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The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

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  • Na Lu

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

  • Shuyi Feng

    (College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China)

  • Ziming Liu

    (School of Social and Public Administration, East China University of Science and Technology, Shanghai 200237, China)

  • Weidong Wang

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

  • Hualiang Lu

    (School of Business, Changzhou University, Changzhou 213146, China)

  • Miao Wang

    (School of Finance and Economics, Jiangsu University, Zhenjiang 212013, China)

Abstract

As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

Suggested Citation

  • Na Lu & Shuyi Feng & Ziming Liu & Weidong Wang & Hualiang Lu & Miao Wang, 2020. "The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1428-:d:320855
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    References listed on IDEAS

    as
    1. Liu, Gengyuan & Yang, Zhifeng & Chen, Bin & Zhang, Yan & Su, Meirong & Ulgiati, Sergio, 2016. "Prevention and control policy analysis for energy-related regional pollution management in China," Applied Energy, Elsevier, vol. 166(C), pages 292-300.
    2. Marbuah, George & Amuakwa-Mensah, Franklin, 2017. "Spatial analysis of emissions in Sweden," Energy Economics, Elsevier, vol. 68(C), pages 383-394.
    3. Jia, Junsong & Deng, Hongbing & Duan, Jing & Zhao, Jingzhu, 2009. "Analysis of the major drivers of the ecological footprint using the STIRPAT model and the PLS method--A case study in Henan Province, China," Ecological Economics, Elsevier, vol. 68(11), pages 2818-2824, September.
    4. Anselin, Luc & Bera, Anil K. & Florax, Raymond & Yoon, Mann J., 1996. "Simple diagnostic tests for spatial dependence," Regional Science and Urban Economics, Elsevier, vol. 26(1), pages 77-104, February.
    5. Hoekstra, Rutger & van den Bergh, Jeroen C. J. M., 2003. "Comparing structural decomposition analysis and index," Energy Economics, Elsevier, vol. 25(1), pages 39-64, January.
    6. Yang, Yuan & Cai, Wenjia & Wang, Can, 2014. "Industrial CO2 intensity, indigenous innovation and R&D spillovers in China’s provinces," Applied Energy, Elsevier, vol. 131(C), pages 117-127.
    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. Vélez-Henao, Johan-Andrés & Font Vivanco, David & Hernández-Riveros, Jesús-Antonio, 2019. "Technological change and the rebound effect in the STIRPAT model: A critical view," Energy Policy, Elsevier, vol. 129(C), pages 1372-1381.
    9. Zoltan J. Acs & Luc Anselin & Attila Varga, 2008. "Patents and Innovation Counts as Measures of Regional Production of New Knowledge," Chapters, in: Entrepreneurship, Growth and Public Policy, chapter 11, pages 135-151, Edward Elgar Publishing.
    10. Zeng, Lin & Xu, Ming & Liang, Sai & Zeng, Siyu & Zhang, Tianzhu, 2014. "Revisiting drivers of energy intensity in China during 1997–2007: A structural decomposition analysis," Energy Policy, Elsevier, vol. 67(C), pages 640-647.
    11. David M. Drukker & Hua Peng & Ingmar Prucha & Rafal Raciborski, 2013. "Creating and managing spatial-weighting matrices with the spmat command," Stata Journal, StataCorp LP, vol. 13(2), pages 242-286, June.
    12. Zhao, Hongli & Lin, Boqiang, 2019. "Will agglomeration improve the energy efficiency in China’s textile industry: Evidence and policy implications," Applied Energy, Elsevier, vol. 237(C), pages 326-337.
    13. Hong, Jingke & Shen, Qiping & Xue, Fan, 2016. "A multi-regional structural path analysis of the energy supply chain in China's construction industry," Energy Policy, Elsevier, vol. 92(C), pages 56-68.
    14. Jean O. Lanjouw & Mark Schankerman, 2004. "Patent Quality and Research Productivity: Measuring Innovation with Multiple Indicators," Economic Journal, Royal Economic Society, vol. 114(495), pages 441-465, April.
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    Cited by:

    1. Haidong Gao & Tiantian Li & Jing Yu & Yangrui Sun & Shijie Xie, 2023. "Spatial Correlation Network Structure of Carbon Emission Efficiency in China’s Construction Industry and Its Formation Mechanism," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    2. Siyao Li & Qiaosheng Wu & You Zheng & Qi Sun, 2021. "Study on the Spatial Association and Influencing Factors of Carbon Emissions from the Chinese Construction Industry," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    3. Yuling Sun & Junsong Jia & Min Ju & Chundi Chen, 2022. "Spatiotemporal Dynamics of Direct Carbon Emission and Policy Implication of Energy Transition for China’s Residential Consumption Sector by the Methods of Social Network Analysis and Geographically We," Land, MDPI, vol. 11(7), pages 1-26, July.
    4. Qiongzhi Liu & Dapeng Zhao, 2023. "Study on the Spatial Characteristics and Spillover Effects of Carbon Emissions in the Yangtze River (Main Stream) Basin," Energies, MDPI, vol. 16(3), pages 1-18, January.
    5. Lu Zhang & Renyan Mu & Nigatu Mengesha Fentaw & Yuanfang Zhan & Feng Zhang & Jixin Zhang, 2022. "Industrial Coagglomeration, Green Innovation, and Manufacturing Carbon Emissions: Coagglomeration’s Dynamic Evolution Perspective," IJERPH, MDPI, vol. 19(21), pages 1-19, October.
    6. Yan Wang & Xi Wu, 2022. "Research on High-Quality Development Evaluation, Space–Time Characteristics and Driving Factors of China’s Construction Industry under Carbon Emission Constraints," Sustainability, MDPI, vol. 14(17), pages 1-19, August.
    7. Panda Su & Yu Wang, 2022. "Does It Help Carbon Reduction in China? A Research Paper about the Mediating Role of Production Automation Based on the Carbon Kuznets Curve," Sustainability, MDPI, vol. 14(23), pages 1-18, November.

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