Enhanced land-resource utilization efficiency and local consumption of renewable energy in cities: identifying heterogeneous treatment effects via machine learning

Engineering notes

Results The results reveal that enhanced land-resource utilization efficiency significantly increases the local consumption of renewable energy in cities, and this policy effect can be decomposed into intensification, synergy, and scale effects. This study makes three contributions: theoretically, it develops a dynamic general equilibrium framework decomposing the transmission mechanism from land-resource utilization efficiency to local renewable energy consumption into intensification, synergy, and scale effects; methodologically, it constructs a city-level coupling–coordination index for local renewable energy consumption using a feedforward neural network and identifies causal effects via a causal forest model; empirically, it provides evidence that enhanced land-resource utilization efficiency significantly promotes local renewable energy absorption, with the effect being stronger in cities with higher secondary industry share and larger industrial electricity demand, yet attenuated in cities with greater local renewable energy production.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Introduction Local consumption of renewable energy in cities can reduce transmission losses and enhance urban sustainability, and this study investigates whether enhanced land-resource utilization efficiency promotes such local consumption. Methods Theoretically, a dynamic general equilibrium model is developed to elucidate the transmission mechanisms through which land-resource utilization efficiency affects renewable energy consumption. Empirically, China’s provincial spatial planning pilot policy is treated as a quasi-natural experiment, with city-level renewable energy production and consumption estimated using a feedforward neural network, and the degree of local renewable energy consumption measured via a coupling–coordination index; a causal forest model is then applied to an unbalanced panel dataset covering 281 prefecture-level cities in China from 2010 to 2022. Results The results reveal that enhanced land-resource utilization efficiency significantly increases the local consumption of renewable energy in cities, and this policy effect can be decomposed into intensification, synergy, and scale effects. This study makes three contributions: theoretically, it develops a dynamic general equilibrium framework decomposing the transmission mechanism from land-resource utilization efficiency to local renewable energy consumption into intensification, synergy, and scale effects; methodologically, it constructs a city-level coupling–coordination index for local renewable energy consumption using a feedforward neural network and identifies causal effects via a causal forest model; empirically, it provides evidence that enhanced land-resource utilization efficiency significantly promotes local renewable energy absorption, with the effect being stronger in cities with higher secondary industry share and larger industrial electricity demand, yet attenuated in cities with greater local renewable energy production. Discussion Heterogeneity analysis further demonstrates that the positive effect is more pronounced in cities with a larger share of secondary industry and higher industrial electricity demand, whereas it is somewhat attenuated in cities with greater local renewable energy production. These findings indicate that territorial spatial planning and industrial–energy coordination constitute critical pathways for improving renewable energy absorption capacity and advancing sustainable urban development.

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