Table 2 Baseline regression results.

From: Utilizing green finance to promote low-carbon transition of Chinese cities: insights from technological innovation and industrial structure adjustment

Variables

(1)

(2)

(3)

(4)

FE

FE

GMM

GMM

GF

−0.016**

−0.014***

−0.011***

−0.014***

 

(0.007)

(0.004)

(0.004)

(0.004)

UR

−0.007***

−0.001

−0.000

−0.002

 

(0.002)

(0.001)

(0.001)

(0.002)

LnSE

−0.001

−0.001

0.002**

0.001

 

(0.001)

(0.001)

(0.001)

(0.001)

LnFI

−0.004***

−0.000

0.001**

0.001*

 

(0.000)

(0.000)

(0.000)

(0.000)

GOV

0.026***

0.018***

0.050***

0.048***

 

(0.004)

(0.003)

(0.008)

(0.007)

L1.CI

 

0.826***

1.010***

1.135***

  

(0.013)

(0.016)

(0.017)

L2.CI

   

−0.154***

    

(0.013)

_cons

0.105***

0.019

−0.048***

−0.024

 

(0.010)

(0.012)

(0.014)

(0.015)

City FE

YES

YES

NO

NO

Year FE

YES

YES

YES

YES

AR(1)

  

0.000***

0.000***

AR(2)

  

0.833

0.919

Sargan test

  

0.000***

0.000***

N

3047

2770

2770

2497

  1. Standard errors in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01; In the GMM estimations, when we simultaneously control for year fixed-effects and city fixed-effects, two-step estimator is not available because of severe collinearity between city dummy variables.
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