درخت حوزه‌های تخصصی

Climate change and infectious diseases have emerged as two of the most significant challenges facing the world and its economic systems today. Recent studies have increasingly focused on understanding how phenomena such as the COVID-19 pandemic and the climate change crisis—including rising greenhouse gas emissions and uncertainties in climate policy—affect macroeconomic variables and capital markets. Many experts assert that climate change poses a serious threat to human civilization. In light of these pressing concerns, this paper examines the impact of Climate Policy Uncertainty (CPU) and Infectious Disease Risk (IDR) on the Islamic Dow Jones Index (IDJ). To achieve this, the study utilizes monthly data spanning from January 2016 to March 2021 and employs a Structural Vector Autoregressive (SVAR) model. Additionally, Contour Plot graphs are applied to estimate the interaction effects on the IDJ. The findings reveal contrasting impacts of the CPU and IDR indices on the IDJ. Specifically, an increase in the CPU index leads to higher utilization of the Earth's resources for producing goods and services by companies included in the index, resulting in a positive effect. Conversely, a rise in the IDR index exacerbates economic recession, thereby causing a decline in the IDJ.

Agricultural products, are mostly perishable, and ensuring their quality through methods like cold storage or optimal conditions can present significant economic challenges for retailers and small-scale producers. In some cases, these products lose their quality and marketability within a span of less than 24 hours. The main challenge in adopting this strategy is determining the timing and amount of the price discount. To optimize price discounting, four functions need to be estimated: a) a time-dependent quality function, b) a price change function, c) a demand function that is a function of price and quality, and d) an objective function, which is considered here as the retailer's profit function. We employ dynamic pricing models that integrate factors such as quality degradation, inventory levels, and consumer behavior. The research data was collected from three fruit and vegetable stores in Gorgan city. The time period was three months, comprising all working days. Each seller was asked to report the price and quantity of sales on an hourly basis from the beginning to the end of the working day. In this way, about 1000 data were recorded from each store. Tomato was selected as case study due to their widespread consumption, continuous supply, and rapid perishability. The results show that price discounts can significantly enhance profitability while reducing the risk of unsold and wasted products. Specifically, the findings indicate that implementing price discounts every 3 to 4 hours yielded the highest profitability. The average discount is 35%, leading to an 82% increase in gross profit. In contrast, shorter discount intervals (every 1 to 2 hours) with an average discount of 18% improved gross profit by 73%. On the other hand, longer discount intervals (every five to seven hours) with an average discount of 46% resulted in a 66% increase in gross profit. Furthermore, this study further emphasizes how crucial consumer perception is to the effectiveness of dynamic pricing methods. The perception of lower product quality brought on by excessive discounting may have a detrimental effect on long-term store credit and customer trust. Perishable product waste can be reduced through the implementation of price reduction strategies. However, the results indicate that sellers’ profits increase in the absence of price reductions. Future research could enhance this framework by incorporating additional factors that influence product quality, such as temperature, humidity, and product variety; examining the effects of consumer price expectations on demand; extending the model to other perishable products, including meat and dairy; and leveraging advanced technologies, such as blockchain and artificial intelligence, to improve dynamic pricing and inventory management.

Climate change, with its gradual and complex impacts, has increasingly inflicted damages on the agricultural sector of arid and semi-arid regions in Iran, including the Zayandeh-Rud Basin. This study aims to develop strategic adaptation policies to enhance agricultural resilience in response to climate change in this basin. The research is based on the Sendai Framework for Disaster Risk Reduction (SFDRR) and the resilience criteria established by the World Economic Forum (2013). A descriptive-analytical methodology was employed, combining documentary studies, field surveys, and the Delphi policy method to achieve expert consensus. Purposive sampling targeted faculties, agricultural experts of the Ministry of Agriculture, as well as producers and exporters of agricultural products with substantial expertise in Climate change issues, adaptation policies, and resilience. Data collection instruments consisted of four structured questionnaires designed to identify strengths and weaknesses, weight resilience criteria, prioritize policies, and select strategies, complemented by semi-structured interviews. Initially, to determine the strategic position of agriculture in the Zayandeh-Rud Basin, Internal Factor Evaluation (IFE), External Factor Evaluation (EFE), and Internal-External (IE) matrices were utilized. Subsequently, the Analytic Hierarchy Process (AHP) and the multi-criteria decision-making PROMETHEE method were employed to prioritize adaptation policies aimed at enhancing resilience to Climate Change. Suitable strategies were developed and extracted through SWOT analysis, and finally, the Quantitative Strategic Planning Matrix (QSPM) was used to evaluate the relative attractiveness and feasibility of each strategy, thereby selecting the implementable final policies. Findings indicated that agriculture in the Zayandeh-Rud Basin, with a final internal factor score of 2.04 and external factor score of 2.12, is in defensive position (survival mode) and exhibits low resilience due to water transfer and scarcity, land fragmentation and subsidence, and weaknesses in modern infrastructure. The most attractive adaptation policies identified under the defensive strategy include equitable water distribution (Total Attractiveness Score (TAS=1.46), development-oriented water resource planning (TAS=1.38), optimization of water. consumption and reduction of losses (TAS=1.35), control of groundwater levels (TAS=1.23), reform of regulations related to land fragmentation (TAS=1.18), and farmer participation in water resource management (TAS=1.16(. The proposed solutions emphasize strengthening integrated and basin-based water governance, implementing participatory strategic management models, investing in modern and soft technologies, gradually revising water-related laws and regulations, and expanding research and national strategy formulation. The results of this study provide actionable, stakeholder-inclusive recommendations for policymakers to enhance sustainability and resilience in vulnerable agricultural regions.