|
Update on Impact Categories, Normalisation and Weighting in LCA 3 Development of normalisation references for different geographic areas
In chapter 1, Introduction to the project, the basic ideas and principles in normalisation of impact categories are outlined. One of the basic ideas is that global impacts are normalised globally, regional impacts are normalised regionally and local impacts are normalised locally. In practise, however, it is not possible to obtain relevant data for specific regions all over the world. That is either because it is too big a job to provide them or because they simply do not exist. In this chapter a method used to predict normalisation factors from one geographical area to another is presented. The specific challenge has been to establish an approximation for a world value ("world proxy") for the following local and regional normalisation factors: acidification, nutrient enrichment (eutrophication), photochemical ozone formation, ecotoxicity and human toxicity. The extrapolation is relevant due to lack of worldwide emission data for the year 1994. The applicability of the outlined method is discussed for the local and regional impact categories. Global warming and stratospheric ozone depletion are global effects with a large amount of available data that can be used in calculations. Therefore, the normalisation factors are based on available global emission/estimates of consumption of ozone depleting substances and global emission of greenhouse gasses. A general extrapolation method is described by using normalisation references for acidification covering approximately 40 European countries (section 3.2) due to easy availability of emission data for substances contributing to the impact category (SO2, NOx, NH3). The present data have been used to identify relations between available acidification data and different technical and economical factors. The application of the outlined methodology is based on the assumption that the substances contributing to the other local and regional impact categories shows the same correlation to the tested factors. However, it is quite obvious that not all impact categories can be described equally qualified by the same parameters. This report provides for specific impact categories relatively good data or estimates for the World, EU and Denmark. However, in many cases it is not known where the actual environmental impact takes place. In these cases, a general proxy for the world might be just as good as the Danish and EU-15 normalisation references. At least such an option could be used as part of sensitivity analysis. The chapter is concluded with recommendations regarding the choice of normalisation references and with considerations of the advantages and limitations of the results obtained thereby. 3.1 General considerationsExtrapolation from countries/groups of countries to a larger geographic scale (e.g. EU-15/the World) has been discussed in other LCA methodology projects, and the conclusion - like in the present study - is that all extrapolation methods will be based on general assumptions and they are therefore never generally valid. The uncertainty introduced by using extrapolation is generally unknown and can only be verified by worldwide emission data i.e. by comparing results of extrapolation with real data. It was neither the aim nor practically possible within the economic frames of the project to develop a detailed or complex model for extrapolation. An extrapolation will always be uncertain regardless of the applied methodology and the uncertainty is assumed to increase as the complexity increase. The expectation is that almost all collected data will represent some uncertainty and by adding, multiplying etc. the uncertainty will be multiplied and thereby increased. Therefore the proposed methodology is based on as few factors as possible and the same method is used for all impact categories. The extrapolation is based on easy available quantitative or qualitative data i.e. data that are available for the whole world in one source if possible (e.g. World Bank reports, United Nations reports, OECD reports, EUROSTAT reports). Examples on available data are:
Examples on advantages and disadvantages with the different data are presented in the Table 3-1. Table 3-1
Of the potential parameters GDP per capita, GNP per capita and energy efficiency have the best coverage, worldwide. These parameters were therefore the first choice in the efforts to establish an extrapolation procedure, while the other parameters were investigated in less detail. The report focuses on GDP and GNP per capita in the discussion while for the other parameters, only the results of the correlation calculations are presented. The possibility of combining several parameters was not tested. It is possible that this may give a better correlation, but this was not investigated. 3.2 General extrapolation methodAcidification depends to a large extend on the emissions of NH3, NOx and SO2; for a detailed description of the impact category see chapter 7, Acidification. According to the CORINAIR 94 summary report (Ritter, 1997) the distribution between the above mentioned substances is 24%, 32% and 44%. The sectors responsible for the acidification in Europe are shown in Table 3-2. Table 3-2
Mentioned in descending order of importance in relation to acidification, the most important sector is seen to be combustion in energy and transformation processes, agriculture and forestry, land use and wood stock change, road transport and combustion in manufacturing industry. Information on the activity in these sectors is not available in statistical material covering the whole world but the three of the four most important sectors are closely related to industrial and economic activity. Economical activity and energy consumption are parameters that are often used as a measure for activity in the different sectors. This information is available in UN or World Bank statistics. The relation between acidification and a number of selected parameters has been tested by linear regression analysis. The parameters tested are:
Economical activity is measured as "gross domestic product" (GDP) and as "gross national product" (GNP). GDP measures the output of goods and services occurring within the domestic territory of a given country whereas GNP also includes foreign income. GDP is therefore supposed to be the best indicator to describe the activity in the above mentioned industrial sectors in a specific well-defined geographical area. Figure 3-1 and Figure 3-2 show potential acidification potentials expressed as sulfur dioxide equivalents (i.e. kg SO2-eq./year/capita) versus GDP/capita for EU-15 respectively Europe including the European part of Asia and Balkan (note: countries with zero values for acidification equivalents or GDP/capita are omitted in the plot and the regression line). Both parameters show very low correlation to acidification. The correlation coefficient R2 is determined to 0.1767 and 0.0052 respectively. The very low correlation with GDP/capita for Europe may be explained by lack of industrial activity or at least lack of reported activity in some of the middle and low-income countries. Figure 3.1 Figure 3.2 By this method the weighting of the single countries is set equal, i.e. the acidification potentials/capita for Luxembourg or Liechtenstein are equally important as the acidification potential for Germany, regardless that Germany contributes with approximately 20% of the total acidification in EU-15. An alternative method is to calculate the acidification potential for groups of countries where the grouping is based on income. This method weighs the acidification potential in relation to the population in the group based on income and is described in the following sections. The World Bank statistical material uses a grouping based on income expressed as GNP/capita resulting in four groups: high income, upper middle income, lower middle income and low income economies; see Table 3-3 for a general description of the different income groups. One argument for using GNP/capita for different income groups is that the level of economy somehow reflects the industrial activity (e.g. the consumption of fossil fuels). Another argument is that the World Bank statistics include average values for a number of other parameters for the same income groups. Figure 3.3 and Figure 3.4 show the acidification versus GDP/capita/income group and GNP/capita/income group respectively. The European countries are divided into groups according to the grouping made by the World Bank i.e. based on GNP/capita in 1997. Average GDP/capita is also calculated for groups based on the above-mentioned grouping in order to maintain consistency in the grouping. The average GDP/capita for the different groupings is based on GDP for the European countries actually assigned to the different income groups. The grouping is presented in Table 3-3 Table 3-3
Figure 3.3 The relation between acidification versus GDP/capita/income group and GNP/capita/income group is tested by regression analysis. The results are presented in Figure 3.4 and Table 3-4 and show that there is a relatively good correlation between acidification and the logarithmic value for both GNP and GDP per capita when the countries are placed in the four income groups. The best correlation coefficient (R2=0,9092) is seen for ln(GNP/capita) and a slightly lower coefficient (R2=0,7626) is seen for ln(GDP/capita). Despite the lower correlation coefficient, the GNP/capita was used in the subsequent calcaulations, the argument being that GNP is a better indicator of (economic) activity in a specific geographic area. Table 3-4
Figure 3.4 The methodology using grouping of countries according to their GNP/capita has the disadvantage that the representation of countries in the different groups are unequal. The number of countries is 20, 7, 8 and 3 in the high income, upper middle income, lower middle income and low income countries, respectively. The average GNP/capita (worldwide) and GDP/capita (worldwide) are both slightly above the average in upper middle income countries. These conditions have not been analysed further. Statistical data regarding consumption of fossil fuels/total energy and energy efficiency were also available in sufficient detail to perform a regression analysis. However, as shown in Table 3-5, the correlation obtained in this way was not as good when using either GNP or GDP/capita. Table 3-5
1 For definition of income groups se World Bank statistics 2 GNP data for 1997. 3 EU-15 except Germany and Luxembourg. Based on the calculations outlined in the previous sections it was determined to use GDP per capita, divided into four income groups, for the extrapolation calculations. In order to estimate the difference between use of GNP and GDP, the normalisation reference based on both papameters was calculated. Based on world population and average income in GDP/capita [4] at 4,515 US$/capita the world normalisation reference for acidification can be calculated to: 59 kg SO2-eq./year/capita Based on average GNP/capita at 5,130 US$/capita (1997) (World Bank, 1998) for the world the similar value can be calculated to: 60 kg SO2-eq./year/capita Both methods results in a world normalisation factor at approximately 60 kg SO2-eq./.year/capita but despite a lower correlation coefficient, the method using GDP/capita is preferred as GDP is more related to a specific geographical area than GNP. The presented method result in the normalisation factors for the different areas and the world as shown in Table 3-6. Table 3-6
1 See Table 3-3 for description of the countries representing the income groups. 2 GDP/capita. 3 GNP/capita. 4 Based on the equation shown in Table 3-4 the normalisation reference for Denmark can be calculated to 84 kg SO2-eq./year/capita. 5 Based on the equation shown in Table 3-4 the normalisation reference for EU-15 can be calculated to 79 kg SO2-eq./year/capita. 3.2.1 Proposed method and assumptionsBased on the results of the acidification scenario a general extrapolation methodology is outlined based on the following assumptions:
Based on the assumption that the relation between the normalisation reference for acidification for EU-15 and the world is the same as the relation for other local and regional impact categories, the relationship between the normalisation reference for acidification and for other local or regional normalisation references can be expressed mathematically as follows:
where Norm.refImpact.cat.,world is the worldwide normalisation reference for a local or regional impact category Norm.refAcid.,world is the worldwide normalisation reference for acidification Norm.refImpact.cat.,EU-15 is the EU-15 normalisation reference for a local or regional impact category Norm.refAcid,EU-15 is the EU-15 normalisation reference for acidification and if Norm.refAcid,EU-15 = 74 kg SO2-eq./year/capita and Norm.refAcid,World = 59 kg SO2-eq./year/capita then the worldwide normalisation references for local and regional impact categories can be expressed as:
The proposed extrapolation methodology can be expressed as:
3.3 Extrapolation applied to different impact categoriesThis section describes advantages and disadvantages of the presented extrapolation method in relation to the effect categories photochemical ozone formation, nutrient enrichment, ecotoxicity and human toxicity. 3.3.1 Photochemical ozone formationThe substances contributing to photochemical ozone formation (anthropogenic emissions of NMVOC, methane and CO) have been compiled worldwide and reported in the EDGAR database. The collection, processing and publishing of 1994 data are not available and therefore only data for 1990 were used in calculating of normalisation factors. Anyway, the worldwide emission data for 1990 were expected to give a more reliable result than using the extrapolation at the European data for 1994. The worldwide normalisation factor based on 1990 data has been calculated to: 22 kg C2H2/capita/year This value can be compared with the normalisation factor calculated by extrapolation, which is 20 kg C2H2/capita/year. Thus, for this impact category, there seem to be a good correlation between the calculated value and the value derived by the extrapolation methodology. 3.3.2 Nutrient enrichmentThe worldwide normalisation factor for nutrient enrichment has been calculated by using the presented extrapolation methodology. Nutrient enrichment differs from a number of the other effect categories as wastewater treatment, agricultural areas, agricultural praxis etc. influence the emission of substances contributing to nutrient enrichment. These conditions are not reflected in the GDP/capita and therefore a systematic error may be introduced by using the presented extrapolation methodology. The worldwide normalisation reference for nutrient enrichment has been calculated to: 19 kg N-equivalents/capita/year or aggregated as 95 NO3- - equivalents/capita/year. 3.3.3 Human toxicityThe proposed extrapolation method is very uncertain for the human toxicity impact category. The emission of toxic substances will be highly influenced by a large number of factors, which are not or poorly reflected in the GDP. For instance, it is not uncommon, that `heavy' and very polluting production facilities are placed in low income (low GDP) regions and the facilities are usually equipped with less pollution abatement devices compared to facilities in high-income countries. Further, the fuel types used for traffic and energy production may differ substantially (for instance in heavy metal content) as may the need for transportation and energy production. Altogether, GDP is a very rough extrapolation parameter, which ideally should be supplemented with a number of infrastructure parameters. The worldwide normalisation reference for human toxicity has been calculated to: 2.45*109 m3 air/capita/year (exposure via air) 4.18*104 m3 water/capita/year (exposure via water) 1.02*102 m3 soil/capita/year (exposure via soil) 3.3.4 EcotoxicityThe proposed extrapolation method is very uncertain for the ecotoxicity impact category. The emission of ecotoxic substances will be highly influenced by a large number of factors, which are not or poorly reflected in the GDP. For instance, it is not uncommon, that `heavy' and very polluting production facilities are placed in low income (low GDP) regions and the facilities are usually equipped with less pollution abatement devices compared to facilities in high-income countries. Altogether, GDP is a very rough extrapolation parameter, which ideally should be supplied with a number of infrastructure parameters. The worldwide normalisation reference for ecotoxicity has been calculated to: 2.82*105 m3/capita/year (etwc; chronic ecotoxicity via water) 2.33*104 m3/capita/year (etwa; acute ecotoxicity via water) 7.71*105 m3/capita/year (etsc; chronic ecotoxicity via soil) 3.3.5 UncertaintiesThe uncertainties have not been quantified for the worldwide normalisation reference for the individual impact categories but are expected to be considerable. There are several reasons for this. The extrapolation methodology has been developed for an impact category depending on three emissions (SO2, NOx and NH3), all of which are determined with a relatively high degree of precision. For the other impact categories, the information regarding actual emissions in the primary area, e.g. Denmark, are not of the same quality, and this introduces a general uncertainty. For the impact categories human toxicity and ecotoxicity, the number of substances potentially contributing to the impacts is very high and the actual emissions are only known in very little detail. At the same time, it is very difficult to relate the emissions to specific economic activities. It is therefore an open question to what extent the GNP and GDP actually can be used reflect environmental impacts and – accordingly – as an extrapolation parameter. The use of both calculated and extrapolated normalisation references should therefore be performed with great caution. As a minimum it should be investigated whether use of other normalisation references changes the overall conclusions of a LCA, e.g. by integrating these calculations in the sensitivity analysis. 3.4 Recommendations on selection of normalisation referencesThe strategy so far has been to normalise global impacts globally as well as regional and local impacts regionally, based on Danish conditions. In the present project, new normalisation references have been developed for global, regional and local effects. For global warming and stratospheric ozone depletion global normalisation references have been calculated. For photochemical ozone formation, acidification, nutrient enrichment, human toxicity and ecotoxicity Danish and European (EU-15) normalisation references have been calculated and worldwide normalisation references have been extrapolated. The intentions with updating and extension of the normalisation references are that:
Furthermore, the new set of normalisation references allows the user to choose a normalisation reference adjusted to a specific purpose. These new possibilities give occasion for choosing the main question being when and how to apply specific normalisation references. 3.4.1 When and how to apply specific normalisationFor use of the updated EDIP97 normalisation references, the following recommendations are given for normalisation of the LCA results:
The recommendations above reflect the inherent uncertainties and lack of knowledge in normalisation, especially if the step has a broad scope. Obviously, the more is known about the product (system) investigated as regards the geographical extent of its potential impacts, the more precisely the normalisation step will mirror the relative importance of different impacts. The recommendations are a modification of earlier recommendations for EDIP97, where Danish normalisation references were recommended for regional and local impacts. The suggested shift to the EU normalisation reference is justified by the better scope for many industrial products, well knowing that the absolute precision for specific products decreases in doing so. 3.5 ReferencesOlivier J.G.J., Bouwman A.F., van der Maas C.W.M., Berdewski J.J.M., Veldt C., Bloos J.P.J., Visschedijk A.J.H., Zandveld P.Y.J., Haverlag J.L. 1996, Description of EDGAR version 2.0: A set of global emission inventories of greenhouse gasses and ozone-depleting substances for all anthropogenic and most natural sources on a per country basis and on 1ox1o grid. RIVM report nr. 771060 002/TNO-MEP report nr. R96/119. Ritter, M. 1997, CORINAIR 94 - Summary Report - European Emission Inventory for Air Pollutants. Copenhagen: European Environment Agency. USBC 1996, Midyear world population 1950 - 1995. United States Bureau of the Census, International Data base. (Available at http://uastr1.math.umass.edu) UN 1996, Statistical Yearbook 1994. Data available as of 31 March 1996. Forty-first issue. United Nations, Department for Economic and Social Information and Policy Analysis Statistics Division. New York. World Bank 1998, World development indicators 1998. (Available at http://www.worldbank.org.) Appendix A: Data sourcesDatabases (paper) UN 1996, Statistical Yearbook 1994. Data available as of 31 March 1996. Forty-first issue. United Nations, Department for Economic and Social Information and Policy Analysis Statistics Division. New York. Databases (electronic) OECD World Bank World Development Indicators 1998 (available at CD-rom; extracts are available at http://www.worldbank.org) Organisations
Footnotes[1] Gross domestic product (GDP) measures the total output of goods and services for final use occurring within the domestic territory of a given country, regardless of the allocation to domestic and foreign claims. [2] Gross national product (GNP) measures the total domestic and foreign income claimed by the residents of the economy. [3] The World Bank statistics use 1987 as baseline in some of their surveys. [4] The total GDP is calculated to 25.284 1012 $ (UN, 1996) and a midyear world population at 5,609,678,819 in 1994 (USBC, 1996). The average GDP/capita equals to 4,515 US$/capita.
|
