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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. [ABSTRACT FROM AUTHOR]

This paper presents an effort pertaining to the simulation of the future production in Pakistan of different primary energy resources, i.e., coal, natural gas and crude oil, thereby constructing Hubbert peaks. In this context, the past 45 years' production data of primary energy resources of Pakistan have been analyzed and simulated using a generic STELLA (Systems Thinking, Experimental Learning Laboratory with Animation) model. The results show that the Hubbert peak of Pakistan's crude oil production has been somehow already achieved in 2013, with the highest production of 4.52 million toe, which is 1.51 times the production in 2000. Similarly, the natural gas peak production is expected in 2024 with a production of 32.70 million toe which shall be 1.96-fold the extraction of the resource in the year 2000. On the other hand, the coal production in the country has been historically very low and with a constant production rate that is gradually picking up, the peak production year for the coal is anticipated to be in the year 2080 with an estimated production of 134.06 million. Based on the results of this study, which provide a greater understanding of future energy patterns, it is recommended that an energy security policy be devised for the country to ensure sustained supplies in the future. [ABSTRACT FROM AUTHOR]

Petroleum is an essential resource for the development of society and its production is huge. There is a great risk of leakage of oil during production, refining, and transportation. After entering the environment, the oil pollutants will be a great threat to the environment and may endanger human health. Therefore, it is very important to remediate oil pollution in the subsurface. However, it is necessary to choose the appropriate remediation technology. In this paper, 18 technologies are evaluated through constructing a parameter matrix with each technology and seven performance indicators, and a comprehensive analysis model is presented. In this model, four MCDA methods are used. They are SWA (Simple Weighted Addition Method), WP (Weighted Product Method), CGT (Cooperative Game Theory), and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Mean ranking and Borda ranking methods are used to integrate the results of SWA, WP, CGT, and TOPSIS. Then two selection priorities of each method (mean ranking and Borda ranking) are obtained. The model is proposed to help decide the best choice of remediation technologies. It can effectively reduce contingency, subjectivity, one-sidedness of the traditional methods and provide scientific reference for effective decision-making. [ABSTRACT FROM AUTHOR]

Due to the high density of buildings and low quality of the drainage pipe network in the city, urban non-point source pollution has become a serious problem encountered worldwide. This study investigated and analyzed the characteristics of non-point source pollution in Baoding City. A simulation model for non-point source pollution was developed based on the Stormwater Management Model (SWMM), and, the process of non-point source pollution was simulated for Baoding City. The data was calibrated using data from two observed rainfall events (25.6 and 25.4 mm, the total rainfall on 31 July 2008 (07312008) was 25.6 mm, the total rainfall amount on 21 August 2008 (08212008) was 25.4 mm) and validated using data from an observed rainfall event (92.6 mm, the total rainfall on 08102008 was 92.6 mm) (Our monitoring data is limited by the lack of long-term monitoring, but it can meet the requests of model calibration and validation basically). In order to analyze the effects of non-point source pollution on Baiyangdian Lake, the characteristics and development trends of water pollution were determined using a one-dimensional water quality model for Baoding City. The results showed that the pollutant loads for Pb, Zn, TN (Total Nitrogen), and TP (Total Phosphorus) accounted for about 30% of the total amount of pollutant load. Finally, applicable control measures for non-point source pollution especially for Baoding were suggested, including urban rainwater and flood resources utilization and Best Management Practices (BMPs) for urban non-point source pollution control. [ABSTRACT FROM AUTHOR]

Energy is crucial in supporting people's daily lives and the continual quest for human development. Due to the associated complexities and uncertainties, decision makers and planners are facing increased pressure to respond more effectively to a number of energy-related issues and conflicts, as well as GHG emission mitigation within the multiple scales of energy management systems (EMSs). This quandary requires a focused effort to resolve a wide range of issues related to EMSs, as well as the associated economic and environmental implications. Effective systems analysis approaches under uncertainty to successfully address interactions, complexities, uncertainties, and changing conditions associated with EMSs is desired, which require a systematic investigation of the current studies on energy systems. Systems analysis and optimization modeling for low-carbon energy systems planning with the consideration of GHG emission reduction under uncertainty is thus comprehensively reviewed in this paper. A number of related methodologies and applications related to: (a) optimization modeling of GHG emission mitigation; (b) optimization modeling of energy systems planning under uncertainty; and (c) model-based decision support tools are examined. Perspectives of effective management schemes are investigated, demonstrating many demanding areas for enhanced research efforts, which include issues of data availability and reliability, concerns in uncertainty, necessity of post-modeling analysis, and usefulness of development of simulation techniques. [ABSTRACT FROM AUTHOR]

In view of the great contribution of coal-fired units to CO2 emissions, the coupled coal and power system with consideration of CO2 mitigation is a typical sub-system of the highly emitting Chinese energy system for low-carbon studies. In this study, an inexact mix-integer two-stage programming (IMITSP) model for the management of low-carbon energy systems was developed based on the integration of multiple inexact programming techniques. Uncertainties and complexities related to the carbon mitigation issues in the coupled coal and power system can be effectively reflected and dealt with in this model. An optimal CO2 mitigation strategy associated with stochastic power-generation demand under specific CO2 mitigation targets could be obtained. Dynamic analysis of capacity expansion, facility improvement, coal selection, as well as coal blending within a multi-period and multi-option context could be facilitated. The developed IMITSP model was applied to a semi-hypothetical case of long-term coupled management of coal and power within a low-carbon energy system in north China. The generated decision alternatives could help decision makers identify desired strategies related to coal production and allocation, CO2 emission mitigation, as well as facility capacity upgrade and expansion under various social-economic, ecological, environmental and system-reliability constraints. It could also provide interval solutions with a minimized system cost, a maximized system reliability and a maximized power-generation demand security. Moreover, the developed model could provide an in-depth insight into various CO2 mitigation technologies and the associated environmental and economic implications under a given reduction target. Tradeoffs among system costs, energy security and CO2 emission reduction could be analyzed. [ABSTRACT FROM AUTHOR]

With support of GIS tools and Theil index, the spatial variance of urban energy consumption in China was discussed in this paper through the parallel comparison and quantitative analysis of the 30 provincial capital cities of mainland China in 2005, in terms of scale, efficiency and structure. The indicators associated with urban energy consumption show large spatial variance across regions, possibly due to diversities of geographic features, economic development levels and local energy source availability in China. In absolute terms, cities with the highest total energy consumption are mostly distributed in economic-developed regions as Beijing-Tianjin-Tangshan Area, Yangtze River Delta and Pearl River Delta of China, however, the per capita urban energy use is significantly higher in the Mid-and-Western regions. With regard to the energy mix, coal still plays the dominant role and cities in Mid-and-Western regions rely more on coal. In contrast, high quality energy carrier as electricity and oils are more used in southeast coastal zone and northern developed areas. The energy intensive cities are mainly located in the northwest, while the cities with higher efficiency are in southeast areas. The large spatial variance of urban energy consumption was also verified by the Theil indices. Considering the Chinese economy-zones of East, Middle and West, the within-group inequalities are the main factor contributing to overall difference, e.g., the Theil index for per capita energy consumption of within-group is 0.18, much higher than that of between group (0.07), and the same applies to other indicators. In light of the spatial variance of urban energy consumptions in China, therefore, regionalized and type-based management of urban energy systems is badly needed to effectively address the ongoing energy strategies and targets. [ABSTRACT FROM AUTHOR]