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Energy Policy 38 (2010) 4783–4796
Contents lists available at ScienceDirect
Energy Policy
0301-42
doi:10.1
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E-m
albart29
journal homepage: www.elsevier.com/locate/enpol
A low-carbon scenario creation method for a local-scale economy and itsapplication 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 606-8530, Japanb Faculty of Economics, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japanc Graduate School of Engineering, Kyoto University, Kyoto-Daigaku Katsura, Nishikyo-ku, Kyoto 606-8530, Japan
a r t i c l e i n f o
Article history:
Received 31 January 2009
Accepted 22 July 2009Available online 25 August 2009
Keywords:
Low-carbon society
Local environmental policy
Backcasting
15/$ - see front matter & 2009 Elsevier Ltd. A
016/j.enpol.2009.07.026
esponding author. Tel.: +8175 383 3370; fax:
ail addresses: [email protected]
@hotmail.com (K. Gomi).
a b s t r a c t
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) low-
carbon 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 2000. Among the measures, the reduction potential of energy
efficiency improvements to residential and commercial sectors was found to be relatively large (15% and
18% of total reductions, respectively). The reduction potential of the passenger transport sector, 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 CO2 emissions, and a 20% increase in
the share of the commuters from outside the city leads to a 3.5% decrease of CO2 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 shouldreduce greenhouse gas (GHG) emissions dramatically by themiddle of this century. In this so-called low-carbon society,technology, institutions and people’s behavior will have to differfrom the current state. Thus, long-term targets and plans toachieve them, which are called ‘‘low-carbon society scenarios’’ or‘‘LCS scenarios’’ in this study, are required. Detailed LCS scenarioshave been proposed in several countries, targeting up to the year2050 and reductions of around 50% compared to current emissionslevel (e.g. Kok et al., 2003; Anderson et al., 2008; Fujino et al.,2008; Mander et al., 2008). On the other hand, local-scale actionswill be important in order to implement concrete measuresbecause usually regions in a country vary in many aspects, andtherefore also the actions to take. This study proposes a method todevelop 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 theirown low-carbon goals and plans targeting after 2020 (Fig. 1).
ll rights reserved.
+8175 383 3371.
.ac.jp,
Many of them aim at emissions reductions of 50% or morecompared to current emissions. Even in the United States, whosefederal government has not set up a long-term target, many statesand cities have their own targets. Nicholas and Sperling (2008)showed that the summation of them covers more than half of theGHG emissions in the United States. In Japan, the Prime Minister’sOffice accepted candidates for ‘‘Environmental Model Cities’’ inApril 2008. A total of 82 municipalities applied, with long-termlow-carbon targets as a necessary condition. Fig. 2 shows thetargets in their proposals.
Some of the municipalities around the world have madequantitative assessment of emissions and measures for long-termtargets after 2020 (Greater London Authority, 2007; CaliforniaEnvironmental 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 climatechange mitigation and adaptation on a regional scale and appliedit to the west of England. They employed the idea of backcastingand 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 abovehave some problems. One is assumptions of activity level,including socio-economic indicators such as population, industrialoutput, and transport demand. These are so crude that they mightexclude possible changes in the region. The indicators are often
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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; Oko 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 Gen�eve, 2006; Environmental Bureau of
Hiroshima city, 2008; Chiyoda ward, 2007; Kashiwa city, 2007; Tokyo Metropo-
litan Government, 2007.
K. Gomi et al. / Energy Policy 38 (2010) 4783–47964784
assumed to be constant or extrapolate current trends separately,therefore they can be internally inconsistent. In order to solve thisproblem of inconsistency, Shimada et al. (2007) developed amethod including a set of tools to estimate socio-economicindicators and carbon dioxide emissions consistently, and appliedit to Shiga prefecture, Japan. They showed three scenarios toreduce carbon dioxide emissions in the prefecture by 30%, 40% and50%, respectively.
Though their method solved the problem of internal consis-tency, a remaining problem is the openness of the local-scaleeconomy. Uncertainty about the future is greater than on a nation-scale because of the more open socio-economic structure oflocals-scale economies. Against this background, this studyimproves the model of Shimada et al and proposes a method todevelop local LCS scenarios considering the openness of localeconomy using backcasting approach. In the definition ofRobinson (1990), backcasting method is ‘‘working backwardsfrom a particular desired end-point to the present in order todetermine the physical feasibility of that future and what policymeasures would be required to reach that point’’. This approachcan be divided into two phases; the first step is to describe adesired goal, and the second step is to seek the way toward therefrom current situation such as investment path or policy schedule.The method and estimation tool we proposed here mainly focuson the first step, to describe a picture of a low-carbon society as agoal of a municipality.
The structure of this paper is as follows. Section 2 discusses therelation of the openness of the local economy and LCS scenariosand proposes how to treat this in LCS scenarios. Sections 3 and 4
show the flow of LCS scenario development and explains thecalculation system of the estimation tool (Extended Snapshot tool,ExSS), which was developed in this study. Section 5 applies themethod to Kyoto city, Japan. Section 6 shows the estimated resultsincluding a sensitivity analysis, and Section 7 offers someconclusions.
2. Openness of local economy and LCS scenarios
2.1. Small-scale economy and LCS scenarios
The amount of GHG emissions in a region depends on itspopulation, industrial structure, and urban structure and so on.With different population paths or economic developmentdirections, the amount and composition of emissions will bedifferent. Thus, the importance of countermeasures changesaccording to the ‘‘upstream’’ socio-economic assumptions aboutthe future society. Furthermore, on a local scale, socio-economicindicators have a wider scope. Between regions within a country,goods, services and people move more easily than betweennations. The nature of small economies strongly relates to thedevelopment of a region. For example, in LCS scenarios, popula-tion demographically estimated is usually given exogenously. Thisassumption is appropriate when population movement is rela-tively small. However, the smaller the target region is – prefecture,metropolitan area, city or town – the more easily people migratein response to socio-economic situations. Thus, even if thepopulation of a country is increasing, the population of a cityoften decreases. Fig. 3 shows a typical example. The population ofYubari city, once prosperous as a coal-mining town, peaked at116,908 in the year 1960. However, along with the closure of themines, the city lost 90% of the peak population by 2005, while theJapanese population increased during the same period. Other thanthe impact of industrial location on the size of population shownin this extreme example, the relationship of commuting is alsoinfluential. Even when a city does not have a large industry, orenough opportunities for employment in its own area, itspopulation can increase if many of the residents commute toneighboring cities. For example, according to the national censusof 2005, 49% of the population in Shiga prefecture’s Otsu city isemployed somewhere, and 40% of these people commute toneighboring cities. The opposite example can be seen in Chiyodaward, Tokyo, whose daytime population is more than 800,000while its nighttime population is only around 40,000 (StatisticsBureau and the Director-General for Policy Planning, HP).
To develop LCS scenarios, one often has to set a long-timescope such as 30 years or more in order to complete the drasticchanges in many sides of society. In such long periods, theopenness of the local economy makes the socio-economic activitylevel highly uncertain. Therefore, for a city or town, major changesof industrial structure, economic activity level and population canbe expected. Even though in the framework of LCS scenarios thosesocio-economic aspects are treated as given assumptions, as longas they affect GHG emissions significantly, the scenarios shouldshow them explicitly and consistently. Therefore we think themethodology of LCS scenarios should consider the region’scharacteristic cross-border relationships.
2.2. The model
In order to treat the openness of local economy in LCSscenarios explicitly, we propose a model based on the ‘‘export-base’’ approach of standard regional economics. Structure of themodel is shown in Fig. 4. This model is intended for use in the first
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Fig. 3. Population transition in the Yubari city, Japan.
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.
K. Gomi et al. / Energy Policy 38 (2010) 4783–4796 4785
stage of backcasting, to picture a desirable goal, and therefore astatic model. The export-base approach considers that demandfrom outside of the region (exports), at least partly, lead to theeconomic growth of the region (Armstrong and Taylor, 2000).Industries producing export goods mainly are called ‘‘basicindustries’’. Production of basic industries induces production inthe other industries through demanding intermediate input andconsumption of laborers. The Garin–Lowry model and itssuccessors 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 governmentexpenditure, drive overall economic activity. First, given exports,other final demand and the inverse matrix of Leontief, output byindustry is calculated using standard IO analysis. Output byindustry, labor productivity and working hours per worker decidesthe number of laborers required to fulfill the output.
Laborers are classified into three categories; those who bothlive and work in the region, commuting FROM outside the region
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Fig. 4. Structure of the model: industry and population.
Fig. 5. Procedure of the methodology.
K. Gomi et al. / Energy Policy 38 (2010) 4783–47964786
and commuting TO outside of the region. To decide the number oflaborers of each category above, we defined two parameters. Oneis called the ‘‘domestic working ratio’’, or DWR, which is definedas the percentage of the laborers working in the region fromamong all laborers living in the region. Lower DWR means highernumbers of commuters to outside of the region. The other is the‘‘domestic employment ratio’’, or DER, which is defined asthe percentage of laborers living outside of the region among allthe laborers working in the region. Lower DER means highnumbers of commuters from outside of the region. If both DERand DWR in a region are high, it tends to be self-sufficient orreclusive in terms of employment.
Population (number of residents) is decided by the number oflaborers working in the region, assumption of a commutingrelationship with surrounding regions (DER and DWR) and laborparticipation ratio of the residents. The residents consume part oftheir income which is handed to IO analysis as private consump-tion expenditure, and the model is closed.
In general, the activity of industries and employment oppor-tunities are considered to induce migration through the labormarket and housing market. However, our model shown abovedoes not treat the phenomenon of migration itself because whilemigration is dynamic, the ‘‘desirable goal’’, which we intend topicture here, is a static idea, as its name suggests. Though, thecontents of the ‘‘goal’’, hereafter called a ‘‘snapshot’’, must bebalanced internally. So the model describes how much thepopulation ‘‘must be’’ under an assumption of economy, insteadof modeling migration directly. When assuming a scenario, ifeconomic development of the target region is lead by a particularindustry, e.g. the car manufacturing industry, setting the exportvolume of the industry will be a critical parameter. If the targetregion is a commuter town, an important variable is the numberof laborers commuting TO outside the region whose keyparameter is DWR. The next section introduces the flow of themethod to develop local LCS scenarios.
3. Methodology of Local LCS scenarios
The flow of the methodology we propose here is shown in Fig. 5.
(1)
Setting the frameworkThe framework of the LCS scenarios consists of a target region,base year, target year(s), target activities, low-carbon target(s)and number of scenarios. In order to achieve the necessarychanges the target year should ideally be in the distant future,whilst in order to make it easy for people to imagine thefuture a nearer target year is preferable. In the scenariostudies of LCS, multiple scenarios are often created andcompared with each other (e.g. IPCC, 2000). However, all ofthe targets and plans shown in Fig. 1 are based on the singleassumption of the socio-economic situation in the target year.The local governments might have intended one vision as anauthorized official goal. Though, considering the uncertaintywe discussed above, we think at least a sensitivity analysis forimportant assumptions should be conducted.
(2)
Depictive scenariosQualitative future image, a depictive scenario, is describedbefore conducting a quantitative estimation. A depictivescenario can include assumptions on speed of economicgrowth, industrial structure, lifestyle of the residents, trans-port, urban structure, land-use and so on. For the industrialstructure, what kinds of industries will have grown/shrunk inthe target year is assumed. Lifestyle assumptions consist ofconsumption patterns, time-use patterns, trends in housingincluding number of family occupants, and balance betweenwork and life. For a LCS scenario, socio-economic assumptionsare prior conditions, not the goals desired to be achieved.Therefore in this study, we do not consider how to realizea socio-economic assumption such as the growth rate ofan industry.(3)
Quantification of socio-economic assumptionsBased on the description of (2), detailed values of the indicesshown in Table 1 are set. Those indices are input to ExSS asexogenous parameters. The relation of socio-economicassumptions and parameter setting is described in detail inSection 4 and 5.(4)
Compilation of low-carbon countermeasuresCountermeasures which are expected to be available in thetarget year in the region are compiled. Examples of candidatesof the countermeasures are equipments with high energyefficiency, insulation of buildings, renewable energy, energy-saving behavior, modal shift, reducing waste and carbon sinks
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Table 1Main parameters in Extended Snapshot Tool.
Parameter Explanation
Population composition Population composition by sex and by age-group
Population allocation Population composition by area
Average number of family occupants Average number of family member per family
Time use Time use of activity per day by individual attribute
Labor participation ratio Labor participation ratio by sex and by age-group
Final consumption composition Composition of final goods and service in private consumption
Average propensity to consume Ratio of consumption in income
Export Demand from outside of the region by goods and service
Government expenditure Government consumption expenditure, public investment by goods and service
Import ratio The share of supply from outside of the region of all demand in the region
Input coefficient Input coefficient of input–output analysis
Labor productivity Labor hours requirement per production
Domestic employment ratio Percentage of the laborer living in the region in the all laborer working in the region
Domestic working ratio Percentage of the laborer living in the region in the all laborer living in the region
Trip number per person Number of trips per person per day by purpose of the trip
Modal share (passenger transport) Share of transport mode by individual attribute and by purpose
Average trip distance Distance of origin and destination per trip by mode
Freight generation unit Weight of transported freight per industrial output by freight
Destination share Share of destination region by freight
Modal share (freight transport) Share of transport mode by freight by destination region
K. Gomi et al. / Energy Policy 38 (2010) 4783–4796 4787
and so on. In this method, countermeasures are limited toonly those which can decrease energy consumption or GHGemissions directly. Policies to enhance the diffusion of thosecountermeasures, such as economic incentives, regulationsand education, are, of course, important to realize LCS, but arenot included in the countermeasures here because they do notaffect GHG emissions directly.
(5)
Setting countermeasures in the target yearAmounts for the countermeasures to be introduced, compiledin (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 countermeasuresare cost-minimization, acceptance of stakeholders, technolo-gical feasibility and so on.(6)
Future estimation and identifying countermeasures setThe parameters set in (3) and (5) are input, and socio-economic indicators, energy demand and GHG emissions areestimated using ExSS. Socio-economic indicators calculatedhere are output by industry, population and number ofhouseholds, regional income, passenger and transport de-mand and so on. If the GHG emissions achieve the low-carbontarget, proceed to (7). Otherwise return to (5), settingcountermeasures, and iterate this process until the low-carbon target is achieved, and then define a portfolio of thecountermeasures.(7)
Proposal of policy setPolicies to enhance the diffusion of the countermeasuresdefined in (6) are set. Since ExSS calculates the reductionamount by countermeasures in detail, it can show measureswith high reduction potential, or the reduction amount of themeasures, in which local government plays a particularlycrucial role.4. Structure of Extended Snapshot tool (ExSS)
4.1. Socio-economic indicators
Fig. 6 shows the structure of ExSS, which consists of sevenblocks and main parameters and variables. ExSS is a bottom-upengineering type model, and formulated as system ofsimultaneous equations, and the solutions are defined uniquely
with given parameters. The model proposed in Section 2 is shownin the upper part of the Fig. 6, and estimates population andoutput by industry. To estimate commercial building stock,passenger and freight transport demand, energy demand andcarbon dioxide emissions, the same method followed by Shimadaet al. (2007) is employed. Since ExSS is a static model, it cannottreat dynamic phenomena like investment or migration in nature.Thus, ExSS cannot assess net cost of low-carbon countermeasuresbecause most of them are related to stock, such as buildingsor vehicles.
The relation of the settings of the main parameters and socio-economic assumptions is explained below. Table 1 shows a shortexplanation including other parameters.
As proposed in Section 2, the size of the economy in the regionis mainly decided by export and government expenditure,including both government consumption and investment. ‘‘Ex-ports’’ here includes both other regions in the country and the restof the world. Both of them are given to the regional economyexternally, and then drive the overall economic activity throughintermediate input and consumption of employee. Composition ofexports defines the industrial structure of the region and affectsenergy consumption significantly. A region with a high exportvolume of energy intensive industry, like steel or petrochemicals,will have large share of the industrial sector in its GHG emissions.For a sightseeing place, exports of the catering industry or hotelswould be large, leading to greater GHG emissions in thecommercial sector.
Labor participation ratio is one of the main parameters todescribe changes in lifestyle. In this model, labor participation ratioaffects the population of a city because a higher ratio means lesspopulation to fulfill labor demand. Working hours also describechanges in the style of working as well as the labor participationratio. For instance, ‘‘work sharing’’ can be expressed by setting highlabor participation and few working hours per laborer.
Domestic working ratio (DWR) and domestic employmentratio (DER) describe the relation of commuting with thesurrounding region as explained in Section 2. In case of Chiyodaward, which we mentioned earlier in this paper, DER is quite lowwhile DWR must be low in the regions where people arecommuting to Chiyoda ward. An assumption of low DWR meansgreater population compared to employment opportunities in theregion. Obviously, the GHG emissions of the residential sectormust be high in such a region.
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Fig. 6. Structure of Extended Snapshot Tool.
K. Gomi et al. / Energy Policy 38 (2010) 4783–47964788
Average number of family occupants, household size in otherwords, expresses the modes of habitation among changes inlifestyle. Architectural Institute of Japan (2006) showed energyconsumption per person is greater in smaller households. Thus,even if population is constant, GHG emissions of the residentialsector will increase when the household sizes of the regiondecreases.
Contents of the expenditure, a way of consumption, describethe lifestyle of the people in the target year as well as the way ofworking and living explained above. In the system of ExSS,consumption expenditure by goods is calculated by multiplyingthe total value of expenditure and composition of the goods andservices. In the relation to GHG emissions, changes in compositionof consumption increase or decrease output of particularindustries, and affect GHG emissions of industrial sector andcommercial sector.
Passenger transport demand is estimated from population andthree parameters: number of trips per person, modal share andaverage trip distance. In the framework of this method, a set ofsocio-economic assumptions is regarded as a precondition fordeveloping a low-carbon society. However, transport demand is atarget of low-carbon countermeasures as well as one of the socio-economic assumptions. In order to assess the effect of low-carboncountermeasures, we recommend developing a scenario without
any countermeasures for a comparison. Modal share, especiallyshare of vehicles, affects energy demand in this sector significantly.In a scenario without countermeasures, constant modal share orincreasing share of vehicles should be set for a region whereincreasing use of vehicles is expected. Shifting modal share awayfrom vehicles to mass-transport such as train and bus, or to bicyclesand walking can be assumed in the scenario with countermeasures.
Average trip distance is influenced by land-use structure in theurban structure of the region. In general, higher populationdensity is thought to lead to shorter trip distances because of theclose location of houses, workplaces and other facilities. Ob-viously, a shorter distance means less energy demand and GHGemissions. If one can assume a more compact urban structure as acountermeasure, in the scenario without countermeasures, con-stant or sparser urban structure and correspondingly constant orlonger trip distances should be set. In the scenario with counter-measures, shorter distances can be assumed based on appropriateassumptions. Share of vehicles can be reduced simultaneously. Inaddition, the distance will be longer where more people commuteto outside of the region.
By setting parameters according to the scenarios’ assumptions,this tool sketches various conditions in the target region and itscharacteristics considering the influence of the openness of thelocal economy while ensuring consistency between variables.
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4.2. Energy demand and carbon dioxide emissions
Calculation of energy demand follows Shimada et al. (2007)and ‘‘Japan Low-Carbon Society’’ scenario team (2008), shown informula (1).
EDeds;esc;e ¼ DFeds � ESVGeds;esc � ESeds;esc;e � EEeds;esc;e ð1Þ
where, EDeds,esc,e is the energy demand (by sector, by service andby fuel); DFeds,esc the driving force of energy demanding sector(by sector); ESVGeds,esc the energy service demand generation unit(by sector and by service); ESeds,esc,e the fuel share (by sector, byservice and by fuel); EEeds,esc,e the energy efficiency (by sector, byservice and by fuel).
Here, energy efficiency (EE) is defined as ‘‘energy demand/energy service supply’’. For example, energy efficiency of a vehicleis shown in ‘‘gallon/mile’’ or using other energy unit and lengthunit, ‘‘toe/km’’. Therefore the more energy efficient technologyshows less value of EE. This definition may seem strange, but toexclude division from the formulation has several merits oncalculation and analysis.
Each low-carbon countermeasure decreases or increases one ofthe variables and the parameters shown in the right hand side ofthe formula above.
Driving force shows the activity level of the sectors, and it isregarded as a fundamental of energy demand. In this tool, drivingforces are: number of household in the residential sector, floorarea of commercial buildings in the commercial sector, output byindustry in the industrial sector, and transport demand in thepassenger and freight transport sectors (passenger-km and ton-km, respectively). Though these variables are calculated asendogenous assumptions, transport demand will often be atarget of countermeasures. Shorter average trip distance lead bycompact urban structure is an example of decreasing volume ofdriving force.
In this formulation, less number of energy efficiency meanshigher energy efficiency. An appropriate unit is chosen for theeach energy consuming technology, e.g. coefficient of perfor-mance (COP) for cooling, warming and hot water supply, and fuelefficiency for passenger vehicles such as kilometer per liter ormile per gallon. The energy efficiency of a particular sector, serviceand fuel, is given by averaging energy efficiency information ontechnologies and their share. In many cases, to reduce energydemand, improvement of energy efficiency in all sectors is anecessary and significant countermeasure. In the future estima-tion, energy efficiency is calculated from the information ontechnologies thought to be available in the target year, which iscompiled in procedure (5) in Fig. 5.
The term ‘‘energy service’’ means utility earned by consumingenergy, for example, available heat for warming and hot watersupply. By definition, energy service demand in the base year isestimated by deviding energy demand by energy efficiency. Theenergy service generation unit is estimated by dividing energyservice demand by driving force. Among the countermeasures toreduce energy demand, living in smaller houses, energy-savingbehavior in the home and offices, and operation improvement inindustries are expressed in the smaller numbers of this parameter.On the other hand, energy service demand per driving force isincreased by the diffusion of various kinds of electric appliances tohousehold and offices, for example.
Other than energy efficiency improvement, greater share offuels with less carbon dioxide emission is also required. This kindof countermeasure is called reduction of carbon dioxide intensity.The share of coal or petroleum should be decreased, whileincreasing the share of natural gas or renewable energies (e.g.photo voltaic power generation, solar heater, wind power
generation and biomass). When one can assume improvementsin carbon dioxide intensity in the power supply sector, which isregarded as exogenous in this method, increasing the share ofelectricity is also an effective countermeasure.
To achieve low-carbon targets as a whole, one can assumevarious (in fact, infinite) combinations of the parameters: drivingforce, energy service generation unit, energy efficiency and fuelshare. However a prominent candidate of the criteria is cost-minimization, this tool does not treat it because of two reasons.One is that costs of low-carbon countermeasures should beassessed across the whole period. As noted above, ExSS is a staticmodel, but energy consuming technologies are stock, and there-fore a dynamic model is required to assess the net cost of thosetechnologies. Another is difficulty of estimating cost. It is almostimpossible to estimate the cost of each countermeasure in thefuture, in say a few decades time, especially for a particular smallarea. Therefore, the criterion to decide the combination ofcountermeasures should depends on what kind of the counter-measures are preferable in the society assumed in the scenario.
Japan Low-Carbon Society scenario team (2008) developed twocountermeasure scenarios: one scenario assumes higher energyefficiency while in the other scenario fuel share is significant. Wecan assess the effect of each countermeasure by estimating energydemand for each sector, fuel and service in detail.
Though we explained the estimation of GHG emissions onlyfrom consumption of fossil fuels, ExSS can estimate other GHGs,such as methane from agriculture, carbon dioxide from wastetreatment, or carbon dioxide emission from and sink by land-usechange by adding appropriate formulae and parameters.
The following Sections show an application example of thismethod in Kyoto city.
5. Application in Kyoto city
We described a low-carbon society quantitatively and identi-fied countermeasures to achieve the emission reduction target byapplying the method to Kyoto city. Recently, studies of participa-tory backcasting have been conducted (Quist and Vergragt, 2006;Larsen and Gunnarsson-Ostling, 2008). This method can beapplied both to participatory and non-participatory scenariodevelopment. However, we chose a non-participatory approachbecause of our intention of showing an example of the applicationof this method, though there was some input from Kyoto cityofficials.
(1)
FrameworkThe base year and the target year are 2000 and 2030,respectively. In its proposal for the ‘‘Environmental ModelCity’’ mentioned in Section 1, Kyoto city set a target reducingits GHG emissions 50% compared to 1990 levels. In thisstudy, the target gas is restricted to only carbon dioxideemissions from fossil fuels. Target activities were thoseconducted within the area of Kyoto city. Therefore theresidents’ activity outside of the city is not included, whileenergy consumption of visitors to Kyoto city, who areestimated to number about 50 million persons per year, isincluded. Activity is divided into five sectors: householdsector, commercial sector including wholesale, retail andservice industry, industrial sector including primary andsecondary industry, and the passenger transport and freighttransport sectors. In the transportation sector, we coveredonly the transportation departing from Kyoto city, so we didnot cover the through traffic. We either did not includeoverseas trip of residents and visitors because there is adifficulty to decide which region is responsible to there
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emission. Especially for the visitors from overseas countries,the problem is more difficult because they likely visitseveral destinations in Japan, such as Kyoto, Osaka andTokyo. However, since emissions from international aviationcan be significant, this problem should be solved in thefuture work.We estimated two scenarios: one was the case not tointroduce countermeasures, called ‘‘2030FX (fix)’’, and theother was the case to introduce countermeasures to achievethe emissions target called ‘‘2030CM (countermeasures)’’. In‘‘2030FX’’, we configured all the parameters related totransport and energy demand identically to the base year.In ‘‘2030CM’’, we introduced countermeasures until thecarbon dioxide emission reduction target, 50% reductioncompared to 1990 was achieved. The socio-economicassumptions like population or industry were common inboth cases. However, we conducted a sensibility analysis incase of fluctuations in two important parameters, DER andexport, in the ‘‘2030CM’’ scenario.
(2)
Socio-economic assumptionsWe referred to ‘‘The Master Concept of Kyoto City (Kyotocity, 2000)’’, a fundamental policy in Kyoto city. It describesthe way of life of Kyoto city and the revitalization of the cityin 2025. However, because the plan is too abstract todevelop a snapshot, we conducted interviews with severalofficials of Kyoto city environmental department and made amore concrete description of several aspects of the society.(i)
Sense of valueIn the sense of value aspect, ‘‘spiritual richness’’ isincreasingly more important than ‘‘material richness’’ associety matures. As a result, the position of Kyoto is goingto be raised to that of a ‘‘hometown of Japanese spiritualculture’’. People balance their work and life, and contribu-tion to the community is one of the important goals ofindividuals.(ii)
LifestyleIn Japan, the population is less than now and the averagefamily size is slightly decreasing because of aging. Up to datetechnologies are diffused quickly in the business world,while relatively slowly in households because of the highershare of elder people, who are relatively conservative.However, the labor participation ratio of elder people andwomen is higher than the current level, and since peopleattach greater importance to balancing work and life,working hours per person are shorter than now. Volunteerwork and community activities are thought to be as valuableas jobs. Housework is shared among family members. Mostof the residents spend their leisure within the Kyoto cityarea, while the way leisure is pursued varies. Lifelong studyaccording to the interests of individuals is popular amongthe residents, and makes their life exciting.(iii)
Land-use and transportBecause of the height restriction of the buildings regulatedby landscape policy, population density is almost constantand therefore urban structure, too. Composition of land-use,forest, farmland, and other green fields are kept almostconstant. The total floor area of the buildings in the city doesnot increase because of population shrinking and thebuilding height regulations.(iv)
Economy and industryEconomic growth rate is relatively low (average annual rateis around 1.3%) as a result of the relatively short workingtime of the residents. Sightseeing related industries con-tinue to be the main industries of Kyoto city. Locallyproduced foods are popular among the residents. Pioneeringintelligence-based industries accumulate within the cityarea. Many venture businesses are born and grow as well astraditional manufacturers with high added value. Driven byolder people who have abundant free time, service busi-nesses related to leisure, recreation, amusement and culturegrow.
(v)
SightseeingThe annual number of visitors to Kyoto city remains around50 million as a result of landscape policy, while the Japanesepopulation declines. While the number of visitors remainsconstant, sightseeing consumption per visitor increasesbecause of a higher preference for authentic traditional artand culture.(3)
Quantification of socio-economic assumptionWe quantified the parameters showed below with referenceto the description in (2).(i)
Population compositionAccording to the assumption of progress in aging, we usedthe population composition estimated by the NationalInstitute of Population and Social Security Research(2008), which shows the composition of people older thanage 64 increases.(ii)
Population allocationSince high populations densities in the city center arerestricted by landscape preservation policy, the populationcomposition of city center area (Kamigyo ward, Nakagyoward, Shimogyo ward, Higashiyama ward) decreases 2 pointscompared to the base year (from 20.2% to 18.2%), while thepopulation composition of the suburban, western area(Nishikyo ward) increases 2 points (from 24.3% to 26.3%).(iii)
Average number of family occupantsThe average household size tends to decrease, with anaverage number of family occupants of 2.15 persons perhousehold (2.36 persons in 2000).(iv)
Time-useChanges related to time-use from the description are:working time is comparatively short, housework is sharedby the family, lifelong study is widespread, volunteeractivity is essential. The resultant setting of time-useassumptions in a day for male workers is: housework timeincreases 0.5 h working hours decrease 1 h, the total forstudy and research, hobby, recreation and volunteer activ-ities increases 0.5 h Those of female workers are: domesticconcern time decreases 1 h, and the total of study andresearch, hobby, recreation and volunteer activities in-creases 1 h.(v)
Labor participation rateAccording to the description of balance of work and life andthe increase in the labor participation of older people andwomen, the setting of labor participation is: men in their60’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 expenditureAccording to the description that ‘‘service businesses relatedto leisure, recreation, amusement and culture grow’’, theratio of expenditure to tertiary industry increases 4.5 pointsfrom the base year (from 86.4% to 90.9%).(vii)
ExportsThe total growth rate of exports is assumed to be 1.3%/yearas shown in the description. Exports of industries men-tioned in the description are assumed to achieve a relativelyhigher than average growth rate (1.43%/year), and the otherindustries set a lower growth rate (1.17%/year). Industriesassumed to achieve higher growth rates are: textile fabrics(mainly silk fabrics), dyeing, commerce, entertainment
ARTICLE IN PRESS
Table 2Results of socio-economic indicators.
2000 2030 2030/2000
FX CM FX CM
Population (10 thousand) 147.4 136.1 137.4 0.92 0.93
Households (10 thousand) 62.2 63.3 63.9 1.02 1.03
GDP (billion yen) 6298 8905 9011 1.41 1.43
GDP per capita (million yen) 4 7 7 1.53 1.54
Production (billion yen) 10541 14875 14948 1.41 1.42
Primary industry 16 23 23 1.42 1.42
Secondary industry 3552 4935 4937 1.39 1.39
Tertiary industry 6684 9503 9569 1.42 1.43
Passenger transport demand (million people * km) 7821 6728 6733 0.86 0.86
Freight transport demand (million tons * km) 2690 3754 3805 1.40 1.41
K. Gomi et al. / Energy Policy 38 (2010) 4783–4796 4791
services, restaurants, hotels and research. For otherexternal final demand, government consumption expendi-ture is assumed to increase at the same rate of exports(1.35%/year) and public investment is assumed to be thesame as the base year.
(viii)
Input coefficientThe scenario description does not mention particularchanges in the input structure of the industries. Therefore,in general, the input coefficient matrix of the industries isassumed constant with the exceptions of input from theenergy and transport industries, which are estimatedendogenously according to changes in energy consumptionand transport demand.(ix)
Labor productivityThe labor productivity of primary and secondary industriesis assumed to improve 2.7%/year, while tertiary industriesare assumed to improve 1.8%/year.(x)
Fig. 7. Carbon dioxide emissions in Kyoto city.
Table 3Primary energy supply (ktoe).
Coal Oil Gas Hydraulic Nuclear Renewable Total
Relation of commuting with surrounding regionsSince the description does not show changes in the placeof employment, the parameters to describe commutingrelations, DER (domestic employment ratio) and DWR(domestic working ratio) are assumed to be the same withthe base year. In addition, the other parameters were thesame as the ones in base year (2000).
power power energy
(4)Primary energy supply
2000 375 1397 1187 73 272 5
3510
2030FX 406 1550 1366 79 294 5
3701
2030CM 152 405 967 42 175 252
199-
4
Composition
2000 10.7% 39.8% 33.8% 2.1% 7.7% 0.1% 100-
%
Compilation of low-carbon countermeasuresWe improved the countermeasure database of Shimadaet al. (2007) to add new countermeasure data for a low-carbon society. Countermeasures were classified into fourcategories; behavior change, energy efficiency improve-ment, fuel shifting (energy demand side) and improvementof carbon dioxide intensity in power supply sector. Inaddition, Kyoto city government recently launched transportdemand management (TDM) policy which aims promotionof walking and plans light rail transit (LRT) construction(Kyoto city, HP). Thus, we regarded modal shift as animportant countermeasure. Table 4 shows the counter-measures 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 andtransportation demand. The population was estimated to be about1.37 million and the result was slightly larger than the oneestimated by the National Institute of Population and Social
Security Research (2008), 1.34 million. Next, the numbers ofhouseholds increased 2% compared to 2000. GDP in Kyoto cityincreased 43% compared to 2000 and the production of tertiaryindustry increases the best in all the industrial sectors. Passengertransportation decreased 14% compared to 2000 because ofdecrease and aging of population. Freight transportationincreased 41% compared to 2000 because of increase ofsecondary industries’ production.
2030FX 11.0% 41.9% 36.9% 2.1% 7.9% 0.1% 100-
%
2030CM 7.6% 20.3% 48.5% 2.1% 8.8% 12.6% 100-
%
6.2. Energy demand and carbon dioxide emissions
Fig. 7 shows carbon dioxide emissions for each case. Carbondioxide emission in ‘‘FX’’ scenario was estimated at 8783 kt-CO2. It
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Table 4Countermeasures.
Sector Low-carbon countermeasure Data Source Category *1 Identified implementation
intensity
Emissions reduction
(kt-CO2)*2 (%)
Household sector High energy efficiency air conditioner COP 6.60 *5 E Diffusion ratio (cooling and
heating)
90 68
High energy efficiency kerosene heating COP 0.88 *4 E Diffusion ratio (heating: kerosene) 100 22
High energy efficiency gas heating COP 0.88 *4 E Diffusion ratio (heating: gas) 100 31
High energy efficiency gas cooker Thermal efficiency (base year ¼ 1) 1.22 *4 E Diffusion ratio (cooking: gas) 100 10
High energy efficiency IH cooker Thermal efficiency (base year ¼ 1) 1.15 *4 E Diffusion ratio (cooking:
electricity)
100 5
Efficiency improvement of other electric
appliances
Electricity consumption (base
year ¼ 1)
0.49 *4 E Electricity consumption (base
year ¼ 1)
0.49 405
Fluorescent light bulb (substitute
incandescent light)
Electricity consumption
(conventional type ¼ 1)
4.35 *4 E Diffusion ratio (incandescent light) 20
LED (substitute incandescent light) Electricity consumption
(conventional type ¼ 1)
8.70 *4 E Diffusion ratio (incandescent light) 80
LED (substitute fluorescent light) Electricity consumption
(conventional type ¼ 1)
2.67 *4 E Diffusion ratio (fluorescent light) 100
House insulation Thermal loss (base year ¼ 1) 0.42 *3 E Diffusion ratio 90 110
Energy saving behavior *3 B 128
cooling Energy service demand reduction
ratio
10% Diffusion ratio 100
heating Energy service demand reduction
ratio
10% Diffusion ratio 100
hot water Energy service demand reduction
ratio
10% Diffusion ratio 100
cooking Energy service demand reduction
ratio
10% Diffusion ratio 100
Other home electric appliances Energy service demand reduction
ratio
10% Diffusion ratio 100
Convened heat and power Generation efficiency 30% *6 S Diffusion ratio 10 16
Photovoltaic generation Potential (ktoe) 295 *7 S Diffusion ratio 20 84
Solar water heating Potential (ktoe) 1037 *7 S Diffusion ratio 10 30
Other energy efficiency improvement E 90
Other fuel shifting S 111
Total 1110
Commercial
sector
High energy efficiency air conditioner
(cooling only)
COP 5.00 *5 E Diffusion ratio (cooling: electricity) 90 67
High energy efficiency absorption tiller (gas) COP 1.35 *16 E Diffusion ratio (cooling: gas) 50 15
High energy efficiency gas heat pump COP 1.60 *15 E Diffusion ratio (cooling: gas) 50 18
High energy efficiency absorption tiller (oil) COP 1.35 *17 E Diffusion ratio (cooling: oil) 100 11
High energy efficiency boiler (oil) COP 0.88 *4 E Diffusion ratio (heating: oil) 100 19
High energy efficiency air conditioner
(heating only)
COP 7.4 E Diffusion ratio (heating: electricity) 90 19
High energy efficiency oil water heater COP 0.87 *4 E Diffusion ratio (hot water: oil) 100 10
High energy efficiency gas water heater COP 0.87 *4 E Diffusion ratio (hot water: gas) 100 29
CO2 cooling medium water heater COP 3.00 *4 E Diffusion ratio (hot water: all) 30 57
High energy efficiency gas cooker Thermal efficiency (base year ¼ 1) 1.15 *4 E Diffusion ratio (cooking: gas) 100 18
IH cooking heater Thermal efficiency (base year ¼ 1) 1.15 *4 E Diffusion ratio (cooking whole) 30 8
Efficiency improvement of other electric
appliances
Electricity consumption (base
year ¼ 1)
0.38 *4 E Electricity consumption (base
year ¼ 1)
0.38 451
LED (substitute incandescent light) Electricity consumption (base
year ¼ 1)
4.55 *4 Diffusion ratio (incandescent light) 100
LED (substitute fluorescent light) Electricity consumption (base
year ¼ 1)
3.95 *4 Diffusion ratio (fluorescent light) 100
Building insulation Thermal loss (base year ¼ 1) 0.50 *4 E Diffusion ratio 100 77
BEMS Energy demand reduction ratio 10% *8 E Diffusion ratio 80 73
K.
Go
mi
eta
l./
En
ergy
Po
licy3
8(2
01
0)
47
83
–4
79
64
79
2
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PRESS
Energy saving behavior *3 B 31
cooling Energy service demand reduction
ratio
10% Diffusion ratio 100
heating Energy service demand reduction
ratio
10% Diffusion ratio 100
Convened heat and power Generation efficiency 30% *6 S Diffusion ratio 20 28
Photovoltaic generation Potential (ktoe) 295 *7 S Diffusion ratio 20 84
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 sector Energy efficient equipments E 292
High energy efficiency boiler Thermal efficiency (base year ¼ 1) 1.09 *3 Diffusion ratio 95
High energy efficiency furnace Thermal efficiency (base year ¼ 1) 1.67 *9 Diffusion ratio 95
High energy efficiency mortar Electricity consumption (base
year ¼ 1)
0.95 *3 Diffusion ratio 95
Inverter control Electricity consumption (base
year ¼ 1)
0.85 *3 Diffusion ratio 95
Fuel shifting From oil to gas S Shifting ratio 60 44
Total 336
Passenger
transport sector
Hybrid vehicle Fuel cost (conventional type ¼ 1) 0.6 *4 E Diffusion ratio 100 258
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 40 300
Eco-drive Fuel efficiency improvement ratio 24% *13 B Diffusion ratio 80 121
Energy efficiency improvement of other
mode
E 75
Total 857
Freight transport
sector
Hybrid vehicle Fuel cost (conventional type ¼ 1) 0.6 *4 E Diffusion ratio 100 262
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 50 349
Energy efficiency improvement of other
mode
E 10
Total 651
Power supply
sector
Improvement of CO2 intensity of power
generation
CO2 emission per generation (tC/
toe)
0.78 919
Fuel shifting *9
Generation efficiency improvement
Coal Generation efficiency 48% *11
Gas Generation efficiency 55% *12
Increase by
changes of
driving force
(28)
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.
Go
mi
eta
l./
En
ergy
Po
licy3
8(2
010
)4
78
3–
47
96
47
93
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K. Gomi et al. / Energy Policy 38 (2010) 4783–47964794
increased 17% compared to 1990 (12% increase compared to2000.). The largest emission was found in the industrial sector,2395 kt-CO2 or 27% of the total emission, followed by thehousehold sector 2106 kt-CO2 (24%) and the commercial sector2025 kt-CO2 (23%). The industrial sector and commercial sectorincreased their emissions 40% and 2.5% respectively because ofeconomic growth. However, population decreases are assumed,emissions of the household sector increased 8.7% because of thedecrease in the average number of family occupants andthe corresponding increase of household numbers. Emissions inthe passenger transport sector alone slightly decreased, from1546 kt-CO2 in the year 2000 to 1323 kt-CO2 in the year 2030,because of the lower population. The freight transport sectorincreased its emissions from 673 kt-CO2 in the year 2000 to934 kt-CO2 in the year 2030, because of growth in the secondaryindustries’ production.
Carbon dioxide emission in the ‘‘CM’’ scenario was estimatedat 3,716 kt-CO2. It showed that the emission reduction target(50% reduction compared to 1990) could be achieved (Fig. 7).Table 3 shows primary energy supply in 2000, 2030FX and2030CM. In 2030CM scenario, share of renewable energyincreased significantly, from 0.1% in 2000 to 12.6% while totalenergy supply decreased 43% from 2000. Table 4 shows detailedinformation of the countermeasures identified. Table 5 shows theamount of reduction by sectors and by countermeasures’categories. The amount of reduction by energy efficiencyimprovement in the domestic sector and commercial sectorwere relatively large in these categories. These dominate 14.6%and 18.0% of the total amount of reduction. The reductionpotential of the passenger transport sector, in which the citygovernment’s policy is especially important, was 16.9% of thetotal amount.
6.3. Sensitivity analysis
We performed a sensitivity analysis based on the followingfour cases. With the assumption of 2030CM case (base case),(i) 10% increase of total export value, (ii) 10% decrease of total
Table 5Reduction amount by category of measures (kt-CO2).
Residential
sector
Commercial
sector
In
se
Energy saving behavior 128 31
Energy efficiency improvement 741 911 2
Fuel shifting 241 279
Total 1110 1221 3
Improvement of power supply
sector
306 293 2
Increase by changes of driving force
Total
Table 6Results of sensitivity analysis.
Variable Reference
Population (10 thousands) 137
GDP (billion yen) 9011
Passenger transport demand (million passenger * km) 6733
Freight transport demand (million ton * km) 3805
Energy demand (ktoe) 1768
CO2 emissions (kt-CO2) 3716
export value, (iii) 20% increase of the ratio of workers commutingfrom the surrounding region, (iv) 20% decrease of the ratio ofthose workers. The assumptions in the cases (i) and (ii) are alarger and a smaller size overall economy in Kyoto city. Because ofthe structure of the model proposed in Section 2, thoseassumptions affect not only the production of industries, but alsoemployment and finally, population. The cases (iii) and (iv) areabout the commuting relation with surrounding regions. Thecorresponding parameter is domestic employment ratio (DER,more precisely, 1-DER). An increased share of commuters fromoutside in case (iii) means a progression of sub-urbanization,which was a prominent trend in many metropolitan areas, andtherefore fewer residents compared to its employment opportu-nities, and (iv) means the opposite, more accumulated work andliving places.
Estimation results of the four cases are shown in Table 6. Incases (i)/(ii), carbon dioxide emissions were estimated to increase/decrease by 8.5% compared to the base case, respectively. In cases(iii)/(iv), carbon dioxide emissions are estimated to decrease/increase by approximately 3.5% compared to the base case,respectively. Case (i) and (ii) indicated that the assumption ofthe value of exports, that is the activity level of core industries, hasa large impact on carbon dioxide emissions.
In case (iii), while carbon dioxide emissions from Kyoto citydecrease, it might lead longer trip distance of commuters fromoutside of the city and greater GHG emissions of transport toKyoto city. Though, in the model ExSS, DER does not affecttransport-related parameters in the model, thus, no change in thetransport demand. The reason is, as noted in Section 3, the modelcalculates transport demand based on population, which meansnumber of residents in the target region, not workers. Howeverthis issue can be significant, transport demand of commuters fromoutside of the region depends on where they live. If they live inplaces very close to the targeted region, even if DER is lower,total transport demand may not increase. Though, where theylive is well beyond the concern of a local government. Therefore,to enable full treatment of this issue, we need to set broadertarget region. In case of Kyoto city, it means the city governmentneeds to co-operate with surrounding municipalities to develop
dustrial
ctor
Passenger transport
sector
Freight transport
sector
Total
0 223 31 413
92 333 272 2549
44 300 349 1213
36 857 651 4175
57 59 5 919
�28
5067
Export 1-DER
(i) +10% (ii) �30% (iii) +20% (iv) �20%
148 127 126 149
9664 8359 8753 9282
7239 6228 7318 6170
4148 4148 3833 3779
1912 1912 1830 1709
4032 3400 3586 3851
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K. Gomi et al. / Energy Policy 38 (2010) 4783–4796 4795
low-carbon scenarios. At the same time, population increase insurrounding region can be considered in a similar way.
7. Conclusion
This study elucidated the following points. We developed amethod for a low-carbon society scenario with regards to the opennature of such economies. This method was applied to Kyoto cityin 2030, and identified countermeasures to reduce its carbondioxide emissions 50% compared to 1990 with 1.3% per yeareconomic growth. Among the countermeasures categories, energyefficiency improvements in the household and commercial sectorswere found to be relatively large. A sensitivity analysis wasconducted on fluctuating exports and commuting from outside,and the assumption of export was found to have a big impact onthe emissions. Future work should be developing a method tomake a road map of diffusion of the countermeasures, includingindirect policies such as economic incentive or regulations, whichcorrespond to the second phase of the method of backcasting.
We think this method is useful for various cities and regions inorder to develop long-term low-carbon society scenarios andaction plans. As a contribution to policy making process, themethod and tool can support the discussion among stakeholdersby providing detailed quantitative information. For example,when a participant argued one of the countermeasures isunrealistic, one can remove the countermeasure, and discusswhat kind of countermeasure can be introduced instead, andcalculate emissions immediately and evaluate the consequence.For another example, a local government usually has varioussocial or economic goals other than low-carbon society. In manycases, the government does not assess consistency, trade-off andsynergy effect among the goals including low-carbon target. Thismethod can asses the effect of those goals to GHG emissions bygiving the future state which achieved them as socio-economicassumptions. The scenario of Kyoto city, which we showed as anexample of the method, is no more than one of many kinds ofcountermeasures portfolios which can achieve the low-carbontarget. However, it can be provided to a policy making process as astarting point of discussion. If various stakeholders participate inthe discussion, and evaluate and modify the scenario, it would bepossible to decide more realistic and acceptable countermeasuresportfolio.
It is especially effective for cities in the developing countries,which intend to achieve high economic growth within next fewdecades, as well as those in developed countries aiming to re-construct their energy consumption structure.
Acknowledgements
This study is part of the research results of the ‘‘Study Projecton Establishment of Method for Multilateral and IntegratedEvaluation, Projection and Planning about Mid-and-Long-TermPolicy Options towards Low-Carbon Society’’ sponsored by theMinistry of the Environment’s Global Environment Research FundS-3-1 and S-6-1. It was also supported by KAKENHI 2056391 andKyoto Collaborative Research Institute of The Consortium ofUniversities in Kyoto. We would like to express our gratitude fortheir support.
References
Anderson, K.L., Mander, S.L., Bowsa, A., Shackley, S., Agnolucci, P., Ekins, P., 2008.The tyndall decarbonisation scenarios—part II: scenarios for a 60% CO2
reduction in the UK. Energy Policy 36, 3764–3773.
Architectural Institute of Japan, 2006. Energy Consumption for ResidentialBuildings in Japan. Maruzen, Tokyo (in Japanese).
Armstrong, H., Taylor, J., 2000. Regional Economics and Policy, third ed. BlackwellPublishing, Oxford.
Berlin Agenda Forum, 2004. Designing the FUTURE Agenda draft summary adoptedby the Berlin Agenda forum.
Bristol City Council, 2004. Bristol Climate Protection and Sustainable EnergyStrategy Action Plan.
California Environmental Protection Agency, 2006. Climate Action Team Report toGovernor Schwarzenegger and the Legislature.
Cheltenham Borough Council, 2005. Climate Change a strategy for Cheltenham.Chiyoda ward, 2007. Chiyoda ward Global Warming Mitigation Ordinance.
(in Japanese).Clean Coal Power Institute, HP. /http://www.ccpower.co.jpS (in Japanese).Connecticut Governor’s Steering Committee on Climate Change, 2005. Connecticut
Climate Change Action Plan 2005 Executive Summary.Environment and Health Administration of City of Stockholm, 2002. Stockholm’s
action program against Greenhouse Gases.Environmental Bureau of Hiroshima city, 2008. Hiroshima City Low-carbon Plan:
Carbon Minus 70 Executive Summary. (in Japanese).Fujino, J., Ehara, T., Matsuoka, Y., Masui, T., Kainuma, M., 2008. Back-casting
analysis for 70% emission reduction in Japan by 2050. Climate Policy 8,108–124 supplement.
Garin, R.A., 1966. A matrix formulation of the Lowry model for intrametropolitanactivity allocation. AIP Journal. .
Governor’s Advisory Group on Global Warming, 2004. Oregon’s Strategy forGreenhouse Gas Reductions.
Greater London Authority, 2007. Action today to protect tomorrow. London.Hitachi Appliances, HP. /http://www.hitachi-ap.co.jp/S.IPCC, 2000. IPCC Special Report on Emission Scenarios Summary for Policy makers.Japan Low-Carbon Society scenario team, 2008. Japan Scenarios and Actions
towards Low-Carbon Societies (LCSs).Kashiwa city, 2007. Global Warming Mitigation Plan. (in Japanese).Kok, M., Vermeulen, W., Faaji, A., Jager, D., 2003. Global Warming and Social
Innovation: The Challenge of a Climate-Neutral Society. Earthscan, London.Kyoto city environmental bureau, 2000. Report on New Energy Vision. (in Japanese).Kyoto city, HP. /http://www.city.kyoto.lg.jp/tokei/soshiki/9-5-0-0-0.htmlS
(in Japanese).Kyoto city, 2000. The Master Concept of Kyoto City. Kyoto.Larsen, K., Gunnarsson-Ostling, U., 2008. Climate change scenarios and citizen-
participation: mitigation and adaptation perspectives in constructing sustain-able futures. Habitat International. 101016/j.habitatint.2008.10.007.
Leicester partnership and Leicester Environment Partnership, 2003. City ofLeicester Climate Change Strategy.
Lowry, L.S., 1964. A model of metropolis. Rand Corporation. .Macgill, S.M., 1977. The Lowry model as an input–output model and its extension
to incorporate full intersectoral relations. Regional Studies 11, 337–354.Mander, S.L., Bowsa, A., Anderson, K.L., Shackley, S., Agnolucci, P., Ekins, P., 2008.
The Tyndall decarbonisation scenarios—part I: development of a backcastingmethodology with stakeholder participation. Energy Policy 36, 3754–3763.
Ministry of Trade, Economy and Industry, 2005. Technology Strategy Map-Ultralong-term energy technology vision. (in Japanese).
Mizuho Information & Research Institute, inc., 2005. Report on research andsimulation for scenario development, Tokyo. (in Japanese).
National Institute of Population and Social Security Research, 2008. Populationprojection for Japan by municipalities: 2010–2035 (in Japanese).
New Energy and Industrial Technology Developing Organization, 2005. Report onfield test of high efficiency industrial furnace. (in Japanese).
Nicholas, L., Sperling, D., 2008. America’s bottom-up climate change mitigationpolicy. Energy Policy 36, 673–685.
Nikkei Inc., 2005. Technology: Its beginning, Nikkei ecology, October 2005, 47–49.(in Japanese).
Nikkei newspaper, May, 14th, 2008. (in Japanese).Oko institute e.V., 2004. Local strategies for the reduction of the emissions around
50% by the example of the city of Munich.Osaka prefecture, HP. /http://www.pref.osaka.jp/S (in Japanese).OSAKAGAS, HP. /http://www.osakagas.co.jp/S (in Japanese).Prime Minister’s Office, HP. /http://www.kantei.go.jpS.Quist, J., Vergragt, P., 2006. Past and future of backcasting: the shift to stakeholder
participation and a proposal for a methodological framework. Futures 38,1027–1045.
Recoo, HP. /http://www.recoo.jp/S (in Japanese).Resource and Energy Investigate Committee, 2005. Energy Supply and Demand
Perspective by 2030. (in Japanese).Robinson, J.B., 1990. Futures under glass: a recipe for people who hate to predict.
Futures (pp. 820–842), 820–842 October 1990.Shimada, K., Tanaka, Y., Gomi, K., Matsuoka, M., 2007. Developing a long-term local
society design methodology towards a low-carbon economy: an application toShiga prefecture in Japan. Energy Policy 35, 4688–4703.
State of New Mexico Office of the Governor, 2005. Governor Bill RichardsonAnnounces Historic Effort to Combat Climate Change.
Statistics Bureau and the Director-General for Policy Planning, HP. /http://www.stat.go.jpS.
The City of New York, 2007. PlaNYC: A Greener, Greater New York.The Energy Conservation Centre Japan, 2007. Catalogue on Energy Saving
Performance winter 2007. Tokyo. (in Japanese).
ARTICLE IN PRESS
K. Gomi et al. / Energy Policy 38 (2010) 4783–47964796
Tokyo Metropolitan Government, 2007. Tokyo Climate Change Strategy—A BasicPolicy for the 10-Year Project for a Carbon-Minus Tokyo.
Toyonaka city, 2007. Regional Plan of Global Warming Mitigation on ToyonakaCity: Challenge 70%. (in Japanese).
Turnpenny, J., Carney, S., Haxeltine, A., O’Riorden, T., 2004. Developing regional andlocal scenarios for climate change mitigation and adaptation, Part 1: A framingof the East of England, Tyndal Centre for Climate Research, Working Paper 54.
Turnpenny, J., O’Riorden, T., Haxeltine, A., 2005. Developing regional and localscenarios for climate change mitigation and adaptation, Part 2: Scenariocreation, Tyndall Centre for Climate Research, Working Paper 67.
Ville de Gen�eve, 2006. �Energie et d �eveloppement durable. (in French).Woking Borough Council, 2005. Climate Change Strategy.Yokohama city Head quarter of Global Warming Mitigation Actions, 2008. CO-DO
30 Low-carbon Action Policy (in Japanese).
