Sectoral trends in global energy use and greenhouse gas emissions

Integrated assessment models have been used to project both baseline and mitigation greenhouse gas emissions scenarios. Results of these scenarios are typically presented for a number of world regions and end-use sectors, such as industry, transport, and buildings. Analysts interested in particular technologies and policies, however, require more detailed information to understand specific mitigation options in relation to business-as-usual trends. This paper presents sectoral trend for two of the scenarios produced by the Intergovernmental Panel on Climate Change's Special Report on Emissions Scenarios. Global and regional historical trends in energy use and carbon dioxide emissions over the past 30 years are examined and contrasted with projections over the next 30 years. Macro-activity indicators are analyzed as well as trends in sectoral energy and carbon demand. This paper also describes a methodology to calculate primary energy and carbon dioxide emissions at the sector level, accounting for the full energy and emissions due to sectoral activities.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

Assessment of bottom-up sectoral and regional mitigation potentials

The greenhouse gas mitigation potential of different economic sectors in three world regions are estimated using a bottom-up approach. These estimates provide updates of the numbers reported in the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). This study is part of a larger project aimed at comparing greenhouse gas mitigation potentials from bottom-up and top-down approaches. The sectors included in the analysis are energy supply, transport, industry and the residential and service sector. The mitigation potentials range from 11 to 15 GtCO₂eq. This is 26–38% of the baseline in 2030 and 47–68% relative to the year 2000. Potential savings are estimated for different cost levels. The total potential at negative costs is estimated at 5–8% relative to the baseline, with the largest share in the residential and service sector and the highest reduction percentage for the transport and industry sectors. These (negative) costs include investment, operation and maintenance and fuel costs and revenues at moderate discount rates of 3–10%. At costs below 100 US$/tCO₂, the largest potential reductions in absolute terms are estimated in the energy supply sector, while the transport sector has the lowest reduction potential.     

Planning regional energy system in association with greenhouse gas mitigation under uncertainty

Greenhouse gas (GHG) concentrations are expected to continue to rise due to the ever-increasing use of fossil fuels and ever-boosting demand for energy. This leads to inevitable conflict between satisfying increasing energy demand and reducing GHG emissions. In this study, an integrated fuzzy-stochastic optimization model (IFOM) is developed for planning energy systems in association with GHG mitigation. Multiple uncertainties presented as probability distributions, fuzzy-intervals and their combinations are allowed to be incorporated within the framework of IFOM. The developed method is then applied to a case study of long-term planning of a regional energy system, where integer programming (IP) technique is introduced into the IFOM to facilitate dynamic analysis for capacity-expansion planning of energy-production facilities within a multistage context to satisfy increasing energy demand. Solutions related fuzzy and probability information are obtained and can be used for generating decision alternatives. The results can not only provide optimal energy resource/service allocation and capacity-expansion plans, but also help decision-makers identify desired policies for GHG mitigation with a cost-effective manner.     

Analysis of policies to reduce oil consumption and greenhouse-gas emissions from the US transportation sector

Even as the US debates an economy-wide CO₂ cap-and-trade policy the transportation sector remains a significant oil security and climate change concern. Transportation alone consumes the majority of the US’s imported oil and produces a third of total US Greenhouse-Gas (GHG) emissions. This study examines different sector-specific policy scenarios for reducing GHG emissions and oil consumption in the US transportation sector under economy-wide CO₂ prices. The 2009 version of the Energy Information Administration’s (EIA) National Energy Modeling System (NEMS), a general equilibrium model of US energy markets, enables quantitative estimates of the impact of economy-wide CO₂ prices and various transportation-specific policy options. We analyze fuel taxes, continued increases in fuel economy standards, and purchase tax credits for new vehicle purchases, as well as the impacts of combining these policies. All policy scenarios modeled fail to meet the Obama administration’s goal of reducing GHG emissions 14% below 2005 levels by 2020. Purchase tax credits are expensive and ineffective at reducing emissions, while the largest reductions in GHG emissions result from increasing the cost of driving, thereby damping growth in vehicle miles traveled.     

Forecasting electric energy consumption using neural networks

An artificial neural network model is developed to relate the electric energy consumption in the Eastern Province of Saudi Arabia to the weather data (temperature and humidity), global solar radiation and population. A two layered feedforward neural network is used for the modelling. The inputs to the neural network are the independent variables and the output is the electric energy consumption. Seven years' of data are used for model building and validation. Model adequacy is established by a visual inspection technique and the chi-square test. Model validation, which reflects the suitability of the model for future predictions is performed by comparing the predictions of the model with future data that was not used for model building. Comparison with a regression model shows that the neural network model performs better for predictions.     

New power generation technology options under the greenhouse gases mitigation scenario in China

Climate change has become a global issue. Almost all countries, including China, are now considering adopting policies and measures to reduce greenhouse gas (GHG) emissions. The power generation sector, as a key source of GHG emissions, will also have significant potential for GHG mitigation. One of the key options is to use new energy technologies with higher energy efficiencies and lower carbon emissions. In this article, we use an energy technology model, MESSAGE-China, to analyze the trend of key new power generation technologies and their contributions to GHG mitigation in China. We expect that the traditional renewable technologies, high-efficiency coal power generation and nuclear power will contribute substantially to GHG mitigation in the short term, and that solar power, biomass energy and carbon capture and storage (CCS) will become more important in the middle and long term. In the meantime, in order to fully bring the role of technology progress into play, China needs to enhance the transfer and absorption of international advanced technologies and independently strengthen her ability in research, demonstration and application of new power generation technologies.     

Preconditions and tools for cross-sectoral regional industrial GHG and energy efficiency policy—A Finnish standpoint

The aim of this paper is to develop an approach and tools for cross-sectoral regional industrial GHG and energy policies. These policies can be conducted at different levels of society. To achieve real results in energy efficiency improvements and in GHG reductions, the policies must be focused on the correct levels in society. In Finland, the province level seems to be a reasonable level for a policy targeted at industry. This paper proposes a category for the ways industry uses energy: building energy users, major users of electricity for process/production, major users of heat for process/production and direct combustion users. This approach gives opportunities for developing regional cooperation among different industries. The approach is also important between industries and society, so that there are integrated solutions which e.g. utilise district heating and biofuel potentials. The individual technologies like electric motors, pumps, fans, heat recovery equipment and boilers do not seem to have any significant potentials for improvements in energy efficiency by the EU target year 2020. However, there are opportunities but they are at system levels: how effectively the individual technologies are applied as a part of industrial systems. This fact supports the idea of energy use categorising.     

Household energy consumption in the UK: A highly geographically and socio-economically disaggregated model

Devising policies for a low carbon society requires a careful understanding of energy consumption in different types of households. In this paper, we explore patterns of UK household energy use and associated carbon emissions at national level and also at high levels of socio-economic and geographical disaggregation. In particular, we examine specific neighbourhoods with contrasting levels of deprivation, and typical ‘types’ (segments) of UK households based on socio-economic characteristics. Results support the hypothesis that different segments have widely differing patterns of consumption. We show that household energy use and associated carbon emissions are both strongly, but not solely, related to income levels. Other factors, such as the type of dwelling, tenure, household composition and rural/urban location are also extremely important. The methodology described in this paper can be used in various ways to inform policy-making. For example, results can help in targeting energy efficiency measures; trends from time series results will form a useful basis for scenario building; and the methodology may be used to model expected outcomes of possible policy options, such as personal carbon trading or a progressive tax regime on household energy consumption.     

Oil and natural gas prices and greenhouse gas emission mitigation

The hikes in hydrocarbon prices during the last years have lead to concern about investment choices in the energy system and uncertainty about the costs for mitigation of greenhouse gas emissions. On the one hand, high prices of oil and natural gas increase the use of coal; on the other hand, the cost difference between fossil-based energy and non-carbon energy options decreases. We use the global energy model TIMER to explore the energy system impacts of exogenously forced low, medium and high hydrocarbon price scenarios, with and without climate policy. We find that without climate policy high hydrocarbon prices drive electricity production from natural gas to coal. In the transport sector, high hydrocarbon prices lead to the introduction of alternative fuels, especially biofuels and coal-based hydrogen. This leads to increased emissions of CO₂. With climate policy, high hydrocarbon prices cause a shift in electricity production from a dominant position of natural gas with carbon capture and sequestration (CCS) to coal-with-CCS, nuclear and wind. In the transport sector, the introduction of hydrogen opens up the possibility of CCS, leading to a higher mitigation potential at the same costs. In a more dynamic simulation of carbon price and oil price interaction the effects might be dampened somewhat.     

Baseload wind energy: modeling the competition between gas turbines and compressed air energy storage for supplemental generation

The economic viability of producing baseload wind energy was explored using a cost-optimization model to simulate two competing systems: wind energy supplemented by simple- and combined cycle natural gas turbines (“wind+gas”), and wind energy supplemented by compressed air energy storage (“wind+CAES”). Pure combined cycle natural gas turbines (“gas”) were used as a proxy for conventional baseload generation. Long-distance electric transmission was integral to the analysis. Given the future uncertainty in both natural gas price and greenhouse gas (GHG) emissions price, we introduced an effective fuel price, p_NGeff, being the sum of the real natural gas price and the GHG price. Under the assumption of p_NGeff=$5/GJ (lower heating value), 650 W/m² wind resource, 750 km transmission line, and a fixed 90% capacity factor, wind+CAES was the most expensive system at ¢6.0/kWh, and did not break even with the next most expensive wind+gas system until p_NGeff=$9.0/GJ. However, under real market conditions, the system with the least dispatch cost (short-run marginal cost) is dispatched first, attaining the highest capacity factor and diminishing the capacity factors of competitors, raising their total cost. We estimate that the wind+CAES system, with a greenhouse gas (GHG) emission rate that is one-fourth of that for natural gas combined cycle plants and about one-tenth of that for pulverized coal plants, has the lowest dispatch cost of the alternatives considered (lower even than for coal power plants) above a GHG emissions price of $35/tC_equiv., with good prospects for realizing a higher capacity factor and a lower total cost of energy than all the competing technologies over a wide range of effective fuel costs. This ability to compete in economic dispatch greatly boosts the market penetration potential of wind energy and suggests a substantial growth opportunity for natural gas in providing baseload power via wind+CAES, even at high natural gas prices.     

Mitigating energy-related GHG emissions through renewable energy

An inventory of greenhouse gas emissions from various economic sectors in Lebanon was conducted following the guidelines set by the World Meteorological Organization and United Nations Environment Programme Intergovernmental Panel on Climate Change. The inventory indicated that the energy sector is the major contributor (74%) to greenhouse gas emissions. This paper describes the inventory of energy related GHG emissions and assesses mitigation options to reduce emissions from electricity generation with emphasis on the usage of renewable energy including biomass, hydropower, solar and wind resources. Policy options for overcoming barriers hindering the exploitation of renewable energy resources are discussed in the context of country-specific characteristics.     

Towards real energy economics: Energy policy driven by life-cycle carbon emission

Alternative energy technologies (AETs) have emerged as a solution to the challenge of simultaneously meeting rising electricity demand while reducing carbon emissions. However, as all AETs are responsible for some greenhouse gas (GHG) emissions during their construction, carbon emission “Ponzi Schemes” are currently possible, wherein an AET industry expands so quickly that the GHG emissions prevented by a given technology are negated to fabricate the next wave of AET deployment. In an era where there are physical constraints to the GHG emissions the climate can sustain in the short term this may be unacceptable. To provide quantitative solutions to this problem, this paper introduces the concept of dynamic carbon life-cycle analyses, which generate carbon-neutral growth rates. These conceptual tools become increasingly important as the world transitions to a low-carbon economy by reducing fossil fuel combustion. In choosing this method of evaluation it was possible to focus uniquely on reducing carbon emissions to the recommended levels by outlining the most carbon-effective approach to climate change mitigation. The results of using dynamic life-cycle analysis provide policy makers with standardized information that will drive the optimization of electricity generation for effective climate change mitigation.     

广东省温室气体减排综合评价模型研究

国外能源模型一般是在发达国家的市场经济基础上开发的,比较适合于市场体系较为完善的国家和地区的能源系统的模拟和预测。中国对能源与环境进行系统建模研究起步较晚,然而还是取得了较大的成果,但省域级别的能源环境经济模型的研究还比较少,尤其是对于像广东省这样经济发展很快,而资源、能源十分匮乏的省份,能源对经济发展的"瓶颈"作用特别突出,因此开展省级能源经济模型研究意义十分重大。利用日本京都大学和国立环境研究所开发的综合模型工具ExSS,建立了适合广东省实际情况的能源与环境评价模型,并设定三种情景,应用模型对广东省2015年的能源消费量、能源结构和温室气体排放进行预测。三种情景分别是基准情景、政策情景和低碳情景。2015年基准情景、政策情景和低碳情景的能源消费量分别为3.4×108t标煤、3.2×108t标煤和3.1×108t标煤;三种情景下二氧化碳排放量分别为6.4×108t、5.6×108t和5.1×108t。广东省在"十二五"期间应加大力度调整能源结构,增加天然气的使用量,减少煤炭的使用,使煤炭的消耗比例控制在合理范围内。     

An energy balance and greenhouse gas profile for county Wexford,Ireland in 2006

In this paper an energy balance and a greenhouse gas profile has been formulated for the county of Wexford, situated in the south east of Ireland. The energy balance aims to aggregate all energy consumption in the county for the year 2006 across the following sectors; residential, agriculture, commerce and industry, and transport. The results of the energy balance are compared with the previous energy balance of 2001 where it is found that the residential sector is the biggest emitter of CO₂ with 38% of total emissions with the transport and industry/commerce sectors sharing second place on 28%. Consumption of oil is seen to have increased significantly in nearly all sectors, accounting for over 70% of the total final energy consumed (TFC) while the total primary energy requirement (TPER) sees oil consumption accounting for 91% of all fuels consumed. To take into account the contribution of agriculture in total GHG emissions the gases CH₄ and N₂O will be estimated from the agricultural and waste sectors. The results show that methane contributes 25% of total GHG emissions with agriculture being the primary contributor accounting for 36% of total emissions.     

Analysis of cooling,heating, and power systems based on site energy consumption

Feasibility of cooling, heating, and power systems frequently is based on economic considerations such as energy prices. However, a most adequate feasibility of CHP systems must be based on energy consumption followed by economic considerations. CHP systems designs must yield economical savings, but more importantly must yield real energy savings based on the best energy performance. For CHP systems, energy savings is related to primary energy and not to site energy. This paper presents a mathematical analysis demonstrating that CHP systems increase the site energy consumption (SEC). Increasing the SEC could yield misleading results in the economic feasibility of CHP systems. Three different operation modes are evaluated: (a) cooling, heating, and power; (b) heating and power; and (c) cooling and power, to represent the operation of the system throughout the year. Results show that CHP systems increase site energy consumption; therefore primary energy consumption (PEC) should be used instead of SEC when designing CHP systems.     

基于时间序列ARMA模型的广东省能源需求预测

广东省能源消耗量大,自给率低,能源供需矛盾已成为影响经济发展的重要因素,准确预测未来能源需求对于制定合理的经济发展战略和能源安全战略有着重要的借鉴意义。采用1979-2006年广东省能源消费总量数据,并根据建模要求对数据进行处理,在此基础上利用时间序列相关理论及ARMA模型对广东省未来能源需求量进行了相关预测,并得出能源需求的模型。从检验结果来看,此模型误差率低,预测效果好。     

Industrial energy efficiency and climate change mitigation

Industry contributes directly and indirectly (through consumed electricity) about 37% of the global greenhouse gas emissions, of which over 80% is from energy use. Total energy-related emissions, which were 9.9 GtCO₂ in 2004, have grown by 65% since 1971. Even so, industry has almost continuously improved its energy efficiency over the past decades. In the near future, energy efficiency is potentially the most important and cost-effective means for mitigating greenhouse gas emissions from industry. This paper discusses the potential contribution of industrial energy-efficiency technologies and policies to reduce energy use and greenhouse gas emissions to 2030.

Popularizing household-scale biogas digesters for rural sustainable energy development and greenhouse gas mitigation

Biogas utilization has undergone great development in rural China since the government systematically popularized household-scale biogas digesters for meeting the rural energy needs in the 1970s. In order to comprehensively estimate the significance of biogas utilization on rural energy development and greenhouse gas emission reduction, all types of energy sources, including straw, fuelwood, coal, refined oil, electricity, LPG, natural gas, and coal gas, which were substituted by biogas, were analyzed based on the amount of consumption for the years from 1991 to 2005. It was found that biogas provided 832749.13 TJ of energy for millions of households. By the employment of biogas digesters, reduction of greenhouse gases (GHG) was estimated to be 73157.59 Gg CO₂ equivalents (CO₂-eq), and the emission by the biogas combustion was only 36372.75 Gg CO₂-eq of GHG. Energy substitution and manure management, working in combination, had reduced the GHG emission efficiently. The majority of the emission reduction was achieved by energy substitution that reduced 84243.94 Gg CO₂, 3560.01 Gg CO₂-eq of CH₄ and 260.08 Gg CO₂-eq of N₂O emission. It was also predicted that the total production of biogas would reach to 15.6 billion m³ in the year 2010 and 38.5 billion m³ in the year 2020, respectively. As a result, the GHG emission reductions are expected to reach 28991.04 and 46794.90 Gg CO₂-eq, respectively.