Does Artificial Intelligence Reduce Urban Carbon Emission Intensity? Evidence from 272 Chinese Cities with Spatial and Threshold Perspectives

This repository provides a comprehensive dataset and supporting materials for analyzing the relationship between artificial intelligence, carbon emissions, and sustainable urban development in China. The collection covers <b>272 cities from 2006 to 2024</b> and is organized to ensure transparency, replicability, and flexibility for further research.<b>Contents</b>:<b>Full panel dataset</b>: Balanced panel data covering economic, technological, and environmental dimensions across cities and years.<b>Code package</b>: Stata code files to replicate the empirical analysis, including data cleaning, model estimation, and robustness checks.<b>Data formats</b>: The dataset is available in Stata (.dta) format for direct use, with supporting documentation for ease of application.<b>Spatial weight matrices</b>: Files constructed to support spatial econometric models, enabling the analysis of spatial dependence and spillover effects.<b>Data processing</b>: All sources were harmonized, standardized, and deflated to constant prices for temporal comparability. Outlier treatments and spatial structure construction were applied to improve robustness and enable advanced econometric modeling.<b>Applications</b>: The dataset and code can be used for econometric, mediation, moderation, and spatial analyses, as well as extended to broader studies on urban low-carbon transition, technological innovation, and policy evaluation. It is designed to be both reproducible and adaptable for future research.This integrated dataset provides a valuable resource for scholars and policymakers interested in climate change mitigation, digital transformation, and sustainable development in urban China.