Towards a Greener Future: How do R&D Factor Market Distortions Affect Green Total Factor Energy Efficiency?

Abstract

This study examines the spatial effect of R&D capital and labor market distortions on the green total factor energy efficiency (GTFEE) using a dynamic spatial Durbin model in China from 2000 to 2018. The results show that 1) China's GTFEE exhibits path‐dependent and agglomeration characteristics in terms of time and space, respectively. In both spatial and temporal dimensions, an increase in local GTFEE stimulates future local GTFEE and inhibits future GTFEE growth in neighboring areas. 2) R&D capital market distortion has a continuous inhibiting effect on the local GTFEE in the short and long terms. R&D labor market distortion has a continuous stimulating effect on the local GTFEE, and its effect gradually strengthens. 3) R&D capital market distortion may indirectly weaken the GTFEE by inhibiting green innovation and industrial upgrading. R&D labor market distortion may indirectly improve the regional GTFEE by suppressing the energy rebound effect and improving green innovation. Based on these results, a series of policy recommendations are proposed.

Toward inclusive green growth for sustainable development: A new perspective of labor market distortion

“…decrease the incentive intensity of wages for laborers, making them unwilling to exert their knowledge and talents, thus discouraging technological innovation (Pang et al, 2022). Based on these findings, Hypothesis 2 is proposed.…”

“…For the spatial weight matrix, considering geographical and economic connections between regions and to guarantee the robustness of the estimation results, the 0–1 adjacency matrix ( ), the geographic distance matrix ( ), and the economic–geographic distance matrix ( ) are set (Pang et al, 2022). Specifically, is constructed based on the Queen space adjacency relation, which means that the elements of the spatial weight matrix take the value of 1 when the regions have common boundaries or vertices and 0 otherwise (Wang et al, 2021).…”

“…For the spatial weight matrix, considering geographical and economic connections between regions and to guarantee the robustness of the estimation results, the 0-1 adjacency matrix (W 0À1 ), the geographic distance matrix (W geo ), and the economicgeographic distance matrix (W ecoÀgeo ) are set (Pang et al, 2022).…”

“…When the labor market is distorted, the actual compensation of workers may deviate from their deserved compensation (Yang et al, 2018). Accordingly, this would decrease the incentive intensity of wages for laborers, making them unwilling to exert their knowledge and talents, thus discouraging technological innovation (Pang et al, 2022). Based on these findings, Hypothesis 2 is proposed.…”