气候变化领域集成服务门户(CCPortal): Exploring the complex origins of energy poverty in The Netherlands with machine learning

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DOI	10.1016/j.enpol.2021.112373
	Exploring the complex origins of energy poverty in The Netherlands with machine learning
	Dalla Longa F.; Sweerts B.; van der Zwaan B.
发表日期	2021
ISSN	03014215
卷号	156
英文摘要	Energy poverty is receiving increased attention in developed countries like the Netherlands. Although it only affects a relatively small share of the population, it constitutes a stern challenge that is hard to quantify and monitor, hence difficult to effectively tackle through adequate policy measures. In this paper we introduce a framework to categorize energy poverty risk based on income and energy expenditure. We propose the use of a machine learning classifier to predict energy poverty risk from a broad set of socio-economic parameters: house value, ownership and age, household size, and average population density. While income remains the single most important predictor, we find that the inclusion of these additional socio-economic features is indispensable in order to achieve high prediction reliability. This result forms an indication of the complex nature of the mechanisms underlying energy poverty. Our findings are valid at different geographical scales, i.e. both for single households and for entire neighborhoods. Extensive sensitivity analysis shows that our results are independent of the precise position of risk category boundaries. The outcomes of our study indicate that machine learning could be used as an effective means to monitor energy poverty, and assist the design and implementation of appropriate policy measures. © 2021 The Authors
关键词	Energy affordability Energy poverty Household energy demand Machine learning SDG 7 The Netherlands
英文关键词	Economic and social effects; Population statistics; Risk assessment; Sensitivity analysis; Developed countries; Energy affordability; Energy poverties; Household energy demands; Machine-learning; Policy measures; SDG 7; Socio-economics; The netherland; Machine learning; demand analysis; energy market; energy policy; household energy; machine learning; poverty; Sustainable Development Goal; Netherlands; Varanidae
语种	英语
来源期刊	Energy Policy
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205068
作者单位	TNO, Energy Transition Department (ETS), Amsterdam, Netherlands; University of Amsterdam, Faculty of Science (HIMS and IAS), Amsterdam, Netherlands; Johns Hopkins University, School of Advanced International Studies (SAIS), Bologna, Italy
推荐引用方式 GB/T 7714	Dalla Longa F.,Sweerts B.,van der Zwaan B.. Exploring the complex origins of energy poverty in The Netherlands with machine learning[J],2021,156.
APA	Dalla Longa F.,Sweerts B.,&van der Zwaan B..(2021).Exploring the complex origins of energy poverty in The Netherlands with machine learning.Energy Policy,156.
MLA	Dalla Longa F.,et al."Exploring the complex origins of energy poverty in The Netherlands with machine learning".Energy Policy 156(2021).