The impact of AI on enterprise energy management: from the perspective of carbon emissions | Science and Technology for Energy Transition (STET)

Emerging Advances in Hybrid Renewable Energy Systems and Integration

Open Access

Table 10

Variable substitution method.

	(1)	(2)	(3)	(4)	(5)
	carbon1	carbon1	carbon1	carbon1	carbon1
AI	−0.0008***	−0.0005***	−0.0011***	−0.0015***	−0.0015***
AI	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
AI²			0.0000***	0.0000***	0.0000***
AI²			(0.000)	(0.000)	(0.000)
AI³				−0.0000***	−0.0000***
AI³				(0.000)	(0.000)
CV	NO	YES	YES	YES	YES
FE	YES	YES	YES	YES	YES
N	30920	30900	30900	30900	30900
R²	0.6069	0.7540	0.7550	0.7552	0.7552

Note: *, **, and ***denote statistical significance at the 10%, 5%, and 1% levels, respectively. CV stands for “Control Variables.” Column (1) presents the regression results without control variables; Column (2) presents the regression results with added control variables; Column (3) presents the regression results with the addition of the squared term of AI; Column (4) presents the regression results with the addition of both the squared and cubic terms of AI; Column (5) presents the regression results with the addition of both the squared and cubic terms of AI, along with cluster processing to obtain more robust standard errors. The regression results show that the level of AI in enterprises has a significant negative impact on carbon emission intensity.

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