THE IEA ENERGY INDICATORS EFFORT

Transcription

1 THE IEA ENERGY INDICATORS EFFORT INCREASING THE UNDERSTANDING OF THE ENERGY/EMISSIONS LINK Lee Schipper, Fridtjof Unander and Céline Marie-Lilliu 1

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3 RÉSUMÉ Since 1995, the International Energy Agency has been developing Energy Indicators. The IEA s first book on the subject, Indicators of Energy Use and Efficiency, appeared in May Since then the IEA has worked to link the indicators to carbon emissions. The Agency is co-operating with a dozen Member countries to help them develop and use national energy indicators. A handbook of energy indicators is under preparation. The title of the most recent publication on the subject, The Link between Energy and Human Activity, highlights an important new thrust in IEA work. Indicators describe the links between energy use and human activity in a detailed way. The goal is to use the disaggregated indicators approach to identify the role of key economic and human activities in energy markets. They show how economic and technical driving factors, like energy prices, economic growth and new technologies, shape energy use, and therefore determine CO 2 emissions. Meaningful differences among countries in their energy use and carbon emissions have been identified. Since carbon emissions are a key source of undesirable climate change, these indicators have taken on important significance in recent years. This paper describes how these indicators are derived and what they can show. Indicators can help individual governments to analyse changes in their CO 2 emissions from energy use and to relate those changes to economic, technological, and human factors. Some countries have begun to undertake this more detailed analysis. 1 Further analysis is needed to understand the factors affecting each country s emissions, and how its plans to limit or reduce carbon are related to these factors. This brief description of the work is intended to show what has been done so far in IEA Member countries, and to offer some suggestions on broadening the effort to a wider number of countries. INTRODUCTION TO INDICATORS Energy indicators measure energy use or emissions in much the same way that stock indices measure economic performance. The indicators relate energy or emissions broken down by activity, end-use or output to a measure of that activity, end-use or output. The IEA energy indicators rely on two other kinds of indicators monetary and physical to measure two kinds of activity, economic and individual (or human) activities. The two types of activity are distinct because individual, or human, activities are not registered directly in national accounts or other economic statistics, such as household energy uses or private transportation. This is important because an increasing share of greenhouse gas emissions arises from these two activities. 1. See the Danish effort at the Canadian effort at or the US effort at 3

4 Indicators are not data. They are derived from basic data on the structure of economic and human activity, combined with measurements or estimates of the energy used in those activities. Using standard coefficients, quantities of carbon emissions are derived from quantities of final energy use. Carbon released in the production of electricity and heat is allocated to final consumption at the average emission rate for power plants per unit of energy supplied to the economy, net of internal uses and distribution losses. Outside the manufacturing sector, very few energy uses are really measured in detailed surveys; so analysis relies on indirect measurements, estimates and regression analysis. For the analyst, the distinction between data and indicators must be kept clearly in mind, as well as the strengths and weaknesses of the data themselves. The indicators are not normative; they are descriptive and analytical. With the help of the indicators, energy use can be normalised, i.e. divided by measures of activity or output, to form intensities. Intensities are more comparable over time or among countries than are unnormalised quantities. This is important, because in some contexts, total energy use or total carbon emissions are less interesting than is either of those quantities normalised to a key parameter like population or Gross Domestic Product. There is a hierarchy of indicators. Descriptive indicators, like shares of energy by fuel or main sector, are the most common. Basic normalised indicators, showing sectoral energy uses divided by population or GDP, are also common, since the data are readily available. Time-series show the development of intensities, shares or other indicators over time. Comparative indicators attempt to show similar features of different countries by introducing appropriate normalisations. Structural indicators measure the distribution of economic or human activity in a number of different modes or output types. Decomposition indicators show how different components of total energy use (or carbon emissions) influence total emissions. Causal indicators suggest which kinds of fundamental economic, demographic, or geographical forces most affect energy uses. And consequential indicators, like carbon emissions, measure the relationships between human activity and energy use and the resulting pollution or other environmental disruption. This project has developed an indicators pyramid based on work carried out at the University of Utrecht (Figure 1) to illustrate the progression from a myriad of technical energy efficiencies at the bottom of the pyramid through the process of aggregation to a multitude of energy intensities of key economic activities in the middle. A difficult step requires quantifying the attributes of various energy uses and the services derived from them: how powerful is a car? how warm is a house? how strong is steel? how big is a refrigerator? If these attributes are known, then the efficiencies can be translated into energy intensities (such as energy use per vehicle kilometre or energy use per dollar of real output) or specific energy requirements (such as energy use per tonne). These intensities can be aggregated further to a list of well-known and easily documented energy uses (the short list shown in Table 1 below), which account for nearly 90% of final energy use and related carbon emissions in most IEA countries. These include automobile traffic, trucking, air travel, heavy manufacturing, fuel and electricity use in the service sector, home space- and water-heat, lighting, and appliances. With further 4

5 structural information, this list can yield a very useful ratio of energy to GDP, a ratio which can now be explained. Take space heating, for example. High energy use for heating, relative to GDP, could arise because of large homes, a cold climate, inefficient heating practices or a combination of all three. Conversely, low heating use could arise because people prefer low temperatures, because the winter climate is mild, because homes are small, or because they are very efficiently heated. In any of these cases, the detailed approach explains the aggregates. Just as important, governments, when they talk about saving energy, focus on technical improvements to heating. Hence energy analysis must identify these components as closely as possible. The pyramid is a useful way of seeing how those components fit into the entire picture. FIGURE 1. ENERGY E INDICATORS PYRAMID 1. E Sectoral Intensities Structure Subsectoral Intensities Attributes: Utilisation, Quality, Etc. Process Efficiencies Understanding the physical efficiencies of individual energy-using processes is important, because it is these that most energy-efficiency strategies seek to improve. If technical data for each process were available as well as data on utilisation, the engineering process efficiencies of the lowest layer in the pyramid could be described in physical terms and transformed, using the various measures of utilisation, into meaningful energy intensities for key end-uses. Usually all that is available is estimates of energy use and surrogates for physical characteristics and utilisation, such as the number of refrigerators in the economy, and an estimate of energy use per refrigerator per year. Those estimates of subsectoral activities and intensities can be aggregated to yield sectoral intensities. Unfortunately the sectoral intensities, like energy use per capita, are often confused with efficiencies in the engineering sense because of a lack of real data on efficiencies. 5

6 Work so far has focused on 14 IEA Member countries: Australia, Canada, Denmark, Finland, France, western Germany 2, Italy, Japan, the Netherlands, New Zealand, Norway, Sweden, the United Kingdom and the United States. For most of these countries the time covered is from the early 1970s to the mid-1990s. Figure 2 summarises energy use per unit of GDP for a dozen end-uses or sectors in a recent year, broken into ten end-uses or sectors. In spite of the sectoral detail, this diagram has little to say about energy efficiency. FIGURE 2. ENERGY E USE BY SECTOR ECTOR, PER UNIT OF GDP, IN IEA COUNTRIES C IN IEA C Cars Home Heat Services Fuel Heavy Manufacturing Trucks Other Travel Other Home Services Electricity Other Manufacturing Other Freight MJ/1990$ PPP of GDP Europe - 4 Nordic-4 US Japan Netherlands Canada Australia New Zealand OTIVATION: : A CLOSER C MOTIVATION LOSER LOOK BEYOND ENERGY The fundamental problem that motivates research on energy indicators is that the most widespread indicator of energy use the ratio of energy use to GDP measures just that, the ratio of energy use to GDP and nothing more. Even with the sectoral detail in Figure 2, little can be said, on the basis of that ratio, about why energy use for any sector has reached a certain level, how efficient that use is, or why levels for otherwise similar countries differ so much. Better tools are required. One problem with using energy/gdp as a tool is that the denominator GDP represents many diverse activities. Thus the aggregate ratio does not really measure efficiency. The mix of activities varies from country to country and over time. Since the 2. Hereafter denoted as w. Germany. 3. In this and other diagrams, NO-4 refers to the four Nordic Countries (Denmark, Finland, Norway, and Sweden). Europe-4 is France, western Germany, Italy, and the United Kingdom. 6

7 energy intensities of these activities vary widely, variations in the mix of activities can cause significant variations in the ratio of energy to GDP over time. They can also explain large differences among countries. Since efficiencies are a function of specific physical processes or of specific economic activities, an aggregate ratio of energy use to GDP is not an indicator of either energy efficiency or economic efficiency because it mixes too many different effects. In response to this weakness, the IEA decided to disaggregate energy uses and activities and to calculate intensities where numerators and denominators match as closely as possible. How Much Information is Necessary? The IEA has conducted a series of workshops to explore the question of how much information is really necessary to understand the link between energy use and human activity. The first workshop, held in 1996, addressed all sectors. Since then, workshops were held on transportation and the service sectors; results can be seen at The conclusion reached at these workshops is that detailed data are preferable, even if obtaining them means taking one s surveys less frequently. Table 1 lists the main relevant sectors and sub-sectors. These include nearly 90% of final energy uses in IEA countries. The desirable level of disaggregation depends on the questions that have to be answered. For the energy technologist, the housing expert or the industrial engineer, dozens of different energy uses have to be separated out, so that each key technology and energy intensity can be identified. Many policy experts need to know how various energy technologies or energy saving programmes have affected energy use, energy uses must be disaggregated. Energy Intensities In almost every discussion of energy use, the question arises How efficient is it? And in nearly every energy-policy document, improved efficiency is listed as a goal. But measuring efficiency is far more difficult than it seems to be. This is because one rarely observes the physical quantities that define an efficiency in the engineering sense. And the economic inputs and outputs that define economic efficiency are rarely measured or estimated. Energy intensities, defined as energy use per unit of activity or output, were introduced in the pyramid of Figure 1 for a large number of economic and human activities. In economics, intensities are often used to measure how much of one or many resources is used to produce a given output. Energy intensities are not the exact inverse of efficiencies. Intensities reflect behaviour, choice, capacity or system utilisation and other factors besides just engineering ones. Is a heavy car less efficient than a lighter one? Most likely the heavy car uses more fuel per kilometre than the light one, but it may well use less fuel per kilogram-kilometre. The heavy car is physically more efficient because it requires less energy per unit of mass per kilometre of travel. It may also be providing more service (comfort and power) to satisfy the driver. Observers can of course debate whether larger or small cars are better from the standpoint of emissions. But it is clear that one must separate the normative better/worse from the objective more/less, higher/lower or rising/falling. 7

8 TABLE 1. MEASURES OF ACTIVITY CTIVITY, SECTORAL STRUCTURE AND ENERGY INTENSITIES Sector Sub-sector (i) Activity (A) Structure (S i ) Residential Passenger Transport Freight Transport Intensity (I i = E i /A i ) Population Space heat Floor area/capita Heat 1 /floor area Water heat Person/household Energy/capita 2 Cooking Person/household Energy/capita 2 Lighting Floor area/capita Electricity/floor area Appliances Ownership 3 /capita Energy/appliance 3 Passenger-km Cars Share of total passenger-km Energy/passenger-km Bus Rail Inland Air Tonne-km Trucks Share of total tonne-km Energy/tonne-km Rail Inland Water Commercial Services total Floor area or GDP (not defined) Energy/floor area or GDP Manufacturing Value-added Paper & Pulp Share total value-added Energy/value-added Chemicals Non-metallic Minerals Iron & Steel Non-ferrous Metals Food and Beverages Other Manufacturing Other Industry Value-added Agriculture Energy/Value-added &Fishing Share total value-added Mining Construction 1. Adjusted for climate variations and for changes in the share of homes with central-heating systems. 2. Adjusted for home occupancy (number of persons per household). 3. Includes ownership and electricity use for six major appliances. Energy intensities are key elements of energy-saving and carbon-reduction plans. This is because most governments expect that various programmes such as carbon taxes or intensified energy research, will accelerate the decline in intensities in some sectors, or reverse the rise in others. All else being equal, such changes lead to less carbon emissions than otherwise, even after certain rebounds are taken into account (the rebound issue is discussed below). Since intensities cannot be added or aggregated, and since intensities change at different rates in different sectors, responding to different stimuli, they must be viewed in a disaggregated manner. We have chosen about three dozen intensities that reflect key energy uses in IEA countries. These are shown in Table 1. 8

9 Let us consider some of these intensities. Take automobiles: each year since 1978 the US government has required manufacturers to meet certain salesweighted fuel economy averages or pay civil penalties. Other countries have introduced voluntary programmes that have now been codified into a formal agreement between the European Union and European car manufacturers. These actions required measuring fuel use/km of all new car models. Many governments have collected such data. Most weight the data by sales to produce an indicator of new-car fuel intensity. Figure 3 shows the data for the US, as well as for a number of other countries that collect it. 4 FIGURE 3. TEST T FUEL INTENSITY (FUEL ECONOMY CONOMY) FOR NEW CARS IN IEA COUNTRIES IEA 20 Liter / 100 km, Gasoline Equivalent France w. Germany UK Sweden Denmark US Netherlands Canada Australia Japan Italy Germany See for example Tableaux des consommations d énergie en France, yearly editions. Paris: Observatoire d énergie, Direction générale de l énergie et des matières premières or the Transportation-Energy Data Book Oak Ridge,Tenn: Oak Ridge National Laboratory. The tests used in the US are not identical to those used in Europe or Japan. Japan actually changed its procedures in the early 1990s. Hence the results among countries are not immediately comparable, but the trends are. 9

10 FIGURE 4. ON-R -ROAD FUEL ECONOMY (FUEL INTENSITY NTENSITY) FOR CARS AND HOUSEHOLD LIGHT TRUCKS l/100 km, gasoline or equivalent US Japan w. Germany Italy Sweden Netherlands Australia Canada New Zealand FIGURE 5. CAR C AR/L /LIGHT TRUCK USE PER CAPITA AND GDP PER CAPITA APITA Vehicle Km / capita (cars and household light trucks) Australia US Canada w. Germany Denmark UK Sweden Netherlands Japan ,000 11,000 13,000 15,000 17,000 19,000 21,000 23,000 25,000 27,000 Per capita GDP, 1990 US$ PPP 5. Note that in this and subsequent Figures with per capita GDP on the X axis, the left-most point generally represents the earliest year covered. Time progresses with higher GDP/capita, except when recession may temporarily reverse GDP. The line joins the points in temporal order. 10

11 What matters for energy use or carbon emissions, however, is real fuel economy on the road, as well as kilometres driven and other factors. Many IEA Member countries publish these key indicators. Figure 4 illustrates the fuel economy indicator for a number of countries. Figure 5 shows how car use (in km/capita,) has evolved. In this case, the study portrays the latter against changes in per capita GDP, which is the key factor driving car use. The product of these two quantities is per capita fuel use for cars and private light trucks. Thus we can explain per capita fuel use for these personal vehicles in terms of both fuel intensity and distance driven. This is crucial to understanding the evolution of total fuel use for this application over time. It is also vital in making comparisons among countries. Differences or changes arise because of differences on both of these components, but it is the former (fuel intensity) that is most often the subject of government actions. Hence changes in the former must be identified. Another important use of energy is space heating. Most IEA Member countries have promulgated policies to reduce energy use for heating. A number of countries (Netherlands, US, France, Sweden) have instituted regular detailed household heating surveys to follow the progress of heat energy-saving efforts. Figure 6 portrays a key indicator of space heating that captures the changes in heating, adjusted to take into account the approximate efficiencies of combustion equipment. The intensity shown is normalised to house size and degree-days, so that figures are both comparable year-to-year within a country and among countries. 6 In essence, this indicator measures what each nation sought to reduce through various policies and new technologies. The data suggest the goals were met in many countries. The flat lines for Japan or the UK, however, reflect significant increases in the standard of indoor comfort during the period shown (greater area heated, longer heating hours, higher indoor temperatures) as standards in these two countries approached those in the other countries shown. This does not mean that energy efficiencies were not improved, but only that other factors were driving heating comfort up, leading to flat or even to rising energy intensities for heating. As a third example, consider energy use in manufacturing. In the aggregate, energy use relative to output (measured by GDP) the use of energy manufacturing has fallen more or less continuously in most IEA countries since the 1950s. This intensity can change both because of shifts towards or away from energy-intensive output or because of changes in individual energy intensities in each manufacturing sub-sector. Governments have encouraged the latter effect which is known as energy saving. It is indeed desirable to measure this effect. If the declines are measured in each separate branch of manufacturing and then combined using the output shares of a single base year, the result, expressed as a single index, is a useful indicator of the overall effects of efficiency improvements. So, both effects changes in intensities and changes in the overall output mix are important, but each should be identified separately. 6. Heating needs are proportional to house size and to the average number of degree-days. Degree-days in their simplest form measure the difference between indoor and outdoor temperature averaged over the entire heating season. They are usually derived for a given country by forming an average over a number of climate zones, weighted by the number of people or number of homes in each zone. 11

12 FIGURE 6. SPACE S HEATING INTENSITIES IN IEA COUNTRIESC 6. S IEA C kj/sq metre/degree-day US Japan UK Sweden Italy Australia Denmark The indicators in Figure 7, indexed to the 1990 values of energy use in manufacturing and the 1990 mix of output, give results for four countries. These show that energy saving over a very long term occurred in four IEA countries with diverse output in manufacturing. Note that the downward trend was present in the 1960s, before the rise in world oil prices in the 1970s. These savings were largely the result of improvements in technology and increases in the scale of manufacturing. The fact that the downward trend is so pervasive is important for estimating future trends in manufacturing. The indicator in Figure 7 was calculated net of effects of changes in the mix of output, a factor which can have an important impact on the aggregate ratio of energy use to output in manufacturing. In Japan and the US, the overall mix of output shifted significantly towards less energy-intensive output, although the intensity effect as shown in Figure 7 still dominated. In some countries not shown here (Australia, the Netherlands, Norway) a shift towards more energy-intensive output occurred, obscuring in part the impacts of individual branch energy intensity reductions, particularly in the case of Norway. In those countries, the aggregate ratio of energy use to output thus understates the real energy saving that took place in individual branches of manufacturing. In either case, detailed branch-bybranch data are important to understanding trends in manufacturing. 12

13 FIGURE 7. LONG ONG-T -TERM TRENDS IN MANUFACTURING ENERGY INTENSITIES 1990 ENERGY E INTENSITY = E = Denmark Germany Japan USA For some countries, intensities in manufacturing can be expressed as energy use per unit of physical output, which are known as specific energy requirements or SER. Figure 8, from the INEDIS data base developed by the Lawrence Berkeley National Laboratory and the University of Utrecht shows the SER for steel-making for several OECD countries. 7 This indicator comes closer to measuring energy efficiency than does an intensity related to monetary output. Unfortunately, such indicators are difficult to come by for more than a handful of products namely raw materials like steel and cement. The advantage of such indicators is that they permit a physical explanation of changes both in intensities over time and differences among countries. These explanations are rooted in differences in actual processes (dry vs. wet process for cement, different kinds of steel-making processes, differences in pulping process or kind of paper output, etc.). 7. Price, L., and Worrell, E., et al., International Network for Energy Demand Analysis in the Industrial Sector. Berkeley, CA: Lawrence Berkeley National Laboratory (LBID-2297) 13

14 FIGURE 8. PHYSICAL P INTENSITIES NTENSITIES: : SPECIFIC S ENERGY REQUIREMENTS OF PRIMARY STEEL GJ Primary Energy/Tonne US w. Germany 5 Japan S. Korea Source: INEDIS Data Base, Lawrence Berkeley National Laboratory. The physical intensities shown in Figure 8 come close to measuring energy efficiency. The differences often reflect real differences in practices that can save energy, of which the most effective are often called best practices. While the technologies behind best practices are important to understand, one must also bear in mind that best is not only a function of energy intensities but also of the costs of other resource inputs, particularly capital and labour. Again, the indicators do just what their name implies: they indicate important areas for further investigation. They do not prove that a particular technology is good or best, only that it is higher or lower in energy intensity for a particular application. The Importance of the Structure of Economic and Human Activities The IEA has not limited its efforts to measuring energy uses and energy intensities. Measuring the underlying structure of activities for which energy is used is just as important. It has already been noted that the analysis uses both economic and human activity for measures of structure and normalisation to determine intensities. Some examples of economic indicators are steel production (in monetary or physical terms), turnover in the for-hire trucking business (in monetary or physical terms, e.g. tonne-kilometres), or activity and output from agriculture, in hectares planted or bushels or value added. Examples of human activity indicators are meals cooked, clothes washed, kilometres driven or areas heated. Figure 5, for example, showed car use per capita, a key structural parameter that tends to 14

15 grow along with GDP per capita. These indicators are as important as those for intensities in explaining energy use. Not all human activities offer accurate measures of output. For example, there are few data to measure production of household activities, like heating, cooking or washing. However, one can employ surrogates, such as the rates of ownership of certain kinds of cooking or washing equipment. Figure 9, for example, shows the ownership of electric appliances in a number of Asian countries. Even if the use of electricity per appliance is not known, these indicators still provide a key to the stage of development of electricity use in the countries shown. In rural areas of developing countries, where equipment is hard to define, there are nevertheless a number of good surveys of basic household activities that reveal how energy is used. FIGURE 9. OWNERSHIP O OF ELECTRIC APPLIANCES IN ASIAN COUNTRIES 9. O 250% Philippines 1989 Thailand % Urban Java 1992 Japan 1968 Korea 1989 Japan 1992 Appliances Per Household 150% 100% 50% 0% Refrigerator Rice Cooker Fan Air Cond. Color or BW TV Source: National Surveys, as reported in IEA (1997). Asian Electricity Study. Paris: IEA. The evolution of the structure of the economy and human activities can itself cause changes in energy consumption that mimic or offset shifts caused by changes in energy intensities. Examples of shifts in the composition of manufacturing output was already noted. Increases in appliance holdings or automobile ownership may be so rapid as to raise energy use much faster than GDP. This was the case in w. Germany and Japan in the 1960s. Alternatively, saturation of ownership (and use) can occur, and in these cases GDP rises faster than energy use for these kinds of energy-related services. These two components structure and energy intensity need to be recognised explicitly. The structural component is driven mainly by incomes and by forces not directly related to energy or energy policies. The energy intensity 15

16 components are related to energy-use technologies and efficiencies that are often the object of public energy policies. To measure the impact of official policies or new technologies on energy uses, governments need to know with some degree of accuracy how each component has affected energy use. Energy policy analysts want to know which changes in energy use are directly caused by, or related to, energy policies, energy prices and energy technologies, and which are attributable to other structural factors. Energy policies may have affected how much energy is used to heat a square metre of an average home. Housing and fiscal policies, on the other hand, may explain why homes are of a given size. With some skill in indexing or other mathematical devices, measures of the structure of activities or output can be produced 8. Some of these measures (such as GDP by sector of origin) are well known. Some others, like the share of passenger transport or freight activity by mode, are less well known but are nonetheless tabulated by most national governments and collected by the OECD through ECMT (European Conference of Ministers of Transport). Some measures are not employed by the OECD or IEA but are commonly used by energy analysts. For example, the structure of household energy use is represented by the kinds of equipment households own, the sizes of the homes and even the size and composition of the households themselves. This is important, because some factors change with rising income while others change mainly in response to demographic forces. Rising income permits individuals to leave the family nest earlier or live apart longer. But, today, both adults in a family often work, leaving the home empty (and many energy uses turned off) for many hours. Thus, the interaction of demography with energy use in the home is very complex. Key Summary Indicators of Energy Intensities and Energy Services Figure 7 employed an indexing technique called Laspeyres indices to combine changes in a number of variables and components into a single index. The Laspeyres index holds all components but one constant at base-year values. It is a kind of all else being equal index. Other more complex indices can also be employed. The key element of an index is that it permits movement upward in the energy indicators pyramid in Figure 1 without losing key information. The indicators for individual uses can be combined to synthesise an aggregate energy-intensity index. This is formed by combining all the energy intensities in the economy at their relative weights in a given base year. The choice of 1990 as the base year was because it has been chosen as the base-year for international climate negotiations. The key message from this index, shown in Figure 10, is the rate at which energy intensities have slowed after There may also be important residual terms that arise as an interaction among the terms in the ASIF decomposition when Laspeyres indices are used. Other indices commonly used that have fewer residuals are Divisia, or Adaptive-Weighted Divisia indices. See Greening, L.A., Davis, W.B., Schipper, L.J., and Khrushch, M.P., Comparison of Six Decomposition Methods: Application to Energy Intensities for Manufacturing in ten OECD Countries, Energy Economics. 16

17 FIGURE 10. IMPACT OF ON FINAL ENERGY USE MPACT OF CHANGES IN ENERGY INTENSITIES 160% Change in Energy Use from change in Intensities, 1990=100% 150% 140% 130% 120% 110% 100% Denmark Japan U.S. U.K. Australia w. Germany New Zealand 90% Now consider the impact on energy use of changes in sectoral structure and activity levels, all other factors being equal. Taken together, these two variables measure energy services, an indicator of what is obtained in economic or physical terms by using energy. Energy services are what drive energy demand: area heated, kilometres travelled, tonnes or dollars produced. Each of these components of activity and structure is indexed to the corresponding energy use in 1990, and results are added across all energy uses for any other year. The results measure a hybrid GDP which includes such physical components as passenger transport, freight, and residential energy use, together with GDP measures of output from the manufacturing and commercial sectors. The total can be compared with the 1990 total for energy use to obtain an overall index of how much energy use changed as a result of changes in energy services alone. Figure 11 shows the overall effect of energy services for selected countries. The index illustrates how energy uses changed as a result of changes in energy services, indexed to a value of 100 in New Zealand started in 1980 with slightly below average values for energy services relative to 1990, yet wound up above average by 1994/5. This happened because energy services grew faster than GDP in New Zealand during the period shown, somewhat offsetting the effect of energy savings. This development was mainly due to growth in car ownership even during a period of declining GDP. In Japan, by contrast, energy service growth was rapid, but GDP grew much faster, thus reducing the ratio of energy use to GDP, all other things being equal. Indeed, in only a few countries studied did energy services growth come close to overall GDP growth. This divergence between GDP and the energy-services index meant that the energy/gdp ratio fell even without changes in energy intensities. This effect is so large in some countries such as Japan that it 17

18 must be analysed carefully on its own. If it continues, it will indeed contribute to energy saving, but not because of efficiency improvements. FIGURE 11. EVOLUTION OF ENERGY SERVICES ERVICES,, MEASURED M WITH LASPEYRES INDICES NDICES (1970S TO 1994/5) Change in Energy Use from Changes in Energy Services, 1990=100% 120% 110% 100% 90% 80% 70% Denmark Japan U.S. U.K. Australia w. Germany New Zealand 60% MOVING FROM ENERGY TO THE ENVIRONMENT NVIRONMENT: : CARBONC EMISSIONS : C In the 1990s, energy ceased to be seen as a scarce commodity. We know that the supply of energy is not running out. In these conditions, measures of energy use taken alone say little about the economy or about the sustainability of growth in energy use. Using energy can cause undesirable side effects, or externalities. One of these is carbon emissions from fossil fuels, which are associated with the threat of climate change. Climate change will be a worse problem for future generations than it is for the present generation. Energy use today is inexorably linked to sustainability. To develop indicators of energy use and of how it relates to sustainability, one must have good indicators of how energy use (and fossil fuel use) is linked to economic and human activity. Indicators serve this purpose. Figure 12 shows the carbon emissions from a variety of sectors and enduses in 1994, normalised to GDP. This compilation rests on much analysis of each country, analysis that goes well beyond simple energy balances. At this point, there is a temptation to draw conclusions. But much more analysis must take place before it is possible to understand the differences among countries. 18

19 FIGURE 12. CARBON C EMISSIONS IN IEA COUNTRIESC OUNTRIES,, NORMALISED N TO GDP 12. C IEA C, N GDP Household Space Heat Services Other Manufacturing Air Travel Truck Freight Other Industry Other Household End Uses Raw Materials Manufacturing Automobile Travel Other Travel Other Freight Denmark '72 Denmark '94 Sweden, '73 Sweden, '95 Norway '73 Norway '93 w. Germany '73 w. Germany '94 tc/10e US$ France '734 France '94 UK, '73 UK, '95 Japan '73 Japan, '94 US '73 US '94 Canada '84 Canada, '95 Australia '74 Australia '95 Seeing Beyond the Surface of Carbon Emissions Disentangling all the factors that contribute to a particular level of carbon emissions (or any other pollutant) is a key theme of indicators work. Figure 13 shows how the indicator approach breaks down changes in carbon emissions into different underlying elements. The figure depicts the links between energy and the general economy, demands for energy service, the energy system that supplies the services and the resulting emissions. Points where governments intervene are indicated by the arrows. Demand for energy services is generated by sectoral activity, expressed as value added and person-kilometre and by structure of sector, such as industry mix and the mix of transport modes. The level of activity and structure developments depend on GDP, population, income distribution and prices, as well as geographic factors like climate. Energy intensity, measured at the end-use point, is the delivered, or final, energy needed per unit of activity. By including supply-side losses for each energy carrier and multiplying all fuels by their emission factor, one calculate the emissions resulting from each of the activities in the various sectors The present approach is limited to carbon emissions. It does not track the full range of greenhouse gases. And it tracks carbon emissions only from direct uses of energy at the point of combustion. More comprehensive approaches are useful. Take carbon leakage, or the passage of carbon across borders (other than in imports or exports of fuels). One could count the carbon in imports and exports of non-energy goods and services. Tracking bunkers, or vehicles that cross national borders can reveal significant border leakage of this kind. It may also be necessary to track carbon beyond the point where energy is converted to useful work and heat. This approach counts the carbon embodied in electric power at an 19

20 FIGURE 13. MODEL M OF ENERGY NERGY/E /EMISSIONS INDICATORS 13. M /E EMISSIONS = Possible governmental programmes and interventions Climate ENERGY SUPPLY -Power Plants -Heat -Heat plants plants -Gas -Gas supply -Oil -Oil supply -Coal -Coal supply supply -etc. -etc. Fuel Mix Supply Conversion Efficiency UTILITY END-USE ENERGY -Heating oil oil -Gasoline -Electricity -District heat heat -etc. -etc. ENERGY SYSTEM Fuel Mix Energy Intensities ENERGY SERVICE DEMAND -Car-km -Motive power -Light -Indoor heat heat -Process steam steam -etc. -etc. Activity Structure ECONOMY -GDP -Population -Income -Prices -etc. -etc. Using the simple rules suggested here, one can move from energy indicators to carbon indicators. Consider for example emissions from home space heating. In most IEA countries, the energy required to heat one square metre of floor space fell by 20%-50% between 1973 and 1993 (Figure 6). Even more interesting is the way emissions per square metre changed. Because the fuel mix generally moved from coal and oil to gas and in some countries to low-carbon electricity, carbon emissions per square metre fell even more than energy used. With the 1986 crash in oil prices, there was little increase in this heating indicator. Space heating of homes accounted for 10%-15% of all carbon emissions in the IEA. The savings from more efficient heating mean that a very considerable important part of emissions is gone for good, and what remains could still shrink. The indicators measure the results of efforts by individual households and building operators to save energy. Unfortunately, the carbon intensity of space heating now is declining only slowly, as shown in Figure 14. Similar indicators can be developed for other energy-use sectors. In transportation, almost all energy is consumed as oil products, which differ only slightly in carbon content. Hence the indicator in Figure 4 (page 10) provides a reasonable measure of the carbon intensity of car use. In manufacturing, there has been much fuel switching over time, and the fuel mix differs significantly among countries, so carbon intensities may vary substantially from energy intensities. In the services/commercial sector, electricity provides as much as 50% of the final energy. So the carbon content of electricity is critically important to overall emissions. average, nation-wide ratio of emissions to electricity (or heat) made available to final demand. Imports and exports of electricity present a thorny accounting problem. Even more sophisticated approaches will be necessary when considering fuel switching to reduce carbon emissions. Diesel fuel contains more carbon per unit of energy released than does conventional petrol. But, where diesel substitutes for gasoline, considerably less carbon is released in the refining process. Even more complicated issues arise in the case of so-called renewables made from biomass feedstocks with a lavish application of fossil fuels, such as US corn-based ethanol. Counting total carbon goes beyond the accounting used in this study. 20

21 FIGURE 14. CARBON C INTENSITY OF SPACE HEATING 14. C 9.00 gramms/sq meter home space/degree-day base 18 o C Denmark Netherlands Sweden Norway w. Germany France UK Japan USA Note: Electricity and district heating emissions apportioned to space heating at the national average ratio of emissions to net energy supplied to end-users. Decomposing Changes in Sectoral Carbon Emissions over Time It is important to separate the components of the carbon intensity effect (which is related to energy efficiency and fuel choices) from those related to demand by people and enterprises for energy service. Moreover, changes in energy services can be biased towards or away from the most carbon-intensive activities within a sector. They change for different reasons and in response to different stimuli. Policies and measures to reduce emissions are most often directed towards the first category. Demand for energy services is related to a country s general welfare and economic development. It is a function of industrial production, travel, appliance ownership, etc. So it is seldom the target for energy and environmental polices. The disaggregated approach presented here improves our understanding of how the various components have shaped and will shape energy and emission developments. Such understanding can help determine what policies can be most effective. The decomposition approach to energy use can be extended to carbon emissions. In addition to changes in each of the factors listed in Table 1 (page 8), final fuel mix and the mix of fuels used to generate electricity must also be counted. Observed changes in the end-use of energy can then be separated into changes in activity, changes in structure and changes in energy intensities. Changes in carbon emissions can be further decomposed into changes in final fuel mix and changes in the emissions of carbon from heat and power generation, which depend both on the fuel mix for those sectors and on the efficiency as well. 21

22 Hence, changes in emissions related to improved end-use energy efficiency (reductions in energy intensity) can be isolated from changes due to other factors. Decomposition of changes in carbon emissions can be summarised by the relation that has come to be known as ASIF ASIF. Emissions G are related to four multiplicative terms: G = A * S i * I i * F i,j A represents overall sectoral activity, say GDP in manufacturing, S represents sectoral structure expressed as share of output or activity or household equipment ownership. I represents the energy intensity of each branch i shown in S (in energy use/real money output). F is the carbon content of each fuel j used in branch i. The j index represents three factors: changes in the primary fuel mix; efficiency in the generation of electricity and district heat; and fuel mix within each end-use sector. The product of the two components of the equation representing changes in the energy system (I and F), is denoted carbon intensity, while energy (or carbon) services is defined as the product of A and S. The Laspeyres indices can be applied again to calculate the relative impact of each term on total carbon emissions from a given sector over time. The indices are constructed by choosing a base year (1990), then taking the ratio between the right-hand side of the equation in any given year and its value in the base year with only one term in the numerator allowed to vary. The result is an index that measures the relative impact of the varying term (energy intensity, for example) on carbon emissions. The results can literally be interpreted as how emissions would have evolved if only one term had changed over time and all other terms had remained the same. The indices are simple to obtain and the results are easy to interpret, since everything is expressed relative to a single base year. An index is calculated for each term denoted respectively as the carboncontent effect, the energy-intensity effect, the structure effect, and the activity effect. As noted above, multiplying the two former terms gives an aggregate carbonintensity term. Combining the structure and activity terms gives an energy-service term. Isolating the effects of the last element of carbon content shows the effect on carbon of actions taken by electric and heat utility companies. Similarly, multiplying the Laspeyres indices for the components of carbon intensity and energy services gives a carbon intensity effect and an energy services effect 10. Figure 15 shows the remarkable path of carbon intensity for the manufacturing sectors in a number of IEA countries, all indexed to 1990 emissions intensity. Fuel use relative to output fell by 25% to 45% in a dozen of the IEA countries studied in the period 1973 to Most of this fall was the result of reductions in fuel requirements for individual industry branches, particularly cement, iron and steel, chemicals, and paper/pulp. 11 Only part of this change in product mix i,j 10. While energy services and energy intensities are not independent of each other, the interaction (also called rebound effect ) is in general small. The June 2000 issue of Energy Policy, which was organised by the IEA, addresses this complex question. 11. A minor part of the savings in Japan, the US, and western Germany, was due to changes in the product mix at branch level not resolved at the level of disaggregation available. For example, shifts from primary to recycled aluminium production give an apparent decline in 22

23 occurred because some firms moved heavy smokestack industries to other countries with less expensive energy or raw materials. In fact, absolute output from heavy manufacturing was higher in almost all IEA countries in the late 1990s than it was in Thus, most of the restraint in emissions from manufacturing in the countries studied occurred because of efforts to save energy within those countries. FIGURE 15. CHANGES C IN THE CARBON INTENSITY OF MANUFACTURING ANUFACTURING, MEASURED USING LASPEYRES INDICES 1990 = % 200% 180% 160% 140% Denmark US Japan Italy w. Germany UK 120% 100% 80% Shifts in the fuel mix similar to those recorded in the household sector reduced carbon emissions somewhat more. Shifts in utility fuel mix also led to reduced emissions in most countries. By mid-1995, IEA industries emitted about the same amount of carbon for manufacturing as they did in 1973, with very much higher output. By the late 1990s, however, the carbon intensity of manufacturing was declining more slowly than it did before The slowdown occurred because energy intensities are falling more slowly than before and because in many IEA countries, the primary fuel mix (including that of electric and heat utilities) is also being de-carbonised more slowly than before The same thing is generally (but not always) true for other sectors. This is an important finding relevant to each country s consideration of its options for reducing carbon intensities further. energy intensity of non-ferrous metals, while increases in pulp production ahead of paper production (for pulp exports) appear as an increase in the energy intensity of paper and pulp production. We estimate these kinds of changes account for at most 20% of the changes in the energy intensity index over time, and a similar amount of the differences in energy intensities among countries of any one subsector of manufacturing. 23

24 Decomposing Changes in Economy-Wide Carbon Emissions over Time Using indices, an overall measure of the carbon intensity of the economy (at constant structure) can be calculated similar to that shown in Figure 8. Figure 16 shows this intensity. In this calculation, A and S are held constant at their 1990 values, while I and F vary. The rapid decline for Sweden is largely a result of modest energy saving and a big switch to hydro-electric and nuclear power as well as biomass. The large decline in Denmark occurred mainly because of energy saving. By contrast, the slow decarbonisation in Australia is due principally to a big increase in the role of coal-fired electricity. Figure 16. Changes in Economy-Wide Carbon Intensities, using Laspeyres Indices Carbon Emissions with 1990 activity, structure and actual carbon intensities, 1990=100% 220% 200% 180% 160% 140% 120% Denmark Sweden w. Germany UK USA Japan Netherlands Australia France 100% 80% The overall changes in the different indices can be compared to see which components were most important. Figure 17 does this for a number of the countries shown in Figure 16, for the period since The first bar shows actual changes in emissions for the period 1990 to 1994 in manufacturing, services, households, travel, and freight. The second shows how much changes in the A and S factors alone increased emissions. (The energy-services effect.) The third bar shows the impact on emissions of changes in the utility fuel mix (the last part of F). The fourth bar measures how energy-intensity changes affected emissions. The fifth bar holds everything but final fuel mix constant (part of F). 24