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Technological progress has transformed the payment industry. Although alternative payment instruments such as bank cards share some characteristics with banknotes, they are not direct competitors to cash. First, cash possesses unique attributes—its functionality without electrical power and its inherent anonymity. Second, statistical data indicate that the ratio of cash to M1 has stabilized at approximately 10% in recent years, primarily due to its substitution with demand deposits. In this paper, we model the demand for banknotes by incorporating technology-related characteristics alongside conventional theories of money demand. Our approach has important implications for central bank, particularly in liquidity management, currency issuance, effective management of physical currency, and continued investment in digital payment infrastructures. We adopt a macroeconomic perspective that considers banknote demand from both transactional and asset viewpoints. To this end, we estimate an autoregressive distributed lag (ARDL) model, which allows us to analyze both short-term and long-term coefficients within a framework of model uncertainty. A key focus of our study is the estimation of a payment system development index using logistic growth and Gompertz models, through which we assess its impact on cash demand. Our empirical evidence supports the notion of S-shaped growth in the adoption of payment technologies. The findings demonstrate that advancements in the payment sector have led to a significant decline in cash demand. Moreover, while increased real economic activity exerts a positive and significant influence on cash demand, a higher opportunity cost of holding cash has a pronounced negative impact.

Objective : This research aims to identify recurring carbon reduction policies and their key variables, and analyze their relationships within pharmaceutical distribution in Iran. Methods : A mixed-method of qualitative and quantitative approaches was adopted. Firstly, a systematic literature review was employed to identify the policies and variables. Afterward, the Intuitive Fuzzy DEMATEL (Decision Making Trial and Evaluation Laboratory) method was used to analyze the causal relationships among identified variables in uncertain conditions. Following snowball sampling, data were gathered through expert questionnaires from 15 specialists in five groups, selected based on relevant expertise in carbon reduction policies, particularly for pharmaceutical distribution companies in Iran.  Results : The study identified three key carbon reduction policies—cap-and-trade, subsidy allocation, and financial penalties—all shaped by distinct variables. Cap-and-trade includes the emission cap, carbon selling price, and demand for carbon emission permits. Subsidy allocation and financial penalties cover the subsidy amount and the penalty rate, respectively. Notably, carbon emission level emerged as the most influential factor in shaping policy effectiveness, while carbon reduction cost was identified as the most impactful variable. These two variables are integral to all three policies, highlighting their pivotal role in policy formulation. While the demand for carbon emission permits remains neutral regarding influence and susceptibility, other variables demonstrate complex interdependencies, creating a dynamic system where policies interact directly through primary variables or indirectly through shared criteria. Conclusion : This study contributes to environmental policy research by offering an analytical framework that integrates uncertainty logic to assess relationships among key variables. The findings suggest that implementing a single policy may not be sufficient—a combination of strategies is recommended for more effective carbon reduction. Understanding how variables interact can help policymakers and businesses design targeted and efficient pharmaceutical distribution strategies in Iran.