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

Area of Science:

• Environmental Science
• Economics
• Urban Studies

Background:

• Big data is crucial for economic development, yet its impact on urban carbon unlocking efficiency (UCUE) is understudied.
• Existing research lacks a clear understanding of how big data policies influence UCUE.

Purpose of the Study:

• To investigate the impact of big data policies on urban carbon unlocking efficiency (UCUE).
• To explore the mechanisms through which big data policies affect UCUE.
• To analyze the heterogeneity of these effects across different city types.

Main Methods:

• Utilized advanced double machine learning (DML) methods.
• Treated big data comprehensive pilot zones (BDCPZ) as quasi-natural experiments.
• Employed panel data from 282 Chinese cities (2011-2022).

Main Results:

• Big data policies significantly enhance UCUE, confirmed by alternative machine learning models.
• UCUE improvement occurs via government modernization, enterprise intelligent development, and economic transformation.
• Benefits are greater in large cities, old industrial bases, digital economy cities, and key environmental protection cities.

Conclusions:

• Big data policies are effective tools for improving urban carbon unlocking efficiency.
• Policy implications include strengthening big data infrastructure and promoting green development.
• Findings support achieving China's 'dual carbon' targets.