Origin and development of IDA

Looking at the origins of index decomposition analysis, one has to distinguish between the methodological background and the conceptual origin. The methodologies used in decomposition analysis date back to index number problems, as discussed in section 4.3. The ideological inspiration of decomposition analysis in the energy/environment field goes back to the debate on deteriorating environmental conditions in the United States at the beginning of the 1970s. During that time the so-called IPAT (Impact, Population, Affluence, Technology) analysis started. The goal of this analysis is to determine the key drivers behind environmental impact, such as air pollution. The IPAT identity states very generally that impact is the result of the product of three different factors: population (P), affluence (A) and technology (T).

_(4.1)

This undefined identity was suggested by Ehrlich and Holdren (1971) as a reaction to researchers doubting any causality between U.S. population growth and environmental impact. Originally, the purpose of this equation was to find the single variable that is most damaging to the environment. Soon a debate started of which variable was to blame. Whereas Commoner and others pointed to new production technologies as the source of more pollution, his opponents at that time, Ehrlich and Holdren, saw population growth as the predominant reason for damage to a broadly defined environment (Ridker 1972). Consequently, population control was considered as an option by Ehrlich and Holdren to control environmental pollution. Other researchers

held the position that negative consequences of population growth and rising affluence would be balanced by technological improvements.

Commoner, the first to make the equation operational, chose production per capita as a measure of affluence and emission per production as a proxy for technology. In many studied examples, he found technology to be behind much of the environmental degradation. A question raised early on concerned the independence of the factors explaining the aggregate of environmental impact, such as interdependencies between affluence and technological improvement. This debate heavily influenced U.S. policy makers in the early 1970s and led to an unprecedented level of environmental legislative activity, leading to policies such as the Clean Air Act. Later on, the IPAT identity was extended to study causal linkages and to be applied to regression analysis (see e.g. Dietz and Rosa 1994; Rosa and Dietz 1998) or to include other factors such as intensity of use, e.g. energy intensity (Waggoner and Ausubel 2002).

The oil price shocks in the 1970s made the broader public aware of the reliance on energy and were a starting point for researchers to look more closely at energy consumption. The academic world was interested in quantifying the drivers behind changes of industrial energy demand and to single out the influence of structural changes in the industry sector. This was the starting point for index decomposition analysis in the context of energy. Although this research stream developed relatively independently from the IPAT analysis, the structure looked broadly similar despite its clear focus on energy. The first studies in the early 1980s therefore investigated how output growth, energy/electricity intensity, structural change and technological change influenced industrial energy/electricity demand (Thomas and MacKerron 1982; Hankinson and Rhys 1983; Jenne and Cattell 1983). New to this approach was an attempt to explain how structural changes in industrial output influenced energy demand. A typical decomposition equation looked like Equation (4.2), where the first term on the right hand side indicates the influence of output, the second the influence of industrial structure and the last the influence of sectoral energy intensity on overall industrial energy consumption.

(4.2)

While in 1987, a survey by Huntington and Myers (1987) found only eight decomposition studies undertaken up to this date in this area, the application of these

techniques was becoming more and more popular in the 1990s so that Ang (1995a) listed 51 studies in the context of industrial energy decomposition. In the 1980s and early 1990s the focus of decomposition analysis was on industrial energy consumption or energy intensity, defined as a unit of energy consumption per unit of output. Differences had appeared, however, regarding the choice of studied fuel, the level of sector disaggregation and in particular the countries studied, ranging from industrialised countries like Japan, UK and Germany to developing countries like Mexico, China and South Korea.

From the 1990s, the focus of decomposition analysis was no longer mainly restricted to industrial energy use, but expanded to other sectors and to the analysis of gas emissions, predominantly CO2 but also SO2 and NOx. Consequently, the last survey on decomposition studies performed by Ang and Zhang (2000) found that 33, out of a total of 124 studies, dealt with the decomposition of gas emissions. The first identity to be specified in the context of the analysis of carbon emission was the Kaya identity (Kaya 1989): (4.3) This identity looks very similar to the original IPAT identity simply with the technology factor detailed into both the energy intensity of the economy and the carbon intensity of energy. Torvanger (1991) was the first to quantify the impact of different drivers, such as industry structure, fuel share and energy intensity, on the development of CO2 emissions within the scope of a cross-country analysis. Traditionally, decomposition techniques have been applied to decompose changes in an aggregate indicator over time. However, there exist some exceptions of the sort of Proops et al. (1992) and Zhang et al. (2001) that decompose the difference in an aggregate indicator between countries. This means that the factors on the right hand side capture differences between countries and not between points in time. Moreover, some studies applied the concept of decomposition analysis beyond energy onto manufacturing and transport issues (Ang and Zhang 2000, p. 1163).

Decomposition techniques have not only been applied to study historical data, but also to analyse future perspectives of the development of environmental indicators. Olsen (1994) for example, based on the traditional IPAT identity, studied three scenarios of future developments. In the same way, the Intergovernmental Panel on Climate Change

(IPCC) used decomposition analysis, based on the Kaya identity, to project future trends in CO2 emissions. In this study, the importance of technological improvement in the light of population growth and economic growth is acknowledged:

“Admittedly, there are many possible combinations of the four Kaya identity components, but with the scope and legitimacy of population control subject to ongoing debate, the remaining two technology-oriented factors, energy and carbon intensities, have to bear the main burden (Rogner et al. 2007, p.108).”

This confirms a more accepted view nowadays that technological systems offer the best possibility to balance environmental impacts of affluence and population increases. After the last survey of studies on decomposition analysis, this research field has extended beyond the industry sector and now includes studies on electricity generation, the residential sector, the service sector and transport (see e.g. International Energy Agency 2004). Moreover, several studies have been published that project future developments of CO2 emissions based on assumptions on key drivers (Kawase et al. 2006; Agnolucci et al. 2009). In addition, decomposition analysis has been used for energy efficiency monitoring and to study material flows (Ang 2004).

Summing up, decomposition analysis is based on the IPAT debate, which started in the 1970s. The quantification of energy intensity changes and structural changes in the industry sector were first studied after the first two oil price shocks. In the 1990s the focus of decomposition studies changed from energy towards environmental indicators, such as CO2 emissions. Nowadays, decomposition analysis is a well-established research area, studying different energy sectors and different energy and environmental indicators. However, except for a couple of studies on cross-country comparisons, analyses have been restricted to decomposing the change of aggregate indicators over time. Further studies have been undertaken that decompose the share of measures towards emissions reduction over time, but do not represent marginal abatement costs. To the knowledge of the author, no studies have been undertaken to decompose changing CO2 emissions over increasing CO2 prices, i.e. to decompose a MAC curve, instead of doing so over time. In summary, the thesis at hand presents a transparent and methodologically detailed approach of bringing together energy system modelling and decomposition analysis to derive MAC curves.