Evaluation of Industrial Urea Energy Consumption (EC) Based on Life Cycle Assessment (LCA)

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.

Quantifying the impact of energy consumption sources on GHG emissions in major economies: A machine learning approach

The Life Cycle of Energy Consumption and Greenhouse Gas Emissions from Critical Minerals Recycling: Case of Lithium-ion Batteries

The potential challenge for the effective GHG emissions mitigation of urban energy consumption: A case study of Macau

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.

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

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

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 ...

Reply to L Aleksandrowicz et al.

Indonesia has targeted 29 % Greenhouse gas (GHG) emissions reduction in 2030 and Industry is one of the big two contributors for GHG emissions. As an industry, mining is an energy-intensive industry, and reducing energy consumption is one of the strategies to improve mining environmental performance. The aim of this paper is to estimate the GHG emission reduction in a mining project through energy reduction initiatives. A copper mine in Indonesia with processing plant capacity of 120,000 t/d and operate 111 Caterpillar 793C Haul Truck was taken as a case study. This mine site has two sources of an electricity namely coal-fired power plant with 112 MW output and diesel power plant with 45 MW output. The analysis method for calculating CO2 emission is using IPCC method where fuel consumption and emission factor are two main variables for GHG emissions. Business as usual scenario (TIER 1) showed that the average of diesel fuel consumption for fleets operation generated 294,006 t CO2-eq/y. A coal-fired power plant with average coal consumption of 350 t/d/unit generated 1.15 Mt CO2-eq/y and diesel power plant consumed 4.35 ML/y produced 11,632 t CO2-eq/y. Two energy initiative programs were identified namely fuel conversion and used oil utilisation program. The initiative scenario focused on substituting, reducing and reusing of fossil fuels including coal, diesel fuel, and used oil. This scenario was estimated to contribute the carbon emission reduction (t CO2-eq) of 258,381 annually. The involvement of mining industry in carbon emission reduction is not only helping Indonesia in achieving its GHG emissions reduction target but also increases mine site environmental performance and company image.

CHAPTER ELEVEN - Life Cycle Assessment of Hydrogen Fuel Cell and Gasoline Vehicles

The energy consumption and greenhouse gas (GHG) emissions associated with urban water systems have come under scrutiny in recent times, as a result of increasing interest in climate change, to which urban water systems are particularly vulnerable. The approach most commonly taken previously to modelling these results has been to consider various urban water system components in great detail, but in isolation from the rest of the system. This piecewise approach is suboptimal, since it systematically fails to reveal the relative importance of the energy consumption and GHG emissions associated with each system component in the context of the entire urban water system. Hence, it was determined that a new approach to modelling the energy consumption and GHG emissions associated with urban water systems was necessary. It was further determined that the value derived from such a model would be greatly enhanced if it could also model the water consumption and wastewater generation associated with each system component, such that integrated policies could be developed, aimed at minimising water consumption, wastewater generation, energy consumption and GHG emissions concurrently. Hence, the following research question was posed: How should the relationships between the water consumption, wastewater generation, energy consumption and GHG emissions associated with the operation of urban water systems be modelled such that the impact of various changes to the system configuration made at different spatial scales can be determined within the context of the entire system? In this research project, life cycle assessment ideas were employed to develop such a new modelling methodology. Initially, the approach was developed at the building-scale, such that the end uses of water present in a selected building and any associated appliances could be modelled, along with the fraction of the citywide water supply and wastewater systems directly associated with providing services to that building. This vast breadth of scope was delivered by considering only the operational life cycle stage of each urban water system component, excluding both the pre- and post-operational life cycle stages of the associated infrastructure. The value of this pilot model was illustrated by several case studies, focused on residential buildings connected to the centralised water supply and wastewater systems in Melbourne, Australia. Later, the approach was extended to the city-scale by using probabilistic distributions of each input parameter, such that all of the end uses of water present in a city, and all of the associated building-scale appliances could be modelled, along with the associated complete water supply and wastewater systems. The value of this city-scale model was illustrated by applying it to model a hypothetical case study city, resembling Melbourne, Australia in many ways. Due to a lack of data, this application was limited to the residential sector of the case study city, along with the fraction of the citywide water supply and wastewater systems directly associated with providing services to that sector. The results generated by the pilot and city-scale models showed that the new modelling methodology could be employed at a wide range of scales to assess the relative importance of each modelled urban water system component in terms of the specified results. Importantly, the high resolution of those results enabled the identification of the underlying causes of the relative importance of each urban water system component, such that efficient and effective approaches to reducing each result for each system component could be developed. Interestingly, for the specific case studies investigated, it was revealed that some commonly neglected system components were actually extremely important, such as domestic hot water services, a trend found to be largely driven by hot water consumption in showers.

Residential buildings generate significant greenhouse gas (GHG) emissions and consume energy throughout their life cycle. In recent years, research on GHG emissions and energy consumption of buildings has developed rapidly in response to the growing climate change and energy crisis. Life cycle assessment (LCA) is an important method for evaluating the environmental impacts of the building sector. However, LCA studies of buildings show widely varying outcomes across the world. Besides, environmental impact assessment from a whole life cycle perspective has been undeveloped and slow. Our work presents a systematic review and meta-analysis of LCA studies on GHG emissions and energy consumption in the preuse, use, and demolition stages of residential buildings. We aim to examine the differences among the results of diverse case studies and demonstrate the spectrum of variations under contextual disparities. Results show that residential building emits about 2928kg GHG emission and consumes about 7430 kWh of energy per m2 of gross building area on average throughout the life cycle. Residential buildings have an average GHG emission of 84.81% in the use phase, followed by the preuse phase and demolition phase; the mean energy consumption in the use stage occupied the largest share of 84.52%, followed by preuse stage and demolition stage. GHG emissions and energy use vary significantly in different regions due to different building types, natural conditions, and lifestyles. Our study stresses the compelling requirement to slash GHG emissions and optimize energy consumption from residential buildings by use of low carbon building materials, energy structure adjustment, consumer behavior transformation, etc.

This study investigates the effect of energy consumption on greenhouse gas (GHG) emissions in 33 African countries from 1995–2017. It contributes to the literature by investigating the effect of disaggregated measures of energy consumption (coal, oil and other liquids, renewable energy, and electricity) on GHG emissions (CO2, N2O, CH4, and total GHG emissions) in Africa and identifies the transmission channels through which energy consumption affects GHG emissions. The system GMM is used in the study as it accounts for possible endogeneity and the potential correlation between the error term and the country fixed effects. The results show that coal consumption significantly increases CO2, CH4, and total GHG emissions and reduces N2O emissions. Oil consumption increases CO2 and total GHG emissions but reduces N2O and CH4 emissions. Renewable energy consumption reduces CO2 and CH4 emissions but increases N2O emissions. Finally, electricity consumption promotes CO2, N2O, CH4 and total GHG emissions in Africa. Further analyses show that foreign trade and economic growth are the channels through which oil consumption increases GHG emissions. The adverse effect of electricity is through urbanization. Renewable consumption could decrease GHG emissions through sustainable urbanization and trade policies. The findings suggest that countries should gradually reduce coal consumption and encourage renewable energy consumption, which has the lowest impact on the environment.

Potential for reducing GHG emissions and energy consumption from implementing the aluminum intensive vehicle fleet in China