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Energy Policy 39 (2011) 4337–4348
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Energy Policy
0301-42
doi:10.1
n Corr
E-m
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journal homepage: www.elsevier.com/locate/enpol
Climate change mitigation in developing countries through interregionalcollaboration by local governments: Japanese citizens’ preference
Hidenori Nakamura a,b, Takaaki Kato c,n
a Governance and Capacity Group, Institute for Global Environmental Strategies, Kamiyamaguchi 2108-11, Hayama, Kanagawa 240-0115, Japanb Department of Social Engineering, Tokyo Institute of Technology, O-okayama 2-12-1, Meguro, Tokyo 152-8552, Japanc Faculty of Environmental Engineering, The University of Kitakyushu, Hibikino 1-1, Wakamatsu, Kitakyushu, Fukuoka 808-0135, Japan
a r t i c l e i n f o
Article history:
Received 7 December 2010
Accepted 21 April 2011Available online 8 May 2011
Keywords:
Carbon crediting
Interregional collaboration
Social survey
15/$ - see front matter & 2011 Elsevier Ltd. A
016/j.enpol.2011.04.051
esponding author: Tel.: þ81 93 695 3237.
ail addresses: [email protected],
@bma.biglobe.ne.jp (T. Kato).
a b s t r a c t
This study explores the motivation of domestic and international interregional collaboration on climate
change mitigation through carbon crediting by Japanese local governments, using a social survey. The study
finds balanced collaboration with domestic partner regions and developing countries is preferred in the case
of collaboration, given that the unit cost of collaboration is assumed lower than that of no collaboration.
Appreciation of benefits such as technology transfer and local environmental improvement in developing
countries increases the preference of collaboration with developing countries. Two factors hinder Japanese
local governments’ collaboration with developing countries from the perspective of citizens: a sense of
environmental responsibility to reduce greenhouse gas (GHG) emissions within the city and a preference for
domestic orientation even if the collaboration with developing countries is less costly and has benefits of
technology transfer and local environmental improvement. The preference for a lower total cost of GHG
emissions reductions is confirmed except for those with a sense of environmental responsibility. The study
also finds that provision of information on mitigation projects and co-benefits would increase the preference
for interregional collaboration with developing countries depending on the types of collaborative project,
except for those with a sense of environmental responsibility.
& 2011 Elsevier Ltd. All rights reserved.
1. Introduction
Global climate change has drawn more and more attention andhas become one of the most significant challenges that require globalcollective actions to solve (United Nations, 2009). One of themeasures to tackle this issue is to put a price on emitting anthro-pogenic greenhouse gases (GHGs) to internalise the negative cost ofthe emissions into economic activities. Mandatory markets such asthe Kyoto mechanism (e.g. clean development mechanism (CDM) andjoint implementation (JI)) and the European Union Greenhouse GasEmission Trading System (EU-ETS) are conspicuous exampleswhereby certificates of emissions reduction are traded among partiesthat have a cap according to regulations (World Bank, 2010). Inaddition, some local governments in the United States, Canada andAustralia have established and engaged in legally binding emissionstrading schemes such as the Regional Greenhouse Gas Initiative(RGGI), Western Climate Initiative (WCI), and New South WalesGreenhouse Gas Reduction Scheme (NSW GGAS), without nationalregulations (Hamilton et al., 2010). Carbon credit trading by the localgovernments in the above developed countries is not linked withclimate change mitigation projects in developing countries, unlike
ll rights reserved.
CDM at the national level. International carbon crediting at the localgovernment level, however, might lead to further promotion ofclimate change mitigation in developing countries, in addition to, orindependently from, the international agreements at a nationalgovernment level.
In Japan, there is not yet a carbon credit trading system at localgovernment level. Recently the Tokyo Metropolitan Government ofJapan established a cap-and-trade scheme that requires large emittersin its jurisdiction to reduce their GHG emissions and to purchasecertified carbon credits when they do not meet the targets (TokyoMetropolitan Government, 2010a). However, this is not a tradingsystem among local governments, and the credits are restricted tothose that are generated domestically. Kitakyushu City, on the otherhand, established the Kitakyushu Asian Center for Low Carbon Societyin 2010 to accelerate the development of low-carbon societies andrelevant businesses using local assets of environmental industries andthe city’s international intercity network (Kitakyushu Asian Center forLow Carbon Society, 2010). Kitakyushu City aims to reduce 150% ofits GHG emissions by means of international cooperation with Asiandeveloping countries, in addition to the reduction of 50% for the city’semissions by 2050 compared to emissions in 2005 (Kitakyushu City,2009). The city has just started examining the idea of city-to-citycarbon crediting by extending the support of low-carbon communityin Surabaya, Indonesia (Kitakyushu City, 2010). However the account-ing method of reductions in Asian developing countries is still underdevelopment and reductions through cooperation are not treated as
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–43484338
offsetting for the reduction target of emissions under the city’sjurisdiction.
Once Japanese local governments are legally obliged to meet theGHG emissions reduction target and if carbon crediting fromdomestic parties and those in developing countries is allowed, itwould open up a new mode of cooperation for low-carbon devel-opment, particularly in developing countries. However citizenswhose support is needed by local government to implement thelocal policies might not agree with large amount of carbon creditingfor several reasons. As psychology studies have shown (Hirose,1995; Hopper and Nielsen, 1991), an individual sense of responsi-bility facilitates the people engaged in environmentally friendlybehaviours and hence citizens supportive to climate change mitiga-tion may not support reducing the burden of mitigation in the nameof division of labour. The criticism of being unethical might be raisedas is the case for CDM and carbon offset (Liverman, 2010). Plus,there is also a criticism that carbon crediting is wasting tax since thetax is not directly used domestically (Yamaguchi, 2009). Thereforethe policy issue from the perspective of citizens would be anappropriate allocation of local government’s budget for domesticand international collaboration and for programmes to reduce GHGemissions within the city, to meet the target of GHG emissionsreduction. Collaboration in this paper is defined as cooperationbetween local governments in which one local government obtainsspecified carbon credits from the other in exchange for financial ortechnical assistance to the other with specified cost.
This study therefore investigates the citizens’ preference ondomestic collaboration and collaboration with developing countriesso that their own city governments can meet the official mandatorytarget of GHG emissions reduction in a hypothetical setting. Thispaper also examines the influence of information on projectdescription and co-benefits in terms of interregional collaborationon climate change mitigation of Japanese local governments.
The study finds three preferences are observed among Japa-nese citizens, given that the unit cost of collaboration is assumedlower than the cost of no collaboration: a sense of environmentalresponsibility (reduction of GHG emissions within the citywithout any collaboration to achieve the target), domestic orien-tation (priority on domestic collaboration), and the preference forcollaboration with developing countries. A sense of environmen-tal responsibility encourages citizens to reduce GHG emissionswithin the city without collaboration. In general, balanced colla-boration with domestic partner regions and developing countriesis preferred in the case of collaboration. Appreciation of benefitssuch as technology transfer and local environmental improve-ment in developing countries increases the preference of colla-boration with developing countries. A preference for a lower totalcost of GHG emissions reduction is confirmed except for citizenswith domestic orientation. Information, including descriptionsand co-benefits of mitigation projects, increases the citizens’support of higher ratio of collaboration with developing countries.
The next section explains the state of local governments’interregional collaboration on climate change mitigation in Japan.The explanation of methodology of the study follows, includingthe questionnaire used in the social survey, the data collectionprocess and the statistical analysis used. Then the results of thestudy are reported and the paper concludes with a discussion andpolicy implications based on the findings.
2. Interregional collaboration on climate change mitigationin Japan
Interregional collaboration among Japanese local governmentshas just prompted the opportunity of accelerating climate changemitigation actions and revitalisation of rural areas, but collaboration
remains domestic so far. Examples include those of Tokyo Metro-politan Government and five prefectures in northern Japan, Yoko-hama City and Doshi village of Yamanashi prefecture, and Shinjukuward of Tokyo and Ina City of Nagano prefecture (Shinjuku ward,2008; Tokyo Metropolitan Government, 2010b; Yokohama City,2010). All cases involve domestic collaboration between localgovernments in urban and rural areas. Tokyo Metropolitan Govern-ment explores the demand of green power in buildings in Tokyowhile encouraging the building owners to use renewable energy(mostly wind power) generated from projects in partner prefectures,in order to meet the GHG emissions reduction targets under therecent cap-and-trade scheme introduced by Tokyo (TokyoMetropolitan Government, 2010b). Yokohama City has a promotioncouncil of climate change mitigation, which makes a donation to thefund for proper management of the forest in Doshi village and at thesame time, the city obtains verified emissions reduction (VER) basedon the increase of GHG absorption in the forest (Yokohama City,2010). Shinjuku ward is expected to carry out carbon offset of two tothree thousand tons of carbon dioxide (CO2) a year in collaborationwith Ina City regarding forest management and conservation(Shinjuku ward, 2008). These emerging cases, however, are exam-ples of domestic collaboration and no attempts are observed toextend such collaboration overseas. The Promotion Council for theLow-Carbon Cities (PCLCC) in Japan, is an association of Japaneselocal governments and relevant organisations, which was estab-lished in 2008 with assistance from national government to promotelow-carbon cities and includes a working group on green economythat seeks interregional collaboration towards low-carbon society(PCLCC, 2010a). This working group has studied and exchangedviews among member cities on regional collaboration. However ithas not yet examined the possibility of extending such collaborationoutside of Japan. It is worth considering and examining to seewhether Japanese local governments can integrate internationalcooperation into their climate policy by means of carbon crediting.This could be cost effective for Japan while it could also be desirablefor entities in developing countries when the collaboration deliversco-benefits for local environment and development. In this way,Japanese local governments would play a role to further promoteclimate change mitigation in developing countries.
3. Methods
3.1. Survey design
In order to study citizens’ attitude towards interregional andinternational collaboration on climate change mitigation, it isdesirable to conduct a social survey in cities that have stated theircommitment to GHG emissions reduction targets and haveextended international cooperation in the field of environment.A survey was conducted in two Japanese cities, namely Yokohamaand Kitakyushu, which are committed to developing low-carbonsocieties as nationally designated Eco Model Cities. These twocities have extensive records of international environmentalcooperation with different focuses. For example, Yokohamaemphasises a contribution to global society while Kitakyushuintends to conduct industrial development in the long term(Nakamura et al., 2010). The location of Yokohama and Kita-kyushu is shown in Fig. 1. Pre-tests were conducted twice withvolunteer subjects to examine the survey loads and clarity of thequestionnaire and to improve the contents of questionnaire.
The survey was implemented to investigate citizens’ attitudetowards the city’s interregional collaboration on climate changemitigation. This survey was a follow-up of the first survey thatfound there was diversity of citizens’ attitudes on internationalcarbon crediting at the national level (Nakamura and Kato, 2010).
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–4348 4339
The first questionnaire was mailed to 1757 citizens over 20 yearsold in both Yokohama and Kitakyushu, randomly drawn from acitizens’ registry, and collected by mail, during February to March2010. The response rates were 38% for Yokohama and 39% forKitakyushu. The survey asked about concerns in climate changeand international development using a five-level scale, dailyactions to mitigate climate change (multiple-choice up to seven-teen), and attitudes on carbon crediting at national level. Thesurvey also included other questions that were used for separatestudy purposes. General individual attributes such as gender, age,and household income were also asked. The second questionnairewas mailed in July 2010 to 539 citizens in Yokohama and 590citizens in Kitakyushu, who had responded to the first survey andhad expressed the possibility of participating in the secondsurvey. The answers were collected over a few months from Julyto September. The response rates for the second survey vis-�a-visthe original target of the first survey were 23% for Yokohama and24% for Kitakyushu.
The second survey inquired about the desirable interregionalcollaboration between the government of the city where therespondent resides and other regions of Japan or some regionsin developing countries, so that the city government could meetthe stated target of GHG emissions reductions. This was assumingthat the city government has to legally meet the target and thatthe interregional collaboration to achieve the target is allowed.Table 1 shows six alternative ratios that were presented for city’sreduction, reduction with domestic collaboration, and reductionwith international collaboration. The question was asked in twocases of different unit cost of GHG emission reductions. In bothcases, the unit cost of GHG emissions reduction with collabora-tion is lower than that without collaboration. Furthermore, theunit cost of reductions in collaboration with developing countriesis lower than that with domestic collaboration in Case 1 and is thesame as that with domestic collaboration in Case 2 (see Table 2).
Yokohama and Kitakyushu
Yokohama
Kitakyushu
Fig. 1. Location of Yokohama and Kitakyushu cities in Japan.
Table 1Alternatives for ratios of collaboration in terms of GHG emissions reduction.
Alternative 1 (%) Alternative 2 (
Ratio of reduction by the city 100 80
Ratio of reduction by domestic collaboration 0 20
Ratio of reduction by international collaboration 0 0
Other combinations of different unit costs could have been added,such as the case where the unit cost of domestic collaboration islower than that of collaboration with developing countries andthe case where cost difference is different from that in Case 1.However, after the trial and confirmation of a pre-test, it wasdecided to use only two cases to keep the respondents’ burden atan acceptable level. These two cases are the most important whenlooking at the effects of unit cost difference between domesticcollaboration and collaboration with developing countries. Actualtargets of yearly reductions for each city as well as calculatedtotal cost of yearly reduction for each alternative are shown as areference. The alternatives 2–6 were defined in two ways: In onecase, alternative 2 has higher ratio of domestic collaborationwhile alternative 6 has lower ratio of domestic collaboration, asshown in Table 1. In another case, alternative 2 has lower ratio ofdomestic collaboration while alternative 6 has higher ratio ofdomestic collaboration. These two cases were randomly assigned.An example of choice representation in Cases 1 and 2 is shown inthe Appendix A. Among items shown in Table 3, multiple choiceswere allowed to identify the reasons of selecting one profileamong six for Cases 1 and 2, respectively. The reasoning that ‘‘thecity should achieve its emissions reduction target without colla-boration with other regions’’ is set to see if there are citizens whoselect an alternative without collaboration based on a sense ofenvironmental responsibility.
The unit costs of GHG emissions reduction are determinedreferring to the estimated marginal cost of abatement in theJapanese case, the actual transaction price of voluntary emissionstrading in Japan, and the cost of carbon offsetting using themitigation project in developing countries though it is difficult tospecify the cost. Expected mitigation methods in Japan can beclassified into those with negative costs, those with relatively lowcosts and those with high costs. As such, mitigation costs formeasures with relatively low costs range from 10,000 to 40,000[JPY/ton CO2eq] and the median against accumulated emissionsreduction is around 20,000 [JPY/ton CO2eq] (Kuriyama, 2010). Onthe other hand, the actual transaction record of the Ministry ofthe Environment-led voluntary emissions trading system in Japanindicates a relatively low cost of mitigation at this moment, atroughly 750–800 [JPY/ton CO2eq] (Ministry of the Environment,2010). This suggests there still are low-cost mitigation options inJapan. Carbon offsetting based on the certified emissions reduc-tion (CER) produced from a biomass power project in Indiarequires JPY5000 to offset one ton of emissions of CO2 (PEARCarbon Offset Initiative, 2010). Considering these estimations andactual transactions, the study uses the above cases of unit costs ofGHG emissions reduction.
%) Alternative 3 (%) Alternative 4 (%) Alternative 5 (%) Alternative 6 (%)
80 80 80 80
15 10 5 0
5 10 15 20
Table 2Unit cost of GHG emissions reduction in Cases 1 and 2.
Unit cost of reduction
Case 1 Case 2
Reduction within the city JPY 20,000 JPY 20,000
Domestic collaboration JPY 12,000 JPY 12,000
Collaboration with developing countries JPY 6000 JPY 12,000
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–43484340
Five types of questionnaire were used to show the effects ofinformation provided in terms of description and co-benefits ofmitigation projects both in Japan and in developing countries(Table 4). Four of the questionnaires provide concrete examples ofmitigation projects with their photographs, a brief explanation ofthe project contents, and the expected co-benefits of the project.Two examples of mitigation projects for domestic collaborationand for collaboration with developing countries were usedrespectively, leading to four (two times two) combinations of amitigation project in Japan and that in developing countries.These examples were used to reflect the representative mitigationmethods that are currently used and that have co-benefits to localcommunities in Japan and in developing Asia (Nakamura et al.,2009; PCLCC, 2010b; Tokyo Metropolitan Government, 2010b).Another type of questionnaire includes only two sentences thatwere also given in other types of questionnaires: ‘‘Collaborationwith other regions in Japan utilises the projects that contribute torevitalisation of the partner regions through mitigation projects.Collaboration with a region in developing countries conducts themitigation projects that are useful for developing countriesthrough mitigation technology transfer and improving livingenvironment.’’ The type of questionnaire was randomly assignedto the target respondents in each city to minimise the effects ofpreferences on specific mitigation methodologies.
The response rate is not high enough to generalise the resultsfor the population of two cities, although the higher rate in themail survey is not easily obtained in Japan. Response rates in mailsurveys in Japan vary from around 15–60% in general, and around20–40% in urban areas in particular. Response rates are evenlower when they are independently conducted by researchorganisations, rather than the government (Shinbo et al., 1993;Terawaki, 2002; Wagawa, 2008; Yoshida et al., 1997). Hence theresponse rate in this study is as high as could be expected for anon government-organised mail survey in Japan. The secondsurvey asked if the respondent also replied to the first survey.Those respondents who replied that they did not answer the firstsurvey questions had their answers for the second surveyremoved from the data set.
The distribution of gender of the samples for the second surveyis consistent with the distribution of the population in two cities(females make up 50.9% of the population), while the distributionof age of the samples for the second survey is different from the
Table 3Reasons for selection of alternatives on collaboration in GHG emissions reduction.
The city should achieve its emissions reduction target without collaboration
with other regions
Collaboration is cost effective
Collaboration encourages mitigation technology transfer to developing
countries
Collaboration contributes to ambient environment improvement in
developing countries
Domestic reduction of emissions is priority
City tax shall be used domestically
Balance between domestic and international collaboration matters
Table 4Types of questionnaire and mitigation projects described for each type.
Questionnaire
type
Concrete explanation of mitigation
projects
Example of mitigation pro
collaboration
Type 1 Yes Utilisation of forest resou
Type 2 Yes Utilisation of forest resou
Type 3 Yes Wind power generation
Type 4 Yes Wind power generation
Type 5 No NA
distribution of the population in two cities (twenties make up14.3%, thirties 18.9%, forties 17.5%, fifties 15.0%, sixties 16.6%, andover seventies 17.6% of the population), at the significant level of5% (gender: w2(1)¼0.202, p¼0.653; age: w2(5)¼29.877,p¼0.000), i.e. the sample used in the study does not representthe population in terms of the distribution of age. The samples areover-representing citizens in their sixties, and under-representingcitizens in their twenties. Table 5 shows the descriptive statisticsof the individual attributes used here.
3.2. Statistical analysis
The basic aggregated data shows the distribution of choicesover the combination of different ratios of domestic and interna-tional collaboration to achieve the city’s GHG emissions reductiontarget for each case of different unit cost of GHG emissionsreduction cost in collaboration with developing countries. Thedifference of distribution for two cases is examined by theWilcoxon signed rank test. The distribution of reasons to selecta specific alternative is then also shown for two cases. Therelationship between the reasons and the alternative selected isanalysed.
The logit model is used to estimate the preference for domesticor international regional collaboration to achieve the city’s targetof GHG emissions reduction. The model includes the variables ofcollaboration dummy, ratio of GHG emission reduction to achievethe target through collaboration with developing countries, andthe unit cost of mitigation for collaboration with developingcountries (See Appendix B for details). Then the relationshipbetween the individual attitude for environmental and develop-mental issues and the preferred ratio of international collabora-tion is analysed by means of logit model.
Lastly the effects of information provided on project descrip-tion and co-benefits of collaboration are analysed using achi-squared test of distribution by questionnaire type. The effectof different questionnaire types is also examined by means oflogit model to find out what significance the types of question-naire had on the selection of alternatives for the ratio of reductionthrough collaboration with developing countries.
4. Results
4.1. Selection results and reasoning for selection
Table 6 shows the basic aggregated data of choice results forCases 1 and 2. In Case 1 where the unit cost of GHG emissionsreduction in collaboration with developing countries is lowerthan that of domestic collaboration, there are three peaks ofdistribution: no collaboration with other regions; balanced colla-boration with domestic and developing-country partner regions,and collaboration with developing countries with maximum ratio.In Case 2 where the unit cost of GHG emission reduction incollaboration with developing countries is the same as that fordomestic collaboration, the peak of balanced collaboration is
ject for domestic Example of mitigation project for international
collaboration
rces Utilisation of stockbreeding waste
rces Composting of municipal organic waste
Utilisation of stockbreeding waste
Composting of municipal organic waste
NA
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–4348 4341
higher and the peak of collaboration with developing countrieswith maximum ratio is lower than those of Case 1. The Wilcoxonsigned rank test shows that the p-value for the differencebetween medians of Cases 1 and 2 is less than 0.001. Thereforethe distributions are considered different at a significance levelof 5%.
Table 6Aggregated data for choices in Cases 1 and 2.
Alternative
Case 1: Unit costs of GHG emissions reduction for within the city, domestic collabor
[yen/ton CO2e], respectively.
Ratio of reduction within the city (%)
Ratio of reduction with domstic collaboration (%)
Ratio of reduction in collaboration with developing countries (%)
Total cost of GHG emissions reduction, normalised by that of alternative 1 (%)
Frequency
Percentage (%)
Case 2: Unit costs of GHG emissions reduction for within the city, domestic collabora
[yen/ton CO2e], respectively.
Ratio of reduction within the city (%)
Ratio of reduction with domstic collaboration (%)
Ratio of reduction in collaboration with developing countries (%)
Total cost of GHG emissions reduction, normalised by that of alternative 1 (%)
Frequency
Percentage (%)
Table 5Descriptive statistics of individual attributes of respondents.
Gender
Female 51.2%
Male 47.4%
Unknown 1.5%
Age
20–29 9.7%
30–39 16.4%
40–49 19.4%
50–59 17.1%
60–69 20.5%
70 or older 15.3%
Unknown 1.7%
Household income
oJPY 2 million 11.3%
JPY 2–5 million 41.6%
JPY 5–10 million 31.8%
JPY 10–15 million 8.7%
JPY 15–20 million 1.9%
JPY 20 million or more 0.7%
Unknown 4.0%
City
Yokohama 48.1%
Kitakyushu 51.9%
Climate change concern
Concerned 45.9%
Concerned, if anything 41.9%
Hard to say 7.4%
Not concerned, if anything 1.6%
Not concerned 1.1%
Unknown 2.1%
International development concern
Concerned 22.7%
Concerned, if anything 43.3%
Hard to say 22.3%
Not concerned, if anything 6.7%
Not concerned 3.4%
Unknown 1.7%
Number of climate protection actions
Mean 6.5
Standard deviation 2.8
As described earlier, the alternatives 2–6 in Table 6 areinversely listed in half of questionnaires which were randomlyassigned to the respondents. In Case 1, 329 respondents answeredthe questionnaire whose order of alternatives is similar to Table 5while 335 respondents answered the questionnaire with oppositeorder. In Case 2, 301 respondents used the questionnaire with theorder of alternatives as in Table 6, while 316 respondents used thequestionnaire with the opposite order. A chi-squared test onindependence of distribution indicates that the difference ofdistribution because of the different order of alternatives is notconfirmed at the significant level of 5% (Case 1: w2(5)¼10.359,p¼0.066; Case 2: w2(5)¼6.305, p¼0.286).
Table 7 shows the distribution of reasons selected by therespondents of appropriate combinations of GHG emission reduc-tion ratios, where multiple answers were allowed among theseven alternatives shown in Table 3. The distributions for Case1 and 2 are shown respectively. The average numbers of alter-natives selected by one respondent were 2.2 and 2.1 for Case1 and 2, respectively. The percentage of respondents who chose areason as against the total number of respondents is also shown.In both cases, around 40–50% of respondents chose the reason ofbenefits of mitigation technology transfer or local environmentalimprovement in developing countries to support collaborationwith developing countries. More than 40% of respondents alsosupport the idea that the balance between domestic collaborationand collaboration with developing countries is significant. On theother hand around 40% of respondents support the idea that thecity should achieve the GHG emissions reduction target withoutcollaborating with other regions. The importance of cost effi-ciency was supported by more respondents in Case 1 where unitcost of emissions reduction in collaboration with developingcountries is lower than that of domestic collaboration, as com-pared to Case 2 where unit cost of emissions reduction incollaboration with developing countries is the same as that ofdomestic collaboration. This is consistent with the expectedrational response.
Table 8 shows a cross-table between the selection of alter-natives for collaboration and the reasons in Case 1 and 2,respectively. More than 40% of respondents who chose thereasoning of the norm to reduce GHG emissions within the cityselected alternative 1, i.e. to reduce GHG emissions reductionwithout collaboration, in both Cases 1 and 2. This is the highestselection ratio of alternative 1 compared to distributions of otherreasoning. The percentages of respondents who did not select thereasoning of tax expenditure within Japan, among the respon-dents who chose alternative 1 and selected the reasoning of
1 2 3 4 5 6 Total
ation, and collaboration with developing countries are 20,000, 12,000, and 6000
100 80 80 80 80 80 –
0 20 15 10 5 0 –
0 0 5 10 15 20 –
100 92 91 89 88 86 –
87 65 89 220 84 119 664
13.1 9.8 13.4 33.1 12.7 17.9 100.0
tion, and collaboration with developing countries are 20,000, 12,000, and 12,000
100 80 80 80 80 80 –
0 20 15 10 5 0 –
0 0 5 10 15 20 –
100 92 92 92 92 92 –
80 76 75 257 50 79 617
13.0 12.3 12.2 41.7 8.1 12.8 100.0
Table 7Distribution of reasoning of selecting appropriate combinations of GHG emission reduction ratios.
Reduction
within city
Cost
efficiency
Technology
transfer
Environmental
improvement
Domestic
reduction
Domestic
expenditure
Balance between domestic and
international collaboration
Case 1
Frequency 196 184 319 250 144 76 286
Percentage (%) 29.5 27.7 48.0 37.7 21.7 11.4 43.1
Note: The number of subjects is 664.
Case 2
Frequency 170 96 278 230 137 79 282
Percentage (%) 27.6 15.6 45.1 37.3 22.2 12.8 45.7
Note: The number of subjects is 617.
Table 8Cross-table between the selection of alternatives for collaboration and the reasons.
Alternative 1 2 3 4 5 6 Total
Case 1: Unit costs of GHG emissions reduction for within the city, domestic collaboration, and collaboration with developing countries are 20,000, 12,000, and 6,000
[yen/tCO2e], respectively.
Reasoning of selection Ratio of reduction within the city (%) 100 80 80 80 80 80 –
Ratio of reduction with domstic collaboration (%) 0 20 15 10 5 0 –
Ratio of reduction in collaboration with developing countries 0 0 5 10 15 20 –
Total cost of GHG emissions reduction, normalised by that of
alternative 1 (%)
100 92 91 89 88 86 –
Reduction within city Frequency 81 22 24 41 13 15 196
Percentage (%) 41.3 11.2 12.2 20.9 6.6 7.7 100.0
Cost efficiency Frequency 4 7 7 38 39 89 184
Percentage (%) 2.2 3.8 3.8 20.7 21.2 48.4 100.0
Technology transfer Frequency 3 5 43 124 59 85 319
Percentage (%) 0.9 1.6 13.5 38.9 18.5 26.6 100.0
Environmental improvement Frequency 2 7 21 96 48 76 250
Percentage (%) 0.8 2.8 8.4 38.4 19.2 30.4 100.0
Domestic reduction Frequency 23 50 45 16 6 4 144
Percentage (%) 16.0 34.7 31.3 11.1 4.2 2.8 100.0
Domestic expenditure Frequency 13 30 15 12 3 3 76
Percentage (%) 17.1 39.5 19.7 15.8 3.9 3.9 100.0
Balance between domestic and international
collaboration
Frequency 2 2 44 178 44 16 286
Percentage (%) 0.7 0.7 15.4 62.2 15.4 5.6 100.0
Case 2: Unit costs of GHG emissions reduction for within the city, domestic collaboration, and collaboration with developing countries are 20,000, 12,000, and 12,000
[yen/tCO2e], respectively.
Reasoning of selection Ratio of reduction within the city (%) 100 80 80 80 80 80 –
Ratio of reduction with domstic collaboration (%) 0 20 15 10 5 0 –
Ratio of reduction in collaboration with developing countries (%) 0 0 5 10% 15 20 –
Total cost of GHG emissions reduction, normalised by that of
alternative 1 (%)
100 92 91 89% 88 86 –
Reduction within city Frequency 73 20 23 35 9 10 170
Percentage (%) 42.9 11.8 13.5 20.6 5.3 5.9 100.0
Cost efficiency Frequency 4 11 7 35 10 29 96
Percentage (%) 4.2 11.5 7.3 36.5 10.4 30.2 100.0
Technology transfer Frequency 2 4 33 141 35 63 278
Percentage (%) 0.7 1.4 11.9 50.7 12.6 22.7 100.0
Environmental improvement Frequency 3 4 17 116 29 61 230
Percentage (%) 1.3 1.7 7.4 50.4 12.6 26.5 100.0
Domestic reduction Frequency 21 53 42 12 5 4 137
Percentage (%) 15.3 38.7 30.7 8.8 3.6 2.9 100.0
Domestic expenditure Frequency 10 38 15 10 4 2 79
Percentage (%) 12.7 48.1 19.0 12.7 5.1 2.5 100.0
Balance between domestic and international
collaboration
Frequency 4 1 36 207 24 10 282
Percentage (%) 1.4 0.4 12.8 73.4 8.5 3.5 100.0
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–43484342
reduction within the city for Cases 1 and 2 came to 85.2% and87.7%, respectively. These respondents’ choices of alternatives areconsidered to be motivated by a sense of environmental
responsibility. Around 50% of respondents who selected thereasoning of cost efficiency chose alternative 6, i.e. a collaborationratio of 20% with developing countries in Case 1 where the unit
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–4348 4343
cost of reduction in collaboration with developing countries islower than that of domestic collaboration. The selection ratio ofalternative 6 for respondents concerned about cost efficiency ishigher than that of distributions for other reasoning. More than80% of respondents who selected the reasons of contribution astechnology transfer and local environmental improvementthrough collaboration with developing countries chose the alter-natives with collaboration ratio with developing countries of 10–20%, while more respondents of this group of reasoning chosealternatives with a higher collaboration ratio with developingcountries in Case 1. On the other hand, 60–70% of respondentswho think that domestic GHG emissions reduction is a priority orwho think that the city tax should be used domestically, chose analternative with a domestic collaboration ratio of 15–20%. Around60% and 70% of respondents who support the idea that balance isimportant between domestic and international collaboration,chose an alternative with equal ratio between domestic colla-boration and collaboration with developing countries in Cases1 and 2, respectively. These selection ratios are higher than thoseof distributions for other reasoning.
4.2. Logit model results
Several logit models have been developed to examine Japanesecitizens’ preference for regional collaboration on climate changemitigation to achieve the GHG emissions reduction target of thecity in which they reside as shown in Appendix B. Please refer toAppendix B for the details of different models.
Table 9 shows the results of two model estimations(See Appendix B for the process of comparison of differentmodels). Model 1 is used to show overall preference structure ofcitizens without considering the effects of individual character-istics. Model 2, on the contrary, is shown to see the effects ofindividual characteristics on their preference of collaboration/nocollaboration in terms of climate change mitigation that aresupposed to be executed in the cities in which they reside.Sample sizes are different depending on the model. This isbecause as many samples as possible are used for estimationwhere available. The more parameters are used in estimation, the
Table 9Logit model results on regional collaboration.
Variable Model 1
Coefficien
No collaboration �6.004
�Household income
�Reduction within the city
�Technology transfer to developing countries
�Environmental improvement in developing countries
�Reduction within Japan
�Tax expendicture within Japan
�Balanced collaboration
Ratio of collaboration with developing countries 16.009
�Reduction within the city
�Cost efficiency
�Technology transfer to developing countries
�Environmental improvement in developing countries
�Reduction within Japan
�Tax expendicture within Japan
�Balanced collaboration
Ratio of collaboration with developing countries, squared �80.546
Total cost of GHG emissions reduction �6.821
Sample size
Log likelihood function
Akaike Information Criterion
smaller the sample size becomes. Akaike Information Criterion(AIC) is defined as �2 ln Lþ2k, where ln L is the value of loglikelihood with estimated parameters and k is the number ofparameters. Model 1 shows that citizens prefer alternatives witha ratio of collaboration with developing countries of around0.099. This is because the coefficients of ratio of collaborationwith developing countries, and that squared, are both significantat the 5% level. The former is positive and the latter is negative:with respect to the variable r the parabolic curve in the obser-vable component of utility function peaks at r¼�16.009/(2(�80.546))¼0.099 in the estimated Model 1. It is also foundthat citizens prefer an alternative with lower total cost of GHGemissions reduction at the 5% significance level. This is rationalbehaviour when it is interpreted as a reduction of the tax payers’burden.
As is the case of Model 1, Model 2 shows that citizens preferalternatives with the ratio of collaboration with developingcountries of around 0.105 since the coefficients of the ratio ofcollaboration with developing countries, and that squared, areboth significant at the 5% level and the former is positive and thelatter is negative: With respect to the variable r the paraboliccurve in the observable component of utility function peaks atr¼�38.541/(2(�184.330))¼0.105. The following are the perso-nal attributes that statistically have a significant effect on choicesof the alternatives at the 5% level. The respondents who areconcerned about cost efficiency, environmental improvement indeveloping countries, and technology transfer to developingcountries, prefer alternatives with higher ratio of collaborationwith developing countries. On the other hand, respondents, whosupport the idea of GHG emissions reduction within Japan, taxexpenditure within Japan and a balance between domesticcollaboration and collaboration with developing countries, preferalternatives with lower ratio of collaboration with developingcountries. It is also found that more respondents who support theidea of GHG emissions reduction within the city prefer analternative without collaboration, while fewer respondents whosupport the idea of balanced collaboration, GHG emissions reduc-tion within Japan, tax expenditure within Japan, technologytransfer to developing countries and environmental improvement
Model 2
t p-value Coefficient p-value
0.020
�0.410 0.017
3.327 0.000
�2.660 0.000
�1.955 0.001
�3.106 0.000
�1.987
�3.712 0.000
0.000 38.541 0.000
�3.633 0.039
15.654 0.000
6.864 0.000
8.601 0.000
�25.841 0.000
�18.575 0.000
�7.133 0.000
0.000 �184.330 0.000
0.015 �2.717 0.000
1296 1242
�2254.7 �1534.6
4517.3 3103.1
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–43484344
in developing countries, as well as with higher household income,prefer an alternative without collaboration, although no prefer-ence is observed on average. The result is that citizens prefer analternative with a lower total cost of GHG emissions reduction onaverage. Hereinafter the interpretation is made based on theresults of Model 2.
Table 10Distribution of alternative selection by questionnaire types.
Questionnare
type
Alternative 1 2 3 4 5 6 Total
Case 1
1 Frequency 24 14 17 37 20 33 145
Percentage (%) 16.6 9.7 11.7 25.5 13.8 22.8 100.0
2 Frequency 16 13 18 51 21 22 141
Percentage (%) 11.3 9.2 12.8 36.2 14.9 15.6 100.0
3 Frequency 10 11 24 45 17 21 128
Percentage (%) 7.8 8.6 18.8 35.2 13.3 16.4 100.0
4 Frequency 16 10 13 49 10 21 119
Percentage (%) 13.4 8.4 10.9 41.2 8.4 17.6 100.0
5 Frequency 21 17 17 38 16 22 131
Percentage (%) 16.0 13.0 13.0 29.0 12.2 16.8 100.0
Total Frequency 87 65 89 220 84 119 664
Percentage (%) 13.1 9.8 13.4 33.1 12.7 17.9 100.0
Case 2
1 Frequency 25 16 9 48 16 23 137
Percentage (%) 18.2 11.7 6.6 35.0 11.7 16.8 100.0
2 Frequency 15 16 17 60 13 12 133
Percentage (%) 11.3 12.0 12.8 45.1 9.8 9.0 100.0
3 Frequency 9 13 17 56 7 14 116
Percentage (%) 7.8 11.2 14.7 48.3 6.0 12.1 100.0
4 Frequency 13 8 14 47 7 18 107
Percentage (%) 12.1 7.5 13.1 43.9 6.5 16.8 100.0
5 Frequency 18 23 18 46 7 12 124
Percentage (%) 14.5 18.5 14.5 37.1 5.6 9.7 100.0
Total Frequency 80 76 75 257 50 79 617
Percentage (%) 13.0 12.3 12.2 41.7 8.1 12.8 100.0
Table 11Estimation results of logit model on the effects of project and co-benefits information.
Variable Model 3
Coefficient
No collaboration �10.306
�Questionnaire type 1 6.172
�Questionnaire type 2 1.816
�Questionnaire type 3 3.699
�Questionnaire type 4 9.743
Ratio of collaboration with developing countries 13.104
�Questionnaire type 1 6.021
�Questionnaire type 2 2.168
�Questionnaire type 3 2.567
�Questionnaire type 4 5.501
Ratio of collaboration with developing countries, squared �82.221
Total cost of GHG emissions reduction �11.457
�Questionnaire type 1 6.014
�Questionnaire type 2 2.195
�Questionnaire type 3 4.695
�Questionnaire type 4 10.344
Sample size 1296
Log likelihood function �2240.9
Akaike Information Criterion 4513.7
It is worth mentioning the potential effect of demographicvariables such as gender, age, and household income, in order tosee the difference of the results obtained from the sample and theactual state that represents the population of two cities. Table 9shows that, at the 5% significance level, household income affectsthe preference to choose the alternative of no collaboration withother regions either in Japan or in developing countries; respon-dents with higher household income chose the alternative ofcollaboration with other regions. Though the representation ofthe sample vis-�a-vis the population in terms of household incomedistribution is considered to affect the result and interpretation,the household income distribution data in two cities is notavailable and hence the difference of distributions between thesample and the population is not confirmed. In addition, althoughthe samples used in the study do not represent the populationwell in terms of the distribution of age, Model 2 in Table 9 showsthat statistically, there is no significant difference of preferencedue to the difference of the age, and hence we can omit the effectof age in our inference.
4.3. Effects of information provided on mitigation projects and their
co-benefits
Table 10 shows distributions of choices according to ques-tionnaire type in terms of the ratio of collaboration with domesticregions and developing countries to achieve the city’s GHGemissions reduction target, for two cases of different unit costof GHG emissions reduction in collaboration with developingcountries. The chi-squared test indicates that the distributionsof choices are not different according to the questionnaire at thesignificance level of 5% for Cases 1 and 2 (Case 1: w2(20)¼21.851,p¼0.349; Case 2: w2(20)¼30.668, p¼0.060).
Table 11 shows the estimation results of the logit model thatincludes the effects of information provided on project descrip-tions and co-benefits of collaboration. The observable compo-nents of utility functions for alternatives are defined in the samemanner as those for Model 1 in Table 9 except that the individualattribute of dummy variable to show questionnaire types 1 to 4 isadded as a cross-term for linear variables. Model 3 includes
Model 4 Model 5
p-value Coefficient p-value Coefficient p-value
0.082 �0.455 0.969 �0.005 0.987
0.436 �3.612 0.820 0.975 0.017
0.825 1.571 0.927 �0.041 0.923
0.664 �8.910 0.639 �0.402 0.402
0.250 �4.328 0.806 0.490 0.277
0.000 18.956 0.000 15.418 0.000
0.004 1.745 0.721 7.334 0.002
0.305 8.124 0.125 2.879 0.213
0.236 �6.378 0.246 3.529 0.135
0.013 3.773 0.503 6.611 0.007
0.000 �109.130 0.000 �100.110 0.000
0.075 �2.561 0.843 – –
0.485 �4.330 0.802 – –
0.805 1.332 0.943 – –
0.611 �8.544 0.679 – –
0.260 �5.345 0.780 – –
306 623
�463.1 �1064.3
958.1 2150.6
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–4348 4345
variables of all questionnaire types in the cross-terms. Model 4is the result of Model 3 when it is applied for respondents whoare considered to have a sense of environmental responsibility,i.e. those who selected the reasoning of reduction within the cityand did not select the reasoning of tax expenditure within Japan.Lastly Model 5 shows the result for only Case 2 where unit cost ofGHG emissions reduction in collaboration with developing coun-tries is the same as that of domestic collaboration. Model 5 doesnot have variables of total cost of GHG emissions reductionbecause alternatives 2–6 in Case 2 have the same total cost.
Model 3 shows that the coefficients of cross-terms of ratio ofcollaboration with developing countries with questionnaire types1 and 4 are greater than that with questionnaire type 5 at the 5%significance level, while the coefficients of cross-terms of ratio ofcollaboration with developing countries with questionnaire types2 and 3 are not statistically significantly different from that withquestionnaire type 5. This indicates that information on projectdescriptions and co-benefits provided in questionnaire types 1 and4 increases the choice of an alternative with a higher ratio ofcollaboration with developing countries, since questionnaire type5 does not include such information. It is hard to draw a systematicinterpretation of citizens’ preference for the types on climate changemitigation projects for collaboration, given the results we obtained.Model 3 also shows that the questionnaire type does not haveinfluence on the choice over collaboration or no collaboration, noron the total cost of GHG emissions reductions.
Model 4 depicts different results for respondents who wereconsidered as having a sense of environmental responsibility. Forthose respondents, information about project description andco-benefits, as well as total cost of GHG emissions reductions,does not affect their choice of alternatives on collaboration, asshown by the fact that the coefficients of cross-term withquestionnaire types 1–4 are not significant at the significant levelof 5%. Variables of no collaboration and total cost are notsignificant in Model 4 either.
Model 5 confirms that the effect of information on projectdescriptions and co-benefits remains even in Case 2 where no costdifference exists between domestic collaboration and collaborationwith developing countries, since Model 5 shows that the coefficientsof cross-terms of ratio of collaboration with developing countrieswith questionnaire types 1 and 4 are greater than those withquestionnaire type 5 at the 5% significance level. This indicates thatthe additional information provided by questionnaire types 1 and4 enhanced the collaboration with developing countries more thandomestic collaboration while questionnaire types 2 and 3 statisti-cally did not make such a significant difference. It was alsoconfirmed that the questionnaire type 1 had the effect of letingrespondents select ‘‘no collaboration’’ more often at the 5% signifi-cance level. Thus additional information on project descriptionsenhanced both of international collaboration and the avoidance ofcollaboration in the case of questionnaire type 1.
5. Discussion and conclusion
The regression analysis finds there is a preference towards theratio of collaboration with developing countries similar to the ratio ofcollaboration with domestic regions. Citizens who are concernedabout cost efficiency, environmental improvement in developingcountries, and technology transfer to developing countries preferalternatives with higher ratio of collaboration with developingcountries, while citizens who support the idea of GHG emissionsreduction within Japan, tax expenditure within Japan, and balancebetween domestic collaboration and that with developing countries,prefer alternatives with a lower ratio of collaboration with develop-ing countries (Model 2 in Table 9). The different preferences of
respondents with different selection reasoning indicated in theregression model are overall consistent with the distribution ofaggregated data in Table 8. The regression analysis also confirmsthat citizens who support the idea of GHG emissions reductionwithin the city prefer to reduce GHG emissions without collaboration(Model 2 in Table 9). Therefore we find that a sense of environmentalresponsibility makes citizens move away from collaboration to acertain degree even when the collaboration has co-benefits.
Regression analysis also finds that respondents prefer a lowertotal cost of GHG emissions reductions (Model 2 in Table 9)although this is not the case for respondents who have a sense ofenvironmental responsibility (Models 5 in Table 11). The generalpreference is consistent with the results of the Wilcoxon signedrank test that confirms the different distributions for Cases 1 and2. The distribution is inclined towards a higher ratio of collabora-tion with developing countries in Case 1 where the total cost ofGHG emissions reduction is lower when the ratio of collaborationwith developing countries is higher.
The analysis of the effect of the different questionnaire typesshows that information provided on projects and co-benefits ofcollaboration for collaborating regions might increase the selectionof profiles that have higher ratio of collaboration with developingcountries depending on the types of low-carbon developmentprojects in Japan and in developing countries, though it does notaffect the selection between no collaboration and collaboration andthe effects of total cost of GHG emissions reduction (Model 3 inTable 11). The pair of projects in domestic collaboration andcollaboration with developing countries, which are used to explainthe details of collaboration, would affect the choice. The respondentswho have a sense of environmental responsibility, i.e. who selectedthe reasoning that ‘‘the city should achieve its emissions reductiontarget without collaboration with other regions’’ and did not selectthe reasoning of ‘‘city tax shall be used domestically’’ are notaffected by provision of information on project details andco-benefits (Model 4 in Table 11). Since descriptions of projectsand co-benefits are provided for both domestic projects and projectsin developing countries, the additional information enhanced thecollaboration with developing countries more than domestic colla-boration as confirmed in particular in Case 2 where a no-costadvantage lies in the alternative with a higher ratio of collaborationwith developing countries in the case of certain project types, whilesuch an effect is not observed in the case of other project types(Model 5 in Table 11).
To conclude, it is confirmed that, in the case of local govern-ment’s regional collaboration on GHG emissions reduction, threepreferences are observed among Japanese citizens, given that theunit cost of collaboration is assumed to be lower than that of nocollaboration: (1) a sense of environmental responsibility (reductionof GHG emissions within the city without any collaboration toachieve the target), (2) domestic orientation (priority on domesticcollaboration), and (3) a preference to collaboration with developingcountries. In general, a balanced collaboration with domestic partnerregions and developing countries is preferred in the case ofcollaboration. Appreciation of benefits such as technology transferand local environmental improvement in partner regions increasesthe preference of collaboration with developing countries. Prefer-ence of lower total cost of GHG emissions reduction is confirmedexcept for citizens with a sense of environmental responsibility.Information provision regarding descriptions and co-benefits ofmitigation projects would increase the citizens’ support of higherratio of collaboration with developing countries, depending on thetypes of climate change mitigation projects for collaboration andtheir combination, though this effect does not exist for citizens witha sense of environmental responsibility.
Although there is some controversy about the utilisation ofcarbon crediting to mitigate global climate change, there is a
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–43484346
chance that international carbon crediting by local governmentsleads to more financing for mitigation projects in developingcountries with the support of citizens when the co-benefits of theprojects are secured and communicated to the citizens in atransparent manner. Citizens who support the idea of interna-tional carbon crediting with developing countries, however, donot seem to be the same as those who would support traditionalenvironmental protection activities, as exemplified by the pre-ference of citizens with a sense of environmental responsibility inthis study.
Acknowledgements
The authors are grateful to the Environment Research andTechnology Development Fund (E-0906), Ministry of the Environ-ment, Japan and Kanagawa Prefectural Government’s grant toIGES for their financial support to the study. Prof. MitsuruTanaka’s encouragement is also greatly appreciated. This workwas inspired by discussion with Prof Takahiro Nakaguchi. Thecomments provided by Prof. Yoichiro Higuchi and anonymousreviewers were helpful to improve the paper.
Appendix A. Part of questionnaire used in the survey
Q.3 (Case 1)
Unit cost of reduction
Reduction within the city
JPY 20,000 Domestic collaboration JPY 12,000 Collaboration with developing countries JPY 60001 2 3 4 5 61 2 3 4 5 6
Domestic 5% 10% Developing Collabo. w/ d l icollabora-
tion (JPY DomesticDomestic
10% p gcountries
15%developingcountries(6 000 /
Reduc-
12,000 / ton) 20%
15%Domestic
10%
15%
5%(6,000 /
ton) 20%
tionwithin Reduc
the city
Reduc Reduc Reduc Reduc
(JPY
-tion -tion -tion -tion -tion
20,000
withinthe
withinthe
withinthe
withinthe
withinthe
/ ton)
with
dev
elop
ing
coun
trie
s
citycitycitycitycity
100% 80%
Rat
io o
fre
duct
ion
with
in th
e ci
ty,
80% 80% 80% 80%
dom
estic
col
labo
ratio
n an
d co
llabo
ratio
n
Total amount f d ti
340,000 ton 340,000 ton 340,000 ton 340,000 ton 340,000 ton 340,000 tonof reduction
Total cost of 6,800 6,256 6,154 6,052 5,950 5,848ota cost oreduction
6,800million yen
6, 56million yen
6, 5million yen
6,05million yen
5,950million yen
5,8 8million yen
Q.4 (Case 2)
Unit cost of reduction
Reduction within the city
JPY 20,000 Domestic collaboration JPY 12,000 Collaboration with developing countries JPY 12,0001 2 3 4 5 61 2 3 4 5 6
Domesticll b
5% 10% Developing Collabo. w/ d l icollabora-
tion (JPY 12 000 /
DomesticDomestic
10% p gcountries
15%developingcountries(6 000 /
Reduc-
12,000 / ton) 20%
15%Domestic
10%
15%
5%(6,000 /
ton) 20%
tionwithin
the city Reduc Reduc Reduc Reduc Reduc
(JPY -tion-tion -tion-tion -tion
within within20,000
withinthe
withinthe
withinthe the the/ ton)
city city city city city100%80% 80% 80% 80% 80%
Total amount f d ti
340,000 ton 340,000 ton 340,000 ton 340,000 ton 340,000 ton 340,000 tonof reduction
Total cost of 6,800 6,256 6,256 6,256 6,256 6,256ota cost oreduction
6,800million yen
6, 56million yen
6, 56million yen
6, 56million yen
6, 56million yen
6, 56million yen
Rat
io o
fre
duct
ion
with
in th
e ci
ty,
dom
estic
col
labo
rati
on a
nd c
olla
bora
tion
with
dev
elop
ing
coun
trie
s
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–4348 4347
Appendix B. Logit models used in the analysis
The variables for the observable component of utility functionin Model 1 include ratio of GHG emissions reduction throughcollaboration with developing countries and total cost of GHGemissions reduction (normalised with total cost of GHG emissionsreduction within the city) to achieve the city’s target. Taking intoaccount the distribution with a peak as shown in Table 5, thesquare of the ratio of GHG emissions reduction through collabora-tion with developing countries is also included in the model.There are six alternatives. The observable component of utilityfunction for the alternative of no collaboration is an alternativespecific constant to be estimated. The observable component ofutility functions for logit model (Model 2) is as follows: r is ratioof GHG emissions reduction through collaboration with develop-ing countries, c is total cost of GHG emissions reduction normal-ised by that for the case in which reduction is achieved withoutcollaboration, X is a vector of individual attributes, and a0, a, br,brr, bc and yr are coefficients to be estimated. The effects ofindividual attributes on the variable of total cost of GHG emis-sions reduction are not examined for the models of Table 9 toavoid over-parameterisation. The alternative 1 is regarded as adummy profile in terms of no collaboration. It should be notedthat the ratio of GHG emissions reduction with domestic colla-boration is calculated as the difference between 0.2 and the ratio
Table A1Logit model results on regional collaboration.
Variable Model 1a
Coefficien
No collaboration �3.680
� Female �0.344
�Age 0.007
�Household income �0.437
�Kitakyushu city 0.131
�Concern in climate change 0.053
�Concern in international development 0.283
�Number of climate protection actions �0.091
�Reduction within the city 3.329
�Cost efficiency 0.112
�Technology transfer to developing countries �2.601
�Environmental improvement in developing countries �1.734
�Reduction within Japan �3.162
�Tax expendicture within Japan �1.817
�Balanced collaboration �3.818
Ratio of collaboration with developing countries 34.094
� Female 2.151
�Age �0.075
�Household income 0.468
�Kitakyushu city 2.425
�Concern in climate change �2.236
�Concern in international development 2.615
�Number of climate protection actions �0.333
�Reduction within the city �4.129
�Cost efficiency 15.658
�Technology transfer to developing countries 6.603
�Environmental improvement in developing countries 9.171
�Reduction within Japan �26.751
�Tax expendicture within Japan �16.767
�Balanced collaboration �7.595
Ratio of collaboration with developing countries, squared �192.902
Total cost of GHG emissions reduction �6.325
IV parameter (No collaboration, fixed)
IV parameter (5 alternatives)
Sample size
Log likelihood function
Akaike Information Criterion
of GHG emissions reduction in collaboration with developingcountries, and therefore it is not an independent variable here.
V1 ¼ a0þaX ð1Þ
Viði¼ 2�6Þ ¼ ðbrþyrXÞrþbrrþr2þbcc ð2Þ
Table A1 shows the different models estimated to select theoptimal models for Table 9. To compare with Model 1 in Table 9,Model 1a in Table A1 shows the effects of individual specificattributes including gender, age, household income, city of resi-dence, concern about climate change, concern about internationaldevelopment, the number of climate protection actions in dailylife, as well as dummy variables if the respondent chose a specificreason for selecting collaboration alternatives. It should be notedthat the reasoning selected could be different for Cases 1 and 2 foridentical respondents and hence the respondent who respondedto Cases 1 and 2 is treated as a different individual in this modelsetting. This treatment was adopted since only 61% of respon-dents selected the same reasoning and a larger sample size ispreferred. Model 2 in Table 9 consists of individual specificattributes that are significant in Model 1a.
The result of the nested logit model for Model 2 is shown asModel 2a in Table A1. The statistics of the likelihood ratio test¼�2(�1534.563�(�1534.551))¼0.024 is smaller than 3.84, chi-squared with a degree of freedom of unity at 5% significance level.
Model 2a
t p-value Coefficient p-value
0.385
0.285
0.947
0.021 �0.407 0.018
0.685
0.823
0.092
0.121
0.000 3.333 0.000
0.832
0.000 �2.683 0.000
0.005 �1.984 0.002
0.000 �3.034 0.000
0.000 �1.944 0.000
0.000 �3.683 0.000
0.000 38.618 0.000
0.135
0.881
0.524
0.089
0.040
0.002
0.216
0.025 �3.621 0.040
0.000 15.734 0.000
0.000 6.845 0.000
0.000 8.596 0.000
0.000 �25.876 0.000
0.000 �18.642 0.000
0.000 �7.156 0.000
0.000 �184.569 0.000
0.149 �2.689 0.000
(1.000) –
0.957 0.001
1205 1242
�1474.3 �1534.6
3012.6 3105.1
H. Nakamura, T. Kato / Energy Policy 39 (2011) 4337–43484348
Therefore IV parameter of Model 2a, i.e. 0.957 is not significantlydifferent from unity (Note that the p-value of IV parameter in TableA1 shows that IV parameter is significantly different from null withthe significance level of 5%). AIC of Model 2a is larger than that ofModel 2. Hence the nested logit model of Model 2a is not consideredappropriate. Therefore the interpretation is made based on theresults of Model 2.
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