Things More Worrisome than AGW: various determinants of human well-being

Source:  Climate Change Reconsidered
by Craig Idso
van Vuuren, D. P., Isaac, M., Kundzewicz, Z. W., Arnell, N., Barker, T. Criqui, P., Berkhout, F., Hilderink, H., Hinkel, J., Hof , A., Kitous, A., Kram, T., Mechler, R., and Scrieciu, S. 2011. The use of scenarios as the basis for combined assessment of climate change mitigation and adaptation. Global Environmental Change 21: 575-591.

This paper summarizes the results of several analyses that investigate the global impacts on various determinants of human well-being, including malaria, agricultural productivity, water stress, sea level rise, and heating and cooling demand through 2100 under a “no climate change” scenario and two “policy” scenarios. The first policy scenario, which serves as the “baseline,” assumes no climate change policies and would increase the global mean temperature by 4 °C above the pre-industrial level by 2100. The second is a Mitigation scenario which would stabilize greenhouse gas concentrations at around 450 ppm CO2-equivalent leading to a 2 °C increase in 2100. Its aim is to assess the effects of an aggressive mitigation policy on the global impacts of climate change.

All scenarios assume that global population will increase to about 9.1 billion in 2050 and stabilize at 9.2 billion by 2100, and that economic growth will follow the IPCC’s B2 scenario till 2050; however, the write-up is vague regarding the subsequent rate of economic growth (p. 576).

Like all impact assessments, the analyses rely on a series of linked models which, as far as the reader can determine, have not been empirically validated using data that were not used in model development. No less important, the uncertain outputs of one model are used as inputs for the next (uncertain) model. To compound matters, no quantitative estimates are provided of the confidence levels or uncertainties that should be attached to any of the impact estimates. In addition, with the partial exception of the malaria analysis (see below), the analyses apparently did not consider advances in adaptive capacity. As pointed out in an earlier NIPCC review, Impacts Assessments Systematically Overestimate Net Damages from Global Warming, this results in overestimating negative impacts and underestimating positive impacts.

Setting these concerns aside, following is a summary of the study’s global-scale impacts for various determinants:

Malaria. This analysis builds in some increases in adaptive capacity due to the economic development assumed in the baseline scenario, but it is silent on increases due to technological change. It indicates that if “no GDP growth” is assumed, then under the baseline scenario (equivalent to “unmitigated climate change”), malaria deaths would increase by about 100% in 2050. Under the mitigation scenario, it would drop by 2% (relative to the unmitigated case). Van Vuuren et al. note: “Adaptation, therefore, has a much more decisive influence on malaria control than mitigation (this finding seems to be robust with available literature)” (p. 583), a finding that others have made previously (Goklany and King, 2004; Goklany 2009a).
Agricultural Yields. There is no suggestion that autonomous adaptations due to advances in adaptive capacity are included in the estimates for the baseline scenario. Unmitigated climate change is projected to decrease yields of maize, wheat and rice by 3-7% in mid- to high-latitude areas in 2100, and 10-35% in low latitude areas (based on visual inspection of the graph provided). Under an adaptation scenario, yields would increase by 2-10% in the former regions, but would still be 5-15% below the no-climate-change case in low latitude areas. Under the mitigation-but-no-adaptation scenario, yields would be a little bit worse for mid- to high-latitude regions but better for the low latitude regions. Finally, with both adaptation and mitigation, yields would be higher in all regions (relative to the no-climate-change case).
Water Stress. Results, based on the HadCM2 model and a global water resources model, indicate that through 2100 fewer people worldwide would be at risk of water stress because of climate change, and that “in some regions mitigation may even increase the numbers of people” (p. 584). These results are consistent with other studies of the global impact of climate change on water resources (e.g., Arnell et al., 2002, 2011). [See, also, Arnell (2004), and Alcamo et al. (2007).]
Sea Level Rise. The study estimates a sea level rise of 71 cm and 49 cm by 2100 under the unmitigated and mitigation scenarios, respectively. It interprets its results as follows: “[I]ndependent of the level of mitigation, adaptation reduces global overall costs rather effectively, which illustrates the necessity for engaging in adaptation even under ambitious mitigation. At the aggregated scale more damages can be avoided through an adaptation-only strategy than through a mitigation-only strategy, although a combination of the two has the strongest positive impact.” It notes that the cost of adaptation would be a “relatively small fraction of global GDP” but could be much larger for the small island states, but no specific numeric results are provided.
Heating and Cooling Energy Demand. Results indicate that on a global basis, net impact on energy demand is small. 

Finally, van Vuuren et al. undertake a cost-benefit analysis using the FAIR model, which has derived its damage and adaptation cost functions from the AD-DICE model (de Bruin et al. 2009)-itself based on Nordhaus’s DICE model-despite the fact that these damage functions are unrelated to the above damages that they themselves estimated (p. 586). Note that neither AD-DICE nor van Vuuren et al. allude to technological change, that is, it seems to have been ignored. Also note that AD-DICE uses generic damage functions that assume damage increases with the temperature increase since 1900 (de Bruin et al., 2009). Implicit in this assumption is that society is optimally adapted to the 1900 temperature, an arguable proposition at best. Other problems with AD-DICE include the following:

? Although it recognizes that it is probably incorrect for many regions, it adopts DICE’s assumption that the net impacts of climate change are always negative (de Bruin et al. 2009, footnote 3). This contradicts IPCC (2001: 940-943; see also Hitz and Smith, 2004).
? De Bruin et al. also note that “According to Nordhaus and Boyer (2000), adaptation is included in the damage estimates and it is implicitly assumed that this is the optimal adaptation.” However, there is no critical evaluation of this claim. Given the state-of-the-art of impacts modeling, one must necessarily be skeptical of claims that adaptation has been appropriately incorporated. See here.

The results of this add-on analysis, based on a 2.5% discount rate (which some would contend is low; e.g., Byatt et al. 2007), are shown in the following figure.


Figure 1: Mitigation costs, adaptation costs, and residual damages due to climate change as share of GDP according to the FAIR model (from van Vuuren et al. 2011, based on Hof et al., 2009). Note “baseline” assumes unmitigated climate change.


In their conclusions, van Vuuren et al. claim that “Integrated scenario analysis … can form a good basis for exploring the different consequences of policy choices (including uncertainties) [but] it is not feasible, given uncertainties to determine an optimal mix between mitigation, adaptation and residual damages.” In light of the second part of the above sentence, the first part seems overly optimistic. They also note that:

? “Effective climate policy includes both adaptation and mitigation” (p. 587). Given that the methodologies used to estimate impacts and damages tend to underestimate the potential of adaptation, however, this statement is open to argument, and cannot be taken as definitive.
? “While climate change may have an impact on millions of people, other challenges are likely to influence people and governance more significantly” (p. 588). They, however, caveat this statement by adding that their analysis “covered only a limited set of impacts and focused mostly on mean estimates of gradual climate change and, for instance, not on catastrophic, very high-impact, extremely low-probability events” (p. 588). But, based on the science, it is difficult, if not impossible to distinguish “extremely low-probability events” from the “extremely” unlikely, if not speculative (IPCC, 2007: 16-17; Goklany, 2009b). 

The conclusion that climate change impacts would likely be overshadowed by other factors echoes conclusions of other studies that have compared future climate change impacts against those of other factors (e.g., Goklany, 2009a).

Additional References
Alcamo, J., Flörke, M., and Märker, M. 2007. Future long-term changes in global water resources driven by socio-economic and climatic changes. Hydrological Sciences Journal 52: 247-275.

Arnell, N.W. 2004. Climate change and global water resources: SRES emissions and socio economic scenarios. Global Environmental Change 14: 31-52.

Arnell, N.W., Cannell, M.G.R., Hulme, M., Kovats, R.S., Mitchell, J.F.B., Nicholls, R.J., Parry, M.L., Livermore, M.T.J., and White, A. 2002. The consequences of CO2 stabilization for the impacts of climate change. Climatic Change 53: 413-46.

Arnell, N.W., van Vuuren, D.P., and Isaac, M. 2011. The implications of climate policy for the impacts of climate change on global water resources. Global Environmental Change 21: 592-603.

Byatt, I., Castles, I., Goklany, I.M., Henderson, D., Lawson, N., McKittrick, R., Morris, J., Peacock, A., Robinson, C., Skidelsky, R. 2007. The Stern Review: A Dual Critique, Part II: Economic Aspects. World Economics 7: 165-232.

De Bruin, K.C., Dellink, R.B., Tol, R.S.J., 2009. AD-DICE: an implementation of adaptation in the DICE model. Climatic Change 95: 63-81.

Goklany, I.M. 2009a. Is Climate Change the “Defining Challenge of Our Age”? Energy & Environment 20: 279-302.

Goklany, I.M. 2009b. Trapped Between the Falling Sky and the Rising Seas: The Imagined Terrors of the Impacts of Climate Change. University of Pennsylvania Workshop on Markets & the Environment.

Goklany, I.M., and King, D.A. 2004. “Climate Change and Malaria.” Letter. Science 306: 55-57.

Hitz, S., and Smith, J. 2004. Estimating Global Impacts from Climate Change. Global Environmental Change 14: 201-218.

IPCC [Intergovernmental Panel on Climate Change]. 2001. Climate Change 2001: Impacts, Vulnerability, and Adaptation. Cambridge: Cambridge University Press.

IPCC. 2007. Climate Change 2007: The Physical Science Basis. Cambridge: Cambridge University Press, 2007.