Regional determinants of energy intensity in Japan: the impact of population density
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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.
KeywordsEnergy intensity Population density Agglomeration Japanese regions
JEL ClassificationQ40 Q50 R10
In Japan, energy demand grew throughout the 1990s and 2000s. It increased at an average annual rate of 0.39% from 1990 to 2010, which was brought about by moderate economic growth (0.78% per annum). On the other hand, energy intensity, which is defined as units of energy per unit of GDP, has decreased by −0.39% per annum on average.1 In particular, energy intensity was greatly reduced in the 2000s compared to the 1990 s. However, there were regional differences in the reduction rate. While energy intensity significantly declined in large metropolitan areas, such as Tokyo, Kansai, and Chubu, the rate of decrease in rural areas was weak; conversely, energy intensity increased in some rural areas, such as Tohoku and Okinawa (Otsuka 2017a).
Boosting regional economic growth, while also curbing greenhouse gas (GHG) emissions via improving energy efficiency, is an important policy goal for Japan because most GHG emissions arise from energy use in modern society. The government has investigated the adoption of several policies for reducing GHGs, mainly centered on green innovation and renewable energy expansion. However, the hot-button issue of climate change has recently become too large to handle with only individual technologies. Japan must also pursue the United Nations Global Compact Cities initiative of urban and regional development, which is designed to reduce carbon emissions by adopting urban and regional policies, including new system design, system changes, and regulation or deregulation.
Population density is the key to decreasing energy intensity. This is because it is known to suppress energy intensity, as shown in a number of previous studies (Newman and Kenworthy 1989; Mindali et al. 2004; Bento and Cropper 2005; Brownstine and Golob 2009; Karathodorou et al. 2010; Su 2011). In Japan, population density has been known to reduce the energy intensity of the manufacturing and commercial sectors (Morikawa 2012; Otsuka et al. 2014). Boyd and Pang (2000) and Otsuka et al. (2014) empirically showed that productivity improvements themselves cause energy-intensity improvements. In other words, the authors claim that energy intensity serves as an indicator of productivity improvements. However, population density might also affect the energy intensity of the residential and transportation sectors as well as the manufacturing and commercial sectors. To accurately predict the future total energy demand of Japanese regions, it is necessary to analyze the energy intensity of the overall sectors. For this purpose, we focus on the energy intensity of all sectors, rather than individual sectors, in Japanese regions. In particular, to contribute to a review of the country’s Basic Energy Plan, it is essential to quantitatively evaluate the impact of population density on energy intensity.
This study aims to provide policy suggestions for the Basic Energy Plan and National Land Planning designed to reduce GHG emissions via improved energy intensity. Using Japanese regional data, we attempt to analyze the effect that population density (the driving force behind sustainable regional economic growth) has on the energy intensity in each regional economy. This study contributes to the existing literature in the following three ways.
First, we clarify the effect of population density on energy intensity from both static and dynamic perspectives. Previous studies that examine the relationship between energy intensity and population density used static models of cross-sectional or panel data (Morikawa 2012; Otsuka et al. 2014). However, population density not only affects the regional disparities of energy intensity, but also its temporal dynamic changes. Considering the interregional migration of the population, the influence of population density on energy intensity is thought to have not only an immediate (static) effect, but also a long-term (dynamic) effect. A static panel model does not allow us to conduct an adequate analysis of dynamic effects, whereas a dynamic panel model enables us to analyze both the short-run effects (short-run elasticity) and long-run effects (long-run elasticity) of population density on energy intensity.
Second, we clarify the difference of the impact of population density on the energy intensity among regions. Populations and firms are agglomerated in a narrow space in Japan’s large metropolitan areas, and the commercial sectors are relatively concentrated in these areas. Japanese commercial sectors have caused energy efficiency to deteriorate to low levels (Otsuka 2017a); therefore, these areas might have less impact on the energy intensity of population density. On the other hand, rural areas might be efficient compared to large metropolitan areas because the manufacturing sectors are concentrated. Manufacturing sectors, which account for a large proportion of the industrial sector, have a high level of energy efficiency, and this level has improved significantly (Otsuka 2017a). Otsuka et al. (2014) showed that the effect of industrial agglomeration on the improvement of energy intensity was significant in the manufacturing sectors. In rural areas, therefore, the impact of population density on energy intensity might be strong, owing to differences in the impact of industrial agglomerations, when compared to large metropolitan areas. We verify this hypothesis.
Third, we focus on the Japanese regional economy with a declining population, which is different from the situations discussed in previous studies. By performing the analysis on Japan, we can provide useful policy implications for energy policymakers in other developed countries that face similar challenges of a potential declining population or falling birth rate.
The remainder of this paper is structured as follows: Section 2 describes the methods and data for empirical analysis. Section 3 summarizes the results of the empirical analysis. Finally, Sect. 4 presents our conclusions and policy suggestions.
2.1 Determinants of energy intensity
In this study, energy intensity (ENERGY) is defined as energy consumption per unit of production value. The focus of this study was to evaluate the effectiveness of population density as a factor affecting energy intensity. Previous studies (e.g., Morikawa 2012; Otsuka et al. 2014) that analyze the relationship between population density and energy consumption efficiency for individual applications (e.g., the manufacturing or commercial sector) show a positive correlation between these factors. This finding suggests that the urban and regional structures represented by population density might improve a sector’s energy intensity, although the effect is not clear in terms of a region’s energy intensity.
Similar to previous studies, this study uses the gross population density (DENS), which is the total population divided by the habitable land area measured in km2. The habitable land area is calculated by deducting the forest, wildlife, and lake areas from the total land area. In addition, several socioeconomic variables that explain variations in energy intensity are included in our model. The selection of the socioeconomic variables is described as follows, and is based on variables used by several previous studies (e.g., Metcalf 2008; Wu 2012; Otsuka et al. 2014; Otsuka and Goto 2015a):
First, we consider energy price (P). According to economic theory, energy consumption decreases when energy prices increase, as long as price elasticity is not zero. Moreover, there is another effect caused by an increase in energy prices: the cost of production increases and producers might respond to it by improving energy intensity. Thus, if the energy market is functioning adequately, higher energy prices are expected to decrease energy intensity through more efficient or reduced energy use. Because of this relationship, the coefficient of P is expected to be negative.
Second, we consider per capita income (Y). This variable is included to reflect the level of economic development in the regions. According to economic theory, rising incomes create greater demand for energy, but also increase people’s ability to adopt more energy-efficient residential lifestyles. Thus, rising incomes would be expected to improve energy intensity. That is, the coefficient of Y is expected to be negative.
Third, we incorporate the capital–labor ratio (KL) to account for the effect of capital intensity on energy intensity. Thompson and Taylor (1995) and Metcalf (2008) showed that capital and energy have a substitution relationship over both the short and long run, whereas Antweiler et al. (2001) held that capital and pollution have a complementary relationship. Here, the capital–labor ratio is employed as a proxy for the level of technology involved. Thus, the KL variable might be negatively related to energy intensity. That is, energy intensity is expected to decline as production technology improves.
Fourth, we indirectly consider the effect of the capital stock’s vintage, which might reflect, to some extent, the speed of old machines and structures being replaced. New capital might be endowed with energy-saving technology and, thus, be more energy efficient. A particular regional industry’s low investment level in upgrading capital stock suggests that the industry’s energy intensity might also be high. Similarly, regional industries that are quick to invest in upgrading capital stock might replace it with more energy-efficient capital, thereby improving the industries’ energy intensity. To measure this capital vintage effect, we consider the investment–capital ratio (IK) (private-sector corporate capital investment divided by capital stock) for each year. The coefficient of this variable is expected to be negative.
Fifth, we introduce temperature data to account for the effects of temperature change on production activities. Specifically, we use cooling degree days (COOL) and heating degree days (HEAT). The annual number of cooling degree days is the cumulative difference of temperatures between 22 °C and the average temperature on each day in a year whose average temperature exceeds 24 °C, while the annual number of heating degree days is the cumulative difference of temperatures between 14 °C and the average temperature on each day in an annual period whose average temperature is below 14 °C. In the energy economic analysis, these indexes are usually used as variables representing cooling and heating, respectively (Metcalf 2008). These indexes are assumed to be related to energy consumption, and their use in this manner has precedent applications. For example, Metcalf and Hassett (1999) and Reiss and White (2008) used cooling and heating degree days to analyze energy consumption.
Finally, a time trend (Time) is also included in the model to capture the general trend of technology change over time, and is expected to have a negative coefficient.
2.2 Empirical models
Note that all of the variables in Eq. (1) are expressed in natural logarithms. The subscript j represents the region (j = 1, …, J) and t represents the time (t = 1, …, T). ENERGY is the energy intensity (final energy consumption per unit of production value), P is the energy price, Y is per capita income, DENS is population density, KL is the capital–labor ratio (capital stock divided by the number of workers), and IK is the investment–capital ratio (annual private-sector corporate capital investment divided by capital stock). To consider the possibility of the non-linearity of the influence of the independent variables, the squared terms of those variables are added to the model.2 Furthermore, as previously mentioned, COOL is the cooling degree days, HEAT is the heating degree days, Time is the time trend, and u is an error term.
Both α and β are the estimated coefficients. Because panel data are used, α represents an individual effect. Energy price increases improve energy intensity and, thus, β1 is expected to have a negative sign. Income increases also improve energy intensity and, thus, β2 is also expected to have a negative sign. If population density improves energy intensity, then the sign of β3 will be negative, whereas if population density worsens energy intensity, its sign will be positive. Moreover, the signs of β4 and β5 will be negative if capital and energy have a substitution relationship, while those of β6 and β7 will be negative if upgrading capital improves energy intensity. Further, β10 is expected to have a negative sign because of technological development.
The problem with Eq. (1) is its assumption that energy intensity immediately reflects changes in economic variables. A more realistic assumption is that energy intensity is affected by changes in economic variables, such as energy price, after a certain time lag. Accordingly, this study considers the factors that affect energy intensity after a time lag by using a partial adjustment model as the second model.3
2.3 Partial adjustment model
Before describing the data source of this study, this section briefly describes the regional distribution of economic activity in 2010. In Japan, the geographical distribution of industries is unique. The population of the Greater Tokyo area (i.e., Saitama Prefecture, Chiba Prefecture, Tokyo, and Kanagawa Prefecture) accounts for 27.36% of the total national population, and production within this area is 32.30% of the total national production. However, the Greater Tokyo area accounts for only 7.34% of the total national livable land. In particular, the population of the Greater Tokyo area is significantly higher than that of the second most populous region, Kansai (16.25%; this region includes Shiga Prefecture, Kyoto, Osaka, Hyogo Prefecture, Nara Prefecture, and Wakayama Prefecture), and of the third most populous region, Chubu (13.46%; this region covers Gifu, Shizuoka, Aichi, and Mie Prefectures). In fact, it is roughly equivalent to the total population of the latter two regions. Similarly, production in the Greater Tokyo area is roughly equivalent to the total production in both Kansai (15.70%) and Chubu (14.14%). Both population and overall economic production show similar distributions, since both are highly concentrated in metropolitan areas.
The data analyzed in this study are the final energy consumption of Japan’s regions, which include 47 prefectures, from 1990 to 2010. These are, therefore, panel data by prefecture and year. All of the data are extracted from official publications of the Japanese government or prominent research institutions. The source of the prefectural final energy consumption data is the Energy Consumption Statistics by Prefecture (Agency for Natural Resources and Energy, Ministry of Economy, Trade and Industry 2015). The production-value data, which are used as the denominator in the calculation of energy intensity, are the actual total prefectural production, extracted from the Annual Report on Prefectural Accounts (Cabinet Office 2013). Energy price data are taken from those published by the International Energy Agency. Income figures are obtained from the Annual Report on Prefectural Accounts (Cabinet Office 2013) and converted to real figures based on the total prefectural expenditure deflator. Population and habitable surface area data are extracted from the Basic Resident Register Population and Society/Population Statistical Survey, respectively (Statistics Bureau, Ministry of Internal Affairs and Communications 2015a, 2015b). Other socioeconomic variables data are drawn from the Central Research Institute of Electric Power Industry’s regional economic database. Finally, the data on heating and cooling degree days are obtained from prefectural capitals and meteorological observation points.
Energy intensity (GJ per million yen)
Energy price (2010 = 100)
Per-capita income (million yen)
Population density (people per km2)
Capital–labor ratio (million yen per
Cooling degree days (degree days)
Heating degree days (degree days)
Total sample, 1990–2010
The average population density during the observation period is 1355 people per km2. The population density increased from the 1990s through the 2000s. This finding indicates that population agglomeration increased during the observation period, which suggests a high likelihood that the observed energy intensity improvements are caused by increased population agglomeration.
Among the socioeconomic variables other than population density, only energy price is a nationwide measure. Energy prices declined in the 1990s before increasing markedly in the 2000s. The average per capita income during the observation period was 2.715 million yen; the per capita income rose steadily during the 1990s and 2000s, and it is highly likely that higher incomes contributed to improved energy intensity. The average capital–labor ratio during the observation period was 15.481. The capital intensity increased from the 1990s through the 2000s; therefore, it is highly likely that product processes were increasingly mechanized. The average investment–capital ratio (0.061) during the period was low, declining from the 1990s through the 2000s. This suggests that little upgrading of production equipment occurred. The Appendix shows the correlation matrix of these explanatory variables.
All explanatory variables have been standardized and, thus, it is possible to compare each estimated coefficient and interpret the magnitude of the effect of different variables measured in different scales.
3 Results and discussion
Estimation results for Eq. (1) (static panel data models)
Number of observations
The results in Table 2 show that population density is associated with low energy intensity. As both the explanatory and explained variables are logarithmic values, coefficients β1 through β10 represent degrees of elasticities. Accordingly, the larger the estimated coefficients (elasticities), the greater the effect of the corresponding explanatory variable on the explained variable. Therefore, as shown in Table 2, the effects of population density significantly exceed those of other explanatory variables. The coefficients of energy price and per capita income have negative signs and, thus, the sign condition mentioned previously is satisfied. In other words, increases in both energy prices and incomes decrease energy intensity. The coefficient of the capital–labor ratio is negative and, therefore, capital and energy consumption have a substitution relationship, meaning that a higher capital–labor ratio translates into lower energy intensity. In addition, the coefficient of the investment–capital ratio is negative, indicating that upgrading capital stock improves energy intensity. Although statistically significant, the estimated parameters are relatively much smaller than are those of income and density. As such, it can be said that the effect of the investment-capital ratio is negligible.
It is important to avoid an endogeneity problem in order to make the fixed-effects model estimates meaningful. In this study, we might need to consider the endogeneity problem between DENS and ENERGY because of the possible influence from some omitted variables in Eq. (1).5 Considering this problem, this study used the instrumental variable method to show the results (Model B) for estimating Eq. (1). The coefficient of population density in Model B is similar to that in Model A. Hence, even if the problem of endogeneity exists in the equation, it is reasonable to assume that the influence would be negligible.
Estimation results for dummy variable model (static panel data models)
Number of observations
Estimation results for Eq. (4) (dynamic panel data models)
Number of observations
Energy (t − 2)
Energy (t − 2)
In this dynamic model, we estimated the model considering the influence of the regional dummy (dum) on population density. The estimation results are shown in Model F. According to the results, the regional dummy coefficients are not statistically significant. This is different from the result of the static model (Model C). This result suggests that the hypothesis (i.e., the effect of population density is different between metropolitan and rural areas) is not supported in the dynamic model. Therefore, the regional difference effects of population density on energy intensity arise solely from differences in regional data, and not from those in the estimated coefficients. The reason for these different results between the static and dynamic models, with respect to the regional dummy, might be population movement over time. Generally, people and firms tend to move among regions according to productive environmental disparities. In the short run, when population movement does not occur, there is a clear difference in the effects of population density between urban and rural regions. Meanwhile, in the long run, when the population moves freely among regions, inter-regional disparities in the effect would be diminished.
Factor elasticities relative to energy intensity
These findings demonstrate the existence of economies of population agglomeration on energy intensity, and this effect is relatively large. We obtained consistent signs of the estimated coefficients on population density regardless of using static or dynamic models. Therefore, we determined that urban and regional development policies that boost population agglomeration improve energy intensity.
Contributions of each factor to changes in energy intensity (1990–2010; annual averages; %)
Rate of change of energy intensity (Δ Energy = a + b + c + d + e + f + g + h + i)
Per capita income
Cooling degree days
Heating degree days
Greater Tokyo area
In addition to population density, other factors that contribute to improving energy intensity are per capita income and the capital–labor ratio. These factors are important in all regions, and the capital–labor ratio, in particular, explains the largest part of the observed changes in energy intensity. Conversely, energy price has little effect on energy intensity, as shown by the low price elasticity of energy demand. Thus, we concluded that energy intensity is affected more by income and capital intensity than by energy price.
4 Conclusion and policy implications
Japan’s aging and declining population, falling birthrates, and tougher environmental restrictions mean that finding a way to achieve regional economic growth is a crucial policy issue for the country. Grounded in the notion that Japan’s prefectures can combine improvements in energy efficiency and productivity for regional economic growth, this study presented an empirical analysis focusing on the effect that population agglomeration has on energy intensity.
Facing global competition, Japanese companies are developing energy-saving technologies. Consequently, the ratio of final energy consumption per unit of production value has continually declined. However, the degree of energy intensity improvement varies from region to region. For example, regions in which the commercial sector accounts for a relatively large share of the industrial structure have made insufficient progress. This suggests that the degree of energy intensity improvement in areas where the commercial sector is concentrated might be small. Previous studies conducted in Japan have shown that the higher a region’s population agglomeration rate, the lower the energy intensity in manufacturing sectors; yet, previous analyses have not adequately evaluated the nature of the effect that population agglomeration has on the entire region’s energy intensity over all sectors.
The results of this study show, for the first time, that population agglomeration brings about improvements in energy intensity across entire regions and sectors. In other words, population agglomeration brings about low energy intensity. We concluded that this improved energy intensity is created by the high elasticity enabled by population agglomeration. This finding may be applicable to other developed countries where population agglomeration is progressing. According to the United Nations (2015), the ratio of the urban population to the total population is greater in the UK, France, Germany, and the US than in Japan. In other words, urbanization is progressing in Europe and the US rather than in Japan. This implies that the improvement of energy intensity will be more advanced in European countries and the US, where higher levels of urbanization have been attained. An examination of the global trend would be an interesting research topic in the future.
Moreover, agglomeration actually reduces the costs of energy-efficiency improvements in two ways. First, proximity lowers the need for infrastructure, public transportation, and the provision of energy-saving services. Simultaneously, spillover effects, by which advances of one company’s efficiency spread rapidly to other companies, lower the overall cost threshold for improvements in energy intensity. In Japan, these effects are stronger in rural areas that contain many industrial cities compared to in large metropolitan areas. In this context, there is a difference in the industrial structure among areas.
To measure the population agglomeration effect on energy intensity, we calculated the degree to which population agglomeration contributes to changes in energy intensity during the observation period from 1990 to 2010. From the result, this study found that the population agglomeration effect is apparent in all regions. However, the trend varies dramatically from region to region. In urban areas with large cities, such as the Greater Tokyo area, Chubu, and Kansai, increased population inflow apparently decreases energy intensity, whereas in rural areas other than Okinawa and Kita-Kanto, its contribution is opposite, due to the decline in population density. Thus, we concluded that the increased population agglomeration results in decreased energy intensity in Japan for entire regions and sectors, but that the effect is not realized in rural areas, in particular, due to declining population density.
This conclusion suggests that the differences in regional population agglomeration may affect regional energy demand through regional energy intensity. This is because energy demand is calculated by multiplying energy intensity by GDP. In other words, in reviewing Japan’s Basic Energy Plan, we need to pay attention to regional energy intensity. Regional energy intensity is affected by various socioeconomic factors such as regional population and industry. Our evidence revealed that the influence of population agglomeration is strong, thus implying that it is necessary to pay attention to the influence of population density when planning national energy policies.
Otsuka and Goto (2015b) and Otsuka (2017b) revealed that many large cities that exist in the regions of the Greater Tokyo area, Chubu, and Kansai enjoy the benefits of agglomeration economies from population agglomerations. According to Fujita and Thisse (2002), these benefits stem from the diversity of the economic structure in these regions. Increasing the diversity of economic structure means improvement in both productivity and energy intensity (Parr 2002). Conversely, rural regions lack diversified cities that play a key role in their economies, and their population is widely dispersed. In order to improve the energy intensity of the whole country in the future, it is necessary to integrate the population by forming a major urban area in each rural region, so that the area can enjoy the economies brought about by population agglomeration. To enjoy the benefits without suffering from the uneconomical aspects of large-city agglomeration (e.g., overcrowding, soaring land prices, and environmental damage) (Marquez-Ramos 2016), a city should, ideally, not become too big by aggregating an excessive number of industries. Determining the appropriate size of a city from the population agglomeration perspective and guiding regions toward such a development path is an important policy issue. As a first step, the policy needs to form medium-sized cities that function as major urban areas in rural regions, in order to improve the energy intensity of Japan as a whole. Furthermore, it might be necessary to rectify the excess concentration of population in the Greater Tokyo area and encourage the migration of the population to rural areas. As such, in order to deepen further consideration, we should evaluate the role of city size in energy intensity from the urban systems perspective, considering the city size distribution. We believe that it is possible to provide clearer suggestions if we can identify the non-linear effects of population size on energy intensity.
Given these findings, this study suggests that growth strategies for urban development should be tailored by region. Exploring tailored strategies by using more detailed data is a research topic that we plan to pursue in the future. In particular, it needs to be further verified whether the hypothesis—that the effect of population density on energy intensity is different between cities and regions—is applicable to other countries by using a more extensive dataset of foreign countries. Moreover, our analysis does not consider spatial effects on energy intensity. For example, logistics, like cargo, generates spatial effects on energy intensity. To introduce spatial spillover effects in the study, we need to examine the energy consumption of cargo in detail, which is grasped in the transport sector. However, in Japan, energy consumption related to cargo is only measured at the country level. This is because it is difficult to distinguish between origin and landing in the energy consumption of cargo. Therefore, our analysis does not analyze the transport sector considering this data constraint. In order to capture the effect of logistics, it is necessary to accurately estimate the actual state of the energy consumption of cargo between regions. This is an important future extension that we should address.
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.
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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