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Spatial Spillover Effects of Directed Technical Change on Urban Carbon Intensity, Based on 283 Cities in China from 2008 to 2019

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  • Hui Zhang

    (Fanli Business School, Nanyang Institute of Technology, Nanyang 473000, China)

  • Haiqian Ke

    (Fanli Business School, Nanyang Institute of Technology, Nanyang 473000, China
    Institute of Central China Development, Wuhan University, Wuhan 430072, China)

Abstract

Technical change essentially drives regional social and economic development, and how technical change influences the regional sustainable development of the ecological environment is also of concern. However, technical change is not always neutral, so how does directed technical change affect urban carbon intensity? Is there a spatial spillover effect between these two? In order to answer these above questions, this article first explores the relationship between directed technical change and carbon intensity through the spatial Durbin model; then, it separately analyses whether the relationship between the two in low-carbon and non-low-carbon cities will differ; finally, we performed a robustness test by replacing weights, replacing the explained variable with a lag of one period, and replacing the explained variable. The conclusions are as follows: (1) There is a positive spatial correlation between the carbon intensity of Chinese cities—that is, there is a positive interaction between the carbon intensity of local cities and of neighboring cities. For every 1% change in the carbon intensity of neighboring cities, the carbon intensity of local cities changes by 0.1027% in the same direction. (2) The directed technical change has a significant inhibitory effect on urban carbon intensity, whether in local cities or neighboring cities. However, it is worth mentioning that the direct negative effect is greater in local cities than in neighboring cities. (3) The directed technical change in low-carbon cities has a stronger inhibitory effect on carbon intensity, with a direct effect coefficient of −0.5346 and an indirect effect coefficient of −0.2616. Due to less green policy support in non-low-carbon cities, the inhibitory effect of directed technical change on carbon intensity is weakened; even if the direct effects and indirect effects are superimposed, it is only −0.0510 rather than −0.7962 for low-carbon cities.

Suggested Citation

  • Hui Zhang & Haiqian Ke, 2022. "Spatial Spillover Effects of Directed Technical Change on Urban Carbon Intensity, Based on 283 Cities in China from 2008 to 2019," IJERPH, MDPI, vol. 19(3), pages 1-19, February.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1679-:d:740324
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    1. Daron Acemoglu & Philippe Aghion & Leonardo Bursztyn & David Hemous, 2012. "The Environment and Directed Technical Change," American Economic Review, American Economic Association, vol. 102(1), pages 131-166, February.
    2. Rolf Färe & Emili Grifell‐Tatjé & Shawna Grosskopf & C. A. Knox Lovell, 1997. "Biased Technical Change and the Malmquist Productivity Index," Scandinavian Journal of Economics, Wiley Blackwell, vol. 99(1), pages 119-127, March.
    3. He, Yongxiu & Xu, Yang & Pang, Yuexia & Tian, Huiying & Wu, Rui, 2016. "A regulatory policy to promote renewable energy consumption in China: Review and future evolutionary path," Renewable Energy, Elsevier, vol. 89(C), pages 695-705.
    4. Fatouros, Nikos & Stengos, Thanasis, 2023. "Nuclear Energy, Economic Growth, and the Environment: Optimal policies in a model with endogenous technical change and environmental constraints," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    5. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    6. Daron Acemoglu & Philippe Aghion & Fabrizio Zilibotti, 2006. "Distance to Frontier, Selection, and Economic Growth," Journal of the European Economic Association, MIT Press, vol. 4(1), pages 37-74, March.
    7. Yu, Jian & Shi, Xunpeng & Guo, Dongmei & Yang, Longjian, 2021. "Economic policy uncertainty (EPU) and firm carbon emissions: Evidence using a China provincial EPU index," Energy Economics, Elsevier, vol. 94(C).
    8. Huang, Junbing & Xiang, Shiqi & Wu, Panling & Chen, Xiang, 2022. "How to control China's energy consumption through technological progress: A spatial heterogeneous investigation," Energy, Elsevier, vol. 238(PC).
    9. Wang, You & Gong, Xu, 2020. "Does financial development have a non-linear impact on energy consumption? Evidence from 30 provinces in China," Energy Economics, Elsevier, vol. 90(C).
    10. 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.
    11. Daron Acemoglu, 2007. "Equilibrium Bias of Technology," Econometrica, Econometric Society, vol. 75(5), pages 1371-1409, September.
    12. Achyuta Adhvaryu & Namrata Kala & Anant Nyshadham, 2020. "The Light and the Heat: Productivity Co-Benefits of Energy-Saving Technology," The Review of Economics and Statistics, MIT Press, vol. 102(4), pages 779-792, October.
    13. Daron Acemoglu, 1998. "Why Do New Technologies Complement Skills? Directed Technical Change and Wage Inequality," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 113(4), pages 1055-1089.
    14. Viliam Druska & William C. Horrace, 2004. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 185-198.
    15. Weber, William L. & Domazlicky, Bruce R., 1999. "Total factor productivity growth in manufacturing: a regional approach using linear programming," Regional Science and Urban Economics, Elsevier, vol. 29(1), pages 105-122, January.
    16. Mulligan, C. B. & Sala-i-Martin, X., 1997. "A labor income-based measure of the value of human capital: An application to the states of the United States," Japan and the World Economy, Elsevier, vol. 9(2), pages 159-191, May.
    17. Daron Acemoglu, 2002. "Directed Technical Change," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 781-809.
    18. Singh, Akshit & Mishra, Nishikant & Ali, Syed Imran & Shukla, Nagesh & Shankar, Ravi, 2015. "Cloud computing technology: Reducing carbon footprint in beef supply chain," International Journal of Production Economics, Elsevier, vol. 164(C), pages 462-471.
    19. Daron Acemoglu, 2003. "Labor- And Capital-Augmenting Technical Change," Journal of the European Economic Association, MIT Press, vol. 1(1), pages 1-37, March.
    20. Virgilio Gómez-Rubio & Roger S. Bivand & Håvard Rue, 2021. "Estimating Spatial Econometrics Models with Integrated Nested Laplace Approximation," Mathematics, MDPI, vol. 9(17), pages 1-23, August.
    21. Charles I. Jones, 2005. "The Shape of Production Functions and the Direction of Technical Change," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(2), pages 517-549.
    22. Anselin, Luc & Hudak, Sheri, 1992. "Spatial econometrics in practice : A review of software options," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 509-536, September.
    23. J. Paul Elhorst, 2014. "Matlab Software for Spatial Panels," International Regional Science Review, , vol. 37(3), pages 389-405, July.
    24. Antonelli, Cristiano & Feder, Christophe, 2020. "The new direction of technological change in the global economy," Structural Change and Economic Dynamics, Elsevier, vol. 52(C), pages 1-12.
    25. Köne, Aylin Çiğdem & Büke, Tayfun, 2019. "Factor analysis of projected carbon dioxide emissions according to the IPCC based sustainable emission scenario in Turkey," Renewable Energy, Elsevier, vol. 133(C), pages 914-918.
    26. Iris, Çağatay & Lam, Jasmine Siu Lee, 2019. "A review of energy efficiency in ports: Operational strategies, technologies and energy management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 170-182.
    27. David A. Stainforth & Raphael Calel, 2020. "New priorities for climate science and climate economics in the 2020s," Nature Communications, Nature, vol. 11(1), pages 1-3, December.
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