基于能源利用的湖北省碳足迹分析<br>Analysis of Carbon Footprint Based on Energy Utilization in Hubei Province

Low-carbon production of iron and steel: Technology options, economic assessment, and policy

First chapter uses California's drought to identify the economic threats of water scarcity on food, energy and environmental systems as a way to introduce the multiple interactions between these resources. The second part of this first chapter introduces the focus of the dissertation, the water-energy nexus, presents a literature review identifying gaps, states the main and specific research objectives and the research questions, explains the research approach, and describes the organization of the dissertation. Second chapter develops an end-use model for water use and related energy and carbon footprint using probability distributions for parameters affecting water consumption in 10 local water utilities in California. Statewide single-family water-related CO2 emissions are 2% of overall per capita emissions, and locally variability is presented. The impact of several common conservation strategies on household water and energy use are assessed simulating different scenarios. Based on the this model, Chapter 3 introduces a probabilistic two-stage optimization model considering technical and behavioral decision variables to obtain the most eco-nomical strategies to minimize household water and water-related energy bills and costs given both water and energy price shocks. Results can provide an upper bound of household savings for customers with well-behaved preferences, and show greater adoption rates to reduce energy intensive appliances when energy is accounted, result-ing in an overall 24% reduction in indoor water use that represents a 30 percent reduc-tion in water-related energy use and a 53 percent reduction in household water-related CO2 emissions. To complete the urban water cycle, Chapter 4 develops first an hourly model of urban water uses by customer category including water-related energy consumption and next I calibrate a model of the energy used in water supply, treatment, pumping and wastewater treatment by the utility, using real data from East Bay Municipal Utility District in California. Hourly costs of energy for the water and energy utilities are assessed and GHG emissions for the entire water cycle estimated. Results show that water end-uses account for almost 95% of all water-related energy use, but the 5% managed by the utility is still worth over $12 million annually. Several simulations analyze the potential benefits for water demand management actions. The total carbon footprint per capita of the urban water cycle is 405 kg CO2/year representing 4.4% of the total GHG emissions per capita in California. Accounting for the results obtained in Chapters 2 to 4, Chapter 5 describes a simple but powerful decision support system for water management that includes water-related energy use and GHG emissions not solely from the water operations, but also from final water end uses, including demands from cities, agriculture, environment and the energy sector. The DSS combines a surface water management model with a simple groundwater model, accounting for their interrelationships, and also includes explicitly economic data to optimize water use across sectors during shortages and calculate return flows from different uses. Capabilities of the DSS are demonstrated on a case study over California's intertied water system over the historic period and some simulations are run to highlight water and energy tradeoffs. Results show that urban end uses account for most GHG emissions of the entire water cycle, but large water conveyance produces significant peaks over the summer season. The carbon footprint of the entire water cycle during this period, according to the model, was 21.43 millions of tons of CO2/year, what was roughly 5% of California's total GHG emissions. The last two chapters discus and summarize the thematic and methodological contribu-tions and looks for further research presenting and discussing the research gaps and research questions that this dissertation left open.

Climate change and global warming are two major concerns over the globe today, and the rapid change in climate became more sensible as it impacts diverse industries and disciplines. Buildings share of the global energy use exceeds 40%, and buildings’ Green House Gas (GHG) emissions resembles one-third of global GHG emissions; hence, Building Energy Simulation (BES) process became an essential step during building design, to study and minimize whole buildings’ energy use, and consequently minimize buildings’ carbon footprint. A significant and underestimated input to the BES process is the weather data; BES process normally uses historical weather data in the form of a weather file to simulate the building’s outdoor conditions, those outdoor conditions impacts the amount of thermal load on the building, therefore they significantly impact building’s estimated thermal comfort and annual energy use. With the rapid change in the outdoor weather conditions year after year, using those historical weather data files must be questioned, as they impact the estimated energy use, and consequently put what’s deemed as the “optimum energy use” in question. Using those historical weather data is hence expected to increase BES degree of uncertainty. This paper studies and quantifies the impact of weather data accuracy on building’s annual energy performance. The paper follows an empirical research approach by simulating an office building located in Cairo, Egypt. The study relies on using a typical weather data file and real weather files to run and compare two simulations for the same case study. Finally, the paper quantifies the impact of weather data’s accuracy on the total annual energy use by comparing both simulation results.

Energy-climate-manufacturing nexus: New insights from the regional and global supply chains of manufacturing industries

Carbon footprint of textile throughout its life cycle: a case study of Chinese cotton shirts

Because of increasing concern about global climate change,there has been a growing research interest in carbon footprint analysis recent years.Carbon footprint analysis on urban energy use takes both direct carbon emissions and indirect emissions into account,so it has great significance for the in-depth analysis on nature process of carbon emissions and scientific formulation on carbon reduction plan.Taking Xiamen as a study area,the hybrid analysis method of carbon footprint was used to access the carbon footprint on the energy use of Xiamen city in 2009.Besides the direct carbon emissions from the urban energy end-use in traditional research,the indirect carbon emissions from the cross-boundary traffic and the embodied energy of key urban materials were also included.The direct emissions include emissions from direct energy use in industrial sector,commercial sector,household sector,transport sector and so on,which often called scope 1 by WRI/WBCSD.The direct emissions also include emissions due to electricity and steam purchases for the sector in the city,often called scope 2 emissions.And indirect carbon emissions,which called scope 3,incorporate the surface,sailing and airline travel′s emissions across cities and the embodied energy of key urban materials: food,water,fuel,and concrete.Research result showed that:(1) Direct carbon emissions from the energy use on the sectors inside the city boundary including industry,transport,commerce and so on,namely scope 1 and scope 2,only take up 64% of the total city carbon footprint.However,the indirect emissions from the cross-boundary traffic and embodied energy of key urban materials account for 36%,which has usually been ignored as scope 3.(2) In the direct emissions,the carbon emissions of industry contributed the largest share,which counts for 55%.And the emissions from the chemical industry account for 25% of the whole industry sector.(3) In the indirect emissions,the shares of emissions from cross-boundary traffic and embodied energy of key urban materials were 27% and 73% respectively.The carbon emissions of long-distance road transport take the largest proportion of cross-boundary traffic,which accounted for 38%.And the embodied energy emissions from the fuel were the most important part of embodied energy emissions,accounting for 51% of embodied emissions.(4) From the perspective of per capital carbon footprint,the per capital direct carbon emissions of Xiamen and Denver were 5.74 t CO2e/cap and 18.9 t CO2e/cap respectively.And the per capital carbon footprint of Xiamen and Denver,including direct and indirect emissions caused by the energy use,were 9.01 t CO2e/cap and 25.3 t CO2e/cap.In the total carbon footprint by Xiamen and Denver,the emissions from the cross-boundary transport all took up 10%,and the emissions caused by embodied energy of key urban materials were 26% and 15% respectively.The embodied emissions by Xiamen were relatively higher than Denver because the urbanization and industrialization consumed more materials in Xiamen.Comparing the per capital carbon emission excluding embodied emissions with typical cities in the worldwide,Denver took the first place with 21.5 t CO2e/cap,and Los Angeles,New York City,London,Bangkok,Cape Town and big city like Shanghai,Tianjin and Beijing in China were more than 10 t CO2e/cap averagely.That of Xiamen was 6.63 t CO2e/cap,which was far less than most cities.

Clarifying carbon and nitrogen emissions of different peanut rotation planting system can provide an effective reference to achieve high yield, high efficiency, and low carbon and nitrogen emissions. Based on field surveys on agricultural inputs and field managements, we calculated the carbon footprint and nitrogen footprint of three planting modes (rape-peanut rotation, wheat-peanut rotation and peanut monoculture) in Huanggang, Hubei Province. The results showed that compared with wheat-peanut rotation, carbon emission per unit area of rape-peanut rotation decreased by 7.8%, carbon emission per unit net present value decreased by 36.9%, the nitrogen emission per unit area decreased by 12.5%, and nitrogen emission per unit net present value decreased by 41.9%. Compared with peanut monoculture, rape-peanut rotation reduced carbon and nitrogen emissions by 19.6% and 30.8%, respectively. The net income of rape-peanut rotation was 1.4 times as that of wheat-peanut rotation and 2.4 times as that of peanut monoculture. It is suggested that rape-peanut rotation could achieve the synergistic benefits of high yield and efficiency and low carbon and nitrogen emissions, which is conducive to the green, high quality, and high efficiency production of oil crops.

As the threat posed by global climate change becomes more acute, action must be taken to mitigate carbon emission. Carbon footprint, as a widely used term and concept, is increasingly being recognized as a valuable indicator in the field of GHG and carbon emissions management. However, few attempts have been made to investigate precise relationship between carbon footprint and influential factors. In this paper, STIRPAT model was employed to reveal the dynamic relationships between carbon footprint and influential factors by means of the ridge regression for a case study of Wuhan city, Hubei province in central China. The results are as follows: (1) Carbon footprint for energy consumption has increased from 188.46 ten thousands hm 2 in 1995 to 331.50 ten thousands hm 2 in 2009, that of the annual average growth rate was 4.12%. (2) Coal consumption accounted for the largest share in carbon footprint for energy consumption. Petroleum and natural gas consumption showed fluctuating trend. (3) According to STIRPAT model, population and economic development were the main influential factors of carbon footprint for energy consumption. 1% increase of population has resulted in 3.229% increase in carbon footprint for energy consumption, and 1% increase of per capita GDP in 0.261%. (4) The relationship between per capita GDP and carbon footprint for energy consumption did not prove the environmental Kuznets curve (EKC). (5) Comparing with other provincial cities in China, the per capita carbon footprint in Wuhan city is lower than Beijing, Shanghai, Tianjin, while much more than Chongqing city.

Identifying drivers of farm-level greenhouse gas (GHG) footprints of crop production can reveal opportunities to improve farming practices and enable more targeted GHG mitigation strategies. Although many studies evaluated the GHG footprints of crop production, differences between and within crops have not been systematically evaluated for a large number of farms so far. Here, we evaluated possible sources of variability in GHG footprints (in terms of kg CO2-eq/kg crop produced) of 26 crops, grown in compliance with Unilever's Sustainable Agriculture Code, using data from 4565 farms in 36 countries from 2013 through 2016. We quantified crop-farm-specific GHG footprints based on four components: (i) emissions from electricity use, (ii) emissions from fossil fuel (petrol and diesel) use, (iii) emissions from crop and pruning residue application, and (iv) emissions from fertilizer use. On average, fertilizer use contributed most to the GHG footprint for 23 out of the 26 crops in our dataset. We further found that variability in GHG footprints was smaller between crops (45%) than within crops (55%). Regression modelling revealed that on average 44% of the GHG footprint variability within crops could be attributed to (a selection of) three explanatory variables, i.e., yield, area of production, and year of production. Of these, yield was the most important explanatory variable. Lower GHG footprints were associated with higher yields for 24 out of the 26 crops. Relationships with area and year of production were less clear, and directions of the relationships were more variable between crops. Strategies to improve fertilizer use efficiencies while maintaining or increasing yields are preferable in a GHG reduction programme.

By calculating carbon sink of vegetation in a natural scenic area and tourist carbon footprint, carbon distribution load rate, carbon distribution equilibrium degree and carbon distribution order degree were introduced to study the carbon distribution order in a scenic area quantitatively. Then Simulink was used to simulate the scenic area model to get the value of carbon distribution load rate, carbon distribution equilibrium degree. On the basis of the carbon distribution situation, managerial staffs of the scenic area could modify tourists’ touring routes to realize an equilibrium state of carbon distribution order and ultimately make sustainable development come true. INTRODUCTION The definition of “low carbon tourism” was officially put forward in a report “move forward low carbon travel and low carbon tourism” at World Economic Forum in 2009. Low carbon tourism is a tourism development model that on the premise of continuous tourism development, carbon emission can be lowered, energy consumption can be reduced during travel by “system adjustment” “technology innovation” “traditional concept transformation” to finally realize sustainable development[Xie Zhihan,2013]. In recent years, researches on low carbon tourism emerged in endlessly. [James E.S. Higham,2011], [Andrew,2010] Hares et al. studied the effect of travel by air to CO2 emission and climate change. [Becken,2004]discussed tourists’ cognitive condition on the offset effect of climate change and forest carbon sink to CO2 emission. [Kuo. N, 2009]adopted LCA ( a Life Cycle Assessment) to conduct quantitative research on tourism energy use, gre\en house gas emission, waste water and solid waste. [Becken,2002] tried top-down and bottom-up approaches to adjust account on tourism carbon footprint in New Zealand. [Barr S,2010] built a low carbon evaluation index system to apply various methods to the evaluation of tourism low carbon degree. BASIC CONCEPTS Drawing on the definition of forest carbon sink, in a natural scenic area, carbon sink means vegetation’s ability to absorb and fix CO2 in itself. The volume of forest carbon sink(volume of the absorbed CO2) in a time unit reflects the forest’s carbon sink ability[Dong Yifei,2013]. The total carbon sink volume in a time unit is the sum of products of vegetation area multiplies per unit vegetation’s absorbed CO2 volume in a time unit. Specifically, if there are m kinds of vegetation in a scenic area, and the fixed carbon sink of each vegetation in a time unit is , the plantation area of each vegetation is , then the total carbon sink volume in a time unit is recorded as C, and C is: C= (1) According to the life cycle method of carbon footprint evaluation, tourists’ carbon footprint refers to the consumed carbon volume during the whole trip either produced by a traveling group or individual tourist. In a natural scenic area, the major influential element for carbon distribution changes is tourists’ carbon emission during the travel, and it includes two aspects: carbon emission from the transportation as well as from the tourists’ breath. This paper has studied the carbon footprint in transportation, and the volume of the produced CO2 by tourist transportation was recorded as t Q , International Conference on Applied Science and Engineering Innovation (ASEI 2015) © 2015. The authors Published by Atlantis Press 1992 . . . t i i i i Q D n β α =∑ (2) t Q refers to CO2 emission volume of transportation tool i ; i D refers to the driving distance of the transportation tool i ; i n refers to the number of i ; i α is the CO2 emission coefficient(kg/MJ) of i’s consumed energy; and i β (MJ/ unit.km) is the energy consumption of per unit i. In line with the carbon footprint study of transportation, a formula to show an individual tourist’s transportation carbon emission volume t Q′ in a time unit could be gained. t Q′ = 1 m i i i i i=1 . . .

The Carbon Footprint (CF) calculates the amount of greenhouse gases, primarily carbon dioxide released into the atmosphere by direct and indirect human activities in a certain time frame. CF provides a measure of effect of human activity on the earth's climate and provides guidance on mitigation potential. This study aims to measure the carbon footprint per household in two Grama Niladhari (GN) divisions and aims to establish relationship between the CF and socioeconomic parameters of the households. Primary data were collected from a survey carried out in Gangodawila South GN division of Colombo District and Galbada GN division of Galle District. Households were selected using random sampling technique and data on activities that emit greenhouse gases including energy use, transport were collected. In addition, secondary data was collected from various sources. Carbon footprint was calculated by using emission factors obtained from 2006 IPCC guidelines for National Greenhouse Gas Inventories. Most relevant and appropriate emission factors for Sri Lankan conditions were selected to increase the accuracy of the study. Results indicate that the CF estimated for Galbada is lower than that of Gangodawila GN division. CF is significantly influenced by income in both locations. Carbon foot print estimations are useful in guiding households towards the choice of options with lesser emissions. Keywords: Carbon footprint, Greenhouse gas, Ecological footprint, Emission factors

Energy consumption and carbon footprint accounting of urban and rural residents in Beijing through Consumer Lifestyle Approach

PDF HTML阅读 XML下载 导出引用 引用提醒 基于土地利用变化的四川省碳排放与碳足迹效应及时空格局 DOI: 10.5846/stxb201506111188 作者: 作者单位: 地理与资源科学学院,地理与资源科学学院,中国科学院资源环境科学数据中心,地理与资源科学学院,地理与资源科学学院,地理与资源科学学院 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目（41371125） Effect of land use changes on the temporal and spatial patterns of carbon emissions and carbon footprints in the Sichuan Province of Western China, from 1990 to 2010 Author: Affiliation: Key Lab of Land Resources Evaluation and Monitoring in Southwest,Ministry of Education,Sichuan Normal University,Key Lab of Land Resources Evaluation and Monitoring in Southwest,Ministry of Education,Sichuan Normal University,Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences RESDC,Key Lab of Land Resources Evaluation and Monitoring in Southwest,Ministry of Education,Sichuan Normal University,Key Lab of Land Resources Evaluation and Monitoring in Southwest,Ministry of Education,Sichuan Normal University,Key Lab of Land Resources Evaluation and Monitoring in Southwest,Ministry of Education,Sichuan Normal 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:土地利用变化的碳排放与碳足迹研究对了解人类活动对生态环境的扰动程度及其机理、制定有效的碳排放政策具有重要意义。采用1990-2010年四川省能源消费数据和土地利用数据，通过构建碳排放模型、碳足迹及其压力指数模型，对研究区20年来土地利用的碳排放及碳足迹进行了定量分析。结果表明：（1）土地利用变化的碳排放和能源消费碳的足迹呈显著增加趋势。碳排放增加5407.839×104 t，增长率达143%；能源消费的碳足迹增加1566.622×104 hm2，四川全省的生态赤字达1563.598×104 hm2。（2）建设用地和林地分别为四川省最大的碳源与碳汇。20年间建设用地的碳排放增加5407.072×104 t，增长率达126.27%，占碳排放总量的88%以上；林地的碳汇减少10.351×104 t，但仍占四川省碳汇的96%以上。（3）土地利用碳排放、碳足迹和生态赤字存在明显区域差异。成都平原区碳排放、碳足迹压力最大，生态赤字严重，西部高山高原区和盆周山区碳排放、碳足迹最小，未出现生态赤字；成都、德阳、资阳和内江等地的碳排放、碳足迹压力最大，生态赤字最严重，甘孜、阿坝等地的碳排放、碳足迹最小，未出现生态赤字。（4）土地利用结构与碳排放、碳足迹存在一定的相互关系，趋高的碳源、碳汇比导致土地利用的碳源效应远大于碳汇效应。因此，四川省减排的重点应该在保持或增加现有的林地的同时，主要以降低建设用地的碳排放、碳足迹为主。 Abstract:Land use changes significantly affect the carbon dynamics of terrestrial ecosystems, and are one of the main factors influencing climate change on a global scale. Analyzing the effects of land use on carbon emissions is important for understanding the mechanisms of carbon emissions and the success of carbon reduction and climate change mitigation efforts. In this study, we developed carbon emission, pressure index, and carbon footprint models to evaluate a carbon budget, and carried out research in the Sichuan Province of western China to estimate carbon sinks and carbon sources, based on energy consumption and land use change data from 1990 to 2010 (obtained from remote sensing technologies). The results showed that:(1) Changes in land use and energy consumption from 1990 to 2010 significantly increased carbon emissions (5407.839×104 t, or 143%), with an average annual rate of increase of 7.151% (1566.622×104 hm2). During the same period, the carbon footprint for energy consumption increased, and the area of ecological deficit reached 1563.598×104 hm2. Overall, the increase in carbon emissions was associated with a rapid increase in fossil fuel consumption as well as land use changes; (2) Land under construction (carbon source) and forests (carbon sink) were the largest carbon pools in the carbon budget. Higher carbon emissions were noted for built-up land than for other land use types. Between 1990 and 2010, there was a continuous increase in carbon sources, and a slight decrease in carbon sinks. Carbon emissions from built-up land increased by 126.27%, which was the largest percentage increase in carbon emissions; (3) There were considerable regional differences in carbon emissions and carbon footprints. The Chengdu plain, and its surroundings regions (e.g., Chengdu, Deyang, Ziyang, and Neijiang), had higher carbon emissions, carbon footprints, and ecological deficits in 2010 than in 1990. In contrast, the west, northwest, and southwest mountainous regions and plateau areas (e.g., the Ganzhi, Aba, and Liangshan autonomous prefectures) had lower carbon emissions in 2010 than in 1990. In general, these regions had low carbon footprints and ecological deficits because of their widespread coverage by forests and grasslands. Compared to the Chengdu plain (and its surroundings regions), these regions had relatively low fossil fuel consumption, slow urbanization rates, and limited industrial development and transportation corridors. Overall, in Sichuan, there was an increase from 1990 to 2010 in the spatial distribution and severity of carbon emissions, carbon footprints, and ecological deficits; and (4) Land use had a greater effect on carbon sources than on carbon sinks. Forests, grasslands, water areas, and unused land were the main carbon sinks, while land under construction and cultivated land were the main carbon sources. The rapid increase in carbon sources and slow decrease in carbon sinks resulted in a substantial increase in carbon emissions in Sichuan from 1990 to 2010, with the ratio of sources to sinks increasing from 4.002 in 1990 to 9.739 in 2010. In conclusion, one key focus of future carbon emission reduction efforts in Sichuan should be to maintain or increase forest areas. It would also be worthwhile to reduce carbon emissions from land under construction. Through targeted land use and land management activities, ecosystems can be managed to enhance carbon sequestration and mitigate fluxes of greenhouse gases. 参考文献 相似文献 引证文献

In 2007, China surpassed the USA to become the largest carbon emitter in the world. China has promised a 60%–65% reduction in carbon emissions per unit GDP by 2030, compared to the baseline of 2005. Therefore, it is important to obtain accurate dynamic information on the spatial and temporal patterns of carbon emissions and carbon footprints to support formulating effective national carbon emission reduction policies. This study attempts to build a carbon emission panel data model that simulates carbon emissions in China from 2000–2013 using nighttime lighting data and carbon emission statistics data. By applying the Exploratory Spatial-Temporal Data Analysis (ESTDA) framework, this study conducted an analysis on the spatial patterns and dynamic spatial-temporal interactions of carbon footprints from 2001–2013. The improved Tapio decoupling model was adopted to investigate the levels of coupling or decoupling between the carbon emission load and economic growth in 336 prefecture-level units. The results show that, firstly, high accuracy was achieved by the model in simulating carbon emissions. Secondly, the total carbon footprints and carbon deficits across China increased with average annual growth rates of 4.82% and 5.72%, respectively. The overall carbon footprints and carbon deficits were larger in the North than that in the South. There were extremely significant spatial autocorrelation features in the carbon footprints of prefecture-level units. Thirdly, the relative lengths of the Local Indicators of Spatial Association (LISA) time paths were longer in the North than that in the South, and they increased from the coastal to the central and western regions. Lastly, the overall decoupling index was mainly a weak decoupling type, but the number of cities with this weak decoupling continued to decrease. The unsustainable development trend of China’s economic growth and carbon emission load will continue for some time.

Integrated assessment of carbon footprint, energy budget and net ecosystem economic efficiency from rice fields under different tillage modes in central China