Energy poverty in Sri Lanka

Jayasinghe, Maneka; Selvanathan, Saroja

doi:10.1016/j.eneco.2021.105450

Cited by 64 publications

(29 citation statements)

References 48 publications

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“…Although China has achieved great economic prosperity in recent decades and declared its success in eliminating absolute poverty in 2020, about 36% of its population still lives in less developed, rural areas with restricted access to modern energy and inadequate healthcare services. In our study, we find about 16–17% rural households in China are classified as energy poor under the EP1 and EP2 measures, which is higher than Australia’s 3–5% [ 20 ], Spain’s 11% to 15% [ 26 ], and France’s 10% [ 27 ], but lower than Pakistan’s 55% [ 71 ], Ghana’s 81% [ 72 ], Uganda’s 66% [ 73 ], and Sri Lanka’s 72% [ 74 ]. Therefore, reducing energy poverty is still important in rural China to increase residents’ welfares.…”

Section: Discussionmentioning

confidence: 99%

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Zhang

2022

IJERPH

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Despite the growing awareness and interest in the impact of energy poverty on depression, studies in developing economies are relative limited, and there is a gap of knowledge of such impact among rural individuals in China. In this study, we investigate the impact of energy poverty on depression among rural Chinese individuals aged 16 and above, and our sample includes 13,784 individuals from 6103 households. With data from the 2018 China Family Panel Studies, we apply the instrumental variable (IV) quantile regression approach to address the potential endogeneity of energy poverty and allow for heterogeneous effects of energy poverty on depression across individuals with different levels of depression. Our estimates from the IV quantile regression suggest a strong positive impact of energy poverty on depression at the upper quantile of depression scores, but no impact at the middle and lower quantiles. The primary results are robust and consistent with alternative energy poverty measures, and we find that energy poverty does not affect depression of low-risk individuals (with low depression scores), but it does affect that of high-risk individuals. We also find individual socio-demographic factors of age, gender, household size, religious belief, education, marriage and employment status play roles in affecting depression. The findings of this study generate policy implications for energy poverty alleviation and mental health promotion.

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Section: Discussionmentioning

confidence: 99%

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Zhang

2022

IJERPH

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“…

Enpov 2

is calculated based on a composite index of appliance deprivation—in accordance with prior energy poverty literature such as Nussbaumer, Nerini, Onyeji, and Howells (2013), M. Jayasinghe, Selvanathan, and Selvanathan (2021), inter alia. The weights of the individual appliances (see Table A1 in Appendix for the list of appliances) in this index are estimated using the Principal Components Analysis (PCA).…”

Section: Model and Methodologymentioning

confidence: 99%

Examining energy poverty in Chinese households: An Engel curve approach

Shafiullah

Jiao

Shahbaz

et al. 2022

Australian Economic Papers

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“…PCA uses orthogonal transformation to convert multiple possibly linearly correlated variables into a set of linearly uncorrelated new variables, namely principal components, and uses the extracted principal components to display the characteristics of the data in a smaller dimension (Ringnér, 2008). Referring to Jayasinghe et al (2021) (Jayasinghe et al, 2021) and Lin and Zhao (2021) (Lin and Zhao, 2021), this paper adopts PCA to determine the weight of each indicator and construct the MEPI as follows:…”

Section: Multidimensional Energy Poverty Index (Mepi)mentioning

confidence: 99%

Assessing the effect of energy poverty on the mental and physical health in China—Evidence from China family panel studies

Xie

Lou

et al. 2022

Front. Energy Res.

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Energy poverty is a crucial issue faced by countries all around the world, as the largest developing country in the world, China is also experiencing energy poverty problems. In order to explore the health effect of energy poverty in China, this paper first uses the principal component analysis (PCA) to construct a comprehensive index to measure energy poverty, and then adopts the ordinary least square method (OLS), fixed effect model (FE), instrumental variable two-stage least squares (IV-2SLS) regression to study the impact of energy poverty on the physical and mental health of Chinese people based on China Family Panel Studies 2018 (CFPS 2018). The study discovers that energy poverty significantly hampers the mental and physical health of Chinese people, an increase in energy poverty might cause 28.74%、18.69% decrease in mental and physical health respectively. Moreover, this paper further explores the influencing paths of energy poverty by intermediary effect and regulatory effect. It is revealed that in addition to directly affecting physical and mental health, energy poverty also have a negative impact on physical and mental health by affecting the accessibility of a series of resources, such as water and food, reducing the opportunities for physical exercise and increasing medical expenses. However, the impact is restricted by age and family income. Finally, under the national strategy of China, this paper further discusses how to give consideration to the joint implementation of heath and emission reduction strategies, then gives specific policy suggestions based on the results.

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Energy poverty in Sri Lanka

Cited by 64 publications

References 48 publications

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Energy Poverty and Depression in Rural China: Evidence from the Quantile Regression Approach

Examining energy poverty in Chinese households: An Engel curve approach

Assessing the effect of energy poverty on the mental and physical health in China—Evidence from China family panel studies

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