Research on the Spatial-Temporal Differentiation Characteristics of Green Total Factor Productivity in China’s Regions
Guangyue Xu et al.
Abstract
As the global climate crisis intensifies, the transition toward green and sustainable development has become a central pillar of global development. This study employs the Super-Efficiency SBM model to calculate the Green Total Factor Productivity (GTFP) of 30 provincial-level administrative regions in China from 2003 to 2022. It further analyzes the spatial agglomeration effects, regional spatiotemporal differentiation patterns, and spatial spillover effects of GTFP. Finally, the Global Malmquist–Luenberger (GML) index decomposition method is applied to explore the dynamic evolution and underlying drivers of GTFP growth across provinces and regions. The findings reveal that: (1) GTFP exhibits an “East-High, West-Low” spatial pattern and a “Decline-then-Rise” evolutionary trajectory; (2) GTFP exhibits a significant but gradually diminishing spatial convergence effect; (3) The overall disparity in GTFP is primarily driven by inter-regional inequality; however, the emerging risk of intra-regional polarization within developed areas presents a new challenge; (4) The spatial spillover effects of GTFP display complex nonlinear characteristics, including driving, inhibitory, and siphoning effects; (5) China’s GTFP growth is predominantly driven by technological change, with the turning points for transformation varying markedly across regions. Based on these findings, we propose a targeted policy framework with specific implementation paths for different types of regions, aiming to enhance policy adaptability and feasibility while advancing the core goals of green and low-carbon transition.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.