Emissions Scenarios - IPCC

Scenario Driving Forces 105
3.1. Introduction
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 xAjfluence x 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 (COj) emissions (e.g., Ogawa, 1991; Parikh et al.,
1991; Nakicenovic et ai, 1993; Parikh, 1994; Alcamo et ai,
1995; Gaffin and O'Neill, 1997; Gürer and Ban, 1997; O'Neill
et al., 2000), in which it is often refeiTcd to as the Kaya identity
(Kaya, 1990), equation (3.2).
CO 2 Emissions = Population X (GDP I Population) X
X (Energy/GDP) X (COJEnergy)
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 (Giirer and Ban,
1997). A property of the multiplicative identity is that
component growth rates are additive. For instance, global
energy-related COj emissions since the middle of the 19*
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 COj 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 (Griibler et al, 1993a).
Although, at face value, the IPAT and Kaya identities suggest
that COj 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