新一代国家人工智能创新发展试验区建设如何推动企业ESG发展？来自中国的经验证据

IF 13.6 2区经济学 Q1 ECONOMICS

Energy Economics Pub Date : 2024-10-29 DOI:10.1016/j.eneco.2024.108011

Yujie Huang , Shucheng Liu , Jiawu Gan , Baoliu Liu , Yuxi Wu

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引用次数: 0

摘要

在人工智能（AI）技术飞速发展、企业ESG绩效日益受到全球关注的背景下，本研究以 "国家新一代人工智能创新发展试验区 "为准自然实验。基于2007-2022年中国沪深两市上市公司的非平衡面板数据，采用多期差分模型（DID）和倾向得分匹配-差分模型（PSM-DID）方法，探讨人工智能试点政策对企业ESG绩效的影响和作用机制。实证结果表明，该政策显著改善了企业的ESG绩效，并通过平行趋势检验、安慰剂检验、PSM-DID检验等验证了结论的稳健性。异质性分析表明，该政策在不同地区、不同行业的效果不同，在东部、中部地区以及非国有企业和重污染行业的响应更为明显。对影响机制的分析证实了绿色技术创新和研发支出水平的关键作用。最后，本文提出了制定差异化政策、搭建创新平台、加强研发投入、建立监测评估机制等政策建议，以促进企业在ESG绩效中有效实施人工智能技术应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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How does the construction of new generation of national AI innovative development pilot zones drive enterprise ESG development? Empirical evidence from China

In the context of the rapid development of artificial intelligence (AI) technology and the growing global attention to the ESG performance of enterprises, this study takes the “National New Generation Artificial Intelligence Innovation and Development Pilot Zone” as a quasi-natural experiment. Based on the unbalanced panel data of Chinese Shanghai and Shenzhen listed companies from 2007 to 2022, it uses the multi-period difference-in-differences model (DID) and the propensity score matching-difference-in-differences (PSM-DID) method to explore the impact and mechanism of the AI pilot policy on the ESG performance of enterprises. The empirical results show that this policy significantly improves the ESG performance of enterprises, and the robustness of the conclusion is verified through parallel trend tests, placebo tests, PSM-DID tests, etc. The heterogeneity analysis shows that the policy has different effects in different regions and industries, and the response is more significant in the eastern and central regions, as well as non-state-owned enterprises and heavily polluting industries. The analysis of the impact mechanism confirms the key role of green technology innovation and the level of R&D expenditure. Finally, this paper puts forward policy suggestions such as formulating differentiated policies, building innovation platforms, enhancing R&D investment, and establishing monitoring and evaluation mechanisms to promote the effective implementation of AI technology application by enterprises in ESG performance.

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来源期刊

Energy Economics ECONOMICS-

CiteScore

18.60

自引率

12.50%

发文量

524

期刊介绍： Energy Economics is a field journal that focuses on energy economics and energy finance. It covers various themes including the exploitation, conversion, and use of energy, markets for energy commodities and derivatives, regulation and taxation, forecasting, environment and climate, international trade, development, and monetary policy. The journal welcomes contributions that utilize diverse methods such as experiments, surveys, econometrics, decomposition, simulation models, equilibrium models, optimization models, and analytical models. It publishes a combination of papers employing different methods to explore a wide range of topics. The journal's replication policy encourages the submission of replication studies, wherein researchers reproduce and extend the key results of original studies while explaining any differences. Energy Economics is indexed and abstracted in several databases including Environmental Abstracts, Fuel and Energy Abstracts, Social Sciences Citation Index, GEOBASE, Social & Behavioral Sciences, Journal of Economic Literature, INSPEC, and more.