Research Article
Effect of Energy Development and Technological Innovation on PM2.5 in China: A Spatial Durbin Econometric Analysis
Table 5
Estimation results of spatial Durbin model.
| | Variables | Two-way fixed effects | Two-way fixed effects (with bias correction) | Random spatial effects and time-period fixed effects |
| | Adjacent weight |
| | δ | 0.3679(0.0000) | 0.4268 (0.0000) | 0.4329 (0.0000) | | ln Ei | 0.1058(0.4343) | 0.0980(0.4896) | 0.0411(0.7629) | | ln Cs | 0.0899 (0.0089) | 0.0880 (0.0147) | 0.0882 (0.0143) | | ln Ppi | -1.0526 (0.0018) | -1.0389 (0.0003) | -1.0866 (0.0021) | | ln Npi | -0.1079 (0.0000) | -0.1043 (0.0002) | -0.0893 (0.0015) | | ln Patent | 0.0626(0.1363) | 0.0657(0.1356) | 0.0905 (0.0310) | | ln Infra | 0.0410(0.4668) | 0.0454(0.4419) | 0.0467(0.4255) | | ln Market | -0.0452 (0.0170) | -0.0439 (0.0000) | -00471 (0.0161) | | W ln Ei | 1.0147 (0.0004) | 0.9882 (0.0010) | 0.4913 (0.0676) | | W ln Cs | 0.0696(0.2970) | 0.0608(0.3844) | 0.0434(0.5334) | | W ln Ppi | -0.4512(0.4771) | -0.3386(0.6104) | -0.5460(0.4074) | | W ln Npi | -0.1558 (0.0076) | -0.1414 (0.0206) | -0.0786(0.1850) | | W ln Patent | -0.3095 (0.0000) | -0.3052 (0.0000) | -0.2093 (0.0016) | | W ln Infra | -0.2437 (0.0474) | -0.2467 (0.0557) | -0.2543 (0.0463) | | W ln Market | -0.0869 (0.0252) | -0.0802 (0.0486) | -0.0864 (0.0312) | | Teta | — | — | 0.0701 (0.0000) | | R2 | 0.9391 | 0.9398 | 0.8902 | | log-likelihood | 141.4070 | 141.4070 | -840.5862 | | Wald spatial lag | 71.8978 (0.000) | 61.5451 (0.000) | 46.1114 (0.000) | | LR spatial lag | 67.7774 (0.000) | 67.7774 (0.000) | — | | Wald spatial error | 87.6175 (0.000) | 75.3315 (0.000) | 54.5307 (0.000) | | LR spatial error | 81.6844 (0.000) | 81.6844 (0.000) | — | | Hausman test | Statistics | DOF | P value | | 95.6264 | 15 | 0.0000 |
| | Distance weight |
| | Δ | 0.4449 (0.0000) | 0.5168 (0.0000) | 0.4399 (0.0000) | | ln Ei | 0.4862 (0.0000) | 0.4867 (0.0011) | 0.3066 (0.0245) | | ln Cs | 0.0752 (0.0421) | 0.0737 (0.0570) | 0.0795 (0.0426) | | ln Ppi | -0.8146 (0.0282) | -0.7859 (0.0431) | -1.1188 (0.0030) | | ln Npi | -0.1472 (0.0000) | -0.1441 (0.0000) | -0.1198 (0.0000) | | ln Patent | 0.0152(0.7189) | 0.0160(0.7184) | 0.0646(0.1287) | | ln Infra | 0.0163(0.7785) | 0.0203(0.7379) | 0.0219(0.7171) | | ln Market | -0.0591 (0.0006) | -0.0579 (0.0057) | -0.0492 (0.0187) | | W ln Ei | -0.4203(0.2145) | -0.4437(0.2106) | -0.5212 (0.0864) | | W ln Cs | 0.3411 (0.0764) | 0.3277(0.1039) | 0.3314(0.1030) | | W ln Ppi | -1.0727(0.1067) | -0.9562(0.1689) | -0.0258(0.9525) | | W ln Npi | -0.2430 (0.0013) | -0.2238 (0.0046) | -0.1340 (0.0685) | | W ln Patent | -0.2628 (0.0210) | -0.2619 (0.0280) | -0.0715(0.5086) | | W ln Infra | -0.2226(0.1075) | -0.2215(0.1261) | -0.0901(0.5263) | | W ln Market | -0.0024(0.9589) | 0.0021(0.9653) | -0.0085(0.8572) | | teta | — | — | 0.0782 (0.0000) | | R2 | 0.9287 | 0.9297 | 0.8679 | | log-likelihood | 105.4847 | 105.4847 | -649.7897 | | Wald spatial lag | 24.3571 (0.0000) | 20.5623 (0.0045) | 9.7989(0.2003) | | LR spatial lag | 23.2803 (0.0015) | 23.2803 (0.0015) | — | | Wald spatial error | 33.2143 (0.0000) | 28.4091 (0.0000) | 14.7569 (0.0392) | | LR spatial error | 30.2017 (0.0000) | 30.2017 (0.0000) | — | | Hausman test | Statistics | DOF | P value | | 60.0659 | 15 | 0.0000 |
|
|
Note., , indicates significance at 10%, 5%, and 1% levels, respectively. |
