Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/240249 
Year of Publication: 
2019
Citation: 
[Journal:] Agricultural and Food Economics [ISSN:] 2193-7532 [Volume:] 7 [Issue:] 1 [Publisher:] Springer [Place:] Heidelberg [Year:] 2019 [Pages:] 1-12
Publisher: 
Springer, Heidelberg
Abstract: 
In this paper, we have constructed and tested a simple methodology for assessing and predicting climate change effects on agricultural yields. The methodology follows two steps. First, we econometrically estimate the marginal product of key production inputs (e.g., labor and land), through the estimation of production functions. Then, we predict future agricultural sector performance, by assuming a future with climate-induced changes in the land use and in agricultural labor use, under different IPCC scenarios. We also assume that no dramatic technological change in agriculture production will occur in the near future, so that the selected inputs will present the same marginal product. We assume that the agricultural sector might develop differently under different climate change-induced scenarios and that the use of land and labor will change accordingly. In this way, we are able to compute predictions on the agricultural sector performance in the future, under very different circumstances. We apply the methodology for predicting the sector performance of the Veneto region in 2030. Results differ according to the selected IPCC scenario and consequent input use variations. In the selected case study, for instance, land presents a very high productivity and climate-induced changes in the land use might dramatically (positively and negatively) affect agricultural yields under different IPCC scenarios. In this perspective, the climate change adaptation and mitigation policies and options should primarily aim at the preservation of land productivity in Veneto.
Subjects: 
Climate change
IPCC
Cobb-Douglas/translog production function estimation
Production inputs marginal productivity
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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