Table 7 Analysis of the mechanism of industrial intelligence.

From: Does digitalization improve firm-level energy efficiency? Evidence from a quasi-natural experiment in China

 

int

Firm energy efficiency

Firm energy consumption

Electricity

Coal

Oil

Electricity

Coal

Oil

(1)

(2)

(3)

(4)

(5)

(6)

(7)

BCP

1.064*** (0.014)

−0.041** (0.021)

0.255*** (0.021)

0.241*** (0.018)

0.056*** (0.020)

−0.239*** (0.020)

−0.226*** (0.017)

int

 

−0.378*** (0.010)

0.346*** (0.011)

0.106*** (0.010)

0.512*** (0.009)

−0.212*** (0.010)

−0.028*** (0.009)

C

3.971*** (0.001)

8.617*** (0.069)

5.542*** (0.075)

7.472*** (0.066)

0.533*** (0.064)

3.608*** (0.071)

1.678*** (0.060)

Firm FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Industry FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Year FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

City FE

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Observations

389,061

389,061

389,061

389,061

389,061

389,061

389,061

R2

0.798

0.601

0.830

0.712

0.707

0.838

0.681

Adj-R2

0.798

0.364

0.729

0.540

0.533

0.743

0.491

F

5450.766

850.992

42.508

34.531

1780.563

41.229

34.611

  1. (1) *** and ** signify significance at the 1% and 5% levels, respectively. (2) High-dimensional fixed effects methods are employed to concurrently control for firm characteristics, year characteristics, industry characteristics, and city characteristics. (3) Firm-level cluster robust standard errors are presented in parentheses. (4) int is measured by the logarithmic values of urban industrial robot stock.
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