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Enhanced land-resource utilization efficiency and local consumption of renewable energy in cities: identifying heterogeneous treatment effects via machine learning
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DOI：10.3389/fenvs.2026.1852224.png)
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Abstract
En 中文
IntroductionLocal 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.MethodsTheoretically; 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.ResultsThe 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.DiscussionHeterogeneity 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.
Keywords:
feedforward neural network
causal forest model
dynamic general equilibrium model
enhanced land-resource utilization efficiency
local consumption of renewable energy in cities
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