Global warming potential and absolute global temperature change potential from carbon dioxide and methane fluxes as indicators of regional sustainability – A case study of Jämtland, Sweden

Ecological Indicators 110 (2020) 105831

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Original Articles

Global warming potential and absolute global temperature change potential from carbon dioxide and methane fluxes as indicators of regional sustainability – A case study of Jämtland, Sweden

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Torbjörn Skytta, , Søren Nors Nielsenb, Bengt-Gunnar Jonssonc a

Mid Sweden University, Department of Ecotechnology and Sustainable Building Engineering, 831 25 Östersund, Sweden Aalborg University, Centre for Bioscience and Techno-anthropology, Department of Chemistry and Bioscience, A.C. Meyers Vænge 15, DK-2450 København SV, Denmark c Mid Sweden University, Department of Natural Sciences, 851 70 Sundsvall, Sweden b

A R T I C LE I N FO

A B S T R A C T

Keywords: Regional sustainability Carbon based fluxes Sustainability indicators Carbon dioxide emissions Methane emissions Nature emissions AGTP response

This study presents a regional model showing the balance of carbon dioxide and methane fluxes in the Swedish county Jämtland, applying a Global Warming Potential 20-year time horizon (GWP20) to meet the Paris agreement horizon and regional policy goals. The results clearly show the necessity to take both anthropogenic and non-anthropogenic emissions into consideration in analyses to be able to make proper priorities in future action strategies. The total annual impact from Jämtland calculated as carbon dioxide equivalents (CO2eq) is an uptake of 2.4 Mton (19 ton per capita). Jämtland shows large annual uptakes in forests (12.7 Mton CO2), but also large emissions of methane (80 kton corresponding to 6.7 Mton CO2eq), mainly from lakes, mires and ruminants. Anthropogenic carbon Greenhous gas emissions are dominated by transportation, working machines and consumption (mainly imported indirect emissions). As a complement to GWP also the Absolute Global Temperature Change Potential (AGTP) as degree K response, is presented per sector and total for Jämtland County, for yearly emissions (as a pulse) and continuous emissions over 200 years. A yearly pulse from Jämtland gives a temperature response of about 0 K after 10 years and about −4 μK (cooling effect) after about 50 years). Using both GWP and AGTP as indicators improves the possibilities to find ways how to optimize regional climate policies to reduce global warming until a specific year. Strategies and action plans should be developed focusing on the following: - Reduced regional transportation and consumption activity. - Increased (prioritized) use of renewable fuels for working machines in forestry and agriculture, as well as for heavy trucks. - Evaluate the potential to reduce emissions of methane from wetlands and mires. - Increase/optimize carbon dioxide assimilation in forests.

1. Introduction Global warming has been debated for some time now and needs no further presentation in terms of causes and effects in a scientific context. The significance of the consequences is reflected in the Paris agreement, which aims to strengthen the global response to this threat by: Holding the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels,

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recognizing that this would significantly reduce the risks and impacts of climate change. (United Nations, 2015, p. Art 2.1 (a)). The Paris agreement is the basis for national and regional climate aims and this article presents the results of a study on the anthropogenic and non-anthropogenic carbon dioxide and methane fluxes in the Swedish county Jämtland in order to create a better platform to the decision making process necessary to fulfil the above goals. The aim is to provide and explore tools for regional carbon flux analyses, including defining relevant system borders that help inform regional decision makers, i.e. suggestion how indicators for regional climate mitigation

Corresponding author. E-mail address: [email protected] (T. Skytt).

https://doi.org/10.1016/j.ecolind.2019.105831 Received 1 July 2019; Received in revised form 8 October 2019; Accepted 13 October 2019 1470-160X/ © 2019 Elsevier Ltd. All rights reserved.

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Jørgensen, 2015). This methodology was found to fit the purposes well, and local authorities also supported this. The University of Aalborg assisted in the evaluation of Jämtland to test the suitability of the methodology for a larger region. Just as in Samsø, Jämtland has a large agricultural sector and from a cultural perspective there are also similarities. For the Samsø study the community activities were divided into relevant sectors. These were used for detailed flow studies of energy (e.g. work energy, or exergy) (Nielsen and Joergensen, 2011) and carbon, from which emissions of carbon dioxide and methane were specified (Jørgensen and Nielsen, 2015). The combined sustainability indicator evaluation of energy and carbon fluxes was found to be a good way to understand a region from a sustainability perspective, mirroring the global problem as regional challenges. Furthermore, an extensive energy analysis of a society is almost necessary to be able to analyse carbon fluxes, since energy use is one of the most relevant parameters involved. Just as in Samsø, Jämtland is actively trying to limit its impact on global warming. Jämtland has the aim to become fossil fuel free by 2030 and carbon dioxide neutral by 2045. The present research project (‘Sustainable Jämtland’) aims at an in-depth understanding of today’s regional situation regarding global warming, thereby gaining a better understanding of the national and the global situation. Furthermore, with an in-depth regional knowledge, the Mid Sweden University can support the county administration and other stakeholders to formulate policies for how to reach the regional (climate related) sustainability goals. Carbon flow analyses at a societal level are of course nothing new, but to model the complete flows of both anthropogenic and natural processes at a regional level in detail is rare. A method for this was developed for the Siena Province (Italy) to be an instrument for testing different future scenarios (Marchi et al., 2012), which also partly served as a base for the Samsø study. A GHG balance of Siena including nonanthropogenic emissions has been presented by Ridolfi et al. (2008), highlighting the need to include all emitting processes in a regional assessment to be able to optimize emission reduction and uptake activities. In order to model Jämtland by scaling up the Samsø methodology implies changes to the methodology. Samsø is an island (114 km2) with about 4000 inhabitants where input and output flows of matter and energy can be controlled (ferry transportation). Jämtland is a large, sparsely populated region, somewhat larger than Denmark (48 945 km2) without “flow control borders” and with about 127 000 inhabitants. Jämtland has one city, Östersund, which has 40% of the regional population, with frequent commuting to the city area. Even if Jämtland is not representative for Sweden as a whole, there are a number of factors, from a sustainability evaluation perspective, which make Jämtland interesting to evaluate. Vast forests and large renewable electricity production should make it possible for the region to show a high degree of sustainability. The evaluation of Samsø shows that about 26 kton carbon net is being removed annually from the atmosphere after the introduction of renewable energy (wind power), and this was one of the focus aspects for the modelling performed (Jørgensen and Nielsen, 2015). The situation in Jämtland is, however, somewhat different. The electricity production is already today to a high extent fully renewable; large hydro power plants generate an abundance of electricity to be exported out of the region. During recent years, large wind power plants have been built, complementing hydro power. Still, even with a fully renewable electricity production, there will be social and natural processes contributing to global warming. When non-anthropogenic (natural) processes are included in a sustainability evaluation, there is a risk that the argument “My-contribution-does-not-matter” gets the upper hand when discussing carbon dioxide contribution from cars or consumption. If a region shows large emissions from natural ecosystems and large uptakes in forests, this might give the impression that anthropogenic flows are less relevant. The situation is a double-edged sword; on one hand we need to increase

can be developed. The concept ‘sustainability’, often vaguely or not at all defined, has been incorporated in most societal policies. However, to evaluate sustainability, specific indicators have to be chosen within the focus areas of economic and social development as well as of environmental protection. In Indicators of Sustainable Development: Guidelines and Methodologies (United Nations, 2007), based on Agenda 21 (United Nations, 1992), the UN has presented appropriate indicators and guidelines for Sustainable Development and there suggests how to adapt them to national conditions and priorities. The UN also has a knowledge web-platform ’Helping governments and stakeholders make the SDGs a reality’ (2018). Sustainability science partly deals with the issue of how to implement scientific knowledge into social action (United Nations, 1992; Kates, 2011; Kates et al., 2001). To enhance such action, it is suitable to work on the regional scale as that is where strategies are to be transformed into activities related to the interaction between ecological processes and human life (Graymore et al., 2008). Furthermore, this fits the role of academia, bridging network actors in regional sustainability initiatives (Zilahy and Huisingh, 2009; Lehmann et al., 2009). A number of regional/territorial scientific greenhouse gas (GHG) emission analyses have been performed, showing variations in methodology (Roibás et al., 2017; Loiseau et al., 2014; Marchi et al., 2012; Xi et al., 2011; Ridolfi et al., 2008). There is no single standardized method of how to assess regions/territories, and many approaches are being used, each showing weaknesses and potentials (Loiseau et al., 2012). Most territorial/regional studies point out the difficulty in finding statistics and the necessity to adapt the assessment to local governmental need and local context, which explains the spread in assessment methodology. Xi et al. discuss the connection between regional assessments to regional governmental targets, to make scientific analysis part of the regional strategy development (2011). They also conclude simple local emission strategies might include a shutdown of the region’s power plants and purchase of electricity and heat, if geographical boundaries are applied without import/export emission counting (p. 6007). This problem is regarded as a producer–consumer responsibility that needs to be addressed in flow analyses (Zhang, 2017; Zhang, 2015; Bastianoni et al., 2004; Munksgaard and Alsted Pedersen, 2001). A study of the trade balance between Scotland and the rest of UK shows that a 45% share of the CO2 generated and emitted in Scotland, actually supports consumption in the rest of UK (McGregor et al., 2008). There is also a corresponding risk in the double-counting of energy and emissions in material and energy flow analyses (Loiseau et al., 2014). From existing research within territorial environmental assessments, we conclude it is important to be clear about the purpose and context of an assessment, to be able to define a relevant method to use. Comparisons with other regions might give valuable input for how to find solutions and develop strategies. It might be less interesting to compare direct results due to regional prerequisites and variations in methodology used. For example the study of the net CO2 emissions per capita in six Italian provinces show a range of 3 tons (Siena) to 12 tons (Modena), mirroring presence of industrialization, urbanization and photosynthetic uptake (Ridolfi et al., 2008). Jämtland has some similarities with Siena, showing low industrialization and high photosynthetic uptake (in large forest areas), but there are major differences, making comparisons between Siena and Jämtland less interesting. However, methodology applied, strategies implemented as well as experiences and lessons learnt, make the studying of other assessments important, thus highlighting the need to publish results from such assessments. To find out if the Jämtland county in Sweden is sustainable from a global warming perspective, Mid Sweden University in 2015 started a research project to model the regional energy flows and carbon based Greenhouse Gas (GHG) fluxes, using a methodology to study societal sustainability developed by the university of Aalborg in Copenhagen in Denmark (AAU) for the small Danish island, Samsø (Nielsen and 2

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Fig. 1. Flow model used for Jämtland as conceptual diagram, showing flows of CO2 equivalents from the Energy sector into the different relevant sectors chosen for Jämtland. In the sector Private, all internal flows are summarized, except for exports of emissions to be summarized in a balance. Grey boxes are emission sources and green boxes represent emission uptakes. Blue arrows show regional transferal of emissions, and grey arrows show exports of emissions out from the region. The Nature sector is a negative export which means it is a CO2 equivalent source but the emissions cannot be exported out of the region since there is no external consumer.

Additionally, one of the major aims of the EU Interreg SMICE-project in financing this research, is to encourage regional sustainability initiatives and to engage people at all levels of society in striving for increased sustainability (Länsstyrelsen Jämtlands län, 2018). The Interreg project covers the Mid Nordic area from the Baltic Sea to the Atlantic, including the Swedish region Västernorrland and also Trondelag in Norway. In parallel with the modelling work, active participation in the county’s climate council was fostered in order to create an infrastructure of stakeholders to work with, thus increasing the understanding of the processes involved in regional climate strategy development. In the next chapter the general method used will be described, and in what way changes have been made compared to the method presented for the Samsø case. In-depth descriptions of certain aspects of the model are presented in chapter 3.

the scientific knowledge, and on the other hand we need to simplify the information to avoid societal passiveness. The modelling results presented in this paper are a summary of the numerical outcome as applied science. However, as a basis for regional knowledge building and regional policy discussions, results from flow models have a clear value both from a research perspective and a regional governing perspective. This work complements our earlier published study on work energy flows in Jämtland, now stressing the related carbon emission issues (Skytt et al., 2018). The linkage to the Paris agreement makes it necessary to connect the evaluation of Jämtland to a relatively near future (20 years). It is however of great importance not to lose sight of the longer perspective (100 years). When large fluxes of both carbon dioxide and methane are involved, the difference in perspective needs to be considered in climate strategy development. The carbon flux analysis of Jämtland therefore also involves sectorial temperature response in the interval from 0 to 100 years, to better clarify how to interpret results presented, when carbon dioxide equivalents are being used. Results from an environmental assessment always need to be interpreted in a regional context also covering other sustainability aims. The main objective with this evaluation of Jämtland is to develop a regional expert body that will make it possible to investigate potential changes to regional prerequisites, in order to enhance sustainable development, and to decrease overall radiative forcing from Jämtland to meet the goals in the Paris agreement. This is done by applying a sectorial analysis following the principles used for the Samsø study (Jørgensen and Nielsen, 2015), adapted for the Jämtland context.

2. Concepts and limitations The carbon flow study of Jämtland presented in this paper is based upon the method developed mainly in (Jørgensen and Nielsen, 2015), but also Marchi et al. (2012) and Ridolfi et al. (2008). The sustainability evaluation of Jämtland focuses on global warming. The inclusion of natural fluxes is needed, to be able to find out in what way Jämtland contributes in terms of radiative forcing to global mean temperature. Since the Paris agreement (United Nations, 2015) focuses specifically on global mean temperature increase, we have chosen not only to calculate GWP as carbon dioxide equivalent (CO2eq) balances but also 3

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considering the long-term effects and the modelling of future temperature changes. However, many regional strategies and discussions focus on a much shorter time perspective, i.e. typically until 2030–2045. An exception is long term climate adaptation strategies where complex infrastructure is involved. IPCC says, regarding GWP that “It is not the role of the IPCC TFI to make any recommendation on which CO2 equivalent factors such as GWP and GTP values should be applied”, but it refers to the 100-year value as the common value used in agreements, such as the Kyoto protocol and the Paris agreement (IPCC, 2018 FAQ). Tropospheric temperatures may respond quicker to variances in RF, which makes the choice of a short time horizon more appropriate (typically GWP20) (Shine, 2009). Referring to the Kyotoprotocol, IPCC says “The choice of time horizon has a strong effect on the GWP values — and thus also on the calculated contributions of CO2 equivalent emissions by component, sector or nation. There is no scientific argument for selecting 100 years compared with other choices…” and continues “The choice of time horizon is a value judgement because it depends on the relative weight assigned to effects at different times” (IPCC, 2014, p. 711ff). The physical state of the earth might be better represented by the Absolute Global Temperature change Potential (AGTP) response in degrees K (GWP is given as kg CO2eq). We present graphs of the temperature response as AGTP for both CO2 and CH4 as sums per sector, from 1 to 100 years. These graphs give a base for evaluation of regional priority activity. One of the main reasons for calculating AGTP is the difficulty how to value CH4 emissions in relation to CO2, and the fact that GWP from CO2eq gives very static information as RF potential at a specific time horizon. In the Jämtland model the CO2eq balance is decided by the given time horizon chosen (calculating the corresponding GWP for CH4 at this specific time). CH4 with its short atmospheric life length, will after a certain time result in a steady-state level as long as the yearly emission remains constant. It also means that relatively fast changes in the flux will result in step changes and after a longer period a new steady-state level will be reached (Frolking et al., 2006). The focus at a relative near future is based on the formulation in the Paris-agreement to limit the temperature increase to max 1.5 °C above pre-industrial temperatures (United Nations, 2015). Article 2.1). From IPCC scenario modelling the government in Sweden has set national emission targets to be carbon dioxide neutral 2050. We therefore apply the GWP20 factor of 84 for the static GWP balance of Jämtland presented in this paper, however, the weight factor is an input to the model. It is worth noticing that it is possible to get results in a wide range, by varying the GWP-factor. For the Samsø study, GWP100 (factor 25 for CH4 to CO2eq, which originates from earlier guidelines, in AR4 from 2007) has been applied, since there is no clear connection to a regional strategy with a corresponding 20–30 year horizon. The regional strategy for Jämtland, as well as for Sweden, focuses on fulfilling the Paris agreement until 2050, but Jämtland has a higher ambition and aims, for example to be fossil fuel free in 2030 and neutral with respect to CO2 -emissions in 2045. Calculations of GWP and AGTP follow IPCC (2014. p. Ch. 8) and Myhre et al. (2013) and the metric values presented in the IPCC Appendix 8.A (IPCC, 2014, p. 731ff) are based on calculations including climate-carbon feedbacks for CO2, while no climate feedbacks are included for other components. However, for CH4 an increase of the direct effect of CH4 is included, from indirect effect on O3 (tropospheric and stratospheric), as well as indirect RF via changes in stratospheric H2O. We have decided not to include climate-carbon feedbacks for CH4 to match the common values used in Sweden and also because compensation parameters in the decay formulas are not clearly presented by IPCC. From an overall perspective the analysis will not change if the feedbacks would be included. AGTP response models are made to cover both yearly emissions per sector regarded as pulses, and continuous emissions calculated as constant yearly pulses. For more detailed explanations and used

temperature response as AGTP (Absolute Global Temperature change Potential) according to (IPCC, 2014. p. Ch 8). The modelling concept is basically a typical (material) flow analysis with relevant sectorial division of the region. Each sector is a ‘black-box’ with an input value and a specific output. The black-box converts input emissions (from energy and matter) to output emissions with a certain efficiency. The output emission can be an ‘emission carrier’ (matter or energy) but also a direct emission (e.g. driving a car a certain distance). The Carbon Cycling Model (CCM) made for Samsø, maps flows of carbon and converts this flow to either CO2 or methane (CH4) depending on the process involved. For Jämtland we calculate the fluxes of CO2 and CH4 as separated flows. The carbon content is used in the sub-models when applicable (forests, mires etc). To sum up sectorial and total balances, the conversion to CO2eq is made both in the Samsø model and in the present model (refer to chapter 2.3). While the Samsø model is built as a STELLA®-model showing seasonal variations, the model of Jämtland is made as a spreadsheet-model with an annual basis to make parameter change simple and to get an overview of all sectors. Matlab® has been used separately to build models for the AGTP-responses. 2.1. Flow model The energy sector is used as a base for emission input into the sectors chosen. Refer to Fig. 1 for an overview of the flow structure. In the spreadsheet-model behind the diagram, flows of CO2 and CH4 are kept apart (to be used as input to calculate AGTP), but summarized as CO2 equivalent in the diagram with the time horizon weight factor for CH4 as input. The sectors studied are (1) Energy, (2) Agriculture, (3) Forestry, (4) Reindeer herding, (5) Industry/Trade, (6) Private, (7) Public, (8) Tourism, (9) Waste, and finally (10) Nature. The numbers refer also to the chapter headline sub-number used in chapter 3 and 4. For a description of each sector, please refer to chapter 3. 2.2. GHG fluxes vs. Carbon based fluxes Following the method developed for the Siena Province (Marchi et al., 2012) and in the Samsø study (Jørgensen and Nielsen, 2015), only carbon GHG fluxes are taken into account. This means that for example fluxes of N2O are not part of the analysis (except for consumption where the database used include all GHG gases). 2.3. GWP and AGTP response The concept “Global Warming Potential” (GWP) was introduced by (IPCC, 1994, p. 32ff) to simplify calculations aiming at climate policy development and to deal with the fact that different GHG contribute in different degrees to Radiative Forcing (RF), i.e. changes of the energy flow per surface unit (Wm−2). The GWP conversion factor mainly focuses on effects on the global mean surface temperature due to differences in RF for a pulse emission of a gas between the present and a chosen time horizon related to a pulse emission of the reference gas CO2. GWP is an easy-to-use concept, however, with limitations when it comes to evaluation of climate and temperature effects. The GWP-factor for a gas gives an RF over a chosen time horizon (time integral) corresponding to what CO2 would give. To convert CH4 into CO2eq typically either a 100-year time horizon factor (GWP100) of 28 or a 20-year time horizon factor (GWP20) of 84 is applied (IPCC, 2014). According to these factors, using the GWP perspective, an emission of 1 kg CH4 corresponds to 28 kg or 84 kg CO2 for 100 or 20 years respectively, and is defined as CO2eq. The atmospheric life of CH4 is only 11–13 years (CH4 oxidizes in the troposphere reacting with OH into CO2), but the RF will be much longer than the atmospheric life of the molecules, i.e. the warming effect persists after the molecules causing it has been broken down. For many purposes the 100-year time horizon is relevant, 4

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and “scientific failure” from several perspectives and they claim that richer countries not only have a larger responsibility for the future, but also for today’s situation from past emissions. On the other hand, as pointed out by Bastianoni et al. (2004), an accounting principle where the final user carries the responsibility, lowers the incentive for the producer/emitter to change the production process (s. 255). To match results from this study with regional policy documents and the Swedish national inventory analysis (Swedish Environmental Protection Agency, 2017) emissions generated in the production of imported goods (and services), i.e. as an import of emissions have been included. Emissions from production in the region, consumed outside the region are regarded as export of emissions (the region does not carry this part). Following the logic used for electricity export at Samsø reducing the actual emissions of CO2 (Jørgensen and Nielsen, 2015), the consumption of matter (and services) are calculated as imported indirect emissions. The decision to use this method, where emissions follow the product in trade (both export and import), does not necessarily imply that we have chosen to put the complete responsibility for emissions with the consumer, thus reducing the producer’s responsibility. From a perspective of fairness in sharing responsibility for emissions, a consumption principle seems to be most reasonable. However, adaptation to available statistics makes it impossible to apply anything but a mixture of approaches, and the allocation of consumption emissions per sector is the result of approximations. The emission source headline “Consumption” per sector, represents calculated emissions for consumption not represented by another emission source defined. Fig. 2 shows a flow chart how emissions from the national database are distributed to regional sectors. Consumption statistics on a national basis for Sweden are collected from ‘Analysis tool for environmental accounts data’ (SCB Consumption, 2018), and

mathematical models see Appendix 2. 2.4. Renewable energy from biomass It is erroneous to presume that biomass combustion always is neutral from a climate perspective (Cherubini et al., 2011). One example is stump harvesting in forestry; if harvested and burnt, the stumps will cause an immediate pulse emission of GHG, but if left in the forest the stumps will slowly decompose (refer to A.3) emitting GHG continuously during these years. Furthermore there is a difference in the carbon to energy GHG emission intensity for biomass (112 ton CO2 per TJ produced) and fossil sources (for example natural gas, 64 ton CO2 per TJ produced) (IPCC, 2006). Instant burning of biomass has after 100 years still not reached full renewability (Grelle, 2013). Hence care should be taken to regard the combustion of woody biomass as climate neutral. To make proper accounting of CO2 emissions from such combustion, a more complex modelling methodology should be developed, corresponding to the suggestions presented in (Cherubini et al., 2011). In the Jämtland model emissions are directly balanced towards uptakes in the Forestry and Nature sectors. This also means that an increased harvest of biomass to be incinerated results in a lower forest uptake potential of carbon dioxide, and the other way round; decreased harvest of biomass for incineration results in larger uptake potential. 2.5. Emissions carried by traded products It is not obvious how to deal with emissions from the production of traded products in a regional study. Basically, we can distinguish between two different accounting principles; consumer responsibility and producer responsibility (Munksgaard and Alsted Pedersen, 2001). Eder and Narodoslawsky (1999) discuss a region’s responsibility for direct and indirect environmental pressure and from this define six different approaches. They also conclude that the statistical situation for input–output model for subnational regions, makes it difficult to find correct data from a specific approach, but varying the approach in the analysis still adds information about a region’s sustainability. Bastianoni et al. (2004) define three different approaches to the problem of assigning emissions as the geographical approach; consumer responsibility approach (typically ecological footprint methodology) and the Carbon Emission Added approach (CEA), where a product (service) carries its own generated emission (comparable to the embodied energy concept). The CEA-approach is a kind of shared responsibility approach and the end-user will not be responsible for the complete chain of emissions carried in the product. Loiseau et al. (2014) apply a “territorial LCA” approach and assign the responsibility for environmental impacts according to a “total responsibility” principle, as defined by Eder and Narodoslawsky (1999). If several regions are to be mapped together as a multi-regional input–output model, it is necessary to have a clear principle when calculating import and export of energy and matter (and services). Zhang (2015) and (2017) analyse the interconnections in this kind of regional modelling, also connecting different principles to different regional economic sizes. Depending on the principle applied in the modelling, the outcome varies for the provincial emission value, thus also the environmental responsibility. Since the Jämtland region does not have any major industries with large emissions, it is obvious that a flow analysis excluding emissions from imported matter will definitely show higher sustainability indicators, compared to an analysis following, for example, the CEA principles. Jayaraman et al. (2010) discuss this as “The Climate Blame Game” from an international perspective and this has parallels also on a regional level, relating to the question of who “owns” available carbon space even if a region as whole might be a carbon sink. To clarify the ethical question: Can inhabitants in Jämtland, refer to higher regional uptakes in growing forest, thus claim the right to emit more CO2 compared to an average Swede? Mahapatra and Vidyapeetham (2017) denote the Paris Agreement (United Nations, 2015) a “toothless deal”

Fig. 2. Flow chart for the sectorial allocation in Jämtland of emissions from consumption starting with national data and the official Swedish GHG inventory analysis of 10.7 ton CO2eq per capita. Please note that the MiR-database includes all GHG and not only carbon based as for the regional analysis. 5

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the total regional balance, the energy sector will be just an exporter (thus contributing only as an emission uptake with the exported electricity).

the Mir-database (MirDb). In MirDb the Swedish consumption statistics from different perspectives are presented (SCB GRDP, 2018; SCB, 2018a). This is also described by Statistics Sweden following the UN statistical methodology COICOP (Classification of Individual Consumption According to Purpose) (SCB, 2016a,b). Allocation per sector has been done at a national level by using data from MirDb (SCB GRDP, 2018) for the Private and Public sector. For the Industry/Trade sector the total sum of emissions from the Private and Public is subtracted from the total national emission (excluding emissions from export products). To get the emissions for Jämtland, the per capita value for Sweden (as explained above) is multiplied by the population of Jämtland, and adjusted with the GRDP (The Gross Regional Domestic Product) for Jämtland compared to rest of Sweden to compensate for lower regional income as a corresponding presumed lower consumption (SCB GRDP, 2018). To get the emissions for the Public sector in Jämtland, the Swedish total Public emissions are multiplied by the regional share of the total national public budget (SCB Consumption, 2018; SCB, 2018b). This should be a better estimation compared to a direct per capita calculation (sparsely populated regions with large surface area typically show higher per capita public costs compared to regions with high population density). A 100-year perspective has probably been applied for GHG emissions in the Mir-database, which means they are lower compared to calculations based on the 20-year horizon value. In most cases CO2 emissions dominate which makes the choice of time horizon less influencing in terms of CO2eq. For a detailed explanation and data please refer to A.11.

Agriculture in Jämtland is typically represented by milk and meat production, not only from cows but also some sheep and goats. CH4 emissions determined for ruminants (and other CH4 emission producing animals) has been calculated as best estimates from different sources (Lodman et al., 1993, Crutzen et al., 1986, IPCC, 1996). CO2 emissions as a result from respiration are not considered in the analysis. Respiration is regarded to correspond to an uptake in plants through captured CO2 due to the photosynthesis process (internal loop within the sector). CH4 emissions from humans are disregarded since they are regarded to be negligible in comparison with methane emissions from ruminants and there are large uncertainties in the calculations (Crutzen et al., 1986, p. 276f). The livestock manure production is calculated from (Barker et al., 2002). Emissions from machinery has been calculated from the calculated sectorial energy use (Baky et al., 2010). General statistics is taken from offical statistics of Sweden (Jordbruksverket, 2018; Yearbook of agricultural statistics, 2014). Horses are considered part of the agriculture sector and the total amount is an estimate from available statistics (Statistics Sweden, 2004). Possible soil carbon respiration is not included in the analysis. Since nitrous oxide is not carbon based, this is also not included.

3. Materials and methods used per sector

3.3. Forestry sector

In this section each of the sectors is presented from a modelling method perspective. A more detailed sectorial quantitative data presentation can be found in Appendix 1. For each sector the total CO2eq balance is given with an estimated margin of error as an attempt to quantify uncertainties as a tolerance given with a positive and negative range from the calculated value. The margin of error is decided from a combined estimation of the quality of the statistics (estimated) and parameter tolerance (typically from IPCC or valuation of uncertainties). We do not apply any quantitative approach to estimate this margin of error and do not claim scientific precision, instead we have assigned ranges based on the uncertainties in the data used. Yet, from an engineering perspective, these margins of error inform the reader of the study about perceived model uncertainties. They also clarify the priorities on where to focus resources to decrease uncertainties.

Forest land covers about 39 000 km2 (80% of the total the county). The large forests in Jämtland are divided into two parts: managed productive forests are allocated to the forestry sector and other forest land including protected forest areas (reserves), and forest land voluntarily set aside, are allocated to the Nature sector. The assignment of forests to two different sectors is made for practical reasons, referring to the difference in biomass productivity and anthropogenic activity between production forests and non-production forests. Forests allocated to the forestry sector are production forests (owned and managed to produce woody biomass). In forest analysis, simplified modelling has to be introduced, resulting in increased uncertainties. In the model presented here we have chosen to regard forest harvest residues in a fast decomposition cycle (typically < 30 years). CH4 fluxes from forest ecosystems are not considered. Emissions from increased soil respiration due to destruction of soil layers from driving of forestry machines are not included; heavy machines compress the upper soil layer resulting in lower air-filled porosity in this layer, affecting CO2 and CH4 fluxes (Epron et al., 2016). Pre-commercially thinning is not included as harvested volume. Growth vs decomposition is regarded as a zero-balance during a 20–30year cycle (due to the small tree diameters cut). The incinerated biomass volume is used for heat production in the energy sector and emissions are accounted for in that sector for further distribution to other sectors (refer to chapter 3.1) and are therefore excluded from the total emission sum in the forestry sector. An increased biomass growth is expected for Jämtland in the future as a result from an increase of mean temperature and shorter winter season (longer growth season). This means a higher/faster uptake of CO2 which is not regarded (ref Heureka/RegVis software simulations (Claesson et al., 2015) showing an increased biomass growth per IPCC scenario RCP4.5 and RCP 8.5). The increased growth means that the situation improves (larger CO2 uptake), but with an associated minor loss in wood density. We regard this as part of the tolerance interval given for the sector and use Swedish biomass functions (Marklund, 1988). Decomposition rate constant for stumps has been estimated from a literature survey (Rock et al., 2008).

3.2. Agriculture sector

3.1. Energy sector Electricity in Jämtland is produced mainly in hydro power plants, but also, to a certain extent, by wind mills and biofuels (CHP). There is some photovoltaic electricity generation, but this is marginal (< 1%). Jämtland has a lower amount of CO2 emissions per produced kWh compared to the Swedish average (Jämtkraft, 2016) (refer to Appendix 1), which also contains import of electricity, giving higher emission values than the Swedish production. Since the electricity production in Jämtland is very high compared to the regional consumption of electricity and 85% of the electricity produced is exported out of the region, import of Swedish electricity mix into the region is not regarded as relevant (if it happens, the time periods for such import are very short). Biofuel incineration (burning of wood material) is a large regional emitter of CO2 (and to some extent CH4). Emission factors are taken from the IPCC guidelines for stationary combustion sources (IPCC, 2006. p. 2.17). Since the energy sector is a supplier to other sectors (and export out of the region), the emissions from production of electricity and heat are distributed to the other (energy consuming) sectors. This means that in 6

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Investments in infrastructure for tourist activities (typically snow groomers, lift installations, buildings etc.) are transferred to the industry/trade statistics (ref to 3.5). This is not fully logical, but efforts to make a proper allocation of such investments to the tourist sector proved to be too intricate given the large number of small companies involved.

Fuels and electricity (trains) used for forestry as well as transportation, is included in the sector balance (Löfroth and Rådström, 2006). In chapter A.3 a detailed description of the method and calculations applied can be found. 3.4. Reindeer herding sector

3.9. Waste sector

Reindeer herding can principally be regarded as a part of the agricultural sector. From a cultural perspective it is, however, a separate sector. Since reindeer in principle are without fixed geographical base, the animal units are connected to ownership. The contribution to global warming comes mainly from CH4 emissions from the animal population. The uncertainty tolerance for methane production given in Crutzen et al. (1986) is ± 15% for the emissions from the total global ruminant population (which is not easily estimated). We have set ± 10% since we regard the figures for the number of animal units rather precise, just as the estimated body weight and energy consumption, which makes this regional value better from a tolerance perspective. The uncertainties regarding CH4 production, depending on type of feed and individual differences, make it still an approximation.

Waste material flows are difficult to follow and map (Skytt et al., 2018). For the carbon-based emissions there are (1) Food waste sent to Gräfsåsen (Östersund) and (2) waste water. The methodology for each one of these parts is described below. Emissions caused by transportation is part of the industry/trade figures, thus not regarded as emission caused by waste as such. Solid waste to be sent for incineration to the Västernorrland region is regarded as a pure export of matter and does not influence the emission balance in Jämtland. In a way this might appear as if regions without a waste incineration facility get a better outcome in terms of balanced emissions, but on the other hand Västernorrland get the energy from the waste, with related offsets in terms of substitution effects in their CO2 balance (refer to (Skytt et al., 2018). 4245 tons of food waste is collected and sent to certain treatment facilities to be composted (Skytt et al., 2018). The emission factor for CH4 is chosen according to (IPCC, 2006) at 4 g CH4 per kg waste (wet weight) and we approximate 30% of the total CH4 emission is combusted at site. The flow of CO2 is regarded as a closed loop system with a short cycle. The calculation principles for waste water has been performed according to IPCC models (2006, pp. 6.6–6.16) to find out CH4 emissions. Instead of the default country factors for BOD (Biochemical Oxygen Demand) giving the amount of carbon in the waste water that is aerobically biodegradable, actual data from Jämtland’s largest waste water treatment facility (Göviken in Östersund) has been used for the total regional population (incoming 1697 ton yr−1 and 66 114 person equivalents from (Gövikens Avlopssreningsverk, 2014) giving 25.67 kg BOD7 capita−1 yr−1).

3.5. Industry and trade sector Jämtland has some industries, but most products used (except from what is being delivered from the agricultural sector) are imported to the region. There are no major emitters in the region, compared with steel manufacturing or pulp and paper production, as in other northern regions in Sweden. 3.6. Private sector All the inhabitants in the region are part of this sector. Typically, emissions from transportation to a working place belong to the private sector, while emissions from transportation of goods in a company truck or car belong to the industry/trade sector. Refer to chapter 2.5 and A.11 for the methodology used for consumption accounting. The private sector is also classified as end consumer of all regional emissions except from what is being exported out from the region. This does, of course, not correspond to the real situation. However, in the flow chart shown in Fig. 1, emissions from energy used, transportation and direct trade (including indirect emissions) are presented as a separate sum. The total sum of emissions, also including imported emissions from the other regional sectors, is given below the sector sum. This follows the Swedish national per capita sum of emissions, also used as a reference value for the regional climate strategy.

3.10. Nature sector The nature sector can be regarded as “pure nature” without anthropogenic contribution. It contains mires and wetlands, lakes and ponds, wild ruminants (moose and deer) and other forest land, i.e. forest land that is not managed productive forests (which belongs to the forestry sector). These separate parts of the nature sector are treated below. The complexity regarding methane and carbon dioxide fluxes in natural processes is high, with associated high uncertainty. Thus, emissions tend to vary with time and space, and for different types of ecosystems. However, calculating approximate levels of emissions makes it possible to get a complete overview of all regional fluxes. Refer to chapter 2.3 for a discussion about the radiative forcing from carbon based GHG fluxes. Surface areas for each type of natural system are taken from (SCB, 2013). In A.3 and A.10 further details about sectorial calculations can be found.

3.7. Public sector Public organizations are typically hospitals, schools, universities, regional and county administration etc. The calculated value for public consumption is given from the share representing the public budget for Jämtland vs Sweden; 559 MSEK vs. 9 GSEK (1,6%). It is presumed that public consumption in terms of content does not vary too much between the Swedish regions. 3.8. Tourism sector

3.10.1. Mires/wetland (Surface area 576 055 ha) Natural wetlands are sources of CH4 to the atmosphere, but they also capture CO2 and store more carbon than they emit (thus acting as carbon sinks), however from a GWP/AGTP-perspective wetlands contribute to global warming (IPCC, 2014; Bäckstrand et al., 2010; Nilsson et al., 2008; Christensen et al., 2012). Also refer to the discussions and references in (Kasimir-Klemedtsson, n.d.). Measurements documented show large spatial variability in production and emission of CH4, thus making estimations difficult. Main factors causing the variability are quality of organic substrate, amount of electron acceptors, pH,

Jämtland is a popular tourist area, with mountains for skiing, walking and snowmobile excursions, but also fishing and hunting attract many visitors. About 2.5 million tourist guest nights occur each year in Jämtland (Swedish agency for economic and regional growth/ Statistics Sweden, 2014) corresponding to a 5% increase in yearly population. Assuming that tourists consume the same as the Swedish average, this would indicate that the CO2eq emission for consumption is about 5% of that in the private sector (5% of 425 237 tons). 7

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Table 1 Review of relevant measurements of CH4-fluxes from boreal mires and wetlands (starting with the oldest). The first column gives the country where the wetland is situated (the first row refers to a review article with a number of measurements from different countries). Lat = Latitude of the wetland area and mg d−1 is the measured daily mean flux per m2 of CH4. Range is the range for the flux variation. The g yr−1 column gives g CH4 per m2 and year, and if this not is given in the article, it is calculated from the daily mean flux times 150 days (which is the approximate growing period in Jämtland). Empty Country/Reference rows refers to the same measurement/reference as the row above. The three last reference papers, from Sweden 68°N, actually refers to the same mire complex (Stordalen, Abisko, in northern Sweden). Country

Lat

mg d−1

Range

g yr−1

Type of soil

References

(Review north) Sweden

45–70 64 63 64 63 63 64 62

91 20.6 38 50.6 25.5 9.4 5.8 49 137 12 −0.2 −0.4 92 75 47

0–664 −4–383 −2–225 −4–265 −2–388 −5–278 −7–185

13.7 3.1 5.7 7.6 3.8 1.4 1 8.0 19.0 3.9 0.3 −0.1 14 11 7.0 5.5 7.1 1.3 12.6 12.0 18.7 3.2 0.5 6.5 36 20 17

Wet, arctic boreal Mesotrophic fen Poor flark fen Poor fine fen Sphagnum bog Sphagnum bog Poor Sphagnum Ombrogenous bog Minerogenous Drained bog Drained fen Peat Collapsed bog Collapsed fen Wet tundra Oligotrophic minerogenic Freshwater wetlands Estuarine wetlands Minerotrophic fen Minerogenic oligotrophic (2004) Minerogenic oligotrophic (2005) Boreal fen Palsa site Sphagnum site Eriophurum site Palsa mire mean Thaw pond, wetland

Bartlett and Harriss (1993) Granberg et al. (1997)

Finland

Canada

Russia Finland North America

67–77 69

Finland Sweden

61 64

84

Canada Sweden

55 68

1.3 1.5 20 113

Sweden Sweden

68 68

0–160

0–96 −10–150 −10–120 0–700

112

Nykänen et al. (1998)

Moore et al. (1998)

Christensen et al. (1999) Hargreaves et al. (2001) Bridgham et al. (2006) Rinne et al. (2007) Nilsson et al. (2008) Long et al. (2010) Bäckstrand et al. (2010)

Christensen et al. (2012) Kuhn et al. (2017)

Quirk et al., 2011). Globally the average sequestration rate in salt marches has been estimated to 210C m−2 yr−1 (Chmura et al., 2003). The differences between salt marshes and subarctic inland wetland mineral soils are considerable, but it underlines the difficulties in estimating the uptake from a collection of scientific data. LORCA gives a long term accumulation of carbon, but these ecosystems are complicated to measure and at least some subarctic mires might today act as sources of carbon to the atmosphere, rather than sinks due to decreased productivity (Malmer and Wallén, 1996), which indicates care should be taken when estimating the uptake value. The studies point in the direction of a LORCA for subarctic mires between 20 and 30 g C m−2 yr−1 and from this, the estimation 25 g C m−2 yr−1 is set. To calculate the actual atmospheric uptake, the carbon content of the emitted CH4 from the estimation above needs to be added (12/16 of 7.5 g CH4 m−2 yr−1 = 5.6 g C m−2 yr−1C). The uptake value is thus set to 30 g C m−2 yr−1corresponding to 110 g CO2 m−2 yr−1. Frolking et al. (2006) present a modelling of northern peatlands from different scenarios and discuss GWP in relation to integrative calculations over shorter and longer time horizons. They conclude from the results of the modelling that atmospheric composition (of gases) and RF respond rapidly to step changes in CH4 fluxes, but more slowly to corresponding changes in CO2. A halving of CH4 flux rates leads after about 50 years to a halving of the RF (p. 6). Joosten et al. (2012) conclude “The combined CO2 and CH4 fluxes from natural, undrained peatlands, result in a radiative forcing that – dependent on peatland type – is slightly positive or slightly negative on the 100-year timescale. In the long run, all natural peatlands sequester carbon from the atmosphere and are climate coolers.” (p. 37). This also corresponds to the modelling results presented by Frolking et al. (2006), but taking the above mentioned RF response to step changes, reduction of CH4 can contribute to lower RF levels than the present steady-state situation. Laine et al. (1996) found that draining of northern peatlands decreased CH4 fluxes, and also enhanced tree growth, at the same time increased

temperature, and level of water table (Granberg et al., 1997). Jämtland has a latitudinal extent from about 61°N to 65°N and wetlands cover about 12% of the county. Dominating wetland types are topogenous fen, soligenous fen and mosaic mixed mires, and according the Finnish mire terminology the wetlands in Jämtland are of Aapa type, characterized by relative high humidity and short growing season (Gunnarsson and Löfroth, 2014). To be able to estimate CH4 emissions from wetlands in Jämtland, some relevant measurements documented have been put together as an overview (Table 1). From Table 1 it seems nutrition rich mires (minerotrophic/minerogenic) show higher emissions than nutrition poor mires. Excluding the highest and lowest emission values gives a probable range between 3 and 18 g CH4 m−2 yr−1. The arithmetic mean value from Table 1 is 9 g m−2 yr−1 and the geometrical mean is about 5 g m−2 yr−1. It appears reasonable to estimate the emissions of CH4 from the mires in Jämtland to a value in the range between 5 and 10 g m−2 yr−1 which gives 7.5 g m−2 yr−1. This also corresponds to the emission factor 76 kg CH4 ha−1 yr−1 for boreal natural wetlands referred to by (IPCC, 2013) 24 Table 5A.2.1) from (Bridgham et al., 2006). The chosen emission value 7.5 g m−2 yr−1 from mires should at least not be too high. The carbon uptake of mires and wetlands cannot easily be measured since it requires measurements of all relevant fluxes. Estimations of the average Long-term apparent Rate of Carbon Accumulation (LORCA) have however been made in a number of studies. Undrained mires in Finland show average LORCA = 17–27 g C m−2 yr−1 depending on geographical region (Turunen et al., 2002). Studies of peatlands in Former Soviet Union show LORCA = 30 g C m−2 yr−1 (Botch et al., 1995) and for peatlands in Canada LORCA = 29 g C m−2 yr−1 (Gorham, 1991). A 2-year evaluation of the carbon budget for a boreal minerogenic oligotrophic mire in Sweden (64°N) shows average LORCA = 24 g C m−2 yr−1 (Nilsson et al., 2008). In salt marshes the rate of carbon storage can be considerably higher; 100–150 g C m−2 yr−1 in stands of Spartina alterniflora and Juncus roemerianus (Elsey8

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needs to be balanced as an output equal to the emission content of the sector, which is emissions caused by the sector from a regional, national and international perspective. From the general methodology applied (ref to chapter 2) and calculations from statistics, estimations and assumptions described in chapter 3 and Appendix, results per sector are summarised below. The total carbon GHG balance (GWP20) of Jämtland is an uptake of 2.4 MTon CO2eq. The private sector, as final end user, balances at 1.5 MTon, which is 11.7 ton per capita. The Swedish national accounting average is 10.7 ton per capita (SCB, 2016a). The total anthropogenic contribution to the total emissions from the region is about 15%. In Fig. 4 the AGTP response for a 1-year summary pulse from Jämtland is shown. It is worth noticing that the CH4 emission has a maximum after 10 years of approx. 5 μK warming, which balances to the 5 μK cooling after 10 years for the CO2 uptake in growing biomass. The AGTP response for continuous (and constant) emissions from Jämtland over 200 years can be seen in Fig. 5. The steady-state temperature response for methane emissions contrasts with the steadily increasing uptake (as the balance of all CO2 emissions with a total uptake). Below are the results per sector (Tables 2 and 3). The regional model shows the sectorial and total GWP20 balance as CO2eq. Other time integrals can be used for additional analysis of the situation in the region, and as pointed out earlier, CH4 emissions are extremely time integral dependent. This also implies the need to evaluate which time horizon is relevant to the study. Absolute Global Temperature Potential responses per sector and total for Jämtland are presented as graphs showing the response (as degree Kelvin) from 0 to 100 years, which visualizes the time impact for each sector. For each sector headline, the sector balance is given as CO2eq as emission (positive value) or uptake (negative value). A tolerance (as estimated uncertainty) of the total balance (given as a plus/minus value equally distributed around the calculated value) is set as explained in chapter 3. Sector 3, Forestry, and sector 10, Nature, are separately presented in Table 3 since they also have uptakes and are interconnected, since productive forests are actually part of nature. In Table 4 the graphical absolute global temperature potential (AGTP) response is given per sector. Summary comments are given per sector after the result tables.

CO2 emissions was found from decomposing peat. The study shows reduced RF during the first decades after draining from CH4 emission reduction and increased CO2 uptake as a net result for 120–160 years. Thereafter the effect decreases. There are shortcomings with the GWP calculations, and it is necessary to take the time horizon into consideration, but accepting that step changes in the flux actually results in changes in RF within a short time horizon makes it necessary to take these fluxes into consideration. Whiting and Chanton (2001) show from measurements of fluxes of subtropical and boreal wetland sites that a short time horizon using GWP20 give the results that all sites were GHG sources and for GWP500 all sites were considered GHG sinks. 3.10.2. Lakes and ponds (Surface area 480 800 ha) Lakes emit large amounts of methane and it is worth noticing that, “Although lakes appear to be a significant component of the non-anthropogenic methane flux, they are rarely considered.” (Wik, 2016). In a study of 11 lakes in North America (NA) and 13 Swedish lakes, three CH4 pathways have been investigated; ebullition, storage and diffusive flux (Bastviken et al., 2004). Total CH4 emissions range from 0.5 to 20 g C m−2 yr−1 (NA) and from 0.2 to 6 g C m−2 yr−1 (Sweden), but the latter does not include ebullition, which in NA gives a contribution of 0.1–16 g C m−2 yr−1. Lakes in Jämtland are typically covered with ice 4–5 months per year and diffusion only occurs during ice free periods and represents 20%-30% of the emissions from all three pathways (Bastviken et al., 2004). There are uncertainties about fluxes from large lakes (few measurements have been made) and the total emissions per m2 seem to be lower from large lakes compared to small lakes. From this we find it reasonable to estimate total emissions of CH4 from ebullition, storage and diffusive flux to be at least 5 g C m−2 yr−1 (corresponding to 7 g CH4 m−2 yr−1 which is used in the model). Uptake of CO2 in lakes and ponds is regarded as small from a GHG perspective in relation to the effect of CH4. 3.10.3. Wild ruminants The number of moose (animal units; au) is approximated from the Swedish moose population per ha forest land as 12 au per 1000 ha (Skogforsk, 2018) which for Jämtland would give 45 000 au. The number of deer is estimated to be less than 5% of the Swedish presumed total deer population of 375 000 (Sveriges Lantbruksuniversitet, 2018). The deer density is assumed to be lower in Jämtland compared to southern Sweden. The deer population is thus set to 15 000 au. CH4 emissions from wild ruminants are taken from (Crutzen et al., 1986); (per au) moose 31 kg CH4 y−1 and deer 15 kg CH4 y−1.

4.1. Results energy (CO2eq balance: Emission 530 000 ton yr−1) Electricity production from hydropower and wind emits about 5 tons CO2eq TJ−1 (from infrastructure) and the corresponding heat production from biomass emits 113 ton CO2eq TJ−1 (as direct emission since this is the totally dominating emission source compared to the infrastructure). The emissions from the energy sector is distributed to other sectors or exported (ref to chapter 3.1). Note that the export of electricity represents an uptake of emissions for Jämtland of about 170 kton CO2eq following the logic of consumer responsibility. To put this figure into context, it corresponds to the emissions from transportation in the private sector, which in turn raises the (ethical) question about compensation of emissions; can a region claim the right to use its resources, i.e. rivers (hydro power) and mountains (wind power), to compensate other (anthropogenic) emissions?

3.10.4. Unproductive forest land and non-production forest land Unproductive forest land is defined as forest which produce less than 1 cubic meter of wood per hectare per year (m3sk ha−1 yr−1) (Skogsstyrelsen, 2014). Woody biomass growth is for Jämtland set to 20% of that in productive non-production forests (20% of 3,43 = 0,68 m3sk/ha ref to Table 16 in A.3) and the decomposition to uptake rate is set to 80%. These forests represent 23% of all forest land in Jämtland. Productive non-production forest land is also included in the Nature sector, since forestry does not take place. Forest land area belonging to the Nature sector represents 18% of the total forest land area in Jämtland. Refer to A.3 for detailed biomass flow calculations. Forest land classified as “Other wooded land” (land within contiguous areas not defined as productive or unproductive forest land following (Skogsstyrelsen, 2014) is not taken into account as it only represent 8% of the land area of Jämtland and does not contribute in terms of woody biomass growth. This forest land area is regarded as climate neutral.

4.2. Results agriculture (CO2eq balance: Emission 160 000 ton yr−1) The GWP20 from Agriculture is dominated by CH4 from ruminants (basically cow/cattle but also other animals contribute). Refer to A.2 for the details about sectorial emissions, summarised in Table 11. Looking at the AGTP response graph in Table 4, the short term CH4 response dominates, while CO2 dominates the long term response. Looking in a longer time perspective with continuous yearly pulses

4. Results Jämtland The outcome from the study of the region is the flow chart showing fluxes summarized as sectorial balances shown in Fig. 3. Each sector 9

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Fig. 3. This chart shows the GWP20 balance for Jämtland as the outcome from the model. The total result is an uptake of 2.4 Mton CO2eq. Blue arrows represent interregional sectorial fluxes and grey arrows emissions. An arrow pointing towards the balance is an export of emissions from the region and an arrow pointing from the balance is a corresponding emission. The negative value pointing from forestry (green arrow) represents an uptake. The arrow from the balance to private “empties” the private sector.

between production forests and non-production forests, since forestry in Jämtland is to be regarded as an activity comparable with agriculture. In A.3 a detailed description can be found regarding how the sector balance has been calculated. Soil fluxes and thinner roots not belonging to stump volumes are not included in the balance. Natural litter fall is also not included (but needles belonging to parts cut are included as a gross and a net volume respectively). Taking the uncertainties and the parts excluded into consideration a tolerance interval of ± 20% is set for the sector balance.

(Fig. 6) we can see that after about 200 years CO2 gives the same response as the steady-state CH4 response and will thereafter be larger. This means that keeping a constant stock of ruminants for a long time does not accelerate global warming, but gives a constant higher steadystate temperature. CO2 emissions, on the other hand, continue to accelerate global warming, due to its extreme slow decay. In the debate about meat and milk production and the alternatives to this, we need to remember the difference between the temperature response of CO2 and CH4 from a time horizon perspective. At the same time the short-term effects of CH4 need to be considered. Part of the production output from the agricultural sector is consumed in private and public sectors, and part is exported out of the sector. A simplified estimation of the sectorial export is made according calculations presented in A.2.

4.4. Results Reindeer herding (CO2eq balance: Emission 53 000 ton yr−1) Reindeer herding is the smallest sector from an economic perspective, still it is a large contributor to global warming. Just as for the agricultural sector, reindeer herding causes relatively large methane emissions, influencing the short-term global warming contribution. Only 3% of the total calculated emissions as CO2eq come from fossil fuels (ref 6% for the agricultural sector referring to Table 11). This sector actually contributes more to short-term global warming than the tourism sector. This mirrors (again) the difference between emissions of

4.3. Results forestry (CO2eq balance: Uptake 7 800 000 ton yr−1) The large uptakes in the Forestry sector do not come from forestry activities as such, but from natural processes, managed by humans (i.e. anthropogenic land-use). It was however decided to distinguish 10

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Fig. 4. AGTP response (as μK) for the 1-year emission and uptake as a pulse from Jämtland County. Blue dashed line is CO2 uptake in growing biomass. Red line is emissions of CH4. Thick black line is the AGTP sum of both CO2 and CH4. Also compare the response with continuous emissions from Jämtland in.

Fig. 5. AGTP response (as 10−4 K) for the continuous and constant yearly emission and uptake from the Nature sector over 200 years (when a cooling effect of 1 mK has been reached). Blue dotted line is CO2 uptake. Red line is emissions of CH4. Thick black line is the AGTP sum of both CO2 and CH4.

would of course gain Jämtland a lot. Direct emissions from the sector are dominated by CO2 from transportation and the use of heat. Indirect emissions from consumption are however totally dominating from an overall perspective. 46% of the total emissions are being exported out of the region (“carried” by products and services).

CH4 and CO2. Comparing graph 4 (AGTP from reindeer herding sector) with graph 8 (AGTP from tourism sector) in Table 4 clarifies the difference between short-term and long-term warming effects in each case. 4.5. Results industry and trade (CO2eq balance: Emission 380 000 ton yr−1)

4.6. Results private (CO2eq balance: Emission 580 000 ton yr−1) The industry and trade sector is the third largest contributor to global warming in Jämtland. Three quarters of the emissions are to be referred to as emissions following import of products. All sectors are interconnected when it comes to material flows. As described in the methodology, allocation of emissions to each sector is not an exact science, but rather an iterative attempt to find some logic in a universe of statistics and calculations. Since Jämtland does not have any large industries (emitters), it becomes obvious that the assigning of the responsibility for emissions caused in other regions and countries needs to be considered. Claiming producer responsibility only for emissions

The private sector is regarded as the end user in the region. Emissions from the other sectors are either exported out of the region or transferred to the private sector. This highlights that there is no production of goods or services without an end user (ref to the methodology part in chapter 2). However, to make an analysis of part of the sectorial emissions possible, the private sector has been allocated only part of the end user responsibility before making the final balance for Jämtland. Import from the other sectors is added to the sector specific value shown and the total sum is then added to the balance. 11

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Table 2 Summary tables per sector of emissions of carbon dioxide, methane and GWP20 carbon dioxide equivalents. Relevant major emission sources are given for each sector. Tolerances are approximated (ref to chapter 3) to quantify uncertainties. Annual emissions and uptakes per sector in JÄMTLAND

1. ENERGY (Margin of error ± 5%)

2. AGRICULTURE (Margin of error ± 10%)

4. REINDEER (Margin of error ± 10%)

5. INDUSTRY/TRADE (Margin of error ± 15%)

6. PRIVATE (Margin of error ± 15%)

7. PUBLIC (Margin of error ± 15%)

8. TOURISM (Margin of error ± 15%)

9. WASTE (Margin of Error ± 30%)

Source

CO2 [ton]

CH4 [ton]

CO2eq [ton]

Electricity District heating EXPORT TOTAL Electricity Fuel (diesel) Animals Manure Consumption EXPORT TOTAL Electricity Fuel Reindeer TOTAL Electricity Heat Transport Fuel Oil Consumption EXPORT TOTAL Electricity Heat Transport Fuel Oil Consumption TOTAL Electricity Heat Transport Fuel Oil Consumpt/Invest TOTAL Electricity Transport Fuel Oil Consumption EXPORT TOTAL Food waste Waste water TOTAL

201 228 495 040 −171 272 524 996 1 144 11 240 0 0 26 674 −9 737 29 322 17 1 178

0 44 0 44 0 0 1 889 189

201 228 498 753 −171 272 528 708 1 144 11 240 158 700 15 870 26 674 −53 254 160 375 17 1 178 51 660 52 855 13 339 71 202 99 481 4 334 519 216 −325 483 382 088 11 217 309 119 165 301 7 313 84 692 577 640 5 139 30 580 52 313 2 167 174 116 264 314 2 383 23 460 745 68 185 −75 818 18 955 998 34 776 35 774

−518 1 560 0 0 615 615

1 195 13 339 70 672 99 481 4 334 519 216 −325 239 381 802 11 217 290 976 165 301 7 313 84 692 559 498 5 139 30 352 52 313 2 167 174 116 264 086 2383 23 460 745 68,185 −75818 18 955

6

−3 3 216

216 3

3

0 12 414 426

0

emissions comes from fuel oil, used mainly for heating of houses. During the latest 20-year period, conversion of fuel oil heating to biofuels and heat-pumps been a clear tendency. Today there is no large potential in such conversion, but of course it is valuable to get rid of the

Even without the transfer of regional sector emissions, the private sector is the largest regional emitter. The largest emitter is transport, representing about 25%. About 50% of the emissions come from renewable resources (production of heat and electricity). A minor part of

Table 3 Summary tables for Nature sector (10) and Forestry sector (3). Emissions of carbon dioxide and methane are given, as well as uptake of carbon dioxide. The last column gives the balance as carbon dioxide equivalents (GWP20). In the balance negative values represent uptake (and positive represent emissions). The margins of error are estimated (ref to chapter 3) to quantify uncertainties. Source

10. NATURE(Margin of error ± 20%)

3. FORESTRY (Margin of Error ± 20%)

Mire Lake Unproductive forests Productive forests Moose Deer TOTAL Biomass growth Decomposition Forestry machines TOTAL

Amount

576 055 ha 480 800 ha 841 000 ha

Emissions [kton]

Balance [kton]

CO2

CH4

CO2

CO2eq

0 0 846 313

43 34 0 0 1,4 0,2 78

635 0 1 057 1 492

2 995 2 827 −211 −1 178 117 19 4 569 −10 151 2 224 89 −7 838

45 000 au 15 000 au 1 159 2 224 89 2 313

12

Uptake [kton]

3 183 10 151

10 151

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Table 4 AGTP response graphs from a one year pulse per sector. Blue dashed line shows carbon dioxide temperature response. Red line shows methane temperature response. Black line shows total temperature response as sum of carbon dioxide and methane response. Sectorial AGTP from CO2 and CH4 1. ENERGY (Margin of error ± 5%)

2. AGRICULTURE (Margin of error ± 10%)

3. FORESTRY (Margin of error ± 20%)

4. REINDEER (Margin of error ± 10%)

(continued on next page)

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Table 4 (continued) Sectorial AGTP from CO2 and CH4 5. INDUSTRY/TRADE (Margin of error ± 15%)

6. PRIVATE (Margin of error ± 15%)

7. PUBLIC (Margin of error ± 15%)

8. TOURISM (Margin of error ± 15%)

9. WASTE (Margin of error ± 30%)

(continued on next page)

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Table 4 (continued) Sectorial AGTP from CO2 and CH4 10. NATURE (Margin of error ± 20%)

fuel oil completely.

Table 5 Tolerance intervals as min and max emissions from which the lowest and the highest balance respectively can be composed. Note that the energy sector is already included in the other sectors as energy input. The total minimum is calculated as the sum of minimum emissions and max uptake in Forestry. The total maximum is calculated as the sum of maximum emissions and minimum uptake in Forestry. Sector

CO2eq [ton]

Tol

Min [ton]

Max [ton]

Energy Agriculture Reindeer herding Industry/Trade Private Public Tourism Waste Forestry Nature TOTAL min resp. max

528 708 160 375 52 855 382 088 577 640 264 314 18 955 36 202 −7 708 796 4 568 769

5% 10% 10% 15% 15% 15% 15% 30% 20% 20%

502 273 144 337 47 569 324 775 490 994 224 667 16 111 25 342 −9 250 555 3 655 015 −4 476 603

555 144 176 412 58 140 439 402 664 286 303 961 21 798 47 063 −6 167 037 5 482 523 922 952

4.7. Results public (CO2eq balance: Emission 260 000 ton yr−1) The public sector in Jämtland uses 1.6% of the national total public budget and has 1.3% of the population, which is typical for sparsely populated regions in Sweden. Compared to regions with higher population density, costs for societal public service will be higher per capita, resulting in higher CO2eq emissions per capita. About 65% of the total emissions come from consumption (indirect emissions imported to the region). Transportation is mainly driven on non-renewable fuels and represents 20%. It is worth considering that Mid Sweden University is part of this sector (with emissions dominated by transportation, i.e. researchers “travelling around the globe”). 4.8. Results tourism (CO2eq balance: Emission 19 000 ton yr−1) Tourism is well known to increase regional environmental pressure and GRDP. Most of the emissions caused by the tourism sector are exported out of the region, namely the part belonging to non-regional inhabitants, who are to carry the responsibility for the emissions (ref to A.8). This follows the logic of consumer’s responsibility. A tourist from outside the region has to carry their own emissions, just as inhabitants do (as part of consumption). On the other hand, claiming full

Fig. 6. AGTP response (as μK) for constant and continuous yearly emission from the Agricultural sector over 200 years. Blue dotted line is CO2 emissions. Red line is CH4. Thick black line is the AGTP sum of both CO2 and CH4. If the animal population is kept constant for a longer time the added temperature response from CH4 will, in principle be steady-state at 3 μK, as can be seen in the red line. 15

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Fig. 7. AGTP response (as 10−4 K) for the continuous and constant yearly emission and uptake from the Nature sector over 200 years. Blue dotted line is CO2 uptake. Red line is emissions of CH4. Thick black line is the AGTP sum of both CO2 and CH4. With a stable nature uptake/emission in Jämtland it takes 100 years to reach balance.

consumer’s responsibility, exporting the emissions out of the region, at the same time leaving the money in the region, in a way might increase the regional incitement to encourage increased (imported) tourism. 4.9. Results waste (CO2eq balance: Emission 36 000 ton yr−1) Food waste and waste water are part of the regional emissions (but solid waste is not). CH4 emissions dominate completely. Note that all types of transportation activities are part of the public sector. Municipal solid waste to be incinerated, is being exported out of Jämtland to the region of Västernorrland. 4.10. Results Nature (CO2eq balance: Emission 4 570 000 ton yr−1) The nature sector is totally dominating the regional contribution from an emission perspective, with CH4 emissions from mires and lakes as the major contributors. As for the agricultural sector, the nature sector has major CH4 emissions, thus making it interesting to study the long term temperature response. Fig. 7 shows the AGTP response graph for a constant yearly and continuous emission. From a short-term perspective the Nature sector shows a warming effect. After about 100 years, cooling effects and warming effects are equal, and thereafter the uptake starts to dominate, thus resulting in a cooling. However, both the long-term steady-state and the short-term pulse CH4 contribution need to be considered from a global warming perspective, just as for the agricultural sector (refer to chapter 4.2). The assimilation of CO2 in growing biomass results in a long-term cooling from the sector.

Fig. 8. Yearly emissions per sector with margins of error as error bars.

4.11. Analysis of the margins of error From the tolerance interval graph in Fig. 8 the dominance of the two sectors of forestry and nature is obvious. The tolerance intervals for each of these sectors is actually larger than the other sectors’ balanced emissions. Refer to Fig. 9 for an overview of the other sectors without the two dominating ones. From Table 5 the minimum value (representing largest uptake) and the maximum value (representing largest emission) can be calculated as an interval ranging from −4.3 Mton to 1.0 Mton which would indicate a higher probability for an uptake than an emission for the balance of Jämtland. MARGIN OF ERROR INVERVALS AS MIN AND MAX VALUES

Fig. 9. Yearly emissions per sector with margins of error as error bars excluding the dominant sectors Forestry and Nature.

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public sector. 3) Decreased methane emissions, especially from wetlands and mires (with afforestation for increased CO2 assimilation). 4) Increased/optimized short term CO2 assimilation in forests.

If the methane CO2eq factor is regarded as an uncertainty, we can study the balance as GWP100 (factor 28 for CH4 to CO2eq) instead of the GWP20 (factor 84 for CH4 to CO2eq). The total balance for the region of Jämtland is then an uptake of about 7 Mton (compared to 2.4 Mton for GWP20). The corresponding tolerance analysis shows a maximum uptake of 8 Mton and a minimum uptake of 4.4 Mton. The uncertainties in the forestry calculations are larger than the total maximum contributions from the sum of all other sectors.

To emphasize this somewhat one-eyed analysis even more, the solutions above can be upscaled to a global level as well, focusing mainly on making the cities much more dense and efficient from a transportation and energy use perspective, and use natural ecosystems to reduce radiative forcing effects. We also need to find ways for how to reduce consumption (a kind of “bike more, buy less” approach). If the sociologist Baumann is right when saying that in a society organized around freedom, the individuals will be defined by their consumption (Bauman, 1988, p. 89 ff), we are facing another problem. Performing a detailed analysis at a regional level is a rather demanding task, and it seems those who have fulfilled such analyses agree on the difficulty to find relevant data, and the need to develop methods for the allocation of emissions to different sectors. Continuous updating of the model is often thus too time consuming (especially since such updating is hardly classified as research).

5. Discussion The study shows that Jämtland has a cooling effect at the global temperature due to the large forests, taking all the regional anthropogenic and natural carbon-based fluxes into consideration. As in the case for several corresponding assessments of regions and cities, major emitters of CO2 (and also CH4) are found to be electricity and/or heat production and transportation, thus resulting in conclusions about the need to reduce the amount of gasoline/diesel cars, improve public transportation, increase energy efficiency etc. How to achieve this is, however, not obvious. In assessments including indirect emissions for imported products and services, the relatively high share of the total emissions from such import, clarifies the need to further analyse consumption. Xi et al. discuss promoting implementation of circular economy to “optimize the use of energy, materials and community resources” (2011, p. 6008). This actually means that reduced consumption at all levels would have a clear effect in reducing GHG emissions. They also raise the question of how to determine the reduction that each sector should be responsible for, taking into consideration that different sectors show different potentials. In the assessment of the GHG balance of the Province of Siena, Ridolfi et al. find that CO2eq per capita is about 3 tons, and conclude that “the low emissions per capita… are in fact due to the vast forest area” (2008, p. 369). This figure includes a CO2 uptake in forests of about 4 ton per capita, which means the emission per capita is actually about 7 tons. If we take the total balance for Jämtland of 2406 tons we would get a total uptake of about 19 tons CO2eq per capita. For Samsø we also get an uptake of about 19 ton per capita as CO2 (the uptake is given as 26 460 tons carbon, corresponding to 96 800 tons CO2 and calculated with 5000 inhabitants, including tourists) (Jørgensen and Nielsen, 2015). Marchi et al. specifically points out in their modelling of the Province of Siena, that if CH4 is included in the analysis, the results can change a lot (2012, s. 59). They further conclude that the indicated best effect in a 10-year perspective to improve the GHG balance is reforestation, i.e. increased CO2 uptake. Cities typically show high emission figures of CO2eq per capita (in ton); Denver 24.3, Los Angeles 15.5, Toronto 14.4, London 10.5, but Barcelona has only 4.2 (Kennedy et al., 2009). These figures include what can be regarded as a full consumer responsibility, thus corresponding to the calculation principles used for Jämtland. Barcelona has high population density, low heating requirements and relatively low emitting electricity production, and for Denver it is the other way round (2009, p. 7300). Barcelona has only 65% of the per capita income compared to Denver which imply a lower consumption activity. The balance for Jämtland would reach 0 if about 230 000 standardized Swedish inhabitants would move into the region, presuming they would emit the mean Swedish CO2eq amount of 10.7 ton. This means a total of 360 000 inhabitants in an area larger than Netherlands (which has about 17 million inhabitants). In a way this is a good visualization of the problem of global warming. If we would optimize Jämtland according to the sectorial study presented and the context above, the following can be concluded:

5.1. The regional goals ‘Fossil fuel free 2030′ and ‘Carbon dioxide neutral 2045′ To achieve the goal to become a fossil fuel free region, the focus should be on machines in forestry and agriculture, the car fleet, trucks, busses, snowmobiles and other terrain vehicles and aviation. The transition to alternate fuels, renewable alternatives to diesel and petrol (and JetA1), partly needs to be adapted to the existing vehicle fleet since an exchange of the complete fleet is probably not realistic in 10 years (30% of the Swedish car fleet was older than 12 years at the end of 2018 (SCB, 2019). It might be worth considering not to focus too hard to become fossil fuel free, to prevent sub-optimization. If some activities result in an increased use of fossil fuels this may still contribute to decreased GTP. Looking at the input/output for the forestry sector (ref to Table 14) it can be seen that an output increase of 1%, balances the complete input emissions in the sector from forestry activities and transportation. This is not to say the input emissions do not matter, but rather to point out the necessity to optimize AGTP rather than focusing too hard on policy goals. If natural processes are included in the balances, Jämtland contributes to global cooling and not warming, but the goal to become CO2 neutral focuses on flows of anthropogenic CO2, which is definitely tougher. How to deal with emissions from biomass combustion is also not fully defined in the regional policy documents. One way to do this is the method to ‘ignore’ emissions from biogenic sources and regard them as ‘renewable’, but from a climate perspective this is not optimal. In principle the two goals ‘Fossil fuel free’ and ‘CO2 neutral’ are one and the same for Jämtland as defined today in regional policy documents. 5.2. Renewability of woody biomass 40% of the harvested annual volumes in Jämtland are exported to become pulp and paper, which circulates in a short-life cycle, soon to become emitted as CO2 again. Another 10% will be incinerated, having a similar destiny. If these emissions are considered to be ‘renewable’ without being balanced the problem might be that no forest is available to care for the uptake of CO2. If the short-life-cycle products (typically paper) would be regarded as if they ‘belong’ to Jämtland, the uptake in the sector decreases from −7 700 Mton to −4 600 tons, and the total balance for Jämtland no longer shows a total uptake. This shows the importance of analysing the complete chain. There are risks when using concepts such as ‘renewable’, and equal emission and uptake. From certain perspectives it might appear strange that the balance accounting in the model does not change if a tree becomes a house, or if it becomes lavatory paper, since Jämtland can calculate the biomass exported as a

1) Concentrate the population to one large city (Östersund) to give short travelling (biking) distances, effective use of energy, and the possibility to improve public transportation. 2) Low per capita income for decreased consumption in the private sector, but this is also valid for the industry/trade sector and the 17

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shows that Jämtland has a cooling effect from an uptake calculated as GWP20 CO2eq of about 2.4 Mton, which corresponds to 19 ton per capita. Jämtland does not show large margins in a sustainability context. From the average Swedish GHG emissions per capita of 10.7 ton, Jämtland’s uptake covers an additional 230 000 persons (excluding Jämtland’s inhabitants themselves) to a total population of about 360 0000 persons, to reach GHG balance. This is not a lot, taking the large natural resources of Jämtland into consideration. From what can be seen in the sector analysis, there are no “low hanging fruits” to pick in today’s situation. To find solutions to the complex problem of societal adaptation to global warming, applying standard solutions is unlikely to be sufficient. Sectorial AGTP analysis has been concluded to be a good complement to GWP as an indicator. The AGTP gives a measure of the sectorial impact from a global warming perspective as degrees (K) in the time interval of 0 to 100 years. Strategies should be developed focusing on the following:

positive RF. This is however just an effect of the logic to keep the boundary conditions as import/export balancing. 5.3. Methane emissions It is important to take the time horizon into consideration when analysing sectors with large CH4 emissions. If the emissions are kept constant during a longer period of time, the contribution from CH4 will reach a steady-state AGTP response and cannot be said to contribute to an increased (accelerated) global warming. However, the effect of a reduction in emissions of CH4 will be a fast response as a reduction of AGTP due to the short lifetime of CH4 in the atmosphere. When emission calculations are reduced to only CO2eq the dynamics of the longterm effects disappear. Reducing CH4 emissions results in step reductions in the temperature response, but if the emissions are regarded as constant they should not be considered as directly corresponding to a certain emission of CO2.

• Reduced regional transportation and consumption activity (private, industry/trade and public sector) • Increased use of renewable fuels (e.g. biodiesel) for working machines in the forestry and agriculture, as well as heavy trucks • Evaluate methane reduction potential from wetlands and mires (and afforestation) • Increase/optimize CO assimilation in forests (biomass will be

5.4. The temperature response from the Nature sector The Nature sector in Jämtland should be seen as a part of the general earth system and also prior to the ongoing anthropogenic driven climate change affecting the atmospheric GHG balance. It is difficult to estimate to what extent recent land-use change has altered the fluxes of GHG. However, we note that mires (wetlands) and lakes are major CH4 emitters. It is important to be aware of the confusion of conceptions often seen (heard) in discussions, both in a regional and a national context, regarding ‘carbon sink’ referring to the accumulation of carbon, vs. total CO2eq balance. Mires are carbon sinks, thus accumulating carbon from a CO2 uptake, but part of this assimilated carbon is emitted as CH4. Taking the mires’ CO2 uptake in Table 32 of 634 kton, this corresponds to 172 kton C. The emission of 43 kton CH4 corresponds to 32 kton C. The carbon accumulation in the regional mires is, in other words 140 kton per year, but they are still negative contributors from a GWP perspective (using GWP20. If the GWP100 is applied, the contribution will still be negative but only 20% of that corresponding to GWP20). Again the AGTP response might be better to use, and looking at the graphs for the yearly pulse from the Nature sector in Table 4 and the continuous emission in Fig. 7, it is obvious that the time perspective is the most important to take into consideration. The fact that moose and deer emit methane does not necessarily mean we should look upon them as ‘environmental problems’ (but as for all ruminants, they contribute to global warming). It is the fate of all living creatures to be consumers in the ecosystems surrounding us. We have not estimated the losses of woody biomass growth from damage to young trees caused by moose and deer. Most methane emissions, such as those from the Nature sector, are not part of the regional GHG inventory analysis. In other words, the complexity will increase quite a lot if these emissions are to be included in the discussion. It is, however, a difference between a scientific understanding, and a policy driven political approach. From the understanding of the fluxes, it is an ethical and philosophical deviation which flows should be considered as belonging to the sphere of potential solutions. We should be aware that GWP-calculations using CO2eq imply an interchangeability between fluxes of CO2 and CH4

Strategies to reduce emissions from the private sector need to include deeper behavioural analysis. Most (not to say all) individuals in the region are probably somehow aware that emissions need to be reduced, but this intellectual insight does not necessarily mean people take action (or help us to find out what action to actually take). Half of the total emissions come from consumption (excluding consumption of accommodation and transportation). Food is a large contributor and there is a wide range of products and services which makes it difficult to pinpoint an obvious way to reduce the emissions, except for a general reduction of the consumption and transportation volumes. Motivators at an individual level for such reduction might be difficult to identify since abstract threats about future global warming are not enough. The time perspective needs to be taken into consideration when developing climate strategies. The short-term effects of methane emission needs to be penetrated deeper and evaluated in relation to the long-term effects of carbon dioxide. If we focus on the 10–20 year perspective (referring to the Paris agreement), alternative strategies might be required, compared to a situation where we apply a 100-year perspective. This is especially true when working out strategies to optimize assimilation of carbon dioxide in forests and reducing emissions of methane. Since the model quite clearly show emissions from each sector and further modelling runs the risk of being classified as “over-engineered”, it can be concluded that an expanded model of forest CO2 assimilation in biomass, for the time horizon of 10 to 30 years, is needed as an input to forestry management strategies. The present model does not cover dynamic changes and scenario analyses of tree growth.

6. Conclusions

Declaration of Competing Interest

With this regional carbon flux analysis, a first step to establish a knowledge platform for the sustainable (climate) management of Jämtland is taken. The platform is constructed from available data with their respective estimated uncertainties. The method applied also serves to indicate important gaps in our knowledge and may be used to set up and improve both research and political statistical reporting. If carbon dioxide equivalents are used as an indicator of sustainability, the Jämtland region can be regarded as sustainable. The model

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

2

needed to produce renewable fuels, but without reducing the GHG balance)

Acknowledgements This research is being financed by the EU Interreg Sweden-Norway program, project SMICE, a network for sustainable development as a 18

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regional cooperation between Trøndelag in Norway and Jämtland in Sweden. We would also like to thank those who have contributed with thoughts and comments to this study.

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