Reducing Uncertainty in LCIAnnex A. Analysis of causes and size of variation of the environmental exchanges in some important sectorsThe different sections in this annex have been written by different authors and may therefore appear somewhat uneven. The main purpose has been to give a survey of available information that could highlight the causes and scale of variation. We have therefore drawn on many different sources and we have not always followed the information back to the original source. The analysis was performed from 1998 and up to the end of 1999. It therefore does not include sources of information that have become available after this date. All litterature references are included in the reference list of the main report. We have selected several of the most important trade sectors for closer analysis. The selection has been based on the environmental importance of the sectors, either as important energy consuming sectors (e.g. the steel industry), important emitters of priority emissions (e.g. VOC’s from petrochemicals) or as determinants for other sectors (e.g. design decisions in the building sector determines energy consumption and production levels of several important material producing sectors). Within each trade sector – and depending on the available data – the analysis seeks to:
The following sectors were examined: Regarding the terminology applied, please see annex B. A.1 Agriculture (CPA 01) Agriculture consumes 3-4% of the primary energy consumption in industrialised countries, and is also important from an environmental point of view, because of its large area use and the many diffuse losses of its inputs (notably fertilisers and agrochemicals, but also carbon in the form of CH4 and CO2). Agricultural inputs (both fertilisers, agrochemicals and use of machinery) are mainly related to the cropping area, and not to the product output6 (yield). This means that a large source of the variation (in inputs and emissions) measured per produced unit is a simple reflection of variations in yield per area. Thus, the variation in agricultural processes is composed of both variation in yield, variation in inputs for the same (average or expected) yield, and variation in emissions to the environment for the same input. In the following sections, these three kind of variations are listed together with the main causes of variation. Besides the obvious variation between different crops and animals (section A.1.1), agriculture is more sensitive than industrial processes to differences in natural conditions such as climate, soil conditions, and availability of water resources (section A.1.2). Differences in economic resources, legislation, and management practices also contribute to a large variation between regions (section A.1.3). Even between farms in the same geographical area, with the same crops, the variation can be very large (section A.1.4). A.1.1 Variation between different crops under the same conditions Since different crops have different optimum conditions, it is not always meaningful to make general comparisons of the yields of different crops. Because of the inherent variation in yield potential, an extrapolation of expected yields from one crop to another, even under the same conditions and within the same group of crops, e.g. grains, can give uncertainties of +/-50%. For inputs and outputs, similar variations should be expected. In a Danish study (Nielsen & Sørensen 1994), total fuel use for field operations varied from 30-170 litres per ha with grass seed in the low end and beet crops in the high end. For grain, a variation between 65 and 100 litres per ha was found. Statistical data allocating fertiliser-N applications to different crops have standard deviations ranging from 0.5% for grain to 6% for vegetables, caused by differences in the sample sizes for different crops (Webb et al. 2000). For animal manure, estimates of application to different crops are uncertain by at least a factor 2. Nutrient emissions are not directly related to inputs, since it is primarily the nutrient surplus, which is lost to the environment. For example, Lor d (1992) report losses in grain crops of 7% of the nitrogen below optimum, but 50% of the surplus N. The same figures for potatoes are 22 and 80% showing the significance of the crop type. Methane emissions from animals with enteric fermentation range from 3 to 8% of gross energy feed intake, but under most conditions vary only from 5.5 to 6.5% of gross energy feed intake (Johnson et al. 1996). However, gross energy feed intake also varies per unit of product and depends largely on the health and feed status of the animals. In cattle on poor quality forage, a number of essential microbial nutrients may be deficient and under these conditions, reductions in animal methane emissions with a factor 3 (for milk production) to 6 (for meat production) has been obtained by improving the quality and nutrient balance of the animal diets (Leng 1993). This is especially relevant in non-industrialised countries. A.1.2 Variation due to differences in natural conditions (climate, water availability, and soil) The same crop will give different yields under different climatic conditions. The main determining climatic factors are solar radiation, temperature and rainfall. Even between regions in a small country like the Netherlands, Blonk et al. (1997) report that solar radia tion is responsible for +/-4% differences in yields. Climatic variation should also reflect itself in annual variation. However, in a study of 20 Danish dairy and pig farms over three years using 10 environmental indicators, Halberg (1999) only found significant yearly variation in one indicator (energy use per kg grain) due to differences in the need for irrigation. Differences in the need for irrigation can be the cause of large variation in energy consumption (+/- 8%; Lindeijer & Meeusen 1998). Soil type may be an even more important factor in explaining variation, also between productions in the same climatic region. The maximum constraint-free yield in different land classes varies +/-40% (Alexandratos 1995). Blonk et al. (1997) report yield differences of +/- 8% between soils in the Netherlands, and much larger differences in nitrogen consumption patterns and emissions (+/- 30%) as well as in pesticide use +/-18%. Also Webb et al. (1997) found soil type to very important in determining both nitrogen demand (+/-25%) and nitrogen emission. If the soil type is unknown, variation may be up to a factor 2 on average leaching values. Nitrogen leaching for a specific N input and a specific crop depends mainly on soil water holding capacity and (winter) precipitation. When these parameters are used in modelling of nitrogen leaching, the model output values are estimated to be correct within a range of +/-10%. A.1.3 Geographical variation in economic and legislative conditions and management Access to adequate amounts of agricultural inputs may be constrained in regions where farmers have less beneficial economic conditions. Legislation may also constrain inputs (especially nitrogen and pesticides) leading to consequent reductions in output. Finally, differences in the educational and advisory services may lead to different management practices in different regions. It is difficult to separate what the part of an observed geographical variation is due to natural conditions and what part is due to economic, regula tory and management differences. A rough estimate is that a variation of +/- 25% should be ascribed to the latter causes. A.1.4 Variation between farms and variation not treated elsewhere In Denmark, average yields per area have been found to increase with increasing farm size (Statistics Denmark 1996). Fuel consumption for a specific field operation is influenced by many factors, e.g. type and structure of the soil, weather conditions, earth moisture, landscape, crops, tractor type (2WD/4WD), tractor size, relation between tractor and implement, driving technique, tractor driver, etc. Nielsen (1989) reports fuel consumption measurements for many specific field operations in Denmark given in litres of fuel per hour or hectare with coefficients of variance generally between 10 and 30%. This is confirmed in Nielsen & Sørensen (1994), where also similar data for harvesting and field spraying are given, with coefficients of variance of 40% and 80%, respectively. It should be noted that the studies were performed on fairly flat terrain and uniform soils. Thus, variation would probably have been larger if a larger variation of climate, soil and landscape types had been included. For harvesting of grass, Nielsen & Sørensen (1994) found fuel consumption to vary from 6.0 to 10.6 l/t of dry matter depending on the harvesting technique. In spite of the large variation on fuel consumption, averages for an entire country calculated by models using the above values showed very good correspondence (within a few percent) to total fuel consumption for the agricultural sector. Nitrogen surplus was found by Dalgaard (2000) to vary less than +/-10% between cattle farms, but more than +/-30% between pig farms and between farms without animals. On animal farms, the variation was closely correlated to the amount of animal manure applied. Dalgaard also found that variation between management practices (organic/conventional) was an important explanatory factor. For fertiliser use, large differences in efficiency can be observed. Worrell et al. (1994b) estimated improvement possibilities, reducing the present use of 220 kg N/ha in the Netherlands to 128 kg/ha, mainly by larger adherence to fertilisation recommendations and improvements in maintenance of fertiliser spreading equipment. However, such large amounts of fertilisers are not used in general. With a European average of 67 kg N/ha only a 10-25% improvement in efficiency can be expected, unless more widespread use of biological N-fixation is introduced (Gielen 1997). Ammonia losses depend mainly on the type of fertiliser applied and the way it is applied, and to a lesser extent on soil type (soil pH for mineral-N, infiltration rate for manure-N), meteorological conditions, and time of application in the cropping cycle. Estimates of ammonia emissions typically lie in the range of 10-40 kg per ha with an estimated uncertainty of +/-30% for manure-N and +/-50% for fertiliser-N (Webb et al. 2000). Current emission factors for N2O are ranging from 0.25 - 2.25% of N inputs (Bouwman 1996). The magnitude of N2O emissions depends on the form of fertiliser applied, the crop type, soil temperature and soil moisture content. However, it has not yet been possible to derive different emission factors for different fertilisers, crop or soil types. NO emissions depend mainly on the mineral N concentration in the soil and Skiba et al. (1997) estimate emissions ranging from 0.003 to 11% of applied fertiliser-N, with a median of 0.3%. A.1.5 Conclusion The variation in the yield is generally smaller, the more that is known about the crop type, the climate, and the soil. The variation in the data for inputs and emissions can only be significantly reduced when more detailed knowledge is available on the individual farm practices, notably fuel use, fertiliser type, and manure handling. This makes it difficult to provide a general guideline for reducing uncertainties in agricultural data for life cycle assessments. A.2 Mining and quarrying (CPA 10-14) For mining of raw materials, there is a parallel to agricultural products, in that the inputs and emissions are not linearly related to the yield. Thus, the yield and ore grade plays an important role in the variation of inputs and emissions per produced unit. Different minerals are mined at different concentrations (with overburden and gangue weighing from 0.1 to 3000 times the mineral or metal content) and from ores of different composition and degrees of contamination (giving different types of waste) and using different benefication methods and chemicals. Energy consumption data from van Tuinen & Moll (1992, cited in Daniëls & Moll 1998) range from 10 MJ/kg for boron, chromium and manganese to 1200-1800 MJ/kg for cobalt, silver, thallium, yttrium, and zirconium, with outliers of 3.5 MJ/kg for arsene and 6 GJ/kg for beryllium and 70 GJ/kg for gold. Thus, it is seldom meaningful to extrapolate from one mineral or metal to another, and even for the same mineral or metal, the use of average values may be questionable. The same mineral or metal may also be mined from different ore grades. For example, bauxite is presently mined in concentrations from 25 to 60%, copper in grades between 0.3 and 2%, and potassium in grades from 9 to 30% K2O-equivalents. As a result, energy consumption may vary +/- 50% between mines and the use of grinding materials and flotation chemicals vary by a factor 2 to 5 (Daniëls & Moll 1998). Also, the same mineral or metal may be mined from open or closed (underground) mining, using different amounts of explosives, causing different amounts of overburden, and different reclamation methods (propping up or levelling and replanting), and using different benefication methods (e.g. KCl is separated from the ore by either recrystallisation or flotation, the latter being less energy demanding, but not always applicable depending on the ore type). Sulphur dioxide is a common by-product from mining, and may in some cases be emitted (mainly in non-industrialised countries), in other cases recovered with efficiencies ranging from 96 to 99.8%. Similarly, particulate removal may vary from 20 to 99%, mainly depending on local regulatory demands and the age of technology. Carbon-gasses (CO2 and CH4) are released from some mining operations. For potassium mining, Gielen (1997) cite a variation from 30-100 m3 gas per Mg K2O-equivalents. The composition of the gas (i.e. the distribution between CO2 and CH4, which is very important for its climate forcing potential) may also vary. From coal mining, Frischknecht (1996) cites OECD for a range of methane emissions from 10-20 cubic metres per Mg extracted coal ore. Up to 90% of these emissions could be recovered by adequate measures, but in practice less than 30% is recovered. For oil and gas drilling, Frischknecht (1996) reports a diesel consumption per metre between 25 and 200 litres for onshore drilling and between 250 and 1250 litres for offshore drilling. The drilling depth varies from 1000 to 5000 metres. Natural gas is found together with oil in quantities ranging from 100 to 600 m3 per 1000 kg oil and 0.05-1% of this is vented and 10-20% flared in the field, while 40-85% is utilised as net natural gas production (Frischknecht 1996). For pure gas fields, the flaring is only 0.2-0.3% of the produced quantity. The variation is geographically dependent and may therefore be substantially reduced with knowledge of the geographical location of the oil field. A.3 Food industry (CPA 15) Vis (1996) investigated datasets from 80 plants under the Unilever food industry and found coefficients of variance of 50% for energy consumption and 220% for COD and other emissions. Even in sub-sets (e.g. for margarine factories only) similar variation in data was found. Vis suggests that the cause of variation is mainly due to differences in management, since the technology is assumed to be fairly even within this one company. A.4 Textile industry (CPA 17-18) This sector has a relatively high water consumption and consumption of chemicals compared to the size of the sector (measured in turnover or employees), while the energy demand is relatively low. Synthetic fibres and cotton fibres constitute about equal percentages and the majority (app. 90%) of the total world production (42×109 kg fibres) in 1994. The consumption of textiles (production + import - export) in Western Europe constitute app.15% and the highest consumption is observed in Asia (excl. China/Taiwan), corresponding to app. 50% of the total consumption of textiles (Körner et al. 1998). The main exporting countries (fabric and apparel) in 1992 were Hong Kong, China, Italy and Germany (Körner et al. 1998). For products on the Danish market the main exporting countries are China, Portugal, Italy and Hong Kong (Pedersen 1992). Imported textiles constitute app. 64% of the Danish market in 1997. It is not possible to obtain statistics on the distribution on different fibre types, but Denmark is known as a cotton and polyester country (Laursen 1998). More than 50% of the dyeing in Denmark is cotton dyeing (Wenzel et al. 1998). The variation in energy and material consumption as well as in emissions is related to:
A.4.1 Variation in the raw material phase The fibres (raw materials) used in the textile industry have very different origin. The fibres may be divided into three main types:
Natural fibres, e.g. cotton and wool, have the ir origin in the agricultural sector, the manufacture of viscose is part of the pulp and paper sector, whereas the manufacture of the synthetic fibres is a part of the petrochemical sector. Therefore, very different processes are involved in the manufacture of textile fibres. The consumption of energy and materials as well as the nature of emissions to the environment varies accordingly. When comparing e.g. the energy consumption among the fibre types a range of 8 MJ/kg (wool) to 158 MJ/kg (acrylic) of energy is required in the production of raw material (Laursen & Hansen 1997). Thus, a very important factor in reducing the uncertainty on textile data is knowledge of the fibre type (raw material) of the studied object. Examples of the variability and nature of consumption and emission from each of the 3 sectors is summarised below in tables A4.1-4 Table A4.1.
Table A4.2.
Table A4.4.
The above tables demonstrate the importance of knowledge of the fibre type. Within each of the 3 types (natural, artificial and synthetic), specific uncertainties can be identified and the variation may be attributed to very different causes as discussed in chapter 2 of the main report. The dominant causes of variation for natural fibres lies within differences between natural regions: Climate, soil type and raw material quality and secondly on differences between administrative regions, predominantly labour costs. For the man-made fibres, geographic variation is primarily related to differences in administrative regions: regulatory differences (environmental taxes, threshold on emissions), processes and differences between plants: age of technology, effectiveness of control. A.4.2 Textile production The use of technology in textile sector is relatively low, except for manufacture of technical fibres. The sector is not technology leading and the basic technologies are the same world-wide. However, several different processes are involved in the production phase of textiles. They may roughly be divided into:
Energy consumption (electric and thermal) and water consumption in the different processes are summarised below in table A4.5. Table A4.5.
1 It is not possible to distinguish between wet or dry finishing 2 Based on 30 wash cycles The overall variation in energy consumption (electric and thermal) when the process is not specified at all may, as seen from the table, be app. 28 to 102 MJ/kg (0.6 to 22 MJ/kg for the dry processes, 6.5-30 MJ/kg for wet processes). For the dry processes, it is not essential to distinguish between different fibre types because the fibres may undergo essentially the same processes, depending on the requirements/purpose of the end use of the textile. The variation in dry processes is primarily explained by the specific process technology involved in the production (e.g. knitting or weaving). For wet treatment, on the other hand, variation depends on both the specific processes involved, e.g. batch or continuous, and the fibre type with associated production lay out. Thus, knowledge of the specific processes and production layout is essential to diminish uncertainty. Other less important causes for variation are:
Geographic variation in consumption and emissions for comparable technologies (processes) and products are mainly related to legislation/environmental costs and labour costs. Pollution prevention measurements and cleaner technologies are generally applied as a result of regulatory demands and a higher degree of automation if the labour costs are high. Cutting introduces a remnant (spill-percentage) of fabric/textile from 6-25% (Laursen & Hansen 1997). The amount of remnant is strongly related to the complexity of the product e.g. bed linen vs. trousers and shirts. As a consequence, the geographic variation is considered to be low. Labour costs has a significant influence on the degree of automation in the making up of textiles. Variation in energy consumption for making up may thus vary geographically caused by different labour costs. To reduce uncertainty related to the production of textiles, it is necessary to have a detailed insight in the layout and specific technologies involved in the production process. A.4.3 The use phase: care and maintenance During the use of textiles the most important variation in energy consumption is caused by consumer behaviour i.e. preferences for laundry/cleaning (wash vs. dry cleaning, machine wash vs. hand wash). Consumer behaviour may be explained by:
The energy consumption of an appliance (washing machine, dryer) is determined by product features (programmes available), consumer behaviour (frequency of wash) and situational aspects (inlet temperatures, used detergent etc.). In a study by Siderius et al. (1995), the specific energy consumption between different efficiency classes of washing machines on a European level varies between 0.159 to 0.560 kWh/kg (60°C cycle), while the country specific averages have been calculated as 0.26 kWh/kg and it is stated that are in the range of ± 10%. In the use phase the availability of resources such as energy and water as well as the cost resources may vary between natural and administrative regions. Also, customs and culture may cause a substantial variation, due to differences in preferences for cleaning methods (laundry vs. cleaning; machine vs. wash by hand). A.4.4 Temporal differences According to Wenzel et al. (1998), the technological development within the textile sector is related to competition on the market. The most important parameters for development of cleaner technology is as a consequence related to:
The development of cleaner technology includes both technological development and development of dyes with enhanced performance. The factors mentioned may also be driving forces for implementation of cleaner technology/new technology in the textile industry. Due to an increase in international agreements on environmental issues regarding e.g. emissions it is predicted that legislative demands and regulatory instruments slowly will be harmonised on a global level. Therefore, a push towards development and implementation of cleaner technologies in the textile sector may be expected on a global scale. Variation in consumption and emission of energy, water and chemicals may in consequence diminish, when comparing the individual technologies. However, the variation between different processes is likely to remain. Concerning labour costs, there is a trend towards an increase in the degree of outsourcing (specialisatio n) and production in countries where the labour costs are low. Ahrens (1998) states that for Danish companies there is an ongoing process towards (in order of present importance):
Labour costs often determines the geographic location of production (c.f. Ahrens 1998) and the technology choice in the textile sector. Labour costs are closely related to the standard of living and the GNP (gross national product). The development in standard of living in less developed countries may be expected, but the time scale of these changes can not be established at present. Variations caused by differences in labour costs may, therefore, be expected to persist over long time. A.5 Wood and wood products (CPA 20) (Based on Hekkert & Worrell 1998) The main energy consuming process in wood products manufacture is drying. Besides the difference between hardwood and softwood, where the energy requirement for drying is approximately the double for hardwoods, factors influencing the variation are mainly the initial water content and the degree of heat recovery. Modern drying plants with heat pumps and variable speed can have an energy use of 60% compared to older equipment. The most common drying technique is using ventila tion with hot air. This is rather energy in-efficient, but the more energy-efficient closed systems are based on electricity and their primary energy consumption is therefore typically in the same range as the open systems based on thermal energy. Other techniques, both the very low-intensive outdoor drying and the high-intensive techniques, such as vacuum or infrared drying, are little used either because of quality problems or high energy requirements. The sawmill industry in Europe is characterised by many small mills with limited ability to install new technology. Durability may differ between tree species and depending on wood preservation technologies. Wood preservation have important toxicological aspects and it is therefore an important information whether (and how) the wood has been preserved. Durability (and suitability for different purposes) may also be increased by modern wood modification techniques, such as high-pressure steam compression. For wood based panels, figures for the energy consumption from different authors agree within less than 10%. Wood based panels include different amounts of glue (from 2% for plywood to 6-9% for particle boards) and the gross energy requirement of the glues differ from 70 to 140 MJ/kg. The average is probably in the very low end of the stated intervals. A.6 Pulp and paper (CPA 21) With 4% of the industrial energy consumption, the pulp and paper industry is the fourth largest energy consuming industry. De Beer et al. (1998) report ranges of energy consumption of 2.3-8.6 MJ heat and 0.36-1.0 kWh electricity per kg paper. The variation mainly depends on differences in energy efficiency of the mills rather than on differences between paper types, although obviously some paper types require more processing than others. The drying process consumes about 90% of the heat, while the electricity is mainly used for pumping and ventilation. If ozone is used for bleaching, this is often produced on-site, resulting in a 5-10% increase in electricity consumption (Gilbreath et al. 1995 cited in Phylipsen et al. 1998). Worrell et al. (1994b) and Farla & Blok (1998 cited in Phylipsen et al. 1998) show that national averages of energy efficiency in 1988 was less than 25% above best practice in most European countries and Japan, while Sweden was 50% above and for Australia, Denmark, Greece, the U.K., and the USA, the energy consumption was approximately 100% above best practice. For Denmark, Greece, and the U.K., this can probably be ascribed to small mills with low capacity utilisation, while for Sweden, Australia and the USA the cause is less obvious. It may be related to the local availability of cheap energy sources. Hanssen & Asbjørnsen (1996) analysed emission data from 27 sulphate pulp mills in Sweden and found differences between the mills with coefficients of variance between 34% (for NOx) over 50-60% (for BOD, COD and Tot-P) to approximately 100% (for SO2, AOX and Tot-N). The emissions were generally reduced over the studied 8-year period, but the variation between mills was not reduced significantly. The coefficients of variance within one plant from year to year were much lower, generally 20-50%. Only for AOX, which is specifically related to chlorine bleaching, which is a technology being phased out during the investigated period, the within-plant variation was still high. The authors conclude that differences in management is a more important cause of differences between plants than differences in technology. Contrary to expectations, Hanssen & Asbjørnsen (1996) found integrated mills (producing both pulp and paper) to have less emissions per kg pulp than stand-alone mills. Hekkert & Worrell (1998) suggest that when the pulp is produced at a different location than the paper, an additional energy consumption for pulp drying of approximately 25% should be needed at the pulp mill, while de Beer et al. (1998) claim that there is no significant differences in specific energy consumption between integrated and non-integrated mills. A.7 Oil refining and petrochemicals (CPA 23.2, 24 and 25) The chemical industry accounts for 10% of the primary energy consumption in Europe (half of which is consumed in the petrochemical industry) and a major part of the VOC emissions. A.7.1 Oil refining The energy requirement of a refinery depends largely on the complexity of its product mix. Simple distillation and thermal cracking will take 2-4% of the feedstock input, while a refinery producing more complex products may require up to 10% of the feedstock input. Thus, a comparison of gross refinery data is not meaningful. The crude distillation unit is the largest energy consumer of a refinery. Its energy consumption may be influenced by the crude oil type with as much as 25% corresponding to 4-8% of the total primary energy consumption of the refinery operations. Since no data are available on the crude oil types used, nor on the specific energy consumption of specific processes within the refineries, this uncertainty cannot be further reduced. An allocation of the overall energy consumption of a specific refinery to the individual processes is discussed in Worrell et al. (1994b) and Frischknecht (1996) resulting in allocation factors based on weight from 0.5 for the heavy fractions (bitumen and diesel) to 2 for the lighter fractions (gasoline and naphta). The age of the refinery and its technology is of importance. Especially, the degree of heat recovery and co-generation is important. A variation of +/-50% can be deduced from the data reported in Frischknecht (1996). Variation in national averages in the EU was below +/-10% with the exception of Ireland, which had an average 50% above the EU average (Phylipsen et al. 1998). Part of the reported variation (up to 30%) in statistical data may be due to different interpretations of the data on crude oil input and product mix (Phylipsen et al 1998). The VOC emissions vary between 0,25 and 2 kg/Mg refinery-output (based on Deslauriers (1996) and Frischknecht (1996). These emissions should also be allocated among the individual processes and products of the refinery. For this purpose, it may be useful to note the following information from Deslauriers (1996):
This information may be combined with the fact that evaporation is larger for the lighter fractions, which in general are also more processed, giving allocation factors from 0.3 to 1.2 for the different fractions. A.7.2 Steamcracking The energy consumption of the steamcracking depends on the feedstock. Measured per product output (olefines, aromates and fuels) the energy consumption increases from 6MJ/kg for the heavier to 12 MJ/kg for the lighter feedstocks. Furthermore, the temperature of the process has an influence on the energy consumption. Since the product mix also changes with the feedstock and temperature, and energy consumption is typically reported in relation to the main output (ethylene) only, it is difficult to compare energy consumption data for processes operating with different feedstocks and temperatures. Differences in feedstock and temperature are for a large part geographically dependant (e.g. naphta is the main feedstock in Europe, while ethane is the main feedstock in the USA). However, these differences only explained 1-2 MJ/kg of an observed 4-5 MJ/kg difference between West Germany and the East European countries (Phylipsen et al. 1995). The remaining 3-4 MJ/kg may be explained by a lower degree of process integration and waste heat recovery, but up to 2MJ/kg could also be caused partly by lack of retrofitting and a higher share of small plants in the East European countries. For the same feedstock and temperature, Phylipsen et al. (1995) estimate the causes of variation as follows:
A.7.3 Polymerisation For the polymerisation of olefins several processes are available. The energy requirement depends on the polymer (from 1.5 to 7 MJ primary energy per kg and polymerisation of PET and PUR even being exothermic) and to lesser extent (+/- 15%) on the process (Joosten 1998). For PVC the differences between processes are reported to be much larger, but also connected to quality differences. A.7.4 Plastics processing Energy consumption for plastics processing may vary from the low energy consuming processes like extrusion (1.4-1.9 MJ electricity per kg plastic) over injection moulding (3.2-7.2 MJ electricity per kg plastic; smaller items being more energy intensive) to thermoforming (13 MJ electricity per kg plastic). (Joosten 1998 based on Novem 1997) A.8 Chlorine (PRODCOM 24.13.11.11) Chlorine is produced together with sodium hydroxide and hydrogen by electrolysis of sodium chloride. Three different technologies are used (mercury, diaphragm and membrane processes) which differ widely in energy consumption and environmental exchanges. Thus, average data are not representative of any of the specific processes and it is not meaningful to express the variation between the technologies as an uncertainty. The membrane process, which has the lowest energy requirement (9.2-9.8 MJ/kg Cl2 against the 11.8 MJ/kg for the mercury process and 12.5 MJ/kg for the diaphragm process at 50% NaOH concentration) and lowest environmental impact, is presently replacing the older technologies (Joosten 1998). Variation between country averages of specific energy consumption may also be explained by differences in the degree of penetration of these technologies (Phylipsen et al. 1998). The variation of the energy requirement of the membrane process is mainly due to differences in current densities and cathode overvoltage, where technological improvements are still made (Joosten 1998). A.9 Sulphuric acid (PRODCOM 24.13.14.33) Sulphuric acid is produced from elemental sulphur with a net export of steam of between 0 and 6 MJ/kg H2SO4. The variation is mainly due to differences in the age of the plant (Kongshaug 1998). A.10 Phosphorous, phosphoric acid, and phosphorous fertilisers (CPA 24) Phosphorous is produced from phosphorus carbonate ore with concentrations from 30 to 40% P2O5-equivalents of phosphorous. The ore is sintered at 1000 °C, mixed with a SiO2 source (gravel) and coal and melted in an electric oven resulting in the process: 2 Ca3(PO4)2 + 6 SiO2 + 10 C ® 6 CaSiO3 + 10 CO + P4 This process is very energy consuming (approximately 85 MJ/kg P). Part of the energy used in this process (up to 25 MJ/kg P) can be recovered when P4 is oxidised to P2O5 (Kongshaug 1998). High-purity phosphoric acid (H3PO4) can be produced from P2O5, but the normal production route is the wet process: Ca3(PO4)2 + 3 H2SO4 ® 3 CaSO4 + 2 H3PO4 Different wet-process routes exist, where the least energy consuming leave more contaminants in the gypsum, which is emitted or deposited (radioactive contaminants make the gypsum unusable for other purposes). A.11 Soda (PRODCOM 24.13.33.10) Soda (Na2CO3) can be produced from NaCl by the Solvay process resulting in low density (light) Solvay soda or from NaOH resulting in high density (heavy) soda. The latter process is significantly less energy consuming (3 MJ/kg versus 9 MJ/kg for the Solvay process). Also, the Solvay process requires as a raw material the energy intensive quicklime (see section A.17) to form CaCl2. A.12 Alkaline silicates (PRODCOM 24.13.52) Fawer (1997) report ranges for energy and raw material consumption for 5 different production routes for sodium silicate at 13 plants covering 93% of the European production of alkaline silicate. For energy consumption, the ranges vary from 40% (for mixing and melting) to 135% (for spray drying) of the average values. The ranges tend to be skew (e.g. -13%/+27%, -42%/+93%), although the number of plants is too small to make general conclusions. Fawer suggests that the causes of the uncertainty are differences in capacity utilisation and age of technology. Raw material use is ranging from 0-1.5% of the average for most processes, up to +5% for metasilicate. The averages are all very close to the theoretical minimum. A.13 Ammonia and nitrogen fertilisers (sub-headings under CPA 24.15) Nitrogen fertiliser production is responsible for 1.2% of the global primary energy demand. Worldwide, ammonia is produced by the same general technology, the Haber-Bosch process, and 80% of the production uses the same feedstock route, namely steam reforming of natural gas. In Europe, 90% of the ammonia is produced from natural gas. The remaining production uses partial oxidation, a route requiring 40-50% more energy but being more flexible with regard to feedstocks. Typically, oil residues and coal are used. (Phylipsen et al. 1998) Since the technology is homogenous, the differences between energy consumption and emissions of individual plants can only be attributed to differences in efficiency between plants, which again can mainly be explained by differences in the age of the plants. The differences are quite large. In 1994, the best practice plant in Europe, using the integrated AMV-process, had an energy consumption of 28 GJ/t NH3, while the European average was 35 GJ/t NH3 and national averages varied from 29 in Spain to 43 in Greece (Worrell et al. 1994b). Obviously, this reflects an even larger variation at plant level. The average energy requirement for production of urea from ammonia in Europe is estimated to 9 MJ/kg N with modern plants at 7.2 MJ/kg N (Kongshaug 1998). The conversion of ammonia into nitric acid is an exothermic process, for which the amount of heat recovered show a large varia tion (5-11 MJ/kg N according to Kongshaug 1998). Emissions of N2O from the process can be substantial. Abatement technology has been developed, and should be able to reduce the emission with 70-85% (Kongshaug 1998). Thus, in present day practice, it is likely to find plants with anywhere between 15 and 100% of the original emissions (0.03 kg N2O or 9 kg CO2-equivalents per kg nitric acid). A.14 Glass (CPA 26.1) Many types of glass with different mechanical and optical properties are made. However, unless exceptional chemical durability and heat resistance is required, soda-lime glass is used, i.e. for containers, flat glass, pressed and blown ware, and lighting products, accounting for nearly 90% of all glass produced world-wide. The most important raw materials (measured by weight) are sand, soda ash, limestone/dolomite and cullet (waste glass). In addition to these materials, small amounts of other raw materials as well as various colorants may be used. Soda-lime glass articles are manufactured by melting the raw materials at 1300-1500 °C and forming into the articles. This is nearly always followed by annealing of the glass products, i.e. reheating to a temperature sufficiently high to relive any stress, followed by slow cooling. Some products additiona lly require secondary or finishing operations, in the form of cutting, grinding, polishing, heat treatment, sintering, chemical treatment, or surface coating. The energy efficiency of the melting process varies due to differences in technology and size (Gielen 1997). Continuous melting, which is the most common, is much more energy efficient (3-8 MJ/kg melt) than pot melting (from 17 MJ/kg for large pots up to 100 MJ/kg for small pots). The larger the tanks, the lower the specific energy requirement. This is likely to be the main cause of the variations between glass works cited in Gielen (1997) from a study covering Germany, Austria and Switzerland showing a variation of fossil fuel fired glass works from 3.4 to 7.9 MJ/kg for both container glass (mean 4.8 MJ/kg) and flat glass (mean 5.7 MJ/kg), while the mean for the electric melts was 4.2 MJ/kg. Tanks where electrical energy is applied directly to the molten glass are more energy efficient than the more common gas-fired regenerative furnaces, which have an efficiency about 30%. All-electric melting units have significant environmental advantages, because they heat the melt from the bottom, which not only results in high energy efficiency but also in low volatilisation. For example, in a fluoride-containing batch as much as 40% volatilisation might occur with gas firing compared to 2% with electric (Gerhartz 1986). Oxygen, when substituted for air, reduces the fuel requirement by 30% (Kirk-Othmer 1994). This option is especially relevant for small furnaces. Regenerative burners, which are used in all modern plants, also increase energy efficiency. Another important factor which influences energy use is the amount of cullet used in the production process. Up to 70% of the raw materials may be substituted by cullet (broken and crushed glass from imperfect articles, trim, and other waste glass from the production process as well as recycled glass). The use of cullet not only reduces the use of raw materials (1 kg of cullet will substitute 1.2 kg of raw materials), but also the amount of energy, since the cullet does not require energy for chemical reactions, which leads to a reduction in energy demand of up to 0.8 MJ/kg for a melt with 70% cullet (Gerhartz 1986). The saved raw material also gives an energy saving, especially for soda, which has a specific energy requirement of approximately 10 MJ/kg. Finally the energy use per unit finished product depends on the amount of glass which is wasted during cutting of the glass into products of the required size. The amount of waste differs significantly for different products, and may for some products be as high as 70% (Kirk-Othmer 1994). The amounts of liquid effluents and solid waste from glass production are limited. The most significant emissions related to the production of glass are the airborne emissions, which mainly occur during the melting of glass. Most airborne emissions are due to the fuels used. However, during melting significant amounts of CO2 are released from the raw materials (not when melting cullet). Substituting raw materials with cullet therefore significantly reduces CO2 emissions (using 70% cullet will reduce CO2 emissions by apx. 140 kg per Mg melted glass). Other factors, which may significantly influence emissions, are cleaning of flue gases, e.g. removal of dust. Pollution prevention measurements and cleaner technologies are generally applied as a result of regulatory demands. A.15 Ceramics (CPA 26.2-26.4) Different qualities of ceramics (with different strength and water absorption rates) have different energy requirements due to different burning temperatures and residence times in the kilns/ovens. For example, Gielen (1997) cite the average energy requirement for Dutch bricks to vary from 0.7 MJ/kg (for inside cladding) over 2.9 MJ/kg (for the standard outside cladding brick) to 3.3 MJ/kg (for pavement bricks). Energy requirement for unglazed tiles is approximately 6 MJ/kg, for table stoneware 10 MJ/kg, for sanitary stoneware 30 MJ/kg and for china 70 MJ/kg (Pratten 1993). Modern tunnel brick kilns have an energy consumption 20% lower than the older designs, and further reductions seems feasible. Large kilns also use less energy per produced unit than smaller kilns. Weight of bricks may vary depending on their degree of perforation (up to 60% perforation is possible) with a corresponding variation in energy requirement. Gielen (1997) estimate that some of this saving is offset by an increased mortar requirement resulting in a corresponding increased consumption of 0.1 kg cement per kg saved brick. A.16 Cement (CPA 26.51) The cement industry is responsible for 1-2% of the global primary energy consumption and more than 2% of the CO2 emissions (Worrell et al. 1995). The term cement is used to designate many different kinds of finely grounded, non-metallic, inorganic materials that are used as hydraulic binders or adhesives. Portland cement is the most widely used cement for construction concrete. All Portland cements are produced with the same basic technologies world-wide. The mineral raw materials (clay and Calcium carbonate) are mixed and ground to a fine powder (dry processing) or a slurry (wet or semi-dry processing). For semi-dry processing the raw materials are wet ground, and then dried before sintering. The ground raw material mixture is slowly heated in a cement kiln to the sintering temperature of 1450 °C, to produce clinker (65% CaO). In Europe, clinker is predominantly burnt in rotary kilns. Cement is then produced by finely grinding either clinker alone (Portland cement) or clinker with other materials, which are capable of hydraulically hardening, e.g. pozzolana, glassy blast-furnace slag, and coal fly ash from power-plants (modified Portland cement). Gypsum or anhydrite may be added in small amounts (~5%) as fillers. These substances are inert or have weakly expressed hydraulic, latent hydraulic, or pozzolanic properties. Coal is the most widely used fuel, with oil as a second. For the production of grey cement an increasing amount of other combustible materials are used, such as waste tyres, waste oils and contaminated waste wood, since the contaminants of e.g. heavy metals are sealed in the clinker, and the high temperature and pH prevents the formation of toxic flue gases. Production of white cement, however, requires a fuel that burns without slag and ashes, which would otherwise colour the cement. Energy requirements for the production of clinker vary significantly depending on the processing method. Wet processing requires up to twice the amount of energy required for dry processing (with semi-dry processing somewhere in between). In general, the dry processing is used in all modern plants. However, the natural water content of the raw materials determine which production method is used, and energy use may therefore vary significantly between different geographical regions. Aalborg Portland - the only Danish producer of cement - produces almost all cement used in Denmark. Aalborg Portland uses both wet and semi-dry processing. The dry method is not used because of the natural high water content of the raw materials. Another factor that may influence energy use is the degree of utilisation of waste heat from the cement oven. The production of clinker being the main energy consuming process in cement manufacture, the overall energy requirement for cement vary significantly depending on the amount of clinker used in the cement. Cement may e.g. contain up to 40% fly ash, or up to 85% blast-furnace slag. Consequently, energy use for the production of Portland fly-ash cement and Portland slag cement may be respectively up to 40% and 85% lower than for cement made from clinker alone. A further advantage of reducing the amount of clinker in the cement is that it also leads to an equivalent reduction in the CO2 release from the calcination process (CaCO3 —> CaO + CO2). Gross energy requirements for Danish produced cement, measured as higher heating value, varies from 5.8 to 9.7 MJ/kg depending on the type of cement (BPS 1998). This is caused both by differences in processing type and the amount of clinker pr. Mg cement. The current average in the EU is 3.7 MJ/kg (Worrell et al. (1994a) with a current minimum of 3.1 MJ/kg (theoretical minimum 1.8 MJ/kg). The types and amount of waste products which may be used as raw material (e.g. fly ash, slag, gypsum from desulfuring of power-plant exhaust gases) or fuel (e.g. waste oil) depend on the types of waste materials available in the region where the cement plant is located, and may therefore vary significantly between geographical regions (Worrell et al. 1995). Some Danish cement types contain up to 15% flyash and gypsum from desulfuring of power-plant exhaust gases (Cement & Beton 1997), while blast-furnace slag is not used in Danish cement. In countries with a low potential of waste materials (like Ireland and in Latin America), a barrier for the increased use of waste materials is the lack of standards for blended cements. Also traditions and building codes for strength testing may limit the development of the market for blended cements. In the USA, some of the potentially available waste materials may currently be treated in a way that makes them less suited for blended cements (Worrell et al. 1995). Including the effects of the different amount of clinker, the national average energy requirements for cement vary from 2.1 MJ/kg in The Netherlands (27% clincker) to 7.3 MJ/kg in Russia (72% clincker) with a potential for energy savings ranging between 0 and 57% of current consumption. The amounts of liquid effluents and solid waste from cement production are limited. The most significant emissions related to the production of cement are the airborne emissions, which mainly occur during the burning of clinker. Most airborne emissions are due to the fuels used, but for dust and CO2 (due to the calcination process) the raw materials are a significant source also. Other factors, which may significantly influence emissions, are cleaning of flue gases, e.g. desulfuring of exhaust gases, and removal of dust. Pollution prevention measurements and cleaner technologies are generally applied as a result of regulatory demands. The summary effects of the above, and including the effect of different fuels (coal, oil and combustible waste materials), is a variation of gross emissions for Danish cement of (BPS 1998): 0,9-1,5 t CO2 per t cement A.17 Lime (CPA 26.52) Quicklime (CaO) is produced by heating calciumcarbonate (CaCO3) under the release of CO2. The released CO2 is often fixed again when the product is subjected to oxidation. Only when incorporated into glass, in steel conversion (where a glass-like slag is formed) and in flue-gas desulphurisation (producing gypsum), this CO2 fixation does not occur. Gielen (1997) cite energy requirements of lime kilns in 1986 varying from 3.7 MJ/kg CaO to 6.9 MJ/kg CaO. Today, the upper limit is probably 5 MJ/kg, with an average at 4 MJ/kg. A.18 Iron and steel (CPA 13.1 and 27.1-27.3) Steel production requires about 5% of the world‘s primary energy demand (Martin et al.1995). Three technologies are used for steel production (IISI 1990 cited in Phylipsen et al. 1998):
Within each of these routes, differences in specific energy consumption are caused by:
Differences in process efficiency between plants are large. The energy input to the blast furnace varied from 12.3 MJ/kg in Japan to 15.3 MJ/kg in the USA in 1994 (Worrell & Moore 1997). In Europe, national averages adjusted for the product mix and a uniform 10% scrap content, showed a variation in specific energy consumption of the BOF process (i.e. not the entire route) up to 8 MJ/kg higher than the best practice, which was 14 MJ/kg slab (Worrell et al. 1994b). Global average data for the entire route (from cradle to factory gate) for hot rolled coil from 26 steel mills (IISI 1998) showed a range of 12 MJ/kg between the lowest and highest primary energy consumption, with a coefficient of variance of 12%. Similar data for the EAF route from 11 sites showed a range of 12 MJ/kg and a coefficient of variance of 40%. This corresponds with the theoretical relation between the range and the coefficient of variance (for sample size 30, the theoretical coefficient of variance is ¼ of the range and for sample size 10, the theoretical coefficient of variance is 1/3 of the range). Explanatory factors for the differences are (Phylipsen et al. 1998):
IISI (1998) report average data on resource use and emissions for the entire route (from cradle to factory gate) for several steel products, e.g. for hot rolled coil from 26 steel mills following the BOF route. The reported coefficients of variance are: 9% for raw material consumption (iron ore), 15% for CO2, The similar data for 11 steel mills following the EAF route show the same variation in material inputs (CV 9% for scrap, 35% for limestone) and many of the emissions, but larger variations for some of the energy related emissions (CO2, NOx). This can mainly be explained by the smaller sample size and by differences in the electricity scenarios for the 11 mills (since the energy supply of the EAF route is mainly in the form of electricity) . Berdowski et al. (1996) report order of magnitude differences and more in BOF emissions of heavy metals to air based on different literature sources. This is in line with the above measured uncertainties for metal ions to water. It should be noted that different speciality steels, coated steels, and alloys may have very different data for energy consumption and emissions, depending on the specific processes and metals involved. For example, both tin-free steel and tinplated coil has an extra energy consumption of 9 MJ/kg compared to hot rolled coil (IISI 1998). Examples with respect to alloys and speciality steels can be found in a Japanese database (Halada 1998) showing especially large energy use for Nickel benefication and large NOx emissions from mining of Nb, Mo, V and W alloying elements. A.19 Aluminium (NACE 13.20.13 and 27.42) Primary aluminium production is electric ity-intensive. Variation in electricity consumption is reported from 13 to 17 kWh/kg with 15 kWh/kg as the US average (Aluminum Association, Inc. 1997, Gielen & van Dril 1997). Production cells normally have current efficiencies ranging from 85 to 95%. The lowest electricity consumption is still the double of the thermodynamic minimum, but only improvements of 5-10% is foreseen as a result of larger cells, continuous anodes, and improved bath composition (Gielen & van Dril 1997). A.20 Combustion processes (CPA 40) Combustion is central to any energy consuming process and is causing a major part of the total global emissions to the environment. The thermal efficiency of the combustion is largely determined by the technology and operating conditions determining the flue gas temperature. Thermodynamic calculations for high quality hard coal, oil, and natural gas, shows the thermal efficiency to vary from approximately 96% at a flue gas temperature of 400 oK (123 oC) over 87-88% at 600 oK (323 oC) to approximately 79% at 800 oK (523 oC). NOx reducing technology and the use of excessive oxygen may decrease the efficiency by a few percent (O‘Callaghan 1993). The loss of heat from stoke depends on technology (primarily isolation) and is approx. 3-5% for normal operation. If the stoke is not used continuously, the loss of heat rise remarkably (O‘Callaghan 1993). For electricity generation, conversion efficiencies depend on fuel type. Natural gas in combined cycle can give efficiencies of 60% while coal and oil typically have efficiencies of 40-50%. Since technology development is ongoing, the age of the plant is of large importance. Phylipsen et al. (1988) cite a study of OECD statistics for the year 1990 showing actual average regional conversion efficiencies ranging from 22 to 44% with a global average of 34%. Variation between countries and individual plants are even larger. Some emissions depend on fuel type. This is true for CO2 emissions and metals emissions, which depend on the origin of the fuel. Also SO2, VOC, and particle emissions depend on type of fuel, but may be reduced by different combustion or cleaning technology. NOx depends entirely on combustion and cleaning technology. For coal combustion, different boiler emission control technologies may remove 5-80% of NOx emissions, 30-95% of SOx emissions, and 97-99% of coal particulate emissions. From oil combustion, control technologies may remove 0-90% of NOx emissions and 70-90% of SOx emissions. For natural gas combustion, different boiler emission control technologies may remove 50-80% of NOx emissions. (Pring et al. 1996) Because of this large variation in emission control, the regulatory emission limits may often be the best estimate for actual emissions of these substances, in the absence of direct measurements. Examples of regulations on emissions of SO2, NOx and particles in Western Europe are shown in table A20.4. Table A20.1 below provides emission ranges from different types of fuels. The low value of some of the ranges implies emission control by technical precautions (except for SO2 of gas oil and for waste). Even lower values may occur depending on the efficiency of control technology, as given above. It follows from the above that very important factors in reducing uncertainty on combustion data is knowledge of the fuel choice and the combustion and control technology. The fuel choice is to some extent dependent on the specific industrial sector, the size of plant, and the need for specific and easily controlled temperature. When fuel choice is not obtainable directly from the involved enterprises, an estimate of the choice may be based on statistical data giving average distributions per sector, see table A20.2. These distributions may vary between countries with price and availability of the individual fuels. When geographical location is known, local statistics may be obtained on the average fuel choice per industrial sector, see table A20.3 for an example. Geographical location may also allow a more specific determination of regulatory requirements. Examples of regulations in different countries are provided in table A20.4. Geographical location may also allow a more specific determination of fuel prices, which may affect the fuel choice. Coal and biomass of low quality – i.e. high moisture, ash and maybe sulphur content - may be preferred due to low price with resulting decrease of combustion efficiency and increase in CO2, particle, and Further to geographical information, the uncertainty on combustion data may be reduced by obtaining information on boiler technology (the ranges in table A20.1 reflects different technologies) and specific fuel composition. Capacity utilisations within normal ranges (25-100%) has little influence on conversion efficiency, but low degree of utilisation leads to decreased efficiency. At 5% utilisation, the conversion efficiency may drop from e.g. 75% to 50% (O‘Callaghan 1993). Poor maintenance may lead to a 10% decrease in efficiency, while emissions, especially concerning particles and VOC may increase considerably. IPCC emission factors for N2O have ranges for coal from 0 to 10 kg/TJ energy input with 1.4 kg/TJ as the recommended value (Houghton et al. 1996). For oil, the recommended emission factor is 0.6 kg/TJ, with a range from 0 to 2.8 kg. The range is smallest for natural gas (0 to 1.1 kilograms), with 0.1 kilogram as the suggested factor. A.21 Building (CPA 45) The construction sector is an important materials consumer. It represents approximately one tenth of the Gross Domestic Product in Europe and about the same fraction of the total CO2 emissions. Most of these, however, do not relate to the construction phase itself, but to the building materials, some of which have been described separately (e.g. wood and wood products, glass, ceramics (bricks), cement, iron and steel, aluminium). Furthermore, heating and cooling of buildings cause approximately one third of the total primary energy consumption in Western Europe. As more new buildings are built than old buildings demolished, buildings materials are increasingly stored in the existing building mass. Differences in constructions can be significant. Gielen (1997) suggest that material use may be reduced with 40-50% by optimisation of current practices. Circular shapes of structural building elements can also save 50-80% of the material use. However, due to lacking availability of special building element shapes, conservative building standards, lacking knowledge regarding advanced calculation methods, and traditional consumer preferences may limit actual variation to 10%. Design and safety criteria may vary considerably between countries. For hollow steel sections, Gielen (1997) give an example where Japanese design criteria for regtangular sections result in a materials consumption 12% below that of the EU, while for bending loads on circular sections, EU and Japan require a factor 2.7 more material used than in the USA. A survey of concrete standards show an even larger variation for the same application (e.g. 15-40 mm for the minimum cover to reinforcement, minimum 200-380 kg cement per cubic metre, and a strength requirement varying from 25 to 40 N/mm2). Similar examples can probably be found for other design, durability and safety criteria. The use of improved quality materials may also reduce materials consumption significantly. Gielen (1997) estimate potential CO2 savings of 20% by the use of engineered wood products, 10-20% by the use of high strength steel, 15-20% by the use of high strength concrete and 10-15% by the use of hollow bricks. The energy use for heating and cooling depends on use characteristics, insulation, ventilation and thermal mass of the building. Each of these factors may vary considerably both within countries and between countries and average values will often be irrelevant for life cycle assessments. A.22 Transport (CPA 60-61) In the OECD countries, transport of goods account for 10-12% of final energy use, out of which 9-17% is for maritime transit between countries (Schipper 1997). The energy consumption for the transport of a specific good of a specific weight depends on two variables:
Emissions are to a large extent linearly related to fuel use, although some important exceptions are mentioned in the last part of the section on energy intensity. A.22.1 Transport distance In the following, only transport within EU and border crossing to EU‘s nearest markets is considered, unless otherwise specified. The calculations are based on information and figures in (Eurostat 1994, 1995, 1998a, 1998b) unless otherwise specified. The distance is calculated by dividing the work of transport (Mgkm) with the weight of transported goods (Mg). Since the information of transport work is not always available for international transport of goods, a long transport distance may be indicated by a high share of tonnage transported internationally. The average transport distance per Mg of goods within EU is 150 km (Eurostat 1998b). 90% of the goods are transported within one country, of which 60% are transported 0-49 km and 22% are transported 50-149 km (Eurostat 1998b). The average distance for national transport within the EU countries is roughly 80 km. The remaining 10% of the goods are transported internationally of which the average distance for inter-EU transport is roughly 500 km. Transports involving flight and ocean-going shipping are not included in the 10%. The tonnage of ocean-going shipping is roughly half the amount of the tonnage for inter-EU transport. The average distance of the transport is influenced by:
A.22.2 Energy intensity The energy intensity of domestic transport of goods by water, rail, and road ranges from 0.2 to 5 MJ/Mgkm, national averages of OECD countries varying from 1 to 3.5 MJ/Mgkm (Schipper 1997). The most important cause of variation in energy intensity per Mgkm is modal choice, in that ships have ranges from 0.05 to 1.2 MJ/Mgkm depending on size, rail ranging from 0.3 to 0.7 MJ/Mgkm and trucks vary from 0.7 to 5 MJ/Mgkm. Differences in modal mix between countries may explain up to one third (0.8 MJ/Mgkm) of the difference between national averages (Schipper 1997). Modal choice is influenced by:
Second most important cause of variation in energy intensity per Mgkm is the size and utilisation of the vehicle, which explains most of the variation in fuel intensity of both ships and trucks. For example, a 14t diesel truck has double the fuel consumption of a 52t truck of the same age and utilising a truck 40-50% instead of 100% also double the fuel consumption (Rydberg 1997). Due to the weight ratio of vehicle to load, the difference is most pronounced for small trucks, but it is still important for larger trucks. The capacity utilisation for trucks typically vary between 40% and 70%. For trucks, vehic le size is influenced by:
Capacity utilisation of the vehicle is influenced by:
Third most important cause of variation in energy intensity per Mgkm is traffic conditions and type of engine/fuel, being the second most important explanation for variation in fuel intensity of trucks. This also leads to differences in emissions other than those linearly related to fuel consumption, see below. Traffic conditions are influenced by:
The type of engine/fuel is first of all a question of diesel versus gasoline. Due to better efficiency, diesel engines use 30-40% less fuel than gasoline engines for the same performance (Samaras & Zierock 1996). For trucks under 6 tonnes, diesel is common in most of Europe, while e.g. Sweden, Norway, US, and Japan have higher shares of gasoline use. The emission characteristics of gasoline and diesel engines are very different and also depends on technology and regulations, see below. In general, diesel engines produce more NOx and particles than gasoline engines but less CO and VOC. For both gasoline and diesel, the fuel composition may vary due to local availability or regulation, e.g. low octane gasoline or poor quality diesel (high sulphur) in e.g. Eastern Europe, which may le ad to slightly increased fuel consumption and large differences in emissions. High sulphur diesel results - besides increased SO2 emissions - also in increased particle emission. For trucks manufactured according to EU standards after 1993 a large part of these particles are however captured by filters. Less important causes of variation are:
For these causes, variation between countries are not expected to be large, but variation between trucking enterprises within one country may be of importance. In general, the variation due to these causes can be assumed smaller than for the causes discussed above, i.e. less than +/- 10%. A.22.3 Pipelines Gas transport by pipeline involve two major environmental problems, namely the leakage and the energy use for transmission. Modern PE pipes have an order of magnitude lower leakage than old steel pipes (Frischknecht 1996). In percentage of the transported gas, leakage has been estimated between 1 and 2% depending on the age of the net and average distribution distances (Ayres & Ayres 1998). For the same reasons, turbo-compressor power for transmission varies from 1% of the transported gas for European conditions (Frischknecht 1996) to the double for the USA (Ayres & Ayres 1998). Frishknecht (1996) reports specific electricity consumption for oil transport by pipeline from 3 to 16 Wh/Mgkm, mainly depending on the height difference but also on pipe diameter and capacity utilisation. However, even when taking these items into account a residual variation of +/-35% was found. A.22.4 Electricity transmission The main loss in electricity transmissio n is caused by voltage conversion. Gärdenäs et al. (1997) and Frischknecht (1996) report 9-14% losses for household electricity (less than 1 kV), 2-6% loss for industrial users around 10 kV and 1-3% loss for industrial users around 100 kV. A.23 Waste treatment (CPA 90) The distribution of the different waste treatment options (incineration with and without heat recovery, landfilling with and without gas and/or leachate collection, etc.) must be estimated from statistical information and literature data, which is often incomplete. Within each waste treatment option, the emission data are typically based on models. The uncertainties on these models vary for different waste treatment options and for different materials. For landfills, the largest uncertainties are due to (Nielsen & Hauschild 1998):
For incineration, the largest uncertainties are on:
A.24 Household energy use The energy use of households has been found to vary +/-25% around an average, which is determined as a linear function of the household income (Kramer et al. 1998). The elasticity of the relation was calculated to 0.8.
6 Exceptions to this general rule is the adjustment of fertiliser use to expected yield and differences in energy use for harvesting depending on the actual yield. |
