Page 115 - Climate Change and Food Systems

required is the ability to model extreme events
and variable climate conditions, as opposed to
the usual treatment of gradual climate change, which is much harder to detect but for which most IAMs are designed. Economic models also need to systematically quantify uncertainties related to the economic models’ structure and parameters and framing economic conclusions in the context of known model limitations. Expressing the model results in probabilistic terms will help decision- makers to understand the risks of under- or over- investing in adaptation to high- or low-probability climate change outcomes.
Increasingly trade is a subject of analysis within the economic modelling of climate change. However, the empirical evidence is incomplete and fraught with the usual caveats related to uncertainty vis-à-vis future climate outcomes and developments in climate and trade policy. More robust trade analyses in the context of climate change should integrate, climate direct impacts on agricultural productivity, demand-side drivers (consumer diets, labelling, subsidies), resource constraints (such as climate-induced irrigation water shortages), as well as climate policies (carbon-taxes, standards, eco-labelling).
As adaptation decisions are inherently local, many economic models at the farm/household level have tackled climate variability. However, better integrated frameworks are required, especially those that integrate biophysical and spatial techniques (GIS, AEZ-based) with socio- institutional analyses (multi-criteria analysis, ABMs) to better appraise vulnerability, adaptive capacity and adaptation required (autonomous and planned). Better data collection for CBA is needed, including improvements in accounting for resource depletion, environmental change as well as distributional issues. Moreover, household models need to do better job incorporating climate risk and intra-seasonal climate variability, and develop the capacity to estimate adaptation options outside the current farmers’ choice, set in line with the size of the climate shocks and increased variability expected under climate change in order to develop better estimates for
local adjustment costs and develop robust climate policies.
References
Agrawala, S. & S. Fankhauser (eds.). 2008.
Economic aspects of adaptation to climate change. Costs, Benefits and Policy Instruments, Paris, OECD9264046038/978-9264046030.
Ahmed, A., N. Diffenbaugh, T. Hertel & W. Martin. 2011b. “Agriculture and trade opportunities
for Tanzania: Past volatility and future climate change.” Working Paper No. 2011/91. United Nations University World Institute for Development Economics Research. Helsinki, Finland.
Ahmed, A., N. Diffenbaugh, T. Hertel, D. Lobell, N. Ramankutty, A. Rios & P. Rowhani. 2011a. “Climate volatility and poverty vulnerability in Tanzania,” Global Environmental Change 21: 46–55.
Ainsworth, E., A., Andrew, B. Leakey, D. Ort, &
S. Ling. 2008. “FACE-ing the facts: inconsistencies and interdependence among field, chamber and modeling studies of elevated CO2 impacts on crop yield and food supply,” New Phytologist 179: 5–9.
Akponikpè, P., G. Michels & K. Bielders. 2010. Use of the APSIM model in long term simulation to support decision making. Eur. J. Agron. 32,144– 154.
Alcamo, J., P. Döll, T. Henrichs, F. Kaspar, T. Rösch & S. Siebert. 2003. Development and testing of
the WaterGAP 2 global model of water use and availability. Hydrol. Sci. J.48 (3), 317–337.
Al-Riffai, P., B. Dimaranan & D. Laborde. 2010.
Global Trade and Environmental Impact Study
of the EU Biofuels Mandate, Final Report for the Directorate General for Trade of the European Commission, International Food Policy Research Institute.
chapter 3: economic modelling of climate impacts and adaptation in agriculture: a survey of methods, results and gaps
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