RESUMO
Electricity plays a pivotal role in the socio-economic development of nations. However, heavy reliance on fossil fuels for electricity generation, as observed in Iran, poses significant environmental challenges. This study proposes a novel hybrid methodology that combines system dynamics modeling and Design of Experiments (DOE) to examine economic and environmental indicators within Iran's electricity sector. The system dynamics model delineates four key subsystems: consumption, production, CO2 emissions, and power trade. By integrating DOE into this framework, various economic and environmental metrics are assessed for the year 2040. Through a comprehensive analysis of variable impacts on these indicators, optimal levels are identified to achieve favorable outcomes. Notably, variables such as the allocation coefficient of export income to capacity development and electricity export price emerge as critical determinants. Due to economic, environmental, and economic-environmental indicators, the most appropriate level of allocation of export income towards capacity development is estimated at 30, 10, and 20 percent, respectively. The study recommends allocating 80 % of the capacity development budget to renewable energy sources and 20 % to thermal power plants to optimize future conditions. In business as usual, the Export CO2 emission damage to export income index will be 0.19. In implementing the proposed scenario, according to the economic-environmental index, this value will decrease and reach 1.73E-06, which indicates the improvement of electricity export from the economic-environmental dimension. This research underscores the importance of balancing economic prosperity with environmental sustainability in electricity industry planning and policy formulation.
RESUMO
Hospital traditional cost accounting systems have inherent limitations that restrict their usefulness for measuring the exact cost of healthcare services. In this regard, new approaches such as Time Driven-Activity based Costing (TDABC) provide appropriate information on the activities needed to provide a quality service. However, TDABC is not flawless. This system is designed for conditions of relatively accurate information that can accurately estimate the cost of services provided to patients. In this study, the fuzzy logic in the TDABC model is used to resolve the inherent ambiguity and uncertainty and determine the best possible values for cost, capacity, and time parameters to provide accurate information on the costs of the healthcare services. This approach has not yet been tested and used in determining the costs of services of a healthcare setting. Therefore, the aim of this study is to present a new Fuzzy Logic-TDABC (FL-TDABC) model for estimating healthcare service costs based on uncertainty conditions in hospitals. The proposed model is implemented in a sample of the hospital laboratory section and the results are compared with the TDABC system. The TDABC model, by allocating the activity costs including fixed costs and not considering the uncertainty regarding the cost, capacity, and time required for each patient, often estimates the unused capacity and costs with a higher margin of error. The results show that the maximum difference in the prescribed costs was 4.75%, 3.72%, and 2.85% in blood bank, microbiology, and hematology tests, respectively, mostly due to uncertainty in the costs of consumables, equipment and manpower (on average 4.54%, 3.8%, and 3.59%, respectively). Also, The TDABC system, in comparison with the proposed system, estimates the unused capacity of the resource with more error. Cost of unused capacity derived using FL-TDABC were 80% of costs derived using TDABC. In conditions where the information is ambiguous, using the new system in hospitals can lead to a more accurate estimate of the cost compared to the TDABC system. Moreover, it helps hospital managers to make appropriate decisions about the use of capacity, capital budgeting, cost control, and etc.
