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Uncertain Decision Making Methods in Energy Policies for Sustainable Development

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 52031

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Guest Editor
Research Institute of Sustainable Construction, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania
Interests: operations research; optimization and decision analysis; multicriteria decision making; multiattribute decision making (MADM); decision support systems; civil engineering; energy; sustainable development; fuzzy sets theory; fuzzy multicriteria decision making; sustainability; management; game theory and economical computing knowledge management
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Department of Business Systems and Analytics, La Salle University, 1900 W Olney Ave, Philadelphia, PA 19141, USA
Interests: decision sciences; business analytics; decision support systems; information sciences

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Guest Editor
Patel College of Global Sustainability, University of South Florida, Tampa, FL 33620, USA
Interests: sustainable development; renewable energy; renewable fuels; energy policy; integration of renewable energy into the fossil infastructure; sustainable power for small island developing states
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Given the critical role of energy in economic growth and the adverse environmental effects often associated with its use, energy policy is an essential factor of sustainable development programs. Managing energy-related environmental impacts is a main objective of energy policy and making energy development more sustainable at the national and international levels. Energy policies for sustainable development are the critical challenge for energy sector development, as the energy sector is a significant driver of economic growth and has a significant negative impact on the environment, especially on global climate change. In recent years, there are numerous economic, technical, social and environmental criteria are used to solve energy policies for sustainable development by decision-makers under the uncertain environments. Multicriteria decision‐making (MCDM) methods are used as effective tools to help decision-makers while solving energy policies problems. Therefore, in this special issue, we invite authors to submit original research and critical survey original research articles that propose uncertain decision-making methods to rationalize the complex process of decision-making in for sustainable development based energy policy making problems.

Dr. Abbas Mardani
Prof. Dr. Edmundas Kazimieras Zavadskas
Prof. Dr. Madjid Tavana
Prof. Dr. George Philippidis
Guest Editors

Manuscript Submission Information

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Keywords

  • Decision-making methods for sustainable development
  • Decision-making methods for energy policies
  • Sustainability assessment in energy
  • Energy policy modeling
  • International climate policy
  • Societal challenges in energy policy
  • Environmental impacts in the energy industry
  • Economic benefits for energy efficiency

Published Papers (16 papers)

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23 pages, 581 KiB  
Article
Research on Spatial Distribution Characteristics of High Haze Pollution Industries Such as Thermal Power Industry in the Beijing-Tianjin-Hebei Region
by Jingkun Zhou and Yating Li
Energies 2022, 15(18), 6610; https://doi.org/10.3390/en15186610 - 9 Sep 2022
Cited by 7 | Viewed by 1259
Abstract
The Beijing-Tianjin-Hebei region is subject to the most severe haze condition in China. Against the backdrop of the coordinated development of Beijing, Tianjin, and Hebei, it is of great significance to explore the space-time distribution characteristics of high haze pollution industries in the [...] Read more.
The Beijing-Tianjin-Hebei region is subject to the most severe haze condition in China. Against the backdrop of the coordinated development of Beijing, Tianjin, and Hebei, it is of great significance to explore the space-time distribution characteristics of high haze pollution industries in the above region. The purpose of this article is to find high haze pollution industries scientifically, analyze the spatial distribution characteristics of high haze pollution industries in Beijing-Tianjin-Hebei correctly, and formulate effective reduction measures for the spatial distribution of high haze pollution industries in different regions of Beijing-Tianjin-Hebei. It hopes that the measures provide a basis for effective to reduce high haze pollution in the Beijing-Tianjin-Hebei region. Using the Gini coefficient and location entropy, this paper explores the space-time characteristics of high haze pollution industries in 13 cities of the Beijing-Tianjin-Hebei region. The results show that those aforesaid high haze pollution industries present an obvious clustering trend in Beijing, Tianjin, Shijiazhuang, Handan, and some areas along the economic axis of China, overall flooding into Eastern coastal and Southern inland areas. By industry, the petroleum processing and coking processing industry, chemical raw materials and chemical products industry, manufacture of non-metallic mineral products, and ferrous metal smelting and rolling processing industry witness a declining clustering trend, as opposed to the electricity, heat production, and supply industry. Meanwhile, core cities of the Beijing-Tianjin-Hebei region have developed high haze pollution industries, and the hinterland lags in economic development. It is necessary to face up to the differences in economic development among areas in the Beijing-Tianjin-Hebei region, promote the staggered development of high haze pollution industries, and build a high haze pollution industry chain with coordinated economic and environmental development, thus reducing the economic gap with each area, and realizing the coordinated development of economy, industry, and environment. Full article
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23 pages, 1283 KiB  
Article
An Approach for the Analysis of Energy Resource Selection Based on Attributes by Using Dombi T-Norm Based Aggregation Operators
by Mujab Waqar, Kifayat Ullah, Dragan Pamucar, Goran Jovanov and Ðordje Vranješ
Energies 2022, 15(11), 3939; https://doi.org/10.3390/en15113939 - 26 May 2022
Cited by 8 | Viewed by 1271
Abstract
Dombi t-norm (DTN) and t-conorm (TCN) are among the most effective triangular norms in fuzzy systems for aggregation purposes. The environment of interval-valued intuitionistic fuzzy (IVIF) set gives us precision in expressing uncertain information by using a membership grade (MG) and non-membership grade [...] Read more.
Dombi t-norm (DTN) and t-conorm (TCN) are among the most effective triangular norms in fuzzy systems for aggregation purposes. The environment of interval-valued intuitionistic fuzzy (IVIF) set gives us precision in expressing uncertain information by using a membership grade (MG) and non-membership grade (NMG) in the form of closed subintervals of 0, 1. The goal of this paper is to introduce DTN-based aggregation operators (AOs) for IVIF numbers (IVIFNs) and study their performance in the evaluation of the worth of energy recourses to be opted in Pakistan to deal with the energy crises situation. We first introduced some DTN and TCN-based operations for IVIFNs and developed two new AOs known as IVIF Dombi weighted averaging (IVIFDWA) and IVIF Dombi weighted geometric (IVIFDWG) operators. The validity and fitness of the proposed operators are tested. A case study is presented where the energy resources of Pakistan are discussed and the problem of the selection of sustainable energy resources in the context of Pakistan is investigated. The sensitivity analysis of the proposed IVIFDWA and IVIFDWG operators is studied and a comparative analysis of the current work with previous studies is established. Full article
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20 pages, 2666 KiB  
Article
A Hybrid Multi-Criteria-Decision-Making Aggregation Method and Geographic Information System for Selecting Optimal Solar Power Plants in Iran
by Jalil Heidary Dahooie, Ali Husseinzadeh Kashan, Zahra Shoaei Naeini, Amir Salar Vanaki, Edmundas Kazimieras Zavadskas and Zenonas Turskis
Energies 2022, 15(8), 2801; https://doi.org/10.3390/en15082801 - 11 Apr 2022
Cited by 10 | Viewed by 2294
Abstract
Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of [...] Read more.
Policy-makers should focus on solar energy due to the increasing energy demand and adverse consequences such as global warming. Conflicting criteria influence choosing the most desirable place to construct a Solar Power Plant (SPP). Researchers have popularized multicriteria decision-making (MCDM) methods because of the potential. Although the simultaneous use of several methods increases the robustness and accuracy of the results, existing methods to integrate MCDM methods mainly consider the same weight for all methods and utilize the alternatives ranking for the final comparison. This paper presents a hybrid decision-making framework to determine the best location for SPPs in Iran using a set of criteria extracted from the literature and expert opinions. An initial list of decision-making alternatives is prepared and evaluated using GIS software in terms of criteria. Decision-makers prioritized the identified alternatives using the MCDM methods, including SWARA and different ranking methods (TOPSIS, TODIM, WASPAS, COPRAS, ARAS, and MULTIMOORA). Finally, the CCSD method aggregates the results and identifies the best location. Results highly correlate with the results of previous methods and demonstrate the robustness of the proposed approach and its capability to overcome the limitations of previous methods. Full article
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24 pages, 5109 KiB  
Article
Performance Evaluation of Solar Energy Cells Using the Interval-Valued T-Spherical Fuzzy Bonferroni Mean Operators
by Maria Akram, Kifayat Ullah and Dragan Pamucar
Energies 2022, 15(1), 292; https://doi.org/10.3390/en15010292 - 1 Jan 2022
Cited by 50 | Viewed by 2327
Abstract
To find the correspondence between every number of attributes, the Bonferroni mean (BM) operator is most widely used and proven to be a flexible approach. To express uncertain information, the frame of the interval-valued T-spherical fuzzy set (IVTSFS) is a recent development in [...] Read more.
To find the correspondence between every number of attributes, the Bonferroni mean (BM) operator is most widely used and proven to be a flexible approach. To express uncertain information, the frame of the interval-valued T-spherical fuzzy set (IVTSFS) is a recent development in fuzzy settings which discusses four aspects of uncertain information using closed sub-intervals of [0,1] and hence reduces the information loss greatly. In this research study, we introduced the principle of BM operators with IVTSFS to develop the principle of the inter-valued T-spherical fuzzy (IVTSF) BM (IVTSFBM) operator, the IVTSF-weighted BM (IVTSFWBM) operator, the IVTSF geometric BM (IVTSFGBM) operator, and the IVTSF-weighted geometric BM (IVTSFWGBM) operator. To see the significance of the proposed BM operators, we applied these BM operators to evaluate the performance of solar cells that play an important role in the field of energy storage. To do so, we developed a multi-attribute group decision-making (MAGDM) procedure based on IVTSF information and applied it to the problem of solar cells to evaluate their performance under uncertainty, where four aspects of opinion about solar cells were taken into consideration. We studied the results obtained using BM operators with some previous operators to see the significance of the proposed IVTSF BM operators. Full article
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22 pages, 4014 KiB  
Article
ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise
by Ewa Chodakowska, Joanicjusz Nazarko and Łukasz Nazarko
Energies 2021, 14(23), 7952; https://doi.org/10.3390/en14237952 - 28 Nov 2021
Cited by 40 | Viewed by 4988
Abstract
The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. [...] Read more.
The paper addresses the problem of insufficient knowledge on the impact of noise on the auto-regressive integrated moving average (ARIMA) model identification. The work offers a simulation-based solution to the analysis of the tolerance to noise of ARIMA models in electrical load forecasting. In the study, an idealized ARIMA model obtained from real load data of the Polish power system was disturbed by noise of different levels. The model was then re-identified, its parameters were estimated, and new forecasts were calculated. The experiment allowed us to evaluate the robustness of ARIMA models to noise in their ability to predict electrical load time series. It could be concluded that the reaction of the ARIMA model to random disturbances of the modeled time series was relatively weak. The limiting noise level at which the forecasting ability of the model collapsed was determined. The results highlight the key role of the data preprocessing stage in data mining and learning. They contribute to more accurate decision making in an uncertain environment, help to shape energy policy, and have implications for the sustainability and reliability of power systems. Full article
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17 pages, 3437 KiB  
Article
Generic Feasibility Assessment: Helping to Choose the Nuclear Piece of the Net Zero Jigsaw
by William Bodel, Kevin Hesketh, Grace McGlynn, Juan Matthews and Gregg Butler
Energies 2021, 14(5), 1229; https://doi.org/10.3390/en14051229 - 24 Feb 2021
Cited by 4 | Viewed by 2221
Abstract
The United Kingdom has declared a climate change policy of 100% reduction in carbon dioxide emissions by 2050. Efforts thus far have been limited solely to electricity generation methods. While progress has been admirable, effort now must be directed at the nation’s non-electrical [...] Read more.
The United Kingdom has declared a climate change policy of 100% reduction in carbon dioxide emissions by 2050. Efforts thus far have been limited solely to electricity generation methods. While progress has been admirable, effort now must be directed at the nation’s non-electrical energy use. Nuclear energy is an essential part of any energy future, since it is low-carbon, firm and supplies synchronous electricity; however the nation’s nuclear strategy to date has been erratic, costly and lacking in strategic oversight. A multitude of reactor designs are on offer for potential uptake, and decision-makers must have clarity of vision on what these systems must deliver before forming a strategy. Choosing between these systems, given the uncharted energy future faced by the UK is a daunting prospect. Generic feasibility assessment offers a tool for decision-makers to assist them in selecting the most suitable nuclear system for chosen future conditions. Generic feasibility assessment offers an alternative to traditional multi-attribute decision analyses, which can be confusing to even committed stakeholders when large numbers of attributes are weighted and compiled. Generic feasibility assessment forms part of a toolkit which will be of utility in achieving net zero by 2050, given the short time that remains. Full article
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22 pages, 3406 KiB  
Article
Algorithm for Reducing Truck Noise on Via Baltica Transport Corridors in Lithuania
by Kristina Čižiūnienė, Jonas Matijošius, Audrius Čereška and Artūras Petraška
Energies 2020, 13(24), 6475; https://doi.org/10.3390/en13246475 - 8 Dec 2020
Cited by 6 | Viewed by 1961
Abstract
The section of Via Baltica going through the territory of the Republic of Lithuania is the most traffic intensive land logistics corridor in the country. The annual transportation volume has been increasing on this road; thus, the reduction of pollution caused by vehicles [...] Read more.
The section of Via Baltica going through the territory of the Republic of Lithuania is the most traffic intensive land logistics corridor in the country. The annual transportation volume has been increasing on this road; thus, the reduction of pollution caused by vehicles has become important. If gas emissions are regulated, and carriers have to pay pollution taxes, this does not apply to noise levels. The article presents the traffic intensity in this logistics corridor, measurements of the noise level at the characteristic points, its relation to the number of vehicles passing through it and an expert evaluation of proposed methods for noise energy reduction. Environmental noise is an unwanted or harmful sound that propagates in terms of both duration and geographical coverage. Noise is associated with many human activities, but road, rail and air traffic noises have the greatest impact. Due to irrationally arranged transport network, the transit flow of freight transport crosses residential areas of the city, places of rest and recreation of the population, causing high noise levels in adjacent areas. This is the biggest problem for the urban environment. Environmental noise affects many Europeans and is therefore considered by society to be one of the biggest environmental problems. This article presents an assessment of a new traffic noise algorithm. The presented expert survey on noise energy reduction allows choosing the most appropriate method for reducing noise energy in Via Baltica transport logistics corridor. Based on the expert survey, a hierarchical table for noise energy reduction was compiled. It will allow assessing the validity of individual noise energy reduction solutions. It has become relevant for improving infrastructure of other transport corridors and choosing the most appropriate solutions to reduce vehicle noise pollution. A further application of this model can be focused on economic evaluation, forecasting of expected benefits and so on. Full article
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22 pages, 2424 KiB  
Article
Estimating the Energy Demand and Growth in Off-Grid Villages: Case Studies from Myanmar, Indonesia, and Laos
by Andante Hadi Pandyaswargo, Mengyi Ruan, Eiei Htwe, Motoshi Hiratsuka, Alan Dwi Wibowo, Yuji Nagai and Hiroshi Onoda
Energies 2020, 13(20), 5313; https://doi.org/10.3390/en13205313 - 13 Oct 2020
Cited by 11 | Viewed by 4313
Abstract
Under the Sustainable Development Goals (SDGs), the world has pledged to “leaving no one behind”. Responding to goal No. 7 on the agenda, efforts to provide modern energy to all the world population must be pushed forward. This is important because electrification in [...] Read more.
Under the Sustainable Development Goals (SDGs), the world has pledged to “leaving no one behind”. Responding to goal No. 7 on the agenda, efforts to provide modern energy to all the world population must be pushed forward. This is important because electrification in the rural area can indirectly support opportunities for social and economic development resulting in an acceleration of the eradication of poverty. The research goal of this study is to contribute insights about the scale of energy demand in unelectrified villages in the Southeast Asian countries and to discuss some factors that might influence the energy demand growth. This is done by making projections based on surveys and interviews, including a time-use survey, in three off-grid villages located in Myanmar, Indonesia, and Laos. Our analysis presented the living condition, highlight the types of energy sources, how, and in what rhythms people use energy on a daily basis in those villages. The demands in each case study villages were then projected based on several constructed scenarios. It was found that the factors of household size, proximity to the city, climate, and topography may influence the present and future growth of energy demands in the villages. The estimated energy demand may be useful for project managers to design a pilot off-grid energy system project in a similar environment and pointed out important factors to consider when formulating off-grid energy policies in the region. Full article
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22 pages, 2101 KiB  
Article
The Analysis of Japan’s Energy and Climate Policy from the Aspect of Anticipatory Governance
by Hiroshi Ohta
Energies 2020, 13(19), 5153; https://doi.org/10.3390/en13195153 - 2 Oct 2020
Cited by 14 | Viewed by 4078
Abstract
This study is a preliminary and experimental one to analyze Japan’s energy transitions to mitigate climate change from anticipatory governance aspects. Japan’s energy policy principles have been energy security, environmental considerations, economic efficiency, and safety (3E + S). According to the energy agency, [...] Read more.
This study is a preliminary and experimental one to analyze Japan’s energy transitions to mitigate climate change from anticipatory governance aspects. Japan’s energy policy principles have been energy security, environmental considerations, economic efficiency, and safety (3E + S). According to the energy agency, the long-term energy outlook is also drawn up by “ambitious multiple track scenarios” and “multilayered and diversified flexible energy supply-demand structure.” This approach resonates with the aspects of anticipatory governance. It promotes the idea of preparing for multiple future scenarios, including the unthinkable worst case future scenario such as a nuclear accident (foresight), the interactions between the policymakers and the public (engagement), and the reflexive processes of policy innovations with a normative decision for the selection of energy mix (integration). However, this study finds that Japan’s energy policy lacks the aspects of anticipatory governance. It sticks to fixed energy policy institutionalized in the 1970s to promote nuclear energy and coal as oil alternatives. It rarely has interactions between the policymakers and the public and thus lacks a societal (normative) decision about a future energy path to energy transitions to mitigate climate change. Instead, Japan’s energy policy has not necessarily met its declared policy objective of 3E + S since the unprecedented Fukushima nuclear accidents occurred and cannot uphold an ambitious target for CO2 emissions reduction. Full article
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23 pages, 3689 KiB  
Article
A Fuzzy-Based Product Life Cycle Prediction for Sustainable Development in the Electric Vehicle Industry
by Yung Po Tsang, Wai Chi Wong, G. Q. Huang, Chun Ho Wu, Y. H. Kuo and King Lun Choy
Energies 2020, 13(15), 3918; https://doi.org/10.3390/en13153918 - 31 Jul 2020
Cited by 12 | Viewed by 4064
Abstract
The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery [...] Read more.
The development of electric vehicles (EVs) has drawn considerable attention to the establishment of sustainable transport systems to enable improvements in energy optimization and air quality. EVs are now widely used by the public as one of the sustainable transportation measures. Nevertheless, battery charging for EVs create several challenges, for example, lack of charging facilities in urban areas and expensive battery maintenance. Among various components in EVs, the battery pack is one of the core consumables, which requires regular inspection and repair in terms of battery life cycle and stability. The charging efficiency is limited to the power provided by the facilities, and therefore the current business model for EVs is not sustainable. To further improve its sustainability, plug-in electric vehicle battery pack standardization (PEVBPS) is suggested to provide a uniform, standardized and mobile EV battery that is managed by centralized service providers for repair and maintenance tasks. In this paper, a fuzzy-based battery life-cycle prediction framework (FBLPF) is proposed to effectively manage the PEVBPS in the market, which integrates the multi-responses Taguchi method (MRTM) and the adaptive neuro-fuzzy inference system (ANFIS) as a whole for the decision-making process. MRTM is formulated based on selection of the most relevant and critical input variables from domain experts and professionals, while ANFIS takes part in time-series forecasting of the customized product life-cycle for demand and electricity consumption. With the aid of the FPLCPF, the revolution of the EV industry can be revolutionarily boosted towards total sustainable development, resulting in pro-active energy policies in the PEVBPS eco-system. Full article
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26 pages, 2288 KiB  
Article
Assessing the Performance of Sustainable Development Goals of EU Countries: Hard and Soft Data Integration
by Ewa Chodakowska and Joanicjusz Nazarko
Energies 2020, 13(13), 3439; https://doi.org/10.3390/en13133439 - 3 Jul 2020
Cited by 17 | Viewed by 2965
Abstract
The European Union (EU) energy policy for sustainable development has been the topic of continuous debate, research, and analysis, which frequently focused on objectives and the evaluation of quantitative and qualitative performance. Different approaches can be used for the assessment of sustainable development [...] Read more.
The European Union (EU) energy policy for sustainable development has been the topic of continuous debate, research, and analysis, which frequently focused on objectives and the evaluation of quantitative and qualitative performance. Different approaches can be used for the assessment of sustainable development goals. The authors of the article conducted a literature review of relevant research papers dated 2016–2020. The most common are quantitative methods based on hard data. Some qualitative studies based on soft data are also available but rare. This article proposes hybrid Rough Set Data Envelopment Analysis (DEA) and Rough Set Network DEA models that integrate both approaches. Also, the models allow the inclusion of uncertainty in the underlying data. The article uses hard data of the International Energy Agency (IEA) and the results of the EU survey regarding the influence of the socio-economic environment on CO2 emissions in EU countries. The authors demonstrate that multifaceted and objective assessment is possible by merging concepts from the set theory and operational research. Full article
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16 pages, 594 KiB  
Article
Financial Liberalisation, Political Stability, and Economic Determinants of Real Economic Growth in Kenya
by Zakaria Yakubu, Nanthakumar Loganathan, Tirta Nugraha Mursitama, Abbas Mardani, Syed Abdul Rehman Khan and Asan Ali Golam Hassan
Energies 2020, 13(13), 3426; https://doi.org/10.3390/en13133426 - 3 Jul 2020
Cited by 11 | Viewed by 3401
Abstract
This study aimed to analyse financial liberalisation, political stability, and economic determinants of Kenya’s real economic growth using time series data over the period of 1970–2016. The authors specified quadratic and interactive models to be estimated by employing a quantile regression analysis. The [...] Read more.
This study aimed to analyse financial liberalisation, political stability, and economic determinants of Kenya’s real economic growth using time series data over the period of 1970–2016. The authors specified quadratic and interactive models to be estimated by employing a quantile regression analysis. The traditional and quantile unit root test was used in testing the stationarity issue. The co-integration findings indicated that the capital account openness and financial development impede on real economic growth; and the political stability also had potential influence on the real economic growth of Kenya. Interestingly, there is a nonlinear U-shape link between financial development and real economic growth that undermined the real economic growth at its onset, but as it advanced, it enhanced the growth of the country in the long run. The policymakers should ensure that the capital account is more liberalised so that it will continue to stimulate the financial development. In the same way, the liberalisation of the domestic financial market should be taken in earnest to overcome the negative effects of financial repression in totality, while maintaining the stable political atmosphere. Full article
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18 pages, 730 KiB  
Article
Analyzing Crude Oil Prices under the Impact of COVID-19 by Using LSTARGARCHLSTM
by Melike Bildirici, Nilgun Guler Bayazit and Yasemen Ucan
Energies 2020, 13(11), 2980; https://doi.org/10.3390/en13112980 - 10 Jun 2020
Cited by 47 | Viewed by 5034
Abstract
Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price [...] Read more.
Under the influence of the COVID-19 pandemic and the concurrent oil conflict between Russia and Saudi Arabia, oil prices have exhibited unusual and sudden changes. For this reason, the volatilities of the West Texas Intermediate (WTI), Brent and Dubai crude daily oil price data between 29 May 2006 and 31 March 2020 are analysed. Firstly, the presence of chaotic and nonlinear behaviour in the oil prices during the pandemic and the concurrent conflict is investigated by using the Shanon Entropy and Lyapunov exponent tests. The tests show that the oil prices exhibit chaotic behavior. Additionally, the current paper proposes a new hybrid modelling technique derived from the LSTARGARCH (Logistic Smooth Transition Autoregressive Generalised Autoregressive Conditional Heteroskedasticity) model and LSTM (long-short term memory) method to analyse the volatility of oil prices. In the proposed LSTARGARCHLSTM method, GARCH modelling is applied to the crude oil prices in two regimes, where regime transitions are governed with an LSTAR-type smooth transition in both the conditional mean and the conditional variance. Separating the data into two regimes allows the efficient LSTM forecaster to adapt to and exploit the different statistical characteristics and ARCH and GARCH effects in each of the two regimes and yield better prediction performance over the case of its application to all the data. A comparison of our proposed method with the GARCH and LSTARGARCH methods for crude oil price data reveals that our proposed method achieves improved forecasting performance over the others in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) in the face of the chaotic structure of oil prices. Full article
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25 pages, 1351 KiB  
Article
A Group Decision Framework for Renewable Energy Source Selection under Interval-Valued Probabilistic linguistic Term Set
by Raghunathan Krishankumar, Arunodaya Raj Mishra, Kattur Soundarapandian Ravichandran, Xindong Peng, Edmundas Kazimieras Zavadskas, Fausto Cavallaro and Abbas Mardani
Energies 2020, 13(4), 986; https://doi.org/10.3390/en13040986 - 22 Feb 2020
Cited by 31 | Viewed by 3576
Abstract
In recent years, the assessment of desirable renewable energy alternative has been an extremely important concern that could change the environment and economic growth. To tackle the circumstances, some authors have paid attention to selecting the desirable renewable energy option by employing the [...] Read more.
In recent years, the assessment of desirable renewable energy alternative has been an extremely important concern that could change the environment and economic growth. To tackle the circumstances, some authors have paid attention to selecting the desirable renewable energy option by employing the decision-making assessment and linguistic term sets. With a fast-growing interest in multi-criteria group decision-making (MCGDM) problems, researchers are tirelessly working towards new techniques for better decision-making. Decision makers (DMs) generally rate alternatives linguistically with different probabilities occurring for each term. Previous studies on linguistic decision-making have either ignored this idea or have used an only a single value for representing the weight of the linguistic term. Since expression of the complete probability distribution is hard and implicit hesitation exists, representation of weights of the linguistic terms using a single value becomes imprecise and unreasonable. To avoid this challenge, an interval-valued probabilistic linguistic term set (IVPLTS) is used, which is a generalization of (probabilistic linguistic term set) PLTS. Inspired by the usefulness of IVPLTS concept, we develop a decision framework for rational decision making. Initially, some operational laws and axioms are presented. Further, a novel aggregation operator known as interval-valued probabilistic linguistic simple weighted geometry (IVPLSWG) is developed for aggregating DMs’ preferences. Also, criteria weights are determined using the newly developed interval-valued probabilistic linguistic standard variance (IVPLSV) approach and alternatives are ranked using the extended VIKOR (VlseKriterijumskaOptimizacijaKompromisnoResenje) method under IVPLTS environment. Finally, a numerical example of renewable energy assessment is demonstrated to show the practicality of the developed decision framework. Also, the strengths and weaknesses of the developed decision framework are illustrated by comparison with existing ones. Full article
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26 pages, 347 KiB  
Article
A Multi-Attribute Decision Making Process with Immediate Probabilistic Interactive Averaging Aggregation Operators of T-Spherical Fuzzy Sets and Its Application in the Selection of Solar Cells
by Shouzhen Zeng, Harish Garg, Muhammad Munir, Tahir Mahmood and Azmat Hussain
Energies 2019, 12(23), 4436; https://doi.org/10.3390/en12234436 - 21 Nov 2019
Cited by 55 | Viewed by 2680
Abstract
The objective of this paper is to present new interactive averaging aggregation operators by assigning associate probabilities for T-spherical fuzzy sets (T-SFSs). T-SFS is a generalization of several existing theories such as intuitionistic fuzzy sets and picture fuzzy sets to handle imprecise information. [...] Read more.
The objective of this paper is to present new interactive averaging aggregation operators by assigning associate probabilities for T-spherical fuzzy sets (T-SFSs). T-SFS is a generalization of several existing theories such as intuitionistic fuzzy sets and picture fuzzy sets to handle imprecise information. Under such an environment, we developed a series of averaging interactive aggregation operators under the features that each element is represented with T-spherical fuzzy numbers. Various properties of the proposed operators are also investigated. Further, to rank the different T-SFSs, we exhibit the new score functions and state their some properties. To demonstrate the presented algorithm, a decision-making process algorithm is presented with T-SFS features. To save non-renewable resources and to the protect environment, the use of renewable resources is important. Solar energy is one of the best renewable energy resources and is also environment-friendly and thus the selection of solar cells is typically a multi-attribute decision-making problem. Therefore, the applicability of the developed algorithm is demonstrated with a numerical example in the selection of the solar cells and comparison of their performance with the several existing approaches. Full article

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23 pages, 6415 KiB  
Review
A Review of Uncertain Decision-Making Methods in Energy Management Using Text Mining and Data Analytics
by Madjid Tavana, Akram Shaabani, Francisco Javier Santos-Arteaga and Iman Raeesi Vanani
Energies 2020, 13(15), 3947; https://doi.org/10.3390/en13153947 - 1 Aug 2020
Cited by 14 | Viewed by 3174
Abstract
The managerial and environmental studies conducted in the energy research area reflect its substantial importance, particularly when optimizing and modifying consumption patterns, transitioning to renewable sources away from fossil ones, and designing plans and systems. The aim of this study is to provide [...] Read more.
The managerial and environmental studies conducted in the energy research area reflect its substantial importance, particularly when optimizing and modifying consumption patterns, transitioning to renewable sources away from fossil ones, and designing plans and systems. The aim of this study is to provide a systematic review of the literature allowing us to identify which research subjects have been prioritized in the fields of energy and sustainability in recent years, determine the potential reasons explaining these trends, and categorize the techniques applied to analyze the uncertainty faced by decision-makers. We review articles published in highly ranked journals through the period 2003–2020 and apply text analytics to cluster their main characteristics; that is, we rely on pre-processing and text mining techniques. We analyze the title, abstract, keywords, and research methodology of the articles through clustering and topic modeling and illustrate what methods and fields constitute the main focus of researchers. We demonstrate the substantial importance of fuzzy-related methods and decision-making techniques such as the Analytical Hierarchy Process and Technique for Order Preferences by Similarity to Ideal Solutions (TOPSIS). We also show that subjects such as renewable energy, energy planning, sustainable energy, energy policy, and wind energy have gained relevance among researchers in recent years. Full article
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