Abstract
The Japanese economy must contend with environmental restrictions; hence, both controlling greenhouse gas emissions by improving energy intensity and boosting national and regional economic growth are important policy goals. Given the potential conflicts between these goals, this study investigates the current energy consumption levels in the Japanese regional economy to determine the factors contributing to improvements in energy intensity. We conduct an empirical analysis using econometric methods to examine whether population density, which is considered a driving force of productivity improvements, contributes to improved energy intensity. The analysis results reveal that population density influences energy intensity improvements. However, the impact differs across regions. In large metropolitan areas, population agglomeration has improved energy intensity, whereas in rural areas, population dispersion has worsened it. The policy implication from this study is that population agglomeration should be encouraged in each region to improve energy intensity, which could protect the environment along with future economic growth.
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Notes
Energy intensity is a measure of the energy efficiency of a nation or region’s economy. High energy intensity indicates a high price or cost of converting energy into GDP, while low energy intensity indicates a lower price or cost of converting energy into GDP.
We check the possible non-linearity of DENS in the estimation and confirm that a coefficient of the square term of DENS was not statistically significant at the 5% level. Therefore, we do not incorporate the square term of DENS into our models.
The change in energy intensity had a correlation coefficient of −0.38 with the change in population density.
It is well known that endogeneity always exists when measuring population agglomeration using production functions (Graham 2009). As discussed in Otsuka and Goto (2015b), strong effects from endogeneity are unlikely when not using production functions to construct data on population agglomeration; however, for verification purpose, this study considers the endogeneity problem.
References
Agency for Natural Resources and Energy, the Ministry of Economy, Trade and Industry (2015) Energy consumption statistics by prefecture
Antweiler W, Copeland BR, Taylor MS (2001) Is free trade good for the environment? Am Econ Rev 91(4):877–908
Arellano M, Bond SR (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–297
Bento AM, Cropper ML (2005) The effects of urban spatial structure on travel demand in the United States. Rev Econ Stat 87(3):466–478
Boyd GA, Pang JX (2000) Estimating the linkage between energy efficiency and productivity. Energy Policy 28(5):289–296
Brownstine D, Golob TF (2009) The impact of residential density on vehicle usage and energy consumption. J Urban Econ 65(1):91–98
Cabinet Office (2013) Annual report on prefectural accounts
Cuddington JT, Dagher L (2015) Estimating short and long-run demand elasticities: a primer with energy-sector applications. Energy J 36(1):185–209
Fujita M, Thisse J (2002) The economics of agglomeration: cities, industrial location and regional growth. Cambridge University Press, Cambridge
Graham DJ (2009) Identifying urbanization and localization externalities in manufacturing and service industries. Pap Reg Sci 88(1):63–84
Karathodorou N, Graham DJ, Noland RB (2010) Estimating the effect of urban density on fuel demand. Energy Econ 32(1):86–92
Marquez-Ramos L (2016) The environmental impact of renewables. Int J Glob Warm, forthcoming
Metcalf GE (2008) An empirical analysis of energy intensity and its determinants at the state level. Energy J 29(3):1–26
Metcalf GE, Hassett KA (1999) Measuring the energy savings from home improvement investment: evidence from monthly billing data. Rev Econ Stat 81(3):516–528
Mindali O, Raveh A, Salomon I (2004) Urban density and energy consumption: a new look at old statistics. Transp Res Part A: Policy Pract 38(2):143–162
Morikawa M (2012) Population density and efficiency in energy consumption: an empirical analysis of service establishments. Energy Econ 34(5):1617–1622
Newman PWG, Kenworthy JR (1989) Gasoline consumption and cities: a comparison of US cities with a global survey. J Am Plan Assoc 55(1):24–37
Nordhaus WD (1979) The efficient use of energy resources. Yale University Press, New Haven and London
Otsuka A (2017a) Regional energy demand and energy efficiency in Japan. Springer Nature, Switzerland
Otsuka A (2017b) Regional determinants of total factor productivity in Japan: stochastic frontier analysis. Ann Reg Sci 58(3):579–596
Otsuka A, Goto M (2015a) Estimation and determinants of energy efficiency in Japanese regional economies. Reg Sci Policy Pract 7(2):89–101
Otsuka A, Goto M (2015b) Agglomeration economies in Japanese industries: the Solow residual approach. Ann Reg Sci 54(2):401–416
Otsuka A, Goto M, Sueyoshi T (2014) Energy efficiency and agglomeration economies: the case of Japanese manufacturing industries. Reg Sci Policy Pract 6(2):195–212
Parr J (2002) Agglomeration economies: ambiguities and confusion. Environ Plan 34:717–731
Reiss PC, White MW (2008) What changes energy consumption? Prices and public pressures. RAND J Econ 39(3):636–663
Statistics Bureau, the Ministry of Internal Affairs and Communications (2015a) Basic resident register population
Statistics Bureau, the Ministry of Internal Affairs and Communications (2015b) Society/population statistical survey
Su Q (2011) The effect of population density, road network density, and congestion on household gasoline consumption in U.S. urban areas. Energy Econ 33(3):445–452
Thompson P, Taylor TG (1995) The capital-energy substitutability debate. Rev Econ Stat 77(3):565–569
United Nations (2015) World urbanization prospects. United Nations, New York
Wu Y (2012) Energy intensity and its determinants in China’s regional economies. Energy Policy 41:703–711
Acknowledgements
The authors thank reviewers whose comments have improved the quality of this study. This study was funded by Japan Society for the Promotion of Science (Grant No. 15K17067, 16K01236). In addition, Dr. Otsuka has received Grant-in-Aid as Young Scientific Research by Yokohama City University.
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Appendix
Appendix
Table 7 shows the correlation matrix of the major explanatory variables in this study. Several variables are correlated with the time trend. For example, KL and the time trend are highly correlated; the correlation coefficient is positive. In addition, IK and the time trend are highly correlated, while the sign of the correlation coefficient is negative. These correlations might affect the sign of the regression coefficient of the time trend. However, since this study uses panel data estimation, the problem of multicollinearity is lessened.
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Otsuka, A., Goto, M. Regional determinants of energy intensity in Japan: the impact of population density. Asia-Pac J Reg Sci 2, 257–278 (2018). https://doi.org/10.1007/s41685-017-0045-1
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DOI: https://doi.org/10.1007/s41685-017-0045-1