An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Rehman, Syed Aziz Ur; Cai, Yanpeng; Fazal, Rizwan; Walasai, Gordhan Das; Mirjat, Nayyar Hussain

doi:10.3390/en10111868

Cited by 82 publications

(43 citation statements)

References 61 publications

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“…These included power generation from biomass, solar, wind, hydropower (dams), hydropower (run-of-river), coal-fired power plants, natural gas-run power plants, and oil-based thermal power plants. The energy demand and supply were further distinctly forecasted in initial studies, which include Rehman, et al [86] and Rehman, et al [87], thereby using different approaches. Nevertheless, the electricity demand forecast by the National Transmission and Dispatch Company (NTDC) NTDC [88] was input in this study model.…”

Section: Generic Details Of the Pak-times Modelmentioning

confidence: 99%

Cleaner and Sustainable Energy Production in Pakistan: Lessons Learnt from the Pak-TIMES Model

et al. 2019

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The energy planning process essentially requires addressing diverse planning objectives, including prioritizing resources, and the estimation of environmental emissions and associated health risks. This study investigates the impacts of atmospheric pollution for Pakistan from the energy production processes under various modalities. A national-scale bottom-up energy optimization model (Pak-TIMES) with the ANSWER-TIMES framework is developed to assess the electricity generation pathways (2015–2035) and estimate GHG emissions and major air pollutants, i.e., CH4, CO, CO2, N2O, NOX, PM1, PM10, PM2.5, PMBC, PMOC, PMTSP, SO2, and VOC under five scenarios. These scenarios are: BAU (business-as-usual), RE-30 (30% renewables), RE-40 (40% renewables), Coal-30 (30% coal), and Coal-40 (40% coal). It is revealed that to reach the electricity demand of 3091 PJ in 2035, both the Coal-30 and Coal-40 scenarios shall cause maximum emissions of GHGs, i.e., 260.13 and 338.92 Mt (million tons) alongside 40.52 and 54.03 Mt emissions of PMTSP in both of the scenarios, respectively. BAU scenario emissions are estimated to be 181.5 Mt (GHGs) and 24.30 Mt (PMTSP). Minimum emissions are estimated in the RE-40 scenario with 96.01 Mt of GHGs and 11.80 Mt of PMTSP, followed by the RE-30 scenario (143.20 GHGs and 17.73 Mt PMTSP). It is, therefore, concluded that coal-based electricity generation technologies would be a major source of emission and would contribute the highest amount of air pollution. This situation necessitates harnessing renewables in the future, which will significantly mitigate public health risks from atmospheric pollution.

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Section: Generic Details Of the Pak-times Modelmentioning

confidence: 99%

Cleaner and Sustainable Energy Production in Pakistan: Lessons Learnt from the Pak-TIMES Model

et al. 2019

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“…These studies highlight the importance of institutional, social, and environmental aspects of energy systems. For instance, evaluation of GHG contribution from household energy usage demonstrates that a 1% rise in per-capita energy usage leads to a 1.65% increase in per-capita carbon emissions [78].…”

Section: Results Of Reference Scenariomentioning

confidence: 99%

Low-Emission Modeling for Energy Demand in the Household Sector: A Study of Pakistan as a Developing Economy

Bashir

Ahmad

2018

Sustainability

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In the developing world, the energy needs of the household sector have grown manifold, due to rapid urbanization and the introduction of affordable technology. However, constraints in the power supply and underutilization of renewable resources, coupled with inefficient fuel use and obsolete technology, have increased the average energy usage cost and emissions. Hence, the current study provides a long-term sustainable energy management plan for the urban household, in terms of energy savings, social cost, and greenhouse gas (GHG) mitigation. To address energy security and climate change challenges, a demand-side management scenario (DSM) is proposed through integration of efficient policies and nationally determined contributions. On the basis of policy analysis and energy consumption patterns, macro-economic modeling was carried out for the period 2011–2050, using the Long-range Energy Alternative Planning (LEAP) modeling tool. Results show that the “efficient water heating” scenario offers the maximum energy-saving potential (up to 270 M.TOE) whereas “efficient space cooling” is the lowest-cost scenario. To achieve the best-fit mitigation scenario (MIT), targets for renewable energy supply were also incorporated. Findings were weighed against the reference scenario (REF), which reveals a huge GHG reduction under the DSM. Moreover, the cost required to implement MIT is estimated to be 3.4 US $/tonne of carbon dioxide-equivalent, less than the REF.

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“…According to the World Bank [26] report, 80% of Pakistan's population has access to electricity whereas 20% of the population is still lacking electricity access. Currently, the demand of electricity in the country is 25,000 MW and is projected to increase to 40,000 MW by 2030 [27][28][29]. Pakistan's energy infrastructure is under developed and insufficient.…”

Section: Electricity Scenario Of Pakistanmentioning

confidence: 99%

Empirical Insights into the Long-Run Linkage between Households Energy Consumption and Economic Growth: Macro-Level Empirical Evidence from Pakistan

et al. 2019

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This study is a maiden empirical attempt to examine the long-run linkage between households’ usage of energy and economic progression in Pakistan from the period of 1972–2017. The Autoregressive Distributive Lag (ARDL) bounds testing method to co-integrate is employed to expose the causality dynamics between the variables such as households’ electricity consumption, households’ gas consumption, population growth, and per capita Gross Domestic Product (GDP) in Pakistan. The study adopted three renowned unit root approaches through the use of the Augmented Dickey-Fuller (ADF), the Phillips-Perron (P-P), and Zivot-Andrews (Z&A) tests to check the stationarity of the variables, while the Johansen cointegration technique is also employed to assess the robustness of the long-run association. The validity of outcomes is also checked with casualty and variance decomposition. The estimated results reveal that, in both the short and long run, households’ electricity and gas usage positively affect economic growth, while population growth in the long-run has a negative impact, but the short-run analysis has a positive impact on economic growth in Pakistan. Additionally, the Granger causality and variance decomposition confirm the robustness of outcomes and suggesting a long run association among the variables, and a unidirectional causal link running from three variables to economic growth of Pakistan in the short run.

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An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan

Cited by 82 publications

References 61 publications

Cleaner and Sustainable Energy Production in Pakistan: Lessons Learnt from the Pak-TIMES Model

Cleaner and Sustainable Energy Production in Pakistan: Lessons Learnt from the Pak-TIMES Model

Low-Emission Modeling for Energy Demand in the Household Sector: A Study of Pakistan as a Developing Economy

Empirical Insights into the Long-Run Linkage between Households Energy Consumption and Economic Growth: Macro-Level Empirical Evidence from Pakistan

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