Can Japan enhance its 2030 greenhouse gas emission reduction targets? Assessment of economic and energy-related assumptions in Japan's NDC

Energy Policy 130 (2019) 328–340

Contents lists available at ScienceDirect

Energy Policy journal homepage: www.elsevier.com/locate/enpol

Can Japan enhance its 2030 greenhouse gas emission reduction targets? Assessment of economic and energy-related assumptions in Japan's NDC

T

Akihisa Kuriyamaa,b,∗, Kentaro Tamuraa, Takeshi Kuramochic,d,∗∗ a

Institute for Global Environmental Strategies, 2108-11, Kamiyamaguchi, Hayama, Kanagawa, 240-0115, Japan Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro, Tokyo, 152-8550, Japan c NewClimate Institute, Clever Straße 13-15, 50668 Cologne, Germany d Copernicus Institute of Sustainable Development, Utrecht University, Postbus 80.115, 3508TC Utrecht, the Netherlands b

ARTICLE INFO

ABSTRACT

Keywords: GHG emissions Climate policy Kaya identity Japan Paris Agreement LMDI analysis

This study investigates the stringency of Japan's greenhouse gas emissions reduction target for 2030 (nationally determined contribution: NDC), focusing on the macroeconomic assumptions of Kaya indicators and others previously overlooked, e.g. GDP per working-age population. It also conducts a decomposition analysis in light of historic political and economic events. We find that the real GDP growth assumption underlying the NDC target is unrealistic. Namely, the real GDP per working-age population, which is an indicator of productivity, needs to be improved annually by 2.5% on average for 15 years, a high level that has not been observed since the collapse of the economic bubble in the early 1990s. Based on these findings and the data from mitigation scenarios, we conclude that Japan can achieve the NDC target (26% below 2013 levels by 2030) with existing mitigation measures and by resuming operations only at those nuclear power plants that come under the conformity assessment, assuming realistic GDP assumptions. If the government enhances mitigation measures promoting low-carbon technologies that are politically affordable in terms of costs with realistic GDP assumptions, then it would be possible to achieve 27–42% of GHG emissions reduction compared to the 2013 level, including the “no nuclear” scenarios.

1. Introduction The Paris Agreement, which was adopted in December 2015, aims to strengthen climate change mitigation measures by keeping a global temperature rise this century well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase even further to below 1.5 °C (UNFCCC, 2015a). It also requests that all countries implement greenhouse gas (GHG) emissions reduction targets and measures that are consistent with the 2 °C or 1.5 °C targets. As of January 2018, 173 countries have ratified the Paris Agreement and 197 countries have submitted nationally determined contributions (NDCs), which determine the GHG emissions reduction targets until 2025 or 2030 (UNFCCC, 2018). To enhance the mitigation targets of the NDCs, the Paris Agreement requests each country to communicate a NDC every five years, which is called “ratchet-up mechanism” (Tamura et al., 2016). In addition to countries' mid-term (2025–2030) emissions reduction targets, the Paris Agreement requests that countries

∗

formulate and communicate long-term low GHG emission development strategies with the UNFCCC Secretariat by 2020 (UNFCCC, 2015a; UNFCCC, 2015b). This study focused on Japan, which is the seventh largest GHG emitting economy in the world followed by China, the United States, India, Indonesia, Russia, and Brazil (Gütschow et al., 2016). 83-88% of Japan's GHG emissions during the period from 1990 to 2016 accounted for energy-related CO2 emissions (NIES, 2018). In its NDC, Japan set a GHG emissions reduction target of 26% below the 2013 emission level, which includes emissions from land use, land-use change, and forestry, by 2030 (hereinafter, NDC mitigation target). As a long-term target, the Global Warming Countermeasures Plan explicitly mentions an 80% reduction in GHG emissions by 2050 (hereinafter, 2050 target) (Cabinet Office, 2016). There are two major points of debate over Japan's NDC mitigation target regarding the sufficiency of Japan's contribution to the Paris Agreement, namely (i) the ambition level of the NDC mitigation target

Corresponding author. Institute for Global Environmental Strategies (IGES), 2108-11, Kamiyamaguchi, Hayama, Kanagawa, 240-0115, Japan. Corresponding author. NewClimate Institute, Clever Straße 13-15, 50668 Cologne, Germany. E-mail addresses: [email protected] (A. Kuriyama), [email protected] (T. Kuramochi).

∗∗

https://doi.org/10.1016/j.enpol.2019.03.055 Received 20 October 2018; Received in revised form 5 February 2019; Accepted 27 March 2019 Available online 22 April 2019 0301-4215/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

compared to the long-term temperature goal of the Paris Agreement and (ii) the stringency level of existing policies for achieving the NDC mitigation target. Regarding the first point, Keidanren, namely the most influential business association in Japan, stated, “It is a very ambitious target to improve energy efficiency to the same level which Japan achieved during the oil crisis of the 1970s” (Keidanren, 2017). On the other hand, Kuramochi et al. (2017) pointed out that Japan's NDC mitigation target is not a sufficient contribution to the global effort to keep global warming within 2 °C. The past discussions on NDCs focused on the carbon price required to achieve the NDC mitigation target (Aldy et al., 2016; RITE, 2015; RITE and NIES, 2015). However, the required carbon prices estimated by the models are not directly comparable because abatement costs are significantly affected by assumptions such as the expected payback period (NIES, 2012). On the second point regarding the stringency of existing policies, the Climate Action Tracker, which evaluates each country's progress towards their NDC mitigation targets annually, stated that “Japan might fall short of achieving its 2030 target in the NDC unless additional measures are implemented” (Climate Action Tracker, 2018). In addition, in the UNEP Emissions Gap Report of 2017, the progress of Japan's mitigation effort was evaluated as “likely to require further action to meet their NDCs” based on recently published current policy scenario projections (UNEP, 2017). While studies have assessed Japan's NDC by focusing not only on technological aspects, but also on multiple dimensions, such as responsibility and equity, only a few assessments have been conducted on the validity of the assumptions on both economic indicators and energy-related indicators underlying the NDC mitigation target. In particular, the assumption of gross domestic product (GDP) has not been discussed in previous studies. In order to conduct such an analysis, a detailed decomposition analysis of historical emissions is necessary. As shown in Fig. 1, the trend in CO2 emissions in Japan has frequently changed over the last 50 years, and has been marked by several external shocks. There are studies to conduct a decomposition analysis related to total energy-related CO2 emissions. The Agency for Natural Resources

and Energy under the Ministry of Economy, Trade and Industry (ANRE, 2018) focused on industrial sector including analysis of structural change but it limited the period of analysis from 2005 to 2015. Ministry of the Environment, Japan (MOEJ, 2017) analysed the changes in Kaya indicators each year for the period from 1990 to 2015. However, neither of these studies include the long-term period from 1960 to 2015, which included the first and second oil crisis when energy efficiency was greatly improved (Mihut and Daniel, 2012; Shibata, 1983). Okushima and Tamura (2007) provided an analysis for 1970–1995 and Lu et al. (2016) analysed energy and economic indices over 66 years from 1946 to 2011, but their analyses did not include the latest trends in energy use after 2011, which was when energy efficiency in the household and commercial sectors was improved (Buckley and Nicholas, 2017; Matsukawa, 2016). To date, no studies have compared the changes in such long time-series data from 1960 and prospective data of energy-economic data according to mitigation levels in 2030 and 2050. Furthermore, none of them decomposes carbon intensity in the whole sector into carbon intensities in the electricity sector and non-electricity sector. The objectives of this study is threefold. First, it investigated the possibility of its enhancement of NDC mitigation target by comparing the prospective development of the Kaya indicators (e.g. each indicator derived from the Kaya identity) such as economic indicators (real GDP) as well as other important macro-economic indicators previously overlooked, such as GDP per working-age population under the NDC mitigation target using the long-term historical data. Second, it quantified the impact of each Kaya indicator on the changes in CO2 emissions using the Logarithmic Mean Divisia Index (LMDI) method that can decompose the impacts of the change in energy intensity into the changes in energy efficiency and changes in economic structure. Third, through sensitivity analysis of CO2 emissions in 2030 by changing the assumptions of each Kaya indicator, this study quantified the potential of enhancing the 2030 NDC mitigation target. This study contributes to the Paris Agreement's ratchet-up mechanism by providing the following new scientific knowledge. First, this study compares each Kaya indicator that affects CO2 emissions in the Fig. 1. Historical trend of Japan's energy-related CO2 emissions from 1960 to 2015 and emission reduction targets in 2030 and 2050. Note: The numbers in the graph indicate the compound average annual growth rate of change in the period indicated by the arrows. CO2 emissions in 2050 shows 80% reduction compared to 2013 level.

329

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

NDC mitigation target with the historical trend from 1960 to 2015 and the outcomes from energy-economic models to assess the stringency of the NDC mitigation target. In particular, it would be helpful for policymakers to show how much Japan needs to improve or update the assumptions on each Kaya indicator to accomplish NDC mitigation target by comparing the level that had been achieved in the past year including the period of the first and second oil crisis and the period after the Great East Japan Earthquake when Japan made a massive effort to implement energy efficiency measures. Second, the emission reduction impact by improving energy intensity is divided into the two effects: saving energy usage on demand side and changes in the industrial structure using the long time-series data. Third, the result of sensitivity analysis on CO2 emissions in 2030 shows the possibility to enhance NDC mitigation target by the setting the assumptions of economic and energy-related indicators. This study is structured as follows. Section 2 provides the methodological framework that includes a comparative analysis of changes in annual rate, LMDI analysis and decomposition analysis. Section 3 shows the data for the three analysis. Section 4 provides analytical results, discussions and limitation of the analytical framework in this study. Section 5 points to a conclusion and policy implication from this study.

quantitative assessment on the extent to which main drivers of emission reduction will bring about changes to the total amount of CO2 emissions in the future. The LMDI analysis also enables to divide the impact on CO2 emission changes by energy intensity improvement into the impact of economic structural change and saving energy usage on demand side. Third, this study performed a sensitivity analysis on 2030 CO2 emissions by each driver to examine how the emissions in 2030 will change depending on the condition of each Kaya indicator. 2.2. Comparative analysis of the annual rate of the changes in each Kaya indicator As shown in Eq. (1), the CO2 emissions are divided into three categories using the Kaya identity: real GDP, energy intensity (amount of energy use per GDP), and emission intensity unit (CO2 emissions per energy consumption).

CO2 = GDP ·

TFC CO2 · = GDP· EI · CI GDP TFC

(1)

where GDP denotes real GDP (based on 2010), TFC is the total final energy consumption of energy, and CO2 is energy-related CO2 emission, EI represents energy intensity, and CI represents carbon intensity. For the value of energy use, it is common to employ either primary energy supply or final energy consumption for the value of energy use. Changes in primary energy supply are affected by not only improvement of energy usage on the demand side but also fuel switch and implementation of energy efficiency measures at the electricity sector. On the other hand, the final energy consumption can focus on analysing the energy usage on the demand side. Therefore, this study uses the final energy consumption as the numerator of energy intensity. When final energy consumption is used for the index of energy usage, energy intensity can be changed by both improvement of energy efficiency on the demand side of the whole country (Filipović et al., 2015; Li et al., 2013; Wang, 2013) and change of industrial structures (Karimu et al., 2017). Consequently, this study conducted an LMDI analysis to further decompose the change of energy intensity into both effects. In recent years, as the increase of renewable energies in the electricity sector, the effect of fuel switch is also important through the indicator of final energy consumption cannot capture this effect. Thus, this study captures the impact of installation of renewable energy on CO2 emissions by the change of carbon intensity as follows. Carbon intensity is an indicator of fossil fuel share in energy usage, which is defined by CO2 emission per final energy consumption in this study. In other words, carbon intensity can be changed by mitigation measures on the supply side of energy. Furthermore, carbon intensity was divided into carbon intensities of electricity sector and non-electricity sector for LMDI analysis to quantify the impact of changes in electricity mix on CO2 emissions.

2. Methodology 2.1. Research framework There are eight types of approach to assessing the level of policy stringency as summarised by Höhne et al. (2018) – this study employs the approach of assessing the trends of aggregate-level decarbonisation indicators. The concept of the analytical framework in this study is shown in Fig. 2. As a first step, this study decomposed historical CO2 emissions into main indicators using the Kaya identity. The decomposition analysis applied in this study is a tool for analysing the effect of various underlying drivers that contribute to changes in some indicators over time. After CO2 emissions were decomposed into each Kaya indicator, this study compared the trend of changes in the drivers of CO2 emissions by historical data and the results of the energy-economic models that show the several states of each Kaya indicator in 2030 as well as the state of achieving the 2050 target, followed by the approach of Spencer et al. (2016). Second, this study conducted LMDI analysis to quantify the impact of each driver on CO2 emissions for both historical and prospective CO2 emissions. In general, the decomposition method is often used to analyse the effects influencing drivers of historical CO2 emissions. Nevertheless, Yi et al. (2016) pointed out a prospective analysis that combines scenario analysis with decomposition analysis, which will provide a theoretical basis for policymakers by conducting a

2.3. Decomposition method As a decomposition method of CO2 emission drivers, this study uses the Kaya identity which generalised relation of I = PAT (impact = population · affluence · technology) (Kaya, 1990). Followed by the Kaya identity, this study decomposes CO2 emissions into GDP, energy intensity, and carbon intensity. While there are a variety of method for decomposition analysis, it can be classified into the following six categories: Refined Laspeyres Decomposition (RLD); Passche index; Simple Average Divisia Method; Fischer Ideal; Parametric Divisia Method (PMD I) and II (PMDII); and Logarithmic Mean Divisia Index (LMDI) (Malpede, 2015). Among the six decomposition methods, the only RLD and LMDI methods can completely decompose the object with a residual term equal to zero to allow easy interpretation of results. In addition, the LMDI method tends to be selected for reasons such as rationality, versatility, ease of understanding of the results and perfect decomposed result (Ang, 2004, 2005; Lima et al., 2016; Yang et al.,

Fig. 2. Conceptual framework of this analysis. 330

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

2016). On the other hand, the RLD method is rarely used for environmental decomposition studies because it is much more complex, especially when the number of factors exceeds three (Ang and Zhang, 2000). Therefore, the LMDI method was preferable for this study. The LMDI method is shown in Eq. (2):

Gjkt Ejkt Qkt · · · Qt = Ejkt Qkt Qt

Gt = j

k

Gc _ jkt · Ge _ jkt ·Gs _ kt · Gy _ t j

j

ln

where Gt denotes the total CO2 emissions in year t and Gjkt expresses the CO2 emissions of sector k with energy type j in year t. To analyse the impact of CO2 emissions according to the changes in economic structure, the category of the sector was classified into three sectors, namely the industry sector, transport and commercial sector, and household and agricultural sector.1 In the industry sector, the shares of total CO2 emissions including direct and indirect emissions from the iron and steel sector and chemical sector were 30% and 18.5% in 2013, respectively, as shown in Fig. 3 (further details about CO2 emissions from the iron and steel sector can be found in Kuramochi (2016) and Appendix I). In addition, only the iron and steel sector and chemical sector had long time-series data from 1960. Thus, the industry sector was divided into the iron and steel sector, chemical sector, and other industry sector. The energy type is classified into two categories: electricity and non-electricity because this study discusses the impact from the change in the carbon CO2 emissions from electricity sector that depends on the electricity mix including the share of nuclear power and renewable energies. Ejkt shows the final energy consumption of sector k, energy type j, in year t. Qkt is real GDP of sector k in year t, and Qt describes real GDP of the whole country. Also, Gc_jkt is carbon intensity of final energy consummation for sector k, energy type j in year t. Ge_jkt is energy intensity for sector k, energy type j in year. Gs_kt denotes a ratio of real GDP of sector k to all the sector in year t. Gy_kt is GDP per capita in year t. By differentiating Eq. (2) with respect to time, Eq. (3) is obtained (Shahiduzzaman and Layton, 2015).

Gt =

Gc _ jkt · Ge _ jkt · Gs _ kt · Gy _ t + j

k

+ j

k

Under the assumption of wjkt = Gc _ jkt ·Ge _ jkt ·Gs _ kt ·Gy _ t the contributions of each item to the CO2 emission change from time 0 to time t can be expressed as in Eq. (4).

wjkt j

ln

k

Ge _ jkt Ge _ jk0

k

ln wjk 0

j

ln wjkt

·ln

Gc _ jkt + Gc _ jk 0

wjkt

+ wjkt

+ j

wjk 0

ln wjkt

k

ln wjkt

wjk 0 ln wjk0

·ln

wjk 0 ln wjk 0 Gy _ t Gy _ 0

wjkt j

·ln

k

ln wjkt

wjk 0 ln wjk 0

k

ln wjkt

wjk 0 ln wjk0

·ln

wjk 0 ln wjk 0 Gy _ t Gy _ 0

wjkt j

·ln

·

k

ln wjkt

wjk 0 ln wjk 0

·

Gs _ kt Gs _ k0

1 t

(5)

(6)

where, GDPj, 2030 denotes the real GDP in 2030 for case j, EIj,2030 shows the energy intensity of 2030 in case j, CIj,2030 describes the carbon intensity of 2030 in case j. Furthermore, GHG emissions in 2030 were calculated by Eq. (7) on the assumption that the reduction of GHG emissions other than energyrelated CO2 emissions can be achieved at the same level as the NDC mitigation target.

k

(3)

Gt =

k

wjkt j

Gc _ jkt + Gc _ jk 0

CO2j,2030 = GDPj,2030· EI j,2030·CI j,2030

Gc _ jkt · Ge _ jkt ·Gs _ kt · Gy _ t j

wjkt ln wjkt

·ln

To conduct a sensitivity analysis of CO2 emissions in 2030, this study developed 24 cases: two assumptions on economic indicators (i.e. real GDP growth) and twelve assumptions on energy-related indicators which consist of three stringencies of mitigation measures on energy use and four level of the share of nuclear power plants in total electricity generation in 2030. For economic indicators, this study used two assumptions of GDP: the GDP at the same level as Japan's NDC assumption and the GDP taken from a median value from the assumptions by the research institutions. For the stringency of mitigation measures that decide the level of saving energy usage on demand side and installation of renewable energies, this study employed the outcome from the energy-economic models by MOEJ (2012b) and METI (2013) in addition to the assumption under the NDC. The energy-economic models have three level of outcomes corresponding to the stringency of mitigation measures based on the discussions with the relevant ministries as shown in Table 1. For the share of nuclear power in total electricity generation, this study made four assumptions: 0%, 15%, 20% and 25%. This range of nuclear share covers the NDC assumption which is 20–22% of nuclear power in total electricity generation. CO2 emissions in 2030 according to the assumption on each indicator was calculated using Eq. (6). The input data for the sensitivity analysis employed both the maximum value and the minimum values in each case.

k

Gc _ jkt ·Ge _ jkt ·Gs _ kt ·Gy _ t +

Ge _ jkt + Ge _ jk0

ln wjk 0

2.4. Sensitivity analysis of CO2 emissions in 2030

Gc _ jkt ·Ge _ jkt · Gs _ kt ·Gy _ t j

k

j

wjk 0

ln wjkt

+

(2)

k

wjkt

¯Gt =

·

GHGj,2030 = CO2j,2030 + OtherCO2 2030 + CH 4 2030 + N2O2030 + IG2030 Abs2030

Gs _ kt Gs _ k0

(7)

where, GHGj,2030 is GHG emissions in 2030 in case j, OtherCO22030 denotes non-energy related CO2 emissions in 2030, CH42030 shows methane emissions in 2030 (CO2-eq/yr), N2O2030 is nitrous oxide emissions in 2030, IG2030 denotes industrial gas emissions such as HFC in 2030 (CO2-eq/yr) and Abs2030 shows the amount of GHG absorption in 2030 (CO2-eq/yr).

(4)

Also, the annual CO2 emission changes during the period from time 0 to t can be calculated as in Eq. (5).

3. Data preparation and scenario design 3.1. Data for factor analysis Historical and prospective data for the population was taken from data published by National Institute of Population and Social Security Research (IPSS, 2017a, 2017b). While IPSS publishes several projections for the population in 2030 and 2050 according to the birth rate and mortality rate, this study referred to the projection based on

1 Due to the limitation of added value data, this study consolidated the transport sector and commercial sector into a single sector.

331

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Fig. 3. Historical trend (1990–2015) of direct and indirect CO2 emissions in industrial sector. Source: Authors based on IEA (2017b) and IEA (2017c).

Table 1 Stringency levels of mitigation measures defined by the ministries. Source: Authors based on MOEJ (2012a) and METI (2013). Stringency level of mitigation measures

Description

Low Medium

Mitigation measures that assume the continuation of current or already planned measures. Mitigation measures that can promote the implementation of major low-carbon technologies and products by taking information policy and reasonable regulations towards the development of a low-carbon society. Mitigation measures that can promote deployment of low-carbon technologies and products of which net present value during the life time is positive despite those high initial costs.

High

medium fertility rate and medium mortality rate. For the historical data of GDP, this study used the World Bank Open Data (World Bank, 2017) that includes long-term real GDP data. Japan's NDC assumes the GDP growth at 1.7% on average until 2030. This number was developed based on the average value of real economic growth target from FY2013 to FY2022 by the government of Japan (METI, 2015). Regarding the prospective data of GDP, it referred to the GDP growth forecasts published by the research institutions such as the Mizuho Research Institute (MHRI, 2017), the Mitsubishi Research Institute (MRI, 2017), the Mitsubishi UFJ Research and Consulting (MURC, 2017), the Institute of Energy Economics, Japan (IEEJ, 2017a), Central Research Institute of Electric Power Industry (Hamagata, 2015), the International Energy Agency (IEA, 2017a) and the Middle of the Road scenario in the Shared Socioeconomic Pathways (Riahi et al., 2017). For the GDP growth assumptions from 2030 to 2050, this study only referred to the analytical outputs of MOEJ (2012a) because there is no available outlook by the government of Japan or domestic research institutions. For the historical data of the final energy consumption of energy and energy-related CO2 emissions, this study used the World Energy Balance (IEA, 2017b) and CO2 emissions from fuel combustion (IEA, 2017c), respectively. The NDC data related to energy and CO2 was taken from the long-term energy outlook (METI, 2015) that is the basis for the development of Japan's NDC. Also, this report used the energyrelated data in the IEA New Policies Scenario that is consistent with the outcomes when the existing policies and announced intentions come into effect as a reference value (IEA, 2017a). For the prospective data in 2030, this report refers the result of the energy-economic models (IEEJ, 2017a; Kainuma et al., 2015; METI and MOEJ, 2013; MOEJ, 2012a; Oshiro et al., 2017) that have been reviewed by the third parties and provide necessary data for this data. For the data in 2050, this study refers to MOEJ (2012b) that is the only available study to provide the energy-economic data for the scenarios consistent with 80% of emission reduction target by 2050.

3.2. Data used for LMDI analysis Using the LMDI analysis, this study further decomposed the impact of energy intensity changes on CO2 emissions changes into the change of industrial structure and saving energy usage on demand side. Based on the GDP data taken from World Bank Open Data, the sectoral added value was calculated using the share of the sectoral added value estimated by Hirata (1998) for the period of 1960–1990 and that of the World Bank (2017) for the period of 1991–2015. For the share data of sub-sectoral added value in the manufacturing sector, this study used the data from UNIDO (2017) for the share of each industrial sector and that of the Cabinet Office (1998) for the share of the whole industrial sector in all the sectors. The sectoral and sub-sectoral data for final energy consumption and energy-related CO2 was taken from IEA (2017b) and IEA (2017c). 4. Results and discussion 4.1. Comparative analysis of annual rates of changes in each Kaya indicator 4.1.1. GDP Fig. 4 shows the historical trend of real GDP from 1960 to 2015 and the range of prospective data in 2030 and 2050. Japan's NDC assumes 1.7% per year on average for the real GDP growth rate from 2015 to 2030. This growth rate has never been achieved over the past quartercentury after the end of the rapid economic expansion by asset price bubble, so-called “Bubble economy”, from 1986 to 1991. Indeed, the research institutes assume around 1.0% of annual average growth between 2016 and 2030. To assess the validity of the GDP assumption by the NDC mitigation target, this study also examined the real GDP per working-age population, which is an index of labour productivity. The working-age population in Japan had reached the peak in 1995, and it started 332

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Fig. 4. Real GDP in Japan: historical trend (1960–2015) and the future levels estimated from 2030 to 2050 GHG emissions reduction targets and scenarios. Note: The numbers in the graph indicate the geometric mean of the rate of change in the period indicated by the arrows. Real GDP is in constant 2010 Japanese Yen.

Fig. 5. Real GDP per working-age population in Japan: historical trend (1960–2015) and the future levels estimated from 2030 to 2050 GHG emissions reduction targets and scenarios. Note: The numbers in the graph indicate the geometric mean of the rate of change in the period indicated by the arrows.

decreasing at the rate of 0.8% until 2015. IPSS (2017b) estimated that the working-age population would decrease at the rate of 0.7% until 2030 and at the rate between 1.0 and 1.5% from 2030 to 2050. Fig. 5 shows the historical trend of real GDP per working-age population and

the range of outlook by the research institutions in 2030 and 2050. The average improvement rate of labour productivity from 2015 to 2030 that was calculated based on the GDP assumption under the NDC and population estimates by IPSS (2017b) results in 2.5% per year. To 333

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Fig. 6. Energy intensity (final energy consumption per real GDP) in Japan: historical trend (1960–2015) and the future levels estimated from 2030 to 2050 GHG emissions reduction targets and scenarios. Note: The numbers in the graph indicate the geometric mean of the rate of change in the period indicated by the arrows. Real GDP is in constant 2010 Japanese Yen. IEA NPS (New Policies Scenario):Mitigation measures will be implemented to achieve the emission reduction target in the NDC (IEA's estimates). 2030 low:Low level of mitigation measures that assumes the continuation of current or already planned measures. 2030 medium: Middle level of mitigation measures that can promote the implementation of major low-carbon technologies and products by taking information policy and reasonable regulations towards the development of a low-carbon society. 2030 high: High level of mitigation measures can promote the massive implementation of low-carbon technologies and products with reasonable cost compared to the benefits for the society even though the initial cost of the measures is huge.

in the industrial sector; Act on Temporary Measures for the Retirees of the Specific Industry in the Recession (provisional translation) in 1977 and Act on Temporary Measures for Stabilizing the Specific Industry in the Recession (provisional translation) in 1978. Combining those policies and the inflated fuel prices after the second oil crisis stimulated the restructuring of material industries such as steel and ceramic sector where international competitiveness has declined (Fujinami, 2014). On the other hand, the share of metal processing and machine sector in the total industry have increased. This change of industrial structure contributes to the decrease in the level of energy intensity (See Appendix II). Section 4.2 provided a further discussion through the LMDI analysis. The improvement of energy intensity from 1986 to 1991 and from 1991 to 2000 were stagnated at the annual rate of −1.4% and +0.3%, respectively. The rate of change in energy intensity between 2000 and 2007 improved to be −1.5% annually. Fujinami (2014) and IEEJ (2017b) pointed out that this change is contributed by the introduction of the top runner programme through the amendment of the Energy Conservation Law in 1999 and Japan's ratification of the Kyoto Protocol in 2002. This political movement raised awareness about energy efficiency improvement. From 2010 to 2013 and from 2013 to 2015, the rate of change in energy intensity was further accreted to −1.8% and −2.7% per year. One of the reasons is that energy-saving efforts have been made in the residential sector since the Great East Japan Earthquake and the old electrical appliances have been replaced with new ones (Wakiyama and Kuramochi, 2017). Based on the assumption under the NDC mitigation target, the energy intensity in 2030 is calculated as 0.40 megatonne of oil equivalent (Mtoe) per trillion yen by decreasing at the average rate of −2.1% per year. Table 2 shows the list of mitigation measures assumed by the energy-economic models reviewed in this study. In the industrial and commercial sectors, mitigation costs for high-efficiency lighting and motors are negative, that is, if people have a long-term perspective, those measures would be promoted without any specific policy support.

achieve this improvement rate, Japan would be required to keep the current improvement rate that has not been achieved even from 2012 to 2015 when the Prime Mister Abe stimulated the economy by the fiscal policy of “Abenomics”. Even though labour productivity is affected by several factors such as the business cycle and innovation of ICT, it would not be feasible to maintain such a rapid improvement rate for the next 15 years. Regarding the business cycle, the length of Japan's long economic boom of Izanagi, Bubble and Izanami were 52 months, 51 months and 57 months, respectively (Cabinet Office, 2018). Therefore, the GDP assumption under the NDC is suggested to be unrealistic. When the NDC mitigation target is updated through “Ratchet-up Mechanism” under the Paris Agreement, it is desirable to use a realistic assumption. 4.1.2. Energy intensity Fig. 6 shows the historical trend of energy intensity from 1960 to 2015 and the range of prospective data in 2030 and 2050. The energy intensity had increased at the rate of 2.6% until 1973 when the first crisis occurred. After the oil crisis, it continuously decreased until 2015 and included rapid changes between 1973 and 1986 when the reverse oil crisis that oil price sharply fell occurred because Saudi Arabia started to increase the crude oil production to keep their share in the oil market (ANRE, 2007). In particular, the government of Japan enacted the Petroleum Supply and Demand Adjustment Act in 1973, which enforces temporary measures to limit the oil usage of companies. This policy aimed at the stable supply of oil, and was not necessarily intended to improve energy efficiency both on supply and demand sides. But, due to the increased price signals, it incentivised companies to take voluntary measures including energy efficiency improvement such as waste heat recovery systems and continuous casting system in iron and steel sector (Fujinami, 2014). Also, Fujinami (2014) pointed out that the implementation of Energy Conservation Act in 1979 that encourages the energy efficiency improvement on demand side have largely influenced on the change of energy intensity. Furthermore, the government enforced the series of laws that promote the restructuring of surplus employees and facilities 334

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Table 2 List of mitigation measures at the final energy consumption by sector, assumed by the energy-economic models. Source: Authors based on MOEJ (2012a) and METI (2013). Sector

Mitigation Cost

List of mitigation measures

Industry

Negative Positive

Commercial

Negative

Household

Negative Positive Negative

Cross-sectoral measures (high-efficiency industrial lighting, high-efficiency industrial motors, etc.) Specific technologies in the energy-intensive sub-sectors Iron and steel: Introduction of next generation coke production technology and utilisation of waste plastics Ceramics: Introduction of innovative cement manufacturing process and glass melting process Paper: Introduction of high-efficiency pulp production technology from wasted paper, utilisation of waste materials Chemical: Introduction of naphtha catalytic cracking technology, utilisation of biomass Petroleum refining: Optimization of a waste heat recovery system, Utilisation of hydrogen Reduction of lighting luminance, high-efficiency air conditioning, high-efficiency motor, improvement of heat insulation in buildings, BEMS, high-efficiency hot water supply, high-efficiency lighting High-efficiency consumer electronics, HEMS, solar power generation, high-efficiency lighting High-efficiency air conditioning, High-efficiency hot water supply, improvement of heat insulation in buildings Introduction of hybrid vehicles, electric vehicles and plug-in hybrid vehicles

Transport

fired power in the electricity mix was considered the dominant effect, as shown in Fig. 9. On the other hand, the carbon intensity decreased at an average rate of 0.9% during the period between 2008 and 2010 owing to the addition of gas-fired power supply. From 2010 to 2013, the carbon intensity rapidly increased at the average rate of 4.1% owing to the halt of nuclear power plants after the Great East Japan Earthquake. However, the carbon intensity improved at the rate of 1.7% from 2013 to 2015, since the share of the gas-fired power plant and renewable energy increased in electricity supply as shown in Fig. 9. To achieve the NDC mitigation target, Japan would need to improve carbon intensity at the rate of 1.1% annually until 3.4 MtCO2/Mtoe. Since the share of the electricity supply by the coal-fired power plants was around 80% in 2015, and there are many plans to build 18 GW of new coal-fired power plants (Kuriyama and Tamura, 2016), it seems to be not easy to achieve the level of carbon intensity by 2030. On the other hand there is a positive fact. Since 2014, the carbon intensity has also decreased owing to the increase in installed capacity of renewable energy at the rate of 15% in annual average. Also, some energy-economic model output suggests that the level of carbon intensity in 2030 is achievable even if the share of nuclear power is 0% in 2030, by increase of share of renewable energy more than 33% in the 2030 electricity mix with medium level of mitigation measures, i.e. implementation of information policy and reasonable regulation for promoting the technologies of energy efficient and renewable energy. Furthermore, if the share of nuclear power is 15% when all the nuclear power plant under conformity assessment that is required to restart its operation would be in operation (NRA, 2018), there is a possibility to improve carbon intensity above the NDC level further. The share of renewable energy under Japan's NDC is 22%, which is almost the same level as the outcomes of the energy-economic models with the low level of mitigation measures. The share of renewable energy with medium and high level of mitigation measures under the energy-economic models shows 29–31% and 25–35%, respectively. Thus, it is expected the assumption of carbon intensity under the NDC is further stringent. Regarding the consistency with the 80% emissions reduction target in 2050, this study differentiated two ranges in carbon intensity, namely that using the value of gross energy-related CO2 emissions and that using the value of net energy-related CO2 emissions after subtracting the CO2 removed by CCS technology. The outcomes of the energy-economic models showed that by 2050, carbon intensity needs to be reduced to 1.54–2.04 MtCO2/Mtoe before the CO2 absorption by CCS technology and to 0.81–1.05 MtCO2/Mtoe after CO2 sequestration. Supposing that the carbon intensity in 2030 is that assumed by the NDC, it is necessary to keep improving the carbon intensity before CCS use from −3.8% to −2.5% of the 2015 level of 3.92 MtCO2/Mtoe and the intensity after CCS use from −6.9% to −5.5% on average during the period from 2030 to 2050. This means that Japan needs to increase its effort to improve the carbon intensity of

Also, since some of the measures such as high-efficient electric appliance in household improve quality of life, it can be promoted by several incentives different from mitigation measures (MOEJ, 2012a). When it is compared with the results from the energy-economic models, the level of energy intensity under the NDC's assumption locates within the range of lower level of mitigation measures. Also, under the assumption of IEA new policy scenario (NPS), it can be estimated that the energy intensity decreases at the rate of 1.5%. In this case, the level of energy intensity in 2030 under the IEA NPS scenario locates at the middle of the range of lower mitigation measure level. If the government takes a medium level of mitigation measures defined as “introduction of major low-carbon technologies and products by taking information policy and reasonable regulations towards the development of a low-carbon society”, it could sufficiently achieve the energy intensity level in 2030. Thus, there is a possibility that the level of energy intensity assumed by Japan's NDC can be achieved the low level of mitigation measures defined as “continuation of current or already planned measures” (METI, 2013; MOEJ, 2012a). From another point of view, the comparison with the past trend suggests that the level of energy intensity under the NDC could be achieved if Japan keeps the average improvement rate from 2000 to 2015. Also, the improvement rate of energy intensity under the NDC assumption is consistent with the 2050 target. 4.1.3. Carbon intensity Fig. 7 shows the historical trend of carbon intensity from 1960 to 2015 and the range of prospective data in 2030 and 2050. The carbon intensity declined at an annual average of −1.6% from 1960 to 1973 because the shift from oil usage to coal usage occurred in the nonelectricity sector as shown in Fig. 7. From 1973 to 1986 when reverse oil crisis occurred, the carbon intensity decreased at the rate of 0.3% in annual average. During this period, the electricity supply from nuclear power plants and gas-fired power plant increased after the implementation of three electricity-related laws consists of Act on Tax for Promotion of Power-Resources Development in 1974, Act on Special Accounts for Electric Power Development Acceleration Measures in 1974 and Act on the Development of Areas Adjacent to Electric Power Generating Facilities in order to reduce the oil consumption. During the periods of 1986–1991 and 1991–2000, the carbon intensity changed at an average rate of +0.2% and −0.6%, respectively. For the indicator to improve carbon intensity, the share of gas usage in the non-electricity sector, first, has increased as shown in Fig. 7. Second, in the electricity sector, the share of gas and nuclear power usage increased, and efficiency of fossil-fuel power plant improved as shown in Appendix III. For the factor to increased carbon intensity, the share of coal among non-electricity use of the total final enegy consumption increased as shown in Fig. 8. From 2000 to 2007, the carbon intensity increased at the average rate of 0.9%. Though there were several factors, the increase in coal335

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Fig. 7. Carbon intensity (CO2 emissions per final energy consumption) in Japan: historical trend (1960–2015) and the future levels estimated from 2030 to 2050 GHG emissions reduction targets and scenarios. Note: The numbers in the graph indicate the geometric mean of the rate of change in the period indicated by the arrows. IEA NPS:Mitigation measures will be implemented to achieve the emission reduction target in the NDC (IEA's estimates). 2050 before CCS: The range of carbon intensity using the value of gross energy-related CO2 emissions. 2050 after CCS:The range of carbon intensity using the value of net energy-related CO2 emission after the part of CO2 was subtracted by CCS technology. The shares of renewable energies and fossil-fueled energy in each 2030 scenarios are as follows; Nuc 0% Scenario: 21%–35% for renewable energies, 65%–79% for fossil-fueled power. Nuc 13–15% Scenario: 21%–35% for renewable energies, and 50%–64% for fossil-fueled power. Nuc 20% Scenario: 21%–43% for renewable energies, and 45%–59% for fossil-fueled power. Nuc 25% Scenario: 21%–35% for renewable energies and 40%–54% for fossil-fueled power.

Fig. 8. Historical trend (1990–2014) of the total final energy consumption by fuel type. Source: Authors based on IEA (2017b).

Fig. 9. Historical trend (1990–2014) of electricity mix. Source: Authors based on IEA (2017b).

gross CO2 emissions without CO2 sequestration compared to the effort during the period from 2015 to 2030.

when the bubble economy collapsed. The improvement of energy efficiency had contributed to the CO2 emission reduction after 1973 except for the period between 1991 and 2000 where the improvement of energy intensity was stagnated. The change of economic structure also contributed to the reduction of CO2 emissions, which implies that Japan's economy has been shifted toward the less energy intensive sector such as service sectors. The change of carbon intensity in electricity sector had the largest annual impact on the increase of CO2 emission during the period between 2010 and 2013 owing to the halt of nuclear power plants after the Great East Japan Earthquake in 2011. On the other hand, the change of carbon intensity in the non-electric sector has less impact on CO2 emission changes in any of the periods. During the period of between 2013 and 2015, even though CO2 emission was decomposed into only four indicators, i.e., real GDP, carbon intensity in the electricity sector and carbon intensity in the non-electricity sector

4.2. LMDI analysis To quantify the impact of the change in economic structure and carbon intensities of both the electricity and non-electricity sectors, LMDI analysis was conducted. The period target analysis was from 1960 to 2015 for the historical data and 2030 data assumed by the NDC as the prospective data. The historical data was divided into eight periods based on the trend of CO2 emission changes as discussed in Section 4.1 and summarised in Table 3. Fig. 10 shows the annual average impact on CO2 emission changes from each Kaya indicator by LMDI analysis. Until 2007, the real GDP growth much influenced on the increases of CO2 emission until 1991 336

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Table 3 Features of economic and energy-related status in the selected period. Source: Authors Period

Features

1960–1973 1973–1986 1986–1991 1991–2000 2000–2007 2007–2010 2010–2013 2013–2015

Rapid economic growth (Olympic economic expansion and Izanagi economic expansion) Energy efficiency improvement owing to the first and second oil crisis Stagnation of energy efficiency improvement due to the reverse oil crisis and the economic bubble Stagnant economic growth following the collapse of economic bubble Izanami economic expansion, Japan's ratification of the Kyoto Protocol and introduction of top runner programme Global financial crisis that caused global economic downturn Energy efficiency improvement at the demand side after the Great East Japan Earthquake and the halt of the operation of nuclear power plants The period when the decoupling of economic growth and CO2 emissions was observed

and energy intensity including energy efficiency and change of economic structure, due to the data limitation, the result shows that the strong decoupling of economy and energy use is observed because the growth of real GDP attributed to the CO2 emissions increase but the changes of energy intensity and carbon intensity contributed to the CO2 emission decrease. The carbon intensity during this period was improved owing to the increase of renewable energy use in the electricity mix as shown in Fig. 9. With regards to the policy implication, Japan had successfully achieved CO2 emission reduction by energy efficient measures in the past many years, while the improvement of carbon intensity both in electricity and non-electricity sectors did not affect the change of CO2 emissions even when the electricity supply from low emissions energy sources such as nuclear power increased. Towards the fulfilment of NDC mitigation target, it is necessary to implement policies that can continuously improve not only energy intensity but also carbon intensity such as fundamental electricity system reform to harness a massive amount of low emissions energy sources including renewable energies (IEA, 2017d). It should also be noted that the NDC mitigation target does not account for the change of economic structure. If the change happens much faster than the past periods owing to the digital technologies (OECD, 2017), the level of energy intensity would be further improved compared to the NDC mitigation target.

is the same level of NDC. Even when the share of electricity from the nuclear power plants is 15% by operating all the nuclear power plants under the conformity assessment, there is a possibility that the NDC mitigation target can be enhanced. In the case where the share of electricity from the nuclear power plants is 0%, no pathway was identified to achieve the NDC emission reduction target even if the government takes the medium level of mitigation measures. But in this case, if the government implements the high level of mitigation measures, the target could be achievable. Under the real GDP assumption that takes the medium value of the forecast by research institutions as shown in Fig. 11b, the GHG emission reduction rate and GHG emissions in 2030 are calculated to be 32% and 955 MtCO2, respectively, if it assumes that the other indicators such as energy intensity and carbon intensity including the share of electricity from nuclear power plants. Also, even if the share of electricity from the nuclear power plants is 0% with the assumption of the alternative GDP and the medium level of mitigation measures, it is suggested that CO2 emission reduction rate could be higher than the assumption in the NDC. Therefore, if the economic assumption is re-estimated downwards, the emission reduction target under current Japan's NDC can be sufficiently achieved, or the target can be enhanced.

4.3. Sensitivity analysis of 2030 emissions

This study also found some methodological and data limitations. First, it uses only economy-wide data for the comparative analysis of the annual rate of the changes in CO2 emissions to compare the historical and prospective values owing to the data limitation, even though it is desirable for energy intensity to decomposed into sub-sectors since the energy intensity is affected by changes in various sectors such as

4.4. Discussion on the research method

Fig. 11 shows the result of the sensitivity analysis of 2030 GHG emission using Eq. (7). Under the real GDP assumption of the NDC in Fig. 11a, there is room for enhancing the NDC mitigation target if the share of electricity from the nuclear power plants is 20% or 25%, which

Fig. 10. Result of LMDI analysis. Note: The numbers above each bar shows annual net change of CO2 emissions during the periods (MtCO2). The period from 1960 to 2015 was divided into eight parts based the features of economic and energy related status as shown in Table 3. 337

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

Fig. 11. Result of sensitivity analysis by mitigation measures and share of electricity supply from nuclear power plants in the electricity mix.

industrial, business, and household sectors and the way of energy use differs across the sectors. Second, as shown in the LMDI analysis, the change of economic structure is one of the major factors to influence national-wide CO2 emissions after 2007. However, this study only analysed the energyeconomic model results that follow the industrial structure under the government's assumption. If the activities in the commercial sector are larger than the governmental assumption, there is a possibility to enhance NDC mitigation target more. Third, when it discusses the possibility to improve carbon intensity further, it will be necessary to refer the result of the latest studies on energy systems to harness a massive installation of renewable energies in the electricity sector.

stimulated by fiscal policies by the government led by Prime Minister Abe. The level of energy intensity assumed under the NDC can be achievable by the low level of mitigation measures (extending current mitigation measures). If the government takes a medium level of mitigation measures (taking information measures and reasonable regulations to implement low carbon technology), the level of energy intensity under NDC can be sufficiently achieved. In particular, some of the measures could be promoted by the low level of mitigation measures because some technologies such as high-efficiency appliances, lighting, motor and hot water supply as well as heat insulation, housing energy management system (HEMS), building energy management system (BEMS) have negative mitigation costs from the view of longterm social benefits. From the comparison with the past trends, Japan needs to keep the improvement rate that achieved during the period of the first and second oil crisis as well as the period after the Great East Japan Earthquake to attain the level of energy intensity under NDC assumption. The level of carbon intensity under the NDC assumption could be sufficiently achievable even if the share of electricity supply from nuclear power is more than 15%, which number would be attained when the all nuclear power plants under the conformity assessment will be in operation. Even when the share of the nuclear power plant is 0%, there is a possibility to achieve the NDC level by increasing the share of renewable energy. For example, the share of renewable energy under the NDC assumption is the about the same as in the case of taking lower mitigation measures. With medium and high mitigation measures, the share will be increased to 30% at least. However, the past trend shows that the carbon intensity was almost constant from 1990 to 2010, while the electricity mix has been changing through the year. Also, after the halt of operating nuclear power plants after the Great East Japan Earthquake in 2011, the share of coal and gas-fired power in the electricity mix increased, causing a significant deterioration in emission intensity. Hence, those facts imply that the level of carbon intensity

5. Conclusion This paper, first, conducted a comparative analysis of the annual rate of the changes in Kaya indicators and other important macroeconomic indicators previously overlooked such as GDP per workingage population under the NDC mitigation target with the historical trends to investigate the possibility of NDC mitigation target using the long-term historical data. Second, it conducted the LMDI analysis to quantify the historical impact of economic and energy-related indicators on the change of CO2 emissions. Third, sensitivity analysis of 2030 emissions against the Kaya identity was performed to discuss the possibility of enhancing the NDC mitigation target. The results from the comparative analysis of annual rate in CO2 emission changes shows that the assumption of the real GDP growth rate under the NDC is assessed as unrealistic due to the following reasons. First, the rate has not been achieved in the past 25 years after the collapse of the bubble economy in 1991. Second, the assumption is higher than any other forecasts by the research institutions. Third, real GDP per working-age population, which is an indicator of productivity, needs to be improved at the average rate of 2.5% for 15 years. This level has not been achieved even when the current economic booming 338

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al.

under the NDC assumption could be achievable with medium or high mitigation measures as mentioned above, but the significant change could be required in the electricity sector to achieve the level of carbon intensity under the NDC assumption. By the LMDI analysis, the impact of each Kaya indicator on the change of CO2 emission was quantified. Also, the impact from energy intensity is decomposed into the impact from energy efficiency and from change of economic structure. The impact of the changes in carbon intensity is decomposed into carbon intensity in electricity and non-electricity sector. From 1960 to 1991 when the bubble economy was collapsed, the real GDP had a dominant impact on the increase of CO2 emissions, but, afterwards, energy efficiency in all sectors and carbon intensity in the electricity sector have mainly influenced. In particular, policy maker needs to prioritise the improvement of carbon intensity in the electricity sector since this indicator has not been much improved in the past years compared to its potential. The other positive aspect is that the strong decoupling of economic growth and energy usage is explicitly observed after 2013. Indeed, the level of decoupling between 2013 and 2015 is greater than the one which is required to achieve the NDC mitigation target. Therefore, the achieving the level of energy intensity and carbon intensity under the target is not always ambitious. The result from sensitivity analysis shows that, if economic indicators, e.g., the growth rate of real GDP, is set to unrealistic assumptions, it is necessary to enhance mitigation actions to achieve the NDC mitigation target excessively. On the other hand, assuming that the GDP growth rate refers the medium values from the forecast by research institutions (i.e. a realistic GDP assumption) and the share of the nuclear power plant in electricity supply is 15%, NDC mitigation target can be achieved with a low level of mitigation measures. In this case, even if the share of nuclear power in the electricity sector 0%, the target is also achievable, this result is consistent with the existing studies such as Kuramochi et al. (2017). If Japan takes medium levels of mitigation measures under the assumption of a realistic GDP growth rate as mentioned above, 27–42% of GHG emission reduction can be achieved. In particular, there is still room for significant reforms in the electricity sector, which has not had much impact on CO2 emissions, in order to retain the decoupling of economic growth and energy use that was observed during the period between 2013 and 2015. This result implies that Japan can make further effort to enhance its mitigation emission reduction and ensure feasibility of achieving the long-term mitigation target by making the most of the “ratchet up mechanism” under the Paris Agreement.

Ang, B., 2004. Decomposition analysis for policymaking in energy. Energy Policy 32, 1131–1139. https://doi.org/10.1016/S0301-4215(03)00076-4. Ang, B.W., 2005. The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33, 867–871. https://doi.org/10.1016/j.enpol.2003.10.010. Ang, B.W., Zhang, F.Q., 2000. A survey of index decomposition analysis in energy and environmental studies. Energy 25, 1149–1176. https://doi.org/10.1016/S03605442(00)00039-6. ANRE, 2018. Status of Energy Balances in 2016 (Heisei 28 Nendo ni Okeru Enerugi Jyukyuu Jisseki, in Japanese). Agency for Natrual Resources and Energy, Tokyo. ANRE, 2007. Energy White Paper 2007. Agency for Natrual Resources and Energy, Tokyo (in Japanese). Buckley, T., Nicholas, S., 2017. Japan: Greater Energy Security through Renewables: Electricity Transformation in a Post-Nuclear Economy. The Institute for Energy Economics and FinancialAnalysis (IEEFA), Cleveland. Cabinet Office, 2018. Keiki Jyunkan Hidsuke (Reference Dates of Business Cycle, in Japanese). [WWW Document]. http://www.esri.cao.go.jp/jp/stat/di/ 150724hiduke.html, Accessed date: 30 May 2018. Cabinet Office, 2016. Chikyu Ondanka Taisaku Keikaku (Plan of Global Warming Countermeasures, in Japanese). Cabinet Office, Tokyo. Cabinet Office, 1998. 1998 Nendo Kokumin Keizai Keisan (System of National Accounts, in Japanese). Cabinet Office, Tokyo. Climate Action Tracker, 2018. Country Summary-Japan. [WWW Document]. https:// climateactiontracker.org/countries/japan/, Accessed date: 3 January 2018. Filipović, S., Verbič, M., Radovanović, M., 2015. Determinants of energy intensity in the European Union: a panel data analysis. Energy 92, 547–555. https://doi.org/10. 1016/j.energy.2015.07.011. Fujinami, T., 2014. Oil Shock no Keikenwo Ikashi Setsuden wo Wagakuino Seityo no Baneni (Saving electricity consumption during the oil crisis contributed economic growth, in Japanese). JRI Rev. 9, 14–34. Gütschow, J., Jeffery, M.L., Gieseke, R., Gebel, R., Stevens, D., Krapp, M., Rocha, M., 2016. The PRIMAP-hist national historical emissions time series. Earth Syst. Sci. Data 8, 571–603. https://doi.org/10.5194/essd-8-571-2016. Hamagata, J., 2015. 2030nen Madeno Makuro Keizai Sangyou -Enerugi Jyukyuu Tenbou ni Muketa Nhonkeizai no Seityouroku no Mikata- (Macroeconomy and Industry up to 2030 - View on Economic Growth Potential towards Energy Supply and Demand Perspective -, in Japanese). (No. Y14017), Research Report. Tokyo. Hirata, J., 1998. Sengono Nihonno Sangyokozo Henka(change of industrial structure after the world war II, in Japanese). Ritsumeikan Econ. 47, 761–784. Höhne, N., Fekete, H., den Elzen, M.G.J., Hof, A.F., Kuramochi, T., 2018. Assessing the ambition of post-2020 climate targets: a comprehensive framework. Clim. Policy 18, 425–441. https://doi.org/10.1080/14693062.2017.1294046. IEA, 2017a. World Energy Outlook 2017. (Paris, France). IEA, 2017b. World Energy Balances 2017. OECD Publishing, Paris France. IEA, 2017c. CO2 Emissions from Fuel Combustion 2017. OECD Publishing, Paris, France. IEA, 2017d. Energy Technology Perspectives 2017: Catalysing Energy Technology Transformations. International Energy Agency, Paris, France. IEEJ, 2017a. IEEJ Outlook 2018. The Institute of Energy Economics, Japan, Tokyo (in Japanese). IEEJ, 2017b. Heisei 28 Nendo Energy Shiyou Gourika Sokushin Kiban Seibi Jigyo (Project for Promotions of Energy Conservation Innovation, in Japanese). Institute of Energy and Economics, Japan, Tokyo. IPSS, 2017a. Population Statistics. National Institute of Population and Social Security Research, Tokyo. IPSS, 2017b. Estimation of Future Population in Japan -Estimated in FY2017-. National Institute of Population and Social Security Research, Tokyo. Kainuma, M., Masui, T., Oshiro, K., Hibino, G., 2015. Pathways to Deep Decarbonization in Japan. SDSN-IDDRI. Karimu, A., Brännlund, R., Lundgren, T., Söderholm, P., 2017. Energy intensity and convergence in Swedish industry: a combined econometric and decomposition analysis. Energy Econ. 62, 347–356. https://doi.org/10.1016/j.eneco.2016.07.017. Kaya, Y., 1990. Impact of Carbon Dioxide emission control on GNP growth: interpretation of proposed scenarios. In: Paper Presented to the IPCC Energy and Industry Subgroup, Response Strategies Working Group. Keidanren, 2017. Recommendations on Future Global Warming Measures (Kongo no Chikyuondanka Taisaku Nikansuru Teigen, in Japanese). KEIDANREN (Japan Business Federation), Tokyo. Kuramochi, T., 2016. Assessment of midterm CO2 emissions reduction potential in the iron and steel industry: a case of Japan. J. Clean. Prod. 132, 81–97. https://doi.org/ 10.1016/j.jclepro.2015.02.055. Kuramochi, T., Wakiyama, T., Kuriyama, A., 2017. Assessment of national greenhouse gas mitigation targets for 2030 through meta-analysis of bottom-up energy and emission scenarios: a case of Japan. Renew. Sustain. Energy Rev. https://doi.org/10.1016/j. rser.2016.12.093. Kuriyama, A., Tamura, K., 2016. Assessing the Emission Impacts of Current Plans for Constructing and Retrofitting Thermal Power Plants in Japan: A Study on the Effectiveness of the Policy Mix in the Electricity Sector on Mid-and Long-Term CO2 Emissions (No. IGES Issue Brief). Hayama. Li, Y., Sun, L., Feng, T., Zhu, C., 2013. How to reduce energy intensity in China: a regional comparison perspective. Energy Policy 61, 513–522. https://doi.org/10.1016/j. enpol.2013.06.007. Lima, F., Nunes, M.L., Cunha, J., Lucena, A.F.P., 2016. A cross-country assessment of energy-related CO2 emissions: an extended Kaya index decomposition approach. Energy 115, 1361–1374. https://doi.org/10.1016/j.energy.2016.05.037. Lu, H., Lin, B., Campbell, D.E., Sagisaka, M., Ren, H., 2016. Interactions among energy consumption, economic development and greenhouse gas emissions in Japan after World War II. Renew. Sustain. Energy Rev. 54, 1060–1072. https://doi.org/10.1016/

Acknowledgement The authors wish to thank Satoshi Tanaka and Taku Ohmura (Ministry of the Environment, Japan) for their valuable advice on key policy implications, and Naoki Matsuo, Mark Elder, Satoshi Kojima (Institute for Global Environmental Strategies, Japan) for their technical guidance and feedback on this study. This work was supported by the IGES Strategic Research Fund in FY 2017 and the Environment Research and Technology Development Fund (2-1501) of the Environmental Restoration and Conservation Agency of Japan. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.enpol.2019.03.055. References Aldy, J., Pizer, W., Tavoni, M., Reis, L.A., Akimoto, K., Blanford, G., Carraro, C., Clarke, L.E., Edmonds, J., Iyer, G.C., McJeon, H.C., Richels, R., Rose, S., Sano, F., 2016. Economic tools to promote transparency and comparability in the Paris agreement. Nat. Clim. Change 6, 1000.

339

Energy Policy 130 (2019) 328–340

A. Kuriyama, et al. J.RSER.2015.10.062. Malpede, M., 2015. A Multi-Model Regional Decomposition of CO2 Emissions: SocioEconomic Developments vs Energy Efficiency and Carbon Intensity Improvements. FEEM Fondazione Eni Enrico Mattei, Milan, Italy. Matsukawa, I., 2016. Consumer Energy Conservation Behavior after Fukushima, SpringerBriefs in Economics. Springer Singapore, Singapore. https://doi.org/10. 1007/978-981-10-1097-2. METI, 2015. Chouki Enerugi Jyukyu Mitoshi (Long-Term Energy Outlook, in Japanese). The Subcommittee on Long-Term Energy Supply and Demand Outlook. Ministry of Economy, Trade and Inidustry, Tokyo. METI, 2013. Heisei 24 Nendo Enerugi Kankyou Sougou Senryaku Tyousa (Overall Study on Energy and Environmental Strategies in 2012, in Japanese). Institute of Energy Economics, Japan, Tokyo. METI, MOEJ, 2013. Toukyoudennryoku no Karyokudenngenn Yuusatsu ni Kannsuru Kannkeikyokuchoukyuukaigi Torimatome (The Summary of a Meeting by Relevant Directors on TEPCO's Bid for Thermal Power, in Japanese). Ministry of Economy Trade and Industry and Ministry of the Environment, Tokyo. MHRI, 2017. Medium Term Outlook for Domestic and Foreign Economies - the Last Opportunity to Reform Japan in the 2020s - 4 Issues to Maintain 1% Growth Even under Declining Population - (Naigai Keizai no Chuki Mitooshi, in Japanese). Mizuho Research Institute, Tokyo. Mihut, M.I., Daniel, D.L., 2012. First oil shock impact on the Japanese economy. Procedia Econ. Financ. 3, 1042–1048. https://doi.org/10.1016/S2212-5671(12)00271-7. MOEJ, 2017. Analysis on Greenhouse Gas Emissions in 2015 (2015 Nendo Onshitsukouka Gasu Haisyutsuryou ni Tsuite, in Japanese). Ministry of the Environment, Tokyo. MOEJ, 2012a. Report on Global Warming Measures after 2013 (2013nen Ikouno Taisaku Shisaku Nikansuru Houkokusho, in Japanese). Central Environment Council, Mihistry of the Environment, Tokyo. MOEJ, 2012b. Summary of Macro Frame Working Group (In Japanese). Central Environment Council, Mihistry of the Environment, Tokyo. MRI, 2017. Mid- to Long-Term Perspective of Domestic and Foreign Economies 2015 2030 (Naigai Keizaino Tyuki Tenbou FY2015-2030, in Japanese). Mitsubishi Research Institute, Tokyo. MURC, 2017. MidTerm Outlook for Japanese Economy in Fiscal Year 2016 - 2030 (Nihon Keizaino Chuki Mitoshi, in Japanese). Mitsubishi UFR Research & Consulting, Tokyo. NIES, 2018. Japan's Greenhouse Gas Inventory 2018: the National Inventory Report and Common Reporting Format Tables. National Institute for Environmental Studies, Tsukuba. NIES, 2012. Outlook for Energy Consumption and GHG Emission Reflecting on the Discussion at the Sub-committee of Post 2013 Global Warming Measures (2013nen Ikou no Taisaku Shisaku Nikansuru Kentousyouiinkai Niokeru Giron Wo Fumaeta Enerugi Shouhiryou Onsitsukouka Gas. National Institute for Environmental Studies, Tsukuba. NRA, 2018. Conformity Assessment Related to Compliance with the New Regulatory Standards (Shinkisei Kijyun Tekigousei ni Kakawaru Shinsa) in Japanese. [WWW Document]. http://www.nsr.go.jp/activity/regulation/tekigousei/power_plants/ index.html, Accessed date: 26 September 2018. OECD, 2017. The Next Production Revolution: Implications for Governments and Business. OECD Publishing, Paris. https://doi.org/10.1787/9789264271036-en. Okushima, S., Tamura, M., 2007. Multiple calibration decomposition analysis: energy use and carbon dioxide emissions in the Japanese economy, 1970–1995. Energy Policy 35, 5156–5170. https://doi.org/10.1016/J.ENPOL.2007.04.001. Oshiro, K., Kainuma, M., Masui, T., 2017. Implications of Japan's 2030 target for longterm low emission pathways. Energy Policy 110, 581–587. https://doi.org/10.1016/ j.enpol.2017.09.003.

Riahi, K., van Vuuren, D.P., Kriegler, E., Edmonds, J., O'Neill, B.C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W., Popp, A., Cuaresma, J.C., KC, S., Leimbach, M., Jiang, L., Kram, T., Rao, S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da Silva, L.A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D., Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G., Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J.C., Kainuma, M., Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A., Tavoni, M., 2017. The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: an overview. Glob. Environ. Chang. 42, 153–168. https://doi. org/10.1016/j.gloenvcha.2016.05.009. RITE, 2015. Yakusokusouan no Haisyutu Sakugen Doryoku no Hyouka to Sekai Haisyutsuryou no Mitoshi (Evaluation of Emission Reduction Efforts of Japan's NDC and Forecast of World CO2 Emissions, in Japanese). Research Institute of Innovative Technology for the Earth, Kizukawa. RITE, NIES, 2015. MILES(Modelling and Informing Low Emissions Strategies) Project -Japan Policy Paper-. Research Institute of Innovative Technology for the Earth (RITE) and National Institute for Environmental Studies (NIES). Shahiduzzaman, M., Layton, A., 2015. Decomposition analysis to examine Australia's 2030 GHGs emissions target: how hard will it be to achieve? Econ. Anal. Policy 48, 25–34. https://doi.org/10.1016/j.eap.2015.10.003. Shibata, H., 1983. The energy crises and Japanese response. Resour. Energy 5, 129–154. https://doi.org/10.1016/0165-0572(83)90010-5. Spencer, T., Pierfederici, R., Sartor, O., Berghmans, N., Samadi, S., Fischedick, M., Knoop, K., Steve, P., Criqui, P., Mathy, S., others, 2016. State of the Low-Carbon Energy Union: Assessing the EU's Progress towards its 2030 and 2050 Climate Objectives (No. Study n08/2016). (Paris, France). Tamura, K., Suzuki, M., Yoshino, M., 2016. Empowering the Ratchet-Up Mechanism under the Paris Agreement (No. IGES WP1605). Institute for Global Environmental Strategies, Hayama. UNEP, 2017. The Emission Gap Report 2017. (Nairobi). UNFCCC, 2018. Paris Agreement - Status of Ratification. [WWW Document]. http:// unfccc.int/paris_agreement/items/9444.php, Accessed date: 18 January 2018. UNFCCC, 2015a. Paris Agreement, Conference of the Parties on its Twenty-First Session. https://doi.org/FCCC/CP/2015/L.9/Rev.1. UNFCCC, 2015b. Report of the Conference of the Parties on its Twenty-First Session, Held in Paris from 30 November to 13 December 2015. (No. FCCC/CP/2015/10/Add.1). Paris. UNIDO, 2017. INDSTAT2 2017. United Nations Industrial Development Organization, Vienna. Wakiyama, T., Kuramochi, T., 2017. Scenario analysis of energy saving and CO2 emissions reduction potentials to ratchet up Japanese mitigation target in 2030 in the residential sector. Energy Policy 103, 1–15. https://doi.org/10.1016/j.enpol.2016. 12.059. Wang, C., 2013. Changing energy intensity of economies in the world and its decomposition. Energy Econ. 40, 637–644. https://doi.org/10.1016/j.eneco.2013.08.014. World Bank, 2017. World Bank Open Data. (Washington, D.C). Yi, B.-W., Xu, J.-H., Fan, Y., 2016. Determining factors and diverse scenarios of CO2 emissions intensity reduction to achieve the 40–45% target by 2020 in China – a historical and prospective analysis for the period 2005–2020. J. Clean. Prod. 122, 87–101. https://doi.org/10.1016/j.jclepro.2016.01.112. Yang, X., Wang, S., Zhang, W., Li, J., Zou, Y., 2016. Impacts of energy consumption, energy structure, and treatment technology on SO2 emissions: a multi-scale LMDI decomposition analysis in China. Appl. Energy 184, 714–726. https://doi.org/10. 1016/j.apenergy.2016.11.013.

340