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– Draft for consultation –

Technical Report

Lifestyle Carbon Footprints Exploration of long-term targets and case studies of carbon footprints from household consumption

Institute for Global Environmental Strategies Aalto University, and D-mat ltd

Lifestyle Carbon Footprints – Draft technical report for consultation 2

Acknowledgments This report is conceived as part of the Absolute REDUCTIONS project (“Reducing Environmental Degradation & Unsustainable Consumption Trends & Impacts On Nature & Society: Research, Policy and Practice”) in collaboration with the Hot or Cool network of scientists and practitioners. This draft report was prepared by the Institute for Global Environmental Strategies (IGES), Aalto University, and D-mat ltd. with support from the Finnish innovation fund, Sitra. The report is released to the public for the purpose of consultation—for receiving comments and suggestions. The authors intend to update the report based on the feedback and publish a final version of the report at a later date. We would appreciate any comments and suggestions regarding the draft report. To provide your feedback, please send an email to: [email protected]

Copyright © 2018 Institute for Global Environmental Strategies, Aalto University, and D-mat Ltd. All rights reserved. Project was conceived and coordinated by Lewis Akenji (IGES) Authors: Aalto University; D-mat ltd.:

Michael Lettenmeier Viivi Toivio

Institute for Global Environmental Strategies (IGES):

Ryu Koide Aryanie Amellina, Lewis Akenji

Contributors: Miho Kamei (IGES). Review and inputs: Chris West (Stockholm Environment Institute, York University), Jennie Moore (British Columbia Institute of Technology), Mariana Nicolau (Collaborating Centre for Sustainable Consumption and Production), Andreas Hauser (Swiss Federal Office for the Environment), Fritz Reusswig (Potsdam Institute for Climate Impact Research), Jun Nakatani (University of Tokyo), Ari Nissinen (Finnish Environment Institute), Taina Nikula, Matti Kuittinen, Pirkko Heikinheimo and Johanna Kentala-Lehtonen (Finnish Ministry of the Environment), Anu Mänty, Aarne Granlund, Markus Terho, Lari Rajantie and Emma Hietaniemi (Sitra), Mikiko Kainuma, Satoshi Kojima, Yuji Mizuno, Diego Silva Herran, Xianbing LIU, Sudarmanto Budi Nugroho, Chen Liu, and Janardhanan Nandakumar (IGES). The contents of this publication do not reflect the official views of IGES, Aalto University, or D-mat ltd. Responsibility for the information and views expressed in the publication lies entirely with the authors.


Table of Contents Executive Summary .............................................................................................................. 5

1. Background ....................................................................................................................... 7

2. Exploration of Long-term Lifestyle Carbon Footprint Target ....................................... 11 2.1. Temperature Targets Under the Paris Agreement ..................................................... 11 2.2. The Identification of Mitigation Pathways to Meet the Targets ................................... 12 2.3. Exploration of Lifestyles Carbon Footprint Targets .................................................... 13

3. Overview of Current Lifestyle Carbon Footprint ........................................................... 16 3.1. Estimating Current Lifestyles Carbon Footprint......................................................... 16 3.2. Comparison of Lifestyles Carbon Footprints ............................................................. 20 3.3. Domain-Specific Comparison ................................................................................... 21

4. Identification of Low-carbon Lifestyle Options ............................................................. 33

5. The Way Forward ............................................................................................................ 40

References .......................................................................................................................... 44


Glossary 3EID Embodied Energy and Emission Intensity Data for

Japan Using Input–Output Tables AR5 IPCC Fifth Assessment Report BECCS Bio-energy with carbon capture and storage CCS Carbon capture and storage CO2 Carbon dioxide COICOP Classification of individual consumption by purpose COP Conference of the parties EGR Emissions gap report GHG Greenhouse gas GLIO Global link input–output HFCs Hydrofluorocarbons IAMs Integrated assessment models I/O Input–output IPCC Intergovernmental Panel on Climate Change ISO International Organization for Standardization LCA Life cycle assessment LCI Life cycle inventory LED Light emitting diode LULUCF Land use, land use change and forestry MRIO Multi-regional input–output tables N2O Nitrous oxide NDC Nationally determined contribution NGO Non-governmental organization PFCs Perfluorocarbons RoW Rest of the world SF6 Sulfur hexafluoride UN Environment United Nations Environment Programme


Executive Summary If humanity wants to keep global warming within a limit of 1.5 °C as in the aspirational target of the Paris Agreement, annual greenhouse gas (GHG) emissions have to drop rapidly (Rockström et al. 2017). Currently, the discussion on solutions to climate change is largely based on technology, despite the importance of behavioral change and systemic infrastructural changes (Creutzig et al. 2016; Akenji and Chen 2016). Also, this study found that the most of the scientific literature on mitigation pathways does not incorporate the impacts from household consumption. The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (IPCC AR5) also indicates the considerable influence of behavior, lifestyles and culture including consumption patterns and dietary changes on emissions (IPCC 2014a). Shifting lifestyles towards low-carbon ones can have relatively quick impacts, especially in consumption domains that are not locked in existing infrastructure (e.g., (Lettenmeier, Laakso, and Toivio 2017). In this study, GHG emissions and reduction potentials are examined from the consumption and lifestyles perspectives. Lifestyles of individuals consist of various elements of daily living including the consumption domains of nutrition, housing, mobility, consumer goods, leisure, and services. This study focuses on the lifestyle carbon footprints, which is defined as the GHG emissions directly emitted and indirectly induced from the final consumption of households, excluding those induced by government consumption and capital formation such as infrastructure. The approach adopted in this study, consumption-based accounting, attributes GHG emissions at production stages—ranging from imported products through to the final demands of households, governments, and capital investments—as indirect emissions. In particular, the study examines the carbon footprint of household consumption (the lifestyle carbon footprint) when compared to the footprint of specific products, organizations, cities, or countries, which are the foci of most of footprint studies. The objective of this report is to establish global lifestyle carbon footprint targets and examine the average consumption and footprint patterns of case countries from lifestyle perspectives. The study proposes globally-unified per capita targets of the carbon footprint from household consumption for the years 2030, 2040 and 2050 (section 2). Current average carbon footprints of Finland, Japan, and also Brazil, India, and China are estimated focusing on the comparison of a physical level of consumption in order to be both comparable to the global targets and compatible with household-level solutions (section 3). These case countries were selected in order to gain a regional balance, to capture the different characteristics of climates and other factors, and to indicate the level of difference between developed and developing countries. The report identifies and structures such promising solutions for reducing lifestyle carbon footprints on the basis of literature (section 4). It finally suggests policy implications in terms of how to proceed towards one-planet lifestyles (section 5). Detailed methodology, data sources, and results of estimation are included in Annex A to C. It should be noted that this draft version of the report does not contain an illustration of the reduction pathways of lifestyle carbon footprints that lead towards meeting the targets established in Section 2. Such an analysis is intended to be included in the forthcoming, updated version of the report. The study suggests that the long-term targets of lifestyle carbon footprints are the ranges of 3.2–2.3, 2.2–1.4 and, 1.5–0.9 t-CO2e per capita for 2030, 2040, and 2050 respectively. In comparison, the estimated current average lifestyle carbon footprint is 10.4 t-CO2e per year in


Finland, 7.6 t-CO2e in Japan, 4.2 t-CO2e in China, 2.8 t-CO2e in Brazil, and 2.0 t-CO2e in India. When comparing the results with the GHG emission targets set for 2050, as much as an 80–91% reduction of the footprint by 2050 would be necessary for developed countries. Annual lifestyle carbon footprints are already at the level of 4.2 tonnes in China, 2.9 tonnes in Brazil, and 2.0 tonnes in India. Therefore, even in the case of developing countries, a 25–79% reduction, depending on the country and on the scenario, would be required by 2050. The estimated current lifestyle carbon footprints revealed large differences in the current footprint contributors of the selected countries, although out of the consumption domains considered, nutrition, housing, and mobility tend to have the largest impact on the total carbon footprints in each country. These differences reflect each country’s unique cultural and societal characteristics consumption habits, and they highlight the various potential opportunities for a reduction of carbon footprints from lifestyle changes. The study aimed to identify and provide preliminary estimates of the most promising solutions for the different domains (nutrition, housing, mobility, and consumer goods), with different impact levels (large, moderate, and small), reduction approaches (demand-side reduction, sharing, modal shift, and efficiency improvement), and relevancy levels targeted for different stakeholders (individuals, the private sector, and governments). Nevertheless, this list of solutions is not an attempt to provide readymade pathways to meet the reduction target, but should be seen as a starting point for discussion on reducing lifestyle carbon footprints in different contexts. One of the important implications of this study is that it is important to adopt a systemic perspective when reducing the lifestyle carbon footprint. Although carbon footprint accounting quantifies the level of GHG emissions’ impacts from every aspect of the lifestyles of households, it does not mean that individual households are solely responsible for reducing the footprints. The consumption behavior of households is locked-in by the existing infrastructure and availability of products and services. A significant part of the footprints comes from the indirect emissions of manufacturing, and delivering products and services (Akenji et al 2016). Households only have influence through purchasing power or voting power, while companies and governments have the power to transform the systems of provision to low-carbon ones in order to reduce lifestyle carbon footprints. Therefore, the globally-unified per capita lifestyle carbon footprint proposed in this study should be considered as a target that is to be achieved through systemic changes of the provision systems and lifestyles—including the supply chain and infrastructure, as well as consumption behavior—achieved through the collective actions of households, the private sector, governments, and other stakeholders.


1. Background Addressing the global temperature targets agreed by the Paris Agreement requires early action towards the rapid reduction of greenhouse gas (GHG) emissions. The annual GHG emissions have to drop very soon, over just a few years, in order to keep the average global temperature rise within a limit of 1.5°C (McSweeney and Pearce 2016; Rockström et al. 2017). This implies that measures to reduce emissions should be implemented on a large scale within a decade. Delaying the early mitigation efforts through 2030 may risk in narrowing cost-effective options even with the 2.0°C temperature target (IPCC 2014a). Although technologies and infrastructures for performing better than today exist (Tynkkynen 2015, 2016; von Weizsacker et al. 2009), their diffusion takes time and investment. Experts strongly agree that early action for ambitious mitigation is necessary in order to address the lock-in of society by infrastructure and long-lived products (IPCC 2014a). The discussion on solutions to climate change is largely based on technology issues despite the importance of behavioral change and systemic infrastructural change (Creutzig et al. 2016). The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) also indicates the considerable influence of behavior, lifestyles, and culture including consumption patterns and dietary changes on emissions, which can complement structural and technological changes (IPCC 2014a). Adaptations in lifestyles can achieve relatively quick changes, especially related to consumption domains that are not locked into existing infrastructure (e.g., Lettenmeier, Laakso, and Toivio 2017; J. L. Moore 2013). Therefore, notable changes to lifestyles are expected to address the targets of the Paris Agreement, including the 1.5°C aspirational target. Most of the literature on low-carbon lifestyles give goals, such as a percentage level of reduction, but does not establish a linkage with a mitigation pathway leading towards achieving the temperature targets of the Paris Agreement. However, there is hardly any science-based evidence on what meeting the Paris Agreement would mean in terms of lifestyles and what opportunities can rise out of that. In this study, GHG emissions and reduction potentials are examined from the consumption and lifestyles perspectives. Unlike the production-based GHG accounting used in the national GHG inventories and climate change negotiations, this study is based on the consumption-based accounting of GHG. Production-based accounting covers only direct emissions from domestic production activities within the geographical boundaries and offshore activities under the control of the country, which does not consider the embodied emission from international trade (Boitier 2012; J. L. Moore 2013). The limitations of this accounting is the possibility of carbon leakage due to international trade and the fact that it might mislead insights on mitigation efforts (Boitier 2012; J. L. Moore 2013). On the contrary, consumption-based accounting (carbon footprint) covers both direct emissions and embedded emissions due to the imports of goods and services, which reflects the global impacts of final consumption and lifestyles of individuals. This approach can address the carbon leakage issue and promote broader options for mitigation, while not forcing developing countries to have high emission commitments (Peters and Hertwich 2008). The majority of the existing footprint studies are either focusing on the footprint of a specific product or aiming at estimating a total footprint of countries. However, only a few of these studies cover the broader perspectives of lifestyles and examine the carbon footprint and characteristics of consumption patterns based on the physical amount of consumption such as food intake, mobility distance, and energy consumption (Schanes, Giljum, and Hertwich 2016; Barrett et al. 2002).


In this report, the carbon footprint is linked to the concept of planetary boundaries (Rockström et al. 2009; Steffen et al. 2015). Planetary boundaries are biophysical thresholds that, if exceeded, would move the Earth’s system irreversibly out of the Holocene period and, in consequence, decrease the operating space for human life. In the 1990s, several approaches were already published that aimed at determining ecological boundaries for human activities, for example, the environmental space concept (Opschoor and Reijnders 1991; Weterings and Opschoor 1992; Buitenkamp, Venner, and Wams 1992), the Factor 10 concept (Schmidt-Bleek 1993; Schmidt-Bleek 1993), and the ecological footprint concept (Wackernagel and Rees 1998). The emerging concept of environmental footprints (Hoekstra and Wiedmann 2014) considers that environmental sustainability requires that footprints are kept below their maximum sustainable level on a global scale. Lifestyle-related targets of footprint per capita based on planetary boundaries have been published for the ecological footprint (J. L. Moore 2013; J. Moore 2015) and the material footprint (Lettenmeier, Liedtke, and Rohn 2014). Only a few concrete lifestyle-related targets have been proposed for the carbon footprint (Nykvist et al. 2013; Dao et al. 2015). However, targets are often defined for a specific country context and concrete, globally unified lifestyle-based targets have not been published so far. The challenges in establishing a long-term per capita carbon target are due to the nature of the balance of GHG emissions and sinks. Unlike the ecological footprint that can, in principle, be directly compared with the biocapacity at any point in the future, establishing a carbon footprint target requires a dynamic assumption of the emission reduction towards the future. To stabilize the concentration of the GHGs in the atmosphere, a carbon budget has been suggested (United Nations Environment Programme 2016). A substantial number of emission scenarios have been published suggesting pathways for the reduction of total emissions at global level. Many of them assume production-based measures and negative emission technologies, but some are also incorporating demand-side reduction measures. Based on the Paris Agreement, countries are taking production-based approaches to monitor and reduce emissions, interpreting the global target into each country’s target in the future. Nevertheless, this approach may not reflect the insights from the real impacts of lifestyles and consumption. Since production and consumption are two sides of the economic activities, the existing emission pathways and carbon budgets have implications on the consumption patterns and lifestyles in the future; however, these scenarios do not provide a picture of how lifestyles will look in the future. In this study, we make the best use of existing emission scenarios through literature review, and propose long-term lifestyles carbon footprint targets based on the selection of scientifically reviewed scenarios. Also, this study suggests an equitable and global long-term target of carbon footprint per capita following the aspiration of “contraction and convergence” for the long term (Meyer 2000). Lifestyle Carbon Footprints

Carbon footprint means the GHG emissions directly and indirectly caused by activities or products throughout their lifecycle from the consumption perspective. It has various definitions ranging from products to daily activities of individuals or organizations (for more information, see review by (Wiedmann and Minx 2008). The focus of this study is on daily activities of individuals determined from the choices they make on ways of life. In this study, the lifestyle carbon footprint is defined as GHG emissions directly emitted and indirectly induced from household consumption excluding those induced by government consumption


and capital formation.

Footprint and Emission - Comparison of boundaries and scopes There are different boundaries and scopes of the measurement of GHGs per capita. ● Territorial boundary–based emission

GHGs directly emitted from households, governments, and private sector activities within the territorial boundary of the country or city. This does not consider indirect emissions caused by the consumption of products and services. This measurement is used in the national GHG inventory and target setting.

● The footprint of products

GHGs directly and indirectly emitted from the production, distribution, use, and disposal of products including those embedded in imported parts and products. This type of measurement is used for carbon footprint labeling and comparison of two or more types of options of products or processes, and typically based on bottom-up process analysis life cycle assessment (LCA). There are globally at least 15 carbon footprint schemes (Peter 2009). The specification for this type of measurement is also published as International Organization for Standardization (ISO) Technical Specification 14067.

● Organizational footprint

GHGs emitted from direct activities of organizations (scope 1), sourcing of energy (scope 2), and other indirect emissions through value chains including production, distribution, use, and disposal of sold products (scope 3). The standard for this type of measurement include ISO 14064 and GHG Protocol (Greenhouse Gas Protocol 2011), and this measurement is typically based on the hybrid method of bottom-up process analysis LCA and top-down input–output (I/O) analysis–based estimation.

● Footprint of countries or cities GHGs directly emitted from activities of households and governments located in the country or city and those indirectly emitted from the final demands of those actors and capital investment including production, distribution, use, and disposal of purchased products and services including those embedded in trades. This type of measurement is typically based on the top-down I/O analysis method. Examples of estimation are Environmental Footprint Explorers (“Environmental Footprint Explorers” n.d.) for countries and C40 (C40 Cities Climate Leadership Group 2018) for cities.

This study focuses on the lifestyle carbon footprint, meaning the carbon footprint of an average household in a country including its direct emissions from the use of fuels and indirect emissions imbedded in the products and services purchased (Figure 2.1). This can be considered as a household version of the organizational carbon footprint or household demand parts of the footprint of countries or cities.


Figure 2.1 Comparison of the boundary of GHG emission and footprint

GHGs covered

The Paris Agreement does not limit the scope of GHGs to be reduced. The reduction of non-CO₂ GHG emissions is equally important to the reduction of carbon dioxide (CO₂) emissions, considering their higher global warming potential and the lifestyle/consumption choices related to them. Therefore, the GHGs covered in this study are, in addition to CO₂, non-CO₂ GHG emissions of methane (CH₄), nitrous oxide (N₂O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulphur hexafluoride (SF6), as in most of the existing global carbon footprint analysis literature and the UN Environment Emission Gap Report 2016 (United Nations Environment Programme 2016). The carbon footprint targets established in this study cover these six types of GHG. For the estimation of the current carbon footprint, we used carbon intensity data covering the six types of gases if explicitly mentioned, or selected GHG intensity data rather than CO₂ data if the types of gases were not explicitly mentioned.


2. Exploration of Long-term Lifestyle Carbon Footprint Target

2.1. Temperature Targets Under the Paris Agreement In 2015, the Paris Agreement secured a clear global commitment to hold the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursue efforts to limit the global average temperature increase to 1.5 °C above pre-industrial levels (UNFCCC 2015). To achieve this global average temperature target, global GHG emissions need to peak as soon as possible and rapidly decrease to achieve zero-emission society in the second half of 21st century (ibid). The Paris Agreement does not include a target of GHG emissions reduction amount or a cumulative carbon budget by 2100. However, the decision of the twenty-first session of the Conference of the Parties (COP 21), which adopted the Paris Agreement, noted that efforts are needed to reduce emissions from the 55 gigatonnes in 2030, projected by the Nationally Determined Contributions (NDC) ambition, to at least 40 gigatonnes in 2030 to keep the global average temperature increase below 2 °C, or to an even lower level to keep the increase below 1.5 °C. The 2 °C and 1.5 °C targets are based on long-running scientific research on GHG emissions projection, climate models, and climate change impacts to the earth and humanity. The currently available research results provide various estimations of the global GHG emissions projection and the maximum amount of GHGs allowed to remain in the atmosphere, which often were obtained from running integrated assessment models (IAMs) under different sets of assumptions. These estimations, called ‘mitigation pathways,’ are frequently accompanied by proposed measures to reduce GHG emissions to surpass the maximum acceptable amount. In 2014, the IPCC published IPCC AR5, with a primary focus on the global target to keep the global average temperature increase below 2 °C, a target established by the 2010 Cancun Agreement (IPCC 2014a). In parallel, results of climate change impact research showed that a 2 °C target may not be enough to prevent existential risks for ecosystems, such as small islands, and extreme weather events (IPCC 2014b). Due to these risks, the global community was urged to pursue a higher ambition of keeping the global average temperature increase below 1.5 °C. As of 2017, however, assessments on mitigation pathways to meet the 1.5 °C target are still very limited in the relevant literature. The forthcoming IPCC special report on the impacts of global warming rising 1.5 ºC above pre-industrial levels and the related global GHG emission pathways is set to be published in 2018 in the context of strengthening the global response to the threat of climate change (IPCC 2018). Nevertheless, it is important to note that 1.5 °C pathways are interconnected with 2 °C pathways, as they are likely the results of further development of 2 °C pathways, with additional more ambitious mitigation measures. Hence, in 1.5 °C scenarios, meeting the 2 °C target are projected with higher probability. In this report, we aim to illustrate the per capita GHG footprint budget for the final consumption of households for both the 2 °C and 1.5 °C targets under the Paris Agreement.


2.2. The Identification of Mitigation Pathways to Meet the Targets To illustrate our proposed targets of carbon footprint of lifestyles to meet the Paris Agreement targets, we reviewed available scenarios on emissions limit from the literature. We looked for scenarios that use assumptions that are close to the set of criteria established for this study, which refers to the IPCC AR5 categories and a literature review. As selection criteria, a selected pathway should;

● Consists of an emission pathway to keep the global average temperature increase below 2 °C with at least 66% probability or to keep the global average temperature increase below 1.5 °C with at least 50% probability

● Provides a quantified estimate of a carbon budget on a time scale up to year 2100 ● Provides enough information on the model and baseline scenario used ● Aims to limit atmospheric CO₂ concentration at 430-480 ppm CO₂eq for 2 °C target and

430 ppm CO₂eq for 1.5 °C target in year 2100 ● Estimates a cumulative carbon budget (2011-2100) at 350-950 GtCO₂ for 2 °C target

and less than 350 GtCO₂ for 1.5 °C target ● Assumes emissions reduction across all sectors ● Assumes that emissions from the energy sector are reduced through the full-scale

utilization of renewable energies, with nuclear power kept at a limited level ● Covers the CO₂, CH₄, N₂O, HFCs, PFCs, and SF6 GHGs in its modeling estimation ● Explains the assumption used on the level of the use of “human carbon sink”

technologies, also known as negative emissions or CO₂ removal technologies, such as industrial carbon capture and storage (CCS) and technologies to capture GHGs and store them in the ground (see Annex A for more information)

● If formulated before the year 2015, the scenario assumes that a global climate policy commitment to reduce GHG emissions and limit the increase in the global average temperature is secured in the near future (as represented by the Paris Agreement).

We reviewed the IPCC AR5 Scenario Database, the United Nations Environment Programme Emission Gap Report 2016 (United Nations Environment Programme 2016), and other peer-reviewed papers in academic journals and publications, and performed a screening study to select the scenarios using the above criteria. More details about methodology for screening and identifying emission scenarios are attached in Annex A. From our literature review and screening study on both the 1.5 °C and 2 °C pathways, we derived two important findings. First, we found only a limited number of papers that performed modeling analysis for pathways compatible with the 1.5 °C target. This is in line with the coverage of the UNEP Emissions Gap Reports for 2016 and 2017, which only included six 1.5 °C pathways from one study. Second, most of the 1.5 °C pathways indicate reliance on the utilization of human carbon sink technologies. The pathways to meet the 1.5 °C target without using those technologies were not often included in the published studies. On the other hand, we found many papers that performed modeling for 2 °C pathways. We could find pathways to meet the 2 °C target both with and without using human carbon sink technologies (see Annex A for more explanation). Based on this screening study, we shortlisted four emissions budgets based on four scenarios available to the public. We included both 1.5 °C and 2 °C temperature targets because the Paris Agreement covers both targets (the scenarios begin with “1.5–” or “2.0–” in Table 2.2). As


was revealed from our study, the assumption of the long-term availability of human carbon sink technologies is a key determinant of the mitigation pathways to meeting those targets; we included two categorizations about its use (the scenario names that end with “–S” for “sink” use human carbon sink technologies and scenarios that end with “–D” for “demand” emphasize more the use of demand-side measures, instead of human carbon sink technologies).

Table 2.1. Scenarios shortlisted for illustration of emissions budget Scenario Description Reference GHGs

covered

1.5S: 1.5 °C with Human Carbon Sink Scenario

Pathway to the 2 °C target with 75% probability and the 1.5 °C target with 50% probability, considering the use of all sinks starting before year 2050

(Rockström et al. 2017)

All GHGs

2S: 2 °C with Human Carbon Sink Scenario

Pathway to the 2 °C target with more than 66% probability, considering the use of CCS technologies

(Rogelj et al. 2011)

All GHGs

1.5D: 1.5 °C with Demand-side Measure Scenario

Pathway to the 1.5 °C target with 60% probability, without the use of CCS

(Ranger et al. 2012)

All GHGs

2D: 2 °C with Demand-side Measure Scenario

Pathway to the 2 °C target with unclear probability, without the use of CCS technologies

(Blanford et al. 2014)

CO2 only

Source: authors It should be noted that the above scenarios are shortlisted from those available at the time at which the review research was conducted. While finalizing this draft version of the report, we noticed that scenarios that are better in line with both the targets of the Paris Agreement and the viewpoint of this report were very recently published (Grubler et al. 2018; van Vuuren et al. 2018; Kriegler, Luderer, and Bauer 2018). We intend to examine these scenarios in the next version of the report and may replace the scenarios with more suitable ones. In consequence, we also might be able to choose one representative scenario or a statistical representation from the scenarios in order to better highlight and communicate the purpose and results of this study, rather than use a range of four scenarios (see Section 2.3).

2.3. Exploration of Lifestyle Carbon Footprint Targets The GHG emission targets identified in the previous section meet the annual emission allowance at the global level. In this section, these emission targets were converted into a per capita carbon footprint target from household consumption in order to examine the implication of these mitigation pathways on household consumption and lifestyles. In order to calculate lifestyle carbon footprint targets per capita, the total GHG emission limit per year was divided by an estimated population of the reference year. Since climate change is a global scale phenomena, the assumption here is that everyone living in the same year in the world, regardless of age, location, and any other status, would have a same carbon footprint target at the national average level. In this study, the median projection from the 2017 Revision of the World Population Prospects (United Nations 2017) was used as a population scenario. To closely investigate the carbon footprint directly relevant to lifestyles of individuals, this study specifically focus on the carbon footprint induced by household final demand (Lifestyles Carbon Footprints). To estimate the proportion of carbon footprint caused by household consumption, data from the existing I/O analysis based carbon footprint estimation were used. As a publicly


available estimation of the share of household consumption covering multiple countries, (Hertwich and Peters 2009) was selected. This article estimates carbon footprint per capita of 73 countries with proportion of 8 consumptions domains and the share between 3 final demands (investment, government, and households) in 2001. According to this article, the share of carbon footprint by household consumption is assumed as 72%. It should be noted that this assumption has certain limitations. First, the share of the carbon footprint of household consumption varies among countries, but the assumed share in this study is a mean of the countries included in the aforementioned study. Second, the difference in the share of household consumption is expected to be affected by the level of economic development of the country and the level of the reduction in the carbon footprint. The countries with different development levels may place a different importance on public services and capital investment, and this may affect the share of the carbon footprint of household consumption. Also, as the per capita lifestyle carbon footprint is to be reduced in the future, the allocation of the carbon footprint between household, government, and capital investment may shift because some of the items are more essential in maintaining well-being while others may have more potential for reduction. These differences are not reflected in this study. Nevertheless, establishing a globally unified lifestyle carbon target is still useful because it can indicate the average level of the necessary reduction. Explored targets of the lifestyle carbon footprints (carbon footprints from household) in the four shortlisted scenarios are summarized in Figure 2.1 and Table 2.2. In terms of all GHGs, the ranges of the estimated lifestyle carbon footprint targets are 3.2–2.3, 2.2–1.4, and 1.5–0.9 tCO2e per capita in 2030, 2040, and 2050 respectively. In terms of CO2, lifestyle CO2 footprint targets are 2.1–0.8, 1.2–0.4, and 0.4–0.3 in 2030, 2040, and 2050 respectively. There are overlaps in the range of targets between the target years due to the different assumptions regarding negative emission technology and the temperature targets. The selection of targets between the lower and higher ends depends on assumptions on long-term availability of negative emission technologies, such as BECCS, and the selection of the global average temperature targets, being either 1.5 or 2.0 °C. It should be noted that lower targets between all GHGs and CO2 are not comparable because they are based on different emission scenarios.


Source: authors based on 1.5S, 2S, 1.5D, and 2D Scenario from (Rockström et al. 2017), (Rogelj et al. 2012), (Ranger et al. 2012), (Blanford et al. 2014), respectively, and population projection from United Nations (2017) and household footprint share from (Hertwich and Peters 2009). Figure 2.1. The lifestyle carbon footprint budget derived from the shortlisted mitigation

pathways

Table 2.2. The lifestyle carbon footprint targets derived from the shortlisted mitigation pathways

Scenario

Type of GHG

Per capita target of lifestyle carbon footprint (Per capita target of total carbon footprint)

2015 2020 2030 2040 2050 2100

1.5S All GHGs (tCO2e/cap/yr) 4.9 (6.8)

4.8 (6.7)

3.2 (4.4)

2.2 (3.1)

1.4 (1.9)

0.8 (1.1)

CO2 (tCO2/cap/yr) 3.6 (4.9)

3.5 (4.8)

2.1 (2.9)

1.2 (1.6)

0.4 (0.6)

0.09 (0.12)

2S All GHGs (tCO2e/cap/yr) 4.4 (6.1)

4.1 (5.6)

3.0 (4.2)

2.2 (3.0)

1.5 (2.1)

0.7 (1.0)

1.5D All GHGs (tCO2e/cap/yr) 4.7 (6.6)

3.7 (5.1)

2.3 (3.1)

1.4 (1.9)

0.9 (1.2)

0.13 (0.18)

2D CO2 (tCO2/cap/yr) 3.1 (4.3)

1.7 (2.3)

0.8 (1.2)

0.4 (0.5)

0.3 (0.4)

0.03 (0.04)

Source: authors based on 1.5S, 2S, 1.5D, and 2D Scenario from (Rockström et al. 2017), (Rogelj et al. 2012), (Ranger et al. 2012), (Blanford et al. 2014), respectively, and population projection from United Nations (2017) and household footprint share from (Hertwich and Peters 2009).

The targets explored and proposed in this section will be used to illustrate the reduction pathways of lifestyle carbon footprints that lead towards meeting the targets. In this draft report, cross-analysis of the established targets and current lifestyle carbon footprints are not included.


3. Overview of Current Lifestyle Carbon Footprint

3.1. Estimating Current Lifestyle Carbon Footprint The lifestyle carbon footprint is defined as the GHG emissions directly emitted and indirectly induced from household consumption, which exclude those induced by government consumption and capital formation in this study. The OECD defines household consumption as the consumption of goods and services by households, and refers to the selection, purchase, use, maintenance, repair, and disposal of products or services. The consumption of products and services by the public sector and by the companies producing goods and services is not part of household consumption (OECD 2002).

In this study, in addition to direct GHG emissions from household activities, GHG emissions from intermediate consumption during production induced by household final demand are accounted as embedded or indirect emissions. Direct and indirect emissions caused by public sector and capital investment are out of the scope, and not considered in the estimation. The exclusion of the footprints of the public sector and capital investment allows us to have a clear focus on the lifestyles of households, which are partly the result of the choices of households and partly due to lock-in effects by socio-technical systems (Akenji and Chen 2016).

This study estimates and analyzes the current average lifestyle carbon footprints at the national level, as of 2017. The analysis of footprints focuses on the average Finnish and Japanese consumers as main case countries, but also covers Brazil, China, and India to illustrate lifestyle carbon footprints in emerging countries. These case countries were selected in order to gain a regional balance, to capture different characteristics of climate and other factors, and to indicate the level of difference between developed and developing countries. Considering the limited coverage of the countries, the expansion of similar studies to other countries should be considered in the future.

1) Lifestyle domains covered and overall methodology

In this study, the resource consumption of a household is divided into six domains: nutrition, housing, mobility, consumer goods, leisure, and services. The division of the total household consumption into domains and components is based on previous studies concerning the importance of different consumption domains (e.g., (Michaelis and Lorek 2004); (Kotakorpi, Lähteneoja, and Lettenmeier 2008); (Lettenmeier, Liedtke, and Rohn 2014). The household system as studied in this paper thus includes the following domains:

1. Nutrition: direct and indirect emissions from intake of all foodstuffs and beverages consumed at home and outside the home. This includes cereals, vegetables, fruits, beans, dairy products, meat, fishes, alcoholic and non-alcoholic beverages, and other food intakes, but excludes direct emissions due to cooking at home, which are included in the housing domain, and emissions from the operation of restaurants, which are included in leisure domain;

2. Housing: direct and indirect emissions from housing infrastructure and supply of utilities. This includes construction and maintenance of houses, the use of energy (electricity and non-electricity) and water for household purposes;

3. Mobility: direct and indirect emissions from the use of owned transport equipment and transportation services for commuting, leisure, and other personal purposes—this includes cars, motorbikes, bicycles, airplanes, and other public transport for personal purposes, but excludes business purposes such as business trips and logistics, which are considered a part of the footprints of each product and service;


4. Consumer goods: goods and materials purchased by households for personal use not covered by other domains. This includes home appliances, electronic appliances, clothes, textiles, furniture, stationary and paper products, entertainment and sports equipment, and any other daily consumer goods. It excludes the electricity used by consumer goods, which is included in the housing domain;

5. Leisure: leisure activities performed outside of home. This includes sports, cultural, and entertainment activities as well as restaurant and hotel services, but excludes the footprint from food ingredients, which is included in nutrition domain. Leisure activities performed at home, which are included in household goods and service domains, are excluded from this domain;

6. Services: consumption of services for personal purposes. This includes finance and insurance, communication and information, broadcasting, ceremony, cleaning and public baths, and public services, but excludes those covered by the government expenditure.

The current carbon footprint is calculated and analyzed per one person in one year, with the reference year being 2017. If specific data for the reference year was not available, the data source from the latest available year was used, assuming that the level of consumption or intensity is constant in these years. The boundary of the estimation covers “from cradle to grave” regarding the consumption of goods and services by households, including resource extraction, material processing, manufacturing, delivery, retail, use, and disposal, but excluding land use, land use change, and forestry (LULUCF). In the case when the scope of the GHG intensity data was not compatible with this boundary setting, supplementary data was used to increase the coverage of the estimation wherever possible. The estimation of the footprints generally uses bottom-up and top-down methods, and each method has strengths and weaknesses. While the top-down method, using I/O analysis, has better coverage, its estimation is based on the monetary units of expenditure but not physical units, and the results are sensitive to the selection of I/O models. On the contrary, the bottom-up method, using LCI databases calculated from the analysis of typical processes, have a better precise estimation of a specific product or service based on physical units, but it is difficult to increase the coverage of items. In this study, the calculations were performed primarily in a bottom-up approach combining micro-level carbon footprint data with national statistical data for major domains and items. This approach was selected in order to examine the lifestyles of households based on the systemic use of physical units (e.g., the weight of food, the distance of transport) rather than being based on the amount of expenditure. The discussion that is based on the physical unit of consumption is suitable for examination of potential carbon footprint reduction actions by households as it can reflect the substitution between different types of consumption modes. In addition, top-down methods were used for other domains and minor items in order to improve the coverage of the estimation. The approach included the following steps:

1. National data was collected for each country to define the average consumption for six consumption domains (see above) and the appropriate components. Data was obtained from national statistics and other public sources. The units of measurement of consumption amounts include weight of food (kg), distance of transport (km passenger), energy consumption from housing (kWh), size of housing space (m2), and expenditure of products or service (USD), depending on data availability.

2. Values for the carbon intensity of goods and activities were mainly obtained from life-cycle inventory (LCI) databases. Where these data did not provide the specific data


required, other public or internal data has been used. For Finland, the carbon intensities used in this study are mainly from the Ecoinvent database (Wernet et al. 2016) as it covers country specific or European data. I/O analysis–based estimations are also used in the domains of consumer goods, leisure, and services (Seppälä et al. 2009) to increase the coverage of the estimations. For Japan, country specific bottom-up LCI data, such as data from the MOEJ (Ministry of the Environment of Japan 2016) and the JEMAI (Japan Environmental Management Association For Industry 2012a), were used for the major items of food, housing, and mobility in the estimation (please refer to Annex B for country specific methodology) because Ecoinvent does not generally cover Japan-specific data. In addition, a top-down I/O analysis–based database, using the Global Link Input-Output (GLIO) model (K. Nansai et al. 2012), was used for other domains and items in order to increase the coverage of the estimation.

3. The carbon footprint for each item (product or service) was calculated by multiplying the amount of direct consumption for each item by the most appropriate carbon intensity value available. As the number of items depends on the availability of consumption data, there are some variations among the case countries (see Annex B).

4. The resulting carbon footprint of single items was then added up to components (e.g., meat, cereals, vegetables) and domains of consumption (six domains: nutrition, housing, mobility, consumer goods, leisure, and services). The number of components is unified among the case countries wherever possible in order to improve the comparability of the results.

The results of the calculation were summed up in tables and figures for comparing the carbon intensities and carbon footprints of different countries, consumption domains and components. The results are given and analyzed in the following sections. In these sections, only the results and discussion are presented. More details of the calculation methods, data sources, and country-specific methodology are described in Annex B.

2) Limitations of this study

It should also be noted that the limitation of this study is that it does not consider the systemic interaction between changes in household consumption and government and capital investments. The consumption by the public sector can still be indirectly related to household consumption, and it can be investigated in future studies.

This study estimates the per capita carbon footprint from household consumption, covering wide ranges of lifestyle aspects. Due to data and methodological limitations, this study should be considered only as a first attempt to examine the carbon footprint of households from this angle, and not as an exhaustive and comprehensive estimation of the footprint. The limitations of this study include the following:

● Coverage of consumption and activities: the activities of individuals are very broad, and some activities may not be reflected in the official statistics. This study covers most of the major activities of individuals that induce GHG emissions, as listed in the previous section, “The consumption domains covered.” However, there might be some minor activities that are not covered in some domains.

● Variation in the selection of data sources: The estimation of carbon footprints in this study is based on a combination of bottom-up and top-down approaches. i) Boundary: The boundary of this study is the cradle-to-grave, direct and indirect GHG emissions from the products and services consumed by the household sector, but excluding LULUCF. We selected the data sources that are mostly compatible with the boundary of this study, but there may be slight differences in boundaries and


assumptions or, in some cases, the detailed information is not clearly indicated in the databases. ii) Country specificity: The carbon intensity of products or activities are subjected to be different among countries because technologies and processes can be different. We selected the intensity data from the specific country whenever available, but the rest of the world (RoW) data or other country data was used if country-specific data was not available. In addition, a domestic technology assumption was used in this study: The intensity of imported products is assumed to be the same as for domestic products1. iii) Reference year: The estimation year of this study is set as 2017. The statistics of consumption amount and carbon intensity data were selected for the reference year whenever available. If the data is not available for 2017, we used the data from the nearest year available2.

● The quality of official statistics: The amount of consumption in this study is estimated from various national and other official statistics available from each country. We have selected the most legitimate and best quality statistics but the methodology of the estimation in each statistics may be different between countries.

● Public services and infrastructure: This study focuses on the GHG emissions induced from the final demand of households, but footprints of consumption of public services are not straightforward to be determined. Public services are partly paid by households but are also heavily subsidized or operated by governments. In this study, for Japan and Finland, public services charged to individuals are included in the estimation. The construction of infrastructure and investment in production equipment are also excluded from this study because they are not considered to be part of the final demand of households. Nevertheless, these two parts are still relevant to lifestyles of consumers because the majority of government services as well as infrastructure are ultimately for the public (see also (Lettenmeier, Liedtke, and Rohn 2014)). The allocation of these parts to the consumption domains can be considered in future research.

These variations from data sources are expected to cause some minor errors in the current footprint estimates. Nevertheless, the presence of errors in footprint estimation is a common issue. Even the top-down I/O analysis–based estimation has errors due to model selection and sectoral aggregation (Steen-Olsen et al. 2014). The level of variation between the estimation of this study and alternative estimations with other methods are discussed in Annex B. Considering these limitations, the individual intensity data of a specific item may not be directly comparable between countries. In this study, the amount of consumption, the footprint, and intensity are only compared between countries at the component level or domain level, as errors are expected to be smaller at the aggregated level. The focus of this study is not on the footprint of particular products or services but on illustrating the overall picture of the GHG emissions induced by the lifestyles of households. The estimates in this study should be

1Except for the top-down based estimates based on the intensity data from the globally-linked input output (GLIO) model in Japan. In Finland, top-down based (ENVIMAT) model applied a hybrid approach, where part of the data on import was replaced by data for specific products obtained from the global databases. Global data coverage of imports was 88%. The missing volume mostly included consumer goods and the missing intensities were assessed using domestic emission factors. 2Food consumption data used for Brazil, China and India is based on the latest FAOSTAT Food Balance Sheet (data from 2013). It is noted that consumption amounts for individual food products might be over- or underestimated compared to present consumption.


considered a first attempt to examine the footprint and consumption patterns based on physical consumption units, and improvement of the accuracy of the estimations should be paid attention to in future research.

3.2. Comparison of Lifestyle Carbon Footprints According to our calculations, total average lifestyle carbon footprints vary considerably between the case countries. The average lifestyle carbon footprint is the highest in Finland, where it is estimated at 10.4 tonnes per year. Japanese have the second highest footprint estimated at 7.6 t-CO2e, followed by China (4.2 t-CO2e), Brazil (2.8 t-CO2e) and India (2.0 t-CO2e) as shown in Figure 3.1. When comparing the results with the GHG emission targets set for 2030 (2.3 t-CO2e per capita in terms of all GHGs, see Section 3.5), the current average lifestyle considerably exceed the targets in Finland and Japan and slightly overshoots in China. These gaps suggests that, by 2030, Finnish, Japanese, Chinese, and Brazilian lifestyle GHG emissions need to decrease by 69–78%, 58–70%, 24–45%, and up to 18% respectively in order to achieve the emission targets. As a comparison, India is currently below the emission targets for 2030.

Figure 3.1. Carbon footprint and its breakdown between consumption domains

Out of the consumption domains considered, nutrition, housing and mobility tend to have the largest impact on the total carbon footprints. The overall difference in the carbon footprint of the three domains compared to the total footprint is relatively small (75 ± 8%) between different case countries, but there are proportionally large differences between these domains. In Finland, mobility and housing contribute the biggest share of the carbon footprint (27% [2.7 t-CO2e] and 24% [2.5 t-CO2e] respectively), followed by nutrition (17%: 1.8 t-CO2e). The leisure and services accounts for the remaining 20% (2.1 t-CO2e). One third (2.4 t-CO2e) of the Japanese footprint originates from housing, followed by mobility (20%: 1.6 t-CO2e) and nutrition (18%: 1.4 t-CO2e). The biggest contributor to the average lifestyle carbon footprint in China is


housing (33%), in Brazil, nutrition (37%) and in India, mobility (36%). Apart from the domains addressed above, the difference between the consumer goods domain’s share in the case countries’ footprint is relatively small (11 ± 3%). The difference in the domain of leisure and services is 15 ± 5.4%. The difference might be due to the limited data coverage, yet the overall effect on the overall lifestyle carbon footprint is relatively small in all case countries.

3.3. Domain-Specific Comparison This section elaborates average lifestyle carbon footprints between case countries by giving more detailed comparison of three domains: nutrition, housing and mobility. Other domains (consumer goods, leisure, and services) are compared only between Finland and Japan due to the data limitation in China, Brazil and India. The description of results begins with Finland, Japan, and other countries, followed by discussion of comparison. The illustration of the results uses “skyline charts” for the carbon footprints in the different consumption domains in the different countries. The charts give the amount of consumption (x-axis) and the carbon intensity (y-axis) for the different components. The total carbon footprint of each component is then displayed as the size of its rectangle. The order of the rectangles from left to right is based on their size, indicating the carbon footprint of the components. In addition, the comparison of the carbon footprint and its breakdown into different components is presented as a table for the housing domain. The following section of domain specific comparison gives a summary and comparison of the average lifestyle carbon footprint estimation of the five countries without referring to the specific data sources and background materials. For specific data sources and detail of estimation results, please refer to Annex B and C, respectively. 1) Nutrition

Finland

The carbon footprint of nutrition in Finland is 1.8 tonnes CO2e per year. The consumption of high intensity (8.0 kg-CO2e/kg) meat products accounts for 37% of this. An average Finn consumes annually approximately 19 kg of beef (24% of total meat consumption), 35 kg of pork (43%), and 27 kg of other meat products (33%). The high consumption of lower-intensity meat products, such as pork, lowers the carbon footprint of meat consumption. Nevertheless beef still contributes 43% to the carbon footprint of meat consumption. Dairy products are responsible for 36% of the footprint (3.2 kg-CO2e/kg), which is especially due to the relatively high consumption of high-intensity cheese products compared to most non-European countries. The amount of cheese consumed annually in Finland is 26 kg (13% of total dairy consumption) and the amount of milk consumed is 120 kg (67%). However, the carbon footprint of cheese consumption accounts 54% and milk consumption 30% of the total carbon footprint of dairy products. The share of beverages is 9% of the carbon footprint of nutrition due to the relatively high consumption of beer and coffee products. Beer products account for 26% of total beverage consumption but 62% of total beverage carbon footprint. The share of coffee consumption is 52% of total beverage consumption, and the carbon footprint of coffee is 17% of total beverage carbon footprint. The carbon intensities of fish (3.5 kg-CO2e/kg) and eggs (2.7 kg-CO2e/kg) are relatively high but the amount consumed is low (16 kg/capita/year and 12 kg/capita/year


respectively). The data used in this study does not separate between domestic and imported nor between wild and farmed fish, which may influence the carbon footprint of fish products. While animal-based products contribute 33% to direct amount of consumption in nutrition, their share of 78% in the carbon footprint is remarkable in relation to plant-based foodstuffs.

Japan - The average Japanese lifestyle carbon footprint for the nutrition domain is 1.4 t-CO2e per year. Meat has the largest share of footprint (23% of nutrition) with an extremely high intensity of 9 kg-CO2e/kg. However, it should be noted that the level of carbon intensity varies among different types of meats from beef (25 kg-CO2e/kg), pork (6 kg-CO2e/kg), and poultry (about 3.5 kg-CO2e/kg), whereas the average Japanese consumes approximately 5 kg of beef (about 14% of meat consumption), 10 kg of chicken (38%), and 21 kg of pork and other meats (58%) per year. The second most high-intensity items are dairy products (3.7 kg-CO2e/kg) and fish (3.5 kg-CO2e/kg), which account for 13% and 7% of the footprint from nutrition domain. The intensity of fish becomes high because non-edible parts of seafood supplied to households are assumed to be higher than other items (assumed as 23% as compared to 15% for eggs and 10% for potatoes). It should also be noted that dairy products include high intensity products such as butter (13 kg-CO2e/kg) as well as low intensity product such as milk (1 kg-CO2e/kg). Cereals, beverages, and vegetables have substantial shares of the footprint (19%, 10%, and 10%, respectively) because of the large amount of consumption; however, the carbon intensity of these items is low to modest (1.7, 0.6, and 0.9 kg-CO2e/kg, respectively). Yet, the intensity of vegetables varies a lot between greenhouse (about 1.9 kg-CO2e/kg) and on-farm (about 0.4 kg-CO2e/kg) produced vegetables. The carbon intensity of cereals (about 1.7 kg-CO2e/kg) is higher than in other countries because the carbon intensity of rice, which has relatively higher consumption in Japan, is higher than other cereals such as wheat (about 1 kg-CO2e/kg). Among beverages, the carbon intensity of alcohol (1.3 kg-CO2e/kg) is more than six times higher than non-alcoholic beverages (0.2 kg-CO2e/kg), while an average Japanese person consumes nearly 40 kg/capita/year of alcoholic beverages (approximately 16% of beverages). The category of others has also a relatively high intensity (2.5 kg-CO2e/kg) because this category contains processed or light-weight products (such as oils and spices). In Japan, food loss at households is estimated as 3.7%, which is reflected in the amount of food consumption in this chart, while food loss in the supply chain is assumed as 4.1%, which is reflected in the carbon intensity. Also, about 15% of the footprint is due to transport and retail (0.2-0.6 kg-CO2e/kg depending cooling requirements), which are reflected in the carbon intensity. Brazil, China, and India In China, the carbon footprint for nutrition is 1.05 t-CO2e per year. The consumption of meat is responsible for the biggest share (44%) of the total carbon footprint of nutrition. Chinese people annually consume approximately 5 kg of beef (9% of total meat consumption), 39 kg of pork (62% of total meat consumption), and 14 kg of poultry (22% of total meat consumption). Yet, beef consumption accounts for 34% of the total carbon footprint of meat consumption due to the high carbon intensity of beef products (approximately 14.0 kg-CO2e/kg). Fish and vegetables each account for approximately 11% of the carbon footprint for nutrition. Fish has a relatively high carbon intensity (3.5 kg-CO2e/kg) and the high consumption level of vegetables (420 kg/cap/a) explains the proportionally high carbon footprint of vegetables. Most of the cereals consumed are rice and wheat. The Chinese value for carbon intensity of rice and wheat production is proportionally small and therefore the share of cereals accounts only 9% of the


total carbon footprint for nutrition. The carbon footprint for nutrition in Brazil is 1.04 t-CO2e per year. The share of meat accounts for the greatest share (43% of the carbon footprint for nutrition). More than half (54%) of the meat consumed is beef that has a higher carbon intensity than other meat products. The consumption of cereals is responsible for the second biggest share (20%). The consumption of rice accounts for 56% of the footprint for cereals due to the high consumption and carbon intensity of rice production. In India, the carbon footprint for nutrition is only 0.5 t-CO2e per year (26% of the total carbon footprint). Dairy products and cereals are responsible for the biggest share (60%) of the total carbon footprint for nutrition. The consumption of rice accounts for 75% of the footprint for cereals due to the high consumption and carbon intensity of rice production in India. The carbon footprint for dairy products is based on milk consumption and the relatively high carbon intensity of milk. Overall, GHG emissions related to nutrition are relatively small.

Finland (1.8 t-CO2e) Japan (1.4 t-CO2e)

China (1.0 t-CO2e) Brazil (1.0 t-CO2e) India (0.5 t-CO2e)

Figure 3.2. A comparison of carbon footprints and their breakdown (nutrition, in t-CO2e/cap/a)

In most of the case countries, meat consumption is the largest contributor to the carbon footprint for nutrition. Yet, the amounts and footprint of meat consumption varies across the countries: from the highest annual consumption of over 80kg (650 kg-CO2e) in Finland to almost 36 kg (330 kg-CO2e) in Japan. Brazil and China already have significant amounts of meat consumption of no less than 46 kg and 62 kg, respectively. In both countries, this contributes


to the carbon footprint with approximately 450 kg-CO2e, reflecting the high share of beef consumption in Brazil (25 kg/cap/year: 54% of total meat consumption). In China and Finland, most of the meat consumed is pork (63% and 43%, respectively) and poultry (22% and 29%). An exception is India, where meat consumption is less than 4 kg per capita (less than 30 kg-CO2e) partly because the majority of the population is vegetarian by tradition. Also most of the meat consumed is poultry (51% of the total meat consumption). Dairy products are also a significant contributor of carbon footprint in Finland, causing no less than 630 kg-CO2e of carbon footprint, nearly equal to the footprint for meat in the country, due to the large consumption of cheese and other dairy products (almost 200 kg per capita). This is not the case in other countries, as average Indian, Japanese, and Brazilian consume about 85 kg, 50 kg, and 35 kg of dairy products, which contributes to 150 kg-CO2e, 180 kg-CO2e, and 110 kg-CO2e of carbon footprint, respectively. Yet, it should be noted that in Brazil, dairy products are the third largest contributor to the footprint for nutrition, and their consumption is generally growing in many countries (“FAOSTAT” 2017). Fish, cereals, and beverages are other major contributors to the footprint depending on dietary habits of the countries. Fish is also a major contributor to the footprints in Japan and China, where consumption of fish is approximately 30-35 kg contributing to no less than 100-120 kg-CO2e of the footprints. Cereals have relatively high intensity in Japan and Brazil compared to other countries (approximately 1.7 kg-CO2e/kg), probably due to the large share of rice consumption which tends to have higher intensity than wheat and other cereals, typically with less than 1 kg-CO2/kg of intensity. Beverage consumption also has a high share of the footprints in Finland and Japan, contributing to approximately 150 kg-CO2 of the footprint. This is not the case in the other countries, but it should be noted that this might be due to limited data in the other three countries. Beans are a relatively low-carbon and protein-rich food and generally have a low-carbon intensity (0.5-1.1 kg-CO2e/kg) among protein-rich food ingredients; however, their consumption is limited in most of the case countries, with 26 kg in Japan, 15 kg in India, and 1-5 kg in Finland and China. An exception is Brazil, where people consume 70 kg of beans annually. 2) Housing

Finland - The carbon footprint of housing is 2.5 t-CO2e/capita per year. An average Finn lives in 40.3 m2 of housing space per person. The carbon intensity per housing space is 62 kg-CO2e/m2, when summing up direct and indirect emissions from construction, maintenance, and energy and water consumption. The share of the habitation is proportionately small compared to the GHG emissions related to the use of energy but does not include the emissions from the land use change affected by the building. Indoor heating demand in Finland is high due to the large living space per capita and the long winter season. Out of the total domestic energy use in Finland, 65% is related to indoor heating, 15% to water heating, 12% to sauna heating and 4% to the use of electronic devices (“Official Statistics of Finland (OSF)” 2017b). The use of electricity (3900 kWh/cap/a) accounts for 34% of total GHG emissions in housing. This includes the use of electricity as the main heating source (43% of the total electricity consumption per capita per year). District heating (3600 kWh/cap/a) forms the largest share of other energy supplies because it is the primary source for indoor and water heating in Finland. District heating accounts for 41% of total GHG emissions in housing due to the high consumption level and relatively high carbon intensity. Depending on the region and power plant, fuels used for


district heat production are wood or other biomass, coal, natural gas, peat, waste and oil (Finnish Energy 2016). However, data of the total amounts of different fuels used for district heat production was not available. Heating oil has one of the highest carbon intensities in housing but the total amount of oil heating (700 kWh/cap/a) is relatively small. The share of other forms of energy is proportionately high (2500 kWh/cap/a), but it is mainly produced by wood that has a low carbon intensity in Finland. Japan - The lifestyles carbon footprint from the housing domain for an average Japanese is 2.4 t-CO2e per year. An average Japanese lives in 39.4 m2 of housing space per person (Statistics Bureau of Japan 2013). This statistic includes not only small-sized apartment rooms in urban centers but also large-sized owned houses in suburbs and rural areas. Some of the latter were once built for a large family but are now occupied by only one or two family members due to the changes in family structure. After summing up direct and indirect emissions from construction, maintenance, and energy and water consumption, carbon intensity per housing space is about 62 kg-CO2e/m2 per year per capita. Constructing and maintaining of housing space contributes 0.5 t-CO2e/capita/year to the lifestyle carbon footprint (approximately 20% of housing domain footprint). 77% of the footprint in housing is from direct energy consumption including both electricity (55%) and other energy sources (22%). In Japan, coal, oil, and LNG account for about 84% of grid electricity sources, while the share of hydropower and other renewable electricity is limited to 15% (Agency for Natural Resources and Energy, Japan 2018a).This fossil-fuel based grid electricity has the highest carbon intensity among energy sources: e.g., 0.99 kg-CO2e/kWh for coal, 0.78 kg-CO2e/kWh for oil, and 0.55 kg-CO2e/kWh for LNG including transmission losses. In terms of direct energy supply, other energy accounts for 49% of energy consumption compared to 51% from electricity. The majority of energy sources are fossil fuel based, such as kerosene mainly used for heating as well as LPG and urban gas used for cooking and heating, which accounts for 22% of lifestyles carbon footprint from housing. Carbon intensity of these non-electricity fossil fuels are lower than fossil fuel based electricity (0.25-26 kg-CO2e/kWh); however, it is still significantly higher than renewable energy sources (0.01-0.06 kg-CO2e/kWh). The impacts of electrification and renewables on the carbon footprint will be discussed later in this section. In total, only 8% of direct energy (both electricity and non-electricity) is supplied by renewable sources. Apart from this, water (freshwater supply and wastewater treatment) consists of about 4% of the total footprint from housing. Brazil, China, and India - The carbon footprint from the housing domain of the average person in China is 1.4 t-CO2e per year (33% of the total carbon footprint: 39 kg-CO2e/m2). The use of other energy sources contributes the biggest share (37%: 0.5 t-CO2e) and electricity consumption contributes the second largest share (35%: 0.5 t-CO2e) of the carbon footprint of housing sector. Most of the energy used (65%) is from non-renewable, carbon-intensive energy sources, such as coal and petroleum. The overall electricity consumption per capita is small, but almost three quarters of electricity in China is produced by coal- and oil-based thermal power that both have high carbon intensity. One quarter (27%) of the emissions in housing are due to the relatively high carbon intensity of living space per one square meter. In Brazil, housing contributes 0.5 t-CO2e per year to the carbon footprint of an average Brazilian (16% of the total carbon footprint: 22 kg-CO2e/m2). The greatest share (46%) of the emissions in the housing domain are due to the relatively high carbon intensity of living space per square meter. The share of energy and electricity is approximately 25% for each due to the low overall consumption and high share of renewable energy sources. Yet, the overall share of housing is approximately only one tenth of the total carbon footprint. In India, housing contributes 0.4 t-CO2e per year to the carbon footprint of an average Indian (22% of the total carbon footprint:


21 kg-CO2e/m2). Emissions related to living space are responsible for the greatest share (51%) of the housing domain’s emission, due to the relatively high carbon intensity of living space per square meter. Use of electricity is responsible for the second largest share (39%; 140 kWh/cap/a), due to the high share of non-renewable energy sources used for electricity production. The total amount of other energy forms is higher (640 kWh/cap/a) but lower carbon intensity compared to electricity is much lower because of the high share of firewood used for heating (88%).



Figure 3.3. A comparison of carbon footprints and their breakdown (housing energy, in t-CO2e/cap/a)


Table 3.1. Comparison of Carbon footprint and its breakdown between different components in the housing domain

Total Construction and maintenance Electricity Other energy Water consumption

CF 1) CF 1) Amount (m2) CF 1) Amount

(kWh) CF 1) Amount (kWh) CF 1) Amount

(m3)

Finland 2,500 400 40.3 860 3,940 1,230 6,850 10 51

Japan 2,430 480 39.4 1,330 2,120 530 2,070 90 110

Brazil 480 220 21.0 120 640 110 750 30 85

China 1,350 360 35.0 470 460 500 1,050 20 49

India 400 200 19.0 30 140 30 640 20 44 1) Carbon footprint, kg-CO2e/capita/year

The two cases of developed countries, Finland and Japan, have similar size of living space of per capita, approximately 40 m2, although it should be noted that the Japanese living space reflects the less populated houses with many empty rooms, while summer cottages in Finland are excluded from this value. The overall carbon footprint from this domain in the two countries are also at the similar level of approximately 2.5 t-CO2e/capita with a carbon intensity of slightly over 60 kg-CO2e/m². In these countries, construction and maintenance of houses accounts for 16-20% (400-480 kg-CO2e) of the footprint from housing. However, direct energy use from housing has a large difference: 10,800 kWh in Finland and 4,200 kWh in Japan. In terms of direct energy use per living space, the difference is nearly 3 times: 270 kWh/m² in Finland in comparison with 110 kWh/m² in Japan. This is partly because of the high energy demand for heating in Finland, whereas 65 %, 15% and 5% of domestic energy use is from indoor heating and water and sauna heating, respectively. Although Japan has relatively high demand for hot water use of 29% partly due to the custom to running hot water into a bathtub, indoor heating accounts for only 22% of house energy consumption while indoor cooling is only 2% (Agency for Natural Resources and Energy, Japan, n.d.).

Electrification of direct housing energy use with renewables can contribute to low-carbon lifestyles, but fossil-fuel-based electricity can be less efficient in comparison with non-electricity energy sources. Japan has a higher electrification rate of direct energy consumption in housing domain, with 51% compared to 37% in Finland. Typically, electricity-based room temperature control system such as heat pump has higher energy conversion efficiency at household level. However, if fossil-fuel is used, carbon intensity for electricity is generally higher than fossil-fuel based non-electricity energy because conversion efficiency at power plants are relatively lower; e.g., 0.26 kg-CO2e/kWh for Kerosene, LPG, and Urban Gas in comparison with 0.55 kg-CO2e/kWh for LNG grid electricity to 0.99 kg-CO2e/kWh for coal grid electricity in Japan. Therefore, the electrification of home energy sources can reduce carbon footprints if the grid electricity is renewable-based, but probably not in other cases.

A comparison between the two countries shows that carbon intensity of grid electricity and non-electricity is significantly lower in Finland than Japan due to the high share of renewables. Within the grid electricity, Finland has a 45% share of renewables, mainly hydropower and from biomass combustion, which result in a relatively low carbon intensity of 0.22 kg-CO2e/kWh. On the contrary, Japan uses no less than 84% of fossil fuels in the grid electricity including over 30% of coal, which results in the high carbon intensity of grid electricity: 0.63 kg-CO2e/kWh.


For non-electricity energy, Japanese house typically use LPG and urban gas for heating (32% of overall energy from housing) and cooking, as well as kerosene for heating (17%), while the use of off-grid renewables and steam and heating system is limited to less than 1%. On the other hand, 48% of the heating energy used for room and water heating by Finnish homes is district heat, which has relatively low carbon intensity despite being largely fossil-fuel based. 34% of heating energy used is from wood for room, sauna and water heating, which is considered as carbon neutral except for its indirect emissions such as transport and production. As a result, the overall renewable share is 37% in Finland, higher than that of 8% in Japan (direct housing energy use).

In the cases from developing countries, the carbon footprint from housing domain is significantly lower than in the above two countries, but varies from 1.4 t-CO2e in China to 0.4 t-CO2e in India, whereas carbon intensity per living space is lower with approximately 20-40 kg-CO2e/m². In terms of per capita living space, the average population in these countries lives in a smaller space, approximately 35 m²/capita in China, 21m²/capita in Brazil and 19 m²/capita in India, and energy use from housing is as low as 1,500 kWh in China, 1400 kWh in Brazil and 800 kWh in India. This is due to a lower heating demand because of the climate conditions, smaller penetration of appliances and electricity, and relatively larger household size and smaller size of housing space per capita in these countries. A comparison between these developing countries shows the share of renewables of total energy consumption is approximately 6% in China, 38% in Brazil and 5% in India. The carbon intensity of grid electricity is significantly higher in China and India compared to Brazil due to the high share of fossil fuels and low share of renewables. The share of fossil fuel based grid electricity is 78%, 15% and 68% in China, Brazil and India, respectively. In Brazil, 85% of grid electricity is renewables, mainly hydropower (490 kWh/cap/a: 77% of total electricity consumption). The main categories of the other energy forms used in China, Brazil and India are coal and derivatives (32% of the total energy consumption), LPG (26% of total energy consumption) and firewood (72% of the total energy consumption), respectively, which notably increases the total share of non-renewables of total energy consumption in China and Brazil.

3) Mobility

Finland Mobility contributes 2.8 t-CO2e per year to the carbon footprint of an average Finn. More than three quarters of this is caused by car transportation (11,200 km/cap/a) due to the notably high amount and carbon intensity of the car kilometers driven. Most of the emissions are from fuel combustion of approx. 80% (VTT Technical Research Centre of Finland Ltd 2017), but the production of vehicles is also included in the calculation. The other important contributor is air transportation because of the relatively high average amount of passenger kilometers (2,260 km/cap/a: 14% of the total transportation demand) and the fuel consumption of air transportation. The total amount of passenger kilometers travelled by land-based public transportation is also notable (1,640 km/cap/a: 10% of the total transportation demand), with 54% of the kilometers travelled by bus, 39% by train, and 7% by tram or metro. The statistics used for this study do not separate urban and long distance journeys and therefore average carbon intensities were used for train and bus transportation. Finland’s railway company uses mainly (over 90% of all the passenger trains; (“VR Group Ltd” 2018) renewable energy for passenger transportation. Therefore, the carbon intensity of public transportation is less than 25% in comparison to car use. Overall, car use contributes more to the carbon footprint of mobility than any other transportation mode, and only a fraction of the carbon footprint is due to other than car or air traffic. The use of other private transportation modes accounts for 800 km per year, the equivalent to 2.2 km per day. This includes trips made by small vehicles that run on gasoline, such as motorcycles, snowmobiles, quad bikes, microcars, etc. The use of


bicycles is moderate, only 260 km per year, which is equivalent to 0.7 km per day. Japan - On average, Japanese have 1.6 t-CO2e of carbon footprint from their annual mobility demand of 11,000 km/capita including walking. Average carbon intensity per mobility distance is 0.14 kg-CO2e/km passenger. Among different transportation modes, automobiles contribute to almost 80% of the carbon footprint from this domain (1.2 t-CO2e), while accounting for about 46% of mobility distance (5,000 km/capita). This is due to its highest carbon intensity among different transportation modes, 0.25 kg-CO2e/km passenger, which is partly due to extremely low average occupancy rate of 1.3 passengers per car while driving and high share of fossil-fuel based combustion engine cars with only 15% of hybrid and electric car use. Among automobiles, taxis have the highest carbon intensity of 0.50 kg/km passenger due to their relatively low occupancy of 1.65 passengers per car and low customer ride rate of approximately 40%. Furthermore, airplane contributes to almost 10% of carbon footprint from mobility (0.15 t-CO2e) accounting for approximately 600 km/capita for domestic and 1,000 km/capita for international air trips. The average carbon intensity of air flights, 0.09 kg-CO2e/km passenger, is not as high as for automobiles, but the overall carbon impacts are high due to the relatively long distance of each trip. In Japan, on-land public transportation (train and bus) accounts for approximately 38% of mobility demand (4,300 km/capita). In particular, trains account for 3,600 km/capita (33%), with a very low carbon intensity of 0.02 kg-CO2e/km, while buses account for 500 km/capita (4%) with a relatively low carbon intensity of 0.09 kg-CO2e/km. The carbon intensity of ferries is as high as 0.65 kg-CO2e/km in Japan, possibly due to the allocation of the footprint between passenger and cargo because many passenger ferry services are combined with cargo. On average, Japanese use bicycles for only 270 km per year, which is equivalent to 0.7 km per day.

Brazil, China, and India - Mobility contributes 1.1 t-CO2e per year (25%) to the carbon footprint of an average Chinese. More than half of this is caused by car transportation due to the high intensity of the car kilometers driven. Another important contributor is motorcycle transportation due to the relatively high amount and carbon intensity of the motorcycle kilometers driven. Also the share of public transportation is high and shows the highest amount of kilometers driven. Most of these kilometers are done by bus which has a relatively high carbon intensity compared to cycling or rail transportation. In Brazil, the carbon footprint of mobility is 0.5 t-CO2e per year (16% of the total carbon footprint). Most of it is from cars (39%) and public transportation (42%). Emissions from car transportation are due to the relatively high carbon intensity of car transportation, although most of the cars and light vehicles are running on ethanol-gasoline blends (flexible-fuel vehicles) in Brazil (Posada and Façanha 2015). The high carbon footprint of public transportation is explained by the high amount of kilometers driven. In addition, most of the kilometers are accounted for by buses which have a relatively high carbon intensity compared to other forms of public transportation. In India, the carbon footprint of mobility is 0.7 t-CO2e per year (34% of the total carbon footprint). Most of it is caused by car transportation (46%) due to its high carbon intensity. Both motorcycle and public transportation account for approximately one fourth of the total carbon footprint of mobility. Compared to car transportation, both have high amounts of kilometers driven. Most of the public transportation is done by bus which has relatively high carbon intensity compared to other forms of public transportation.




Figure 3.4. A comparison of carbon footprints and their breakdown (mobility, in t-CO2e/cap/a)

As a comparison between the case countries, Finland has the highest mobility demand per capita with a demand of nearly 16,500 km/capita in comparison with moderate demand of nearly 11,000 km/capita in Japan and the low demand of 4,000-8,000 km/capita in the other three countries. This probably reflects higher population density and metropolitan development in Japan than Finland, with lower welfare in developing countries. Automobiles are the largest contributor to the carbon footprint from this domain in most case countries, except for Brazil where the heavy use of buses contributes to the footprint. The mobility modal share of automobiles is very high at 68% (11,200 km/capita) in Finland, moderate at nearly 46% (5,000 km/capita) in Japan, and relatively low at 21-27% (1,100-1,800 km/capita) in China and Brazil, and much lower at 13% (800 km/capita) in India. The carbon intensity of cars is slightly higher in Japan (0.25 kg-CO2e/km) than Finland, probably reflecting the high replacement cycle of cars and relatively low occupancy rate of 1.3 persons/car. In comparison, the average occupancy rate is 1.7 person/car in Finland. The intensity is much higher in developing countries such as China and India, partly because of lower fuel efficiency of the cars being used but also due to intensity data that is based on global average values for different car classes and fuel types.

Airplane travel is the second largest contributor to the footprints in the two developed countries, accounting for nearly 10-15% of the footprint from mobility. In Finland, flights induce as much as 370 kg-CO2e/capita while accounting for nearly 2,200 km/capita (13%) of mobility demand. In Japan, airplanes contribute to 150 kg-CO2e/capita of the footprint and account for over 1,600


km/capita (15%) of mobility needs. The contribution of flights to the carbon footprint is higher in Finland than Japan partly due to the higher intensity of flights with less occupancy rate. The average occupancy rate of flights in Finland is 53-73% (domestic and intercontinent, (Lähteenoja, Lettenmeier, and Saari 2006)), which is slightly lower than in Japan with an occupancy rate of 69-83% (domestic and international, (All Nippon Airways 2018; Japan Airlines 2017)).

The use of land public transport is higher (33%: 3,600 km/capita) in Japan than in Finland (10%: 1,640 km/capita), partly reflecting the high coverage of public transport services backed by high population density, while its share reaches almost 50% in Brazil. Although Japan has a higher share of trains (almost 30% of mobility demand), the use of buses is limited in both countries (5% in Japan to 6% in Finland). Trains have a relatively low carbon intensity (e.g., 0.02 kg-CO2e/km in Japan) and have almost zero intensity in Finland due to carbon neutral policy of national train services (“VR Group Ltd” 2018), in comparison with that of buses (approximately 0.1 kg-CO2e/km). However, the use of train services in developing countries is also limited (6-7% in Brazil, China, and India), while buses play a higher role (24-43%). In addition, China has a higher share of motorcycles (over 20%). Although its carbon intensity is lower than that of cars, it is still much higher than public transport. The use of bicycles in most case countries is limited to 250 km/capita/year or less than 0.7 km per day. Exceptions are China (over 1,100 km/capita) and India (almost 500 km/capita), where average population travel by bicycle from 1km to 3 km per day.

4) Other domains (consumer goods, leisure, and services) Finland For the consumption domains of household goods, leisure and services, detailed data or product lists concerning total amounts and carbon intensities of consumption were not publically available. Therefore, the carbon footprints of consumer goods, leisure and services are based on the study of (Seppälä et al. 2009). Detailed analysis of the consumption domains of and components under consumer goods, leisure and services is beyond the scope of this study. The carbon footprint of consumer goods is 1.3 t-CO2e/capita, which is 13% of the average annual lifestyle carbon footprint in Finland. Mixed goods and services related to furnishing and housekeeping (categorized under furniture) account for the greatest share of the consumer goods (360 kg-CO2e/capita), followed by clothes, outdoor equipment (categorized under sport/entertainment) and mixed products and services (all from 240 to 270 kg-CO2e/capita). ICT/AV equipment (110 kg-CO2e/capita) and paper products/stationery (90 kg-CO2e/capita) are minor contributors to the carbon footprint of consumer goods domain. Leisure-related products and services accounted for only 570 kg-CO2e/capita (6% of the total carbon footprint), and the impact of this domain is therefore minor. Leisure-related components included recreational and cultural services (180 kg-CO2e/capita), travel expenses abroad (200 kg-CO2e/capita) and hotel services (200 kg-CO2e/capita). Services accounted for 1.5 t-CO2e/capita, which is 14% of the average annual lifestyle carbon footprint in Finland. Healthcare, social services, and education-related services are the largest contributors to the carbon footprint of the service domain (contributing 590, 460, and 220 kg-CO2e/capita respectively, accounting for 85% of the service domain’s footprint). Japan - Consumer goods account for 1.0 t-CO2e/capita, which is 14% of the average annual lifestyles carbon footprint in Japan. Among different items of consumer goods, home appliances and ICT/AV equipment are the largest contributors to the carbon footprint, accounting for no less than 320 kg-CO2e/capita (31% of consumer goods domain). The second largest contributor to the footprint is clothing, which accounts for 220 kg-CO2e/capita (21%).


Apart from daily essential consumables such as sanitation (120 kg-CO2e/capita) and other items (160 kg-CO2e/capita), entertainment and luxury products are also a major contributor; e.g., sports and entertainment tools, jewellery, and tobacco accounts for almost 150 kg-CO2e/capita of the carbon footprint (14%). On another front, leisure services consumed outside the home account for nearly 600 kg-CO2e/capita of the lifestyle carbon footprint in Japan. The most significant part of the carbon footprint for this domain is from travel and eating out. Restaurants and hotels account for 250 kg-CO2e (42% of leisure domain) and 135 kg-CO2e (19%) of the lifestyle footprint excluding the footprint derived from food ingredients, which is reflected in the nutrition domain. The per monetary value carbon intensity for hotel and restaurant services including the footprint induced from food ingredients is over 0.3 kg-CO2e per JPY100, the highest among leisure items. Furthermore, amusement facilities and racing stadiums including those for legal gambling as well as nightlife and bars contribute no less than 165 kg-CO2e to the carbon footprint (30%), whereas these leisure items have relatively high carbon intensity of 0.25 kg-CO2e per JPY100. The remaining parts of the footprint of 40 kg-CO2e are accounted for by cultural, sports, and outdoor leisures including movies, theater plays, sports facilities, and outdoor parks; these leisure items have relatively low carbon intensity per monetary value of less than 0.20 kg-CO2e per JPY100. Yet, it should be noted that the footprint due to the access to these leisure facilities is not included in these intensity values but reflected in the mobility domain. The average Finn has a slightly higher footprint (1.3 t-CO2e) than average the Japanese from consumer goods (1.0 t-CO2e), possibly due to slightly more expenditure on consumer goods (over EUR 3,000/capita/year in Finland compared to JPY 360,000/capita/year in Japan, which is equivalent to EUR 2,700) and slightly higher carbon intensity in Finland (0.42 kg-CO2/Euro in Finland compared with 0.0029 kg-CO2e/JPY in Japan, equivalent to 0.38 kg-CO2/Euro). However, it should be noted that Finnish and Japanese consumer goods data are not directly comparable due to the differences in classification method. Finnish data is based on product groups based on the Classification of Individual Consumption by Purpose (COICOP) and the breakdown of product groups into individual products is not possible. Therefore Finnish consumer goods data might include some products or services that would be classified differently in Japanese data. As per leisure services, both countries have a similar carbon footprint of 600 kg-CO2e/capita. Japanese have a higher footprint from restaurants and hotels (380 kg-CO2e, over 60%), while the distribution of the leisure footprint in Finland has more variety among recreational and cultural activities, travel abroad, and hotel services. It should be noted that the data in Japan does not include the footprint from travel abroad, and this data is not directly comparable due to different methodology of estimation. From Japanese carbon intensity data, it was implied that the shift from material purchase to leisure and experience consumption may not immediately contribute to low-carbon lifestyles. On average, carbon intensity per monetary value expenditure for consumer goods and leisure services are almost the same (0.29 kg-CO2e per JPY100) including footprint induced from food ingredients. This implies that the shift of expenditure from material-based consumption to experience or service-based consumption may not immediately reduce carbon footprints, but the choice between low-carbon to high-carbon leisures and absolute amount of consumption of goods may be more relevant in reducing lifestyle carbon footprint. Non-leisure service consumption accounts for approximately 600 kg-CO2e/capita in Japan, with slightly lower carbon intensity of 0.22 kg-CO2e per 100 JPY, partly reflecting the labor intensive characteristics of the service industry.


4. Identification of Low-carbon Lifestyle Options For reducing global GHG emissions in line with the targets proposed in Section 2, both demand and supply side measures have to be implemented. While a huge amount of literature and suggestions for reducing lifestyle carbon footprints have been published, these are often not well structured nor synthesized with respect to the different consumption domains, carbon reduction strategies or their potential effects.

We have conducted a review of existing literature to identify promising options to reduce lifestyle carbon footprints. The literature was scientific articles, technical reports and some other literature that indicate lifestyle and behavior-related carbon reduction options with quantified potential impacts published by academic and policy research institutes. The focus of the literature review was global but most of the existing articles were from Europe and North America. The sources used for most of the examples were Project Drawdown (Hawken 2017), Capital Consumption (Hersey et al. 2009), Sitra’s Green to Scale (Tynkkynen 2016, 2015) and Sitra’s 100 options for smart and sustainable living (Finnish Innovation Fund Sitra 2017) and their background materials.

As a result of the literature review, we identified low-carbon lifestyle options for three domains (nutrition, housing, mobility, and consumer goods) in Tables 4.1–4.4. Similar types of options proposed by different studies were combined into one group of options in each line. For compiling these tables, we applied the following criteria:

● To cover both production- and consumption-related options;

● To consider different carbon reduction approaches: absolute reduction, modal shift, sharing, efficiency improvement;

● To avoid or to at least notice overlapping of different options; and

● To include sufficiently impactful options, and indicate the size.

While a huge range of solutions could be identified for the different consumption domains, it is worth mentioning that the promising options identified do not necessarily cover the whole range of possible solutions. There can be new issues emerging once the need for reductions is defined in the different domains and the full range of required change becomes visible (Akenji et al 2016). Therefore, the reduction options identified here should be seen as promising rather than the only solutions. Reflecting the identified solutions together with the change required can open new aspects on reductions in lifestyles carbon footprints. This has been shown for the material footprint in earlier research (Lettenmeier, Liedtke, and Rohn 2014; Lettenmeier 2018). Nevertheless, this draft report does not contain an illustration of the reduction pathways of lifestyle carbon footprints that lead towards meeting the targets established in Section 2. Such an analysis is intended to be included in the forthcoming, updated version of the report.


1) Four Approaches of Low-carbon lifestyles

In order to structure the solutions proposed in each lifestyle domain, the collected options were first categorized into four basic types of reduction approaches. These approaches were demand-side reduction, modal shift, efficiency improvement, and sharing:

● Demand-side reduction means an absolute reduction in the amount of of goods or services consumed, for example a smaller amount of food intake, a reduction in kilometres driven, or a reduction in dwelling space.

● Modal shift means a shift from one consumption mode to another, for instance a change in diet replacing meat-products by bean-based products, replacing car trips by public transportation, or switching to renewable energy sources in electricity or heating.

● Efficiency improvement means decreasing emissions mainly by replacing technologies by less carbon-emitting ones, for example by improving agricultural practices, making vehicles consume less fuels or energy, or decreasing heating demand by insulation.

● Sharing means products or services are shared among households directly or indirectly through third party sharing service providers, for example by increasing occupancy rate of cars through ridesharing, sharing of available housing space.

For decades already (e.g., (F. Schmidt-Bleek 1993), scholars have warned of the risk that efficiency improvements might increase total consumption and even increase emissions by making consumption cheaper. For example, reducing fuel consumption might increase either the size or the travel performance of cars thus potentially eating up or even turning negative the absolute amount of resource use or emissions. Recently, the risk of rebound effects has also be linked to, e.g., modal shifts or demand reductions if these provide consumers additional time or money for increasing other consumption (Buhl 2014; Ottelin, Heinonenb, and Junnilaa 2017). The potential rebound and other negative effects of sharing solutions have also recently become subject of discussion (Clausen et al. 2017). For example, car-sharing might, in addition to increasing car kilometres of people that were car-free earlier, increase car-use especially outside rush-hours thus weakening the demand for public transportation.

The four reduction approaches described can vary largely and show different angles for different solutions and in different consumption domains, and they are not always mutually exclusive. In Tables 4.1 to 4.4, relevant approaches for each option are indicated including allowing a hybrid of more than one approach. In addition, it should be noted that some of the options listed here still have some overlaps between or within domains. There is also a distinction between individual and systemic actions. The shift of lifestyles is not the sole responsibility of consumers based on their individual choices (Akenji 2014). In addition to the choices of individuals, the improvement of production processes, the supply of low-carbon products or services by the private sector, and both a shift of infrastructure and the introduction of policies by governments are also required in order to realize many options. The choices of the consumers are locked in by the options available in the market, infrastructure, and their community (Akenji and Chen 2016). Therefore, each of the low-carbon lifestyle options needs action to be taken by individuals, private sectors, governments, or combination of these actors, and usually the effort should be the collaborative effort of more than one stakeholder. The relevance of low-carbon options to each stakeholder is indicated in Tables 4.1 to 4.4 using three levels: directly relevant options with a power to change (A), indirectly relevant options with essential roles in realizing the change (B), and indirectly relevant options with


some roles in supporting the shift (C). 2) Size of Impacts of Low-carbon Lifestyle Options

Strategically identified and promoted impactful carbon reduction options are essential in addressing the Paris Agreement target as the reductions required towards 2030 and 2050 are not incremental but drastic (e.g., 60-70% reduction by 2030). A recent study on the carbon reduction potential of lifestyle choices (Wynes and Nicholas 2017) noticed that both governmental suggestions and educational materials often fail to address impactful recommendations and instead focus on a large number of incremental or otherwise low-impact issues. This is probably not especially helpful when targeting at limiting global warming to well below 2°C. Therefore, in this study, we tried to indicate the level of impacts for each option and included options with large impacts in each domain.

To indicate the relative size of the reduction potentials, we have compared the per capita annual reduction potential suggested by different studies for each category of option. Different literature quantifies reduction potentials with different scopes, for example in terms of target year, unit of reduction potential, or object of calculation. Where the quantified impacts indicated in literature were not given per capita, we made a quick estimate of the annual reduction potential of each option per capita by dividing the total saving potentials of an option by the expected population of the area in question for the reference year. After that, we indicated the relative size of the impacts in three levels: large (reduction potential approximately over 250 kg-CO2e/cap/year), moderate (approximately 75 up to 250 kg-CO2e/cap/year), and small (approximately up to 75 kg-CO2e/cap/year), based on the mean of minimum and maximum reduction potentials estimated from literature.

As a limitation of this quick estimates, the carbon reduction potentials estimated for the options listed are based on very different assumptions, geographical areas and diffusion rates, and refer to different reference years. Therefore, this exercise shall be considered as a qualitative assessment of carbon reduction potentials with three levels of relative impacts rather than as a quantitative estimation of precise impacts of each option.

Tables 4.1, 4.2, 4.3, and 4.4 give examples of promising solutions for reducing lifestyle carbon footprints in the domains of nutrition, housing, mobility, and consumer goods. The solutions represent both production and consumption-oriented measures.


Table 4.1. Low-carbon lifestyle options identified for nutrition

Options Impact Level

Approaches Stakeholders

References Reduction

Sharing

Modal

shift

Efficienc

y

Individu

al Private

Governmen

t

Plant-rich food (incl. vegetarian, vegan)

Large V A B C

(Hawken 2017; Henkel AG & Co. KGaA, Wuppertal Institute for Climate, Environment and Energy 2018; “Shrink That Footprint” n.d.; Finnish Innovation Fund Sitra 2017)

Low-carbon protein instead of red meat (inc. poultry, fish)

Large V A B C (“Shrink That Footprint” n.d.; Finnish Innovation Fund Sitra 2017)

Reduction of food loss and waste Moderate V V V A A C

(Hawken 2017; Tynkkynen 2015)

Supply chain improvement after farms

Moderate V C A C (Hersey et al. 2009)

Production improvement at farms

Moderate V C A C (Hersey et al. 2009; Hawken 2017; Tynkkynen 2015)

Reduction of excess nutrition Moderate V A C C (Finnish Innovation

Fund Sitra 2017)

Renewable electricity in food supply chain

Moderate V C A A (Hersey et al. 2009)

Alternative dairy products (plant-based)

Moderate V A B C (Finnish Innovation Fund Sitra 2017)

Reduction of sweets and alcohol Small V V A C C (Hersey et al. 2009)

Manure management and composting

Small V C A C (Tynkkynen 2015)

Note: impact level is based on the size of reduction potential per capita: large (over 250 kg-CO2e/cap/year), moderate (over 75 up to 250 kg-CO2e/cap/year), small (up to 75 kg-CO2e/cap/year). The classifications of the relevance of low-carbon options to the stakeholders are: directly relevant options with a power to change (A), indirectly relevant options with essential roles in realizing the change (B), and indirectly relevant options with some roles in supporting the shift (C).


Table 4.2. Low-carbon lifestyle options identified for housing




Sharing

Modal shift

Efficiency

Individual

Private

Government

Renewable energy (geothermal, solar, wind)

Large V A A A

(Yue, You, and Darling 2014; Hawken 2017; Rohn, Pastewski, and Lettenmeier 2013; Finnish Innovation Fund Sitra 2017)

Smaller living space Large V A B C

(Yue, You, and Darling 2014; Hawken 2017; Rohn, Pastewski, and Lettenmeier 2013; Finnish Innovation Fund Sitra 2017)

Improved efficiency of construction


Heat pump for temperature control

Moderate V V A B C

(Hawken 2017; Salo et al. 2017; “Official Statistics of Finland (OSF)” 2017b); (Finnish Innovation Fund Sitra 2017)

Sharing of housing space

Moderate V A B C (Finnish Innovation

Fund Sitra 2017)

Reduction of air conditioning needs (optimized room temperature)

Moderate V A B C (Finnish Innovation

Fund Sitra 2017)

Reduction of new housing infrastructure

Moderate V B A A (Hersey et al. 2009)

Energy efficient home appliances

Moderate V V A B C

(Tynkkynen 2015; Finnish Innovation Fund Sitra 2017)

Improved insulation (inc. window sealing, curtains)

Moderate V V A A C

(Hawken 2017; Finnish Innovation Fund Sitra 2017)

Saving of hot water Small V A C C (Finnish Innovation Fund Sitra 2017)

District heating Small V C B A (Hawken 2017)

Reduction of home electricity use (monitoring, peak management)

Small V A B C (Finnish Innovation Fund Sitra 2017)

Recycled and low-impact building materials

Small V C A C (Hersey et al. 2009)

LED lighting Small V V A B C (Finnish Innovation Fund Sitra 2017)

Bioenergy for heating Small V A A A (Tynkkynen 2015)

Note: impact level is based on the approximate size of annual reduction potential per capita: large (over 250 kg-CO2e/cap/year), moderate (over 75 and up to 250 kg-CO2e/cap/year), small (up to 75 kg-CO2e/cap/year). The


classifications of the relevance of low-carbon options to the stakeholders are: directly relevant options with a power to change (A), indirectly relevant options with essential roles in realizing the change (B), and indirectly relevant options with some roles in supporting the shift (C).

Table 4.3. Identified low-carbon lifestyles options (mobility)




Sharing

Modal

shift Efficienc

y Individu

al Private

Government

Traveling less often/closer destination

Large V A B C (Finnish Innovation Fund Sitra 2017)

Reduction of flights Large V V A B B

(Hersey et al. 2009; Finnish Innovation Fund Sitra 2017)

Reduction of car use (inc. public transport, Mobility as a Service i.e. MaaS, bicycle)

Large V V A B B (Finnish Innovation Fund Sitra 2017)

Electric vehicles Large V V A A C (Hawken 2017; Tynkkynen 2015; Finnish Innovation Fund Sitra 2017)

Moving closer to work and services Large V A A A (Hersey et al. 2009)

Carsharing Moderate V A B C (Hersey et al. 2009; Finnish Innovation Fund Sitra 2017)

Vehicle fuel efficiency Small V C A C (Hersey et al. 2009;

Tynkkynen 2015)

Efficient airplanes Small V C A C (Hersey et al. 2009; Hawken 2017)

Hybrid cars Small V V A B C (Hawken 2017)

Telework/telepresence Small V A A C

(Hawken 2017; Hersey et al. 2009; Finnish Innovation Fund Sitra 2017)

Improved public transport Small V C B A (Hersey et al. 2009;

Hawken 2017) Closer weekend leisure/hobbies Small V A B B (Finnish Innovation

Fund Sitra 2017)

Biogas-fueled vehicles Large V V C A B

(Tynkkynen 2015; Finnish Innovation Fund Sitra 2017)

Ridesharing Small V A A C (Hawken 2017; Finnish Innovation Fund Sitra 2017)

Note: impact level is based on the approximate size of annual reduction potential per capita: large (over 250 kg-CO2e/cap/year), moderate (over 75 and up to 250 kg-CO2e/cap/year), small (up to 75 kg-CO2e/cap/year). The classifications of the relevance of low-carbon options to the stakeholders are: directly relevant options with a power to change (A), indirectly relevant options with essential roles in realizing the change (B), and indirectly relevant options with some roles in supporting the shift (C).


Table 4.4. The identified low-carbon lifestyles options (for consumer goods)




Sharing

Modal

shift Efficienc

y Individu

al Private

Government

Renewable electricity in the manufactured goods supply chain

Moderate V B A A (Hersey et al. 2009)

Improved manufacturing efficiency


Remanufacturing Moderate V V V C A C (Parker et al. 2015)

Reuse, repair, and refurbish Small V V A A C (Parker et al. 2015;

Willis 2010)

Waste prevention Small V A B C (Hersey et al. 2009)

Reduction in clothes consumption

Small V A B C (Hersey et al. 2009)

Reduction in electronics consumption


Reduction in smoking Small V A B C (Hersey et al. 2009)

Reduction in floor coverings Small V A B C (Hersey et al. 2009)

Reduction in chemical product consumption

Small V A B C (Finnish Innovation Fund Sitra 2017)

Reduction in jewellery consumption


Increased use of Bioplastic Small V V C A C (Hawken 2017)

The sharing of manufactured goods

Small V V A B C (Finnish Innovation Fund Sitra 2017)

Household waste recycling (plastic, metal, glass, rubber, textile)

Small V V B B A (Hawken 2017)

Paper recycling Small V V B A A (Hawken 2017; Hersey et al. 2009)

Note: impact level is based on the approximate size of annual reduction potential per capita: large (over 250 kg-CO2e/cap/year), moderate (over 75 and up to 250 kg-CO2e/cap/year), small (up to 75 kg-CO2e/cap/year). The classifications of the relevance of low-carbon options to the stakeholders are: directly relevant options with a power to change (A), indirectly relevant options with essential roles in realizing the change (B), and indirectly relevant options with some roles in supporting the shift (C).


5. The Way Forward

Highlights from research results

The objective of the project is to analyze and demonstrate opportunities in pursuing and achieving the Paris Agreement targets, including the 1.5 °C aspirational target, as seen from the lifestyle perspective. It examines the carbon footprint of household consumption and showcases especially Finland and Japan but also Brazil, China, and India in the global discussion and action on sustainable lifestyles. The study focuses on the lifestyle carbon footprint which is defined as the GHG emissions directly emitted and indirectly induced from household consumption. This study is the first attempt to look at the detailed patterns and characteristics of lifestyle carbon footprint of households from the broad range of individual consumption based on the systematic use of physical units. It established the long-term targets of lifestyle carbon footprints with providing lifestyle-relevant interpretation of the scientifically reviewed emission scenarios and identified the gaps, hot spots, and potential opportunities for reducing the carbon footprint from household consumption. The study identified the long-term targets of lifestyle carbon footprint as the ranges of 3.2–2.3, 2.2–1.4, and 1.5–0.9 tCO2e per capita in 2030, 2040, and 2050 respectively. In terms of CO2, lifestyle CO2 footprint targets are 2.1–0.8, 1.2–0.4, and 0.4–0.3 in 2030, 2040, and 2050 respectively. These targets are based on the shortlisted mitigation pathways for the targets of 1.5 °C (Rockström et al. 2017; Ranger et al. 2012) and 2.0 °C (Rogelj et al. 2012; Blanford et al. 2014), both with and without the use of negative emission technology—targets that are based on the screening of the scenarios published in the scientific articles. This study proposed an equitable and global long-term target for the carbon footprint per capita following the aspiration of “contraction and convergence” for the long term (Meyer 2000). The per capita lifestyle carbon footprint targets are calculated assuming the footprint share of household consumption as 72% (Hertwich and Peters 2009) and using the UN’s population projection (United Nations 2017). The review of scenarios has indicated that the target level is sensitive to the assumption of long-term use of negative emission technologies such as CCS and BECCS, as well as the probability of achieving the targets. Consumers and policymakers should be aware of the critical fact that the levels of required reduction in the long run largely depend on the choice of technology. However, the long-term availability and costs of technology are uncertain, and solely relying on the assumed massive application of negative emission technology is a risky societal decision. This is why this study includes mitigation pathways that do not assume that negative emission technology will be used in the future when forming a basis for the carbon footprint targets. In comparison with the estimated current lifestyle carbon footprints, 10.4 tonnes in Finland and 7.6 tonnes in Japan, as much as an 80–91% reduction of the footprint by 2050 would be necessary in the case developed countries, assuming that an early action of the level of 58–78% reduction by 2030 is achieved. If early action is not achieved at the global level, the long-term target can be much tighter. The lifestyle carbon footprints are already at the level of 4.2 tonnes in China, 2.9 tonnes in Brazil, 2.0 tonnes in India. Therefore, even in the case of developing countries, a 25–79% reduction, depending on the country and the scenario,


would be required by 2050. The examination of the carbon footprint from household consumption revealed a large difference in the current consumption patterns and footprint contributors between countries. As each country has a unique culture and society, consumption patterns such as purchased amount and types of food, energy and living spaces, transportation varies. This leads to the very different characteristics of lifestyle carbon footprint in each country, which is also due to the difference in carbon intensity of products and services. For example, among the cases of developed countries, an average Finn consumes more than double the amount of meats and dairy products as a Japanese. In the housing domain, higher heating demand in Finland leads to more than double the energy consumption for housing, while the high share of renewables of 37% in Finland makes the intensity significantly lower than Japan. The mobility distance of the average Finn, of which 70% is by car, is over 40% higher than in Japan, where trains and buses serve almost 33% of demand. Conversely, China, Brazil, and India all have significantly lower energy demand for housing and mobility than the other two countries, but footprints from some high-carbon items such as private cars, meats, and fossil-fuel based electricity are already high in some countries. These characteristics highlight the various potential opportunities for a reduction to carbon footprints from lifestyle changes (Akenji et al 2016). This implies that, even if we establish per-capita unified targets for lifestyle carbon footprints at the global level by 2030 and 2050, solutions should be tailored to each context. In section 4 of this study, we have identified a global list of promising solutions as a preliminary attempt based on the literature review. These lists should be considered as a starting point for discussion on reducing lifestyle carbon footprints in different contexts, but further efforts to recontextualize the solutions and identify feasible but high-impact solutions will be necessary.


Policy and practical recommendations

The outcomes of this study are useful for policymakers and practitioners promoting sustainable lifestyles (e.g., non-governmental organizations, research institutes, municipal governments). The results can contribute to a better understanding of the scientific background of their interventions and communication and stimulates policy discussions alongside the impacts of household consumptions in climate change. For policymakers, including national governments and international organizations, the study suggests the importance of footprint and lifestyle perspectives in addressing the Paris Agreement including the 1.5 °C aspirational target as follows:

● Carbon footprint accounting: To reflect the realistic causes of GHG emissions, not only territorial boundary-based accounting for GHG emissions, but also consumption-based accounting should be used for assessing the carbon footprint of countries, cities, organizations, and the lifestyles of individuals. The consumption perspective should be more incorporated in the context of the global assessments such as by the IPCC and emissions gap analyses to inform the implementation of the Paris Agreement.

● Guidelines for lifestyle carbon footprint accounting: There are existing guidelines for carbon footprint accounting for products and organizations, while some studies focus on the footprint of countries or cities. The development of guidelines for the carbon footprint accounting of households, developed to facilitate more standardized and comparable examinations of the carbon impacts of lifestyles, should be considered in order to also facilitate consumption-based policy-making.

● Globally unified per-capita footprint target by 2030-2050: Our study suggests the establishment of the globally unified per capita targets of lifestyle carbon footprint (carbon footprints from household consumption) as the ranges of 3.2–2.3, 2.2–1.4, and 1.5–0.9 tCO2e per capita in 2030, 2040, and 2050 respectively. In terms of CO2, lifestyle CO2 footprint targets are 2.1–0.8, 1.2–0.4, and 0.4–0.3 in 2030, 2040, and 2050 respectively. The case studies suggest an average footprint reduction of 80–90% by 2050 (58–75% by 2030) in developed countries, and of 25–79% by 2050 in developing countries, depending on the countries.

● Lifestyle-relevant low-carbon policy options: The existing emission scenarios and discourses related to climate change mainly focus on technological solutions, including negative emission technology and production side efficiency improvement, while non-technological and consumption perspectives are considered less. Solely relying on the production-side technology solutions may not be effective and can be more costly compared to already existing solutions, and policy options to facilitate low-carbon lifestyles beyond awareness raising (considering rebound effects and lock-in effects) should be incorporated in the policymaking. Since the gap between current and target footprint are significant (e.g., 80-90% reduction by 2050), especially impactful options should be emphasized and tailored to the context of each country and society.

For practitioners, NGOs, and community-based organizations, the findings suggest the following actions to address the gaps in existing knowledge, solutions, and best practices:

● Situational studies on carbon footprint and consumption patterns - The existing attempts to examine carbon footprints of lifestyles are usually limited to individual footprint calculators. Practitioners can implement studies on the level of cities or communities, and communicate the results of the studies in an interactive manner.


● Creation and testing of lifestyle-relevant low-carbon options: New and existing solutions to reducing lifestyle carbon footprint can be simulated and tested in different contexts. These solutions should not just be regarded as restrictive, as they can provide opportunities for new business, employment, and the improvement of wellbeing. Such testing can be expanded to wider communities engagements at larger scales, so that scientific evidence of the effectiveness and feasibility of policy and community-level interventions would be strengthened and mainstreamed.

The study contributes to a better understanding of the necessity of rapid decarbonization of household consumption, long-term targets and gaps, current problems of carbon footprint calculation in relation to consumption patterns, and potential low-carbon options. The results also identified the further necessity of research including the following thematic areas:

● Expansion of lifestyle carbon footprint study: Analyzing carbon footprints and consumption patterns from the broad aspects of lifestyle proposed in this study—including nutrition, housing, mobility, consumer goods, and leisure—can be expanded to other countries in order to derive important insights into the fundamental causes of GHG emissions.

● Analysis of consumer segments - More detailed examination of lifestyle carbon footprint beyond national average, such as comparison between cities or consumer segments, can provide more insights on who emits how much, and why. Such insights can determine the priority strategy for reducing carbon footprints to meet the temperature goals of the Paris Agreement.

● Analysis of low-carbon lifestyles interventions - Study of impacts, effectiveness, and feasibilities of policy and practical interventions to facilitate low-carbon lifestyles beyond information-provision or environmental taxation are important next steps. It should be noted that behavioral changes should not be seen as a separate solution to those provided by technological changes, nor as the sole responsibility of consumers. Rather, these are two sides of the same coin which consists of production and consumption within the provision systems.

● Development of technical tools and scenarios based on lifestyle carbon footprint to inform policymakers and consumers - The results and methodology proposed in this study can be equipped as information tools, which can inform decision-makers, practitioners, and consumers about the real impacts of the current patterns of consumption as well as hotspot domains, and estimated impacts of low-carbon lifestyles. Further scenario studies adopting demand-side reductions and behavioral changes towards rapid decarbonization of society in line with the Paris Agreement are necessary. Scenarios based on backcasting towards the long-term target can increase the understanding on the desired steps towards a rapid decarbonization of society.


References Agency for Natural Resources and Energy, Japan. 2015. “Standards of Power Generation

Efficiency towards Efficiency Improvement of Fire Power Generation Plants (Karyoku Hatsuden No Koukouritsuka Ni Muketa Hatsuden Kouritsu No Kijun Nado Nitsuite).” November 17. http://www.meti.go.jp/committee/sougouenergy/shoene_shinene/sho_ene/karyoku/pdf/003_01_00.pdf.

———. 2018a. “Energy Supply and Demand Report 2016 (Heisei 28 Nendo Niokeru Enerugi Jukyu Jisseki Kakuhou).” http://www.enecho.meti.go.jp/statistics/total_energy/pdf/stte_024.pdf.

———. 2018b. “Energy Supply and Demand Report 2016 (Heisei 28 Nendo Niokeru Enerugi Jukyu Jisseki Kakuhou).” http://www.enecho.meti.go.jp/statistics/total_energy/pdf/stte_024.pdf.

———. n.d. “Energy White Paper 2017 (Enerugi Hakusho 2017).” http://www.enecho.meti.go.jp/about/whitepaper/2017pdf/whitepaper2017pdf_2_1.pdf.

Akenji, Lewis. 2014. “Consumer Scapegoatism and Limits to Green Consumerism.” Journal of Cleaner Production 63. Elsevier Ltd:13–23. https://doi.org/10.1016/j.jclepro.2013.05.022.

Akenji, Lewis, Magnus Bengtsson, Raimund Bleischwitz, Arnold Tukker, and Heinz Schandl. 2016. “Ossified Materialism: Introduction to the Special Volume on Absolute Reductions in Materials Throughput and Emissions.” Journal of Cleaner Production 132. Elsevier Ltd:1–12. https://doi.org/10.1016/j.jclepro.2016.03.071.

Akenji, Lewis, and Huizhen Chen. 2016. “A Framework for Shaping Sustainable Lifestyles.” http://www.scpclearinghouse.org/sites/default/files/a_framework_for_shaping_sustainable_lifestyles_determinants_and_strategies_0.pdf.

All Nippon Airways. 2018. “ANA Gurupu Jisseki（performance of ANA Group）.” March 2018. https://www.ana.co.jp/group/pr/pdf/20180508-2.pdf.

Automobile Inspection and Registration Information Association. 2017. “Number of Owned Hybrid and Electric Vehicles (Haiburido Sha Denki Jidousha No Hoyuu Daisuu Suii).” https://www.airia.or.jp/publish/file/r5c6pv000000g7wt-att/r5c6pv000000g7x8.pdf.

Barrett, John, Harry Vallack, Adrew Jones, and Gary Haq. 2002. “A Material Flow Analysis and Ecological Footprint of York.” Stockholm Environment Institute. https://www.researchgate.net/profile/Gary_Haq/publication/257494298_A_Material_Flow_Analysis_and_Ecological_Footprint_of_York/links/02e7e525555fbc95ab000000.pdf.

Berners-Lee, Mike. 2011. How Bad Are Bananas?: The Carbon Footprint of Everything. Greystone Books.

Biomass Power Association. 2016. “Promotion Measures of Biomass Power Generation (Baiomasu Hatsuden Jigyou No Fukyu Sokushin Saku Nitsuite).” November 29. http://www.meti.go.jp/committee/chotatsu_kakaku/pdf/026_01_00.pdf.

Blanford, Geoffrey, James Merrick, Richard Richels, and Steven Rose. 2014. “Trade-Offs between Mitigation Costs and Temperature Change.” Climatic Change 123 (3-4): 527–41.

Boitier, Baptiste. 2012. “CO2 Emissions Production-Based Accounting vs Consumption: Insights from the WIOD Databases.” http://www.wiod.org/conferences/groningen/paper_Boitier.pdf.

Buhl, Johannes. 2014. “Revisiting Rebound Effects from Material Resource Use. Indications for Germany Considering Social Heterogeneity.” Resources 3 (1): 106–22.

Buitenkamp, Maria, H. Venner, and T. Wams. 1992. “Sustainable Netherlands: Action Plan for a Sustainable Development of the Netherlands.” Vereniging Milieudefensie: Amsterdam.

C40 Cities Climate Leadership Group. 2018. “Consumptionbased GHG Emissions of C40 Cities.” http://www.c40.org/researches/consumption-based-emissions.


Central Research Institute of Electric Power Industry. 2010. “Evaluation of Lifecycle CO2 Emission per Power Source (Dengen Betsu No Raifusaikuru CO2 Haishutsu Ryou Wo Hyouka).” https://criepi.denken.or.jp/research/news/pdf/den468.pdf.

Clausen, Jens, Katrin Bienge, Jaya Bowry, and Martina Schmitt. 2017. “The five Shades of Sharing.” Ökologisches Wirtschaften - Fachzeitschrift 32 (4): 30–34.

“Coffee and Roasting Federation in Finland 2016.” n.d. Coffee Consumption 2016. Accessed April 25, 2018. http://www.kahvi.fi/kahvi-lukuina/tilastot/kahvin-kulutus.html.

Creutzig, Felix, Blanca Fernandez, Helmut Haberl, Radhika Khosla, Yacob Mulugetta, and Karen C. Seto. 2016. “Beyond Technology: Demand-Side Solutions for Climate Change Mitigation.” Annual Review of Environment and Resources 41 (1): 173–98.

Dao, Hy, Pascal Peduzzi, B. Chatenoux, Andrea De Bono, Stefan Schwarzer, and Damien Friot. 2015. “Environmental Limits and Swiss Footprints Based on Planetary .” Swiss Federal Office for the Environment.

Dardiotis, Christos, Georgios Fontaras, Alessandro Marotta, Giorgio Martini, and Urbano Manfredi. 2015. “Emissions of Modern Light Duty Ethanol Flex-Fuel Vehicles over Different Operating and Environmental Conditions.” Fuel 140 (January): 531–40.

Edenhofer, Ottmar, Brigitte Knopf, Terry Barker, Lavinia Baumstark, Bertrand Chateau, Patrick Criqui, Morna Isaac, et al. 2010. “The Economics of Low Stabilization : Model Comparison of Mitigation Strategies and Costs Source : The Energy Journal , Vol . 31 , Special Issue 1 : The Economics of Low Stabilization Published by : International Association for Energy Economics Stable URL.” Energy Journal 31 (Special1): 11–48.

“Electricity Generation Record (Hatsuden Jisseki).” 2018. Agency for Natural Resources and Energy, Japan. 2018. http://www.enecho.meti.go.jp/statistics/electric_power/ep002/results.html.

“Environmental Footprint Explorers.” n.d. Norwegian University of Science and Technology. Accessed April 19, 2018. https://environmentalfootprints.org/.

EPE – Empresa de Pesquisa Energética. 2017. “Brazilian Energy Balance.” http://www.epe.gov.br/sites-en/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-46/topico-82/Relatorio_Final_BEN_2017.pdf.

“FAOSTAT.” 2017. Food Balance Sheet. December 12, 2017. http://www.fao.org/faostat/en/#data/FBS.

Finnish Energy. 2016. “Finnish Energy.” Electricity Generation. January 20, 2016. https://energia.fi/en/energy_sector_in_finland/energy_production/electricity_generation.

Finnish Innovation Fund Sitra. 2017. “100 Smart Ways to Live Sustainably - Sitra.” 100 Smart Ways to Live Sustainably - Sitra. 2017. https://www.sitra.fi/en/projects/100-smart-ways-to-live-sustainably/.

Finnish Transport Agency. 2012. “National Travel Survey 2010-2011.” https://julkaisut.liikennevirasto.fi/pdf3/lr_2012_henkiloliikennetutkimus_web.pdf.

———. 2018. “National Travel Survey.” https://julkaisut.liikennevirasto.fi/pdf8/lti_2018-01_henkiloliikennetutkimus_2016_web.pdf.

Finnish Travel Agency. 2012. “National Travel Survey 2010-2011. Suomalaisten Liikkuminen.” https://julkaisut.liikennevirasto.fi/pdf3/lr_2012_henkiloliikennetutkimus_web.pdf.

Fuss, Sabine, Josep G. Canadell, Glen P. Peters, Massimo Tavoni, Robbie M. Andrew, Philippe Ciais, Robert B. Jackson, et al. 2014. “COMMENTARY: Betting on Negative Emissions.” Nature Climate Change 4 (10): 850–53.

Global CCS Institute. n.d. “The Largest Carbon Capture Power Plant in the World Goes Online | Global Carbon Capture and Storage Institute.” http://www.globalccsinstitute.com/news/institute-updates/largest-carbon-capture-power-plant-world-goes-online.

Greenhouse Gas Protocol. 2011. “Corporate Value Chain (Scope3) Accounting and Reporting Standard.” http://www.ghgprotocol.org/sites/default/files/ghgp/standards/Corporate-Value-Chain-Accounting-Reporing-Standard_041613_2.pdf.

Grubler, Arnulf, Charlie Wilson, Nuno Bento, Benigna Boza-Kiss, Volker Krey, David L. McCollum, Narasimha D. Rao, et al. 2018. “A Low Energy Demand Scenario for Meeting


the 1.5 °C Target and Sustainable Development Goals without Negative Emission Technologies.” Nature Energy 3 (6): 515–27.

Hawken, Paul. 2017. Drawdown: The Most Comprehensive Plan Ever Proposed to Roll Back Global Warming. Penguin.

Henkel AG & Co. KGaA, Wuppertal Institute for Climate, Environment and Energy. 2018. “Footprint Calculator.” 2018. https://footprintcalculator.henkel.com/en.

Hersey, J., N. Lazarus, T. Chance, S. Riddlestone, P. Head, A. Gurney, and S. Heath. 2009. “Capital Consumption: The Transition to Sustainable Consumption and Production in London.” BioRegional and the London Sustainable Development Commission, London.

Hertwich, Edgar G., and Glen P. Peters. 2009. “Carbon Footprint of Nations: A Global, Trade-Linked Analysis.” Environmental Science and Technology 43 (16): 6414–20.

Hieta, Antti. 2010. “Carbon Footprint of District Heating.” Bachelor’s Theses, HAMK University of Applied Sciences. http://www.theseus.fi/bitstream/handle/10024/14813/Kaukolammon+hiilijalanjalki_Antti+Hieta.pdf;jsessionid=1172151409ADEF2C10D98607185CA065?sequence=1.

Hoekstra, Arjen Y., and Thomas O. Wiedmann. 2014. “Humanity’s Unsustainable Environmental Footprint.” Science 344 (6188): 1114–17.

Honkapuro, Samuli, Jarmo Partanen, Juha Haakana, Salla Annala, and Jukka Lassila. 2015. “Selvitys Sähkö- Ja Kaasuinfrastruktuurin Energiatehokkuuden Parantamismahdollisuuksista.” Lappeenranta University of Technology, June. https://energia.fi/files/1224/Selvitys_sahko-_ja_maakaasuinfrastruktuurin_energiatehokkuuden_parantamismahdollisuuksista_2015.pdf.

IIASA Energy Program. 2004. “AR5 Scenario Database Version 1.0.2.” 2004. https://tntcat.iiasa.ac.at/AR5DB/dsd?Action=htmlpage&page=about.

“India Commute Stat.” 2011-2014. Nation Master. 2011-2014. http://www.nationmaster.com/country-info/profiles/India/Transport/Commute.

IPCC. 2014a. “Summary for Policymakers.” In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

———. 2014b. “Summary for Policymakers.” In Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

———. 2018. “IPCC: Global Warming of 1.5 °C.” 2018. http://www.ipcc.ch/report/sr15/. Itoen Co., Ltd. 2014. “Financial Results Briefing, Second Quarter, April 2015 (Kessan

Setsumeikai Shiryou 2015 Nen 4 Gatsu Ki Daini Shihan Ki).” https://www.itoen.co.jp/files/user/pdf/ir/material/201410.pdf.

Japan Airlines. 2017. “JAL Bin Goriyou Jisseki (record of Usage of JAL Flights).” 2017. http://press.jal.co.jp/ja/items/17019/6d358b246374b5d557c4bcd575ffea3e49dc5b8e.pdf.

Japan Automobile Manufacturers Association. 2016. “Motorcycle Market Trend Survey 2015 (Nirin Jidousya Shijou Doukou Chousa).” http://www.jama.or.jp/lib/invest_analysis/pdf/2015Motorcycle.pdf.

Japan Automobile Research Institute. 2011. “Analysis of Comprehensive Efficiency and GHG Emissions (Sougou Kouritsu to GHG Haishutsu No Bunseki Houkokusho).” http://www.jari.or.jp/Portals/0/jhfc/data/report/2010/pdf/result.pdf.

Japan Environmental Management Association For Industry. 2012a. “Guide for Use of Basic Database of Carbon Footprint Communication Programme (Kabon Futto Purinto Comyunikeshon Puroguramu Kihon Detabesu Katsuyou Gaido).”

———. 2012b. “Secondary Data for CFP Calculation: Basic Database (CFP Santei You Nijideta: Kihon Detabesu).” https://www.cfp-japan.jp/calculate/verify/data.html.

Japan LP Gas Association. 2009. “Contribution of LP Gas to Environment: Towards Low-Carbon Society (LG Gasu Ga Ninau Kankyo Heno Kouken: Teitanso Syakai No Jitsugen Ni Mukete).” November 20. http://www.j-lpgas.gr.jp/data/files/20091120_kishakon.pdf.


Japan Travel Bureau Foundation. 2014. “Annual Report on the Tourism Trends Survey 2014 (Ryokou Nenpou).” https://www.jtb.or.jp/publication-symposium/book/annual-report/annual-report-2014.

JTB Tourism Research and Consulting, Co. 2016. “Survey on International Trip in 2016 (2016 Nen No Kaigai Ryokou Ni Tsuite No Kinkyu Chousa).” https://www.jtbcorp.jp/scripts_hd/image_view.asp?menu=news&id=00044&news_no=42.

Jundo, Yoshida, and Keitaro Fujioka. n.d. “Analysis of Pedestrian Transport Considering Variation of Attribution of Pedestrians (Hokousha Zokusei No Tayouka Wo Kouryo Shita Hokousha Koutsu Bunseki Ni Kansuru Kenkyu).” http://www.uit.gr.jp/members/thesis/pdf/honb/486/486.pdf.

Kajikawa, Takashi. n.d. “LCA of Green Tea and Coffee Considering the Use of Containers (Youki No Shiyou Joukyou Wo Kouryo Shita Ryokucha Kohi Inryou No LCA: Mai Botoru Nadono Inryou Youkini Tyakumoku Shite).” http://www2.kpu.ac.jp/life_environ/mat_cycle_soc/report/10kajikawa.pdf.

Kaskinen, Tuula, Outi Kuittinen, Saija-Riitta Sadeoja, and Anna Talasniemi. 2011. Kausiruokaa - Ilmastonystävän Keittokirja. TEOS.

Kaskinen, Tuuli, Outi Kuittinen, Saija-Riitta Sadeoja, Ewa Skiöldebrand, Milton Crawford, Robin Donovan, Lise Nymark, et al. 2011. Kausiruokaa.

Kawamoto, Keiichi. 2011. “Life Cycle Analysis of Photovoltaic Power Generation System : Examples, Guidelines and Perspectives (Taiyoukou Hatsuden Shisutemu No Raifusaikuru Hyouka: Kizon Jirei to Gaidorain Kongo No Tenbou).” OYO BUTURI 80 (8): 679–86.

Kayo, Chihiro, Ryota Ojimi, Masahiro Iwaoka, and Koji Yasuda. 2016. “Greenhouse Gas Emission Reductions in a District Heating and Cooling System by Using Woody Biomass (Mokushitsu Baiomasu Chiiki Netsu Kyokyu Shisutemu No Onshitsu Kouka Gasu Haishutsu Sakugen Kouka: Iwateken Shiwa Chou Wo Taishou Toshite).” Journal of the Japan Wood Research Society 62 (5): 172–81.

Keith, David W., and David W. Keith. 2009. “Why Capture CO 2 from the Atmosphere?” 1654. https://doi.org/10.1126/science.1175680.

Kotakorpi, Elli, Satu Lähteneoja, and Michael Lettenmeier. 2008. Household MIPS - Natural Resource Consumption of Finnish Households and Its Deduction. THE FINNINSH ENVIRONMENT 43en. Ministry of the Environment.

Kriegler, E., G. Luderer, and N. Bauer. 2018. “Pathways Limiting Warming to 1.5° C: A Tale of Turning around in No Time?” Phil. Trans. R. http://rsta.royalsocietypublishing.org/content/376/2119/20160457.abstract.

Lähteenoja, Satu, Michael Lettenmeier, and Arto Saari. 2006. “TransportMIPS - The Natural Resource Consumption of the Finnish Transport System.” Ministry of the Environment. https://helda.helsinki.fi/bitstream/handle/10138/40369/FE_820en.pdf?sequence=3.

Lettenmeier, Michael. 2018. “A Sustainable Level of Material Footprint – Benchmark for Designing One-Planet Lifestyles.” Edited by Eva Heiskanen. Doctor of Arts, Aalto University, School of Arts, Design and Architecture.

Lettenmeier, Michael, Senja Laakso, and Viivi Toivio. 2017. “Future Households: Smaller Footprint, Better Life.” In Boosting Resource Productivity by Adopting the Circular Economy, edited by Christian Ludwig and Cecilia Matasci, 293–97. Villigen: Paul Scherrer Institut.

Lettenmeier, Michael, Christa Liedtke, and Holger Rohn. 2014. “Eight Tons of Material Footprint—suggestion for a Resource Cap for Household Consumption in Finland.” Resources 3 (3): 488–515.

Magné, Bertrand, Socrates Kypreos, and Hal Turton. 2010. “Technology Options for Low Stabilization Pathways with MERGE.” Energy Journal 31 (SPECIAL ISSUE): 83–108.

Mastrandrea, Michael D., Christopher B. Field, Thomas F. Stocker, Ottmar Edenhofer, Kristie L. Ebi, David J. Frame, Hermann Held, et al. 2010. “Guidance Note for Lead Authors of the IPCC Fifth Assessment Report on Consistent Treatment of Uncertainties IPCC Cross-Working Group Meeting on Consistent Treatment of Uncertainties.” Intergovernmental Panel on Climate Change (IPCC).


McSweeney, Robert, and Rosamund Pearce. 2016. “Analysis: Only Five Years Left before 1.5C Carbon Budget Is Blown | Carbon Brief.” Carbon Brief. May 19, 2016. https://www.carbonbrief.org/analysis-only-five-years-left-before-one-point-five-c-budget-is-blown.

Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley. 2011. “Emulating Coupled Atmosphere-Ocean and Carbon Cycle Models with a Simpler Model, MAGICC6 - Part 1: Model Description and Calibration.” Atmospheric Chemistry and Physics 11 (4): 1417–56.

Meyer, Aubrey. 2000. Contraction & Convergence: The Global Solution to Climate Change. Green Books.

Michaelis, Laurie, and Sylvia Lorek. 2004. “Consumption and the Environment in Europe — Trends and Futures.” In Danish Environmental Protection Agency, Environmental Project No. 904 2004. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.614.7501.

Minister of Land, Infrastructure, Transport and Tourism, Japan. 2012. “Passenger Movement in Cities: From Urban Transport Character Survey 2010 Results (Toshi Ni Okeru Hitono Ugoki: Heisei 22 Nen Zenkoku Toshi Koutsuu Tokusei Chousa Shuukei Kekka Kara).” http://www.mlit.go.jp/common/001032141.pdf.

Ministry of Agriculture, Forestry and Fisheries, Japan. 2012a. “Crop Situation Survey 2012 (Sakkyou Chousa).” http://www.maff.go.jp/j/tokei/kouhyou/sakumotu/.

———. 2012b. “Situation of Gardening Facilities and Waste Plastics from Agriculture 2012 (Engei You Shisetsu Oyobi Nougyou You Hai Purasuchiku Ni Kansuru Chousa).” http://www.maff.go.jp/j/seisan/ryutu/engei/haipura.html.

———. 2014. “Food Loss Statistical Survey 2014 (Syokuhin Rosu Toukei Chousa).” 2014. http://www.maff.go.jp/j/tokei/kouhyou/syokuhin_loss/.

———. 2016. “Food Balance Sheet 2016 (Syokuryou Jukyu Hyou).” 2016. http://www.maff.go.jp/j/zyukyu/fbs/.

Ministry of Environment, Japan. 2009. “LCA of Ships (Senpaku No LCA).” https://www.env.go.jp/council/35hairyo-keiyaku/y356-01/ref02.pdf.

———. 2015. “Greenhouse Gas Emission from Water Supply and Waste Water Treatment (Jousuidou Kougyou You Suidou Gesuidou Bumon Niokeru Onshitsu Kouka Gasu Haishutsu Nado No Joukyou).” https://www.env.go.jp/council/38ghg-dcgl/y380-07/mat03.pdf.

———. 2016. “Carbon Intensity Database for Calculation of Greenhouse Gas Emissions of Organizations through Supply Chain (version 2.3) (Sapuraichen Wo Tsuujita Soshiki No Onshitsu Kouka Gasu Haisyutu Nado No Santei No Tameno Haisyutsu Gentani Detabesu).” http://www.env.go.jp/earth/ondanka/supply_chain/gvc/files/tools/DB_v2.3_r.pdf.

———. n.d. “Situation of Utilization of Food Waste: Conceptual Diagram (2014) (Syokuhin Haikibutsu Nado No Riyou Joukyou Nado, Heisei 26 Nendo Suikei, Gainenzu).” http://www.env.go.jp/press/files/jp/105509.pdf.

Ministry of Environment of Japan. 2016. “Emission Intensity Database for Estimation of Greenhouse Gas Emission through Supply Chain of Organizations Ver. 2.3 (Sapurai Chain Wo Tsuujita Soshiki No Onshitsu Kouka Gasu Haishutsu Nado No Santei No Tameno Haishutsu Gentani Detabesu Ver. 2.3).”

Ministry of Health, Labour and Welfare, Japan. 2016. “National Health and Nutrition Survey 2016 (Kokumin Kenkou Eiyou Chousa).” 2016. http://www.mhlw.go.jp/seisakunitsuite/bunya/kenkou_iryou/kenkou/kenkounippon21/en/eiyouchousa/.

Ministry of Internal Affairs and Communications, Japan. 2009. “2005 Input-Output Tables for Japan (Heisei 17 Nen Sangyou Renkan Hyou).” http://www.soumu.go.jp/english/dgpp_ss/data/io/io05.htm.

Ministry of Justice, Japan. 2017. “Immigration Statistics 2017 (Syutsunyukoku Kanri Toukei).” 2017. http://www.moj.go.jp/housei/toukei/toukei_ichiran_nyukan.html.

Ministry of Land, Infrastructure and Transport, Japan. 2017a. “Annual Statistics of Automobile Fuel Consumption 2017 (Jicousha Nenryou Shouhiryou Toukei Nenpou).”


http://www.mlit.go.jp/k-toukei/22/annual/index.pdf. ———. 2017b. “Automobile Origin and Destination Survey 2015, Quick Estimation (Heisei 27

Nendo Zenkoku Douro Gairo Koutsuu Jousei Tyousa: Jidousya Kishuten Chousa OD Chousa Shuukei Kekka No Sokuhou Nitsuite).” http://www.mlit.go.jp/common/001194564.pdf.

Ministry of Land, Infrastructure, Transport and Tourism. 2016. “Overview of Japanese Water Resources 2016 (Nihon No Mizu Shigen No Genkyou).” http://www.mlit.go.jp/mizukokudo/mizsei/mizukokudo_mizsei_fr1_000036.html.

Ministry of Land, Infrastructure, Transport and Tourism, Japan. 2005. “The Actual Condition of Taxi Business (Takushi Jigyou No Jittai).” http://www.mlit.go.jp/singikai/koutusin/rikujou/jidosha/taxi/01/images/05.pdf.

———. 2015a. “Nationwide Person Trip Survey 2015 (Zenkoku Toshi Koutsu Tokusei Chousa).” 2015. http://www.mlit.go.jp/toshi/tosiko/toshi_tosiko_fr_000024.html.

———. 2015b. “Passenger Transport Record 2015 (Ryokaku Yusou Jisseki).” 2015. http://www.mlit.go.jp/common/001211650.pdf.

———. 2016a. “Annual Statistics of Automobile Transport 2016 (Jidousya Yusou Toukei Nenpou).” 2016. http://www.mlit.go.jp/k-toukei/06/annual/06a0excel.html.

———. 2016b. “Annual Statistics of Rail Transport 2016 (Tetsudou Yusou Toukei Nenpou).” http://www.mlit.go.jp/k-toukei/10/annual/10a0excel.html.

———. 2017a. “Annual Statistics of Air Transport 2017 (Koukuu Yusou Toukei Nenpou).” http://www.mlit.go.jp/k-toukei/11/annual/11a0pdf.html.

———. 2017b. “Car Ownership Number of Vehicles Statistics 2017 (Heisei 29 Nen 12 Gatsu Matsu No Jidousya Hoyuu Sharyou Suu).” http://www.mlit.go.jp/common/000990608.pdf.

———. 2017c. “Housing Groundbreaking Statistical Survey 2017 (Jyutaku Chakkou Toukei Chousa).” 2017. http://www.mlit.go.jp/toukeijouhou/chojou/gaiyo_b1t1.html.

Ministry of the Environment of Finland. 2017. “National Building Code of Finland, Housing Design.” http://www.ym.fi/download/Ymparistoministerion_asetus_asuin_majoitus_ja_tyotiloista/fceef0dd-27d1-4a38-bcc5-6484a4810238/133661.

Mi, Raymond, Helal Ahammad, Nina Hitchins, and Edwina Heyhoe. 2012. “Development and Deployment of Clean Electricity Technologies in Asia : A Multi-Scenario Analysis Using GTEM.” Energy Economics 34: S399–409.

Moore, J. 2015. “Ecological Footprints and Lifestyle Archetypes: Exploring Dimensions of Consumption and the Transformation Needed to Achieve Urban Sustainability.” Sustainability: Science Practice and Policy. http://www.mdpi.com/2071-1050/7/4/4747/htm.

Moore, J. L. 2013. “Getting Serious about Sustainability: Exploring the Potential for One-Planet Living in Vancouver.” https://open.library.ubc.ca/collections/ubctheses/24/items/1.0074187.

“Motiva Ltd.” 2018. Water Consumption. 2018. https://www.motiva.fi/koti_ja_asuminen/hyva_arki_kotona/vedenkulutus.

Nansai, Keisuke, Yasushi Kondo, Shigemi Kagawa, Sangwon Suh, Kenichi Nakajima, Rokuta Inaba, and Susumu Tohno. 2012. “Estimates of Embodied Global Energy and Air-Emission Intensities of Japanese Products for Building a Japanese Input--Output Life Cycle Assessment Database with a Global System Boundary.” Environmental Science & Technology 46 (16): 9146–54.

Nansai, Ryosuke. 2013. “Estimation Methods of the Sectoral Energy Consumption and Greenhouse Gas Emissions Based on the 2005 Input-Output Table (2005 Nen Sangyou Renkan Hyou Ni Motoduku Bumonbetsu Enerugi Shouhiryou Oyobi Onshitsu Kouka Gasu Haishutsuryou No Suikei Houhou).” National Institute for Environmental Studies, Japan. http://www.cger.nies.go.jp/publications/report/d031/jpn/pdf/6/3EID2005_Method_jp.pdf.

National Institute for Health and Welfare, and Official Statistics of Finland. 2017. “Tobacco Statistics 2016,” November. http://www.julkari.fi/bitstream/handle/10024/135579/Tr41_2017.pdf?sequence=4&isAllow


ed=y. “Natural Resource Institute Finland.” 2017. Balance Sheet for Food Commodities. 2017.

http://statdb.luke.fi/PXWeb/pxweb/fi/LUKE/LUKE__02%20Maatalous__08%20Muut__02%20Ravintotase/01_Elintarvikkeiden_kulutus.px/table/tableViewLayout1/?rxid=76de6cf2-d0c3-4255-93a6-8908d3b7565f.

Nemoto, Shihoko. 2009. “CO2 Emissions Associated with Foods throughout Distribution Channels from Points of Production to a Consumer (Syokuryou No Kouri Oyobi Ryuutsu Katei Niokeru CO2 Haisyutsuryou Shisan).” Journal of Life Cycle Assessment, Japan 5 (1): 113–21.

Next Generation Vehicle Promotion Center. 2012. “Survey on Electric Vehicle Prevalence 2011 (Heisei 23 Nendo Denki Jidousya Nado No Fukyu Ni Kansuru Chousa: Chousa Houkokusho).” http://www.cev-pc.or.jp/chosa/pdf/2011_1_honpen.pdf.

Nykvist, Björn, Åsa Persson, Fredrik Moberg, Linn Persson, Sarah Cornell, and Johan Rockström. 2013. “National Environmental Performance on Planetary Boundaries.” Swedish Environmental Protection Agency. https://www.naturvardsverket.se/Documents/publikationer6400/978-91-620-6576-8.pdf.

Official Statistics of Finland. n.d. “Fuel classification 2018.” Tilastokeskus. Accessed May 11, 2018. https://www.stat.fi/tup/khkinv/khkaasut_polttoaineluokitus.html.

———. 2017, Appendix table 6. Finnish Travel [e-publication]. ISSN=1798-8837. 2017, Appendix table 6. Ulkomaan vapaa-ajanmatkat lähialueille 2015-2017 ja vuosimuutos. Helsinki: Statistics Finland [referred: 18.5.2018]. Access method: http://www.stat.fi/til/smat/2017/smat_2017_2018-03-29_tau_009_fi.html. 2017, Appendix table 6. http://www.stat.fi/til/smat/2017/smat_2017_2018-03-29_tau_009_fi.html.

“Official Statistics of Finland (OSF).” 2017a. Habitation and living conditions. [e-publication]. ISSN=1798-6745. 2016, Appendix table 3. Area per habitation (m2) according to house type 1970-2016, whole housing stock. Helsinki: Statistics Finland. May 22, 2017. http://www.stat.fi/til/asas/2016/asas_2016_2017-05-22_tau_003_fi.html.

———. 2017b. Energy Consumption in Households [e-Publication]. ISSN=2323-329X. 2016, Appendix Table 1. Energy Consumption in Households 2010-2016, GWh (Corrected on 1 February 2018) . Helsinki: Statistics Finland. November 17, 2017. http://www.stat.fi/til/asen/2016/asen_2016_2017-11-17_tau_001_en.html.

Opschoor, Hans, and Lucas Reijnders. 1991. “Towards Sustainable Development Indicators.” In In Search of Indicators of Sustainable Development, edited by Onno Kuik and Harmen Verbruggen, 7–27. Dordrecht: Springer Netherlands.

Ottelin, Juudit, Jukka Heinonenb, and Seppo Junnilaa. 2017. “Rebound Effects for Reduced Car Ownership and Driving.” In Nordic Experiences of Sustainable Planning: Policy and Practice. Routledge.

Owaki, Tetsuya. 2010. “A Calculation Method of the Bicycle’s Vehicle-Km in Japan (Nihon Zenkoku No Jitensya Soukou Daikiro No Sansyutsu Houhou Ni Tsuite).” Traffic Science 40 (2): 69–72.

Parker, David, Kate Riley, Seigo Robinson, Harry Symington, Jane Tewson, Kim Jansson, Shyaam Ramkumar, David Peck, and Oakdene Hollins. 2015. “Remanufacturing Market Study.” https://www.remanufacturing.eu/wp-content/uploads/2016/01/study.pdf.

“Paulig.fi.” 2016. Coffee Measurements. 2016. https://www.paulig.fi/kahvijutut/kuinka-paljon-kahvia-kuuluu-mitata.

Peter, Bolwig Simon Gibbon. 2009. “Counting Carbon in the Marketplace: Part I - Overview Paper.” OECD. http://www.oecd.org/trade/envtrade/42886201.pdf.

Peters, Glen P., and Edgar G. Hertwich. 2008. “Post-Kyoto Greenhouse Gas Inventories: Production versus Consumption.” Climatic Change 86 (1): 51–66.

Posada, Francisco, and Cristiano Façanha. 2015. “Brazil Passenger Vehicle Market Statistics - International Comparative Assessment of Technology Adoption and Energy Consumption.” The International Counsil of Clean Transportation. https://www.theicct.org/sites/default/files/publications/Brazil%20PV%20Market%20Statistics%20Report.pdf.

Ranger, N., L. K. Gohar, J. A. Lowe, S. C. B. Raper, A. Bowen, and R. E. Ward. 2012. “Is It


Possible to Limit Global Warming to No More than 1.5C?” Climatic Change 111 (3): 973–81.

Rockström, Johan, Owen Gaffney, Joeri Rogelj, Malte Meinshausen, Nebojsa Nakicenovic, and Hans Joachim Schellnhuber. 2017. “A Roadmap for Rapid Decarbonization.” Science 355 (6331): 1269–71.

Rockström, Johan, Will Steffen, Kevin Noone, Asa Persson, F. Stuart Chapin 3rd, Eric F. Lambin, Timothy M. Lenton, et al. 2009. “A Safe Operating Space for Humanity.” Nature 461 (7263): 472–75.

Rogelj, Joeri. 2013. “2020 Emissions Levels Required to Limit Warming to below 2 Degree C” 3 (December 2012). https://doi.org/10.1038/NCLIMATE1758.

Rogelj, Joeri, William Hare, Jason Lowe, Detlef P. van Vuuren, Keywan Riahi, Ben Matthews, Tatsuya Hanaoka, Kejun Jiang, and Malte Meinshausen. 2011. “Emission Pathways Consistent with a 2 °C Global Temperature Limit.” Nature Climate Change 1 (8): 413–18.

Rogelj, Joeri, Gunnar Luderer, Robert C. Pietzcker, Elmar Kriegler, Michiel Schaeffer, Volker Krey, and Keywan Riahi. 2015. “Energy System Transformations for Limiting End-of-Century Warming to below 1.5 °C.” Nature Climate Change 5 (6): 519–27.

Rogelj, Joeri, David L. McCollum, Brian C. O’Neill, and Keywan Riahi. 2012. “2020 Emissions Levels Required to Limit Warming to below 2 °C.” Nature Climate Change 3 (December): 405.

Rohn, Holger, Nico Pastewski, and Michael Lettenmeier. 2013. “Ressourceneffizienz: Potenziale von Technologien, Produkten Und Strategien (Resource Efficiency: Potentials of Technologies, Products and Strategies).” http://publica.fraunhofer.de/dokumente/N-280123.html.

Salo, Marja, Ari Nissinen, Maija Mattinen, and Juha Pekkanen. 2017. “How Is the Carbon Footprint Calculated in the Ilmastodieetti Tool?” https://beta.ilmastodieetti.fi/pdf/Ilmastodieetti_documentation_2017-10-13.pdf.

Sano, Itsumi, Shuhei Masuda, Yu-You Li, Osamu Nishimura, and Hideki Harada. 2012. “Evaluation of Greenhouse Gas Emission Factors and Reduction Measures in Sewage Treatment Plants (Gesui Shorijou Niokeru Onshitsu Kouka Gasu No Haishutsu Keisuu Hyouka to Teigen Taisaku).” Proceedings of Environmental Engineering Research 68 (7): 565–73.

Schanes, Karin, Stefan Giljum, and Edgar Hertwich. 2016. “Low Carbon Lifestyles: A Framework to Structure Consumption Strategies and Options to Reduce Carbon Footprints.” Journal of Cleaner Production 139 (December): 1033–43.

Schmidt-Bleek, F. 1993. “Wieviel Umwelt Braucht Der Mensch--MIPS, Das Maß Für Ökologisches Wirtschaften.” Verlag Birkhäuser, Basel, Boston, Berlin.

Schmidt-Bleek, Friedrich. 1993. “Revolution in Resource Productivity for a Sustainable Economy. A New Research Agenda.” Fresenius Environmental Bulletin 2 (8): 485–90.

Semeniuk, Ivan. 2017. “Could a Canadian Company Hold the Key to Generating Fuel from CO2 Emissions?” The Globe and Mail. November 12, 2017. https://www.theglobeandmail.com/technology/science/a-canadian-companys-attempt-to-get-a-grip-on-the-carbon-emissionsproblem/article27970800/.

Seppälä, Jyri, I. Mäenpää, S. Koskela, T. Mattila, A. Nissinen, J. M. Katajajuuri, T. Härmä, M. R. Korhonen, M. Saarinen, and Y. Virtanen. 2009. “Assessment of the Environmental Impacts of Material Flows Caused by the Finnish Economy with the ENVIMAT Model (Suomen Kansantalouden Materiaalivirtojen Ympäristövaikutusten Arviointi ENVIMAT-Mallilla).” The Finnish Environment 20: 2009.

Shibahara, Naoki, Yuri Hattori, Ryoko Morimoto, Hirokazu Kato, and Yoshitsugu Hayashi. 2009. “Comparison of CO2 Emission from Airplane and Bullet Train Based on LCA (LCA Wo Mochiita Koukuu to Shinkansen No CO2 Haishutsu Ryou No Hikaku).” Proceedings of the 1st Symposium of Global Environment, September, 19–25.

“Shrink That Footprint.” n.d. Shrink That Footprint. Accessed May 23, 2018. http://shrinkthatfootprint.com/.

“Statista.” 2015. Eating out Frequency in Europe 2015. 2015. https://www.statista.com/statistics/683959/eating-out-frequency-europe/.


Statistics Bureau of Japan. 2013. “Housing and Land Survey 2013 (Jyutaku Tochi Tokei Chousa).” 2013. http://www.stat.go.jp/data/jyutaku/index.html.

———. 2016. “Family Income and Expenditure Survey 2016 (Kakei Chousa).” 2016. http://www.stat.go.jp/data/kakei/.

Steen-Olsen, Kjartan, Anne Owen, Edgar G. Hertwich, and Manfred Lenzen. 2014. “EFFECTS OF SECTOR AGGREGATION ON CO2 MULTIPLIERS IN MULTIREGIONAL INPUT–OUTPUT ANALYSES.” Economic Systems Research 26 (3): 284–302.

Steffen, Will, Katherine Richardson, Johan Rockström, Sarah E. Cornell, Ingo Fetzer, Elena M. Bennett, Reinette Biggs, et al. 2015. “Sustainability. Planetary Boundaries: Guiding Human Development on a Changing Planet.” Science 347 (6223): 1259855.

Tynkkynen, Oras, ed. 2015. Green to Scale. Low-Carbon Success Stories to Inspire the World. Vol. 105. Sitra Studies. Helsinki: Sitra.

———. 2016. Nordic Green to Scale: Nordic Climate Solutions Can Help Other Countries Cut Emissions. Nordic Council of Ministers.

UNFCCC. 2015. “Report of the Conference of the Parties on Its Twenty-First Session, Held in Paris from 30 November to 13 December 2015.” Addendum-Part Two: Action Taken by the Conference of the Parties 01194 (January): 1–36.

United Nations. 2017. “World Population Prospects - Population Division - United Nations.” World Population Prospects - 2017 Revision. 2017. https://esa.un.org/unpd/wpp/.

United Nations Environment Programme. 2016. The Emissions Gap Report 2016. ———. 2017. The Emissions Gap Report 2017 - A UN Environment Synthesis Report. Van Vuuren, Detlef P., Michel G. J. Den Elzen, Paul L. Lucas, Bas Eickhout, Bart J.

Strengers, Bas Van Ruijven, Steven Wonink, and Roy Van Houdt. 2007. “Stabilizing Greenhouse Gas Concentrations at Low Levels: An Assessment of Reduction Strategies and Costs.” Climatic Change 81 (2): 119–59.

“VR Group Ltd.” 2018. Environment and Responsibility. 2018. http://www.vrgroup.fi/en/vrgroup/responsibility/environment/.

VTT Technical Research Centre of Finland Ltd. 2017. “LIPASTO - Unit Emissions.” 2017. http://lipasto.vtt.fi/yksikkopaastot/indexe.htm.

Vuuren, Detlef P. van, Elke Stehfest, David E. H. J. Gernaat, Maarten van den Berg, David L. Bijl, Harmen Sytze de Boer, Vassilis Daioglou, et al. 2018. “Alternative Pathways to the 1.5 °C Target Reduce the Need for Negative Emission Technologies.” Nature Climate Change 8 (5): 391–97.

Wackernagel, Mathis, and William Rees. 1998. Our Ecological Footprint: Reducing Human Impact on the Earth. New Society Publishers.

WBCSD. 2015a. “Sustainable Lifestyles Report Brazil.” http://wbcsdpublications.org/wp-content/uploads/2015/12/WBCSD_SLWG_BRAZIL2015.pdf.

———. 2015b. “Sustainable Lifestyles Report China.” http://wbcsdpublications.org/wp-content/uploads/2015/12/WBCSD_SLWG_CHINA2015.pdf.

———. 2016. “Sustainable Lifestyles Report India.” http://wbcsdpublications.org/wp-content/uploads/2016/03/SL_INDIA_2016.pdf.

Weizsacker, Ernst Ulrich von, Charlie Hargroves, Michael H. Smith, Cheryl Desha, and Peter Stasinopoulos. 2009. Factor Five: Transforming the Global Economy through 80% Improvements in Resource Productivity. 1 edition. Routledge.

Wernet, Gregor, Christian Bauer, Bernhard Steubing, Jürgen Reinhard, Emilia Moreno-Ruiz, and Bo Weidema. 2016. “The Ecoinvent Database Version 3 (part I): Overview and Methodology.” International Journal of Life Cycle Assessment 21 (9): 1218–30.

Weterings, Rapm, and J. B. Opschoor. 1992. “The Environmental Capacity as a Challenge to Technology Development.” RMNO (Advisory Council for Research on Nature and Environment), Rijswijk, The Netherlands.

Wiedmann, Thomas, and Jan Minx. 2008. “A Definition of ‘carbon Footprint.’” Ecological Economics Research Trends 1: 1–11.

Wilkinson, Tara Loader. 2018. “On the Green Path.” The Business Times. February 13, 2018. https://www.businesstimes.com.sg/magazines/wealth-february-2018/on-the-green-path.

Willis, Peter. 2010. “Market Failures in Remanufacturing: An Examination against Major


Categories by Centre for Remanufacturing & Reuse.” http://www.remanufacturing.org.uk/pdf/story/1p352.pdf.

Wynes, S., and K. A. Nicholas. 2017. “The Climate Mitigation Gap: Education and Government Recommendations Miss the Most Effective Individual Actions.” Environmental Research Letters: ERL [Web Site]. http://iopscience.iop.org/article/10.1088/1748-9326/aa7541/meta.

Yue, Dajun, Fengqi You, and Seth B. Darling. 2014. “Domestic and Overseas Manufacturing Scenarios of Silicon-Based Photovoltaics: Life Cycle Energy and Environmental Comparative Analysis.” Solar Energy 105 (July): 669–78.