Does green innovation reduce carbon emission intensity of Chinese cities? Analysis based on GS2SLS

ABSTRACT

In the 14th Five-Year Plan, China’s economic and social development aims to focus on carbon peaking and carbon neutrality. On the basis of panel data of 284 prefecture-level cities in China from 2011 to 2020, this study uses the generalized spatial two-stage least squares method (GS2SLS) to analyse empirically the relationship and mechanism between the green innovation and carbon emission intensity（CEI） of Chinese cities. The research reveals several findings. (1) The CEI of Chinese cities has a significant spatial spillover effect. Moreover, green innovation has reduced the CEI of Chinese cities. The robustness and endogenous tests further verified this conclusion. (2) The carbon emission reduction effect of green innovation shows significant regional heterogeneity, which is more significant in eastern regions and cities with high innovation capabilities. In contrast, this outcome is not significant in regions with weaker innovation capabilities and in the central and western regions. (3) Green innovation mainly reduces the CEI of Chinese cities by promoting the upgrading of industrial structures. A higher level of digital economy development will also have a significantly positive catalytic effect on green innovation to inhibit CEI.

KEYWORDS:

• Green innovation
• CEI
• industrial structure upgrading
• digital economy
• GS2SLS

JEL CLASSIFICATION:

• Q55
• Q56
• O18

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ The economic distance weight matrix represents the economic distance by the regional GDP per capita. difference. Given these variables,

${\mathrm{W}}_2=W_{1}diaog({\overline{gd}}_1/\overline{gd},{\overline{gd}}_2/\overline{gd}\dots {\overline{gd}}_n/\overline{gd})$ is the average per capita GDP of region i over the years, and$\overline{gd}$ is the average per capita GDP of the whole sample.

² The difference in financial technology investment represents the R&D capability distance weight matrix. Therefore,${\text{W}}_3={\text{W}}_1(\raisebox{1ex}{$\overline{{\text{T}}_1}$}\!\left/ \!\raisebox{-1ex}{$\overline{\text{T,}}$}\right.\raisebox{1ex}{$\overline{{\text{T}}_2}$}\!\left/ \!\raisebox{-1ex}{$\overline{\text{T,}}$}\right.\mathrm{...}\raisebox{1ex}{$\overline{{\text{T}}_n}$}\!\left/ \!\raisebox{-1ex}{$\overline{\text{T}}$}\right.)$. Among them, $\overline{{\mathrm{T}}_{\mathrm{i}}}$is the average value of R&D investment in region i over the years, and $\bar{\mathrm{T}}$is the average value of the technical input of the whole sample.

³ Given the small differences among Moran’s I values under the three different matrices, this paper only reports information based on inverse distance matrix calculations.