Abstract
In recent years, many countries and firms seek the new and renewable energy to cope with the impending global environmental crisis, such as depletion of fossil-based energy, climate change to control emissions of greenhouse gases. This paper aims to take the perspective of the firm, which undertakes the energy R\&D project to maximize profits implying minimization of total cost as well. Incorporating technical and market risks into energy R\&D project is crucial, in that the managers often face the rapidly changing environment full of uncertainties. The firms should incorporate managerial flexibility into energy R\&D project decision not only reducing uncertain risks, but also increasing potential market payoff. This research considers a multi-stages decision model in which real-option-based analysis is applied for energy R\&D project under fuzzy environment. Specifically, the market payoff is obtained when the new and renewable energy product is commercialized to market, while energy R\&D investment costs are exhausted gradually. Furthermore, the uncertain development performance and market information are described as fuzzy variables by credibility theory. Instead of the traditional real option pricing methods, the dynamic programming methodology that captures the uncertain product development performance and final market return is developed to more effectively characterize the managerial flexibility. This method can reflect the multi-stages nature of R\&D programme, while helping decision-makers take the optimal investment decision and capture future market opportunities of energy products.
Highlights
The accelerating shortage of fossil-based energy resources and shocks of oil prices, coupled with regulatory responses to impending global warming such as the climate change policy for reduction of greenhouse gas emissions, have prompted clearly the increasingly important sources of developing new and renewable energy such as wind, photovoltaic, thermal heat, and biological organisms (Kim etc., 2014)
This paper considers a multi-stages decision model for the optimal investment policy of energy research and development (R&D) project, taking into account R&D variabilities as risk events described by fuzzy variable that have a optimistic or pessimistic effects on the policy targets
Dynamic programming model, which is composed of uncertain R&D information and described as more realistic formulation of investment planing by mathematical equations, is proposed to analyze crucial development risks methodically and determine proper decision operations ahead of increasing managerial flexibility based on real option analysis in accordance with Santiago and Vakili (2005)
Summary
The accelerating shortage of fossil-based energy resources and shocks of oil prices, coupled with regulatory responses to impending global warming such as the climate change policy for reduction of greenhouse gas emissions, have prompted clearly the increasingly important sources of developing new and renewable energy such as wind, photovoltaic, thermal heat, and biological organisms (Kim etc., 2014). Dynamic programming model, which is composed of uncertain R&D information and described as more realistic formulation of investment planing by mathematical equations, is proposed to analyze crucial development risks methodically and determine proper decision operations ahead of increasing managerial flexibility based on real option analysis in accordance with Santiago and Vakili (2005). Since managerial flexibility is not the internal attribute of energy R&D project, the presented method can recognize the potential risky factors, develops a serious of real options to capture the risky factors, afterwards the fuzzy decision model is used to evaluate the value of opportunity and take the decision options, which can maximize the market payoff of R&D project.
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