Some of the major driving forces of past and future anthropogenic greenhouse gas (GHG) emissions, which include demographics, economics, resources, technology, and (non-climate) policies, are reviewed in this chapter. Economic, social, and technical systems and their interactions are highly complex and only a limited overview is provided in this chapter. The discussion of major scenario driving forces herein is structured by considering the links from demography and the economy to resource use and emissions. A frequently used approach to organize discussion of the drivers of emissions is through the so-called IPAT identity, equation (3.1).

Impact = Population × Affluence × Technology (3.1)

The IPAT identity states that environmental impacts (e.g., emissions) are the product of the level of population times affluence (income per capita, i.e. gross domestic product (GDP) divided by population) times the level of technology deployed (emissions per unit of income). The IPAT identity has been widely discussed in analyses of energy-related carbon dioxide (CO₂ ) emissions (e.g., Ogawa, 1991; Parikh et al., 1991; Nakicenovic et al., 1993; Parikh, 1994; Alcamo et al., 1995; Gaffin and O'Neill, 1997; Gürer and Ban, 1997; O'Neill et al., 2000), in which it is often referred to as the Kaya identity (Kaya, 1990), equation (3.2).

CO₂ Emissions = Population × (GDP/Population) × (Energy/GDP) × (CO₂ /Energy) (3.2)

The Kaya multiplicative identity also underlies the analysis of the emissions scenario literature (Chapter 2). It can be broken down into further subcomponents. For instance, the energy component can be decomposed into fossil and non-fossil shares, and emissions can be expressed as carbon emissions per unit of fossil energy, as shown in Figure 3-1 (Gürer and Ban, 1997). A property of the multiplicative identity is that component growth rates are additive. For instance, global energy-related CO₂ emissions since the middle of the 19 th century are estimated to have increased by approximately 1.7% per year (Watson et al., 1996). This growth rate can be decomposed roughly into a 3% growth in gross world product (the sum of a 1% growth in population and a 2% growth in per capita income) minus a 1% per year decline in the energy intensity of world GDP (the third term in equation (3.2)) and a decline in the carbon intensity of primary energy (the fourth term) of 0.3% per year (Nakicenovic et al., 1993; Watson et al., 1996).

While the Kaya identity above can be used to organize discussion of the primary driving forces of CO₂ emissions and, by extension, emissions of other GHGs, there are important caveats. Most important, the four terms on the right-hand side of equation (3.2) should be considered neither as fundamental driving forces in themselves, nor as generally independent from each other.

Global analysis is often not instructive and even misleading, because of the great heterogeneity among populations with respect to GHG emissions. The ratios of per capita emissions of the world's richest countries to those of its poorest countries approach several hundred (Parikh et al., 1991; Engelman, 1994). Of course, some level of aggregation is necessary. In practice, the models used to produce emissions scenarios in this report, for example, operate on the basis of 9-15 regions (see Appendix IV, Table IV-1). This level of detail isolates the most important differences, particularly with respect to industrial versus developing countries (Lutz, 1993).

The spatial and temporal heterogeneity of emission growth that becomes masked in the global aggregates is shown in Figure 3-1, in which the growth in energy-related CO₂ emissions since 1970 is broken down into a number of subcomponents. For industrial countries the population growth has been modest and their emissions have evolved roughly in line with increases (or declines) in economic activity. For developing countries both population and income growth appear as important drivers of emissions. However, even in developing countries the regional heterogeneity becomes masked in the aggregate analysis (Grübler et al., 1993a).

Although, at face value, the IPAT and Kaya identities suggest that CO₂ emissions grow linearly with population increases, this depends on the real (or modeled) interactions between demographics and economic growth (see Section 3.2) as well as on those between technology, economic structure, and affluence (Section 3.3). In principle, such interactions preclude a simple linear interpretation of the role of population growth in emissions.

Demographic development interacts in many ways with social and economic development. Fertility and mortality trends depend, among other things, on education, income, social norms, and health provisions. In turn, these determine the size and age composition of the population. Many of these factors combined are recognized as necessary to explain long-run productivity, economic growth, economic structure, and technological change (Barro, 1997). In turn, long-run per capita economic growth and structural change are closely linked with advances in knowledge and technological change. In fact, long-run growth accounts (e.g., Solow, 1956; Denison, 1962, 1985; Maddison, 1989, 1995; Barro and Sala-I-Martin, 1995) confirm that advances in knowledge and technology may be the most important reason for long-run economic growth; more important even than growth in other factors of production such as capital and labor. Abramovitz (1993) demonstrates that capital and labor productivity cannot be treated as independent from technological change. Therefore, it is not possible to treat the affluence and technology variables in IPAT as independent of each other.

Pollution abatement efforts appear to increase with income, growing willingness to pay for a clean environment, and progress in the development of clean technology. Thus, as incomes rise, pollution should increase initially and later decline, a relationship often referred to as the "environmental Kuznets curve." This process seems well established for traditional pollutants, such as particulates and sulfur (e.g., World Bank, 1992; Kato, 1996; Viguier, 1999), and there have been some claims that it might apply to GHG emissions. Schmalensee et al. (1998) found that CO₂ emissions have flattened and may have reversed for highly developed economies such as the US and Japan. Other researchers argue that the Kuznets curve does not apply to GHG emissions (Pearce, 1995; Galeotti and Lanza, 1999, Viguier, 1999). The flattening in emissions can be explained by normal market processes and does not appear to result from a willingness to pay to protect the global environment. Urbanization, infrastructure, poverty, and income distribution are other factors in the complex interplay between population, economy, and environment (see, e.g., Rotmans and de Vries, 1997; de Vries et al., 1999; O'Neill et al., 2000).

Technological, economic, and social innovation have long been means by which a greater number of people can live from the same environmental resources. The best known historical examples of major periods of innovation include the Neolithic revolution (beginnings of organized agriculture from around 10,000 years ago); and the industrial revolution that began two centuries ago (Rosenberg and Birdzell, 1986). In each case, changes in patterns of primary production (food, energy, materials) are linked to changes in social organization, institutions, economy, and technology (e.g., Mumford, 1934; Campbell, 1959; Landes, 1969; Hill, 1975; Wilber, 1981; Buchanan, 1992; Reynolds and Cutcliffe, 1997). The most remarkable change in recent decades is the so-called demographic transition, which has led to a stabilization of population in many parts of the world. No single one of these changes can be considered as the primary driver, and they cannot be considered as independent from each other: each play a role in an interconnected system.

Most innovative efforts in the past two centuries were devoted to improving labor productivity and the human ability to harness resources for economic purposes. While material and energy efficiency improved slowly, economic growth was faster and thus aggregate resource use increased.

Finally, and importantly, the high uncertainty with regard to the nature and extent of the relationships between driving forces of GHG emissions means that, with current knowledge, it is not possible to develop probabilistic future emission scenarios. Even if it were possible to derive (subjective) probability distributions of the future evolution of individual scenario driving-force variables (like population, economic growth, or technological change), the nature of their relationships is known only qualitatively at best or remains uncertain (and controversial) in many instances.

The next five sections review the major driving forces of GHG emissions within the IPAT identity. Section 3.2 discusses the role of population, Section 3.3 addresses economic and social development processes (including technological change), Section 3.4 examines energy resources and technology in more detail, and Section 3.5 addresses agriculture, forestry, and land-use change. Section 3.6 considers other sources of non-CO₂ GHGs. The chapter concludes with a discussion of non-climate policies and their potential impact on the principal driving forces of future emissions. Each section briefly reviews past trends, available scenarios, and important new methodological and empirical advances since the publication of previous International Panel on Climate Change (IPCC) emissions scenarios in 1992 (IS92). This chapter provides the background to establish recommendations for the range of driving-force variables to be explored in the new set of scenarios. The available literature and current understanding of the inherent uncertainties in developing very long-term scenarios are reflected. Each section elucidates in detail the important relationships between scenario driving forces, as the question of relationships is a new and important mandate for SRES. Nonetheless, most attention is paid to the possible relationships between population and economic growth, because this is the area most intensively discussed in the literature.

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