فهرست های گازهای گلخانه ای ملی مبتنی بر تولید و مبتنی بر مصرف: مفهوم استونی
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|20708||2012||13 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Ecological Economics, Volume 75, March 2012, Pages 161–173
Two national greenhouse gas (GHG) inventories were prepared for Estonia: (1) an inventory that includes GHG emissions from the production of goods and services (i.e., commodities) within its national territory and (2) an inventory of GHG emissions occurring within and outside its national boundaries due to Estonia's consumption of commodities, whether produced domestically or traded bilaterally. The inventories included estimates of energy-related and non-energy-related carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions (converted to CO2-equivalent, CO2eq) associated with the production and consumption of commodities, grouped in three main sectors: energy, industrial processes and agriculture. Input–output (IO) analysis, emissions embodied in bilateral trade (EEBT) approaches and the basic methods of the 2006 IPCC Guidelines were used to perform the estimates. The results of the study illustrated that the total CO2eq emissions associated with consumption in Estonia in 2005 were 18% higher than those associated with production, primarily due to the net import of CO2eq emissions from countries outside of the European Union.
Two types of national greenhouse gas (GHG) inventories have been developed intensively during recent years. The first type of national GHG inventory, referred to as production-based (Peters, 2008), was established under the United Nations Framework Convention on Climate Change (UNFCCC (1992)) to (1) analyse the magnitude of each country's influence on climate as a result of its annual GHG emissions (i.e., direct GHGs, including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), the halocarbons (HFCs), the perfluorocarbons (PFCs) and sulphur hexafluoride (SF6), and ozone precursors,1 including carbon monoxide (CO), oxides of nitrogen (NOx), non-methane volatile organic compounds (NMVOCs) and sulphur dioxide (SO2)); (2) develop GHG emission-reduction targets (i.e., under the Kyoto Protocol (UN, 1998) and the Copenhagen Accord (UNFCCC, 2010a)); and (3) monitor progress towards achieving the targets that have been set up to mitigate human influence on the climate system. This type of inventory focuses and defines GHG emissions and removals occurring within the territory of a country due to production activities, which are aggregated into the following six sectors: energy, solvents and other product uses, industrial processes, agriculture, land use and land use change (LULUCF) and waste (IPCC, 1997). The second type of national GHG inventory, which is consumption-based (Peters, 2008), has its roots in the academic community and is becoming an increasingly important alternative accounting method for GHG emissions in the context of globalisation. Globalisation ‘breaks down’ the national boundaries of countries by integrating their economies and societies through the liberalisation of trade and the emergence of a worldwide production market. The latter promotes access to a wide range of goods and services for consumers in different parts of the world due to exports and imports between countries (i.e., international trade). International trade, in its turn, obliterates ‘GHG emission boundaries’. In other words, GHG emissions caused by the production of commodities for export in one country (i.e., country–producer) are accounted for as GHG emissions associated with a country–producer, and this country should attempt to reduce the emissions. However, the exported commodities are consumed in another country (i.e., country–consumer) that does not account for the GHG emissions associated with their production and uses the commodities without any ‘responsibility for climate change’. Therefore, considering GHG emissions associated only with production of commodities and omitting GHG emissions embodied in traded commodities leads to an incomplete understanding of the overall GHG emissions associated with each country on the global scale. The consumption-based inventory explicitly includes GHG emissions embodied in imported commodities and excludes GHG emissions associated with exported commodities. The results obtained by estimating GHG emissions using a consumption-based inventory reflect a more full and adjusted picture of the overall GHG emissions in relation to the actual living standards (i.e., consumption level) of a country. To date, numerous global-scale and individual case studies analysed GHG emissions estimated by production-based and consumption-based inventories. A brief overview of such studies was summarised in Wiedmann, 2009 and Wiedmann et al., 2007; a short list of the latest studies includes Chen and Chen, 2011, Davis and Caldeira, 2010, Edens et al., 2011, Lin and Sun, 2010, Muñoz and Steininger, 2010, Peters et al., 2011a, Peters et al., 2011b, Rodrigues et al., 2010, Su and Ang, 2010 and Su et al., 2010. On the whole, GHG emissions associated with production and consumption of commodities were evaluated and recorded for more than 110 countries. The results obtained in the framework of the global-scale studies make possible the quantification of economic and ‘GHG emissions’ linkages between countries and the identification of the main importer-countries and exporter-countries of GHG emissions embodied in international trade. The individual case studies evaluated for several countries, primarily the members of the Organisation for Economic Co-operation and Development (OECD) (including the 15 old member states2 of the European Union (EU27)), provide a basis for detailed analysis of the differences between GHG emissions that occur due to production and those that are associated with the consumption of commodities at the sectoral or the product level in these countries and provide a broader understanding of the factors (e.g., trade structure, volume, trade partners) that result in differences in the levels of the emissions. Hence, the global-scale and individual case studies reinforce and benefit each other and can be considered to provide a solid basis for the development of further climate policy. Estonia is one of the less studied countries. The GHG emissions associated with the production and consumption of commodities in Estonia were recorded only in the framework of global-scale studies (Bang et al., 2008, Davis and Caldeira, 2010, Peters et al., 2011a, Peters et al., 2011b and Rodrigues et al., 2010). No detailed estimated data on GHG emissions embodied in commodities imported and exported and no analysis of differences in GHG emissions on sector level between the production-based and consumption-based inventories were performed. However, Estonia, as a full member of the EU, ratified the Kyoto Protocol; the EU is taking the lead in establishing global agreement to minimise adverse effects of countries' activities on the climate and to implement domestic actions to achieve reductions in GHG emissions by each member country (EC, 2009). It is reasonable to assume that further rational climate policy should be developed based on sound knowledge and understanding of the objective situation. In the present study, we compiled national production-based and consumption-based inventories of three main gas emissions, CO2, CH4 and N2O for three main inventory sectors (energy, industrial processes and agriculture) of Estonia for 2005. For CH4 and N2O, emissions were converted to CO2-equivalent (CO2eq) emissions using the corresponding global warming potential of the 100-year time horizon provided by (IPCC, 1995) and established to be used under the Kyoto Protocol (UN, 1998). Specifically, we investigated energy-related CO2eq emissions caused by the combustion of energy sources (i.e., the energy sector) associated with the production, bilateral trade and consumption of commodities as well as those that occurred in the process of primary fuel extraction (i.e., fugitive emissions; energy sector) and those associated with the production and consumption of the primary fuels. In addition, we examined non-energy-related CO2eq emissions resulting from manufacturing processes and agricultural activities (i.e., industrial processes and the agriculture sector of national inventories) in the production of goods consumed in Estonia or exported abroad and emissions associated with the importation of these goods. The main principles of input–output (IO) analysis (Eurostat, 2008), the basic methods reported in the 2006 IPCC Guidelines (IPCC, 2006) and the main rules of the emissions embodied in bilateral trade (EEBT) approach (Peters, 2008) were employed in completing the inventories. The potential uncertainties and challenges associated with completing a detailed and accurate consumption-based GHG inventory of Estonia are also discussed in the present study.
نتیجه گیری انگلیسی
The present study contributes to current research that investigates the production-based and consumption-based GHG inventories of Estonia and analyses the differences between the emissions associated with these inventories (Bang et al., 2008, Davis and Caldeira, 2010, Peters and Hertwich, 2008, Peters et al., 2011a, Peters et al., 2011b and Rodrigues et al., 2010). The results of the studies completed to date differ, and the differences in the results can be explained by differences in the methods, including the level of sector disaggregation, geographical coverage and the choice of sources of CO2 emissions for analysis employed in the calculations. Bang et al. (2008) estimated that CO2 emissions associated with production were higher than emissions associated with consumption in Estonia in 2001. Their study relied on the EEBT approach and considered 57 commodities of 87 countries/regions; CO2 emissions due to fuel combustion and cement production were analysed. In addition to these two categories of CO2 emissions, Peters et al. (2011a) also considered CO2 emissions from gas flaring. In that study, the EEBT approach was employed with 57 commodities of 113 countries; the results showed that CO2 emissions associated with production were lower than CO2 emissions associated with consumption in Estonia in 2001. Peters and Hertwich (2008) used the EEBT approach with 57 commodities of 87 countries but focused only on combustion-based CO2 emissions. Their results showed an excess of consumption-based CO2 emissions over production-based CO2 emissions in Estonia in 2001. Davis and Caldeira (2010) and Peters et al., 2011a and Peters et al., 2011b used the MRIO approach to perform an analysis involving 57 commodities of 113 countries. Davis and Caldeira addressed only CO2 emissions due to fuel combustion, while Peters et al. additionally considered CO2 emissions from cement production and gas flaring. The results of both studies showed that CO2 emissions associated with production were lower than CO2 emissions associated with consumption of commodities in Estonia in 2004. In this study, the emissions of three main gases associated with the production and consumption of commodities, CO2, CH4 and N2O, were evaluated in terms of CO2eq emissions (IPCC, 1995). The study focused on energy-related CO2eq emissions associated with primary fuel production (i.e., fugitive emissions) and the combustion of primary and secondary fuels and on non-energy-related CO2eq emissions, i.e., those resulting from manufacturing processes and agricultural activities. The IO analysis, the EEBT approach and the basic methods outlined in the 2006 IPCC Guidelines were employed to estimate the emissions. Data on the production, import and export of commodities, measured in monetary and physical terms, were obtained from different statistical datasets. The results demonstrated that the total CO2eq emissions (i.e., energy-related and non-energy-related) associated with the consumption of commodities in Estonia were 18% higher than the CO2eq emissions associated with their production. The energy sector was the main contributor to emissions associated with production and consumption. The major share of CO2eq emissions embodied in Estonian exports was traded to countries with lower total energy-related CO2eq emission intensities arising from the production of commodities, mainly to the countries of the EU27, e.g., Finland and Sweden. On the whole, energy-related CO2eq emissions due to the production of commodities in Estonia play a special role because they are the highest energy-related emission intensities associated with commodities produced by countries of the EU27 (Table 2; Andrew et al., 2009). The CO2eq emissions embodied in Estonian imports consisted mainly of CO2eq emissions from Russia and included commodities associated with higher emission intensities. The differences between the two types of GHG inventories studied in this work lie not only in the values of GHG emissions associated with production and consumption but also in the evaluation of the total uncertainty values related to each type of inventory. The production-based GHG inventory requires less input data to estimate emissions, which can be obtained from national statistics with country- and technology-specific emission factors. The consumption-based GHG inventory requires more data that depends on international input, and this increases the level of uncertainty in determining the total GHG consumption-based emissions (Peters, 2008 and Peters et al., 2011a). Despite the high potential uncertainty, the practice of calculation of uncertainties related to input data and methodological choices is not introduced in the framework of global-scale or country-specific consumption-based GHG inventories. A number of studies have identified, described and discussed uncertainties related to consumption-based inventories (Hertwich and Peters, 2010, Lenzen, 2001, Peters, 2008, Peters et al., 2011a and Wiedmann, 2009). The present study contributes to the discussions provided in those studies and lists major important uncertainties. First, uncertainties are inherent in the IO tables because the process of development of the IO table assumes a linear relationship between input and output (Lenzen, 2001). Hence, technological, price and structural changes in the economy, which can occur, would have impact the results of the consumption-based inventory. Furthermore, the level of sector disaggregation of the IO tables chosen when performing the consumption-based inventory and harmonising its results with the energy data on primary and secondary fuels combusted brings additional uncertainties related to the GHG emissions associated with consumption. Su et al. (2010) have demonstrated that the difference in CO2 emissions embodied in exports, which affects the total consumption-based emissions, is approximately 12% in the case of 10 major sector aggregations and 40 sector (i.e., commodities) disaggregations applied in the IO tables. The data on energy combusted itself involves a number of uncertainties; these are mainly associated with missing and incorrectly allocated data and unrealistic outliers in time-series variations (Fujimori and Matsuoka, 2011). In the latter study, the authors argued that the error in the data on energy consumption could reach 35–45% for some countries. The choice of calculation approach between the EEBT and the MRIO primarily determines the differences in the total GHG emissions associated with consumption because of the different ways these two approaches treat data on imported commodities in intermediate production. The difference in consumption-based GHG emissions estimated using the two methods may reach approximately 20–30% for some countries (Peters, 2008 and Peters et al., 2011a). Moreover, the differences in GHG emission associated with the bilateral trade balances calculated using the both approaches may attain approximately 50–60% or even had negative or positive GHG emission values embodied in the bilateral trade for the same country (Su and Ang, 2011). However, the MRIO approach produces additional uncertainties related to imported commodities. Because the sectoral use of commodities imported by countries–producers within the economy of a country–consumer is not available, the adjustment of import data brings uncertainties (Peters et al., 2011a and Wiedmann, 2009). International trade data also deserve special attention. In the present study, data from the Eurostat datasets (Eurostat, 2010b and Eurostat, 2010d) were used. The quality of these datasets is generally high but is heterogeneous among the EU27 countries (Eurostat, 2010e). Several types of uncertainties are also important in this area, including uncertainties related to the classifications applied in the IO tables and trade datasets, the methodology used to report trade data (i.e., all goods that cross the border of a country are recorded as imports; in a similar manner, all outgoing goods are included in exports) and the method used to convert trade data expressed in national currency into a common currency, the euro (i.e., monthly averages of daily exchange rates or annual exchange rates are used for different goods) (Eurostat, 2006). In addition, Lenzen et al. (2004) have listed trade-related uncertainties due to “time lags between shipping of export and receipt of imports, reporting errors, losses due to accident in transits”. Perhaps most important to the total uncertainty in determining total consumption-based GHG emissions are issues related to optimal spatial disaggregation of the input data used in the consumption-based inventories (e.g., the IO tables, energy data, trade data, emission factors, and other data). To date, most studies have relied on country-average input data and emission factors. However, countries are diverse with respect to their primary and secondary fuel energy-mixes and technologies, technology processes and agricultural practices. Optimal disaggregation is especially important for large countries (e.g., China, the United States, Russia and Brazil), but small countries are not excluded (e.g., Slovakia, Estonia). Su and Ang (2010) present a good example of the difference in the total energy-related CO2eq emissions in exports accounted for by spatial disaggregation. The authors showed that the CO2eq energy-related emissions embodied in exports from China, a large country, based on an eight-region-model were approximately 14% lower than CO2eq emissions estimated from country-level average data. The understanding of uncertainty information is primarily needed in order to identify areas in which the inventory might need improvement. However, for further detailed and comprehensive development of a consumption-based GHG inventory that covers all GHGs released from all sources, the minimisation of uncertainties related to already available and used input data is not the only activity required. In particular, two sectors omitted from the study (i.e., the LULUCF and waste sectors), should be included in the analysis of GHG emissions. The consideration of non-energy-related CO2eq emissions associated with the LULUCF sector could make a remarkable difference in the calculated balance of GHG emissions between production-based and consumption-based GHG inventories. For example, Gavrilova et al. (2010) showed that the annual ‘import’ of CO2eq embodied in soybeans imported from Brazil and Argentina for use in Austria's livestock production was 1017 tCO2eq due to deforestation in these countries, an emission source of the LULUCF sector. This particular import contributed more than 30% of the total CO2eq emissions released due to domestic production of livestock and related products in Austria in 2000. Estonia also imported soybeans from Germany and the Netherlands (FAOSTAT, 2010), each of which imported crops from Brazil and Argentina. Although Estonia imported soybeans in lower amounts than Austria (e.g., 22,085 t versus 499,931 t by Austria in 2005), the non-energy-related CO2eq emissions embodied in the soybeans exported from Brazil and Argentina to Germany and the Netherlands could change the emission balance of Estonia, perhaps to a lower degree than that of Austria. Estonia also imported beef from Brazil and Argentina, countries in which the production of beef is considered a driving force for deforestation (Zaks et al., 2009). Hence, accounting for non-energy-related GHG emissions embodied in beef imports from Brazil and Argentina would alter Estonia's GHG emission balance. The examination of non-energy-related GHG emissions associated with the waste sector would contribute less to the difference between the two GHG inventories than inclusion of the LULUCF sector. GHG emissions released from the waste sector make up approximately 2–10% of the total GHG emissions of a country (UNFCCC, 2010b), and the major quantity of GHG from the waste sector consists of emissions from municipal waste disposal. The latter could be directly considered under the consumption-based inventory because the emissions are associated with consumption. The particular aspects of the energy, industrial processes and agricultural sectors that were omitted from the development of the method used in the present study to estimate energy-related and non-energy-related GHG emissions should eventually be addressed to make the consumption-based GHG inventory more complete, e.g., to include fugitive emissions due to the refining of petroleum products and solid fuel transformation, GHG emissions associated with the remaining (more than 30) products considered under the industrial processes sector and the agricultural sector (e.g., crops: legumes, fodder roots or dairy products such as butter and cheese). The estimation of GHG emissions due to the use of solvents and other products would be directly considered under the consumption-based inventory. Undoubtedly, accounting for non-energy-related and energy-related GHG emissions will additionally require the development of currently absent large datasets. In particular, accounting for non-energy-related GHG emissions calculated under the land use change sectors would require the development of large datasets on the influence of production of particular commodities on changes in land use practices (e.g., deforestation). In addition, the use of other methodological approaches, e.g., full carbon accounting (Gavrilova et al., 2010), is also advisable. In the waste sector, the GHG emissions released due to industrial wastewater generation associated with manufactured products (e.g., food and beverages, pulp and paper) could be estimated based on the tonnage-output of the products produced and bilaterally traded. Furthermore, accounting for GHG emissions released due to industrial waste disposal would also require large datasets on the type of solid waste generated in the production of each commodity within a given year, permitting use of the first-order decay method in calculating the emissions from the landfills (IPCC, 2006). The estimation of energy-related and non-energy-related GHG emissions associated with the items omitted from the present study could be based on existing datasets on energy statistics (Eurostat, 2010c) or on records of products produced, imported and exported in physical terms recorded under the dataset of the Community Survey of Industrial Production (Prodcom, 2010). Moreover, the examination of all sources of energy-related and non-energy-related GHG emissions could be completed using both the EEBT and MRIO approaches. This allows the transparent analysis of GHG emissions associated with the bilateral trade balance and detailed examination, considering GHG emissions embodied in (re)exported commodities. In summary, because the further development of the GHG inventory system is a very important tool in implementing successful action to mitigate human pressures on the climate system, all of the abovementioned steps should be taken in order to estimate GHG emissions in internationally traded commodities more accurately and to establish consumption-based GHG national inventories for Estonia and other countries.