Emissions in the stream: estimating the greenhouse gas impacts of an oil and gas boom

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

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).

In order to manage strategies to curb climate change, systemic benchmarking at a variety of production scales and methods is needed. This study is the first life cycle assessment (LCA) of a large-scale, vertically integrated organic dairy in the United States. Data collected at Aurora Organic Dairy farms and processing facilities were used to build a LCA model for benchmarking the greenhouse gas (GHG) emissions and energy consumption across the entire milk production system, from organic feed production to post-consumer waste disposal. Energy consumption and greenhouse gas emissions for the entire system (averaged over two years of analysis) were 18.3 MJ per liter of packaged fluid milk and 2.3 kg CO(2 )equiv per liter of packaged fluid milk, respectively. Methane emissions from enteric fermentation and manure management account for 27% of total system GHG emissions. Transportation represents 29% of the total system energy use and 15% of the total GHG emissions. Utilization of renewable energy at the farms, processing plant, and major transport legs could lead to a 16% reduction in system energy use and 6.4% less GHG emissions. Sensitivity and uncertainty analysis reveal that alternative meat coproduct allocation methods can lead to a 2.2% and 7.5% increase in overall system energy and GHG, respectively. Feed inventory data source can influence system energy use by -1% to +10% and GHG emission by -4.6% to +9.2%, and uncertainties in diffuse emission factors contribute -13% to +25% to GHG emission.

Plastics and climate change—Breaking carbon lock-ins through three mitigation pathways

Embodied greenhouse gas (GHG) emissions and their structure of inducement by the supply-chain networks of 480 goods and services in the United States are analyzed for 44 GHGs. Producing a dollar of a product or service generates an average of 0.36 kg of CO2 equivalent GHGs onsite, increasing to 0.83 kg when supply-chain-induced emissions are taken into account. Services produce less than 5% of total U.S. GHG emissions directly, and their direct GHG emission intensities per dollar output are much less (0.04 kg C02 equiv/dollars) than those of physical products, even when supply-chain-induced emissions are included (0.47 kg C02 equiv/dollars). When both supply-chain effects and the volume of household expenditures are taken into account, however, household consumption of services excluding electric utilities and transportation services proves to be responsible for 37.6% of total industrial GHG emissions in the United States, almost twice the amount due to household consumption of electric utility and transportation services. Given the current structure of GHG emissions, a shift to a service-oriented economy is shown to entail a decrease in GHG emission intensity per unit GDP but an increase, by necessity, in overall GHG emissions in absolute terms. The results are discussed in the context of U.S. climate change policy.

This paper presents a model to quantify total greenhouse gas (GHG) emissions from dairy farms. The model, which is based on a whole farm management approach, accounts for the variability that occurs in GHG emissions among farm production and management practices. The variation is accommodated in six dairy farm management systems (FMS), which broadly include typical dairy production systems in South Africa. These are pasture-based with high or low stocking rates, total mixed ration with high or low stocking rates, and partial mixed ration with high or low stocking rates. Three variations of functional units that were used to evaluate the environmental impacts of various FMS are defined as per animal unit = kg CO 2-eq head -1 yr -1 ; per unit of farm area = kg CO 2-eq ha -1 yr -1 , and per unit of product = kg CO 2-eq kg FPCM -1 , where FPCM is fat and protein corrected milk. The results show a range of GHG emissions in CO 2-eq among the FMS with various methodological approaches because of the large impact from different emission factors, which vary between accounting methods. The more detailed equations were recommended to effectively improve environmental impacts. These more detailed non-linear equations tended to predict more biologically realistic emissions when compared with the linear equations in which over or under-predictions of GHG were observed. The most prominent drivers for GHG emissions across all FMS were from enteric methane (CH 4 ) and nitrous oxide (N 2 O) from soil management. Rankings among FMS varied according to output methodology and functional units. GHG emissions expressed per animal or per unit area differ greatly from those expressed from a given level of product. In conclusion, the accounting methodologies that are described in this paper to predict GHG emissions of animal-related origin performed sufficiently across all FMS, and could be applied to quantify the carbon footprint of dairy production systems in South Africa. Keywords : Carbon dioxide equivalents, dairy production, methane, nitrous oxide

Drained peat soils contribute significantly to global human-caused CO2 emissions and reducing peat degradation via rewetting is high on the political agenda. Ceasing agricultural activities on rewetted soils might lead to land owner resistance and high societal expenses to compensate farmers. Continued biomass production adapted for wet conditions on peat soils potentially minimizes these costs and helps supplying the growing demand for e.g. materials, fuels and feed. Here we used a life cycle assessment approach (cradle to farm gate) to investigate the greenhouse gas (GHG) emissions related to three cases by applying IPCC (Intergovernmental Panel on Climate Change) emission factors and specific site conditions at a bog and a fen site that represent widely distributed temperate peat soils. Besides soil emissions, upstream emissions from input, operational emissions and emission related to rewetting construction work were included. The analyzed systems were deeply drained cash cropping on agricultural bog (potatoes (Solanum tuberosum L.), spring barley (Hordeum vulgare L.) and oat (Avena sativa L.), permanent Reed canary grass (RCG) (Phalaris arundinacea L.) production on non-drained bog and permanent RCG production on shallow-drained fen. The annual mean water table depths (WTD) were −70, −38 (estimated) and −13 cm, respectively. Results showed estimated GHG emissions of 40.5, 26.1 and 20.6 Mg CO2eq ha−1, respectively, corresponding to a 35% GHG reduction for the non-drained bog case as compared to the drained bog case, despite that the obtained WTD due to ceased drainage did not adhere to the IPCC rewetting threshold of −30 cm. Emissions related to crop management represented 7, 14 and 19% of total emissions. In the RCG cultivation on fen case, the WTD were controlled primarily by the water table of the nearby stream and total GHG emissions were even lower as compared to the RCG production on the non-drained bog reflecting the difference in WTD. Rewetting projects need to include careful knowledge of the specific peat area to foresee the actual reduction potential.

The Australian dairy industry contributes ~1.6% of the nation’s greenhouse gas (GHG) emissions, emitting an estimated 9.3 million tonnes of carbon dioxide equivalents (CO2e) per annum. This study examined 41 contrasting Australian dairy farms for their GHG emissions using the Dairy Greenhouse Gas Abatement Strategies calculator, which incorporates Intergovernmental Panel on Climate Change and Australian inventory methodologies, algorithms and emission factors. Sources of GHG emissions included were pre-farm embedded emissions associated with key farm inputs (i.e. grains and concentrates, forages and fertilisers), CO2 emissions from electricity and fuel consumption, methane emissions from enteric fermentation and animal waste management, and nitrous oxide emissions from animal waste management and nitrogen fertilisers. The estimated mean (±s.d.) GHG emissions intensity was 1.04 ± 0.17 kg CO2 equivalents/kg of fat and protein-corrected milk (kg CO2e/kg FPCM). Enteric methane emissions were found to be approximately half of total farm emissions. Linear regression analysis showed that 95% of the variation in total farm GHG emissions could be explained by annual milk production. While the results of this study suggest that milk production alone could be a suitable surrogate for estimating GHG emissions for national inventory purposes, the GHG emissions intensity of milk production, on an individual farm basis, was shown to vary by over 100% (0.76–1.68 kg CO2e/kg FPCM). It is clear that using a single emissions factor, such as milk production alone, to estimate any given individual farm’s GHG emissions, has the potential to either substantially under- or overestimate individual farms’ GHG emissions.

Executive Summary: The California Climate Action Registry, which was initially established in 2000 and began operation in Fall 2002, is a voluntary registry for recording annual greenhouse gas (GHG) emissions. The purpose of the Registry is to assist California businesses and organizations in their efforts to inventory and document emissions in order to establish a baseline and to document early actions to increase energy efficiency and decrease GHG emissions. The State of California has committed to use its ''best efforts'' to ensure that entities that establish GHG emissions baselines and register their emissions will receive ''appropriate consideration under any future international, federal, or state regulatory scheme relating to greenhouse gas emissions.'' Reporting of GHG emissions involves documentation of both ''direct'' emissions from sources that are under the entity's control and indirect emissions controlled by others. Electricity generated by an off-site power source is consider ed to be an indirect GHG emission and is required to be included in the entity's report. Registry participants include businesses, non-profit organizations, municipalities, state agencies, and other entities. Participants are required to register the GHG emissions of all operations in California, and are encouraged to report nationwide. For the first three years of participation, the Registry only requires the reporting of carbon dioxide (CO2) emissions, although participants are encouraged to report the remaining five Kyoto Protocol GHGs (CH4, N2O, HFCs, PFCs, and SF6). After three years, reporting of all six Kyoto GHG emissions is required. The enabling legislation for the Registry (SB 527) requires total GHG emissions to be registered and requires reporting of ''industry-specific metrics'' once such metrics have been adopted by the Registry. The Ernest Orlando Lawrence Berkeley National Laboratory (Berkeley Lab) was asked to provide technical assistance to the California Energy Commission (Energy Commission) related to the Registry in three areas: (1) assessing the availability and usefulness of industry-specific metrics, (2) evaluating various methods for establishing baselines for calculating GHG emissions reductions related to specific actions taken by Registry participants, and (3) establishing methods for calculating electricity CO2 emission factors. The third area of research was completed in 2002 and is documented in Estimating Carbon Dioxide Emissions Factors for the California Electric Power Sector (Marnay et al., 2002). This report documents our findings related to the first areas of research. For the first area of research, the overall objective was to evaluate the metrics, such as emissions per economic unit or emissions per unit of production that can be used to report GHG emissions trends for potential Registry participants. This research began with an effort to identify methodologies, benchmarking programs, inventories, protocols, and registries that u se industry-specific metrics to track trends in energy use or GHG emissions in order to determine what types of metrics have already been developed. The next step in developing industry-specific metrics was to assess the availability of data needed to determine metric development priorities. Berkeley Lab also determined the relative importance of different potential Registry participant categories in order to asses s the availability of sectoral or industry-specific metrics and then identified industry-specific metrics in use around the world. While a plethora of metrics was identified, no one metric that adequately tracks trends in GHG emissions while maintaining confidentiality of data was identified. As a result of this review, Berkeley Lab recommends the development of a GHG intensity index as a new metric for reporting and tracking GHG emissions trends.Such an index could provide an industry-specific metric for reporting and tracking GHG emissions trends to accurately reflect year to year changes while protecting proprietary data. This GHG intensity index changes while protecting proprietary data. This GHG intensity index would provide Registry participants with a means for demonstrating improvements in their energy and GHG emissions per unit of production without divulging specific values. For the second research area, Berkeley Lab evaluated various methods used to calculate baselines for documentation of energy consumption or GHG emissions reductions, noting those that use industry-specific metrics. Accounting for actions to reduce GHGs can be done on a project-by-project basis or on an entity basis. Establishing project-related baselines for mitigation efforts has been widely discussed in the context of two of the so-called ''flexible mechanisms'' of the Kyoto Protocol to the United Nations Framework Convention on Climate Change (Kyoto Protocol) Joint Implementation (JI) and the Clean Development Mechanism (CDM).

ABSTRACTClimate change is a consequence of greenhouse gas emissions. Greenhouse gas (GHG) emissions from the waste sector contribute to 3% of total anthropogenic emissions. In this study, applicable solutions for municipal solid waste (MSW) management in Luangprabang (LPB) and Laos were examined. Material flow analysis of MSW was performed to estimate the amount of MSW generated in 2015. Approximately 29,419 tonnes of MSW is estimated for 2015. Unmanaged landfilling was the main disposal method, while MSW open burning was also practiced to some extent. The International Panel on Climate Change 2006 model and the Atmospheric Brown Clouds Emission Inventory Manual were used to estimate GHG emissions from existing MSW management, and total emissions are 33,889 tonnes/year carbon dioxide-equivalents (CO2-eq). Three scenarios were developed in order to reduce GHG emissions and environmental problems. Improvement of the MSW management by expanding MSW collection services, introducing composting and recycling, and avoiding open burning, can be considered as solutions to overcome the problems for LPB. The lowest GHG emissions are achieved in the scenario where composting and recycling are proposed, with the total GHG emissions reduction by 18,264 tonnes/year CO2-eq.

Well-to-wheel greenhouse gas emissions of electric versus combustion vehicles from 2018 to 2030 in the US

Global warming contributions from wheat, sheep meat and wool production in Victoria, Australia – a life cycle assessment

Current and future greenhouse gas (GHG) emissions from the management of municipal solid waste in the eThekwini Municipality – South Africa

This study examines the relationship between inflation and greenhouse gas (GHG) emissions in three major economies: the United States of America (USA), the European Union (EU), and China. The analysis spans from 1960 to 2021 for the USA and EU, and from 1971 to 2021 for China. A feedforward neural network model, optimized using the Levenberg–Marquardt backpropagation algorithm, was employed to predict GHG emissions based on annual inflation rates and fossil fuel energy consumption. The study integrates historical data on inflation trends with GHG emissions, measured in CO2 equivalents, and fossil fuel energy consumption, expressed as a percentage of total energy use. This multidimensional approach allows for a nuanced understanding of the economic-environmental interplay in these regions. Key findings indicate a nonlinear response of GHG emissions to inflation rates. In the USA, GHG emissions begin to decrease when inflation rates exceed 4.7%. Similarly, in the EU, a steep reduction in emissions is observed beyond a 7.5% inflation rate. China presents a more complex pattern, with two critical inflection points: the first at a 4.5% inflation rate, where GHG emissions start to decline sharply, and the second at a 7% inflation rate, beyond which further increases in inflation do not significantly reduce emissions. A critical global insight is the identification of a uniform inflation rate, around 4.4%, across all regions, at which GHG emissions consistently increase by 1%, hinting at a shared global economic behavior impacting the environment. This discovery is vital for policymakers, emphasizing the need for tailored regional strategies that consider unique economic structures, energy policies, and environmental regulations, alongside a coordinated global approach.

Well-to-wheel life cycle assessment of transportation fuels derived from different North American conventional crudes