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1 Energy Policy 38 (2010) Contents lists available at ScienceDirect Energy Policy journal homepage: A low-carbon scenario creation method for a local-scale economy and its application in Kyoto city Kei Gomi a,, Kouji Shimada b, Yuzuru Matsuoka c a Graduate School of Environmental Studies, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-ku, Kyoto , Japan b Faculty of Economics, Ritsumeikan University, Nojihigashi, Kusatsu, Shiga , Japan c Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-ku, Kyoto , Japan article info Article history: Received 31 January 2009 Accepted 22 July 2009 Available online 25 August 2009 Keywords: Low-carbon society Local environmental policy Backcasting abstract On May 2008, Kyoto city government set up a low-carbon target of a 50% GHG reduction by 2030 compared to the 1990 level. To contribute to these discussions, we developed a local (city-scale) lowcarbon scenario creation method. An estimation model was developed to show a quantitative and consistent future snapshot. The model can explicitly treat the uncertainty of future socio-economic situations, which originate from the openness of local economy. The method was applied to Kyoto city, and countermeasures to achieve the low-carbon target were identified. Without countermeasures, emissions would increase 12% from Among the measures, the reduction potential of energy efficiency improvements to residential and commercial s was found to be relatively large (15% and 18% of total reductions, respectively). The reduction potential of the passenger transport, in which the city government s policy is especially important, was 17% of the total amount. A sensitivity analysis showed that a 10% increase in exports leads to an 8.5% increase in CO 2 emissions, and a 20% increase in the share of the commuters from outside the city leads to a 3.5% decrease of CO 2 emissions because of the smaller number of residents in the city. & 2009 Elsevier Ltd. All rights reserved. 1. Introduction To avoid the huge risk of climate change, the world should reduce greenhouse gas (GHG) emissions dramatically by the middle of this century. In this so-called low-carbon society, technology, institutions and people s behavior will have to differ from the current state. Thus, long-term targets and plans to achieve them, which are called low-carbon society scenarios or LCS scenarios in this study, are required. Detailed LCS scenarios have been proposed in several countries, targeting up to the year 2050 and reductions of around 50% compared to current emissions level (e.g. Kok et al., 2003; Anderson et al., 2008; Fujino et al., 2008; Mander et al., 2008). On the other hand, local-scale actions will be important in order to implement concrete measures because usually regions in a country vary in many aspects, and therefore also the actions to take. This study proposes a method to develop LCS scenarios on a local scale, such as in municipalities, especially considering the open structure of local economies. A large number of municipalities around the world have their own low-carbon goals and plans targeting after 2020 (Fig. 1). Corresponding author. Tel.: ; fax: addresses: g.kei@iwyh.mbox.media.kyoto-u.ac.jp, albart29@hotmail.com (K. Gomi). Many of them aim at emissions reductions of 50% or more compared to current emissions. Even in the United States, whose federal government has not set up a long-term target, many states and cities have their own targets. Nicholas and Sperling (2008) showed that the summation of them covers more than half of the GHG emissions in the United States. In Japan, the Prime Minister s Office accepted candidates for Environmental Model Cities in April A total of 82 municipalities applied, with long-term low-carbon targets as a necessary condition. Fig. 2 shows the targets in their proposals. Some of the municipalities around the world have made quantitative assessment of emissions and measures for long-term targets after 2020 (Greater London Authority, 2007; California Environmental Protection Agency, 2006; Berlin Agenda Forum, 2004). In research on the methodology of local-scale LCS, Turnpenny et al. (2004, 2005) developed a method for climate change mitigation and adaptation on a regional scale and applied it to the west of England. They employed the idea of backcasting and developed four scenarios. Three of them achieve 60% reductions in GHG emissions using different sets of measures. However, the methods applied in the plans and scenarios above have some problems. One is assumptions of activity level, including socio-economic indicators such as population, industrial output, and transport demand. These are so crude that they might exclude possible changes in the region. The indicators are often /$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi: /j.enpol

2 4784 K. Gomi et al. / Energy Policy 38 (2010) show the flow of LCS scenario development and explains the calculation system of the estimation tool (Extended Snapshot tool, ExSS), which was developed in this study. Section 5 applies the method to Kyoto city, Japan. Section 6 shows the estimated results including a sensitivity analysis, and Section 7 offers some conclusions. 2. Openness of local economy and LCS scenarios 2.1. Small-scale economy and LCS scenarios Fig. 1. Low-carbon targets of the local governments around the world. The emission reduction targets are shown in relative emission amount compared to base-year emission of each municipality. The base year, target gases and target activities vary among the municipalities. Abbreviations of names of states in the US are: CA; California, CT; Connecticut, NM; New Mexico, OR; Oregon. Source: Greater London Authority, 2007; California Environmental Protection Agency, 2006; Connecticut Governor s Steering Committee on Climate Change, 2005; Governor s Advisory Group On Global Warming, 2004; Environment and Health Administration of City of Stockholm, 2002; Öko institute e. V., 2004; Toyonaka city, 2007; The City of NewYork, 2007; Woking Borough Council, 2005; Cheltenham Borough Council, 2005; State of New Mexico Office of the Governer, 2005; Bristol City Council, 2004; Leicester partnership and Leicester Environment Partnership, 2003; Berlin Agenda Forum, 2004; Yokohama city Head quarter of Global Warming Mitigation Actions, 2008; Ville de Geneve, 2006; Environmental Bureau of Hiroshima city, 2008; Chiyoda ward, 2007; Kashiwa city, 2007; Tokyo Metropolitan Government, assumed to be constant or extrapolate current trends separately, therefore they can be internally inconsistent. In order to solve this problem of inconsistency, Shimada et al. (2007) developed a method including a set of tools to estimate socio-economic indicators and carbon dioxide emissions consistently, and applied it to Shiga prefecture, Japan. They showed three scenarios to reduce carbon dioxide emissions in the prefecture by 30%, 40% and 50%, respectively. Though their method solved the problem of internal consistency, a remaining problem is the openness of the local-scale economy. Uncertainty about the future is greater than on a nationscale because of the more open socio-economic structure of locals-scale economies. Against this background, this study improves the model of Shimada et al and proposes a method to develop local LCS scenarios considering the openness of local economy using backcasting approach. In the definition of Robinson (1990), backcasting method is working backwards from a particular desired end-point to the present in order to determine the physical feasibility of that future and what policy measures would be required to reach that point. This approach can be divided into two phases; the first step is to describe a desired goal, and the second step is to seek the way toward there from current situation such as investment path or policy schedule. The method and estimation tool we proposed here mainly focus on the first step, to describe a picture of a low-carbon society as a goal of a municipality. The structure of this paper is as follows. Section 2 discusses the relation of the openness of the local economy and LCS scenarios and proposes how to treat this in LCS scenarios. Sections 3 and 4 The amount of GHG emissions in a region depends on its population, industrial structure, and urban structure and so on. With different population paths or economic development directions, the amount and composition of emissions will be different. Thus, the importance of countermeasures changes according to the upstream socio-economic assumptions about the future society. Furthermore, on a local scale, socio-economic indicators have a wider scope. Between regions within a country, goods, services and people move more easily than between nations. The nature of small economies strongly relates to the development of a region. For example, in LCS scenarios, population demographically estimated is usually given exogenously. This assumption is appropriate when population movement is relatively small. However, the smaller the target region is prefecture, metropolitan area, city or town the more easily people migrate in response to socio-economic situations. Thus, even if the population of a country is increasing, the population of a city often decreases. Fig. 3 shows a typical example. The population of Yubari city, once prosperous as a coal-mining town, peaked at 116,908 in the year However, along with the closure of the mines, the city lost 90% of the peak population by 2005, while the Japanese population increased during the same period. Other than the impact of industrial location on the size of population shown in this extreme example, the relationship of commuting is also influential. Even when a city does not have a large industry, or enough opportunities for employment in its own area, its population can increase if many of the residents commute to neighboring cities. For example, according to the national census of 2005, 49% of the population in Shiga prefecture s Otsu city is employed somewhere, and 40% of these people commute to neighboring cities. The opposite example can be seen in Chiyoda ward, Tokyo, whose daytime population is more than 800,000 while its nighttime population is only around 40,000 (Statistics Bureau and the Director-General for Policy Planning, HP). To develop LCS scenarios, one often has to set a long-time scope such as 30 years or more in order to complete the drastic changes in many sides of society. In such long periods, the openness of the local economy makes the socio-economic activity level highly uncertain. Therefore, for a city or town, major changes of industrial structure, economic activity level and population can be expected. Even though in the framework of LCS scenarios those socio-economic aspects are treated as given assumptions, as long as they affect GHG emissions significantly, the scenarios should show them explicitly and consistently. Therefore we think the methodology of LCS scenarios should consider the region s characteristic cross-border relationships The model In order to treat the openness of local economy in LCS scenarios explicitly, we propose a model based on the exportbase approach of standard regional economics. Structure of the model is shown in Fig. 4. This model is intended for use in the first

3 K. Gomi et al. / Energy Policy 38 (2010) Fig. 2. Low-carbon targets of the proposal of 82 municipalities for Environmental Model Cities. (Prime Minister s Office). The emission reduction targets are shown in relative emission amount compared to base-year emission of each municipality. The base year, target gases and target activities vary among the municipalities. Fig. 3. Population transition in the Yubari city, Japan. stage of backcasting, to picture a desirable goal, and therefore a static model. The export-base approach considers that demand from outside of the region (exports), at least partly, lead to the economic growth of the region (Armstrong and Taylor, 2000). Industries producing export goods mainly are called basic industries. Production of basic industries induces production in the other industries through demanding intermediate input and consumption of laborers. The Garin Lowry model and its successors are classic examples of the application of this approach (Lowry, 1964; Garin, 1966; Macgill, 1977). In our model, exogenously-given exports of each industry, and government expenditure, drive overall economic activity. First, given exports, other final demand and the inverse matrix of Leontief, output by industry is calculated using standard IO analysis. Output by industry, labor productivity and working hours per worker decides the number of laborers required to fulfill the output. Laborers are classified into three categories; those who both live and work in the region, commuting FROM outside the region

4 4786 K. Gomi et al. / Energy Policy 38 (2010) Fig. 4. Structure of the model: industry and population. and commuting TO outside of the region. To decide the number of laborers of each category above, we defined two parameters. One is called the domestic working ratio, or DWR, which is defined as the percentage of the laborers working in the region from among all laborers living in the region. Lower DWR means higher numbers of commuters to outside of the region. The other is the domestic employment ratio, or DER, which is defined as the percentage of laborers living outside of the region among all the laborers working in the region. Lower DER means high numbers of commuters from outside of the region. If both DER and DWR in a region are high, it tends to be self-sufficient or reclusive in terms of employment. Population (number of residents) is decided by the number of laborers working in the region, assumption of a commuting relationship with surrounding regions (DER and DWR) and labor participation ratio of the residents. The residents consume part of their income which is handed to IO analysis as private consumption expenditure, and the model is closed. In general, the activity of industries and employment opportunities are considered to induce migration through the labor market and housing market. However, our model shown above does not treat the phenomenon of migration itself because while migration is dynamic, the desirable goal, which we intend to picture here, is a static idea, as its name suggests. Though, the contents of the goal, hereafter called a snapshot, must be balanced internally. So the model describes how much the population must be under an assumption of economy, instead of modeling migration directly. When assuming a scenario, if economic development of the target region is lead by a particular industry, e.g. the car manufacturing industry, setting the export volume of the industry will be a critical parameter. If the target region is a commuter town, an important variable is the number of laborers commuting TO outside the region whose key parameter is DWR. The next section introduces the flow of the method to develop local LCS scenarios. 3. Methodology of Local LCS scenarios TheflowofthemethodologyweproposehereisshowninFig. 5. (1) Setting the framework The framework of the LCS scenarios consists of a target region, base year, target year(s), target activities, low-carbon target(s) Fig. 5. Procedure of the methodology. and number of scenarios. In order to achieve the necessary changes the target year should ideally be in the distant future, whilst in order to make it easy for people to imagine the future a nearer target year is preferable. In the scenario studies of LCS, multiple scenarios are often created and compared with each other (e.g. IPCC, 2000). However, all of the targets and plans shown in Fig. 1 are based on the single assumption of the socio-economic situation in the target year. The local governments might have intended one vision as an authorized official goal. Though, considering the uncertainty we discussed above, we think at least a sensitivity analysis for important assumptions should be conducted. (2) Depictive scenarios Qualitative future image, a depictive scenario, is described before conducting a quantitative estimation. A depictive scenario can include assumptions on speed of economic growth, industrial structure, lifestyle of the residents, transport, urban structure, land-use and so on. For the industrial structure, what kinds of industries will have grown/shrunk in the target year is assumed. Lifestyle assumptions consist of consumption patterns, time-use patterns, trends in housing including number of family occupants, and balance between work and life. For a LCS scenario, socio-economic assumptions are prior conditions, not the goals desired to be achieved. Therefore in this study, we do not consider how to realize a socio-economic assumption such as the growth rate of an industry. (3) Quantification of socio-economic assumptions Based on the description of (2), detailed values of the indices shown in Table 1 are set. Those indices are input to ExSS as exogenous parameters. The relation of socio-economic assumptions and parameter setting is described in detail in Section 4 and 5. (4) Compilation of low-carbon countermeasures Countermeasures which are expected to be available in the target year in the region are compiled. Examples of candidates of the countermeasures are equipments with high energy efficiency, insulation of buildings, renewable energy, energysaving behavior, modal shift, reducing waste and carbon sinks

5 K. Gomi et al. / Energy Policy 38 (2010) Table 1 Main parameters in Extended Snapshot Tool. Parameter Population composition Population allocation Average number of family occupants Time use Labor participation ratio Final consumption composition Average propensity to consume Export Government expenditure Import ratio Input coefficient Labor productivity Domestic employment ratio Domestic working ratio Trip number per person Modal share (passenger transport) Average trip distance Freight generation unit Destination share Modal share (freight transport) Explanation Population composition by sex and by age-group Population composition by area Average number of family member per family Time use of activity per day by individual attribute Labor participation ratio by sex and by age-group Composition of final goods and service in private consumption Ratio of consumption in income Demand from outside of the region by goods and service Government consumption expenditure, public investment by goods and service The share of supply from outside of the region of all demand in the region Input coefficient of input output analysis Labor hours requirement per production Percentage of the laborer living in the region in the all laborer working in the region Percentage of the laborer living in the region in the all laborer living in the region Number of trips per person per day by purpose of the trip Share of transport mode by individual attribute and by purpose Distance of origin and destination per trip by mode Weight of transported freight per industrial output by freight Share of destination region by freight Share of transport mode by freight by destination region and so on. In this method, countermeasures are limited to only those which can decrease energy consumption or GHG emissions directly. Policies to enhance the diffusion of those countermeasures, such as economic incentives, regulations and education, are, of course, important to realize LCS, but are not included in the countermeasures here because they do not affect GHG emissions directly. (5) Setting countermeasures in the target year Amounts for the countermeasures to be introduced, compiled in (4), are set. Technical parameters related to energy demand, such as energy efficiency and fuel share, are then decided. Candidate criteria for deciding a portfolio of countermeasures are cost-minimization, acceptance of stakeholders, technological feasibility and so on. (6) Future estimation and identifying countermeasures set The parameters set in (3) and (5) are input, and socioeconomic indicators, energy demand and GHG emissions are estimated using ExSS. Socio-economic indicators calculated here are output by industry, population and number of households, regional income, passenger and transport demand and so on. If the GHG emissions achieve the low-carbon target, proceed to (7). Otherwise return to (5), setting countermeasures, and iterate this process until the lowcarbon target is achieved, and then define a portfolio of the countermeasures. (7) Proposal of policy set Policies to enhance the diffusion of the countermeasures defined in (6) are set. Since ExSS calculates the reduction amount by countermeasures in detail, it can show measures with high reduction potential, or the reduction amount of the measures, in which local government plays a particularly crucial role. 4. Structure of Extended Snapshot tool (ExSS) 4.1. Socio-economic indicators Fig. 6 shows the structure of ExSS, which consists of seven blocks and main parameters and variables. ExSS is a bottom-up engineering type model, and formulated as system of simultaneous equations, and the solutions are defined uniquely with given parameters. The model proposed in Section 2 is shown in the upper part of the Fig. 6, and estimates population and output by industry. To estimate commercial building stock, passenger and freight transport demand, energy demand and carbon dioxide emissions, the same method followed by Shimada et al. (2007) is employed. Since ExSS is a static model, it cannot treat dynamic phenomena like investment or migration in nature. Thus, ExSS cannot assess net cost of low-carbon countermeasures because most of them are related to stock, such as buildings or vehicles. The relation of the settings of the main parameters and socioeconomic assumptions is explained below. Table 1 shows a short explanation including other parameters. As proposed in Section 2, the size of the economy in the region is mainly decided by export and government expenditure, including both government consumption and investment. Exports here includes both other regions in the country and the rest of the world. Both of them are given to the regional economy externally, and then drive the overall economic activity through intermediate input and consumption of employee. Composition of exports defines the industrial structure of the region and affects energy consumption significantly. A region with a high export volume of energy intensive industry, like steel or petrochemicals, will have large share of the industrial in its GHG emissions. For a sightseeing place, exports of the catering industry or hotels would be large, leading to greater GHG emissions in the commercial. Labor participation ratio is one of the main parameters to describe changes in lifestyle. In this model, labor participation ratio affects the population of a city because a higher ratio means less population to fulfill labor demand. Working hours also describe changes in the style of working as well as the labor participation ratio. For instance, work sharing can be expressed by setting high labor participation and few working hours per laborer. Domestic working ratio (DWR) and domestic employment ratio (DER) describe the relation of commuting with the surrounding region as explained in Section 2. In case of Chiyoda ward, which we mentioned earlier in this paper, DER is quite low while DWR must be low in the regions where people are commuting to Chiyoda ward. An assumption of low DWR means greater population compared to employment opportunities in the region. Obviously, the GHG emissions of the residential must be high in such a region.

6 4788 K. Gomi et al. / Energy Policy 38 (2010) Fig. 6. Structure of Extended Snapshot Tool. Average number of family occupants, household size in other words, expresses the modes of habitation among changes in lifestyle. Architectural Institute of Japan (2006) showed energy consumption per person is greater in smaller households. Thus, even if population is constant, GHG emissions of the residential will increase when the household sizes of the region decreases. Contents of the expenditure, a way of consumption, describe the lifestyle of the people in the target year as well as the way of working and living explained above. In the system of ExSS, consumption expenditure by goods is calculated by multiplying the total value of expenditure and composition of the goods and services. In the relation to GHG emissions, changes in composition of consumption increase or decrease output of particular industries, and affect GHG emissions of industrial and commercial. Passenger transport demand is estimated from population and three parameters: number of trips per person, modal share and average trip distance. In the framework of this method, a set of socio-economic assumptions is regarded as a precondition for developing a low-carbon society. However, transport demand is a target of low-carbon countermeasures as well as one of the socioeconomic assumptions. In order to assess the effect of low-carbon countermeasures, we recommend developing a scenario without any countermeasures for a comparison. Modal share, especially share of vehicles, affects energy demand in this significantly. In a scenario without countermeasures, constant modal share or increasing share of vehicles should be set for a region where increasing use of vehicles is expected. Shifting modal share away from vehicles to mass-transport such as train and bus, or to bicycles and walking can be assumed in the scenario with countermeasures. Average trip distance is influenced by land-use structure in the urban structure of the region. In general, higher population density is thought to lead to shorter trip distances because of the close location of houses, workplaces and other facilities. Obviously, a shorter distance means less energy demand and GHG emissions. If one can assume a more compact urban structure as a countermeasure, in the scenario without countermeasures, constant or sparser urban structure and correspondingly constant or longer trip distances should be set. In the scenario with countermeasures, shorter distances can be assumed based on appropriate assumptions. Share of vehicles can be reduced simultaneously. In addition, the distance will be longer where more people commute to outside of the region. By setting parameters according to the scenarios assumptions, this tool sketches various conditions in the target region and its characteristics considering the influence of the openness of the local economy while ensuring consistency between variables.

7 K. Gomi et al. / Energy Policy 38 (2010) Energy demand and carbon dioxide emissions Calculation of energy demand follows Shimada et al. (2007) and Japan Low-Carbon Society scenario team (2008), shown in formula (1). ED eds;esc;e ¼ DF eds ESVG eds;esc ES eds;esc;e EE eds;esc;e where, ED eds,esc,e is the energy demand (by, by service and by fuel); DF eds,esc the driving force of energy demanding (by ); ESVG eds,esc the energy service demand generation unit (by and by service); ES eds,esc,e the fuel share (by, by service and by fuel); EE eds,esc,e the energy efficiency (by, by service and by fuel). Here, energy efficiency (EE) is defined as energy demand/ energy service supply. For example, energy efficiency of a vehicle is shown in gallon/mile or using other energy unit and length unit, toe/km. Therefore the more energy efficient technology shows less value of EE. This definition may seem strange, but to exclude division from the formulation has several merits on calculation and analysis. Each low-carbon countermeasure decreases or increases one of the variables and the parameters shown in the right hand side of the formula above. Driving force shows the activity level of the s, and it is regarded as a fundamental of energy demand. In this tool, driving forces are: number of household in the residential, floor area of commercial buildings in the commercial, output by industry in the industrial, and transport demand in the passenger and freight transport s (passenger-km and tonkm, respectively). Though these variables are calculated as endogenous assumptions, transport demand will often be a target of countermeasures. Shorter average trip distance lead by compact urban structure is an example of decreasing volume of driving force. In this formulation, less number of energy efficiency means higher energy efficiency. An appropriate unit is chosen for the each energy consuming technology, e.g. coefficient of performance (COP) for cooling, warming and hot water supply, and fuel efficiency for passenger vehicles such as kilometer per liter or mile per gallon. The energy efficiency of a particular, service and fuel, is given by averaging energy efficiency information on technologies and their share. In many cases, to reduce energy demand, improvement of energy efficiency in all s is a necessary and significant countermeasure. In the future estimation, energy efficiency is calculated from the information on technologies thought to be available in the target year, which is compiled in procedure (5) in Fig. 5. The term energy service means utility earned by consuming energy, for example, available heat for warming and hot water supply. By definition, energy service demand in the base year is estimated by deviding energy demand by energy efficiency. The energy service generation unit is estimated by dividing energy service demand by driving force. Among the countermeasures to reduce energy demand, living in smaller houses, energy-saving behavior in the home and offices, and operation improvement in industries are expressed in the smaller numbers of this parameter. On the other hand, energy service demand per driving force is increased by the diffusion of various kinds of electric appliances to household and offices, for example. Other than energy efficiency improvement, greater share of fuels with less carbon dioxide emission is also required. This kind of countermeasure is called reduction of carbon dioxide intensity. The share of coal or petroleum should be decreased, while increasing the share of natural gas or renewable energies (e.g. photo voltaic power generation, solar heater, wind power ð1þ generation and biomass). When one can assume improvements in carbon dioxide intensity in the power supply, which is regarded as exogenous in this method, increasing the share of electricity is also an effective countermeasure. To achieve low-carbon targets as a whole, one can assume various (in fact, infinite) combinations of the parameters: driving force, energy service generation unit, energy efficiency and fuel share. However a prominent candidate of the criteria is costminimization, this tool does not treat it because of two reasons. One is that costs of low-carbon countermeasures should be assessed across the whole period. As noted above, ExSS is a static model, but energy consuming technologies are stock, and therefore a dynamic model is required to assess the net cost of those technologies. Another is difficulty of estimating cost. It is almost impossible to estimate the cost of each countermeasure in the future, in say a few decades time, especially for a particular small area. Therefore, the criterion to decide the combination of countermeasures should depends on what kind of the countermeasures are preferable in the society assumed in the scenario. Japan Low-Carbon Society scenario team (2008) developed two countermeasure scenarios: one scenario assumes higher energy efficiency while in the other scenario fuel share is significant. We can assess the effect of each countermeasure by estimating energy demand for each, fuel and service in detail. Though we explained the estimation of GHG emissions only from consumption of fossil fuels, ExSS can estimate other GHGs, such as methane from agriculture, carbon dioxide from waste treatment, or carbon dioxide emission from and sink by land-use change by adding appropriate formulae and parameters. The following Sections show an application example of this method in Kyoto city. 5. Application in Kyoto city We described a low-carbon society quantitatively and identified countermeasures to achieve the emission reduction target by applying the method to Kyoto city. Recently, studies of participatory backcasting have been conducted (Quist and Vergragt, 2006; Larsen and Gunnarsson-Östling, 2008). This method can be applied both to participatory and non-participatory scenario development. However, we chose a non-participatory approach because of our intention of showing an example of the application of this method, though there was some input from Kyoto city officials. (1) Framework The base year and the target year are 2000 and 2030, respectively. In its proposal for the Environmental Model City mentioned in Section 1, Kyoto city set a target reducing its GHG emissions 50% compared to 1990 levels. In this study, the target gas is restricted to only carbon dioxide emissions from fossil fuels. Target activities were those conducted within the area of Kyoto city. Therefore the residents activity outside of the city is not included, while energy consumption of visitors to Kyoto city, who are estimated to number about 50 million persons per year, is included. Activity is divided into five s: household, commercial including wholesale, retail and service industry, industrial including primary and secondary industry, and the passenger transport and freight transport s. In the transportation, we covered only the transportation departing from Kyoto city, so we did not cover the through traffic. We either did not include overseas trip of residents and visitors because there is a difficulty to decide which region is responsible to there

8 4790 K. Gomi et al. / Energy Policy 38 (2010) emission. Especially for the visitors from overseas countries, the problem is more difficult because they likely visit several destinations in Japan, such as Kyoto, Osaka and Tokyo. However, since emissions from international aviation can be significant, this problem should be solved in the future work. We estimated two scenarios: one was the case not to introduce countermeasures, called 2030FX (fix), and the other was the case to introduce countermeasures to achieve the emissions target called 2030CM (countermeasures). In 2030FX, we configured all the parameters related to transport and energy demand identically to the base year. In 2030CM, we introduced countermeasures until the carbon dioxide emission reduction target, 50% reduction compared to 1990 was achieved. The socio-economic assumptions like population or industry were common in both cases. However, we conducted a sensibility analysis in case of fluctuations in two important parameters, DER and export, in the 2030CM scenario. (2) Socio-economic assumptions We referred to The Master Concept of Kyoto City (Kyoto city, 2000), a fundamental policy in Kyoto city. It describes the way of life of Kyoto city and the revitalization of the city in However, because the plan is too abstract to develop a snapshot, we conducted interviews with several officials of Kyoto city environmental department and made a more concrete description of several aspects of the society. (i) Sense of value In the sense of value aspect, spiritual richness is increasingly more important than material richness as society matures. As a result, the position of Kyoto is going to be raised to that of a hometown of Japanese spiritual culture. People balance their work and life, and contribution to the community is one of the important goals of individuals. (ii) Lifestyle In Japan, the population is less than now and the average family size is slightly decreasing because of aging. Up to date technologies are diffused quickly in the business world, while relatively slowly in households because of the higher share of elder people, who are relatively conservative. However, the labor participation ratio of elder people and women is higher than the current level, and since people attach greater importance to balancing work and life, working hours per person are shorter than now. Volunteer work and community activities are thought to be as valuable as jobs. Housework is shared among family members. Most of the residents spend their leisure within the Kyoto city area, while the way leisure is pursued varies. Lifelong study according to the interests of individuals is popular among the residents, and makes their life exciting. (iii) Land-use and transport Because of the height restriction of the buildings regulated by landscape policy, population density is almost constant and therefore urban structure, too. Composition of land-use, forest, farmland, and other green fields are kept almost constant. The total floor area of the buildings in the city does not increase because of population shrinking and the building height regulations. (iv) Economy and industry Economic growth rate is relatively low (average annual rate is around 1.3%) as a result of the relatively short working time of the residents. Sightseeing related industries continue to be the main industries of Kyoto city. Locally produced foods are popular among the residents. Pioneering intelligence-based industries accumulate within the city area. Many venture businesses are born and grow as well as traditional manufacturers with high added value. Driven by older people who have abundant free time, service businesses related to leisure, recreation, amusement and culture grow. (v) Sightseeing The annual number of visitors to Kyoto city remains around 50 million as a result of landscape policy, while the Japanese population declines. While the number of visitors remains constant, sightseeing consumption per visitor increases because of a higher preference for authentic traditional art and culture. (3) Quantification of socio-economic assumption We quantified the parameters showed below with reference to the description in (2). (i) Population composition According to the assumption of progress in aging, we used the population composition estimated by the National Institute of Population and Social Security Research (2008), which shows the composition of people older than age 64 increases. (ii) Population allocation Since high populations densities in the city center are restricted by landscape preservation policy, the population composition of city center area (Kamigyo ward, Nakagyo ward, Shimogyo ward, Higashiyama ward) decreases 2 points compared to the base year (from 20.2% to 18.2%), while the population composition of the suburban, western area (Nishikyo ward) increases 2 points (from 24.3% to 26.3%). (iii) Average number of family occupants The average household size tends to decrease, with an average number of family occupants of 2.15 persons per household (2.36 persons in 2000). (iv) Time-use Changes related to time-use from the description are: working time is comparatively short, housework is shared by the family, lifelong study is widespread, volunteer activity is essential. The resultant setting of time-use assumptions in a day for male workers is: housework time increases 0.5 h working hours decrease 1 h, the total for study and research, hobby, recreation and volunteer activities increases 0.5 h Those of female workers are: domestic concern time decreases 1 h, and the total of study and research, hobby, recreation and volunteer activities increases 1 h. (v) Labor participation rate According to the description of balance of work and life and the increase in the labor participation of older people and women, the setting of labor participation is: men in their 60 s: 70% (57% in 2000), men over 70: 40% (18% in 2000), women from their 30 s to 50 s: 70% (52 58% in 2000), women in their 60 s: 50% (31% in 2000), women over 70: 15% (7% in 2000). (vi) Composition of private consumption expenditure According to the description that service businesses related to leisure, recreation, amusement and culture grow, the ratio of expenditure to tertiary industry increases 4.5 points from the base year (from 86.4% to 90.9%). (vii) Exports The total growth rate of exports is assumed to be 1.3%/year as shown in the description. Exports of industries mentioned in the description are assumed to achieve a relatively higher than average growth rate (1.43%/year), and the other industries set a lower growth rate (1.17%/year). Industries assumed to achieve higher growth rates are: textile fabrics (mainly silk fabrics), dyeing, commerce, entertainment

9 K. Gomi et al. / Energy Policy 38 (2010) Table 2 Results of socio-economic indicators /2000 FX CM FX CM Population (10 thousand) Households (10 thousand) GDP (billion yen) GDP per capita (million yen) Production (billion yen) Primary industry Secondary industry Tertiary industry Passenger transport demand (million people * km) Freight transport demand (million tons * km) services, restaurants, hotels and research. For other external final demand, government consumption expenditure is assumed to increase at the same rate of exports (1.35%/year) and public investment is assumed to be the same as the base year. (viii) Input coefficient The scenario description does not mention particular changes in the input structure of the industries. Therefore, in general, the input coefficient matrix of the industries is assumed constant with the exceptions of input from the energy and transport industries, which are estimated endogenously according to changes in energy consumption and transport demand. (ix) Labor productivity The labor productivity of primary and secondary industries is assumed to improve 2.7%/year, while tertiary industries are assumed to improve 1.8%/year. (x) Relation of commuting with surrounding regions Since the description does not show changes in the place of employment, the parameters to describe commuting relations, DER (domestic employment ratio) and DWR (domestic working ratio) are assumed to be the same with the base year. In addition, the other parameters were the same as the ones in base year (2000). (4) Compilation of low-carbon countermeasures We improved the countermeasure database of Shimada et al. (2007) to add new countermeasure data for a lowcarbon society. Countermeasures were classified into four categories; behavior change, energy efficiency improvement, fuel shifting (energy demand side) and improvement of carbon dioxide intensity in power supply. In addition, Kyoto city government recently launched transport demand management (TDM) policy which aims promotion of walking and plans light rail transit (LRT) construction (Kyoto city, HP). Thus, we regarded modal shift as an important countermeasure. Table 4 shows the countermeasures and the introduction amount of them. 6. Results 6.1. Socio-economic indices Table 2 shows the main results of the socio-economic indices. The differences between the results of 2030FX and 2030CM were caused by the spillover effects of the changes in energy and transportation demand. The population was estimated to be about 1.37 million and the result was slightly larger than the one estimated by the National Institute of Population and Social Table 3 Primary energy supply (ktoe). Fig. 7. Carbon dioxide emissions in Kyoto city. Coal Oil Gas Hydraulic power Nuclear power Primary energy supply FX CM Composition % 39.8% 33.8% 2.1% 7.7% 0.1% 100- % 2030FX 11.0% 41.9% 36.9% 2.1% 7.9% 0.1% 100- Security Research (2008), 1.34 million. Next, the numbers% of households 2030CM 7.6% increased 20.3% 2% 48.5% compared 2.1% to % GDP12.6% in Kyoto 100- city increased 43% compared to 2000 and the production of tertiary % industry increases the best in all the industrial s. Passenger transportation decreased 14% compared to 2000 because of decrease and aging of population. Freight transportation increased 41% compared to 2000 because of increase of secondary industries production Energy demand and carbon dioxide emissions Renewable energy Total Fig. 7 shows carbon dioxide emissions for each case. Carbon dioxide emission in FX scenario was estimated at 8783 kt-co 2.It

10 Table 4 Countermeasures Sector Low-carbon countermeasure Data Source Category *1 Identified implementation intensity Emissions reduction (kt-co 2 ) *2 (%) Household High energy efficiency air conditioner COP 6.60 *5 E Diffusion ratio (cooling and heating) High energy efficiency kerosene heating COP 0.88 *4 E Diffusion ratio (heating: kerosene) High energy efficiency gas heating COP 0.88 *4 E Diffusion ratio (heating: gas) High energy efficiency gas cooker Thermal efficiency (base 1.22 *4 E Diffusion ratio (cooking: gas) High energy efficiency IH cooker Thermal efficiency (base 1.15 *4 E Diffusion ratio (cooking: electricity) Efficiency improvement of other electric Electricity consumption (base 0.49 *4 E Electricity consumption (base appliances Fluorescent light bulb (substitute Electricity consumption 4.35 *4 E Diffusion ratio (incandescent light) 20 incandescent light) (conventional type ¼ 1) LED (substitute incandescent light) Electricity consumption 8.70 *4 E Diffusion ratio (incandescent light) 80 (conventional type ¼ 1) LED (substitute fluorescent light) Electricity consumption 2.67 *4 E Diffusion ratio (fluorescent light) 100 (conventional type ¼ 1) House insulation Thermal loss (base 0.42 *3 E Diffusion ratio Energy saving behavior *3 B 128 cooling Energy service demand reduction 10% Diffusion ratio 100 ratio heating Energy service demand reduction 10% Diffusion ratio 100 ratio hot water Energy service demand reduction 10% Diffusion ratio 100 ratio cooking Energy service demand reduction 10% Diffusion ratio 100 ratio Other home electric appliances Energy service demand reduction 10% Diffusion ratio 100 ratio Convened heat and power Generation efficiency 30% *6 S Diffusion ratio Photovoltaic generation Potential (ktoe) 295 *7 S Diffusion ratio Solar water heating Potential (ktoe) 1037 *7 S Diffusion ratio Other energy efficiency improvement E 90 Other fuel shifting S 111 Total 1110 Commercial High energy efficiency air conditioner COP 5.00 *5 E Diffusion ratio (cooling: electricity) (cooling only) High energy efficiency absorption tiller (gas) COP 1.35 *16 E Diffusion ratio (cooling: gas) High energy efficiency gas heat pump COP 1.60 *15 E Diffusion ratio (cooling: gas) High energy efficiency absorption tiller (oil) COP 1.35 *17 E Diffusion ratio (cooling: oil) High energy efficiency boiler (oil) COP 0.88 *4 E Diffusion ratio (heating: oil) High energy efficiency air conditioner COP 7.4 E Diffusion ratio (heating: electricity) (heating only) High energy efficiency oil water heater COP 0.87 *4 E Diffusion ratio (hot water: oil) High energy efficiency gas water heater COP 0.87 *4 E Diffusion ratio (hot water: gas) CO 2 cooling medium water heater COP 3.00 *4 E Diffusion ratio (hot water: all) High energy efficiency gas cooker Thermal efficiency (base 1.15 *4 E Diffusion ratio (cooking: gas) IH cooking heater Thermal efficiency (base 1.15 *4 E Diffusion ratio (cooking whole) 30 8 Efficiency improvement of other electric Electricity consumption (base 0.38 *4 E Electricity consumption (base appliances LED (substitute incandescent light) Electricity consumption (base 4.55 *4 Diffusion ratio (incandescent light) 100 LED (substitute fluorescent light) Electricity consumption (base 3.95 *4 Diffusion ratio (fluorescent light) 100 Building insulation Thermal loss (base 0.50 *4 E Diffusion ratio BEMS Energy demand reduction ratio 10% *8 E Diffusion ratio K. Gomi et al. / Energy Policy 38 (2010) ARTICLE IN PRESS

11 Energy saving behavior *3 B 31 cooling Energy service demand reduction 10% Diffusion ratio 100 ratio heating Energy service demand reduction 10% Diffusion ratio 100 ratio Convened heat and power Generation efficiency 30% *6 S Diffusion ratio Photovoltaic generation Potential (ktoe) 295 *7 S Diffusion ratio Solar water heating Potential (ktoe) 1037 *7 S Diffusion ratio 5 12 Other energy efficiency improvement E 38 Other fuel shifting S 154 Total 1221 Industrial Energy efficient equipments E 292 High energy efficiency boiler Thermal efficiency (base 1.09 *3 Diffusion ratio 95 High energy efficiency furnace Thermal efficiency (base 1.67 *9 Diffusion ratio 95 High energy efficiency mortar Electricity consumption (base 0.95 *3 Diffusion ratio 95 Inverter control Electricity consumption (base 0.85 *3 Diffusion ratio 95 Fuel shifting From oil to gas S Shifting ratio Total 336 Passenger transport Freight transport Power supply Hybrid vehicle Fuel cost (conventional type ¼ 1) 0.6 *4 E Diffusion ratio Modal shift From vehicle to; B 102 Intra-area trip walking and bicycle Shifting ratio 10 train and bus Shifting ratio 30 Inter-area trip bicycle Shifting ratio 5 train and bus Shifting ratio 30 Trip to outside of the city train Shifting ratio 30 Bio-fuel From oil to bio-fuel S Diffusion ratio Eco-drive Fuel efficiency improvement ratio 24% *13 B Diffusion ratio Energy efficiency improvement of other E 75 mode Total 857 Hybrid vehicle Fuel cost (conventional type ¼ 1) 0.6 *4 E Diffusion ratio Modal shift From large freight vehicle to; B 31 cargo ship Shifting ratio 5 ferry Shifting ratio 5 train Shifting ratio 20 Bio-fuel From oil to bio-fuel S Diffusion ratio Energy efficiency improvement of other E 10 mode Total 651 Improvement of CO 2 intensity of power generation Fuel shifting *9 Generation efficiency improvement Coal Generation efficiency 48% *11 Gas Generation efficiency 55% *12 CO 2 emission per generation (tc/ toe) Increase by (28) changes of driving force Total 5067 Note:*1 The categories corresponds those of Table 5. E: energy efficiency improvement, B: energy saving behavior, S: fuel shifting. *2 Reduction amounts are changes from BaU sceanrio. Sources of countermeasures information are; *3: Shimada et al (2007), *4: Mizuho Information Research Institute and inc. (2005), *5: The Energy Conservation Centre Japan (2007), *6: Nikkei Inc. (2005), *7: Kyoto city environmental bureau (2000), *8: Ministry of Trade, Economy and Industry (2005), *9: New Energy and Industrial Technology Developing Organization (2005), *10: Resource and Energy Investigate Committee (2005), *11: Clean Coal Power Institute, *12:Nikkei newspaper (2008), *13:Recoo, *14:Osaka prefecture, *15:Osaka prefecture, *16:OSAKAGAS, *17:Hitachi. K. Gomi et al. / Energy Policy 38 (2010) ARTICLE IN PRESS