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ORIGINAL RESEARCH
How does Artificial Intelligence Affect Carbon
Emission Efficiency? Empirical Evidence
from the Pearl River Delta in China
1
School of Economics and Management, Huaibei Normal University, Huaibei 235000, China
Submission date: 2024-10-08
Final revision date: 2024-11-17
Acceptance date: 2024-12-16
Online publication date: 2025-03-03
Publication date: 2026-01-30
Corresponding author
Pol. J. Environ. Stud. 2026;35(1):1455-1468
KEYWORDS
carbon emission efficiencyartificial intelligencesuper-EBM modelindustrial structure upgradingenergy efficiency
TOPICS
ABSTRACT
In China, the Pearl River Delta (PRD) plays a leading role as not only an artificial intelligence (AI)
innovation hotspot but also a pilot zone for green and low-carbon development. The Super-EBM model
was used to measure the PRD’s carbon emission efficiency (CEE) from 2006 to 2021. On this basis, dual
fixed effect, mediation effect, and threshold effect regression estimation approaches are used to analyze
the influence of AI on CEE and its internal mechanism. The results show that AI can significantly
improve the CEE, and this conclusion remains true after endogenous and robustness tests such as
difference-in-difference (DID), time lag effect, independent variable replacement, and split‑sample
tests. Mechanism analysis reveals that industrial structure upgrading and energy efficiency are two
basic paths for improving CEE. The analysis of the panel threshold regression model and heterogeneity
test shows that with industrial structure upgrading and energy efficiency improvement, AI has a more
significant effect on promoting CEE, with that effect being more prominent in the PRD’s core cities.
The government should vigorously promote the deep integration of AI and the low-carbon economy,
give full play to the indirect driving role of industrial structure upgrading and energy efficiency,
strengthen regional cooperation, promote the coordinated development of various regions, and
implement differentiated low-carbon transformation policies.
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