• Energy Research

Abstract Commercial potential in developing countries has always received a great attention from international investors, but this is not the case in Waste-to-Energy sector. Waste-to-Energy is bound up with various uncertainties rooted in its long-term nature therefore incorporating risks regarding political matters in developing countries makes it more complex. The present study substantiates the incompatibility of classic valuation methods in risky projects. Consequently, to deal with the riskiness of Waste-to-Energy investment in less developed countries, the combination of binomial tree analysis and Decoupled NPV is proposed. The hybrid approach is deployed to value a Waste-to-Energy project in Iran, and all evidence attest to the robustness of the method. The contribution of this paper can open up new vistas for investing in Waste-to-Energy industry, thus abating the catastrophic effects of landfill gas emissions.

Abstract Irrefutably, energy transition has recently gathered momentum thanks to the Paris Agreement and renewable energy (RE) falling costs. However, unlocking the full socio-economic potential of energy transition requires encouraging Foreign Direct Investment (FDI) in developing countries where energy demand and the risk of stranded assets are growing. Encouraging FDI in developing countries necessitates addressing two highly controversial issues: measuring political risk and carrying out proper valuation of long-term RE investments. In this regard, current paper investigates the rationale behind classic risk-adjusted discount rate (RADR) approach. It finds that since RADR requires heuristics, it may lead to misrepresentation of real worth of RE projects in developing countries. Addressing this issue, the paper offers a hybrid method based on risk pricing which can incorporate political risk into valuation straightforwardly. The hybrid method is applied to a photovoltaic case in Iran. Surprisingly, while the analysis based on classic RADR approach indicates that the investment is not financially viable, the hybrid analysis demonstrates attractive return on the investment. This can contribute to unlocking a considerable annual amount of 270 TWh solar power in Iran. The empirical study also shows that the suitable rate to discount RE investments in Iran is 10.22%.

Abstract Today, oil and natural gas are the most important sources of human energy. These two essential sources have strategic importance in economic and political equations of the world and can play a significant role in the process of international relations. To this end, this research design a multi-echelon network for the oil and gas supply chain includes extraction, purification, storage and shipping to the target market. Furthermore, a bi-objective mathematical model is formulated which attempts to maximize total profit from the sale of fossil fuels and to maximize the reliability of processing plants to meet the applicants’ demand. Besides, there may be disturbances in the extraction phase due to the failure of the extractors. To this end, several disruption scenarios are provided to deals with possible disorders. Moreover, a real-world case study in the Iranian oil and gas industry is applied to verifying the proposed model and it solved using augmented e-constraint and goal programming methods. Also, the sensitivity analysis is performed to provide some useful managerial insights. The results show that the e-constraint method was selected as the best approach in terms of CPU time and objective functions’ values. Moreover, increment in demand led to the use of more gathering centers, which increased the inventories and economic factors such as costs. Likewise, shipping costs of products to gathering centers are also very effective in selecting them. Therefore, locating gathering centers is a very important and precise task that is affected by geographical conditions, transport equipment, and proximity to key applicants.

Abstract Received a plethora of attention by both practitioners and researchers, oil price forecasting remains a challenging issue due to the particular characteristics of oil price and its prodigious impact on various economic sectors. Motivated by this issue, the authors aim to introduce a robust hybrid model for reliable forecasting of Brent oil price. For this purpose, the Adaptive Neuro Fuzzy Inference System (ANFIS), Autoregressive Fractionally Integrated Moving Average (ARFIMA), and Markov-switching models are employed in the proposed hybrid model. The cardinal merit of this hybridization lies in the fact that the constituent models are capable of capturing particular features like nonlinearity, lag, and market interrelationships existing in oil price time series. Then, specific weights are assigned to each model to achieve an accurate prediction of the empirical time series. Three weighting scenarios, namely equal weights, error-value-based weights, and genetic algorithm weighting function, are applied. The authors use root mean square error, mean absolute error, and mean absolute percentage error to measure errors. Robustness of results and prediction quality of the hybrid model compared with counterparts are also investigated by Diebold-Mariano test. Finally, numerical results reveal that the hybrid model weighted by genetic algorithm generally outperforms the constituent models, hybrid model with equal weights, and hybrid model weighted based on the error values. Reliable forecasting of crude oil prices is especially beneficial to producer and importer nations to optimize their production and order rates and mitigate the adverse effect of possible shocks.