The Impact of New Energy Sources on Industrial Green Total Factor Productivity
Journal of Decision Making and Healthcare, Volume 3, Issue 2, August 2026, Pages: 134–146
BO-LIN REN
Business School, Beijing Information Science and Technology University, Beijing 100192, People’s Republic of China
Beijing Knowledge Management Research Center, Beijing Information Science and Technology University, Beijing 100192, People’s Republic of China
XIAO-LI MENG
Business School, Beijing Information Science and Technology University, Beijing 100192, People’s Republic of China
Beijing Knowledge Management Research Center, Beijing Information Science and Technology University, Beijing 100192, People’s Republic of China
Abstract
The green and low-carbon transformation of the energy sector is the critical pathway to achieving China's ``carbon peak and carbon neutrality'' goals. Although considerable progress has been made in separate studies on the new energy industry and Green Total Factor Productivity (GTFP), research exploring their relationship, especially at the industrial level, remains scarce. Based on panel data of 30 Chinese provinces from 2013 to 2023, this study proposes the Convexificated Support Vector Frontiers-Malmquist method, a novel approach based on structural risk minimization, to measure industrial GTFP. The entropy weight method is employed to construct composite scores for three sub-systems of the new energy industry: innovation, coordination, and greenness (foundation). A two-way fixed effects model is then applied to examine the impact of these sub-systems on industrial GTFP. The empirical results demonstrate that New Energy Industry Innovation (NEII) and Coordination of New Energy Industry (CNEI) significantly promote industrial GTFP, with coefficients of 0.4530 and 0.5088 respectively, both significant at the 1 % level. In contrast, the New Energy Industry Foundation (NEIF) does not exhibit a statistically significant effect. These findings remain robust across endogeneity tests using the two-stage least squares (2SLS) method with lagged instrumental variables, as well as robustness checks including Winsorization and exclusion of pandemic-affected samples. The study contributes to the literature by introducing the CSVF--Malmquist model with superior generalization ability and frontier estimation accuracy, and by providing empirical evidence that innovation capacity and supply-demand coordination in the new energy sector are key drivers of industrial green productivity improvement in China.
Cite this Article as
Bo-Lin Ren and Xiao-Li Meng, The Impact of New Energy Sources on Industrial Green Total Factor Productivity, Journal of Decision Making and Healthcare, 3(2), 134–146, 2026