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*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| AI2 | 0.0000*** | 0.0000*** | 0.0000*** | ||
| (0.000) | (0.000) | (0.000) | |||
| AI3 | −0.0000*** | −0.0000*** | |||
| (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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