 Will alleviating energy poverty enhance social trust in China? An approach based on dual machine learning modelingYulin Liu and Haoran Wei Energy Economics, 2025, vol. 147, issue C Abstract: Improving social trust is crucial for social stability and development. Based on the data of China Family Panel Studies from 2012 to 2020, this paper measures the energy poverty index from the four dimensions of energy accessibility, energy service, energy efficiency, and energy demand. In our analysis, to study the impact of energy poverty on social trust and its internal mechanism, we employ dual machine learning (DML) model, such as Random Forest (RF). The main results indicate that the reduction of energy poverty will promote the improvement of social trust. This is related to the mechanism that the alleviation of energy poverty will improve residents' health and increase their education level. Further, we observe differences in the impact of energy poverty on social trust by gender and domicile: it is more significant for males and rural dwellers. These findings help us devise solutions for improving social trust by designing policies related to energy poverty. Keywords: Energy poverty; Social trust; Dual machine learning model; Residents' health; China (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:147:y:2025:i:c:s0140988325003846 DOI: 10.1016/j.eneco.2025.108560 Access Statistics for this article Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant More articles in Energy Economics from Elsevier |
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