Examining the multidimensional energy poverty trap and its determinants: An empirical analysis at household and community levels in six provinces of China

Huang, Yatao; Jiao, Wenxian; Wang, Kang; Li, Erling; Yan, Yutong; Chen, Jingyang; Guo, Xuanxuan

doi:10.1016/j.enpol.2022.113193

Cited by 19 publications

(3 citation statements)

References 73 publications

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“…Tavares et al [ 8 ] proposed two indexes for measuring and indexing the vulnerability of the COVID-19 poverty based on the evidence from a fuzzy multidimensional perspective. Huang et al [ 9 ] examined the poverty trap through a multidimensional energy and its determinant in typically six provinces in China. They used the household-scale variables and community-scale variables for analyzing the effect.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach

et al. 2023

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Poverty is a glaring issue in the twenty-first century, even after concerted efforts of organizations to eliminate the same. Predicting poverty using machine learning can offer practical models for facilitating the process of elimination of poverty. This paper uses Multidimensional Poverty Index Data from the Oxford Poverty and Human Development Initiative across the years 2019 and 2021 to make predictions of multidimensional poverty before and during the pandemic. Several poverty indicators under health, education and living standards are taken into consideration. The work implements several data analysis techniques like feature correlation and selection, and graphical visualizations to answer research questions about poverty. Various machine learning, such as Multiple Linear Regression, Decision Tree Regressor, Random Forest Regressor, XGBoost, AdaBoost, Gradient Boosting, Linear Support Vector Regressor (SVR), Ridge Regression, Lasso Regression, ElasticNet Regression, and K-Nearest Neighbor Regression algorithm, have been implemented to predict poverty across four datasets on a national and a subnational level. Regularization is used to increase the performance of the models, and cross-validation is used for estimation. Through a rigorous analysis and comparison of different models, this work identifies important poverty determinants and concludes that overall, Ridge Regression model performs the best with the highest R 2 score.

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Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach

et al. 2023

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“…Currently, fuel poverty is a complex concept due to its multidimensional approach (Abbas et al 2022). In simple terms, fuel poverty could be defined as the lack of accessibility and availability of safe and reliable energy services (Huang et al 2022). Energy poverty can result from the combination of different factors, such as households with low income levels and limited equity resources (Sharma et al 2019;Mulder et al 2023), energy-inefficient housing (Drescher and Janzan 2021), home ownership status and place of residence (Abbas et al 2022;Taltavull de La Paz et al 2022), low levels of education (Awan et al 2022), and high energy costs (Sharma et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, Latin America has an average rate of access to electricity that exceeds 98%; however, behind this figure, there is a significant gap in terms of energy inequality (Thomson et al 2022;Dehays and Schuschny 2019). In 2022, about 17 million people did not have access to electricity and 75 million did not have access to clean cooking fuels (Comisión Económica para América Latina y el Caribe 2022), which are harmful to human health (Huang et al 2022). In addition, 15% of the population living in precarious conditions did not have access to electricity.…”

Section: Introductionmentioning

confidence: 99%

How Do Remittances Influence the Mitigation of Energy Poverty in Latin America? An Empirical Analysis Using a Panel Data Approach

González Bautista,

Zurita Moreano,

Vallejo Mata

et al. 2024

Economies

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Energy poverty represents a critical challenge in Latin America today, given the social disparities the region faces. In this context, this study focuses on exploring the effects of remittances on the energy poverty of 13 Latin American countries during the period 2000–2020. Panel estimations with fixed and random effects, along with the generalized method of moments, are employed to address potential endogeneity issues. The results suggest that remittances play a significant role in mitigating energy poverty in the Latin American region, particularly in rural areas. Furthermore, it is observed that economic growth and financial development act as mediators, allowing remittances to indirectly contribute to mitigating energy poverty. Although inequality was examined as a potential mediator, the findings suggest that it does not play a significant role in this context. It is concluded that remittances are an appropriate mechanism to improve the quality of life of the population, and their impact is strengthened in a more robust economic environment.

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Modeling the impact of energy poverty on income poverty, health poverty, educational poverty, and environmental poverty: a roadmap towards environmental sustainability

Batool

Sun

Irfan

2023

Environ Sci Pollut Res

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Examining the multidimensional energy poverty trap and its determinants: An empirical analysis at household and community levels in six provinces of China

Cited by 19 publications

References 73 publications

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach

A Comparative Analysis of Multidimensional COVID-19 Poverty Determinants: An Observational Machine Learning Approach

How Do Remittances Influence the Mitigation of Energy Poverty in Latin America? An Empirical Analysis Using a Panel Data Approach

Modeling the impact of energy poverty on income poverty, health poverty, educational poverty, and environmental poverty: a roadmap towards environmental sustainability

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