A Life Cycle Assessment of Carbon Mitigation Possibilities in Metropolitan Areas

Transcription

1 A Life Cycle Assessment of Carbon Mitigation Possibilities in Metropolitan Areas Jukka Heinonen Researcher Aalto University School of Science and Technology Finland Professor Seppo Junnila, Aalto University School of Science and Technology, Finland, Project Development Manager Matti Kuronen, YIT Corporation, Finland, 1. Introduction Climate change mitigation is one of the greatest challenges of coming decades with the focus on carbon emissions. Cities seem to earn special attention, since they have been estimated to produce up to 80 % of global greenhouse gases. Life cycle assessment (LCA) method has been in focus for the present decade in search for reliable and easy-to-use tools that could provide the information to support the decision making. Input-output life cycle assessment method (IO-LCA) seems to offer a way to create useful platform for strategic decision making. The method is fast and rather easy to use, and it relies mostly on existing data from existing administrative systems. In this study we examine the possibilities that IO-LCA based screening-lca approach provides for modeling consumer behavior and its climate change implications for urban development purposes. The study also tests whether urban structure and income level related GHG emissions can be identified with the method. 2. Method and design of the study The study was conducted utilizing IO-LCA based screening-lca method. The screening-lca method refers to quick modeling of a phenomenon for strategic decision making. Two input databases were utilized in the study. Primary database analyzed was Finnish consumer survey To confirm the results, a longitudinal study using Finnish consumer survey 2001 data was conducted. The IO-LCA model utilized in the study is an adjusted Carnegie Mellon University s EIO-LCA 2002 model. The model was chosen, since with its 428 sectors it provides better sector matching accuracy than any other model available. In addition, the Finnish economy a small open economy with more than 50 % of the value of total consumption oriented to import goods. The screening-lca was conducted for average consumers of seven Finnish metropolitan case cities: Espoo, Helsinki, Hyvinkää, Kerava, Nurmijärvi, Porvoo and Vantaa. All of the cities are situated in Helsinki region, and represent the area where the urban sprawl occurs in Finland. The study advanced in three phases. First, the consumption data were aggregated into 41 groups

2 from the original over categories. Second, the groups were matched with EIO-LCA industry sectors, and merged further to 10 consumption classes. In the third phase, the per capita carbon footprints for the seven case cities in Helsinki region were calculated and analyzed. 3. Results The screening-lca results indicate that there are significant differences in the emissions caused by consumers living in different types of metropolitan areas. It also seems that a significant share of the difference comes from urban structure factors. The carbon footprint of a consumer is dominated by energy consumption related to housing, other housing related activities and private driving. These are also categories with high variation, making them the key points when analyzing the results, especially with an urban development perspective. The cities without commuter train connection, and with less dense urban structure, seemed to cause higher GHG emissions. The overall scores in tons of CO2 e were (private consumption in parenthesis) Hyvinkää 12.1 ( ), Vantaa 13.8 ( ), Kerava 15,2 ( ), Porvoo 15.1 ( ), Helsinki 15.3 ( ), Espoo 17.3 ( ) and Nurmijärvi 18.4 ( ). 4. Discussion In the light of the study it seems that consumer carbon footprint can be effectively modeled with EIO-LCA method based on private consumption. The method seems to provide good information with rather high robustness according to the sensitivity analyzes conducted. According to this study an average Finnish consumer is responsible for 13.6 t CO2 e GHG emissions on annual basis. For validation of this result we calculated a comparable figure with the data from the Finnish ENVIMAT study, this result being 10.1 t CO2 e. Junnila, in his earlier study, has calculated a carbon footprint per capita of 14.6 t CO2 e for a Finnish consumer. In addition the Finnish national emissions inventory showed a per capita footprint of 15.2 t CO2 e. The carbon footprint of a consumer is dominated by building energy, other housing related activities and private driving. These are also categories with high variation among the case cities, making them the key points when analyzing the results with an urban development perspective. It would seem that the difference in housing related energy consumption is only moderate between apartment building based metropolitans and those dominated by detached houses. More variation was found from private driving, indicating, that less driving is needed in more dense metropolitan areas due to better proximity of workplaces and services. Also, the cities without commuter train connection seemed to cause higher GHG emissions. The case studies indicate also, that the correlation between the level of consumption and the carbon emissions, while tight, is not linear but diminishing. Also, the carbon intensity of consumption of goods seems only slightly higher than that of services, when housing and transportation are left out of the examination. 5. Conclusions The basic intention of this study was to test EIO-LCA based screening-lca method in modeling carbon footprint of a consumer for urban development purposes using consumption data as inputs. It would seem that the approach is applicable in creating effective and easy-to-use management tools for urban development purposes. The study brought new and relevant information about the significance of urban structure into further discussion. The study revealed a difference in emissions of over 50 % between the best and the worst case city. This result indicates that there is room for significant development even without any new technology breakthroughs.

3 A Life Cycle Assessment of Carbon Mitigation Possibilities in Metropolitan Areas Jukka Heinonen Researcher Aalto University School of Science and Technology Finland Professor Seppo Junnila, Aalto University School of Science and Technology, Finland, Project Development Manager Matti Kuronen, YIT Corporation, Finland, Summary Climate change mitigation is one of the greatest challenges of coming decades. This study tests the applicability of EIO-LCA based screening-lca method for modeling GHG emissions caused by consumers living in metropolitan areas, and creates new perspective on utilization of the method in urban development. We propose a consumption based approach that allows modeling of emissions regardless of their regional origin. The results show that carbon footprint of consumers can be effectively and reliably modeled with the screening-lca. In addition, an analysis of the factors causing regional variations can be conducted with the method. The model utilized in this study seems to reveal well both urban structure and income related differences in consumer carbon footprint. The case study of seven Finnish metropolitan cities shows a variation of over 50 % in carbon emissions per capita. This would indicate that consumption based screening-lca approach could provide an usable platform for creation of management tools for future urban development. Keywords: screening-lca, EIO-LCA, GWP, climate change, carbon footprint, carbon emissions, urban development, environmental management, consumption 1. Introduction Climate change mitigation is one of the greatest challenges of coming decades [1, 2] with the focus on carbon emissions [3]. Recognition, control and reduction of green house gas (GHG) emissions together with adaptation strategies are argued to be the keys to maintain and raise our standard of living in the future [4, 5, 6]. A lot of research has already taken place to develop clean production technologies. In addition to the technological improvements, we also need system level information about the emissions related to consumer behavior, where larger infrastructure, life style and technology systems are being examined from the end user point of view [24]. Recently, built environment has been found as the single most significant source of economically viable climate mitigation potential [7, 8]. Cities seem to earn special attention, since they have been estimated to produce up to 80 % of global greenhouse gases [30]. It has also been stated that a new focus on urban development is needed, since local authorities are the key in effective implementation of mitigation programs [19]. Radical improvements on urban development call for management tools to create fast, reliable and comparable information about the effects of development projects on the environment.

4 Life cycle assessment (LCA) method has been in focus for the present decade in search for reliable and easy-to-use tools that could provide the needed climate change and other environmental information to support the decision making. Until recent development of input-output LCA method, the more often used process based LCA approach has been too laborious to be used widely outside scientific communities at least when complex systems are considered. [9, 10, 11] Input-output life cycle assessment method (IO-LCA) seems to offer a way to create useful platform for strategic decision making. The method is fast and rather easy to use, and it relies mostly on existing data from existing administrative systems [11]. However, the practical management circumstances of how and where it can be used needs further research. In this study we step on an important, but yet narrowly covered, field by examining the possibilities that IO-LCA based screening-lca approach provides for modeling consumer behavior and its climate change implications for urban development purposes. With screening-lca we mean a holistic and efficient monetary based modeling of carbon emissions for decision making. We propose that demand and consumption approach is especially relevant for urban development since it provides a possibility to predict consumer behavior and its environmental implications and further to simulate environmental effects of the coming development projects with the consumer behavior taken into account. The purpose of this study is to examine the applicability of IO based screening-lca method in modeling GHG emissions of consumption in a specific urban structure, and to create new perspective on utilization of the method in urban development. The study also tests whether urban structure and income level related GHG emissions can be identified with the method. In the study we utilize city level Finnish consumer survey data as inputs to an adjusted economic input-output life cycle assessment model (EIO-LCA) [13], to investigate the differences between different types of metropolitan areas to bring out the most important factors contributing to the GHG emissions. Finally we interpret our findings and create a synthesis on their significance for urban development. 2. Method and design of the study 2.1 Method The study was conducted utilizing life cycle assessment (LCA) method. An LCA, measuring the full life-cycle effects of the studied system, has become widely acknowledged method for environmental impact assessments. The screening-lca method utilized in this study refers to quick modeling of a phenomenon for strategic decision making. In this study we use an adjusted IO-LCA model, described below in more detail. 2.2 Input data We utilized two input databases in our study. Primary database analyzed with the IO-LCA model is Finnish consumer survey 2006 [12]. To confirm the results, a longitudinal study using Finnish consumer survey 2001 data was conducted simultaneously and the results analyzed with the same model. The primary input data of Finnish consumer survey includes observations and telephone interviews of consumers' consuming behavior. The level of detail of the survey data is high including over categories and sub-categories of goods and services. The data are available on national, regional and city level, of which the national and city/municipality level data were utilized in this study. Whereas the level of detail of the data is high, a few adjustments were needed to fit the data with

5 the IO model. First, in Finland, a notable share of consumed heat and electricity are actually embedded in rent and housing management charges. These expenditures were aggregated to heat and energy consumption according to Kiiras et al. [37] study and our updating study with 10 financial statements of housing corporations located in Helsinki metropolitan area. These studies showed that the average share of energy costs of the overall maintenance and operation costs is notable, approximately 25 %. Second, as the property costs include costs of both building and land, these were disaggregated to two different cost categories. The division was made according to the statistics published by The Housing Finance and Development Centre of Finland (ARA) [38]. As the third step, lease payment and housing management charges were divided into more detailed categories. The disaggregation was made according to Kiiras et al. [37] and the financial statements study mentioned above. 2.3 IO-LCA model The IO-LCA model utilized in the study is an adjusted Carnegie Mellon University Green Design Institute's Economic Input-Output Life Cycle Assessment US 2002 Industry Benchmark model [11, 13]. The US industry benchmark model was chosen, since with its 428 sectors it provides far better accuracy in sector matches with the input data than any other model available. For example, recently developed Finnish input-output model (ENVIMAT) divides the economy for only 151 industry sectors [14], which did not seem to offer enough diversity for the purpose of this study. In addition, the Finnish economy is highly dependent on import and export sectors with more than 50 % of the value of total consumption oriented to import goods [34]. At the same time, the share of environmentally significant industries in economy, such as mining of coal and extraction of crude petroleum and natural gas, is moderate. There exist some uncertainties related to temporal (inflation and currency rate differences) and regional (industry structure differences) uniformity of the input data and the model as the model utilized is US industry benchmark model. To overcome the temporal compatibility problem, we used purchasing power parity (PPP) adjustment based on the World Bank s The International Comparison Program (ICP) data [35]. The same method was utilized in the recent study of Weber and Matthews concerning the global aspects of the American household carbon footprint [22]. The second challenge, the potential incompatibility of sectoral carbon emissions profiles, seems to have minor significance. Junnila has compared the process and IO emissions profiles concluding that the profiles are similar especially among the most important sectors in our study [9, 36]. In addition, the robustness of the result was tested with a comparison to the ENVIMAT study, which showed similar results especially concerning dominant sectors. Further discussion about the model and data uncertainties may be found from Data quality analysis section. 2.4 Case cities The carbon footprint assessment and analysis was conducted for average consumers of seven Finnish metropolitan case cities: Espoo, Helsinki, Hyvinkää, Kerava, Nurmijärvi, Porvoo and Vantaa. With the assessments we were able to both analyze the composition of the footprints and demonstrate the division of the emissions to urban structure and life style related emissions. The seven Finnish cities were chosen for the case study as Finland provides well fitting regional basis for the study and data availability is excellent. In 2005 Finland ranked 20th in global ranking of carbon footprints per capita [33]. All of the cities are situated in Helsinki region, and represent cities with high dependence of Helsinki metropolitan area, as well as the area where the urban sprawl occurs in Finland. Helsinki is the capital of Finland and the centre of Helsinki region, an area of over one million inhabitants. Helsinki has also the most diverse public transportation system in Finland with metro, commuter train, tram and bus connections available. Espoo and Vantaa were chosen because they

6 are the closest neighbours of Helsinki with diverse public transportation systems, high urban density and high workplace dependency on Helsinki. Together with Helsinki they form the core of metropolitan area. Kerava and Hyvinkää represent cities further away (30-50 km), but with good commuter train connection. Porvoo and Nurmijärvi are situated within the same range (30-50 km) from Helsinki, but their public transportation systems are based on bus connections. All the studied cities around Helsinki also have fast highway connections that motivate private driving, and lower urban density than Helsinki measured in both residential area per inhabitant and inhabitants per total land area of the city [15, 16]. With this selection of cities we aimed to demonstrate how urban and transportation infrastructure together with standard of living are reflected in the carbon footprint of an average inhabitant of these cities. Table 1 represents some key figures of the cities. Table 1 Key figures of the case cities Espoo Helsinki Hyvinkää Kerava Nurmijärvi Porvoo Vantaa Inhabitants Inhabitants / km Living space / inhabitant (m2) Private consumption ( ) Design of the study The study started with aggregation of the consumption data. The data were grouped into 41 consumption groups from the original over categories on basis of consumption type, object and relevance for urban design. These groups were then matched with EIO-LCA model's 428 industry sectors for the calculations. After the sector matching, a first rough picture of the environmental impact of an average Finnish consumer was created by using national consumption averages as inputs. The 41 consumption groups were then grouped into 10 consumption classes from housing related lifestyle issues (building, purchased energy, etc.) to daily goods and consumption of services. The classes are: 1. Heat and electricity 2. Building and property 3. Maintenance and operation 4. Private driving 5. Public transportation 6. Consumer goods 7. Leisure goods 8. Leisure services 9. Travelling abroad 10. Health, nursing and training services Of these 10 major classes, Heat and electricity include all household energy and a share of building energy. Building and property class consists of construction and planning. Maintenance and operation class comprise expenses of maintenance construction, waste, water, home appliances and property management. Private driving comprises all expenses related to driving, purchases and maintenance of private vehicles. Other domestic travelling is comprised to class Public transportation. Goods and services classes include daily consumption and consumption of durable goods. Of these, leisure related expenses are separated for demonstration of the allocation of emissions and lifestyle differences. Flying and accommodation abroad form the class Travelling abroad. Finally, Health, nursing and training services comprise consumption groups where free or heavily subsidized public services dominate consumption. Thus, the monetary expenses related to these are small and consist primarily of consumption of private services. At this point we tested our selection of EIO-LCA industry sectors by altering them to find out the

7 sensitiveness of the results in relation to sectoral choices. This testing was executed for those consumption categories that had multiple somewhat similarly fitting industry sectors available. With the tested selection of sectors we calculated the per capita carbon footprints for the seven case cities in Helsinki region: Espoo, Helsinki, Kerava, Hyvinkää, Nurmijärvi, Porvoo and Vantaa. 3. Results The screening-lca results indicate that there are significant differences in the environmental load and the distribution of emissions caused by consumers living in different types of metropolitan areas. Part of the difference can be explained by differences in disposable income, but a significant share of the difference comes from regional factors such as infrastructure and type of urbanization. According to the Finnish consumer survey data, an average consumer in Finland spent in consumption in Along with the screening-lca model this average consumer directly or indirectly was responsible for GHG emissions of approximately 13.6 tons of carbon dioxide equivalents (t CO2 e). Table 2 summarizes the results of the screening-lca calculations. Table 2 Annual carbon footprint (t CO2 e) and private consumption ( ) per capita in the case cities Hyvinkää Vantaa Kerava Porvoo Helsinki Espoo Nurmijärvi Private consumption Carbon footprint, total Building energy Building and property Maintenance & operation Private driving Public transportation Travelling abroad Consumer goods Leisure goods Leisure services Health, nursing and training services When looking at Table 2, we see that clearly the most significant source of carbon emissions in all cities is Building energy accounting approximately for % of the total emissions. Also the most significant differences between the cities (in absolute terms) are found from this group, from 5.6 t CO2 e in Hyvinkää and Vantaa to 7.4 in Nurmijärvi. Other housing related categories, Building and property and Maintenance and operation, form together another significant part of the carbon footprint. Together they seem to account for % of the carbon emissions. Another source of differences between the cities is found from emissions related to private driving. Here the cities with commuter train connections, Helsinki, Hyvinkää, Kerava, Vantaa and Espoo all have lower emissions than the cities where public transport is based on bus connections, namely Nurmijärvi and Porvoo. The figure of Espoo and Porvoo is actually approximately the same, 2.2 t CO2 e, but the level of private consumption is notably higher in Espoo. The differences between the regions within the category of public transportation are significant only in relative level, as public transport form a substitute to private driving. Thus, the figures should be evaluated together with emissions from private driving. The rest of the consumption groups, Consumer goods, Leisure goods, Leisure services, Travelling abroad, and Health, nursing and training services, account together for up to one third of the total emissions. While this expenditure on goods and services is further away from the domain of urban development, some interesting notions are made in the next chapter.

8 These remaining groups are the ones often left out from production oriented carbon studies. However, living lifestyles affect these emissions notably. According to this study, it seems that in more urban lifestyles consumers eat more outside, go to movies more and use health and beauty services more, and thus outsource their carbon emissions to services. The Figure 1 exhibit the division of emissions and the differences between the cities. The line indicating private consumption exhibits relatively high dependence between GWP and private consumption. However, in a more detailed analysis, the line also demonstrates the advantage of higher urban density, more effective public transportation and higher proximity to workplaces and services, as Porvoo and Nurmijärvi have the weakest consumption-gwp ratios. This ratio is exhibited by the distance between the line indicating private consumption and the emissions bar. 20,00 18, Health, nursing and training services Leisure services Annual carbon footprint, t CO2 e 16,00 14,00 12,00 10,00 8,00 6,00 4, Private consumption, per capita Leisure goods Consumer goods Travelling abroad Public transportation Private driving Maintenance & operation 2,00 0,00 0 Building and property Building energy Private consumption Fig. 1 The division of emissions and private consumption in the case cities 4. Data quality analysis We utilized two databases in this study. Primary database used was Finnish consumer survey For validation of the results, earlier survey from 2001 was analyzed. While the sample is large in consumer surveys (9 858 consumers in 2006 survey and in 2001 survey) and the data reliable on national level, results concerning smaller regions might be biased at some points due to small number of observations. The most reliable figures concern daily consumption and other frequently occurring expenditures. Contrary, the expenditures on durable goods may have more variations due to coincidence. In addition, public services are mostly excluded from the data. In Finland, he difference is notable especially concerning our merged consumption area "Health, nursing and training services" which comprises expenditure categories where public participation is predominant. Taking heavily subsidized public services into account would change the overall picture within the above mentioned category [14], but since the category was presumed to have a minor influence in the scope of the study (urban development) no amendatory actions were taken. A more comprehensive discussion about the uncertainties related to consumer survey data can be found from Weber and Matthews [22]. Another type of data reliability problem arises from temporal (inflation and currency rate differences)

9 and regional (industry structure differences) uniformity of data and the model. In this study, the EIO-LCA 2002 US Industry benchmark model [13] forms the basis of our model. We assessed this as the most reliable model for two reasons. First the EIO-LCA 2002 model is the most disaggregated IO-LCA model available with its 428 economy sectors, which is relevant in our study for the high level of disaggregation of the input data. Second, imports account for over 50 % of total consumption in Finland [39]. In addition, Junnila has shown that Finnish and US economies are similar enough for a model built for one economy to describe the other [10]. Especially similar enough are sectors important for this study. Concerning the temporal uniformity problem, we used purchasing power parity (PPP) adjustment according to the data published by The World Bank [40]. The same method was recently utilized by Weber and Matthews [22]. Related to these is the problem of prices used. The model is built on producer prices whereas we utilize consumer prices. The difference in Finnish taxation system consists mainly of value added tax (VAT), meaning scale type of bias. This affects little the results of our study, as we don t make international comparisons, and the conclusions are based on relative rather than absolute figures. There are some sectors where product taxes affect the prices substantially, such as energy and fuel sectors, but a sensitivity analysis here showed no change in the overall results. 5. Discussion This study aimed at both testing the applicability of EIO-LCA based screening-lca for modeling GHG emissions caused by private consumption in different regions, and creating new perspective on utilization of the method in urban development. In addition, the study tested if screening-lca can be used to indicate urban structure related and income level related carbon emissions. In the light of the study it seems that consumer carbon footprint can be effectively modeled with EIO-LCA method based on private consumption. The method seems to provide good information with rather high robustness according to the sensitivity analyzes conducted. The model is also easy to use, and with good quality input data quick overall results can be obtained. The challenges, discussed in more detail in chapter Data quality analysis above, include problems related to temporal and regional uniformity of the input data and the model, problems due to lack of observations, random variations caused by small samples, and variations caused by data collection methods of the used surveys. According to this study an average Finnish consumer is responsible for 13.6 t CO2 e GHG emissions on annual basis. When positioning this study with earlier assessments, the result seems to be quite accurate. For validation of the results we calculated a comparable figure from ENVIMAT data [14], this result being 10.1 t CO2 e. Junnila, in his earlier study, has calculated a carbon footprint per capita of 14.6 t CO2 e for a Finnish consumer [23]. In addition, the same consumption categories were found to have the highest impact on carbon emissions in the three studies. We also calculated per capita carbon emissions from the Finnish national emissions inventory [26], which showed a footprint of 15.2 t CO2 e. When the city level carbon footprints were calculated, we observed that the carbon footprint of a consumer is dominated by energy consumption related to housing, other housing related activities and private driving. These are also categories with high variation among the case cities, making them the key points when analyzing the results with an urban development perspective. The overall scores in tons of CO2E were Hyvinkää 12.1, Vantaa 13.8, Kerava 14.6, Porvoo 15.1, Helsinki 15.3, Espoo 17.3 and Nurmijärvi 18.4 respectively. It would seem that the difference in housing related energy consumption is only moderate between apartment building based metropolitans and those where housing is dominated by detached houses. However, more variation was found from private driving, indicating, that less driving is needed in more dense metropolitan areas due to better proximity of workplaces and services. Also, the cities without commuter train connection seemed to cause higher GHG emissions. These

10 results and conclusions about the significance of urban density correspond to some extent with the results of recent studies of Dodman and Norman et al. [19, 29], as well as with those of Finnish LYYLI study [27]. Norman et al. and Dodman, though, reported rather high relation between more dense living and energy consumption, a result our study seems to question. The case studies indicate also, that the correlation between the level of consumption and the carbon emissions, while tight, is not linear but diminishing. Exactly the same finding was reported in Weber and Matthews [22]. Interesting point here is that the carbon intensity of consumption of goods seems only slightly higher than that of services, when housing and transportation are left out of the examination. This would mean that often aspired change in consumption habits towards consumption of services wouldn't lead to a sustainable society as such. Some earlier LCA studies have noted this same result when comparing the emissions of consumption of goods and services, the explanation being the recognition of the whole supply chain instead of just direct emissions [9]. A longitudinal validation of the results was conducted with a similar analysis of Finnish consumer survey The same overall pattern was found, with Helsinki and Vantaa being at the top end and Porvoo and Nurmijärvi having the weakest consumption-carbon emissions -ratio. In addition to this, the same categories dominated emissions, namely energy related to housing, housing (without energy) and private driving. Some relevant studies were found for comparison from literature. Weber and Matthews [22] analyzed household carbon footprint and trading patterns in US using similar consumer survey data as that used in this study and the EIO-LCA method. Their key findings, similar to those of our study, include significant variation between different households within same country or region and close relationship between private consumption and carbon footprint. They also found the relationship between private consumption and carbon footprint being non-linear especially at higher consumption levels. Cicas et al. studied regional modeling of emissions with regionalized EIO-LCA for eight US geographical regions concluding that the US industry benchmark EIO-LCA model can be effectively utilized in regional level studies if valid data are available [28]. Dodman studied carbon emissions of large cities concluding that higher urban density and effectiveness of transportation infrastructure mitigate emissions in studied cities compared to other areas in same countries [19]. In the study that our results question, Norman et al. concluded that low-density suburban development is times more GHG intensive than high-density urban core on per capita basis [29]. And finally, the recent Finnish IO-LCA study ENVIMAT ended up with the same significant sources of carbon emissions [14]. 6. Conclusions The basic intention of this study was to test EIO-LCA based screening-lca method in modeling carbon footprint of a consumer for urban development purposes using consumption data as inputs. It seems that the method can be used to describe the economy at this level. It would seem that the approach is applicable in creating effective and easy-to-use management tools for urban development purposes. Further research is needed, but the approach seems to offer promising possibilities. Further, the study brought new and relevant information about the significance of urban structure into further discussion. The study revealed a difference in emissions of over 50 % between the best and the worst case city. This result indicates that there is room for significant development even without any new technology breakthroughs. For urban development, this can be seen as an activating challenge since large share of the carbon emissions variation can be explained by structural factors. 7. Acknowledgment The authors thank Sitra, the Finnish Innovation Fund, YIT Corporation and the Confederation of Finnish Construction Industries RT, for enabling this study.

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