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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
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.
Aiginger K, Davies SW (2004) Industrial specialization and geographic concentration: two sides for the same coin? Not for the European Union. J Appl Econ 7(2):231–248
Aigner DJ, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function model. J Econ 6:21–37
Aranda-Uson A, Ferreira G, Mainar-Toledo MD, Scarpellini S, Liera E (2012) Energy consumption analysis of Spanish food and drink, textile, chemical and non-metallic mineral products sectors. Energy 42:477–485
Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20(2):325–332
Batty PWJ (2009) Accessibility: in search of a unified theory. Environ Plan Plan Des 36:191–194
Beeson PE, Husted S (1989) Patterns and determinants of productive efficiency in the state manufacturing. J Reg Sci 29(1):15–28
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
Boix R, Trullen J (2007) Knowledge, networks of cities and growth in regional urban systems. Paper Reg Sci 86(4):551–574
Boyd GA (2008) Estimating plant level energy efficiency with a stochastic frontier. Energy J 29:23–43
Boyd GA, Pang JX (2000) Estimating the linkage between energy efficiency and productivity. Energy Pol 28(5):289–296
Boyd G, Dutrow E, Tunnessen W (2008) The evolution of the ENERGY STAR® energy performance indicator for benchmarking industrial plant manufacturing energy use. J Clean Prod 16:709–715
Brownstine D, Golob TF (2009) The impact of residential density on vehicle usage and energy consumption. J Urban Econ 65(1):91–98
Buck J, Young D (2007) The potential for energy efficiency gains in the Canadian commercial building sector. Energy 32:1769–1780
Camagni R (1993) From city hierarchy to city networks: Reflections about an emerging paradigm. In: Lakshmanan LTR, Nijkamp P (eds) Structure and change in the space economy: festschrift in honor of Martin Beckmann. Springer, Berlin, pp 66–90
Camagni R, Capello R (2004) The city network paradigm: theory and empirical evidence. In: Capello R, Nijkamp P (eds) Urban dynamics and growth. Elsevier, Amsterdam, pp 495–532
Capello R (2000) The city network paradigm: measuring urban network externalities. Urban Stud 37(11):1925–1945
Chang T-P, Hu J-L (2010) Total-factor energy productivity growth, technical progress, and efficiency change: an empirical study of China. Appl Energy 87(10):3262–3270
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2(6):429–444
Chen Y-Y, Schmidt P, Wang H-J (2014) Consistent estimation of the fixed effects stochastic frontier model. J Econ 181(2):65–76
Coelli TJ (1995) Recent development in frontier modelling and efficiency measurement. Aust J Agric Resour Econ 39(3):219–245
Coelli TJ (1996) A guide to frontier version 4.1: a computer program for stochastic frontier production and cost function estimation (CEPA Working Papers No. 96/07). University of New England, London
Combes PP, Gobillon L (2015) The empirics of agglomeration economies. In: Duranton G, Henderson JV, Strange W (eds) Handbook of regional and urban economics, vol 5A. Elsevier, Amsterdam, pp 247–348
Driffield N, Munday M (2001) Foreign manufacturing, regional agglomeration and technical efficiency in UK industries: a stochastic production frontier approach. Reg Stud 35(5):391–399
Du K, Lin B (2017) International comparison of total-factor energy productivity growth: a parametric Malmquist index approach. Energy 118:481–488
Energy Information Administration (1995) Measuring energy efficiency in the United States’ economy: A beginning. Energy Information Administration, DOE/EIA-0555(95)/2, Washington DC, USA
Energy Information Administration (2013) International energy outlook 2013. U.S. Energy Information Administration, DOE/EIA-0484, Washington DC, USA
Feijoo ML, Franco JF, Hernández JM (2002) Global warming and the energy efficiency of Spanish industry. Energy Econ 24:405–423
Filippini M, Hunt LC (2011) Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach. Energy J 32:59–79
Filippini M, Hunt LC (2012) U.S. residential energy demand and energy efficiency: a stochastic demand frontier approach. Energy Econ 34:1484–1491
Filippini M, Lin B (2016) Estimation of the energy efficiency in Chinese provinces. Energy Effic 9(6):1315–1328
Filippini M, Hunt LC, Zoric J (2014) Impact of energy policy instruments on the estimated level of underlying energy efficiency in the EU residential sector. Energy Policy 69:73–81
Fritsch M, Slavtchev V (2011) Determinants of the efficiency of regional innovation systems. Reg Stud 45(7):905–918
Goto M, Atris AM, Otsuka A (2018) Productivity change and decomposition analysis of Japanese regional economies: application of HMB productivity index. Reg Stud 52(11):1537–1547
Graham DJ (2007) Variable returns to urbanization and the effect of road traffic congestion. J Urban Econ 62(1):103–120
Greene W (2004) Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization’s panel data on national health care systems. Health Econ 13(10):959–980
Greene W (2005) Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. J Econ 126(2):269–303
Hansen WG (1959) How accessibility shapes land use. J Am Inst Plan 25:73–76
Hirano Y, Fujita T (2012) Evaluation of the impact of the urban heat island on residential and commercial energy consumption in Tokyo. Energy 37:371–383
Holl A (2012) Market potential and firm-level productivity in Spain. J Econ Geogr 12(6):1191–1215
Hu J-L, Wang S-C (2006) Total-factor energy efficiency of regions in China. Energy Policy 34(17):3206–3217
Ihara T, Kikegawa Y, Asahi K, Genchi Y, Kondo H (2008) Changes in year-round air temperature and annual energy consumption in office building areas by urban heat-island countermeasures and energy-saving measures. Appl Energy 85:12–25
International Energy Agency (2009) Progress with implementing energy efficiency policies in the G8. International Energy Agency, Paris
Jondrow J, Knox Lovell CA, Materov IS, Schmidt P (1982) On the estimation of technical inefficiency in the stochastic frontier production function model. J Econ 19(2):233–238
Karathodorou N, Graham DJ, Noland RB (2010) Estimating the effect of urban density on fuel demand. Energy Econ 32(1):86–92
Ke S, Yu Y (2014) The pathways from industrial agglomeration to TFP growth—the experience of Chinese cities for 2001–2010. J Asia Pac Econ 19(2):310–332
Lall SV, Shalizi Z, Deichmann U (2004) Agglomeration economies and productivity in Indian industry. J Dev Econ 73(2):643–673
Lee BS, Jang S, Hong SH (2010) Marshall’s scale economies and Jacobs’ externality in Korea: the role of age, size and the legal form of organisation of establishments. Urban Stud 47(14):3131–3156
Li J, Liu H, Du K (2019) Does market-oriented reform increase energy rebound effect? Evidence from China’s regional development. China Econ Rev 56:101304
Lin B, Du K (2013) Technology gap and China’s regional energy efficiency: a parametric metafrontier approach. Energy Econ 40:529–536
Lin B, Du K (2014) Measuring energy efficiency under heterogeneous technologies using a latent class stochastic frontier approach: an application to Chinese energy economy. Energy 76(1):884–890
Lin B, Du K (2015) Modeling the dynamics of carbon emission performance in China: a parametric Malmquist index approach. Energy Econ 49:550–557
Lin B, Long H (2015) A stochastic frontier analysis of energy efficiency of China’s chemical industry. J Clean Prod 87:235–244
Lin B, Wang X (2014) Exploring energy efficiency in China’s iron and steel industry: a stochastic frontier approach. Energy Pol 72:87–96
Lin B, Yang L (2013) The potential estimation and factor analysis of China’s energy conservation on thermal power industry. Energy Pol 62:354–362
Marshall A (1890) Principles of economics. Macmillan, London
Matthews HS, Williams E (2005) Telework adoption and energy use in building and transport sectors in the United States and Japan. J Infrastruct Syst 11(1):21–30
McCoy K, Moomaw RL (1995) Determinants of manufacturing efficiency in Canadian cities: a stochastic frontier approach. Rev Reg Stud 25(3):317–330
Melo PS, Graham DJ, Levinson D, Aarabi S (2016) Agglomeration, accessibility and productivity: evidence for large metropolitan areas in the US. Urban Stud 54(1):179–195
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
Ministry of Land, Infrastructure, Transport and Tourism (2006) The survey on transport energy. Tokyo
Mitra A (1999) Agglomeration economies as manifested in technical efficiency at the firm level. J Urban Econ 45(3):490–500
Mitra A (2000) Total factor productivity growth and urbanization economies: a case of Indian industries. Rev Urban Reg Dev Stud 12(2):97–108
Montolio D, Solé-Ollé A (2009) Road investment and regional productivity growth: the effects of vehicle intensity and congestion. Paper Reg Sci 88(1):99–118
Morikawa M (2012) Population density and efficiency in energy consumption: an empirical analysis of service establishments. Energy Econ 34:1617–1622
Murillo-Zamorano LR (2004) Economic efficiency and frontier techniques. J Econ Surv 18(1):33–77
Newman PWG, Kenworthy JR (1989) Gasoline consumption and cities. J Am Plan Assoc 55(1):24–37
Otsuka A (2017) Regional energy demand and energy efficiency in Japan. Springer, Switzerland
Otsuka A (2018a) Dynamics of agglomeration, accessibility, and total factor productivity: evidence from Japanese regions. Econ Innovat New Technol 27(7):611–627
Otsuka A (2018b) Determinants of energy demand efficiency: evidence from Japan’s industrial sector. In: Intech Open (eds) Energy policy [working title]. https://doi.org/10.5772/intechopen.81482
Otsuka A, Goto M (2013) Regional policy and the productive efficiency of Japanese industries. Reg Stud 49(4):518–531
Otsuka A, Goto M (2015) Estimation and determinants of energy efficiency in Japanese regional economies. Reg Sci Pol Pract 7(2):89–101
Otsuka A, Goto M (2018) Regional determinants of energy intensity in Japan: the impact of population density. Asia Pac J Reg Sci 2(2):257–278
Otsuka A, Goto M, Sueyoshi T (2010) Industrial agglomeration effects in Japan: productive efficiency, market access, and public fiscal transfer. Paper Reg Sci 89(4):819–839
Otsuka A, Goto M, Sueyoshi T (2014) Energy efficiency and agglomeration economies: the case of Japanese manufacturing industries. Reg Sci Pol Pract 6(2):195–212
Porter ME, Van der Linde C (1995) Toward a new conception of the environment competitiveness relationship. J Econ Perspect 9:97–118
Reiss PC, White MW (2008) What changes energy consumption? Prices and public pressures. RAND J Econ 39(3):636–663
Rice P, Venables AJ, Patacchini E (2006) Spatial determinants of productivity: analysis for the regions of Great Britain. Reg Sci Urban Econ 36(6):727–752
Rosenthal S, Strange W (2004) Evidence on the nature and sources of agglomeration economies. In: Henderson JV, Thisse JF (eds) Handbook of regional and urban economics, vol 4. Elsevier, Amsterdam, pp 2119–2171
Shui H, Jin X, Ni J (2015) Manufacturing productivity and energy efficiency: a stochastic efficiency frontier analysis. Int J Energy Res 39(12):1649–1663
Stelder D (2016) Regional accessibility trends in Europe: road infrastructure, 1957–2012. Reg Stud 50(6):983–995
Stern DI (2012) Modeling international trends in energy efficiency. Energy Econ 34(6):2200–2208
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
Tsekeris T, Papaioannou S (2017) Regional determinants of technical efficiency: evidence from the Greek economy. Reg Stud 52(10):1398–1409
Wei YM, Liao H, Fan Y (2007) An empirical analysis of energy efficiency in China’s iron and steel sector. Energy 32(12):2262–2270
Zhou P, Ang BW, Zhou DQ (2012) Measuring economy-wide energy efficiency performance: a parametric frontier approach. Appl Energy 90(1):196–200
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.
Conflict of interest
The author declares no conflicts of interest. The founding sponsors had no role in the design of the study; collection, analyses, or interpretation of the data; writing of the manuscript; or the decision to publish the results.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
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.
About this article
Cite this article
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