Population Agglomeration and Residential Energy Consumption: Evidence from Japan

Otsuka, Akihiro

doi:10.3390/su10020469

Cited by 15 publications

(8 citation statements)

References 25 publications

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“…Population density has a negative effect on EP, which indicates population density contributes to decreasing energy consumption and this finding conforms to the study of Otsuka [61] in Japanese residential sector. The effect of population density on EP is not significant at the 25% quantile, which suggests population density in high-EP areas is more effective in energy intensive utilization than low-EP areas.…”

Section: Decomposition Results In Different Yearssupporting

confidence: 89%

What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach

Dong

Zhang

2018

Sustainability

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Given the binding provincial goals of energy intensity reduction and total energy consumption control in China, the main purpose of this study is to analyze the regional disparities of energy consumption from the perspectives of energy consumption per capita (EP) and energy intensity (EI), as well as to propose differentiated energy conservation policies. In doing so, quantile regression and regression-based Shapley value decomposition are performed in the case of 30 provinces in China during 2000-2015. The results of quantile regression specify that the impact of each determinant on EP differs distinctly at different quantiles. Income has a positive effect on EP, conversely, industrial structure, population density and transportation infrastructure have negative effects on EP. Similarly, the effect of each influencing factor on EI presents distinct dynamic varying process at different quantiles. Industrial structure, FDI and technological progress have significantly negative effects on EI, while energy mix has a positive effect on EI. Furthermore, based on the results of median regression, the assessment of contributions of individual variables to regional disparities of energy consumption per capita and energy intensity (i.e., EPD and EID) is conducted by the Shapley value decomposition method. It is found that inequality in income level is the most important reason for EPD and its annual average contribution rate is 70%. In addition, differences in population density play an important role in explaining EPD, while the inequality in transportation infrastructure contributes little to EPD. By contrast, EID is mainly due to differences in technological progress, whose annual average contribution rate is up to 46%. Following technological progress, the inequalities of FDI and energy mix are also important factors accounting for EID. On the whole, the contribution of industrial structure or regional factors is always small. Then, this study explores the provincial energy-saving development path based on the actual conditions of all provinces.

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Section: Decomposition Results In Different Yearssupporting

confidence: 89%

What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach

Dong

Zhang

2018

Sustainability

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“…The research agenda from the macro viewpoint clarifies how the increase in urban population density affects energy efficiency, as discussed in Otsuka and Goto [40] and Otsuka [41]. In developed countries, urban compactification is being promoted from the viewpoint of city sustainability.…”

Section: Conclusion and Policy Implicationsmentioning

confidence: 99%

Determinants of Energy Demand Efficiency: Evidence from Japan’s Industrial Sector

Otsuka¹

2020

Energy Policy

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With the growing demand for energy, improving energy efficiency has become a key policy issue in Japan. Therefore, this study estimates the energy demand function of Japan's industrial sector using a stochastic frontier model and analyzes the level of energy efficiency and its determinants. An empirical analysis based on the data of 47 Japanese prefectures presents three main findings. First, installment in large production facilities deteriorates energy efficiency and second, it is effective in increasing the electrification rate to improve energy efficiency. Finally, improving productivity leads to an increase in the electrification rate. These results suggest that policies aimed at increasing electrification by enhancing the productivity of factories and offices significantly contribute to improving energy efficiency.

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“…Since the working-age population and non-working-age population display distinct heterogeneity in REC, it is clear that analyzing the compositional change of the population, especially the age composition and the distribution of people in urban and rural areas, is very important for understanding future needs and the potential for energy saving and emission reduction in residential sector. In addition to the above-mentioned socio-economic variables, climate is also known to be an important determinant of REC through influencing heating and cooling [ 17 , 34 , 35 , 36 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Comparative Analysis of Residential Energy Consumption in Urban and Rural China: Determinants and Regional Disparities

Dong

Hua

et al. 2018

IJERPH

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Residential energy consumption (REC) has become increasingly important in constructing an energy-saving and environment-friendly society in China. The main purpose of this study is to provide a more in-depth analysis of the determinants of REC from an urban-rural segregation perspective, and quantify the contributions of individual determinants to the regional disparities of REC. Based on the extended STIRPAT (the stochastic impacts by regression on population, affluence, and technology) model, seemingly unrelated regression (SUR) estimation is employed to examine the impacts of various determinants of urban REC per capita (URECP) and rural REC per capita (RRECP) in a sample of China’s 30 provinces over the period 2007–2016. Then, following the results of SUR, this paper tries to explore the reasons for interprovincial disparities of URECP and RRECP by using the Shapley value decomposition. The empirical results show that income level and heating lead to an increase in URECP, while other factors, including the share of natural gas, average temperature, child dependency ratio and gross dependency ratio, significantly decrease URECP. In terms of RRECP, it is shown that old-age dependency ratio, income level and the share of coal consumption positively influence RRECP, while average temperature has a negative effect on RRECP. Specially, the effect of gross dependency ratio on RRECP is positive, indicating the non-working-age population causes more energy use than the working-age population in rural areas. According to the Shapley decomposition, rather than social-economic variables, climate and heating factors contribute the most to the interprovincial differences in URECP. Furthermore, it is found that income level is the most important factor accounting for inter-provincial differences in RRECP. The findings of this research are of great interest, not only to scholars in REC-related fields, but also to decision makers.

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Population Agglomeration and Residential Energy Consumption: Evidence from Japan

Cited by 15 publications

References 25 publications

What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach

What Contributes to Regional Disparities of Energy Consumption in China? Evidence from Quantile Regression-Shapley Decomposition Approach

Determinants of Energy Demand Efficiency: Evidence from Japan’s Industrial Sector

A Comparative Analysis of Residential Energy Consumption in Urban and Rural China: Determinants and Regional Disparities

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