Long-term policy evaluation: Application of a new robust decision framework for Iran's energy exports security

Alipour, Mohammad; Hafezi, Reza; Ervural, Bilal; Kaviani, Mohamad Amin; Kabak, Özgür

doi:10.1016/j.energy.2018.05.176

Cited by 36 publications

(22 citation statements)

References 69 publications

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“…Energy scenarios produced by influential global organizations such as Shell, WEC and IEA matter because they may affect what would actually take place. These organizations may shape the expectations of decision-makers in related energy sectors (Alipour et al 2018;Carrington and Stephenson 2018;Hafezi et al 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

An overview of global energy scenarios by 2040: identifying the driving forces using cross-impact analysis method

Ghasemian

Faridzad

Abbaszadeh

et al. 2020

Int. J. Environ. Sci. Technol.

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This study presents a novel comparative analysis on global energy scenarios in terms of main characteristics, scenario building frameworks and time frames. The study analyzes each energy scenario's outputs and outlooks by 2040 such as gross domestic product growth, technology developments, primary energy demand by sector, energy supply by fuel, energy intensity and carbon emissions. Based on the foresight semiquantitative method, global energy driving forces which play significant roles in reshaping the world energy future by 2040 have been identified. The study applies mathematical principles to quantify the rational judgments of an expert panel in social, technological, economic, environmental and political framework through cross-impact analysis. Among the 10 global energy driving forces by 2040, population growth rate and climate change are identified as social and environmental driving forces, respectively. Investment in infrastructures, financial shocks, consumer behavior and energy efficiency are marked as economic driving forces, while global governance and geopolitical relationships are identified as the global energy market's political driving force. And finally, technological development in renewable energies as well as in oil and gas upstream sector is recognized as a technological driving force.

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Section: Literature Reviewmentioning

confidence: 99%

An overview of global energy scenarios by 2040: identifying the driving forces using cross-impact analysis method

Ghasemian

Faridzad

Abbaszadeh

et al. 2020

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Kabak and Ruan (), Ervural and Kabak (), and Alipour, Hafezi, Ervural, Kaviani, and Kabak () suggested some transformation formulae for numerical value and interval value assignments, linguistic terms, and intuitionistic fuzzy values. The current study provides a new formula for interval value assignments and intuitionistic fuzzy values and also does so for 2‐tuple and hesitant fuzzy sets, for the first time, as described in the following.…”

Section: Cumulative Belief Degreementioning

confidence: 99%

A cumulative belief degree approach for group decision‐making problems with heterogeneous information

Ervural

Kabak

2019

Expert Systems

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In some complex group decision‐making (GDM) problems, the information needing to be processed may be heterogeneous. This may involve consideration of objective and subjective criteria by experts who have their own particular set of criteria, their own preference format for assessing alternatives under these criteria, and who may themselves be assigned differing importance weights as experts. This paper presents a cumulative belief degree approach to cope with heterogeneous information in multiple attribute GDM problems. The proposed approach focuses to aggregate subjective expert assessments and objective criteria that are presented in various representation formats and scales. The methodology employs transformation formulae for several preference representation scales to belief structure, including 2‐tuple representation, classical fuzzy sets, hesitant fuzzy sets, and intuitionistic fuzzy sets. Aggregation formulae are proposed to combine expert criteria evaluations and find a collective preference. A consensus degree is calculated for measuring the agreement between the experts. An illustrative example is presented to clarify the steps of the methodology, and validity of the approach is assured through comparative analysis with the existing methods.

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“…The world energy demand increased in the two past decades and even predictions implying the growing trends for the next decades [1][2][3][4]. Still, fossil fuels play a critical role in the energy…”

Section: Introductionmentioning

confidence: 99%

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

et al. 2019

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Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679).

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Long-term policy evaluation: Application of a new robust decision framework for Iran's energy exports security

Cited by 36 publications

References 69 publications

An overview of global energy scenarios by 2040: identifying the driving forces using cross-impact analysis method

An overview of global energy scenarios by 2040: identifying the driving forces using cross-impact analysis method

A cumulative belief degree approach for group decision‐making problems with heterogeneous information

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

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