Can big data policy drive urban carbon unlocking efficiency? A new approach based on double machine learning

Abstract

In recent years, data has increasingly become the "new oil" for 21st-century economic development. However, there is still a gap in how the development of big data promotes the improvement of urban carbon unlocking efficiency (UCUE). Utilizing advanced double machine learning (DML) methods, and treating the big data comprehensive pilot zone (BDCPZ) as a quasi-natural experiment, we employ panel data from 282 Chinese cities spanning 2011 to 2022 to study the impact of big data policies on UCUE and its mechanisms. The study finds that: (1) Big data policies significantly enhance carbon unlocking efficiency, and their importance in carbon unlocking is confirmed even when alternative machine learning models are used.(2) Regarding the mechanisms, big data policies improve carbon unlocking efficiency through three pathways: government modernization, enterprise intelligent development, and economic transformation.(3) Heterogeneity analysis reveals that the carbon unlocking benefits of big data policies are more pronounced in large cities, old industrial base cities, digital economy dividend cities and key environmental protection cities. We also provide insights for strengthening the construction of big data, alleviating carbon emission pressures, and achieving the goals of "dual carbon".

Keywords: Big data policy; Double machine learning; Mechanism analysis; Urban carbon unlocking efficiency.