Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors
Current energy efficiency indicators (such as energy intensity) do not properly reflect the inherent relationship between “energy-environment-health”. Therefore, this study introduces the indicator of energy intensity of human well-being (EIWB) to depict the efficiency problem be...
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doaj-091a9008a27b44bfadda0495665779802020-11-25T03:30:13ZengMDPI AGInternational Journal of Environmental Research and Public Health1660-46012020-01-0117135710.3390/ijerph17010357ijerph17010357Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing FactorsRuyin Long0Qin Zhang1Hong Chen2Meifen Wu3Qianwen Li4School of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Management, China University of Mining and Technology, Xuzhou 221116, ChinaCurrent energy efficiency indicators (such as energy intensity) do not properly reflect the inherent relationship between “energy-environment-health”. Therefore, this study introduces the indicator of energy intensity of human well-being (EIWB) to depict the efficiency problem between energy consumption and residents’ health. In this paper, panel data of 30 provinces in mainland China from 2005 to 2016 is used to calculate the EIWB of each province and analyze its spatial distribution. Moreover, the effect of influencing factors on EIWB is investigated by using the spatial Durbin model. The results show that: (1) The EIWB presents a spatial agglomeration. The provinces with high EIWB mostly cluster in the northern China. (2) Industrial structure and energy structure have positive effects on EIWB in local area through increasing energy consumption and damaging residents’ health. (3) The effect of urbanization and income on local EIWB is significantly positive because it will promote energy consumption. (4) Industrial structure, health expenditure, foreign direct investment and technological progress have spatial spillover effects due to its significant impact on residents’ health in neighboring areas. Based on conclusions, the corresponding policy recommendations are proposed.https://www.mdpi.com/1660-4601/17/1/357energy intensity of human well-beinginfluencing factorsspatial econometric analysischina |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ruyin Long Qin Zhang Hong Chen Meifen Wu Qianwen Li |
spellingShingle |
Ruyin Long Qin Zhang Hong Chen Meifen Wu Qianwen Li Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors International Journal of Environmental Research and Public Health energy intensity of human well-being influencing factors spatial econometric analysis china |
author_facet |
Ruyin Long Qin Zhang Hong Chen Meifen Wu Qianwen Li |
author_sort |
Ruyin Long |
title |
Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors |
title_short |
Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors |
title_full |
Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors |
title_fullStr |
Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors |
title_full_unstemmed |
Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors |
title_sort |
measurement of the energy intensity of human well-being and spatial econometric analysis of its influencing factors |
publisher |
MDPI AG |
series |
International Journal of Environmental Research and Public Health |
issn |
1660-4601 |
publishDate |
2020-01-01 |
description |
Current energy efficiency indicators (such as energy intensity) do not properly reflect the inherent relationship between “energy-environment-health”. Therefore, this study introduces the indicator of energy intensity of human well-being (EIWB) to depict the efficiency problem between energy consumption and residents’ health. In this paper, panel data of 30 provinces in mainland China from 2005 to 2016 is used to calculate the EIWB of each province and analyze its spatial distribution. Moreover, the effect of influencing factors on EIWB is investigated by using the spatial Durbin model. The results show that: (1) The EIWB presents a spatial agglomeration. The provinces with high EIWB mostly cluster in the northern China. (2) Industrial structure and energy structure have positive effects on EIWB in local area through increasing energy consumption and damaging residents’ health. (3) The effect of urbanization and income on local EIWB is significantly positive because it will promote energy consumption. (4) Industrial structure, health expenditure, foreign direct investment and technological progress have spatial spillover effects due to its significant impact on residents’ health in neighboring areas. Based on conclusions, the corresponding policy recommendations are proposed. |
topic |
energy intensity of human well-being influencing factors spatial econometric analysis china |
url |
https://www.mdpi.com/1660-4601/17/1/357 |
work_keys_str_mv |
AT ruyinlong measurementoftheenergyintensityofhumanwellbeingandspatialeconometricanalysisofitsinfluencingfactors AT qinzhang measurementoftheenergyintensityofhumanwellbeingandspatialeconometricanalysisofitsinfluencingfactors AT hongchen measurementoftheenergyintensityofhumanwellbeingandspatialeconometricanalysisofitsinfluencingfactors AT meifenwu measurementoftheenergyintensityofhumanwellbeingandspatialeconometricanalysisofitsinfluencingfactors AT qianwenli measurementoftheenergyintensityofhumanwellbeingandspatialeconometricanalysisofitsinfluencingfactors |
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1724576778953949184 |