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Iran is expected to experience increasingly severe economic consequences from climate change, given that its annual precipitation is less than one-third of the global average. This study investigates the potential impacts of climate change on economic output across different sectors of the Iranian economy using a dynamic input-output (IO) modeling framework. The results indicate that climate change could reduce annual aggregate economic output growth from approximately 2.7% under a no climate change baseline to 1.6–2.0% across the simulated climate scenarios. The effects vary significantly among different sectors, with the output structure gradually shifting toward services and manufacturing, while agriculture and energy experience comparatively greater losses. In particular, agricultural activities are projected to experience a reduction of 1–2% in their annual growth rate from the current level of approximately 2.8%, whereas the growth of most non-agricultural activities is expected to decline by less than 1%. In order to improve the flexibility of the traditional IO framework in addressing climate-induced disruptions, a fuzzy optimization method is integrated to determine adjustments that reduce output losses associated with climate change. The optimization results suggest that output losses could be reduced by approximately 22% by the end of the simulation period, primarily through an expansion of the services sector. Moreover, a 10% rise in final demand is observed to exert relatively minor impacts on total output growth and the composition of sectors throughout the simulation period. Under the optimization framework; however, this increase in final demand could raise aggregate output by approximately 0.4% annually. Overall, the findings highlight the substantial and heterogeneous economic effects of climate change in Iran and demonstrate that flexible resource reallocation, particularly toward the services sector, can partially mitigate climate-induced output losses.
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keshavarz et al. (2026) studied this question.
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