The purpose of this paper is analyzing the influence of climate change on the evolution of land use, and the assessment of economic change on agricultural productivity in the Region of Basilicata. Understanding and analyzing the impacts of climate on agricultural production in the evolutionary dynamics is very complex, because there are many other local variables (physical, social, etc.) that contribute in the determination of the variations. Therefore it is not possible to talk of a unique phenomenon that involves an entire continent or a nation, but of local phenomena, in climatic, economic, political and geographical similar conditions. It is therefore necessary to use a method of research able to take into account numerous variables (climatic, physical, economic, social, environmental, etc..), in order to define reliable scenarios of change. The multivariate analysis of the possible future transitions, realized through the use of neural network (ANN, Artificial Neural Network), simulating a logical-deductive reasoning like that of the human brain, are excellent models of space-time simulation. In the case under examination, we have used a special ANN model based on the use of the Multi-Layer Perceptron (MLP). Therefore, it is possible to consider such variables, some specific actions, such as tax reductions, incentives for agro-environmental activities, temperature and atmospheric CO2, reduction of rainfall, economic aid to the production and / or incentives for agricultural production sector. The output is a territory mapping, where a higher degree of risk corresponds to a higher capacity of soils to change their use. Comparing the current land cover with the future maps derived from the model MLP, you can make a dual analysis: - evaluating the effects of climate on land use changes; - estimating the economic impacts of climate change on agricultural production. The economic analysis was conducted comparing the current production levels with the future productivity, estimated using the scenarios made with the MLP technique previously described. Overall, the primary sector in Basilicata could be significantly affected by the effects of climate change, effects that could be mitigated by the adoption of new measures under the CAP for the period 2014-2020.

CLIMATE CHANGES AND ECONOMIC IMPACT EVALUATION IN PRIMARY SECTOR: PREDICTIVE MODEL OF LAND USE CHANGE

GIGLIO, PAOLO;COZZI, Mario;ROMANO, Severino;VICCARO, MAURO
2014

Abstract

The purpose of this paper is analyzing the influence of climate change on the evolution of land use, and the assessment of economic change on agricultural productivity in the Region of Basilicata. Understanding and analyzing the impacts of climate on agricultural production in the evolutionary dynamics is very complex, because there are many other local variables (physical, social, etc.) that contribute in the determination of the variations. Therefore it is not possible to talk of a unique phenomenon that involves an entire continent or a nation, but of local phenomena, in climatic, economic, political and geographical similar conditions. It is therefore necessary to use a method of research able to take into account numerous variables (climatic, physical, economic, social, environmental, etc..), in order to define reliable scenarios of change. The multivariate analysis of the possible future transitions, realized through the use of neural network (ANN, Artificial Neural Network), simulating a logical-deductive reasoning like that of the human brain, are excellent models of space-time simulation. In the case under examination, we have used a special ANN model based on the use of the Multi-Layer Perceptron (MLP). Therefore, it is possible to consider such variables, some specific actions, such as tax reductions, incentives for agro-environmental activities, temperature and atmospheric CO2, reduction of rainfall, economic aid to the production and / or incentives for agricultural production sector. The output is a territory mapping, where a higher degree of risk corresponds to a higher capacity of soils to change their use. Comparing the current land cover with the future maps derived from the model MLP, you can make a dual analysis: - evaluating the effects of climate on land use changes; - estimating the economic impacts of climate change on agricultural production. The economic analysis was conducted comparing the current production levels with the future productivity, estimated using the scenarios made with the MLP technique previously described. Overall, the primary sector in Basilicata could be significantly affected by the effects of climate change, effects that could be mitigated by the adoption of new measures under the CAP for the period 2014-2020.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/98520
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