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

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.

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Publication Details

Journal
Sustainability
Published
2024-10-18
DOI
https://doi.org/10.3390/su16209028
Citations
2
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
2.26

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article

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

Chuanming Sun, Chen Lu, Tianyao Hao, junliang ren et al.
2 citations
Sustainability
Energy, Environment, Economic Growth
2.26
article

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

Chuanming Sun, Chen Lu, Tianyao Hao, junliang ren, Tao Liu
article en
2 citations

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.

SustainabilityVol. 16(20)
Wuhan Polytechnic University (CN), Central China Normal University (CN)
National Office for Philosophy and Social Sciences
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Energy, Environment, Economic Growth
2.26
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Cultural Industry Agglomeration and Carbon Emission Performance: Empirical Analysis Based on 276 Cities in China — Chuanming Sun, Chen Lu, et al. · Sustainability (2024) | TGRS Research Map | TGRS