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Impact of Urban Form on CO 2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China

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  • Ran Guo

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

  • Hong Leng

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

  • Qing Yuan

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

  • Shiyi Song

    (School of Architecture, Xi’an University of Architecture and Technology, Xi’an 710055, China)

Abstract

The accurate estimation of the impact of urban form on CO 2 emissions is essential for the proposal of effective low-carbon spatial planning strategies. However, few studies have focused on the relationship between urban form and CO 2 emissions in small and medium-sized cities, and it is especially unclear whether the relationship varies across cities with different socioeconomic characteristics. This study took 132 small and medium-sized cities in the Yangtze River Delta in China to explore how urban form affects CO 2 emissions, considering the socioeconomic factors of industrial structure, population density, and economic development level. First, nighttime light data (DMSP-OLS and NPP-VIIRS) and provincial energy data were used to calculate CO 2 emissions. Second, four landscape metrics were used to quantify the compactness and complexity of the urban form based on Chinese urban land-use data. Finally, panel data models were established to analyze whether and how different socioeconomic factors impacted the relationship between urban form and CO 2 emissions. The results showed that the three socioeconomic factors mentioned above all had obvious influences on the relationship between urban form and per capita CO 2 emissions in small and medium-sized cities. The effect of compactness on per-capita CO 2 emissions increased with a rise in the proportion of the tertiary industry, population density, and per-capita GDP. However, compactness shows no effects on per-capita CO 2 emissions in industrial cities and low-development-level cities. The effect of complexity on per-capita CO 2 emissions only increased with the rise in population density. The results may support decision-makers in small and medium-sized cities to propose accurate, comprehensive, and differentiated plans for CO 2 emission control and reduction.

Suggested Citation

  • Ran Guo & Hong Leng & Qing Yuan & Shiyi Song, 2022. "Impact of Urban Form on CO 2 Emissions under Different Socioeconomic Factors: Evidence from 132 Small and Medium-Sized Cities in China," Land, MDPI, vol. 11(5), pages 1-20, May.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:5:p:713-:d:811961
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    as
    1. Zhao, Jincai & Ji, Guangxing & Yue, YanLin & Lai, Zhizhu & Chen, Yulong & Yang, Dongyang & Yang, Xu & Wang, Zheng, 2019. "Spatio-temporal dynamics of urban residential CO2 emissions and their driving forces in China using the integrated two nighttime light datasets," Applied Energy, Elsevier, vol. 235(C), pages 612-624.
    2. Tao Lin & Yunjun Yu & Xuemei Bai & Ling Feng & Jin Wang, 2013. "Greenhouse Gas Emissions Accounting of Urban Residential Consumption: A Household Survey Based Approach," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-12, February.
    3. 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).
    4. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    5. Wang, Dongeen & Lin, Tao, 2014. "Residential self-selection, built environment, and travel behavior in the Chinese context," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(3), pages 5-14.
    6. Wang, Shaojian & Liu, Xiaoping, 2017. "China’s city-level energy-related CO2 emissions: Spatiotemporal patterns and driving forces," Applied Energy, Elsevier, vol. 200(C), pages 204-214.
    7. Zhou, Yang & Liu, Yansui, 2016. "Does population have a larger impact on carbon dioxide emissions than income? Evidence from a cross-regional panel analysis in China," Applied Energy, Elsevier, vol. 180(C), pages 800-809.
    8. Karen C. Seto & Robert K. Kaufmann, 2003. "Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data," Land Economics, University of Wisconsin Press, vol. 79(1), pages 106-121.
    9. Wang, Shaojian & Liu, Xiaoping & Zhou, Chunshan & Hu, Jincan & Ou, Jinpei, 2017. "Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO2 emissions in China’s megacities," Applied Energy, Elsevier, vol. 185(P1), pages 189-200.
    10. He, Sanwei & Yu, Shan & Li, Guangdong & Zhang, Junfeng, 2020. "Exploring the influence of urban form on land-use efficiency from a spatiotemporal heterogeneity perspective: Evidence from 336 Chinese cities," Land Use Policy, Elsevier, vol. 95(C).
    11. Tian, Guangjin & Jiang, Jing & Yang, Zhifeng & Zhang, Yaoqi, 2011. "The urban growth, size distribution and spatio-temporal dynamic pattern of the Yangtze River Delta megalopolitan region, China," Ecological Modelling, Elsevier, vol. 222(3), pages 865-878.
    12. Fang, Chuanglin & Wang, Shaojian & Li, Guangdong, 2015. "Changing urban forms and carbon dioxide emissions in China: A case study of 30 provincial capital cities," Applied Energy, Elsevier, vol. 158(C), pages 519-531.
    13. Camagni, Roberto & Gibelli, Maria Cristina & Rigamonti, Paolo, 2002. "Urban mobility and urban form: the social and environmental costs of different patterns of urban expansion," Ecological Economics, Elsevier, vol. 40(2), pages 199-216, February.
    14. Yu Song & Guofan Shao & Xiaodong Song & Yong Liu & Lei Pan & Hong Ye, 2017. "The Relationships between Urban Form and Urban Commuting: An Empirical Study in China," Sustainability, MDPI, vol. 9(7), pages 1-17, July.
    15. 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.
    16. Harris, Richard D. F. & Tzavalis, Elias, 1999. "Inference for unit roots in dynamic panels where the time dimension is fixed," Journal of Econometrics, Elsevier, vol. 91(2), pages 201-226, August.
    17. Littlefair, Paul, 1998. "Passive solar urban design : ensuring the penetration of solar energy into the city," Renewable and Sustainable Energy Reviews, Elsevier, vol. 2(3), pages 303-326, September.
    18. Shi, Kaifang & Chen, Yun & Yu, Bailang & Xu, Tingbao & Chen, Zuoqi & Liu, Rui & Li, Linyi & Wu, Jianping, 2016. "Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis," Applied Energy, Elsevier, vol. 168(C), pages 523-533.
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    2. Yichen Ding & Yaping Huang & Lairong Xie & Shiwei Lu & Leizhou Zhu & Chunguang Hu & Yidan Chen, 2022. "Spatial Patterns Exploration and Impacts Modelling of Carbon Emissions: Evidence from Three Stages of Metropolitan Areas in the YREB, China," Land, MDPI, vol. 11(10), pages 1-18, October.

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