IDEAS home Printed from
   My bibliography  Save this article

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


  • 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)


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

    Download full text from publisher

    File URL:
    Download Restriction: no

    File URL:
    Download Restriction: no

    References listed on IDEAS

    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    Full references (including those not matched with items on IDEAS)


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Song, Yi & Huang, Jianbai & Zhang, Yijun & Wang, Zhiping, 2019. "Drivers of metal consumption in China: An input-output structural decomposition analysis," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    3. Ling Yang & Michael L. Lahr, 2019. "The Drivers of China’s Regional Carbon Emission Change—A Structural Decomposition Analysis from 1997 to 2007," Sustainability, MDPI, vol. 11(12), pages 1-18, June.
    4. Demidova, Olga, 2021. "Methods of spatial econometrics and evaluation of government programs effectiveness," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 107-134.
    5. Shang, Hua & Jiang, Li & Pan, Xianyou & Pan, Xiongfeng, 2022. "Green technology innovation spillover effect and urban eco-efficiency convergence: Evidence from Chinese cities," Energy Economics, Elsevier, vol. 114(C).
    6. Sun, Xudong & Li, Jiashuo & Qiao, Han & Zhang, Bo, 2017. "Energy implications of China's regional development: New insights from multi-regional input-output analysis," Applied Energy, Elsevier, vol. 196(C), pages 118-131.
    7. Valerien O. Pede & Raymond J. G. M. Florax & Henri L. F. De Groot, 2007. "Technological Leadership, Human Capital and Economic Growth: a Spatial Econometric Analysis for US Counties, 1969-2003," Annals of Economics and Statistics, GENES, issue 87-88, pages 103-124.
    8. Kenneth Zahringer & Christos Kolympiris & Nicholas Kalaitzandonakes, 2017. "Academic knowledge quality differentials and the quality of firm innovation," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(5), pages 821-844.
    9. Hongyun Han & Shu Wu, 2018. "Structural Change and Its Impact on the Energy Intensity of Agricultural Sector in China," Sustainability, MDPI, vol. 10(12), pages 1-23, December.
    10. Bivand, Roger & Piras, Gianfranco, 2015. "Comparing Implementations of Estimation Methods for Spatial Econometrics," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i18).
    11. Wang, Miao & Feng, Chao, 2018. "Decomposing the change in energy consumption in China's nonferrous metal industry: An empirical analysis based on the LMDI method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2652-2663.
    12. Li, Li & Hong, Xuefei & Wang, Jun, 2020. "Evaluating the impact of clean energy consumption and factor allocation on China’s air pollution: A spatial econometric approach," Energy, Elsevier, vol. 195(C).
    13. Ron Boschma & Ernest Miguelez & Rosina Moreno & Diego B. Ocampo-Corrales, 2021. "Technological breakthroughs in European regions: the role of related and unrelated combinations," Papers in Evolutionary Economic Geography (PEEG) 2118, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jun 2021.
    14. Pfister, Curdin & Koomen, Miriam & Harhoff, Dietmar & Backes-Gellner, Uschi, 2021. "Regional innovation effects of applied research institutions," Research Policy, Elsevier, vol. 50(4).
    15. PU, Zhengning & YUE, Shujing & GAO, Peng, 2020. "The driving factors of China's embodied carbon emissions," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    16. Tom Broekel & Thomas Brenner, 2011. "Regional factors and innovativeness: an empirical analysis of four German industries," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 47(1), pages 169-194, August.
    17. Pieter E. Stek, 2021. "Identifying spatial technology clusters from patenting concentrations using heat map kernel density estimation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 911-930, February.
    18. Pede, Valerien O. & Florax, Raymond J.G.M. & de Groot, Henri L.F., 2006. "The Role of Knowledge Externalities in the Spatial Distribution of Economic Growth: A Spatial Econometric Analysis for US Counties, 1969-2003," 2006 Annual meeting, July 23-26, Long Beach, CA 21157, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    19. Darío Serrano-Puente, 2021. "Are we moving toward an energy-efficient low-carbon economy? An input–output LMDI decomposition of CO $$_{2}$$ 2 emissions for Spain and the EU28," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(2), pages 151-229, June.
    20. Alexandre Almeida & Aurora A.C. Teixeira, 2007. "Does Patenting negatively impact on R&D investment?An international panel data assessment," FEP Working Papers 255, Universidade do Porto, Faculdade de Economia do Porto.


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:4:p:1428-:d:320855. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.