Note: climate scenarios based on HadCM2 simulations: the range with unmitigated emissions reflects variation between ensemble simulations.
Note: climate scenarios based on HadCM2 simulations: the range with unmitigated emissions reflects variation between ensemble simulations.
Aggregation of impacts to regional and global scales is another key problem with such geographically-distributed impact assessments. Tables 20.4 to 20.6, for example, keep track of people living in watersheds who will face increased water-related stress. Of course, many people live in watersheds where climate change increases runoff and therefore may apparently see reduced water-related stress (if they see increased risk of flooding). Simply calculating the 'net' impact of climate change, however, is complicated, particularly where 'winners' and 'losers' live in different geographic regions, or where 'costs' and 'benefits' are not symmetrical. Watersheds with an increase in runoff, for example, are concentrated in east Asia, while watersheds with reduced runoff are much more widely distributed. Similarly, the adverse effects felt by 100 million people exposed to increased water stress could easily outweigh the 'benefits' of 100 million people with reduced stress.
The Defra Fast Track and ATEAM studies both describe impacts along defined scenarios, so it is difficult to infer the effects of different rates or degrees of climate change on different socio-economic worlds. A more generalised approach applies a wide range of climate scenarios representing different rates of change to estimate impacts for specific socio-economic contexts. Leemans and Eickhout (2004), for example, show that most species, ecosystems and landscapes would be impacted by increases of global temperature between 1 and 2°C above 2000 levels. Arnell (2006) showed that an increase in temperature of 2°C above the 1961 to 1990 mean by 2050 would result in between 550 and 900 million people suffering an increase in water-related stress in both the SRES (Special Report on Emissions Scenarios, Nakicenovic and Swart, 2000) A1 and B1 worlds. In this case, the range between estimates represents the effect of different changes in rainfall patterns for a 2°C warming.
20.7 Implications for regional, sub-regional, local and sectoral development; access to resources and technology; equity
The first sub-section here addresses issues of equity and access to resources as measured by the likelihood of meeting Millennium Development Targets by 2015 and Millennium Development Goals until the middle of this century. Vulnerability to climate change is unlikely to be the dominant cause of trouble for most nations as they try to reach the 2015 Targets. However, an assortment of climate-related vulnerabilities will seriously impede progress in achieving the mid-century goals. The second sub-section considers the range of these vulnerabilities across regions and sectors in 2050 and 2100 before the last offers portraits of the global distribution of vulnerability with and without enhanced adaptive capacity and/or mitigation efforts.
20.7.1 Millennium Development Goals -a 2015 time slice
The Millennium Development Goals (MDGs) are the product of international consensus on a framework by which nations can assess tangible progress towards sustainable development; they are enumerated in Table 20.7. UN (2005) provides the most current documentation of the 8 MDGs, the 11 specific targets for progress by 2015 or 2020 and the 32 quantitative indicators that are being used as metrics. This chapter has made the point that sustainable development and adaptive capacity for coping with climate change have common determinants. It is easy, therefore, to conclude that climate change has the potential to affect the progress of nations and societies towards sustainability. MA (2005) supports this conclusion. Climate-change impacts on the timing, flow and amount of available freshwater resources could, for example, affect the ability of developing countries to increase access to potable water: Goal #7, Target #10, Indicator #30 (UN, 2005). It is conceivable that climate change could have measurable consequences, in some parts of the world at least, on the indicators of progress on food security: Goal #1, Target #2, Indicators #4 and #5 (UN, 2005). Climate-change impacts could possibly affect one indicator in Goal #6 (prevalence and death rates associated with malaria), over the medium term (UN, 2005). The list can be extended.
The anthropogenic drivers of climate change, per se, affect MDG indicators directly in only two ways: in terms of energy
Table 20.7. The Millennium Development Goals.
1. Eradicate extreme poverty and hunger
2. Achieve universal primary education
3. Promote gender equality and empower women
4. Reduce child mortality
5. Improve maternal health
6. Combat HIV/AIDS, malaria and other diseases
7. Ensure environmental sustainability
8. Develop a global partnership for development
use per dollar GDP and CO2 emissions per capita. While climate change may, with high confidence, have the potential for substantial effects on aspects of sustainability that are important for the MDGs, the literature is less conclusive on whether the metrics themselves will be sensitive to either the effects of climate change or to progress concerning its drivers, especially in the near term. The short-term targets of the MDGs (i.e., the 2015 to 2020 Targets) will be difficult to reach in any case. While climate impacts have now been observed with some levels of confidence in some places, it will be difficult to blame climate change for limited progress towards the Millennium Development Targets.
In the longer term, Arrow et al. (2004) argue that adaptation decisions can reduce the effective investment available to reach the MDGs. They thereby raise the issue of opportunity costs: perhaps investment in climate adaptation might retard efforts to achieve sustainable development. Because the determinants of adaptive capacity and of sustainable development overlap significantly; however, (see Section 20.2) it is also possible that a dollar spent on climate adaptation could strengthen progress towards sustainable development.
Whether synergistic effects or trade offs will dominate interactions between climate impacts, adaptation decisions and sustainable development decisions depend, at least in part, on the particular decisions that are made. Decisions on how countries will acquire sufficient energy to sustain growing demand will, for example, play crucial roles in determining the sustainability of economic development. If those demands are met by increasing fossil fuel combustion, then amplifying feedbacks to climate change should be expected. There are some indications that this is now occurring. Per capita emissions of CO2 in developing countries rose from 1.7 tonnes of CO2 per capita in 1990 to 2.1 tonnes per capita in 2002; they remained, though, far short of the 12.6 tonnes of CO2 per capita consumed in developed countries (UN, 2005). Resources devoted to expanding fossil fuel generation could, therefore, be seen as a source of expanded climate-change impacts. On the other hand, investments in forestry and agricultural sectors designed to preserve and enhance soil fertility in support of improved food security MDGs (e.g., Goal #1) might have synergies for climate mitigation (through carbon sequestration) and for adaptation (because higher economic returns for local communities could be invested in adaptation). It is simply impossible to tell, a priori, which effect will dominate. Each situation must be analysed qualitatively and quantitatively.
These complexities make it clear that not all development paths will be equal with respect to either their consequences for climate change or their consequences for adaptive capacity. Moreover, the Millennium Ecosystem Assessment (MA, 2005) and others (e.g., AfDB et al., 2004) argue that climate change will be a significant hindrance to meeting the MDGs over the long term. There is no discrepancy here because stresses from climate change will grow over time. Some regions and countries are already lagging in their progress towards the MDGs and these tend to be in locations where climate vulnerabilities over the 21st century are likely to be high. For example, the proportion of land area covered by forests fell between 1990 and 2000 in sub-Saharan Africa, South-East Asia and Latin America and the Caribbean, while it appeared to stabilise in developed countries (UN, 2005). Energy use per unit of GDP fell between 1990 and 2002 in both developed and developing regions, but developed regions remained approximately 10% more efficient than developing regions (UN, 2005). In short, regions where ecosystem services and contributions to human well-being are already being eroded by multiple external stresses are more likely to have low adaptive capacity.
The range of increase in global mean temperature that could be expected over the next several centuries is highly uncertain. The compounding diversity in the regional patterns of temperature change for selected changes in global mean temperature is depicted elsewhere in IPCC (2007b, Figure SPM.6); so, too, are illustrations of geographic diversity in changes in precipitation and model disagreement about even the sign of this change (IPCC, 2007b, Figure SPM.7). Earlier sections of this chapter have also underscored the difficulty in anticipating the development of adaptive capacity and the ability of communities to take advantage of the incumbent opportunities. Despite all of this complexity, however, it is possible to offer some conclusions about vulnerability across regions and sectors as reported throughout this report.
Locating the anticipated impacts of climate change on a map is perhaps the simplest way to see this point. Figure 9.5, for example, shows the spatial distribution of the projected impacts that are reported for Africa in Chapter 9. The power of maps like this lies in their ability to show how the various manifestations of climate change can be geographically concentrated. It is clear, as a result, that climate change can, by virtue of its multiple dimensions, be its own source of multiple stresses. It follows immediately that vulnerability to climate change can easily be amplified (in the sense that total vulnerability to climate change is greater than the sum of vulnerabilities to specific impacts) in regions like the southeastern coast of Africa and Madagascar.
Maps of this sort do not, however, capture sensitivities to larger indices of climate change (such as increases in global mean temperature); nor do they not offer any insight into the timing of increased vulnerabilities.
Tables 20.8 and 20.9 address these deficiencies by summarising estimated impacts at global and regional scales against a range of changes in global average temperature. Each entry is drawn from earlier chapters in this report, and assessed levels of confidence are indicated. The entries have been selected by authors of the chapters and the selection is intended to illustrate impacts that are important for human welfare. The criteria for judging this importance include the magnitude, rate, timing and persistence/irreversibility of impacts, and the capacity to adapt to them. Where possible, the entries give an indication of impact trend and its quantitative level. In a few cases, quantitative measures of impact have now been estimated for different amounts of climate change, thus pointing toward different levels of the same impact that might be avoided by not exceeding given amounts of global temperature change.
The time dimension is captured by the bars drawn at the top of Table 20.8; they indicate the range of global average temperature increase that could be expected during the 2020s, the 2050s and the 2080s among the SRES collection of unmitigated scenarios as well as a range of alternative stabilisation pathways (Nakicenovic and Swart, 2000). The real message to be drawn from their inclusion is that no temperature threshold associated with any subjective judgment of what might constitute 'dangerous' climate change can be guaranteed by anything but the most stringent of mitigation interventions, at least not on the basis of current knowledge. Moreover, there is an estimated commitment to warming of 0.6°C due to past emissions, from which impacts must be expected, regardless of any future efforts to reduce emissions in the future.
20.7.3 The complementarity roles of mitigation and enhanced adaptive capacity
IPCC (2001a) focused minimal attention on the co-benefits of mitigation and adaptation, but this report has added a chapter-length assessment of current knowledge at the nexus of adaptation and mitigation. An emphasis on constructing a "portfolio of adaptation and mitigation actions" has emerged (Chapter 18, Sections 18.4 and 18.7). Moreover, the capacities to respond in either dimension are supported by 'similar sets of factors' (Chapter 18, Section 18.6). These factors are, of course, themselves determined by underlying socio-economic and technological development paths that are location and time specific.
Yohe et al. (2006a, b) offer suggestive illustrations of potential synergies within the adaptation/mitigation portfolio; complementarity in the economic sense that one makes the other more productive. Figures 20.5 and 20.6 display the geographic distribution of these synergies in terms of a national vulnerability index with and without mitigation, and with and without enhanced adaptive capacity by 2050 and 2100, respectively. Vulnerabilities that were assigned to specific countries on the basis of a vulnerability index derived from national estimates of adaptive capacity provided by Brenkert and Malone (2005) and the geographic distribution of temperature change derived from a small ensemble of global circulation models. The upper left panels of Figures 20.5 and 20.6 present geographical distributions of vulnerability in 2050 and 2100, respectively, along the SRES A2 emissions scenario with a climate sensitivity of 5.5°C under the limiting assumption that adaptive capacities are fixed at current levels; global mean temperature climbs by 1.6°C and 4.9°C above 1990 levels by 2050 and 2100, respectively. These two panels are benchmarks of maximum vulnerability against which other options can be assessed. Notice that most ofAfrica plus China display the largest vulnerabilities in 2050 and that nearly every nation displays extreme vulnerability by 2100. A2 was chosen for illustrative clarity with reference to temperature change only. Moreover, none of the interpretations depend on the underlying storyline of the A2 scenario; Yohe et al. (2006b) describes comparable results for other scenarios.
The upper right panels present comparable geographic distributions under the assumption that adaptive capacity improves everywhere with special emphasis on developing countries; their capacities are assumed to advance to the current global mean by 2050 and 2100 for Figures 20.5 and 20.6,
Global mean annual temperature change relative to 1980-1999 (°C) 12 3 4
CO2 stabilisation: TAR
SRES: AR4 WG1 multiple sources B1 •
Was this article helpful?