Assessment of Greenhouse Gas Emissions from Various Energy Sources

Avoiding the Doldrums: Evaluating the Need for Change in the Offshore Wind Permitting Process

Background According to the international requirements Russian Federation must decrease the value of greenhouse gases in 40% up to 2012. In order to reach that value energy conservation programs must be applied. However, it is impossible to establish the energy efficiency of the industry and amount of exhaust and greenhouse gases without proper analysis of the present day rate of energy consumption. Aims and Objectives To provide the quantitive analysis of the amount of greenhouse gases produced by present production rates based on the energy consumption rates. To investigate the most gas blow-out areas. To analyze the possible ways of blow-out decrease by energy conservation programs application. Methods The calculating methods of greenhouse gases blow-out determination is provided by IPCC (Intergovernmental Panel on Climate Change). Estimation of the influence of the applied programs on the greenhouse gases blow-out. Reduce of environment pollution by energy consumption decrease and usage of alternative energy resources. Conclusion Development and application of energy conservation programs in industry, introducing of biogas units for methane emission decrease, usage of wind and the sun as the alternative energy resources in the local units (road illumination) and hydraulic energy - all of these methods will really decrease the greenhouse effect.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

The key to coping with climate change is to control carbon emissions from energy consumption. Scientific prediction of energy consumption carbon emissions based on influencing factors is of great significance to the determination of carbon control aim and emission reduction strategies. Given the lack of previous studies on county-level carbon emissions, this paper proposed a systematic approach to study the influencing factors of county-level energy consumption carbon emissions and to predict future emissions. Firstly, the annual energy consumption carbon emissions were calculated based on the method proposed by the Intergovernmental Panel on Climate Change (IPCC). Then the expanded Kaya equation and existing research were combined to select influencing factors for the establishment of the optimal Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, which was used to quantitatively analyze the influencing factors of carbon emissions from energy consumption at the county level. Finally, the emission reduction aims and low-carbon strategies were determined based on scenario analysis. The method was applied to Changxing, a typical county with large energy consumption and carbon emissions. Based on 16 years of data, the STIRPAT carbon emission prediction model was established and the forecast results of future emissions under three different scenarios were obtained. The results indicated that population size, industrial structure, and affluence degree were the three most influential factors, and the influence degree of each factor was quantified to support targeted low-carbon strategies for county-level cities.

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

Elucidating the mechanism underpinning ultra-clean coal production from Victorian brown coal and its application as a gasification fuel

With the increasingly prominent environmental problems and the decline of fossil fuel reserves, the reduction of energy consumption (EC) has become a common goal in the world. Urea industry is a typical energy-intensive chemical industry. However, studies just focus on the breakthrough of specific production technology or only consider the EC in the production stage. This results in a lack of evaluations of the life cycle of energy consumption (LcEC). In order to provide a systematic, scientific, and practical theoretical basis for the industrial upgrading and the energy transformation, LcEC of urea production and the greenhouse gas (GHG) emissions generated in the process of EC are studied in this paper. The results show that the average LcEC is about 30.1 GJ/t urea. The EC of the materials preparation stage, synthesis stage, and waste-treatment stage (ECRMP, ECPP, ECWD) is about 0.388 GJ/t urea, 24.8 GJ/t urea, and 4.92 GJ/t urea, accounting for 1.3%, 82.4%, and 16.3% of LcEC, respectively. Thus, the synthesis stage is a dominant energy-consumer, in which 15.4 GJ/t urea of energy, accounting for 62.0% of ECpp, supports steam consumption. According to the energy distribution analysis, it can be concluded that coal presents the primary energy in the process of urea production, which supports 94.4% of LcEC. The proportion of coal consumption is significantly higher than that of the average of 59% in China. Besides, the GHG emissions in the synthesis stage are obviously larger than that in the other stage, with an average of 2.18 t eq.CO2/t urea, accounting for 81.3% of the life cycle of GHG (LcGHG) emissions. In detail, CO2 is the dominant factor accounting for 90.0% of LcGHG emissions, followed by CH4, while N2O is negligible. Coal is the primary source of CO2 emissions. The severe high proportion of coal consumption in the life cycle of urea production is responsible for this high CO2 content of GHG emissions. Therefore, for industrial urea upgrading and energy transformation, reducing coal consumption will still be an important task for energy structure transformation. At the same time, the reformation of synthesis technologies, especially for steam energy-consuming technology, will mainly reduce the EC of the urea industry. Furthermore, the application of green energy will be conducive to a win-win situation for both economic and environmental benefits.

Operationalizing marketable blue carbon

Koh Mak Island was promoted as the low carbon destination in Thailand. Transportation represents the main contributor of greenhouse gas (GHG) emissions which is linked to climate change. These GHG emission from surface transport is quite complicated as data is scarce on the distances travelled for tourism purposes. The aim of this study is to estimate the amount of CO2 emission from energy consumption by tourist transportation in Koh Mak Island, Trat province, Thailand. The methodology of a bottom up approach was observed by using questionnaire surveys. Firstly, the questionnaire design ensured the validity of the questionnaire by calculating the Item-Objective Congruence (IOC) index which was found to be 0.96 which is acceptable. Secondly, CO2 emission from energy consumption by transportation was calculated by the 2006 Intergovernmental Panel on Climate Change (IPCC) criteria. The CO2 emission of local transportation was estimated by using the 465 copies of questionnaire that were distributed to the tourists. The tourism demographic information of male and female in Koh Mak Island were 42 % and 58 %. Most of the tourist age was 26-35 years old. The average local transport between beginning of the journey in Thailand and Koh Mak destination was 468 ± 139 km person-1. The total consumption of gasoline and diesel for road transportation of the 465 tourists were 7,954.01 and 15,199.80 L. Gasoline used in boat transportation was 1,357.80 L. The total CO2 emissions in transportation due to consumption of gasoline and diesel were 20,389.14 and 23,715.83 kg CO2-eq. The average CO2 emission was 23.83 kg CO2 person-1. The alternative to reduce CO2 emission in transportation by low carbon tourism is to ride bicycles on the island as the distance between landmarks are quite short and there is very good scenery between the roads.

Multi-input, Multi-output Hybrid Energy Systems

This article examines the short- and long-run association among carbon emissions, energy consumption and economic growth through deploying the environmental Kuznets curve (EKC) using combined (aggregated) and separated (disaggregated) energy consumption data for Zimbabwe from 1980 to 2014. The ARDL bounds tests and Johansen cointegration tests found long-run relationships among the variables. In the long-run, total energy consumption and primary coal consumption produce statistically significant positive relationships with carbon emissions. However, petroleum consumption demonstrates a statistically significant negative association with carbon emissions. The results show the validity of the EKC in total energy and primary coal consumption in the long-run but are invalid for petroleum consumption. In the short run, the findings reveal that total energy, primary coal and petroleum consumption have statistically significant positive relationships with carbon emissions. Furthermore, in the short run, the EKC is evident in petroleum consumption but invalid in both total energy and primary coal consumption. The short- and long-run Granger causality tests results based on the VECM are also discussed. The article concludes that, if carbon emissions are to be reduced in developing economies, alternative energy sources in the form of green technologies should be adopted as substitutes for coal and petroleum.

This article examines the short- and long-run association among carbon emissions, energy consumption and economic growth through deploying the environmental Kuznets curve (EKC) using combined (aggregated) and separated (disaggregated) energy consumption data for Zimbabwe from 1980 to 2014. The ARDL bounds tests and Johansen cointegration tests found long-run relationships among the variables. In the long-run, total energy consumption and primary coal consumption produce statistically significant positive relationships with carbon emissions. However, petroleum consumption demonstrates a statistically significant negative association with carbon emissions. The results show the validity of the EKC in total energy and primary coal consumption in the long-run but are invalid for petroleum consumption. In the short run, the findings reveal that total energy, primary coal and petroleum consumption have statistically significant positive relationships with carbon emissions. Furthermore, in the short run, the EKC is evident in petroleum consumption but invalid in both total energy and primary coal consumption. The short- and long-run Granger causality tests results based on the VECM are also discussed. The article concludes that, if carbon emissions are to be reduced in developing economies, alternative energy sources in the form of green technologies should be adopted as substitutes for coal and petroleum.

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020.

Climate Change and Health: Strengthening the Evidence Base for Policy

Debate over climate change is nothing new. Scientists have been arguing about whether greenhouse gases released by human activity might change the climate since the late nineteenth century, when Swedish chemist Svante Arrhenius first proposed that industrial emissions might cause global warming.1 Fueled by partisan bickering, this dispute now is more bellicose than ever.