A roadmap towards a low-carbon society in Japan using backcasting methodology: Feasible pathways for achieving an 80% reduction in CO2 emissions by 2050

Energy Policy 41 (2012) 584–598

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A roadmap towards a low-carbon society in Japan using backcasting methodology: Feasible pathways for achieving an 80% reduction in CO2 emissions by 2050 Shuichi Ashina a,n, Junichi Fujino a, Toshihiko Masui a, Tomoki Ehara b, Go Hibino b a b

National Institute for Environmental Studies, Onogawa 16-2, Tsukuba, Ibaraki 305-8506, Japan Mizuho Information & Research Institute, Inc., Kanda-Nishikicho 2-3, Chiyoda-ku, Tokyo 101-8443, Japan

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 April 2011 Accepted 7 November 2011 Available online 22 November 2011

The purpose of the study is to analyze feasibility and a roadmap of a low-carbon society in Japan by 2050, while satisfying required demands. Future technology roadmaps, CO2 emission pathways and energy mix transitions leading Japan are calculated using the AIM/Backcasting Model based on backcasting methodology with taking into consideration that one of the keys for technological market penetration is the preferences of consumers. Under the CO2 emission target of 80% reduction as compared to 1990 level by 2050, it is found from the results that the target is feasible in Japan by implementing actions toward low-carbon society as early as possible. From the perspective of minimizing the total costs, it would be best to target a reduction rate of 16–20% in 2020, 31–35% in 2030 and 53–56% in 2040 within the range of Scenarios A and B. During this process, major investment will be needed in the early stage of the analytical periods, especially in the residential, commercial and transport sectors. However, viewed in the long term, this can be recovered by reduction in energy consumption. Moreover, the analysis suggests that returns that balance the total investment may be possible. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Low-carbon society Technology roadmap Climate change mitigation

1. Introduction The objective of the study is to depict a feasible technology roadmap, CO2 emission pathways and energy mix transitions towards a low-carbon society in Japan, while satisfying required demands, by using simulation model which considers the technology diffusion process based on consumer preference. In the past, a variety of research has been conducted to analyze CO2 mitigation pathways (or pathways towards a low-carbon society) both on the global and regional/national scale. Global CO2 mitigation scenarios are studied in Akimoto et al. (2004), Calvin et al. (2009), den Elzen et al. (2009), Ekholm et al. (2010), Gurney et al. (2009), Shultz and Kasting (1997), Syri et al. (2008), and regional studies were conducted in Aki et al. (2006), Dagoumas and Barker (2010), Gomi et al. (2010), Kannan (2009) and Wu et al. (1994). These studies evaluate energy mix and economic impacts, however, except for some research such as Pugh et al. (2011), Chen et al. (2011) and Ashina et al. (2010), no study has tried to depict future technology and/or policy roadmaps towards a low-carbon society.

n

Corresponding author. Tel.: þ81 29 850 2227; fax: þ81 29 850 2422. E-mail address: [email protected] (S. Ashina).

0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.11.020

To achieve a low carbon society by 2050, a major transformation of social infrastructure will be needed: the creation of compact cities, the widespread adoption of super-insulated residences and so on. Moreover, further innovations in energy use technologies will be indispensable. For example, in Fujino et al. (2008), in order to achieve a 70% reduction in Japan CO2 emissions by 2050, the coefficient of performance (COP) for household air conditioners should be improved to COP 8 on a stock basis. In 2007, the COP for household air conditioners on a stock basis was 4.26 for heating and 3.85 for cooling (The Energy Conservation Center Japan, 2011), and technological advances that can double these figures will be needed in order to achieve the required performance by 2050. Even for the same technology, it will be difficult to achieve the technical performance that will be needed in the future unless ongoing technical development is conducted step-by-step. Again, for example, an air conditioner with COP 8 is unlikely to enter the market immediately following those with COP of 4.26 or COP of 3.85; rather, air conditioners that represent a gradual improvement in efficiency of COP 6 and COP 7 will first appear on the market, and COP 8 air conditioners will enter the market as a consequence of their success. Such technology innovation and market penetration processes require public and/or private funding of the R&D process

S. Ashina et al. / Energy Policy 41 (2012) 584–598

(Research and Development) as well as consumer’s (including companies and businesses) support through the market. Although innovative technology is successfully launched in the market with the support of R&D investments and/or policies, if consumers do not choose them when they appear in the market, it becomes less certain that subsequent technological innovation will be carried out and even more innovative technologies will be unable to appear in the market. The effect of R&D investments and policies are discussed in Gerlagh (2007), Goulder and Schneider (1999), Kemfert (2005), Kemfert and Truong (2007) and Rosendahl (2004), however, few studies were made which consider the effect of consumer’s preferences in the market on CO2 mitigation strategies. Previously, in Ashina et al. (2010), the authors have reported on the results of a quantitative study with policy roadmaps in order to achieve Japan a low-carbon society by 2050 by using the AIM/Backcasting Model. For the current study, the AIM/Backcasting Model was improved to include technology diffusion processes in the market based on consumer’s preferences, and analyzed optimal CO2 pathways and the costs for implementation as well as technology roadmaps towards low-carbon society in Japan on the premise of proper implementation of policies such as those shown in Ashina et al. (2010). As for the CO2 emission cap in 2050, we set a strengthened target in comparison to the previous study. At the G8 L’Aquila Summit (2009), and in the ‘‘Japan–U.S. Joint Message on Climate Change Negotiations’’ (2009), the Government of Japan agreed with a longterm target of 80% reduction of greenhouse gas emissions in 2050. Japan’s greenhouse emissions in 2009 consist of 94.6% from CO2 and 5.4% from other gases, such as methane, nitrous oxide and chlorofluorocarbons (Energy Data and Modeling Center, 2011), therefore, in the study, the CO2 emission reduction target is set at an 80% reduction by 2050 as compared to the 1990 level.

2. Methodology 2.1. Overview of the AIM/Backcasting Model The AIM/Backcasting Model used here is bottom-up type model based on a backcasting methodology. Unlike a forecast,

585

that is, the prediction of future images based on currently available information such as technology levels and industrial structures, a backcasting draws up a target image and/or an outcome to avoid and investigates for roadmap or pathways satisfied to achieve it or to avoid it. The AIM/Backcasting Model has two boundary conditions of socio-economic conditions and/or energy consumptions in 2005 (base year) and 2050 (target year). The energy consumption, industrial structure and the composition of CO2 emissions for the base year are estimated based on the existing statistics such as Energy Data and Modeling Center (2011) and Japan Statistics Bureau (2010). The energy consumption, industrial structure and the composition of CO2 emissions for the target year are needed for estimation using other models or tools before carrying out the analysis of the AIM/Backcasting Model. In order to achieve the target visions the model seeks to identify the types of technology that must be introduced and when and to what degree they must be introduced, while satisfying energy service demands. The mixed integer programming method (MIP) was used for formulation, and the optimal solution was derived using the Cplex solver in the General Algebraic Modeling System (GAMS). Fig. 1 shows an overview of the estimation process using the AIM/Backcasting Model. The volume of activity in each sector for the intermediate year is established taking into account social changes, changes in population composition and other factors in each scenario, and is provided as an exogenous condition. However, energy consumption, the composition of technologies and CO2 emission pathways in intermediate years are estimated endogenously through the optimization by minimizing the sum of total costs during the analytical period, 2005–2050. Some bottom-up type models such as AIM/Enduse (Kainuma et al., 2003) employs a recursive dynamic optimization, however, the AIM/Backcasting Model as well as the backcasting approaches stand on simultaneous optimization during analytical periods. Fig. 2 shows the reason illustratively. In the figure, x-axis is the time period, and Tb and Tt are base year and target year, respectively. The y-axis represents indicators such as CO2 emissions and GDP. Points A and D reveal a certain quantity determined by the base year condition or target vision as mentioned above. Lines AB, AC, DE, DF are upper or lower bounds of solution space. These bounds are determined by constraints, and in the

Fig. 1. Schematic of the estimation process using a Backcasting Model.

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ABC: Space of solution for satisfying conditions in the base year DEF: Space of solution for satisfying conditions in the target year AGDH: Space of solution in the problem

B

E

CO2 emissions, GDP, Energy consumption etc.

G

A

D

H C F

Tb (Base year)

T

Tt (Target year)

Fig. 2. Illustrative explanation of selection in optimization approach in the AIM/ Backcasting Model.

AIM/Backcasting Model, bounds are derived from population trends, GDP assumptions, etc. Although a curved bound is allowed in general backcasting approach, the AIM/Backcasting Model is structured based on the MIP, thus, linear boundary is shown as representative example. In forecasting, a model only seeks solution within DABC, i.e. reachable area from point A, a base year condition. Even the DABC includes point D, a model encounters difficulties in finding feasible pathways to target vision because the model disables to know future consequences of a choice of pathways in each year under the forecasting approach. In the backcasting approach, a model needs to find solution within intersection area of DABC and DDEF, i.e. reachable area to the target vision, thus, space of solution limits within &AGDH. In order to formulate the intersection area properly, the model looks at the base year condition and target vision simultaneously, thus, the AIM/Backcasting Model investigates solutions through simultaneous optimization during analytical periods. 2.2. Modeling of the technology diffusion process based on consumer preference In order for further improvement in technological performance in a low-carbon technology or market introduction of a more energy-efficient technology than those that exist at present to occur, one of the catalysts is the indication by consumers of a preference for innovative technologies (or low-carbon technologies) in the market. Accordingly, for the purpose of this study, technological options are classified into three categories: the average stock technology that exists at present, the best available technology (BAT) that exists at present, and the future BAT. Depending on the prospects for technology development, each technology could be assumed to have several future BATs. For example, options for industrial motors consist of one stock technology, one current BAT technology that has a 12.5% higher efficiency over the stock, and one future BAT that has 25% higher efficiency over stock. Airconditioners in the residential sector are set at four options: one stock technology with COP 3.68 for cooling, one current BAT technology with COP 4.94 and two future BAT with COP 7 and 8. In the AIM/Backcasting Model, an analysis was conducted with the condition that, if the current BAT does not acquire a certain degree of market share, the future BAT will not be introduced in the market. Moreover, there is not necessarily only one future BAT. It is established that the period for market introduction of

future BAT will not be advanced to a point earlier than the time indicated in the ultra-long term energy technology roadmap (Institute of Applied Energy, 2006). Performances of both, the average stock technology and the current/future BATs such as energy efficiency are set based on the estimates from the Energy Conservation Center (2009), International Energy Agency (IEA) (2009), Ministry of the Environment (1995), Japan Automobile Manufacturers Association, Inc. (2008) and other statistical sources. In addition, a market survey was also conducted. Some technological parameters such as future cost of technologies are difficult to quantify, and we conduct expert interview as complementary to statistical data review and market survey. Fig. 3 shows a diagram of the process of incorporating new technology in the market in the AIM/Backcasting Model. When the market share of a current or future BAT drops below a certain threshold value, described later, the next BAT is introduced to the market, and simultaneously the oldest technology is eliminated. When consumers actively select the BAT (Fig. 3(a)), only the stock technology and the current BAT are present in the market at periods t0 and t1, and consumers select one or the other. When the current BAT drops below the threshold value at period t1, the future BAT becomes a candidate for selection in the market at period t2 and the stock technology is eliminated from the market, so consumers are able to select only the current or future BAT. Furthermore, the share of future BAT at period t2 comes to exceed the threshold value, so the future BAT2 becomes a candidate for selection at period t3. In the case where consumers are not eager to select the BAT (Fig. 3(b)), many select the stock technology even in period t1, so new technologies do not enter the market. When the market share for the current BAT finally drops below the threshold value in period t2, the future BAT technology enters the market in period t3 and the stock technology is eliminated, so the future BAT2 does not appear. In this study, the threshold at which new technologies are introduced to the market was set at 16%. This was based on the assumption that, according to Rogers (2003), explosive popularity of new products comes after the product has been adopted by the innovators that make up 2.5% and the early adopters that make up 13.5% of all consumers—in other words, the early majority stratum. At the point at which this threshold value (16%) has been exceeded, it is assumed that the next new product will be introduced to the market. The model employs the Weibull distribution that uses the number of years elapsed since introduction of a technology as a variable for the percentage of installed technology that is discarded. It is assumed that all the installed technologies are discarded when the number of years since introduction reaches double the life of the technology (Eq. (1)). Here Sm(t, h) is the residual ratio at period (in time) t for technology m of cohort h. th is the year in which cohort h was introduced, and lm is the life of the technology. am and bm are the shape parameters for the Weibull distribution and are provided exogenously for each technology. ( expfam  ðtt h Þbm g if tt h r2  lm Sm ðt,hÞ ¼ ð1Þ 0 otherwise

am ¼

logð2Þ b

lmm

ð2Þ

2.3. Objective function for optimization As a rule, the flow amount for introduction of technology m in each period, CAPf, m(t) [unit], and the service supply quantity for service type j, OUTm(t, j) [Mtoe], were calculated through total

S. Ashina et al. / Energy Policy 41 (2012) 584–598

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(1) If consumers prefer energy efficient (low-carbon) technology BAT3 into market BAT out from market

BAT2 into market STOCK out from market 0% BAT2

Market share

BAT

BAT3 STOCK BAT STOCK

Threshold for deployment new technology into market

BAT2 STOCK

100%

t0

t1

t2

t3

(2) If consumers does not so much prefer energy efficient (low-carbon) technology BAT2 into market STOCK out from market

BAT

BAT2

0%

Market share

BAT BAT

STOCK

STOCK

Threshold for deployment new technology into market

STOCK STOCK

100%

t0

t1

t2

t3

BAT: Best Available Technology Fig. 3. Pattern diagram of the process of incorporating new technological progress.

cost minimization during the analysis period. The unit for the quantity of technology introduced were different for each technology (for example, number of items in the case of air conditioners, refrigerators and automobiles and kW in the case of power generation equipment). Mtoe was used to measure service supply quantity and energy consumption. Costs were made up of investment costs, operation and maintenance costs (both fixed and variable) and fuel costs. Costs were converted to present value for evaluation, using a discount rate of r ¼3%. Hereafter, capital letters denote endogenous variables, those determined through optimization, while small letters denote indices and exogenous variables or input parameters. min TC ¼

tL X

t ¼ t0

(

1 ð1 þ rÞtt0

X  ðINV m ðtÞþ FOMm ðtÞ þ VOM m ðtÞ þVARm ðtÞÞ

)

m

ð3Þ TC is the total costs during analysis period (JPY), INVm(t) the investment costs for technology m in period t (JPY), FOMm(t) the operation and maintenance costs (fixed) for technology m in period t (JPY), VOMm(t) the operation and maintenance costs (variable) for technology m in period t (JPY), VARm(t) the fuel costs for technology m in period t (JPY). The investment cost, INVm(t) [JPY], for technology m was determined by multiplying the flow amount for introduction of technology for period t, CAPf, m(t) [unit], by the cost per unit of installed capacity, ucfixm [JPY/unit]. INV m ðtÞ ¼ ucf ixm  CAP f ,m ðtÞ

ð4Þ

Fixed operation and maintenance costs, FOMm(t) [JPY], were calculated by multiplying the remaining capacity of technology in period t, CAPm(t) [unit], by the operation and maintenance costs per unit of installed capacity, ucfomm [JPY/unit]. FOMm ðtÞ ¼ ucf omm  CAP m ðtÞ

ð5Þ

Variable operation and maintenance costs, VOMm(t) [JPY], were calculated from the service supply quantity, OUTm(t,j) [Mtoe], and

the operation and maintenance costs per unit of service supply quantity, ucvomm [JPY/Mtoe]. VOMm ðtÞ ¼ ucvomm  OUT m ðt,jÞ

ð6Þ

Fuel costs, VARm(t) [JPY], were determined from the primary energy supply quantity for the overall energy system. Fuel costs for individual technologies were determined after derivation of the optimal solution. 2.4. Other equations and constraints in the AIM/Backcasting Model 2.4.1. Calculation of energy service demands The energy service demand, d(t,i,j) [Mtoe], was calculated by multiplying the service demand j in sector i, a(t,i,j)[unit], by the energy service demand per unit of service, u(t,i,j) [Mtoe/unit] dðt,i,jÞ ¼ aðt,i,jÞ  uðt,i,jÞ

ð7Þ

Among the measures leading to a low-carbon society, those that relate to a transformation of the societal infrastructure, such as the popularization of super-insulated residences and the creation of compact cities to reduce passenger transport demand will be indispensable. For the purpose of this study, such measures have been incorporated into the definition of energy service demand, and the model focuses primarily on the introduction of technologies and its roadmaps. 2.4.2. Balance of energy consumption In the model, the exogenous service demand must be satisfied and the energy supply and demand for the target energy system as a whole must be equal, regardless of whether or not restrictions on CO2 restrictions are imposed. In other words, the sum total of the energy service supply quantity from all target technologies, OUTm(t) [Mtoe], must match the energy service demand (for enduse technology), d(t,i,j) [Mtoe], and the enduse technology fuel demand (for supply-side technology), INm(t) [Mtoe].

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S. Ashina et al. / Energy Policy 41 (2012) 584–598

For enduse technology: X OUT m ðtÞ ¼ dðt,i,jÞ

ð8Þ

m A Enduse technology

For supply-side technology: X X OUT m ðtÞ ¼ m A Supply technology

IN m0 ðtÞ

ð9Þ

m0 A f uel k

2.4.3. CO2 emissions and its constraint CO2 emissions for period t, CO2(t) [Mt-CO2], were determined by multiplying the energy resource consumption for energy supply technologies, INm(t) [Mtoe], by the CO2 emissions factor for each energy resource r, emfr [Mt-CO2/Mtoe]. 8 0 19 = X< X emf r  @ IN m ðtÞA ¼ CO2 ðtÞ ð10Þ : ; r m A Supply technology

The CO2 emissions factor for each type of fuel, emfr [Mt-CO2/ Mtoe], was assumed as constant throughout the analysis period. In the roadmap study, a requirement that the CO2 emissions in 2050 and thereafter, CO2(t) [t-CO2], must be reduced by at least 80% of CO2 emissions in 1990, CO2, 1990 [MtC], was imposed as a restriction. CO2 ðtÞ9t Z 2050 rð10:8Þ  CO2,

1990

ð11Þ

2.4.4. Restrictions on the quantity of technologies introduced Unless there are additional restrictions, the installed capacity for countermeasure technology m in period t, CAPm(t) [unit], is expressed as the sum total of the stocks for cohort h that are operating during that period, CAPm, h(h,t) [unit]. Here Ht is an aggregate that expresses the cohorts that can be operated in period t: X CAP m ðtÞ ¼ CAP m,h ðh,tÞ ð12Þ

Fig. 4. Relationship between the span of time needed for implementing technologies and assessment of the pathways for introducing the technology.

For enduse technology: P dðt,i,jÞ CAP m,f ðhÞ r sm For supply-side technology: P INm0 ðtÞ 0 CAP m,f ðhÞ r m A fuel k sm

ð14Þ

ð15Þ

2.5. Specific constraints for the Backcasting approach 2.5.1. Service supply in the target year The service supply quantity from technologies in 2050, OUTm(2050) [Mtoe], was matched to the service supply quantity for each technology, qm, 2050 [Mtoe], in the future visions. OUT m ð2050Þ ¼ qm,2050

ð16Þ

h A Ht

The stocks of cohort h that are operating in each period, CAPm, [unit], were determined by multiplying the flow amount of countermeasures introduction for cohort h, CAPm, f(h) [unit], by the residual ratio, Sm(t,h) [–] as described in Section 2.2 h(h,t)

CAP m,h ðh,tÞ ¼ Sm ðt,hÞ  CAPm,f ðhÞ

ð13Þ

Among the technologies some could be deployed immediately, such as the fuel shift to imported biofuels and co-firing of woody biomass in coal-fired plants. In most cases, however, dissemination would require from several years to several decades. Moreover, promoting the introduction of countermeasure technologies with excessive speed would invite unnecessary consumption of excessive resources, and it may actually place an additional burden on the environment. Accordingly, the minimum number of years needed for introduction and dissemination of each technology was estimated and used as a restriction, as a rule, together with the life of that technology (Fig. 4). When the minimum number of years was unclear, figures derived through appropriate references and interviews with experts were collected and organized. The flow amount of technology introduction for cohort h in period t, CAPm, f(h) [unit], was restricted based on the minimum number of years required for introduction of that technology, sm [years] as mentioned above. Specifically, the sum total of energy service demand for service j in sector i for each year was divided by the minimum number of years required for introduction of that countermeasure technology, sm [years], to determine the maximum quantity that can be introduced each year (flow), and the flow amount for the introduction of countermeasure technologies was restricted so it would not exceed this value.

2.5.2. Restrictions on introduction of technology As mentioned in Section 2.4.4, dissemination of technology requires from several years to several decades, and over-rapid installation leads to another environmental problem such as excessive consumption of material resources and generation of waste. In the AIM/Backcasting Model, installed capacity of technology at period t restricts by the installed capacity at period t þ1: CAP m ðrÞ Z

X Sm ðt þ 1,hÞ  CAPm0 ðhÞ Sm ðt,hÞ hAH

ð17Þ

t

X h A Ht

Sm ðt,hÞ  CAPm0 ðhÞ r CAP m ðt þ1Þ Sm ðt þ 1,hÞ

ð18Þ

3. Target vision of a low-carbon society in the analysis Before analyzing technology roadmaps toward the Japan LowCarbon Society, target vision needs to be clarified and quantified. In the analysis, quantified target visions were established following the approach of Fujino et al. (2008) and that of the ‘‘2050 Japan Low-Carbon Society’’ scenario team (2009). Specifically, the approach used here is as follows: i. Describing different future directions of society and economy up to 2050. ii. For each direction of society, life (use of time, the type of services needed, etc.), city and transport forms (what type of city and residence people live in, whether mobility is needed, etc.) and

S. Ashina et al. / Energy Policy 41 (2012) 584–598

industrial structure were quantified, and the energy service demand under the assumed conditions (for example, cooling calories, tons of raw steel production, etc.) was estimated. iii. Next, in each society, a search was conducted for the energy service demand that would support socio-economic activities and meet 80% reduction in CO2 emissions, as well as the enduse energy technologies (air conditioners, iron and steel production plants, hybrid cars, etc.), the types of energy supplied, and the combination of energy supply technologies, taking the amount of energy that can be supplied, economic viability and policy achievability into consideration. The type and share of energy supply and demand technologies were identified to get a quantitative picture of the vision of a society that achieves 80% reduction.

3.1. Set future visions in 2050 It is difficult to predict accurately what direction the society and economy in Japan will take in the years leading up to 2050.

589

For this reason, it was decided to establish two different future scenarios as directions, and then to depict the feasible vision that can be achieved 80% reduction in CO2 emissions in Japan by 2050. Two scenarios established were: (i) a technology-oriented economic development scenario as Scenario A and (ii) a renewable resource-oriented, decentralized scenario as Scenario B. Table 1 summarizes an overview of the vision of society for each of the proposed scenarios. Table 2 gives major socio-economic indicators for each scenario. Table 3 envisions the quantity of renewable energy that can be introduced into the energy supply, in addition to nuclear power generation and carbon capture and storage (CCS). In the IPCCSRES (2000), six scenarios have been developed under four families; A1, A2, B1 and B2. The A1 scenario family has three varied scenarios, A1FI, A1T and A1B. Scenarios developed here is similar to the A1B scenario for the Scenario A and the B2 scenario for the Scenario B, but technological innovation prospects and future economic development employs more positive in the Scenarios A and B. As for the future socio-economic indicators for the both scenarios, most of the parameters, including economic parameters, material productions and transportation demands, are based on the study

Table 1 Overview of the future vision of society in 2050. Overview Scenario A

   

Population and capital is centered in the city center in pursuit of convenience and efficiency High proportion of apartment residence and low number of residents per household GDP growth rate of 1.0% per year is achieved (per capita 1.7% per year) Industrial products shifts to high-value-added goods

Scenario B

   

Population and capital is dispersed to regional areas in pursuit of quality of life Slight increase in the proportion of apartment residents but trend toward living together with family GDP growth rate of 0.5% per year is achieved (per capita 1.0% per year) Mature society that represents a departure from material affluence is formed

Table 2 Major socio-economic indicators for the future vision of societies assumed to exist in 2050.

Population (1000 persons) No. of households (1000 households) Proportion of apartment residence (%) Commercial floor area (1 million m2) GDP (JPY 1 billion) Raw steel production (1000 t) Cement production (1000 t) Automobile ownership (1000 units) Passenger transport demand (million person-km) Freight transport demand (million t-km)

2005

2050

Actual values

Scenario A

Scenario B

127,768 48,962 43 1759 506,000 112,720 73,931 73,888 825,687 334,979

94,480 43,195 58 1721 770,000 106,787 50,680 63,900 572,091 246,176

100,297 42,065 50 1781 596,000 77,519 44,643 63,900 572,091 246,176

Table 3 Major conditions for energy supply. Unit

Renewable energy Photovoltaic power Wind power Hydroelectric power (including small and medium-scale) Geothermal power Biomass power Solar power Biomass heat use Nuclear power generation capacity Equipment use capacity CCS

10,000 kl 10,000 kl [10,000 kW] 10,000 kl [10,000 kW] 10,000 kl [10,000 kW] 10,000 kl [10,000 kW] 10,000 kl [10,000 kW] 10,000 kl 10,000 kl 10,000 kW % Mt-CO2

2005

2050

Actual values

Scenario A

Scenario B

2808 35 [144] 44 [109] 1660 [2061] 76 [53] 462 [409] 61 470 4947 72 –

14,108 4227 [17,300] 2525 [5000] 3664 [3656] 515 [361] 1002 [886] 490 1687 6806 90 200

15,712 4931 [20,180] 2525 [5000] 3664 [3656] 515 [361] 1002 [886] 490 2587 6806 90 200

590

S. Ashina et al. / Energy Policy 41 (2012) 584–598

from Central Environmental Council (2010). Besides them, population-related parameters are set based on population projections by National Institute of Population and Social Security Research (2007), although some of the parameters such as international and internal net migration are adjusted so as to fit with the narrative scenario descriptions. The parameters for the Scenario A are based on the low variant case of the national study while Scenario B adopted the middle variant case parameters in its projection. Following population projection, the number of households by type, structure and insulation levels in each region are estimated. People’s choice of building type (apartment or detached house) is also an important variable since the insulation level of the residence is heavily influenced by the building type, so, an increase in apartment share is assumed even in Scenario B because of the inertia of the current trend towards a higher apartment share for new buildings.

Figs. 5 and 6 show the quantified visions of primary energy consumption and the makeup of CO2 emissions for the society that achieves 80% reduction in CO2 emissions as compared to 1990 level. 3.3. Estimation of intertemporal demand of services The intertemporal demand of service j (cooling demand, passenger demand for ordinary vehicles, etc.) in sector i (industrial sector, residential sector, etc.) is established exogenously in accordance with the society envisioned in Scenario A and Scenario B. For example, for the iron and steel sector, this is calculated from future iron and steel production, while for the residential sector, it is calculated from the number of households and the activity level per household. As an example, Fig. 7 shows the

3.2. Feasible energy mix for achieving 80% reduction in 2050 In this analysis, only the CO2 reduction technologies that are presently thought achievable have been considered, while technologies such as nuclear fusion and space photovoltaics, which have been pointed out to be promising in terms of CO2 reduction but whose prospects are still unclear have been excluded. Specifically, approximately 470 types of technology in all sectors were assumed: energy-efficient air conditioners, LED lights, etc. in the residential sector, energy-efficient boilers and fuel conversion in the industrial sector, hybrid automobiles and electric vehicles in the transport sector, and energy-efficient fossil fuel-fired power generation, CCS-equipped power generation, etc. in the energy supply sector. Table 4 shows the major technologies considered in this study. For each technology, the following three types of parameter were prepared: (i) Data on technological characteristics, such as initial stocks both in the base and target year, capacity factor and energy efficiency. (ii) Period of time needed for introduction (minimum number of years for introduction). (iii) Costs of technology, such as investment costs and operation and maintenance (O&M) costs. The data relating to technological characteristics were estimated based on the Energy Technology Vision 2100 (Institute of Applied Energy, 2006) compiled by the Ministry of Economy, Trade and Industry and other references shown in Section 2.2.

Fig. 5. Estimated primary energy consumption for achieving 80% reduction of CO2 emissions in 2050.

Table 4 Major technologies considered in the study. Major technologies Residential and commercial Energy-efficient air conditioners, district heat and cooling (cooling/heating/hot water), electric heat pump hot water heaters, fuel cell hot sector water heaters, solar water heaters, energy-efficient lighting, energy-efficient electrical appliances, energy-efficient commercial power equipment, etc. Transport sector

Electric vehicles, hybrid vehicles, plug-in hybrid vehicles, fuel cell vehicles, improved automobile fuel consumption (lightweight vehicles, etc.), energy-efficient railways, energy-efficient shipping, energy-efficient aviation, biofuels, etc.

Industrial sector

Innovative steel manufacturing processes, innovative chemical manufacturing processes, increased efficiency for paper and pulp manufacturing processes, increased efficiency for cement manufacturing processes, high-performance industrial furnaces, energyefficient motors, energy-efficient boilers, increased efficiency for home generators, fuel conversion, etc.

Energy supply sector

Energy-efficient coal-fired thermal power generation, energy-efficient gas thermal power generation, nuclear power generation, general hydroelectric power generation, small and medium-scale hydroelectric power generation, geothermal power generation, photovoltaic power generation, onshore wind power generation, offshore wind power generation, biomass power generation, waste power generation, hydrogen production through fossil fuel reforming, hydrogen production through electrolysis, carbon capture and storage technologies, etc.

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trends in the service demand in the residential sector under Scenario A.

4. CO2 emission, energy pathways and technology roadmap leading to 80% reduction in CO2 emissions In the study, an analysis was conducted with 2005 as the base year t0 and 2050 as the target year. To eliminate the end effect in the simulation, 2070 was used as the final year tL, and service demand for the years after 2050 was extrapolated from the trends in the years up to 2050. The same restrictions for CO2 reduction as used for 2050 were imposed for the onward years. The year 2010 had already elapsed but statistical information of the year is not

Fig. 6. CO2 emissions in 2050 under the vision A and B.

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fully public yet, thus, the trends for 2008 and 2009 were extrapolated and set for 2010, and parameters for 2005 and 2010 were given exogenously for the analysis. The target values for announced energy policy were also treated as given conditions, and a restriction that the renewable energy introduction ratio for 2020 should be at least 10% of primary energy consumption was imposed for the purpose of this study. Renewable energy options included photovoltaics, solar heaters, onshore/offshore wind power, biomass, geothermal and small and medium-size as well as large-scale hydroelectric power. 4.1. CO2 emission pathways towards Japan low-carbon society by 2050 In accordance with Scenario A and Scenario B, as set in Chapter 3, the CO2 emission pathways to the achievement of an 80% reduction in CO2 emissions by 2050 while satisfying service demand was studied using the AIM/Backcasting Model that used total cost minimization during the period as an objective function (Fig. 8). The ‘‘technology frozen’’ case shown in the figure refers to the case in which it is assumed that no CO2 reduction restrictions for 2050 and afterwards are imposed and only the equipment in use in 2005 can be selected for the introduction of new investment, all the way up to 2050. Moreover, the reduction in transport demand due to the widespread adoption of superinsulated residences and the creation of compact cities, and a portion of the measures to reduce demand such as the imposition of traffic flow measures, have been incorporated into the volume of activity, and this is true for the ‘‘technology frozen’’ case as well. In addition, it should be noted that restrictions are imposed for the introduction targets for renewable energy in 2020 (10% of primary energy), and therefore these are not strictly results for cases in which CO2 emissions reduction targets are not imposed. In the ‘‘technology frozen’’ case, CO2 emissions decline following a peak in 2015 in case of both Scenario A and Scenario B. In Scenario A (technology frozen case), emissions in 2050 are 1075 Mt-CO2 (1990 levels þ2%), while in Scenario B, emissions in 2050 are 930 Mt-CO2 (1990 levels 12%). Even though these figures include the impact of demand reduction measures, they are a long way from achieving the target of 80% reduction. In the case in which an 80% reduction restriction as compared to 1990 levels is imposed as a CO2 emissions reduction target for 2050, the path to the 80% reduction is the same for both Scenario A and Scenario B. The introduction of technologies that contribute

Fig. 7. Example of future service demand (residential sector, Scenario A).

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1,400

CO2 emissions [Mt-CO2]

1,200 1,000 800 600 400

Scenario A: Technology Frozen Scenario A: 80% reduction

200

Scenario B: Technology Frozen Scenario B: 80% reduction

0 2005

2010

2015

2020

2025

2030

2035

2040

2045

2050

Fig. 8. CO2 emission pathways towards 80% reduction by 2050.

Fig. 9. CO2 reduction wedges by sector (compared to technology frozen case). (a) Scenario A and (b) Scenario B.

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to a reduction of CO2 emissions begins in 2010, and the rate of reduction as viewed from 1990 levels for the range of Scenario A and Scenario B is minus 16–20% in 2020, minus 31–35% in 2030, and minus 53–56% in 2040. This indicates that rapid measures are part of a desirable path from the standpoint of total cost minimization for the purpose of achieving a low-carbon society. Fig. 9 shows CO2 reductions per sector for the case in which CO2 emissions reduction restrictions are imposed. Not counting CCS, for both Scenario A and Scenario B reductions are greatest in the power sector, followed by the industrial sector, the transport sector, the residential sector and the commercial sector. This is one reason that the effect of CO2 reductions through changes in power demand on the part of end users is seen as equivalent to reductions in the power sector. Moreover, in each sector, CO2 reductions begin from the start of the analysis period. CCS will be introduced gradually beginning in 2030, but due to differences in the energy structure and the activity level assumed in 2050, the amount of CO2 captured in 2050 by CCS is only 167 Mt-CO2 in

593

Scenario B as compared to 200 Mt-CO2 in Scenario A.

4.2. Energy mix for achieving an 80% reduction Fig. 10 shows the status of dissemination of countermeasure technologies with regard to the demand for agricultural services in the industrial sector, accomplished by means of imposing CO2 reduction targets. Fig. 11 shows the demand for ordinary passenger automobile services. Total service demand is assumed to be an externally given condition that does not change whether or not CO2 emissions targets are imposed. As a consequence of dissemination of technologies and fuel shifts shown above, energy mix for achieving 80% reduction of CO2 emission are obtained. Fig. 12 shows the trends in primary energy consumption leading to low-carbon society by 2050, for each fuel source and scenario. For nuclear power and renewable energy, as in the case of the statistics from the EDMC (2011), the

8 7

Electric appliances (BAT) Electric appliances (Stock) Biomass boiler (BAT2) Biomass boiler (BAT1) Gas boiler (BAT2)

Service demand [Mtoe]

6 5 4 3

Gas boiler (BAT1) 2

Oil boiler (BAT)

1

Oil boiler (Stock)

0 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Fig. 10. Dissemination of technologies in agriculture industry.

20 18 Plug-in hybrid (BAT3)

Service demand [Mtoe]

16

Plug-in hybrid (BAT2)

14

Plug-in hybrid (BAT1)

12 Hybrid car (BAT2)

10 Hybrid car (BAT1)

8

Hybrid car (Stock)

6

Gasoline car (BAT2)

4

Gasoline car (BAT1)

2

Gasoline car (Stock)

0 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 Fig. 11. Dissemination of technologies in passenger car.

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Fig. 12. Changes in primary energy consumption with 80% reduction target. (a) Scenario A and (b) Scenario B.

values were converted into primary energy using a factor of 2105 kcal/kWh (40.88% of the power generation efficiency). In each scenario, in addition to assuming BAT for the analysis, it is assumed that a high degree of energy conservation is achieved at the beginning of the analysis period as compared to other times, because the introduction of best available technologies progresses rapidly as compared to the periods prior to 2010 due to the rapid implementation of countermeasures. In Scenario A, the impact of the introduction of nuclear power and equipment in combination with CCS is great, and renewable energy is not introduced until just before 2050. In Scenario B, as in Scenario A, the introduction of nuclear power and equipment in combination with CCS progresses, but the introduction of renewable energy proceeds from a relatively early point in time. Fig. 13 shows the trends in the makeup of power generation for Scenario A and Scenario B. Unlike primary energy consumption, the total demand for power remains constant even if CO2 emissions reduction restrictions are imposed. This indicates that the imposition of restrictions to reduce CO2 emissions promotes a conversion on the part of end users from technologies that directly use fossil fuels such as kerosene heaters and oil burners

to power-using equipment such as air conditioners and heat pump heat sources (electrification). In both Scenario A and Scenario B, the proportion of thermal power plants that use fossil fuels decreases as 2050 approaches, and there is an increase in the proportion of nuclear power plants and renewable energy, particularly hydroelectric power. Moreover, beginning in 2035, the introduction of coal-fired thermal power plants that include CCS and natural gas thermal power plants progresses, and as a result the power generation sector achieves zero emissions in 2050 in both scenarios. For 2010, the actual figures for 2008 and 2009 are assumed to have been extended, and as a result the usage ratio of nuclear power is assumed to be low at 62%. On the other hand, the installed capacity of new power plants that began operating between 2005 and 2010 is determined as 5.8 GW (by extrapolating the increase of 3.5 GW (Energy Data and Modeling Center, 2011) for the period between 2005 and 2008), which is insufficient to make up for the decrease in power generation by nuclear power plants. For this reason, the power demand is made up for by an increase in power generation by oil-fired thermal power plants. In reality too, in the results for 2007, the decrease in the

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Fig. 13. Generation of electricity by power plant types under an 80% reduction constraint. (a) Scenario A and (b) Scenario B.

amount of power generated by nuclear power plants was made up for by the operation of oil-fired thermal power plants. Accordingly, this is thought to be an appropriate result in line with assumptions. 4.3. Technology roadmaps towards a low-carbon society Figs. 14–17 show the analyzed technology roadmap by service in each sector in the scenario A toward a low-carbon society Japan by 2050. In the study, future changes in technological innovation and their consequences such as the degree of improvement in energy efficiency are exogenously given, and analysis using AIM/ Backcasting Model puts emphasis on the market diffusion process of each innovative technology and its contributions to achieving a low-carbon society in Japan by 2050. The number of domains in the roadmap indicates the minimal amount of technological innovation required in order to attain the technological level by 2050 shown in Scenarios A and B, such as COP 8 for air-conditioners in the residential sector and a thermal efficiency of 55% in HHV for gas combined cycle. Roughly speaking, technologies in the industrial sector require single technological innovation, in contrast, the residential sector, commercial sector and transportation sector need to promise multiple technological innovation to achieve an 80% reduction by the deadline.

Domain and their proportion shifts indicate cost-minimizing technology transition pathways. For example, in the agriculture industry, stock technology will be replaced completely by 2020, and by 2030, half of the technologies will need to shift to more innovative technology. Each service is composed of several types of technologies; for example, passenger cars can be classified into three types of cars, gasoline cars, hybrid cars and plug-in hybrid cars as shown in Fig. 11. In principle, plug-in hybrid cars will be developed through the technology of gasoline and hybrid car, thus, the plugin hybrid car is treated as best available technology with respect to gasoline and hybrid cars in the process of configuration of technology roadmap, and a similar manner is adopted for other technologies. In either scenario, in the ‘‘technology frozen’’ case, the share of each technology in 2005 is maintained until 2050; therefore the technology roadmap includes only stock technology. Imposing CO2 emissions restrictions and a fixed target for technology volumes in 2050 promotes the introduction of BAT from the beginning of the analysis period, and when multiple BATs were assumed, dissemination of these technologies began in sequence from the point at which market introduction becomes possible. The same trend was evident for sectors and services in both Scenario A and Scenario B.

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2010

2020

2030

2040

2010

2050

2020

2030

2040

2050

Transportation sector: Passenger transportation

Industrial sector Agriculture

Light vehicle

Mining

Small vehicle

Construction

Car

Food

Bus

Paper

Ship Aviation

Textile

Transportation sector: Freight transportation

Petrochemical

Light vehicle

Chemical

Small vehicle

Cement

Car

Ceramics

Train

Steel

Ship

Metal

Aviation

Machine

Stock technology

BAT1

BAT2

BAT3

Other industry Stock technology

CCS

BAT2

BAT1

BAT: Best Available Technology, CCS: Carbon Capture and Storage

Fig. 14. Technology roadmap in the industrial sector (Scenario A).

Fig. 16. Technology roadmap in the transportation sector (Scenario A).

Electricity sector Fossil fuel-fired plant Stock technology

Residential sector

CCS

Fig. 17. Technology roadmap in the electricity sector (Scenario A).

Hot water supply Cooking Lighting Other appliances

Commercial sector Air-conditioning Hot water supply Cooking Lighting Other appliances Stock technology

BAT

BAT: Best Available Technology, CCS: Carbon Capture and Storage

Air-Conditioning

BAT1

BAT2

BAT3

BAT4

BAT5

Fig. 15. Technology roadmap in the residential and commercial sector (Scenario A).

4.4. Costs leading to an 80% reduction From the standpoint of the total cost minimization needed to achieve an 80% reduction in CO2 emissions by 2050, if one focuses on the trends in yearly additional investment as compared to the ‘‘technology frozen’’ case, an additional investment of approximately JPY 4 trillion will be needed in 2010 in Scenario A (Fig. 18). This investment amount does not include the cost of superinsulated residences and structures and the cost needed to change city infrastructures to create more compact cities. Moreover, it does not include the cost needed for system power measures required for frequency changes and the absorption of voltage fluctuations due to the expanded quantity of renewable energy.

An additional investment for the residential sector is the largest, followed by the commercial, the transport and the industrial sectors. Beginning in 2015, annual additional investment costs will amount to JPY 2–3 trillion within a range of Scenarios A and B. However, as it will be possible to reduce fuel costs by JPY 2–3 trillion due to reduced energy consumption, the total additional cost will be a maximum of minus JPY 2 trillion for Scenario A and minus JPY 1 trillion for Scenario B. This suggests that it is very likely that these scenarios will be less expensive than the ‘‘technology frozen’’ case for the energy system as a whole. Investment costs in the power sector will decrease by approximately JPY 0.5 trillion at the beginning of the analysis period due to reduced power demand as compared to the ‘‘technology frozen’’ case. However, as the introduction of thermal power that includes CCS and other expensive equipment will progress as 2050 draws nearer, additional investment costs will be needed. In 2045, an additional investment cost of approximately JPY 3 trillion will be needed due to the introduction of countermeasure technologies, particularly in the power sector, that are effective in reducing CO2 emissions but are expensive. For this reason, total additional costs will be JPY 0.5 trillion for Scenario A and JPY 0.3 trillion for Scenario B. The average additional investment costs needed annually up to 2050 are JPY 2.2 trillion (Scenario A) to JPY 2.4 trillion (Scenario B). On the other hand, the reduction in fuel costs is expected to be an average of JPY 2.3 trillion per year for both Scenario A and Scenario B. Accordingly, the net additional costs are minus JPY 0.15 trillion for Scenario A and plus JPY 0.09 trillion for Scenario B. In this study, the learning effect relating to investment costs for technologies has not been included, however, if a gradual reduction in investment costs for technologies can be anticipated

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Fig. 18. Annual additional cost compared to technology frozen case with discount rate 3% in Scenario A.

due to the learning effects, it is possible that the case in which restrictions are imposed for the achievement of an 80% reduction in CO2 emissions may be less expensive than the ‘‘technology frozen’’ case for the energy system as a whole. Although investments will be almost covered by fuel cost savings in the long-term prospects, from the macro viewpoint of Japan’s economic activity, additional investment in the residential sector leads to cuts in the other household consumption in the specific period. On the other hand, the additional investment could reduce the energy demand in residential sector in the future. Moreover, from the results on the necessary costs, the reduction on social welfare might be small. To avoid deep reduction of social welfare, the low-carbonizing strategy in Japan should include establishing public and/or private financial supporting schemes.

5. Conclusion The 80% reduction target of CO2 emissions by 2050 compared to the 1990 level is tough but feasible in Japan if all stakeholders including businesses, citizens and policy decision makers take appropriate actions for technology innovation and diffusion of CO2 mitigation options as early as possible. Future pathways leading Japan are calculated using the AIM/Backcasting Model, which is an analytical model based on backcasting methodology. Although some studies such as Wigley et al. (1996) pointed out that taking CO2 mitigation actions should wait until future technologies have been developed, we conclude from this study that implementing actions toward low-carbon society is recommended to take steps sooner rather than later (early action), and taking early action could lead to a cost-minimizing way to achieve a low-carbon society in Japan. The cost-minimizing CO2 pathway is to reduce 16–20% in 2020, 31–35% in 2030 and 53–56% in 2040 as compared to 1990 level. In order to take early action, a large investment is needed in the initial stage, particularly in energy-efficient appliances in the residential and commercial sectors, in next-generation automobiles in the transportation sectors, and in other technologies also in which there is great room for efficiency improvement as a result of future technological innovations. Total additional investments will be balanced by the total of fuel cost reductions. Although this study excludes the learning-by-doing effects for

future investment costs, considering the learning effect with regard to technology, it is likely that the early market entry of technologies will lead to reductions in the cost of future technologies. This is strongly depends on how much the degree of reduction in investment due to learning-by-doing effects are assumed to be, but early action might help to build a low-carbon society in a more cost-effective manner. The importance of early action is derived from the analysis which takes into consideration the technology penetration process with preference of consumers in the market, and considers the lead time needed to convert and/or prepare buildings and urban systems, road, railway and other transportation systems, power plants and transmission lines, gas pipelines and other parts of the energy infrastructure to a low-carbon infrastructure, taking steps to reduce CO2 emissions sooner rather than later will increase the likelihood of achieving a low-carbon society. The achievement of innovative technologies needed to build a low-carbon society by the deadline might be dependent on the accumulation of whatever technologies are developed up to that time, changes in systems, the preparation of a social infrastructure that will enable these new technologies to be incorporated, and other factors. For example, service stations that can supply electricity must be in place before electric vehicles can achieve widespread adoption. Similarly, in order to achieve widespread adoption of energy-efficient residences that are appropriate for the climatic characteristics of the region and are able to reduce energy demand, various systems must be changed: insulation standards with more detailed categories than those that currently exist must be in place, a system for energy-saving performance assessments by third party organizations must be set up and so on. Moreover, it is essential for government leadership to take the first step in the right direction, based on quantitative analysis that is founded on scientific knowledge and a long-term, all-embracing perspective. In March 11, 2011, Japan suffered big earthquake and tsunami (so-called: the 2011 Tohoku earthquake and tsunami). Nuclear power plants in north part of the main island of Japan are shut down. Assuming that all nuclear power plants will replace to a gas combined cycle with 60% of efficiency in HHV, CO2 emissions in 2050 increase 151.5 Mt-CO2, which corresponds to 14.3% of 1990 level. In the summer 2011, areas owned by Tohoku Electric Power Companies and Tokyo Electric Power Companies face a deficit in their electrical supply due to the lack of electricity supplied from

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nuclear power. Our scenarios shown in this analysis assumes that living standards remain at least the same both now and in the future. After the earthquake, Japan shifts its lifestyle to lower amounts of electricity and fossil fuel consumption. This can be regarded as a kind of big experiment towards a low-carbon society. By including these effects and enhancing renewable energy, it is still quite likely that the 80% reduction of CO2 emissions by 2050 could be feasible in Japan. Further discussion is beyond the scope of this study but we might continue our research to develop technology/policy roadmaps toward the lowcarbon and sustainable society which also robust against all the various risks.

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