How Does Artificial Intelligence Empower Urban Pollution Governance? Evidence from the Spatial Durbin Model of 274 Cities in China

Ruizhi Chen; Lingfeng Zhu

doi:10.54097/pft10c02

Authors

Ruizhi Chen
Lingfeng Zhu

DOI:

https://doi.org/10.54097/pft10c02

Keywords:

Artificial Intelligence, Urban Pollution, Spatial Durbin Model

Abstract

Against the backdrop of the "dual-carbon" goals and the green development strategy, to explore the role of artificial intelligence (AI) in local pollution and emission reduction, this study employs the Spatial Durbin Model (SDM) and uses data from 274 prefecture-level and above cities in China spanning 2013 to 2022. The explained variable is the environmental pollution index (incorporating industrial wastewater discharge, industrial sulfur dioxide emission, and industrial smoke and dust emission) constructed via the entropy weight method. The core explanatory variable is the logarithmic value of the number of AI enterprises in each city, with control variables including urbanization rate and fiscal decentralization. The results show that: AI development exerts a significantly negative impact on local pollution (the main effect coefficient is -0.011, significant at the 1% level, under both geographic distance matrix and economic-geographic nested matrix); pollution exhibits strong spatial dependence (rho value ranges from 0.828 to 0.876); the spatial spillover effect of AI is heterogeneous; both environmental pollution and AI development demonstrate significant spatial agglomeration (Moran’s I index is significantly positive). This study provides a reference for leveraging intelligence to empower local pollution and emission reduction and facilitate the achievement of "dual-carbon" goals.

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References

[1] People’s Network. Report on the Development of Intelligent Internet [J]. Grand Society, 2023(12): 11.

[2] Lü Y, Ma M H, Chen Y C, et al. Artificial Intelligence Empowers Green Development [J]. Chinese Journal of Population, Resources and Environment, 2023, 33(10): 100-111.

[3] Shen Y, Guo X Y, Zhang X W. Intelligent Manufacturing, Environmental Pollution and Labor Price Distortion [J/OL]. Journal of Beijing University of Aeronautics and Astronautics (Social Sciences Edition): 1-9 [2024-05-06].

[4] Li Q, Liu Q F. Environmental Rule of Law and Environmental Pollution Level — An Empirical Analysis of 108 Cities in the Yangtze River Economic Belt [J]. Journal of Chongqing University (Social Sciences Edition), 2022, 28(05): 32-45.

[5] Liu Y W, Wang Y, Hu Z Y. An Empirical Study on the Impact of the Central Environmental Protection Inspection on Urban Air Quality in China — From the Perspective of the Institutional Change from "Environmental Inspection" to "Environmental Protection Inspection" [J]. China Soft Science, 2021(10): 21-31.

[6] Zhang R, Ye Y Y. Can the Central Environmental Protection Inspection Improve Corporate Environmental Performance? — Empirical Evidence from Listed Industrial Enterprises [J]. Journal of Jiangxi University of Finance and Economics, 2023(06): 13-26.

[7] Shen K R, Jin G. The Policy Effect of Local Government Environmental Governance in China — A Study Based on the Evolution of the "River Chief System" [J]. Social Sciences in China, 2018(05): 92-115+206.

[8] Wang S X, Ouyang P. How Do Free Trade Pilot Zones Promote Green Development? — An Empirical Study from the Perspective of Environmental Pollution [J/OL]. Journal of North China University of Water Resources and Electric Power (Social Sciences Edition): 1-12 [2024-05-06].

[9] Yang S Y, Li Z, Li J C. Research on the Environmental Effect of Industrial Intelligent Transformation and Its Mechanism [J]. Nankai Economic Studies, 2023(11): 186-209.

[10] Sun X H, Fu L, Yu R Q. The Environmental Pollution Effect of Inter-Regional Energy Misallocation [J]. Journal of Lanzhou University (Social Sciences Edition), 2023, 51(04): 26-43.

[11] Wang P, Xie L W. Pollution Control Investment, Corporate Technological Innovation and Pollution Control Efficiency [J]. Chinese Journal of Population, Resources and Environment, 2014, 24(09): 51-58.

[12] Fan D Z, Yu S. Can Large Generative Artificial Intelligence Models Boost the High-Quality Development of the Real Economy? Theoretical Mechanism, Practical Foundation and Policy Path [J]. Journal of Yunnan Minzu University (Philosophy and Social Sciences Edition), 2024, 41(01): 152-160.

[13] Liu J Q, Xue F, Ru S F. Research on the Impact of Artificial Intelligence Technology on Urban Economic Resilience [J]. Soft Science, 2024, 38(06): 13-19+60.

[14] He Q, Qiu Y, Xu G. Artificial Intelligence, Fiscal Expenditure Structure Bias and High-Quality Employment [J]. Research on Economics and Management, 2024, 45(02): 70-86.

[15] Sun W S, Sun X. Whose Jobs Will Artificial Intelligence "Take Away"? — Employment Substitution Risk of Heterogeneous Workers [J]. Journal of Hebei University of Economics and Business, 2024, 45(02): 66-76.

[16] Ye Y L, Zhang Q Z. Artificial Intelligence, Industrial Agglomeration Externality and Carbon Emission Efficiency of the Yangtze River Economic Belt [J]. Journal of Nantong University (Social Sciences Edition), 2024, 40(02): 55-65+159.

[17] Chai Z M, Zhang P D, Han X F. Can Industrial Intelligence Become a New Driver for Industrial Structure Upgrading? [J/OL]. Science & Technology Progress and Policy: 1-11 [2024-05-06].

[18] Liao G P, Qin J M. Can Green Technological Innovation Effectively Improve Environmental Quality? — From the Perspective of Fiscal Decentralization [J]. Technology Economics, 2022, 41(04): 17-29.

[19] Chen Z, Chen G H. Research on the Dynamic Strategy of Enterprises Participating in Voluntary Environmental Regulation Under Dual External Pressures [J]. Soft Science, 2022, 36(08): 130-137.

[20] Georg G, Pascual R, Nordström O S. Technology and the Labor Market [J]. Labour Economics, 2022, 76.

[21] Chang L, Taghizadeh-Hesary F, Mohsin M. The Role of Digitalization on Green Economic Growth: Does Industrial Structure Optimization and Green Innovation Matter? [J]. Resources Policy, 2023, 85: 103890.

[22] Acemoglu D, Restrepo P. Robots and Jobs: Evidence from US Labor Markets [J]. Journal of Political Economy, 2020, 128(6): 2188-2244.

[23] Vivanco D F, Kemp R, van der Voet E. How to Deal With the Rebound Effect? A Policy-Oriented Approach [J]. Energy Policy, 2016, 94: 114-125.

[24] Li Y, Zhang Y, Pan A, et al. Carbon Emission Reduction Effects of Industrial Robot Applications: Heterogeneity Characteristics and Influencing Mechanisms [J]. Technology in Society, 2022, 70: 102034.