**Table 3.**

*Estimation results for 1980–2010.*

#### *Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector DOI: http://dx.doi.org/10.5772/intechopen.81482*

of installing power facilities and an increase in the electrification rate from office automation are more significant than the negative impact of installing large productive capital equipment.

Due to these effects, this study estimates Model B, which account for the nonlinear effects of determinants. Statistically, significant values are obtained for the quadratic term of the capital-labor ratio. The sign of the quadratic term is negative. This shows there is a threshold for the impact of the capital-labor ratio on energy efficiency. Specifically, the increase in mechanization improves energy efficiency, but it exacerbates energy efficiency when it exceeds a threshold value. On the other hand, the quadratic terms for electrification are not statistically significant, and the nonlinear effects of electrification are not recognizable.

During the observation period (1990–2010), there was financial crisis in 2008. As the economic depression spreads worldwide from the US, Japan's economic growth rate fell greatly in 2008. This economic downturn had a significant influence on the production system of the regional industry. Hence, when considering result stability, this influence must be considered. Therefore, the analysis period is reset from 1990 to 2007, and Model A and Model B reestimated. **Table 4** shows the reestimation results. Model C represents the result of reestimation of Model A and Model D of Model B. There is no significant difference in the regression coefficients in **Table 4**. Therefore, the results in **Table 3** can be judged as robust.

**Table 5** presents the descriptive statistics for the energy efficiency values of each prefecture, obtained from the estimation results (Model A). An efficiency value of 1 denotes the highest efficiency, while a value below 1 indicates lower energy efficiency. The average energy efficiency value is 0.617 and the median value 0.685. More importantly, a maximum value of 0.950 and a minimum of


*Note: \*\* and \* denote significance at the 1 and 5% levels, respectively.*

#### **Table 4.**

*Estimation results for 1980–2007.*

*Energy Policy*

**3. Results and discussion**

demand in the industrial sector.

**Table 3** presents the estimation results for the energy demand frontier function. Model A shows the estimation results of (2) and (3). Model B shows the estimation result of the model considering a nonlinear effect in the inefficiency determinant. First, let us consider the results of Model A. The estimated coefficients show the expected signs, and all variables are statistically significant. Since each variable is a logarithmic variable, the estimated parameter can be interpreted as elasticity. Estimated price elasticity is 0.046 and income elasticity 0.707, indicating that income elasticity significantly exceeds price elasticity. Price elasticity is inelastic and denotes the nature of energy goods as essential goods. The capital-labor ratio and the coefficient on vintage are positive and have reasonable signs. This suggests that there is more energy demand in areas where industries for which mechanization is progressing are located. It also shows that capital investment increases energy demand. The coefficients for cooling degree day are slightly significant, but their magnitude is small. Additionally, the coefficients for heating degree day are not significant. These results show that temperature is a weak determinant of energy

Next, this study evaluates the estimation results for the factors determining energy efficiency. The capital-labor ratio is positive and reports the expected sign. This indicates that energy efficiency deteriorates with an increase in mechanization. In other words, energy efficiency is lower in regions where numerous industries with large-scale facilities are located. The electrification rate is negative, reporting the expected sign. The results show that an increase in the electrification rate enhances energy efficiency. The coefficient value for the electrification rate is considerably larger than that for the capital-labor ratio. That is, the positive impact

*Constant* (*α*) −0.584\*\* (0.038) −0.571\*\* (0.040) *α<sup>P</sup>* −0.046\*\* (0.009) −0.039\*\* (0.009) *α<sup>Y</sup>* 0.707\*\* (0.010) 0.705\*\* (0.010) *αKL* 0.10 \*\* (0.023) 0.109\*\* (0.021) *αIK* 0.065\*\* (0.010) 0.073\*\* (0.011) *αCDD* −0.021\* (0.009) −0.021\* (0.009) *αHDD* −0.005 (0.007) −0.006 (0.007) *Constant* (*β*) 0.534\*\* (0.043) 0.556\*\* (0.047) *βKL* 0.093\*\* (0.025) 0.096\*\* (0.024)

<sup>2</sup> −0.028\*\* (0.009) *βER* −0.522\*\* (0.011) −0.556\*\* (0.026)

<sup>2</sup> −0.019 (0.011)

Number of observations 987 987

*Note: \*\* and \* denote significance at the 1 and 5% levels, respectively.*

<sup>2</sup> 0.062\*\* (0.004) 0.063\*\* (0.004)

) 0.692\*\* (0.096) 0.690\*\* (0.092)

**Model A Model B Coefficient Standard error Coefficient Standard error**

**92**

*βKL*

*βER*

*σu* <sup>2</sup> + *σ<sup>v</sup>*

*σu* 2 /(*σ<sup>u</sup>* <sup>2</sup> + *σ<sup>v</sup>* 2

**Table 3.**

*Estimation results for 1980–2010.*


**Table 5.**

*Descriptive statistics of energy efficiency scores.*

0.114 point to a significant difference in energy efficiency levels among regions. **Figure 1** shows the time-series transition of the national average energy efficiency scores. The average energy efficiency score of Japan's industrial sector has been consistently increasing until 2007, after declining from 1990 to 1994. Energy conservation progressed from the latter half of the 1990s to the 2000s, and energy efficiency thus improved. However, in 2008, energy efficiency worsened and then increased slightly.

**Table 6** presents the average energy efficiency scores for each prefecture and ranks them accordingly. Nara, Tokyo, Yamanashi, Ishikawa, Saga, and Yamagata are among the high-ranking areas. The factories and offices located in these areas are likely to report an increasing rate of electrification. On the other hand, Oita has the lowest energy efficiency. Okayama, Chiba, and Yamaguchi have petrochemical complexes and, thus, low energy efficiency, because petrochemical complexes have large-scale production facilities and are lagging in electrification, given the large demands for coal, kerosene, and gas for production.

**Table 6** also shows the change rate of the average scores for the energy efficiency between the 1990s and the 2000s. The table highlights two key characteristics. First, Mie, Wakayama, and Fukuoka report improved average scores. Specifically, Mie has the highest improvement score, and its energy efficiency value shows an annual improvement of 1.87%. Wakayama and Fukuoka's scores improve by 1.36 and 1.12%

**95**

*Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector*

**Rank Prefecture Average efficiency score Change rate of score** Nara 0.93 −0.22 Tokyo 0.92 −0.10 Yamanashi 0.91 −0.10 Ishikawa 0.90 −0.20 Saga 0.87 −0.06 Yamagata 0.86 −0.01 Nagano 0.86 0.17 Kyoto 0.85 −0.19 Okinawa 0.84 −0.07 Gunma 0.84 0.00 Akita 0.84 −0.46 Fukui 0.83 −0.01 Kumamoto 0.79 −0.38 Fukushima 0.79 0.73 Shiga 0.77 0.81 Shimane 0.77 0.61 Tochigi 0.77 −0.18 Nagasaki 0.76 0.79 Gifu 0.72 −0.13 Saitama 0.72 −0.14 Kagoshima 0.72 −0.29 Tottori 0.71 −1.07 Miyagi 0.71 −0.59 Iwate 0.68 0.15 Toyama 0.67 0.51 Shizuoka 0.65 0.81 Tokushima 0.64 1.39 Miyazaki 0.63 0.82 Osaka 0.61 0.16 Niigata 0.60 0.11 Aichi 0.53 0.23 Aomori 0.53 0.38 Hokkaido 0.51 0.10 Kochi 0.49 0.43 Kagawa 0.47 0.77 Hyogo 0.42 0.35 Fukuoka 0.42 1.12 Ehime 0.40 0.15 Kanagawa 0.37 −0.58 Wakayama 0.31 1.36 Hiroshima 0.29 0.53

*DOI: http://dx.doi.org/10.5772/intechopen.81482*

**Figure 1.**

*Time trend of national average energy efficiency scores.*


*Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector DOI: http://dx.doi.org/10.5772/intechopen.81482*

*Energy Policy*

**Table 5.**

increased slightly.

*Descriptive statistics of energy efficiency scores.*

0.114 point to a significant difference in energy efficiency levels among regions. **Figure 1** shows the time-series transition of the national average energy efficiency scores. The average energy efficiency score of Japan's industrial sector has been consistently increasing until 2007, after declining from 1990 to 1994. Energy conservation progressed from the latter half of the 1990s to the 2000s, and energy efficiency thus improved. However, in 2008, energy efficiency worsened and then

Mean 0.617 Std. dev. 0.234 Minimum 0.114 Maximum 0.950 Median 0.685 *Note: The energy efficiency scores in the table are calculated using the estimation results for Model A.*

**Table 6** presents the average energy efficiency scores for each prefecture and ranks them accordingly. Nara, Tokyo, Yamanashi, Ishikawa, Saga, and Yamagata are among the high-ranking areas. The factories and offices located in these areas are likely to report an increasing rate of electrification. On the other hand, Oita has the lowest energy efficiency. Okayama, Chiba, and Yamaguchi have petrochemical complexes and, thus, low energy efficiency, because petrochemical complexes have large-scale production facilities and are lagging in electrification, given the large

**Table 6** also shows the change rate of the average scores for the energy efficiency between the 1990s and the 2000s. The table highlights two key characteristics. First, Mie, Wakayama, and Fukuoka report improved average scores. Specifically, Mie has the highest improvement score, and its energy efficiency value shows an annual improvement of 1.87%. Wakayama and Fukuoka's scores improve by 1.36 and 1.12%

demands for coal, kerosene, and gas for production.

**94**

**Figure 1.**

*Time trend of national average energy efficiency scores.*


#### **Table 6.**

*Average energy efficiency scores and change rate of the score.*


**Table 7.** *Results of rank correlation.*

annual rates. These prefectures rank low in average energy efficiency. Therefore, it is highly likely these regions have several electrical machineries and equipment manufacturing units, and their machinery industry has progressive electrification rates, thus contributing to the improvement of energy efficiency. Second, energy efficiency is deteriorating in regions with high energy efficiency levels, including Nara, Tokyo, Yamanashi, Ishikawa, and Saga. This suggests that the regional disparities in energy efficiency are decreasing.

This study verifies the possibility of reducing regional disparities in energy efficiency by calculating the rank correlation coefficient between the average energy efficiency score and its change rate. **Table 7** shows the results of the rank correlation coefficient. The Kendall rank correlation coefficient is −0.2396, which is statistically significant. Spearman's rank correlation coefficient is −0.3207, also being statistically significant. The sign of any rank correlation coefficient is negative, and the improvement in energy efficiency is progressing in the region with a low energy efficiency.

The energy efficiency level is highly related to electrification. **Figure 2** is a cross-sectional plot of the average values for the electrification rate and energy efficiency. The figure clearly illustrates an upward trend. In other words, regions with advanced electrification have high energy efficiency levels. Specifically, regions where offices are concentrated (e.g., Tokyo) are located in the upper right corner, while those with petrochemical complexes (e.g., Oita, Okayama, and Chiba) are in the lower right.

Furthermore, it is also highly possible that energy efficiency improvements have progressed to electrification. **Figure 3** plots the time-series relationship between the electrification rate and energy efficiency and shows an upward trend. In other words, it is highly likely that advanced electrification contributes to energy efficiency improvements. As described above, Mie is likely to report improved energy efficiency, given its progress in electrification. On the other hand, Chiba has lower energy efficiency, given the low energy efficiency of petrochemical complexes.

**97**

**Figure 3.**

**Figure 2.**

*Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector*

Finally, this study analyzes the determinants of the electrification rate, which is one of the key factors to improve energy efficiency. **Table 8** presents the estimation results for Eq. (4). First, the F-test checks for fixed effects and rejects the null hypothesis that there is no fixed effect at the 1% significance level. Additionally, the Hausman test rejects the null hypothesis that the fixed effect is a random effect

*Dynamic relationship between energy efficiency score and electrification rate.*

*DOI: http://dx.doi.org/10.5772/intechopen.81482*

*Static relationship between energy efficiency score and electrification rate.*

*Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector DOI: http://dx.doi.org/10.5772/intechopen.81482*

**Figure 2.** *Static relationship between energy efficiency score and electrification rate.*

**Figure 3.** *Dynamic relationship between energy efficiency score and electrification rate.*

Finally, this study analyzes the determinants of the electrification rate, which is one of the key factors to improve energy efficiency. **Table 8** presents the estimation results for Eq. (4). First, the F-test checks for fixed effects and rejects the null hypothesis that there is no fixed effect at the 1% significance level. Additionally, the Hausman test rejects the null hypothesis that the fixed effect is a random effect

*Energy Policy*

**Table 6.**

**Table 7.**

*Results of rank correlation.*

annual rates. These prefectures rank low in average energy efficiency. Therefore, it is highly likely these regions have several electrical machineries and equipment manufacturing units, and their machinery industry has progressive electrification rates, thus contributing to the improvement of energy efficiency. Second, energy efficiency is deteriorating in regions with high energy efficiency levels, including Nara, Tokyo, Yamanashi, Ishikawa, and Saga. This suggests that the regional

**Rank correlation method Rank correlation coefficient P-value** Kendall's tau −0.2396 0.0175 Spearman −0.3207 0.0280

**Rank Prefecture Average efficiency score Change rate of score**

 Mie 0.28 1.83 Ibaraki 0.25 −0.26 Yamaguchi 0.20 −0.25 Chiba 0.15 −0.32 Okayama 0.14 0.49 Oita 0.13 0.95

*Note: The energy efficiency scores in the table are calculated using the estimation results for Model A.*

This study verifies the possibility of reducing regional disparities in energy efficiency by calculating the rank correlation coefficient between the average energy efficiency score and its change rate. **Table 7** shows the results of the rank correlation coefficient. The Kendall rank correlation coefficient is −0.2396, which is statistically significant. Spearman's rank correlation coefficient is −0.3207, also being statistically significant. The sign of any rank correlation coefficient is negative, and the improvement in energy efficiency is progressing in the region with a

The energy efficiency level is highly related to electrification. **Figure 2** is a cross-sectional plot of the average values for the electrification rate and energy efficiency. The figure clearly illustrates an upward trend. In other words, regions with advanced electrification have high energy efficiency levels. Specifically, regions where offices are concentrated (e.g., Tokyo) are located in the upper right corner, while those with petrochemical complexes (e.g., Oita, Okayama, and Chiba) are in

Furthermore, it is also highly possible that energy efficiency improvements have progressed to electrification. **Figure 3** plots the time-series relationship between the electrification rate and energy efficiency and shows an upward trend. In other words, it is highly likely that advanced electrification contributes to energy efficiency improvements. As described above, Mie is likely to report improved energy efficiency, given its progress in electrification. On the other hand, Chiba has lower energy efficiency, given the low energy efficiency of

disparities in energy efficiency are decreasing.

*Average energy efficiency scores and change rate of the score.*

low energy efficiency.

the lower right.

petrochemical complexes.

**96**


*Note: 1. \*\* and \* indicate significance at the 1 and 5% levels, respectively. 2. The values between parentheses are p-values.*

#### **Table 8.**

*Panel estimation results on determinants of the electrification rate.*

at the 1% significance level. Therefore, the fixed effect model is appropriate for the panel regression analysis. Further, to test the validity of the panel GMM estimation, this study performs a Sargan-Hansen test for the exogeneity of the instrumental variables. From Hansen J's statistical results, the number of instrumental variables is appropriate and satisfies the condition of heteroskedasticity.

The signs for all the variables are consistent under both models. The sign for the establishment size is positive, meaning establishments with a larger number of employees have a higher electrification rate. Further, the higher the proportion of offices, the greater the electrification rate. It is also noteworthy that the sign of an establishment's productivity is positive. This indicates that an increase in the establishment's productivity is proportional to that in the electrification rate. The magnitude of the coefficient on productivity is between 0.475 and 0.676, and it significantly influences the electrification rate. Neither cooling nor heating degree days are statistically significant.

In sum, the establishment scale and productivity are closely related to the electrification rate, which may influence energy efficiency. That is, productivity improves energy efficiency through an increase in electrification at factories and business establishments. Therefore, the efforts to increase the office productivity could improve energy efficiency.

#### **4. Conclusions and policy implications**

This study analyzed the energy efficiency levels and their determinants in Japan's industrial sector using an energy demand frontier function. To the best of the author's knowledge, this is the first attempt to do so. Energy intensity has been traditionally used as a proxy for energy efficiency and depends on economic

**99**

**Acknowledgements**

**Conflict of interest**

manuscript, or the decision to publish the results.

no. 18K01614).

*Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector*

variables such as price and income. However, this study specified energy demand and controlled for price, income, production environment, and climate factors,

This study focused on compact mechanization and electrification as the two main determinants of the improvements in the energy efficiency of the industrial sector. The analysis presented three key findings. First, an installment in large capital facilities deteriorates energy efficiency. Therefore, policies aimed at promoting small- or medium-sized production facility installments lead to improvements in energy efficiency. Second, an increase in the electrification rate of a given region can improve its energy efficiency. Finally, it is necessary to increase the productivity and also the electrification rate, that is, raising the productivity of factories and offices promotes electrification, which considerably contributes to increased energy efficiency. This finding highlights the relationship between increasing productivity and improvements in energy efficiency, suggesting the possibility of the Porter

It can be concluded that the energy efficiency of the industrial sector can be improved by developing an appropriately competitive environment and encouraging electrification in each region's energy market. Additionally, electrification increases environmental efficiency by reducing carbon dioxide emissions. Therefore, the promotion of electrification is critical to the achievement of not only energy efficiency but also improving environmental efficiency. Nevertheless, further research is needed to verify whether this trend also applies to other countries to

The future research agenda relates to both the micro and macro viewpoints. The former indicates that future studies should examine the energy efficiency of electric power as an energy source from a more diversified viewpoint, including power saving. An important factor that warrants consideration in power consumption efficiency is an appropriate way to account for the efficiency of plant facilities and performance of air conditioning. Since this research could not account for the performance of each device, quantitatively examining this factor warrants further research. The research agenda from the macro viewpoint clarifies how the increase in urban population density affects energy efficiency, as discussed in Otsuka and Goto [40] and Otsuka [41]. In developed countries, urban compactification is being promoted from the viewpoint of city sustainability. The rise in urban population density exacerbates energy efficiency by causing a heat island phenomenon. Meanwhile, population concentration in cities has the merit of promoting the use of public transportation. Further, cities have more dwelling units than detached houses, and apartments have high thermal insulation and energy efficiency. As such, living in the city center may increase the energy efficiency. It seems that clarifying these problems would deepen the understanding of energy efficiency.

This study was funded by the Japan Society for the Promotion of Science (grant

The author declares no conflict of interest. The funders had no role in the design of the study; the collection, analyses, or interpretation of data; the writing of the

*DOI: http://dx.doi.org/10.5772/intechopen.81482*

hypothesis being established.

ensure the effectiveness of electrification.

thus rendering energy efficiency a more accurate index.

#### *Determinants of Energy Demand Efficiency: Evidence from Japan's Industrial Sector DOI: http://dx.doi.org/10.5772/intechopen.81482*

variables such as price and income. However, this study specified energy demand and controlled for price, income, production environment, and climate factors, thus rendering energy efficiency a more accurate index.

This study focused on compact mechanization and electrification as the two main determinants of the improvements in the energy efficiency of the industrial sector. The analysis presented three key findings. First, an installment in large capital facilities deteriorates energy efficiency. Therefore, policies aimed at promoting small- or medium-sized production facility installments lead to improvements in energy efficiency. Second, an increase in the electrification rate of a given region can improve its energy efficiency. Finally, it is necessary to increase the productivity and also the electrification rate, that is, raising the productivity of factories and offices promotes electrification, which considerably contributes to increased energy efficiency. This finding highlights the relationship between increasing productivity and improvements in energy efficiency, suggesting the possibility of the Porter hypothesis being established.

It can be concluded that the energy efficiency of the industrial sector can be improved by developing an appropriately competitive environment and encouraging electrification in each region's energy market. Additionally, electrification increases environmental efficiency by reducing carbon dioxide emissions. Therefore, the promotion of electrification is critical to the achievement of not only energy efficiency but also improving environmental efficiency. Nevertheless, further research is needed to verify whether this trend also applies to other countries to ensure the effectiveness of electrification.

The future research agenda relates to both the micro and macro viewpoints. The former indicates that future studies should examine the energy efficiency of electric power as an energy source from a more diversified viewpoint, including power saving. An important factor that warrants consideration in power consumption efficiency is an appropriate way to account for the efficiency of plant facilities and performance of air conditioning. Since this research could not account for the performance of each device, quantitatively examining this factor warrants further research.

The research agenda from the macro viewpoint clarifies how the increase in urban population density affects energy efficiency, as discussed in Otsuka and Goto [40] and Otsuka [41]. In developed countries, urban compactification is being promoted from the viewpoint of city sustainability. The rise in urban population density exacerbates energy efficiency by causing a heat island phenomenon. Meanwhile, population concentration in cities has the merit of promoting the use of public transportation. Further, cities have more dwelling units than detached houses, and apartments have high thermal insulation and energy efficiency. As such, living in the city center may increase the energy efficiency. It seems that clarifying these problems would deepen the understanding of energy efficiency.

### **Acknowledgements**

This study was funded by the Japan Society for the Promotion of Science (grant no. 18K01614).
