This study investigates the impact of population agglomeration and interregional networks on the energy efficiency of the Japanese industrial and commercial sector. The empirical analysis employs a stochastic frontier model and reveals a nonlinear relation between population agglomeration and energy efficiency. External diseconomies prevail until reaching a threshold level of agglomeration; when the threshold is exceeded, external economies come into play. Enhanced accessibility is found to significantly increase energy efficiency. These results suggest that policies aimed at strengthening regional agglomeration and interregional networks can greatly contribute to improving energy efficiency.
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Energy intensity is defined as the ratio of energy consumption to the gross domestic product. It depends on various socioeconomic factors, such as energy prices and production size, and whether energy intensity is a suitable indicator for energy efficiency is open for debate (Energy Information Administration 1995, 2013; International Energy Agency 2009).
Thompson and Taylor (1995) argued that there is a both a short-term and long-term relationship between capital and energy consumption.
Not only can SFA deal with statistical noise, it is also very flexible in describing technological heterogeneity. In the traditional SFA, a fixed-effects SFA model is used. For this model, Battese and Coelli (1995) use the maximum-likelihood estimation, but Greene (2004, 2005) uses true fixed effects estimates. However, it has been pointed out that there is a problem with Greene's estimation approach, and Chen et al. (2014) and Du and Lin (2017) have suggested ways to improve on it. For more information, see Lin and Du (2013, 2014, 2015) and Du and Lin (2017).
This energy demand function can be derived from the producer's profit maximization behavior. That is, it is assumed that (1) is the reduced form of the energy demand function. See Li et al. (2019) for details on the derivation method.
This accessibility variable has a constant distance attenuation parameter. We need to consider variable attenuation parameters as more realistic countermeasures. This task is a future extension.
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The authors thank reviewers whose comments have improved the quality of this study.
This study was supported by the Japan Society for the Promotion of Science under Grant No. 18K01614.
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Appendix A: data sources
The final energy consumption (E) of each prefecture was obtained from the “Energy Consumption Statistics by Prefecture” (Ministry of Economy, Trade and Industry). Final energy consumption includes data that aggregate the industrial and commercial sectors. Final energy refers to the amount of energy actually consumed in the industrial, commercial, transportation, and household sectors. Energy is generally composed of primary and secondary energy. Primary energy is directly obtained from nature such as crude oil, coal, natural gas, hydropower, nuclear power, wind power, geothermal power, while secondary energy is converted and processed for easy use of primary energy, such as petroleum products refined at refineries, electricity generated at power plants, city gas, and coke for steelmaking. The final energy consumption is the total primary energy consumed directly as it is as well as consumption of secondary energy. However, energy consumed during processing and conversion, such as electricity and petroleum refining, is counted separately as part of the energy conversion sector. In other words, final energy consumption is the total amount of energy consumed at the consumer level, such as by industrial activities, transportation, and households. This study covers the energy consumption of industrial activities (industrial and commercial sector), that is, the total amount of energy consumed in the factory and office.
The real energy price (P) is the real energy price index of the industrial and commercial sector published by the International Energy Agency. Corporate income (Y) data were obtained from the “Annual Report on Prefectural Accounts” (Cabinet Office) and deflated by the gross regional product deflator. Capital–labor ratio (KL) is the ratio of capital stock to the number of employees. The vintage of capital (IK) is the ratio of capital investment to capital stock. The data on capital investments and capital stock were procured from the Central Research Institute of Electric Power Industry. The number of employees was obtained from the “Annual Report on Prefectural Accounts.” Data on the cooling degree day (CDD) and heating degree day (HDD) were obtained from prefectural capital and meteorological observation points. A cooling degree day is the difference between the average temperature above 24 °C and 22 °C. A heating degree day is the difference between the average temperature below 14 °C and 14 °C. Population density is the ratio of the population to the residential land area. Information regarding the size of the population was obtained from the “Basic Resident Register Population” (Ministry of Internal Affairs and Communications). Data for the residential land area were obtained from the “Social Indicators by Prefecture” (Ministry of Internal Affairs and Communications). The value of the output used in the computation of accessibility indicator was obtained from the “Annual Report on Prefectural Accounts.” Data regarding the time distances between regions were obtained from the National Integrated Transport Analysis System (Ministry of Land, Infrastructure, Transport and Tourism). The manufacturing ratio is based on the information obtained from the “Annual Report on Prefectural Accounts.”
Appendix B: descriptive statistics of the variables used in the analysis
The descriptive statistics of the variables used in this study are presented in Table 4. The industrial and commercial sector’s final energy consumption rose from the 1990s to the 2000s, as energy prices declined and corporate incomes increased. The capital–labor ratio had been rising and mechanization continued to progress. However, between 2000 and 2013, the industrial and commercial sector’s energy consumption declined. Income was rising, but it may have been affected significantly by rising energy prices. Population densities gradually increased throughout the observation period, and population agglomeration to urban areas was accelerating. The accessibility indicator, however, rose sharply between 1990 and 2000, and then, it began declining in 2013. The results indicate a significant impact of shortening time distances due to the development of public transportation systems in the 1990s.
Appendix C: characteristics of each region in Japan
Table 5 reports the characteristics of each region in Japan. The values in the table are regional averages, not total amounts. The regions with the highest regional average energy use in the industrial and commercial sector are in the Greater Tokyo Area, followed by Hokkaido, Chugoku, and Chubu. Okinawa and Hokuriku consume less energy. The mechanized regions are Chubu, Hokuriku, and Kansai. Highly populated and accessible areas include the large metropolitan areas, such as the Greater Tokyo Area, Kansai, and Chubu. This reflects the fact that the Japanese public transportation network extends radially around large metropolitan areas. In other words, the large metropolitan areas function as passenger transportation hubs. Therefore, regional agglomeration is advanced and accessible in this area. The regions where manufacturing is concentrated include North-Kanto, Chubu, and, in local areas, Chugoku.
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Otsuka, A. How do population agglomeration and interregional networks improve energy efficiency?. Asia-Pac J Reg Sci 4, 1–25 (2020). https://doi.org/10.1007/s41685-019-00126-7
- Population agglomeration
- Interregional networks
- Energy efficiency