Abstract: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, 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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Economic strategies and planning are critical to a country’s growth and development. China, like many other countries, is seeking the most cost-effective trade deals. Using the Global Vector Auto Regression (GVAR) model, this study examined the impact of a shock to China’s macroeconomic factors on trading economies. The major findings reveal that there is no co-movement between the shock in Chinese gross domestic product (GDP) and German macroeconomic indicators; however, the shock has a positive and substantial influence on Japan’s GDP and Unites States (US)’ exchange rate. It is also worth noting that a shock to Chinese trade volume is more susceptible and more disturbing than a shock to US trade volume since it reduces trade volume and causes the Ren Min Bi (RMB) to devalue permanently. Furthermore, the analysis shows that Chinese stock prices have a major influence on German economy since China’s GDP, trade volume, and currency appreciate over time when its stock price rises. Finally, the exchange rate shock is beneficial to Germany as it boosts GDP and trade volume but has a negative influence on US stock prices. The current study is, therefore, expected to be a suitable beginning point for the governments and policymakers of trading partners to design an effective trade policy to minimize the impact on major economic variables.

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This paper developed and modify Peter and Clark (PC) causality algorithm to revisit the causal linkages between Pakistan and the leading foreign stock markets. Initially, the PC algorithm was conceived to determine causality in cross sectional data. Later on, (Swanson & Granger, 1997) for the first time used VAR residuals in PC algorithm to determine the causal ordering in time series. However, the weak point attached to VAR residuals are that it carries only contemporaneous causal information and remove all the past information. This study modify the PC algorithm based on recursive residuals proposed by (Rehman & Malik, 2014) and explore the causality among exchange rate, interest rate and stock market prices. The overall empirical results of modified PC algorithm indicate that causality is running from exchange rate, interest rate and stock market of India and Bangladesh to Pakistani stock market. The results observed from GARCH-GJR model show spill over effect from the leading foreign stock markets toward Pakistan stock market excluding Sir Lanka. The results of the study will guide the investors to be vigilant in decision making in diversified portfolio investment and hedging. Keywords: Financial Markets, PC algorithms, Causality, Graph theoretic Approach, GARCH, GJR.

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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.