Table 4 Robustness check.
From: Green finance policy and corporate carbon emissions: advancing corporate sustainability
Panel A: Delete the special samples | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Delete samples affected COVID-19 | Delete samples for the year of policy implementation | |||||||||||
Variable | DID | DML | DID | DML | ||||||||
tce | dce | ice | tce | dce | ice | tce | dce | ice | tce | dce | ice | |
gfp | −0.206*** | −0.139*** | −0.068*** | −0.213*** | −0.144*** | −0.069*** | −0.277*** | −0.179*** | −0.098*** | −0.282*** | −0.182*** | −0.100*** |
(0.062) | (0.040) | (0.023) | (0.062) | (0.039) | (0.023) | (0.070) | (0.046) | (0.025) | (0.072) | (0.047) | (0.025) | |
N | 10,764 | 10,764 | 10,764 | 10,764 | 10,764 | 10,764 | 11,960 | 11,960 | 11,960 | 11,960 | 11,960 | 11,960 |
Panel B: Consider macro control variables and replace the explanatory variables | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Consider macro control variables | Replace the dependent variables | |||||||||||
Variable | DID | DML | DID | DML | ||||||||
tce | dce | ice | tce | dce | ice | tce | dce | ice | tce | dce | ice | |
gfp | −0.226** | −0.155** | −0.071** | −3.109*** | −2.609*** | −1.032*** | −0.627*** | −0.401*** | −0.221*** | −0.660*** | −0.420*** | −0.233*** |
(0.094) | (0.063) | (0.032) | (0.115) | (0.094) | (0.040) | (0.211) | (0.135) | (0.074) | (0.211) | (0.135) | (0.074) | |
N | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 |
x | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
xn | No | No | No | Yes | Yes | Yes | No | No | No | Yes | Yes | Yes |
industry | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
firm | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
year | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
province | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Panel C: Reset double machine learning model | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Variable | K-folds = 3 | K-folds = 8 | NNET | SVM | Interaction | ||||||||||
tce | dce | ice | tce | dce | ice | tce | dce | ice | tce | dce | ice | tce | dce | ice | |
gfp | −0.242*** | −0.158*** | −0.084*** | −0.241*** | −0.157*** | −0.085*** | −0.485*** | −0.222*** | −0.077*** | −0.281*** | −0.171*** | −0.088*** | −0.548*** | −0.347*** | −0.201*** |
(0.066) | (0.043) | (0.024) | (0.065) | (0.042) | (0.024) | (0.023) | (0.015) | (0.007) | (0.068) | (0.044) | (0.024) | (0.053) | (0.034) | (0.019) | |
x | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
xn | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
industry | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
firm | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
year | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
province | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
N | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 | 13,156 |