Environmental decision-making commonly involves multifaceted problems that demonstrate considerable uncertainty. Monte Carlo simulation approaches have been employed in a variety of environmental planning venues to address these uncertain aspects. Simulation-based outputs are frequently presented in the form of probability distributions. Recently an approach referred to as simulation decomposition (SD) has been introduced that extends the analysis of Monte Carlo results by enhancing the explanatory power of the cause-effect relationships between the multi-variable combinations of inputs and the simulated outputs. SD constructs sub-distributions of the simulation output by pre-classifying some of the uncertain input variables into states, clustering the various combinations of these different states into scenarios, and then collecting simulated outputs attributable to each multi-variable input scenario. Since the contribution of subdivided scenarios to the overall output is easily portrayed visually, SD can highlight and disclose previously unidentified connections between the multi-variable combinations of inputs on the outputs. An SD approach is generalizable to any Monte Carlo model with negligible additional computational overhead and, hence, can be readily used for environmental analyses that employ simulation models. This study illustrates the efficacy of SD in environmental analysis using a carbon capture and storage project from China.
This paper presents a new method to enhance simulation-based analysis of complex investments that contain multi-variable uncertainty. The method is called "simulation decomposition". Typically the result of simulation-based investment analysis is in the form of histogram distributions-here we propose a method for first classifying the possible outcomes of selected uncertain variables into states and then using combinations of the created states in the decomposition of the simulated distribution into a number of sub-distributions. The sub-distributions that can be matched to state-combinations of the variables contain relevant actionable information that helps managers in decision-making with regards to the studied investments. A numerical illustration of a renewable energy investment is used to demonstrate the usability, the enhanced analytical power, and the intuitively understandable benefits that can be reached by using the simulation decomposition method. The proposed method is generally usable and can be utilized independent of the investment context.
Russian renewable energy policy, introduced in May 2013, is a capacity mechanism-based approach to support wind, solar, and small hydro power development in Russia. This paper explores the effect of the new mechanism on the profitability of new renewable energy investments with a numerical example. The sensitivity of project profitability to selected factors is studied and the results are compared ceteris paribus to results from a generic feed-in premium case. Furthermore, the paper gives a complete and detailed presentation of the capacity price calculation procedure tied to the support mechanism.The results show that the new Russian renewable energy capacity mechanism offers a significant risk reduction to the investor in the form of dampening the sensitivity to external market factors. At the same time it shields the energy market system from excessive burden of renewable energy support. Even if the complexity of the method is a clear drawback to the detailed understanding of how the mechanism works, the design of the incentive policy could be an appealing alternative also for other emerging economies.
Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.
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