Identifying the Impact of Industrial Agglomeration on China’s Carbon Emissions Based on the Spatial Econometric Analysis

Table 5

Spatial panel econometric results of the influence of SA and DA on carbon emissions.

Variable	W1		W2		W3
	(1)	(2)	(3)	(4)	(5)	(6)
L.lnCE	0.969	1.022	0.965	0.999	0.973	0.989
	(0.022)	(0.021)	(0.023)	(0.021)	(0.023)	(0.021)
W × L.lnCE	−0.231	−0.192	−0.263	−0.273	−0.350	−0.309
	(0.068)	(0.069)	(0.089)	(0.089)	(0.150)	(0.149)
W × lnCE	0.287	0.305	0.356	0.396	0.558	0.672
	(0.062)	(0.062)	(0.084)	(0.082)	(0.141)	(0.135)
lnSA	0.091		0.087		0.065
	(0.040)		(0.037)		(0.038)
lnDA		−0.013		−0.020		−0.018
		(0.008)		(0.007)		(0.007)
lnpgdp	0.004	0.008	0.004	0.014	−0.005	−0.004
	(0.031)	(0.032)	(0.031)	(0.032)	(0.032)	(0.032)
lner	0.054	0.054	0.048	0.041	0.049	0.043
	(0.021)	(0.021)	(0.021)	(0.021)	(0.021)	(0.021)
[lner]^2	−0.006	−0.007	−0.006	−0.005	−0.006	−0.005
	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
ur	−0.428	−0.552	−0.395	−0.466	−0.386	−0.409
	(0.148)	(0.150)	(0.150)	(0.151)	(0.150)	(0.151)
lninf	−0.087	−0.116	−0.077	−0.088	−0.079	−0.090
	(0.023)	(0.024)	(0.023)	(0.023)	(0.023)	(0.023)
Log-likelihood	875.111	860.107	877.054	870.349	876.962	870.310
Obs	510	510	510	510	510	510

Standard errors are reported in parentheses. , , and represent significance at 1%, 5%, and 10% levels, respectively.