IPCC Fourth Assessment Report: Climate Change 2007
Climate Change 2007: Working Group II: Impacts, Adaptation and Vulnerability

7.3 Assumptions about future trends

Defining possible future socio-economic conditions is a key to understanding future vulnerabilities to climatic change and assessing the capacity to adapt in the face of new risks and opportunities. A range of tools, including scenarios and storylines, has been used to develop characterisations of the future (Chapter 2). While specific characterisations have been developed for vulnerability and adaptation studies in certain climate-sensitive sectors (for example, Arnell et al., 2004; Nicholls, 2004), few characterisations have been developed that relate specifically to climate impacts as they could affect industry, settlement and society. Where such characterisations have been done (e.g., NACC, 2000; London Climate Change Partnership, 2004; Raskin et al., 2005), they have common roots in the perspectives embedded in the IPCC Special Report on Emissions Scenarios (SRES; Naki?enovi? and Swart, 2000; see also Chapter 2, Section 2.4.6). Drivers in the SRES scenarios – population, economic growth, technology and governance – are all highly relevant for the development of industry, settlement and society.

A key future condition, for instance, is human population and its distribution. According to the latest United Nations projections (i.e., post-SRES), even as the rate of population growth continues to decline, the world’s total population will rise substantially. The total is expected to reach between 8.7 and 9.3 billion in 2030 (UN, 2004). More than half these people live in urban centres, and practically all live in settlements, many depending on industry, services and infrastructures for jobs, well-being and mobility. Most population growth will take place in cities, largely in urban areas of developing countries, especially from Asia and Africa (Table 7.1). Some mega-cities will grow very substantially, but the major population growth will take place in medium cities of 1 to 5 million people and in small cities of under 500,000 people, which still represent half of the world population (Table 7.1, see also UN-Habitat, 2003).

Table 7.1. Urban indicators.

 Percentage urban Percent of the world’s urban population living in the region Percent of urban population in different size-class of urban centre, 2000 
Year 1950 1975 2000 2030* 1950 1975 2000 2030* Under 0.5 m 0.5-1 m 1-5 m 5-10 m 10 m + 
Northern America  63.9  73.9  79.1  86.7  15.0  11.9  8.8  7.1  37.4 11.0  34.3  5.4 11.9 
Latin America and the Caribbean  42.0  61.2  75.4  84.3  9.6  13.0  13.9  12.4  49.8  9.0  21.7  4.9 14.7 
Oceania  62.0  71.5  70.5  73.8  1.1  1.0  0.8  0.6  41.9  58.1 
Europe  50.5  67.9  71.7  78.3  37.8  29.2  18.4  11.1  67.8  9.8  15.1  5.4  1.9 
Asia  16.8  24.0  37.1  54.1  32.0  37.9  47.9  53.7  49.0 10.0  22.6  8.8  9.7 
Africa  14.7  25.4  36.2  50.7  4.5  7.0  10.3  15.1  60.2  9.6  22.1  4.6  3.5 
WORLD 29.0  37.2  46.8  59.9 100 100 100 100  52.6  9.8  22.4  6.8  8.4 

* These are obviously speculative (projections based largely on extrapolating past trends) and, since any nation’s or region’s level of urbanisation is strongly associated with their per capita income, economic performance between 2000 and 2030 will have a strong influence on the extent to which regional populations continue to urbanise. Source: taken from or derived from statistics in United Nations (2006).

Features of development relevant to adaptation, such as access to resources, location and institutional capacity, are likely to be predominantly urban and to be determined by differences in economic growth and access to assets, which tend to be increasingly unequal (e.g., the income gap between the richest and the poorest 20% of the world population went from a factor of 32 to 78 between 1970 and 2000: UN-Habitat, 2003). It is estimated that one third of the world’s urban population (923.9 million) live in “overcrowded and unserviced slums, often situated on marginal and dangerous land” (i.e., steep slopes, food plains, and industrial zones), and that 43% are in developing countries (UN-Habitat, 2003). It is projected that in the next 30 years “the total number of slum dwellers will increase to about 2 billion, if firm and concrete action is not taken” (UN-Habitat, 2003).

Risk-prone settlements such as in coastal areas are expected to experience not only increases in weather-related disasters (CRED, 2005) but also major increases in population, urban area and economic activity, especially in developing countries (Chapter 6). Growing population and wealth in exposed coastal locations could result in increased economic and social damage, both in developing and developed countries (Pielke et al., 2005; Box 7.4).

Global economic growth projections in SRES and SRES-derived scenarios (Chapter 2) vary significantly - more than population projections. Under low-growth scenarios (A2 and B2), world GDP would double by 2020 and increase more than 10-fold by 2100. Under a high-growth scenario (A1), world GDP would nearly triple by 2020 and grow over 25-fold by 2100. Under all these scenarios, more valuable assets and activities are likely to become exposed to climate risks, but it is assumed that the economic potential to respond will also vastly increase. Economic development will be central to adaptive capacity (Toth and Wilbanks, 2004). SRES scenarios also assume convergence of national per capita incomes, which is contrary to historical tendencies for income gaps between the rich and the poor to increase. While the ratio of per capita incomes in developed as compared with developing countries stood at 16.1 in 1990, SRES scenarios assume a narrowing of this ratio to between 8.4 and 6.2 in 2020, and between 3.0 and 1.5 in 2100. Smaller differences in relative incomes are likely to have important consequences for the perception of climate vulnerability and for the pattern of response.

Because it is potentially highly dynamic, the treatment of technology varies greatly between global scenario exercises. For instance, three qualitatively-different technology scenarios were developed for SRES scenario A1 alone (A1FI, A1T and A1B). An even broader universe of technological change scenarios can be developed for global and downscaled national, regional and sectoral scenarios (e.g., Berkhout and Hertin, 2002). In this chapter we make no specific assumptions about the rate and direction of technological change into the future, recognising that very wide ranges of potentials will exist at the local and organisational levels at which climate vulnerability and responses will often be shaped, and also that the knowledge base referenced in the chapter reflects a range of assumptions about future trends. Governance is likewise a topic about which different scenario families make divergent assumptions. The SRES scenarios include both globally-integrated systems of economic and political and sustainability governance, as well as more fragmented, regionalised systems. The Global Scenarios Group set of scenarios include characterisations in which institutions and governance as we know them persist with minor reform; ‘barbarisation’ scenarios consider futures in which “absolute poverty increases and the gap between rich and poor …[and] national governments lose relevance and power relative to trans-national corporations and global market forces…” (Gallopin et al., 1997); ‘great transitions’ scenarios contain storylines in which sustainable development becomes an organising principle in governance. In this chapter we also have made no specific assumptions about the nature of future pattern of governance, while recognising that institutional capacity will be central to adaptive capacity (Section 7.6.5; also see Chapter 2).