Avoided global warming emissions with the adoption of biofuel policies in Spain

Life cycle greenhouse gas emissions impacts of the adoption of the EU Directive on biofuels in Spain. Effect of the import of raw materials and land use changes

The body of life cycle assessment (LCA) literature is vast and has grown over the last decade at a dauntingly rapid rate. Many LCAs have been published on the same or very similar technologies or products, in some cases leading to hundreds of publications. One result is the impression among decision makers that LCAs are inconclusive, owing to perceived and real variability in published estimates of life cycle impacts. Despite the extensive available literature and policy need formore conclusive assessments, only modest attempts have been made to synthesize previous research. A significant challenge to doing so are differences in characteristics of the considered technologies and inconsistencies in methodological choices (e.g., system boundaries, coproduct allocation, and impact assessment methods) among the studies that hamper easy comparisons and related decision support. An emerging trend is meta-analysis of a set of results from LCAs, which has the potential to clarify the impacts of a particular technology, process, product, or material and produce more robust and policy-relevant results. Meta-analysis in this context is defined here as an analysis of a set of published LCA results to estimate a single or multiple impacts for a single technology or a technology category, either in a statisticalmore » sense (e.g., following the practice in the biomedical sciences) or by quantitative adjustment of the underlying studies to make them more methodologically consistent. One example of the latter approach was published in Science by Farrell and colleagues (2006) clarifying the net energy and greenhouse gas (GHG) emissions of ethanol, in which adjustments included the addition of coproduct credit, the addition and subtraction of processes within the system boundary, and a reconciliation of differences in the definition of net energy metrics. Such adjustments therefore provide an even playing field on which all studies can be considered and at the same time specify the conditions of the playing field itself. Understanding the conditions under which a meta-analysis was conducted is important for proper interpretation of both the magnitude and variability in results. This special supplemental issue of the Journal of Industrial Ecology includes 12 high-quality metaanalyses and critical reviews of LCAs that advance understanding of the life cycle environmental impacts of different technologies, processes, products, and materials. Also published are three contributions on methodology and related discussions of the role of meta-analysis in LCA. The goal of this special supplemental issue is to contribute to the state of the science in LCA beyond the core practice of producing independent studies on specific products or technologies by highlighting the ability of meta-analysis of LCAs to advance understanding in areas of extensive existing literature. The inspiration for the issue came from a series of meta-analyses of life cycle GHG emissions from electricity generation technologies based on research from the LCA Harmonization Project of the National Renewable Energy Laboratory (NREL), a laboratory of the U.S. Department of Energy, which also provided financial support for this special supplemental issue. (See the editorial from this special supplemental issue [Lifset 2012], which introduces this supplemental issue and discusses the origins, funding, peer review, and other aspects.) The first article on reporting considerations for meta-analyses/critical reviews for LCA is from Heath and Mann (2012), who describe the methods used and experience gained in NREL's LCA Harmonization Project, which produced six of the studies in this special supplemental issue. Their harmonization approach adapts key features of systematic review to identify and screen published LCAs followed by a meta-analytical procedure to adjust published estimates to ones based on a consistent set of methods and assumptions to allow interstudy comparisons and conclusions to be made. In a second study on methods, Zumsteg and colleagues (2012) propose a checklist for a standardized technique to assist in conducting and reporting systematic reviews of LCAs, including meta-analysis, that is based on a framework used in evidence-based medicine. Widespread use of such a checklist would facilitate planning successful reviews, improve the ability to identify systematic reviews in literature searches, ease the ability to update content in future reviews, and allow more transparency of methods to ease peer review and more appropriately generalize findings. Finally, Zamagni and colleagues (2012) propose an approach, inspired by a meta-analysis, for categorizing main methodological topics, reconciling diverging methodological developments, and identifying future research directions in LCA. Their procedure involves the carrying out of a literature review on articles selected according to predefined criteria.« less

Effects of land-use change on the carbon balance of 1st generation biofuels: An analysis for the European Union combining spatial modeling and LCA

Increases in soil sequestered carbon under conservation agriculture cropping decrease the estimated greenhouse gas emissions of wetland rice using life cycle assessment

Mitigation of greenhouse gas emissions from beef production in western Canada – Evaluation using farm-based life cycle assessment

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Based on the localized data of environmental load, this study has establishedthe life cycle assessment (LCA) model of battery electric passenger vehicle(BEPV) that be produced and used in China, and has evaluated the energyconsumption and greenhouse gases (GHGs) emission during vehicle pro-duction and operation. The results show that the total energy consumptionand GHG emissions are 438GJ and 37,100kg (in terms of CO2 equivalent)respectively. The share of GHG emissions in total emissions at the productionstage is 24.6%, and 75.4% GHG emissions are contributed by the operationalstage. The main source of energy consumption and GHG emissions at vehicleproduction stage is the extraction and processing of raw materials. TheGHG emissions of raw materials production accounts for 75.0% in the GHGemissions of vehicle production and 18.0% in the GHG emissions of fulllife cycle. The scenario analysis shows that the application of recyclablematerials, power grid GHG emission rates and vehicle energy consumption rates have significant influence on the carbon emissions in the life cycle ofvehicle. Replacing primary metals with recycled metals can reduce GHGemissions of vehicle production by about 7.3%, and total GHG emissionscan be reduced by about 1.8%. For every 1% decrease in GHG emissionsper unit of electricity, the GHG emissions of operation stage will decrease byabout 0.9%; for every 1.0% decrease in vehicle energy consumption rate, thetotal GHG emissions decrease by about 0.8%. Therefore, developing cleanenergy, reducing the proportion of coal power, optimizing the productionof raw materials and increasing the application of recyclable materials areeffective ways to improve the environmental performance of BEPV.

Feeding a growing, increasingly affluent population while limiting environmental pressures of food production is a central challenge for society. Understanding the location and magnitude of food production is key to addressing this challenge because pressures vary substantially across food production types. Applying data and models from life cycle assessment with the methodologies for mapping cumulative environmental impacts of human activities (hereafter cumulative impact mapping) provides a powerful approach to spatially map the cumulative environmental pressure of food production in a way that is consistent and comprehensive across food types. However, these methodologies have yet to be combined. By synthesizing life cycle assessment and cumulative impact mapping methodologies, we provide guidance for comprehensively and cumulatively mapping the environmental pressures (e.g., greenhouse gas emissions, spatial occupancy, and freshwater use) associated with food production systems. This spatial approach enables quantification of current and potential future environmental pressures, which is needed for decision makers to create more sustainable food policies and practices.

The transportation sector accounts for over 20 percent of greenhouse gas (GHG) emissions in Colorado which without intervention will grow to over 30 million metric tons (MMT) of GHG emissions per year. This study seeks to develop a specific characterization of the Colorado fuel and transportation system using a customized life cycle assessment (LCA) model. The model (CO-GT) was developed as an analytical tool to define Colorado’s 2020 baseline life cycle GHG emissions for the transportation sector, and to examine Colorado-specific pathways for GHG reductions through fuel types and volumes changes that might be associated with a state clean fuel standard (CFS). By developing a LCA of transportation fuels that is specific to the state of Colorado’s geography, fleet makeup, policies, energy sector and more, these tools can evaluate various proposals for the transition towards a more sustainable state transportation system. The results of this study include a quantification of the Colorado-specific roles of clean fuels, electricity, extant policies, and fleet transition in projections of the state’s 2030 transportation sector GHG emissions. Relative to a 2020 baseline, electrification of the vehicle fleet is found to reduce state-wide lifecycle GHG emissions by 7.7 MMT CO2e by 2030, and a model CFS policy able to achieve similar reductions in the carbon intensity of clean fuels as was achieved by California in the first 10 years of its CFS policies is found to only reduce state-wide lifecycle GHG emissions by 0.2 MMT CO2e by 2030. These results illustrate the insensitivity of Colorado’s transportation fleet GHG emissions reductions to the presence of CFS policies, as proposed to date.

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 ...

Taking Stock of Strategies on Climate Change and the Way Forward: A Strategic Climate Change Framework for Australia

Uncertainty in life cycle greenhouse gas emissions of sustainable aviation fuels from vegetable oils

PDF HTML阅读 XML下载 导出引用 引用提醒 基于生命周期评价的上海市水稻生产的碳足迹 DOI: 10.5846/stxb201304240794 作者: 作者单位: 上海市农业科学院,上海市农业科学院,上海市农业科学院,上海市农业科学院,江西农业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技部支撑计划后世博专项资助项目（2010BAK69B18）；上海市科委崇明科技攻关专项资助项目（10DZ1960101） Life cycle assessment of carbon footprint for rice production in Shanghai Author: Affiliation: Shanghai Academy of Agricultural Sciences,Seed management station of Shanghai,,,Jiangxi Agricultural University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:碳足迹是指由企业、组织或个人引起的碳排放的集合。参照PAS2050规范并结合生命周期评价方法对上海市水稻生产进行了碳足迹评估。结果表明：（1）目前上海市水稻生产的碳排放为11.8114 t CO2e/hm2，折合每吨水稻生产周期的碳足迹为1.2321 t CO2e；（2）稻田温室气体排放是水稻生产最主要的碳排放源，每吨水稻生产的总排放量为0.9507 t CO2e，占水稻生产全部碳排放的77.1%，其中甲烷（CH4）又是最主要的温室气体，对稻田温室气体碳排放的贡献率高达96.6%；（3）化学肥料的施用是第二大碳排放源，每吨水稻生产的总排放量为0.2044 t CO2e，占水稻生产总碳排放的16.5%，其中N最高，排放量为0.1159 t CO2e。因此，上海低碳水稻生产的关键在降低稻田甲烷的排放，另外可通过提高氮肥利用效率，减少氮肥施用等方法减少种植过程中碳排放。 Abstract:Global climate change has become an urgent issue of concern. Climate change will increasingly threaten our food production, security and even the survival of the human race. It also has a serious impact on natural ecosystems and the socioeconomic system. With the increasing scale and improvement in mechanization levels, the economic linkage between agricultural production and reduction of Greenhouse Gas (GHG) emissions is even closer in the agricultural production system. Therefore, the development of a low-carbon agricultural model is one of the long-term strategies for low-carbon economic growth throughout the country.This research of carbon footprint is introduced to measure the GHG emission over the rice production cycle. The carbon footprint can be defined as the total carbon emissions caused by an organization, event, product or person. At present, carbon footprints are used to measure GHG emissions in products, services, organizations, cities and countries and offer the decision basis for the formulation of GHG emission reduction schemes.Agricultural ecological systems, every year, also produce a lot of GHG emissions. The whole process of prenatal, intrapartum and postpartum agricultural production are closely related to energy consumption and GHG emission. In the process, all the agricultural inputs, such as chemical fertilizers, pesticides, seeds, cultivation, plant protection, agricultural machinery, irrigation and harvest also produce greenhouse gas emissions.The whole cultivation of rice involves methane (CH4) emission. This study shows that rice cultivation is one of the biggest sources of GHG emissions in crop cultivation. Rice paddies emit a large amount of methane in their water logged mode. Different irrigation modes have a great influence on the emission of GHG. Straw return is another factor that promotes GHG emissions. Soil organic content increases with the return of straw, with an increase in the soil methanogen activity, leading to increased methane emissions.The current carbon footprint research is the first time it has been used to measure the carbon emissions involved in rice production. The carbon footprint for rice production in Shanghai was assessed by the PAS2050 paradigm and life cycle assessment. The study area, located in Changjiang Farm, which belongs to the Guangming Group in Chongming County Shanghai City atlatitude 121°32'22' E, longitude31°40'23' N. Chongming County, in the Yangtze River Estuary, is a typical sub tropical monsoon climate with mild climate, abundant rainfall, annual average temperatures of 15.3 ℃, and annual precipitation of 1245 mm. It is the major grain production base for Shanghai city with winter wheat and summer rice forming their main planting patterns, which are typical for the middle and lower reaches of the Yangtze River rice-wheat rotation cropping pattern.The entire carbon emission of rice production in Shanghai was 11.8114 t CO2e (CO2-equivalents)/hm2, corresponding to a 1.2321 t CO2e/t rice grain yield. GHG emissions from paddy fields were the major source, which emitted 0.9507 t CO2e/t rice and accounted for 77.1% of total carbon emissions during rice production. Moreover, CH4 was the largest source for GHG emissions with a contribution rate of 96.6%.Chemical fertilizers were the second largest emission source in rice production. Chemical fertilizers emitted 0.2044 t CO2e for each ton of rice production, contributing 16.5% of total carbon emissions in rice production. N fertilizer was the biggest emission source, which released 0.1159 t CO2e/t rice.This research investigates the GHG emissions over the whole process of the Shanghai rice production cycle and reveals the energy consumption and GHG emissions in rice production. Thus, a rice carbon footprint is calculated by assessing the GHG emissions in Shanghai rice production. The results are beneficial for producing reduction plans of reducing GHG emissions in Shanghai rice production. Furthermore, the results will supply both practicable and theoretical foundations for drafting carbon footprint formulations in other industrial areas. 参考文献 相似文献 引证文献

Understanding spatial and temporal variations of greenhouse gas (GHG) emissions at local level is important for both companies in the agri-food sector working to improve sustainability in their supply chains and local governments following a low-carbon development pathway. For this reason, it is necessary to count with methodologies that do not only estimate GHG emissions, but provide analytics about their location and temporal variations. Factors such as spatial distribution of farms, variations on the requirement of inputs depending on the age of the farms, as well as transportation distances for the main and complementary products, should be assessed at farm level. To accomplish this, spatial and temporal analytics can be incorporated into life cycle assessment (LCA) by joining it with geographic information systems (GIS). This paper explores how spatial statistics tools can be applied to identify spatial and temporal trends in GHG emission results obtained from an LCA conducted on cocoa farming in the region of San Martin in Peru. Results indicate that it is possible to identify temporal and spatial trends of statistically significant clusters of farms with high GHG emissions (hot spot analysis).

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.