Energy intensity improvement is an influential policy agenda for global environmental problem-solving and sustainability enhancement. This study focuses on inter-regional networks’ impact on energy intensity and statistically examines whether the improvement of such networks promotes a modal shift from passenger vehicles to railways. The results indicate the nonlinear relationship between the improvement of inter-regional networks and the variation of the passenger vehicle sector’s energy consumption. Furthermore, the results reveal that the laying of high-speed railways, which helps strengthen inter-regional networks, can significantly improve regional energy intensity. This result has valuable implications for Japan’s national land plan. The high-quality transportation infrastructure, such as high-speed railways, to be introduced in the country can potentially change the energy consumption patterns in inter-regional travel and improve energy intensity. Our results suggest that the installation of the high-speed railway will effectively reduce carbon dioxide emissions. This revelation is expected to provide new policy evidence to scholars and policymakers.
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.
See MLIT (2017) for more details on the calculation of travel time.
Available data on Japanese regional production activities ranges till 2016. However, the 2008 SNA data standard was employed from 2015, thus making it impossible to connect data before 2006. A long-term time series is necessary to obtain reliable estimation results. Therefore, this study employs the 1993 SNA standard to utilize the long-term time series. The most recent year of the data standard is 2014, which can be connected to data from the 1990s and 2000s.
See the Sect. 5 for calculations of the time-distance changes as a result of the opening of the Linear Chuo Shinkansen.
Alberini A, Filippini M (2011) Response of residential electricity demand to price: the effect of measurement error. Energy Econ 33:889–895
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(2):277–297
Batty PWJ (2009) Accessibility: in search of a unified theory. Environ Plan B 36:191–194
Blázquez L, Boogen N, Filippini M (2013) Residential electricity demand in Spain: new empirical evidence using aggregate data. Energy Econ 36:648–657
Burger MJ, Meijers EJ (2016) Agglomerations and the rise of urban network externalities. Pap Reg Sci 95(1):5–15
Camagni R, Capello R, Caragliu A (2016) Static vs. dynamic agglomeration economies. Spatial context and structural evolution behind urban growth. Pap Reg Sci 95(1):133–158
Cameron CA, Trivedi PK (2009) Microeconometrics using Stata. Stata Press, College Station
Chen Z, Haynes KE (2015a) Chinese railway in the era of high-speed. Emerald Group Publishing Limited, Bingley
Chen Z, Haynes KE (2015b) Impact of high speed rail on housing values: an observation from the Beijing-Shanghai line. J Transp Geogr 43:91–100
Chen Z, Haynes KE (2015c) Multilevel assessment of public transportation infrastructure: a spatial econometric computable general equilibrium approach. Ann Reg Sci 54(3):663–685
Chen Z, Haynes KE (2015d) Impact of high-speed rail on international tourism demand in China. Appl Econ Lett 22(1):57–60
Chen Z, Xue J, Rose AZ, Haynes KE (2016) The impact of high-speed rail investment on economic and environmental change in China: a dynamic CGE analysis. Transp Res Part A 92:232–245
Costantini V, Martini C (2010) The causality between energy consumption and economic growth: a multi-sectoral analysis using non-stationary cointegrated panel data. Energy Econ 32(3):591–603
Hansen WG (1959) How accessibility shapes land use. J Am Inst Plan 25:73–76
Ministry of Land, Infrastructure, Transport and Tourism (2015). National spatial strategy (national plan). http://www.mlit.go.jp/common/001127196.pdf. Accessed 20 Jan 2020
Ministry of Land, Infrastructure, Transport and Tourism (2017) The development of land policy simulation model (FY2017 Edition). http://www.mlit.go.jp/kokudoseisaku/kokudoseisaku_tk3_000085.html. Accessed 6 Jan 2020
Otsuka A (2017) A new perspective on agglomeration economies in Japan. Springer, Singapore
Otsuka A (2018) Dynamics of agglomeration, accessibility, and total factor productivity: evidence from Japanese regions. Econ Innov New Tech 27(7):611–627
Otsuka A (2019) Natural disasters and electricity consumption behavior: a case study of the 2011 Great East Japan Earthquake. Asia-Pacif J Reg Sci 3(3):887–910
Otsuka A (2020a) Inter-regional networks and productive efficiency in Japan. Pap Reg Sci 99(1):115–133
Otsuka A (2020b) A new approach to inter-regional network economies in Japan. Reg Sci Policy Pract. https://doi.org/10.1111/rsp3.12291
Otsuka A (2020c) How do population agglomeration and interregional networks improve energy efficiency? Asia-Pacif J Reg Sci 4(1):1–25
Otsuka A, Goto M (2018) Regional determinants of energy intensity in Japan: the impact of population density. Asia-Pacif J Reg Sci 2(2):257–278
Pedroni P (2001) Purchasing power parity tests in cointegrated panels. Rev Econ Stat 83(4):727–731
Pesaran MH, Shin Y, Smith RP (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16(3):289–326
UIC (2018) High speed lines in the world, UIC High Speed Department. https://uic.org/IMG/pdf/20181001-high-speed-lines-in-the-world.pdf. Accessed 17 May 2020
Van Meeteren M, Neal Z, Derudder B (2016) Disentangling agglomeration and network externalities: a conceptual typology. Pap Reg Sci 95(1):61–81
Westin J, Kågeson P (2012) Can high speed rail offset its embedded emissions? Transp Res Part D Transp Environ 17(1):1–7
Wetwitoo J, Kato H (2017) Inter-regional transportation and economic productivity: a case study of regional agglomeration economies in Japan. Ann Reg Sci 59(2):321–344
The author thanks the reviewers whose comments have improved the quality of this paper.
This study was supported by the Japan Society for the Promotion of Science under Grant no. 18K01614.
Conflict of interest
The author declares that the manuscript does not involve any conflict of interest. The funding 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.
Calculation of travel time reduction as a result of the opening of the Linear Chuo Shinkansen.
The reduction of the travel time as a result of the opening of the Linear Chuo Shinkansen is calculated as follows. First, the current travel time when the Linear Chuo Shinkansen is not opened is employed as a criterion for the calculation. In other words, it is assumed that the current Shinkansen is used as a means of transportation. Thus, when any two of Shinagawa, Nagoya, and Shin-Osaka Stations are selected as the transportation route, the travel time realized by the opening of the Linear-Chuo Shinkansen is calculated by subtracting the value in Table 6 from the travel time before the opening; see MLIT (2017) for details.
About this article
Cite this article
Otsuka, A. Assessment of the improvement in energy intensity by the new high-speed railway in Japan. Asia-Pac J Reg Sci (2020). https://doi.org/10.1007/s41685-020-00165-5
- Inter-regional network
- High-speed railway
- Modal shift
- Energy intensity
- Panel data analysis