Time Series Evidence on Artificial Intelligence and Green Transformation: The Impact of AI Policy on Corporate Carbon Performance

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🔬研究者:Offers a robust causal identification strategy using panel time series methods for evaluating AI's impact on carbon performance.

🏢実務担当者:Demonstrates how AI policy can be leveraged to improve corporate carbon performance, informing corporate sustainability strategy.

🏛政策担当者:Provides evidence that AI pilot zones can enhance carbon performance, suggesting policy design for green transformation.

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development.

• semanticscholar https://doi.org/10.3390/math14091489first seen 2026-05-15 21:24:24