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

Zaman Sajid, Asma Javaid, Muhammad Kashif Khan, Hamad Sadiq and Usman Hamid
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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
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

Resources, 2021, vol. 10, issue 9, 1-26

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.

Keywords: energy; modelling; Pakistan; policy; uncertainty; Monte Carlo simulation; policymaking; energy resource management (search for similar items in EconPapers)
JEL-codes: Q1 Q2 Q3 Q4 Q5 (search for similar items in EconPapers)
Date: 2021
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