How Artificial Intelligence Pilot Zones Enhance Corporate Green Resilience? Evidence from China’s Listed Firms with Double Machine Learning

In the context of extreme climate events and increasingly stringent environmental regulation, insufficient corporate green resilience has become a micro-level bottleneck to achieving China’s “dual-carbon” targets. Using panel data on Chinese A-share listed firms from 2015 to 2023, this study treats the approval of the National Pilot Zone for Artificial Intelligence Innovation Applications as a quasi-natural experiment and employs a double machine learning (DML)–augmented difference-in-differences framework to estimate the causal impact of the policy on firms’ green resilience. We find that the pilot-zone policy significantly increases corporate green resilience by about 32%, with stronger effects among high-tech firms, non-heavily polluting industries, regulated sectors, and large enterprises. Mechanism analyses show that the policy improves green resilience through four channels—accelerating green innovation, enhancing supply-chain efficiency, alleviating financing constraints, and reducing operating costs—with innovation and supply-chain efficiency playing dominant roles. These findings provide firm-level causal evidence that AI-oriented place-based policies can strengthen firms’ capability to sustain green development under disturbances and inform the coordination of the “Digital China” and “Dual Carbon” agendas.

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Publication Details

Journal
Sustainability
Published
2026-05-27
DOI
https://doi.org/10.3390/su18115388
Primary Topic
Supply Chain Resilience and Risk Management
Type
article
Field-Weighted Citation Impact
0.00
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article

How Artificial Intelligence Pilot Zones Enhance Corporate Green Resilience? Evidence from China’s Listed Firms with Double Machine Learning

Xihao Zeng, Jingru Gao, Guilin Xu, Yuzeng Xin
Sustainability
Supply Chain Resilience and Risk Management
article

How Artificial Intelligence Pilot Zones Enhance Corporate Green Resilience? Evidence from China’s Listed Firms with Double Machine Learning

Xihao Zeng, Jingru Gao, Guilin Xu, Yuzeng Xin
article en

Abstract

In the context of extreme climate events and increasingly stringent environmental regulation, insufficient corporate green resilience has become a micro-level bottleneck to achieving China’s “dual-carbon” targets. Using panel data on Chinese A-share listed firms from 2015 to 2023, this study treats the approval of the National Pilot Zone for Artificial Intelligence Innovation Applications as a quasi-natural experiment and employs a double machine learning (DML)–augmented difference-in-differences framework to estimate the causal impact of the policy on firms’ green resilience. We find that the pilot-zone policy significantly increases corporate green resilience by about 32%, with stronger effects among high-tech firms, non-heavily polluting industries, regulated sectors, and large enterprises. Mechanism analyses show that the policy improves green resilience through four channels—accelerating green innovation, enhancing supply-chain efficiency, alleviating financing constraints, and reducing operating costs—with innovation and supply-chain efficiency playing dominant roles. These findings provide firm-level causal evidence that AI-oriented place-based policies can strengthen firms’ capability to sustain green development under disturbances and inform the coordination of the “Digital China” and “Dual Carbon” agendas.

SustainabilityVol. 18(11)
Guangxi University (CN), Hainan University (CN), Hainan Agricultural School (CN), Nanning Normal University (CN)
Climate action
Openalex Percentile: Top 5%
Supply Chain Resilience and Risk Management
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