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Integration of Regression Analysis and Monte Carlo Simulation for Probabilistic Energy Policy Guidelines in Pakistan

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  • Zaman Sajid

    (Department of Process Engineering, Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada
    Department of Business Administration, University of the People, Pasadena, CA 91101, USA)

  • Asma Javaid

    (Department of Computer Science, Memorial University of Newfoundland, St. John’s, NL A1B 3X5, Canada)

  • Muhammad Kashif Khan

    (School of Mechanical Engineering, Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon 16419, Gyeonggi-do, Korea)

  • Hamad Sadiq

    (Institute of Chemical Engineering and Technology, University of the Punjab, Lahore 54590, Pakistan)

  • Usman Hamid

    (Department of Chemical Engineering, University of Engineering and Technology, Lahore 54590, Pakistan)

Abstract

Forecasting energy demand and supply is the most crucial concern for energy policymakers. However, forecasting may introduce uncertainty in the energy model, and an energy policy based on an uncertain model could be misleading. Without certainty in energy data, investors cannot quantify risk and trade-offs, which are compulsory for investments in energy projects. In this work, the energy policies of Pakistan are taken as a case study, and flaws in its energy policymaking are identified. A novel probabilistic model integrated with curve fitting methods was proposed and was applied to 17 different energy demand and supply variables. Monte Carlo simulation (MCS) was performed to develop probabilistic energy profiles for each year from 2017 to 2050. Results show that the forecasted energy supply of Pakistan in the years 2025 and 2050 would be 70.69 MTOE and 131.65 MTOE, respectively. The probabilistic analysis showed that there is 14% and 6% uncertainty in achieving these targets. The research shows the expected energy consumption of 70.33 MTOE and 189.48 MTOE in 2025 and 2050, respectively, indicating uncertainties of 65% and 31%. Based on the results, eight energy policy guidelines and recommendations are provided for sustainable energy resource management. This study recommends developing a robust and sustainable energy policy for Pakistan with the help of transparent governance.

Suggested Citation

  • Zaman Sajid & Asma Javaid & Muhammad Kashif Khan & Hamad Sadiq & Usman Hamid, 2021. "Integration of Regression Analysis and Monte Carlo Simulation for Probabilistic Energy Policy Guidelines in Pakistan," Resources, MDPI, vol. 10(9), pages 1-26, August.
    • Handle: RePEc:gam:jresou:v:10:y:2021:i:9:p:88-:d:621928
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    References listed on IDEAS

    as
    1. Zaman Sajid & Asma Javaid, 2018. "A Stochastic Approach to Energy Policy and Management: A Case Study of the Pakistan Energy Crisis," Energies, MDPI, vol. 11(9), pages 1-18, September.
    2. Valasai, Gordhan Das & Uqaili, Muhammad Aslam & Memon, HafeezUr Rahman & Samoo, Saleem Raza & Mirjat, Nayyar Hussain & Harijan, Khanji, 2017. "Overcoming electricity crisis in Pakistan: A review of sustainable electricity options," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 734-745.
    3. Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2016. "Process simulation and life cycle analysis of biodiesel production," Renewable Energy, Elsevier, vol. 85(C), pages 945-952.
    4. Urban, F. & Benders, R.M.J. & Moll, H.C., 2007. "Modelling energy systems for developing countries," Energy Policy, Elsevier, vol. 35(6), pages 3473-3482, June.
    5. Ziad Alahdad, 2012. "Pakistan’s Energy Sector: From Crisis to Crisis-Breaking the Chain," PIDE Monograph Series 2012:6, Pakistan Institute of Development Economics.
    6. Zaman Sajid & Nicholas Lynch, 2018. "Financial Modelling Strategies for Social Life Cycle Assessment: A Project Appraisal of Biodiesel Production and Sustainability in Newfoundland and Labrador, Canada," Sustainability, MDPI, vol. 10(9), pages 1-19, September.
    7. Fumo, Nelson & Rafe Biswas, M.A., 2015. "Regression analysis for prediction of residential energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 332-343.
    8. Urban, F. & Benders, R.M.J. & Moll, H.C., 2007. "Corrigendum to "Modelling energy systems for developing countries": [Energy Policy 35 (2007) 3473-3482]," Energy Policy, Elsevier, vol. 35(9), pages 4764-4765, September.
    9. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio & Minea, Alina A., 2010. "Analysis and forecasting of nonresidential electricity consumption in Romania," Applied Energy, Elsevier, vol. 87(11), pages 3584-3590, November.
    10. Di Leo, Senatro & Caramuta, Pietro & Curci, Paola & Cosmi, Carmelina, 2020. "Regression analysis for energy demand projection: An application to TIMES-Basilicata and TIMES-Italy energy models," Energy, Elsevier, vol. 196(C).
    11. Yang, Guangfei & Li, Wenli & Wang, Jianliang & Zhang, Dongqing, 2016. "A comparative study on the influential factors of China's provincial energy intensity," Energy Policy, Elsevier, vol. 88(C), pages 74-85.
    12. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
    13. Atom MIRAKYAN & Laurent LELAIT & Nikolai KHOMENKO & Igor KAIKOV, 2009. "Methodological Framework for the analysis and development of a sustainable, integrated, regional energy plan - A French region case study," EcoMod2009 21500066, EcoMod.
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