Energy planning and policy development require an in-depth assessment of energy resources and long-term demand forecast estimates. Pakistan, unfortunately, lacks reliable data on its energy resources as well do not have dependable long-term energy demand forecasts. As a result, the policy makers could not come up with an effective energy policy in the history of the country. Energy demand forecast has attained greatest ever attention in the perspective of growing population and diminishing fossil fuel resources. In this study, Pakistan’s energy demand forecast for electricity, natural gas, oil, coal and LPG across all the sectors of the economy have been undertaken. Three different energy demand forecasting methodologies, i.e., Autoregressive Integrated Moving Average (ARIMA), Holt-Winter and Long-range Energy Alternate Planning (LEAP) model were used. The demand forecast estimates of each of these methods were compared using annual energy demand data. The results of this study suggest that ARIMA is more appropriate for energy demand forecasting for Pakistan compared to Holt-Winter model and LEAP model. It is estimated that industrial sector’s demand shall be highest in the year 2035 followed by transport and domestic sectors. The results further suggest that energy fuel mix will change considerably, such that oil will be the most highly consumed energy form (38.16%) followed by natural gas (36.57%), electricity (16.22%), coal (7.52%) and LPG (1.52%) in 2035. In view of higher demand forecast of fossil fuels consumption, this study recommends that government should take the initiative for harnessing renewable energy resources for meeting future energy demand to not only avert huge import bill but also achieving energy security and sustainability in the long run.

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, NO_X, PM₁, PM₁₀, PM_2.5, PM_BC, PM_OC, PM_TSP, 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 PM_TSP in both of the scenarios, respectively. BAU scenario emissions are estimated to be 181.5 Mt (GHGs) and 24.30 Mt (PM_TSP). Minimum emissions are estimated in the RE-40 scenario with 96.01 Mt of GHGs and 11.80 Mt of PM_TSP, followed by the RE-30 scenario (143.20 GHGs and 17.73 Mt PM_TSP). 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.

This paper explored the energy–environment–economy (EEE) causal nexus of Pakistan, thereby reporting the causal determinants of the EEE nexus by employing the newly developed modified Peter and Clark (PC) algorithm. The modified PC algorithm was employed to investigate the causal ordering of energy consumption, CO₂ emissions and economic growth across Pakistan’s domestic, industrial, transportation and agricultural sectors. An empirical comparison, i.e., following Monte Carlo simulation experiments demonstrates that the proposed modified PC algorithm is superior to the original PC proposition and can differentiate between true and spurious nexus causalities. Our results show that significant causality is running from energy consumption in industrial and agricultural sectors towards economic growth. There is no causal association between energy consumption and economic growth in the domestic and transportation sectors. On the other hand, causality runs from energy consumption in the transportation, domestic and industrial sectors towards CO₂ emissions. It is concluded that energy consumption in industrial and agricultural sectors leads to economic growth alongside the associated CO₂ emissions. On the other hand, the contribution of domestic and transportation sectors in economic growth is trivial with significant CO₂ emissions. This paper provides novel empirical evidence of impacts of energy mismanagement at sectoral levels, economic output and environmental consequences; alongside policy recommendations for sustainable energy-based development on the national scale.