An evaluation of the economic impact of Climate Change through a three-stages Discrete Stochastic Programming model
The climate change in the agricultural sector acting on multiple weather variables at different times of the various crop cycles. In several cases by changing the mean level of variables (rainfall, temperature, etc..), in other cases by changing the distribution of events. This work provides an evaluation of the economic impact due to changes in multiple events, and to the associated uncertainty. For this reason, a classical two-stage stochastic programming model was extend into a three-stages model. The model is specified for an area of Sardinia, and examines the impact of climate change on rainfall and hence on the availability of water for agriculture, and on maximum temperatures and, therefore, on the requirements of some irrigated crops relevant to the agricultural economy of the area. The effect of climate change is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future, obtained by projecting to 2015 the climate trends of the last fifty years. The results show that the agricultural sector of the area adapt itself with a low cost by use of land and cultural practices. This cost, however, is very high for some farms that suffer a significant reduction of the income. There is also an increase of the use of natural resources, in particularly groundwater. The economic impact of these changes is due primarily to the decreased of water availability in the future. The availability of water becomes the crucial factor to adapting to climate change, because the effects of temperature can be compensate by increased the use of water resources.
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- Ingham, Alan & Ma, Jie & Ulph, Alistair, 2007. "Climate change, mitigation and adaptation with uncertainty and learning," Energy Policy, Elsevier, vol. 35(11), pages 5354-5369, November.
- Heinemann, A. B. & Hoogenboom, G. & de Faria, R. T., 2002. "Determination of spatial water requirements at county and regional levels using crop models and GIS: An example for the State of Parana, Brazil," Agricultural Water Management, Elsevier, vol. 52(3), pages 177-196, January.
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