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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Main Authors: Ruyin Long, Qin Zhang, Hong Chen, Meifen Wu, Qianwen Li
Format: Article
Language:English
Published: MDPI AG 2020-01-01
Series:International Journal of Environmental Research and Public Health
Subjects:
Online Access:https://www.mdpi.com/1660-4601/17/1/357
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spelling 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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