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Cultural Industry Agglomeration and Carbon Emission Performance: Empirical Analysis Based on 276 Cities in China

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  • Tinglei Hao

    (School of Management, Wuhan Polytechnic University, Wuhan 430048, China)

  • Jiajie Ren

    (School of Management, Wuhan Polytechnic University, Wuhan 430048, China)

  • Chuanming Sun

    (National Research Center of Cultural Industries, Central China Normal University, Wuhan 430079, China)

  • Lu Chen

    (School of Management, Wuhan Polytechnic University, Wuhan 430048, China)

  • Tao Liu

    (National Research Center of Cultural Industries, Central China Normal University, Wuhan 430079, China)

Abstract

This study investigated the influence of cultural industry agglomeration on the energy carbon emission performance (CEP). Based on panel data from 276 cities in China, we used the Super-SBM model to measure the CEP. We then used the Tobit regression model to calculate the influence coefficient of cultural industry agglomeration and eight control variables on the CEP and analyzed the complex effects of cultural industry agglomeration on the CEP. The results showed that there is the phenomenon of “diseconomies of agglomeration” in cultural industry agglomeration, which cannot improve the CEP. For each unit of cultural industry agglomeration increase, the CEP decreases by 0.055; however, this phenomenon is not linear. Further research showed that the effects of cultural industry agglomeration showed a trend from good to inferior in the order of east, central, and west and did not improve with time. Finally, we used the panel quantile regression model and found that as the CEP levels rise, the negative impact of cultural industry agglomeration improves. Our research results show that strengthening the technical level to promote the upgrading of the cultural industry is the best way to achieve sustainable development. Governments at all levels should pay attention to the emission reduction potential of cultural industry agglomeration under high CEP levels and strengthen the benign agglomeration of the cultural industry.

Suggested Citation

  • Tinglei Hao & Jiajie Ren & Chuanming Sun & Lu Chen & Tao Liu, 2024. "Cultural Industry Agglomeration and Carbon Emission Performance: Empirical Analysis Based on 276 Cities in China," Sustainability, MDPI, vol. 16(20), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9028-:d:1501587
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    1. C. Cindy Fan & Allen J. Scott, 2003. "Industrial Agglomeration and Development: A Survey of Spatial Economic Issues in East Asia and a Statistical Analysis of Chinese Regions," Economic Geography, Taylor & Francis Journals, vol. 79(3), pages 295-319, July.
    2. Kunpeng Ai & Ning Xu, 2023. "Does Regional Integration Improve Carbon Emission Performance?—A Quasi-Natural Experiment on Regional Integration in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    3. Yang, Zikun & Zhang, Mingming & Liu, Liyun & Zhou, Dequn, 2022. "Can renewable energy investment reduce carbon dioxide emissions? Evidence from scale and structure," Energy Economics, Elsevier, vol. 112(C).
    4. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    5. Abdullah A. Aljughaiman & Ngan D. Cao & Mohammed S. Albarrak & Abdulateif A. Almulhim, 2024. "Influence of Cultural and Environmental Values of CEOs on Greenhouse Gas Emission Intensity," Sustainability, MDPI, vol. 16(2), pages 1-24, January.
    6. Erli Dan & Jianfei Shen & Xinyuan Zheng & Peng Liu & Ludan Zhang & Feiyu Chen, 2023. "Asset Structure, Asset Utilization Efficiency, and Carbon Emission Performance: Evidence from Panel Data of China’s Low-Carbon Industry," Sustainability, MDPI, vol. 15(7), pages 1-20, April.
    7. Wang, Yafei & Bai, Ying & Quan, Tianshu & Ran, Rong & Hua, Lei, 2023. "Influence and effect of industrial agglomeration on urban green total factor productivity—On the regulatory role of innovation agglomeration and institutional distance," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1158-1173.
    8. Polemis, Michael L. & Stengos, Thanasis & Tzeremes, Panayiotis & Tzeremes, Nickolaos G., 2021. "Quantile eco-efficiency estimation and convergence: A nonparametric frontier approach," Economics Letters, Elsevier, vol. 202(C).
    9. Wen, Yuyuan & Yu, Zilong & Xue, Jingjing & Liu, Yang, 2024. "How heterogeneous industrial agglomeration impacts energy efficiency subject to technological innovation:Evidence from the spatial threshold model," Energy Economics, Elsevier, vol. 136(C).
    10. Guangliang Li & Chunlan Tan & Weikun Zhang & Wolin Zheng & Yong Liu, 2023. "Carbon Emission Efficiency, Technological Progress, and Fishery Scale Expansion: Evidence from Marine Fishery in China," Sustainability, MDPI, vol. 15(8), pages 1-20, April.
    11. Umar, Muhammad & Mirza, Nawazish & Hasnaoui, Jamila Abaidi & Rochoń, Małgorzata Porada, 2022. "The nexus of carbon emissions, oil price volatility, and human capital efficiency," Resources Policy, Elsevier, vol. 78(C).
    12. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    13. Grebitus, Carola & Steiner, Bodo & Veeman, Michele M., 2016. "Paying for sustainability: A cross-cultural analysis of consumers’ valuations of food and non-food products labeled for carbon and water footprints," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 63(C), pages 50-58.
    14. Liu, Xiaoxi & Razzaq, Asif & Shahzad, Mohsin & Irfan, Muhammad, 2022. "Technological changes, financial development and ecological consequences: A comparative study of developed and developing economies," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    15. Haibo Chen & Jiawei Lu, 2023. "Does Cultural Agglomeration Affect Green Total Factor Productivity? Evidence from 279 Cities in China," Sustainability, MDPI, vol. 15(9), pages 1-23, April.
    16. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
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