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,
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)
Figure 3-1: Historical trends in energy-related
CO2 emissions ("carbon emissions" shown as bold gray line) and broken
down into the components of emission growth: growth or declines of population,
gross domestic product (GDP) at purchasing power parities (PPPs), energy
use per unit of GDP (Energy/GDP), share of renewables in energy use
(Renewable energy/Energy), and carbon intensity per fossil energy (Carbon/Fossil
energy) since 1970, in million tons elemental carbon (MtC). From top
to bottom: Organization for Economic Cooperation and Development (OECD90,
countries that belong to the OECD as of 1990), former USSR (FSU), Developing
Countries (ASIA and Africa, Latin America and the Middle East (ALM)),
and World. Source: Gürer and Ban, 1997.
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
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