Exploring the impact of urban green spaces on household electricity consumption in summer through explainable machine learning

The household electricity consumption (HEC) directly contributes to energy demand and carbon emissions that are crucial in mitigating climate changes. This study applied linear regression and machine-learning models to examine the associations between HEC and green space attributes, individual and household characteristics, and user satisfaction. The city of Xi’an, China, was selected to collect data by residents’ survey and spatial analysis. The linear model results showed that key factors were mainly related to individual and household characteristics, as well as satisfaction with green spaces. In contrast, the random forest model revealed threshold effects, indicating that keeping the distance to the nearest green space within 1,200 m and providing at least four green spaces within a 1 km radius can effectively promote outdoor visits and reduce HEC. The analysis of dual-factor interactions revealed that simultaneously enhancing both accessibility and landscape quality of nearby green spaces represents an effective pathway for reducing HEC. These findings not only elucidate the mechanisms linking green space accessibility and HEC, but also provide quantitative guidance and actionable thresholds for optimizing the spatial configuration, accessibility, and service efficiency of urban green spaces. This study contributes practical guidance on embedding energy-saving and carbon-reduction strategies within green space system planning, advancing the transition toward sustainable and climate-responsive urban development.