Yuefen Li, 2022. "Carbon Emission Intensity Characteristics and Spatial Spillover Effects in Counties in Northeast China: Based on a Spatial Econometric Model," Land, MDPI, vol. 11(5), pages 1-19, May.
    15. Xiao, Huijuan & Duan, Zhiyuan & Zhou, Ya & Zhang, Ning & Shan, Yuli & Lin, Xiyan & Liu, Guosheng, 2019. "CO2 emission patterns in shrinking and growing cities: A case study of Northeast China and the Yangtze River Delta," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    16. Yong Wang & Guangchun Yang & Ying Dong & Yu Cheng & Peipei Shang, 2018. "The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model," Energies, MDPI, vol. 11(5), pages 1-25, May.
    17. Nag, Barnali & Parikh, Jyoti, 2000. "Indicators of carbon emission intensity from commercial energy use in India," Energy Economics, Elsevier, vol. 22(4), pages 441-461, August.
    18. Joseph E. Aldy, 2007. "Divergence in State-Level Per Capita Carbon Dioxide Emissions," Land Economics, University of Wisconsin Press, vol. 83(3), pages 353-369.
    19. Wang, Ping & Wu, Wanshui & Zhu, Bangzhu & Wei, Yiming, 2013. "Examining the impact factors of energy-related CO2 emissions using the STIRPAT model in Guangdong Province, China," Applied Energy, Elsevier, vol. 106(C), pages 65-71.
    20. Knapp, Tom & Mookerjee, Rajen, 1996. "Population growth and global CO2 emissions : A secular perspective," Energy Policy, Elsevier, vol. 24(1), pages 31-37, January.
    21. Poumanyvong, Phetkeo & Kaneko, Shinji, 2010. "Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis," Ecological Economics, Elsevier, vol. 70(2), pages 434-444, December.
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