春玉米-晚稻与早稻-晚稻种植模式碳足迹比较

量化作物生产的碳足迹有助于为农业生态系统温室气体减排提供理论依据。利用生命周期法研究了我国南方地区稻田春玉米-晚稻水旱轮作种植模式和早稻-晚稻连作种植模式下粮食生产的碳足迹,并定量分析粮食生产过程中各种碳排放源的相对贡献。结果表明,与早稻-晚稻的连作模式相比,春玉米-晚稻轮作模式的单位面积碳排放降低了6724 kg CO₂-eq/hm²,单位产量的碳足迹降低了0.56 kg CO₂-eq/kg。春玉米比早稻少排放6228 kg CO₂-eq/hm²;与早稻-晚稻模式中晚稻碳排放相比,春玉米-晚稻轮作模式晚稻碳排放降低了497 kg CO₂-eq/hm²。早稻-晚稻种植模式的碳足迹主要来源于甲烷（CH₄）,其碳排放为9776 kg CO₂-eq/hm²（54.8%）,氮肥生产和施用的碳排放为2871 kg CO₂-eq/hm²（16.1%）,灌溉电力消耗的碳排放2849 kg CO₂-eq/hm²（16.0%）。春玉米-晚稻轮作模式的碳足迹主要来源于CH₄的碳排放4442 kg CO₂-eq/hm²（39.9%）,氮肥生产和施用的碳排放2871 kg CO₂-eq/hm²（25.8%）,灌溉电力消耗的碳排放1508 kg CO₂-eq/hm²（13.6%）。该模式中晚稻的碳足迹组成情况与春玉米-晚稻模式的碳足迹相似。但是,对于春玉米而言,其碳足迹主要来源氮肥生产和施用的碳排放1436 CO₂-eq/hm²（50.1%）,氧化亚氮（N₂O）的碳排放为579 kg CO₂-eq/hm²（20.2%）,CH₄的碳排放为378 CO₂-eq/hm²（13.2%）。同时,相比于早稻-晚稻中晚稻的产量（6333 kg/hm²）,春玉米-晚稻轮作模式下的晚稻产量（7270 kg/hm²）提高了14.8%。因此,引入春玉米-晚稻轮作模式有利于提升稻田生产力,降低稻田连作系统碳排放和碳足迹。     

基于碳足迹的传统农业系统环境影响评价——以青田稻鱼共生系统为例

现代农业在带来粮食高产的同时也产生许多生态环境问题,这促使人们再次把目光转向传统农业系统。一些传统农业系统不仅具有突出的经济、社会和文化价值,还具有多种重要的生态功能,如温室气体减排。然而,已有研究缺乏对传统农业系统整个生命周期的固碳减排能力的测算及其环境影响的评价。为此,基于农户调研数据,运用生命周期评价法对青田稻鱼共生系统的碳足迹进行了测算,并与当地水稻单作系统进行比较。研究发现:(1)青田稻鱼共生系统和水稻单作系统碳足迹分别为6266.7 kgCO_2-eq/hm~2和7520.0 kgCO_2-eq/hm~2,单位产值碳足迹分别为0.12 kgCO_2-eq/元和0.21 kgCO_2-eq/元。与水稻单作系统相比,稻鱼共生系统排放的温室气体更少,环境影响更小,生态和经济效益更高。(2)农业生产过程中积累的CH_4是碳足迹的最主要来源,农业生产资料投入中的化肥是碳足迹的的第二大来源。农业生产资料投入中的饲料则是稻鱼共生系统碳足迹的另一重要来源。通过碳足迹的方法对青田稻鱼共生系统的环境影响进行量化,不仅丰富了碳足迹在实际应用中的适用类型,对于其他传统农业系统的环境影响评价也具有借鉴意义。     

产业园区温室气体排放清单

温室气体排放所导致的全球气候变化是国际社会长期关注的热点问题,它严重限制了人类社会的发展并威胁着人类的生存。产业园区通常集中了一个区域主要的生产要素与生产能力,也代表着特定产业在该区域的发展水平,理应作为发展低碳经济的基础单元和减少温室气体排放的重要控制点,也可以成为解决区域资源、环境问题的突破口。明确了产业园区温室气体排放的系统边界和内部结构,梳理了产业园区全生命周期温室气体排放行为,综合考虑产业园区能源消耗、工业生产、物质材料消耗、仪器设备投入、废弃物处理处置、景观绿化等过程,建立产业园区温室气体排放核算方法,并对案例园区进行了清单分析。结果表明:案例园区整个生命周期的温室气体排放量为1872177 t CO2-eq,其中运行管理阶段占全生命周期排放的比例最高,为95.35%。建设阶段的温室气体排放总量中建筑材料消耗引起的排放占到96.95%,主要集中在建筑工程、内部装修工程和外部装饰工程3个环节。运行管理阶段电力消耗、热力消耗和污水处理过程的排放量占到总量的98.69%。根据核算及分析结果提出了案例园区在建设和运行管理阶段实现温室气体减排的建议。     

城市能源利用碳足迹分析——以厦门市为例

城市能源利用碳足迹分析综合考虑直接与间接碳排放,对于深度分析碳排放的本质过程、制定科学全面的碳减排计划具有重要意义。以厦门市为研究案例,应用碳足迹的混合分析方法,对厦门市2009年能源利用碳足迹进行了分析,除了包括传统研究中的城市能源终端利用产生的直接碳排放,还计算了跨界交通和城市主要消耗物质的内含能引起的间接碳排放。研究结果表明:(1)城市边界内的工业、交通、商业等部门的能源消耗产生的直接碳排放(即层次1和层次2)只占到总碳足迹的64%,而一直被忽略的跨界交通和城市主要消耗物质的内含能引起的间接碳排放(层次3)占到36%;(2)在直接碳排放中,工业部门的碳排放贡献率最大,占到直接碳排放的55%,其中化工行业带来的碳排放占到工业部门的25%;(3)在间接碳排放中,跨界交通引起的碳排放占间接碳排放的27%,其中长途道路运输贡献率最大,占跨界交通碳排放的38%;主要材料内含能碳排放占间接碳排的73%,其中燃料的内含能碳排放占总内含能的份额最大,达51%。;(4)从人均碳足迹角度比较,厦门市人均碳足迹和丹佛市的人均直接碳排(层次1+层次2)分别为5.74 t CO2e/人、18.9 t CO2e/人,包含3个层次的人均碳足迹分别为9.01 tCO2e/人、25.3 t CO2e/人,其中跨界交通引起的碳排放均占总碳足迹的10%左右,主要材料的内含能引起的碳排放分别占到总碳足迹的26%、15%;通过国内外典型城市不同层次碳足迹比较可见厦门还是相对低碳的,但有个显著的特点是主要消耗物质的内含碳排放比例较高,这在一定程度上说明了发展中国家城市消耗更多的基础材料,进一步证明了传统核算中忽略的第3层次碳排放核算与管理的重要性。     

北京市居民食物消费碳足迹

碳足迹作为一种评价碳排放影响的全新测度方法,已被用来衡量人类活动对大气环境和气候变化的影响。食物是人类的首要消费品,其消费的碳足迹反应维持一个区域人口的基本食物需求的碳排放以及对气候变化的影响。在碳足迹理论和模型的基础上,根据北京市食物的供应和消费现状情况,利用生命周期法(Life cycle analysis LCA),计算和分析了北京市居民食物消费的碳足迹。得到北京市居民食消费碳足迹为476.8×104t,约占北京市总碳足迹的6%,人均碳足迹为310.0kgCO2/人,占北京市家庭消费碳排放的23.3%,只占北京市能源消费人均碳排放量的5.96%,反映了居民食物消费对全球气候变化造成的影响有限。食物消费碳足迹最大的为粮食,其次为瓜果蔬菜豆类,总共占到65%以上,而在食物生命周期过程中,食物的再加工炊事过程碳排放最大,超过50%,合理减少食物加工炊事过程中碳排放将是减少食物消费碳排放的重要途径之一。其次为化肥农药施用,占到23.23%,减少食物生产过程中化肥农药使用,提高化肥农药的使用效率,或者进行生态农业尽量不使用化肥农药,北京市每年可减少135.1×104t CO2排放,人均87.84kgCO2/人,是有效的减排途径之一。     

Water,land and carbon footprints of sheep and chicken meat produced in Tunisia under different farming systems

Meat production puts larger demands on water and land and results in larger greenhouse gas emissions than alternative forms of food. This study uses footprint indicators, the water, land and carbon footprint, to assess natural resources use and greenhouse gas emissions for sheep and chicken meat produced in Tunisia in different farming systems in the period 1996–2005. Tunisia is a water-scarce country with large areas of pasture for sheep production. Poultry production is relatively large and based on imported feed. The farming systems considered are: the industrial system for chicken, and the agro-pastoral system using cereal crop-residues, the agro-pastoral system using barley and the pastoral system using barley for sheep. Chicken meat has a smaller water footprint (6030 litre/kg), land footprint (9 m²/kg) and carbon footprint (3 CO₂-eq/kg) than sheep meat (with an average water footprint of 18900 litre/kg, land footprint of 57 m²/kg, and carbon footprint of 28 CO₂-eq/kg). For sheep meat, the agro-pastoral system using cereal crop-residues is the production system with smallest water and land footprints, but the highest carbon footprint. The pastoral system using barley has larger water and land footprints than the agro-pastoral system using barley, but comparable carbon footprint.     

Carbon footprints of rice production in five typical rice districts in China

Five provinces located in the five main rice-growing regions in China were selected as study areas, which were Jiangsu, Heilongjiang, Sichuan, Guangdong and Hunan province respectively in the middle and lower reaches of the Yangtze River, northern, southwest, southern and central rice districts. Carbon footprints of rice production in these five provinces were calculated through the life cycle assessment method using governmental statistical data, industrial standards and relevant technical data separately. Material and energy consumptions were estimated, key stages of energy consumptions and carbon emissions were identified as well. Moreover, improving measurements had been suggested correspondingly. The results indicated that: the energy consumptions of rice production in these five provinces ranked as following (high to low): Guangdong, Heilongjiang, Hunan, Sichuan and Jiangsu. The carbon footprints of rice production were 2504.20 kg carbon dioxide equation per ton rice (kgCO₂-eq./t) (Guangdong province), 2326.47 kgCO₂-eq./t (Hunan province), 1889.97 kgCO₂-eq./t (Heilongjiang province), 1538.90 kgCO₂-eq./t (Sichuan province) and 1344.92 kgCO₂-eq./t (Jiangsu province) respectively. Reducing the quantities of urea and using the intermittent irrigation method could decrease energy consumption as well as carbon footprint.     

FoodPrints of households

Purpose

Food consumption is one of the main drivers of environmental impacts. To develop meaningful strategies for the reduction of impacts, food consumption patterns need to be understood on the household level, as purchasing decisions are taken on this level. The goals of this study were to develop a model that estimates food demand and environmental impact as a function of household characteristics, to assess variability between households, and to provide a basis for the development of consumer-targeted political interventions. We titled the study “FoodPrints of households,” as we assessed food consumption in terms of carbon footprint (in analogy to (Stoessel et al. Environ Sci Technol 46(6):3253–3262 2012)).

Methods

We used data from the Swiss household budget survey and applied multiple linear regressions based on generalized linear models to quantify food and beverage demand of individual households. Seven household characteristics, such as size, income, and educational level, served as input variables for the regressions. In a case study, food and beverage demand of 3238 individual households of a Swiss municipality was environmentally assessed with life cycle assessment, and scenarios for different reduction strategies were evaluated.

Results and discussion

We found that the carbon footprints of in-home food consumption per household member and year vary from 0.08 t CO₂ eq. to 5 t CO₂ eq. with a median value of 1 t CO₂ eq. This variability is significantly smaller than the carbon footprint variability for the consumption areas of housing and mobility, where 25 % of the people are responsible for 50 % of the environmental impacts. Differences between high- and low-impact households can be primarily explained by differences in meat and dairy consumption.

Conclusions

This paper presents a model for quantifying food demand and impacts on a household level in Switzerland and represents a basis for developing targeted political measures to mitigate food consumption impacts. Household budget data is also available for many other countries, and the methods presented in this paper could therefore also be applied to other geographical regions.

     

甘南高原不同生计方式农户的碳足迹分析

二氧化碳增加导致的全球气候变暖已成为当前人类社会面临的最严峻挑战,减少碳排放迫在眉睫。以地处青藏高原东缘的甘南高原为研究区,基于农户调查数据,采用生命周期评价法估算了甘南高原不同生计方式农户的生活直接能源消费碳足迹和间接能源消费碳足迹,并运用最小二乘法分析了影响农户碳足迹的关键因素。结果发现:(1)甘南高原农户年人均碳足迹达2.67 t CO2,其中,生活直接能源消费碳足迹比例达76.53%,间接能源消费碳足迹比例仅占23.47%;(2)随着非农化水平的提高,甘南高原农户的碳足迹依次下降,其中,纯农户人均碳足迹达4.32 t,兼业户与非农户分别为2.37 t和1.07 t;(3)随着农户家庭规模的增大、收入水平的提高、距县城距离的增加和消费水平的提高,农户碳足迹不断增加;随着劳动力受教育程度的提高、恩格尔系数的增加和非农化程度的提高,农户碳足迹不断减少。     

Reducing methane on-farm by feeding diets high in fat may not always reduce life cycle greenhouse gas emissions

Purpose

To consider whether feed supplements that reduce methane emissions from dairy cows result in a net reduction in greenhouse gas (GHG) intensity when productivity changes and emissions associated with extra manufacturing and management are included.

Methods

A life cycle assessment was undertaken using a model farm based on dairy farms in Victoria, Australia. The system boundary included the creation of farm inputs and on-farm activities up to the farm gate where the functional unit was 1 L of fat and protein corrected milk (FPCM). Electricity and diesel (scaled per cow), and fertiliser inputs (scaled on farm size) to the model farm were based on average data from a survey of farms. Fertiliser applied to crops was calculated per area of crop. Animal characteristics were based on available data from farms and literature. Three methane-reducing diets (containing brewers grain, hominy or whole cotton seed) and a control diet (cereal grain) were modelled as being fed during summer, with the control diet being fed for the remainder of the year in all cases.

Results and discussion

Greenhouse gas intensity (kg CO₂-eq/L FPCM) was lower than the control diet when the hominy (97 % compared with control) and brewers grain (98 %) diets were used but increased when the whole cottonseed diet was used (104 %). On-farm GHG emissions (kg CO₂-eq) were lower than the control diet when any of the methane-reducing diets were used (98 to 99.5 % of emissions when control diet fed). Diesel use in production and transport of feed supplements accounted for a large portion (63 to 93 %) of their GHG intensity (kg CO₂-eq/t dry matter). Adjusting fertiliser application, changing transport method, changing transport fuel, and using nitrification inhibitors all had little effect on GHG emissions or GHG intensity.

Conclusions

Although feeding strategies that reduce methane emissions from dairy cows can lower the GHG emissions up to the farm gate, they may not result in lower GHG intensities (g CO₂-eq/L FPCM) when pre-farm emissions are included. Both transport distance and the effect of the feed on milk production have important impacts on the outcomes.     

An appraisal of carbon footprint of milk from commercial grass-based dairy farms in Ireland according to a certified life cycle assessment methodology

Purpose

Life cycle assessment (LCA) studies of carbon footprint (CF) of milk from grass-based farms are usually limited to small numbers of farms (<30) and rarely certified to international standards, e.g. British Standards Institute publicly available specification 2050 (PAS 2050). The goals of this study were to quantify CF of milk from a large sample of grass-based farms using an accredited PAS 2050 method and to assess the relationships between farm characteristics and CF of milk.

Materials and methods

Data was collected annually using on-farm surveys, milk processor records and national livestock databases for 171 grass-based Irish dairy farms with information successfully obtained electronically from 124 farms and fed into a cradle to farm-gate LCA model. Greenhouse gas (GHG) emissions were estimated with the LCA model in CO₂ equivalents (CO₂-eq) and allocated economically between dairy farm products, except exported crops. Carbon footprint of milk was estimated by expressing GHG emissions attributed to milk per kilogram of fat and protein-corrected milk (FPCM). The Carbon Trust tested the LCA model for non-conformities with PAS 2050. PAS 2050 certification was achieved when non-conformities were fixed or where the effect of all unresolved non-conformities on CF of milk was?<?±5 %.

Results and discussion

The combined effect of LCA model non-conformities with PAS 2050 on CF of milk was <1 %. Consequently, PAS 2050 accreditation was granted. The mean certified CF of milk from grass-based farms was 1.11 kg of CO₂-eq/kg of FPCM, but varied from 0.87 to 1.72 kg of CO₂-eq/kg of FPCM. Although some farm attributes had stronger relationships with CF of milk than the others, no attribute accounted for the majority of variation between farms. However, CF of milk could be reasonably predicted using N efficiency, the length of the grazing season, milk yield/cow and annual replacement rate (R ²?=?0.75). Management changes can be applied simultaneously to improve each of these traits. Thus, grass-based farmers can potentially significantly reduce CF of milk.

Conclusions

The certification of an LCA model to PAS 2050 standards for grass-based dairy farms provides a verifiable approach to quantify CF of milk at a farm or national level. The application of the certified model highlighted a wide range between the CF of milk of commercial farms. However, differences between farms’ CF of milk were explained by variation in various aspects of farm performance. This implies that improving farm efficiency can mitigate CF of milk.     

新疆风力发电减碳效益全生命周期评估

风力发电减碳效益评估有助于从减碳角度更好制定能源发展相关政策。以风力资源总体丰富且亟需发展风力发电以实现能源系统脱碳的新疆为研究区,将生命周期方法与风力发电模型结合,在省、市级尺度分别评估了风力发电全生命周期的排放水平及发电效益,核算了风力发电相对于火力发电和光伏发电的减碳效益,有效弥补了传统生命周期评估中空间差异考虑不充分的问题。结果表明,风机全生命周期平均发电量为13.1×10⁷ kWh,风力发电全生命周期共排放3944.24 tCO₂-eq,通过材料处置回收和循环再利用可减少1424.79 tCO₂-eq。新疆发展风力发电具有低排放强度和高减碳效益的特点,与火电相比可减少97.44%排放,减碳效益平均可达906.72 gCO₂-eq/kWh,并且应优先布局在哈密、巴音郭楞蒙古自治州和北屯市;与光伏相比,减碳效益可分别达到43.85 gCO₂-eq/kWh(衰减率DR=1%)和169.84 gCO₂-eq/kWh(DR=3%),此情景下风电应主要部署在克孜勒苏柯尔克孜自治州、喀什和和田。在风电减碳效益较差地区如石河子市、铁门关市和双河市应考虑利用本地充足太阳能资源发展光伏发电。需注意风电的排放强度和减碳效益在局地小尺度评估中存在不确定性,获取更精细的结果仍需进一步评估。未来应大力发展新疆本地的风电产业,打造绿色供应链和加快发展处置回收技术以增加减碳效益。     

Life-cycle greenhouse gas inventory analysis of household waste management and food waste reduction activities in Kyoto, Japan

Purpose

Source-separated collection of food waste has been reported to reduce the amount of household waste in several cities including Kyoto, Japan. Food waste can be reduced by various activities including preventing edible food loss, draining moisture, and home composting. These activities have different potentials for greenhouse gas (GHG) reduction. Therefore, we conducted a life-cycle inventory analysis of household waste management scenarios for Kyoto with a special emphasis on food waste reduction activities.

Methods

The primary functional unit of our study was ??annual management of household combustible waste in Kyoto, Japan.?? Although some life-cycle assessment scenarios included food waste reduction measures, all of the scenarios had an identical secondary functional unit, ??annual food ingestion (mass and composition) by the residents of Kyoto, Japan.?? We analyzed a typical incineration scenario (Inc) and two anaerobic digestion (dry thermophilic facilities) scenarios involving either source-separated collection (SepBio) or nonseparated collection followed by mechanical sorting (MecBio). We assumed that the biogas from anaerobic digestion was used for power generation. In addition, to evaluate the effects of waste reduction combined with separate collection, three food waste reduction cases were considered in the SepBio scenario: (1) preventing loss of edible food (PrevLoss); (2) draining moisture contents (ReducDrain); and (3) home composting (ReducHcom). In these three cases, we assumed that the household waste was reduced by 5%.

Results and discussion

The GHG emissions from the Inc, MecBio, and SepBio scenarios were 123.3, 119.5, and 118.6 Gg CO₂-eq/year, respectively. Compared with the SepBio scenario without food waste reduction, the PrevLoss and ReducDrain cases reduced the GHG emissions by 17.1 and 0.5 Gg CO₂-eq/year. In contrast, the ReducHcom case increased the GHG emissions by 2.1 Gg CO₂-eq/year. This is because the biogas power production decreased due to the reduction in food waste, while the electricity consumption increased in response to home composting. Sensitivity analyses revealed that a reduction of only 1% of the household waste by food loss prevention has the same GHG reduction effect as a 31-point increase (from 50% to 81%) in the food waste separation rate.

Conclusions

We found that prevention of food losses enhanced by separate collection led to a significant reduction in GHG emissions. These findings will be useful in future studies designed to develop strategies for further reductions in GHG emissions.     

Consequential cradle-to-gate carbon footprint of water treatment chemicals using simple and complex marginal technologies for electricity supply

Purpose

Chemicals produced via chlor-alkali electrolysis are widely used throughout the water industry worldwide, with treatment chemicals often the second largest source of environmental impacts from potable water production after electricity use. Population-driven increases in the future demand for potable water will require concomitant increases in the production of water treatment chemicals, with the associated environmental impacts of chemicals production primarily arising from the additional demand for electricity. Due to the dominance of electricity in the environmental performance of chlor-alkali chemicals, assessment of the future environmental impacts of potable water production is largely dependent on proper identification of the marginal source of electricity. In this paper, we present a consequential cradle-to-gate carbon footprint (cCF) for the most widely used chlor-alkali-produced disinfectant (sodium hypochlorite (13 % w/w)) and coagulant (ferric chloride (42 % w/w)) in Australia, with special emphasis placed upon the identification of future marginal electricity supply and the substitution of hydrogen gas and sodium hydroxide during production. While this analysis is presented in an Australian context, commonalities in potable water and chlor-alkali chemical production processes internationally give the findings a broader relevance.

Methods

Consequential models for sodium hypochlorite (13 % w/w) and ferric chloride (42 % w/w) production were developed, and the identification of the marginal source of electricity was modelled using a “simple marginal technology” approach via operationalisation of the Weidema framework and a “complex marginal technology” using a partial equilibrium model. For the simple marginal technology, the levelised cost of electricity was used to select the most competitive energy generation technologies and those most relevant for the Australian market. For the complex marginal technology, the energy sector model was used to simulate the most likely electricity supply mix. Details of the different paths taken in the substitution of hydrogen gas and sodium hydroxide are also presented. To allow for proper incorporation of uncertainties arising from these key factors in the cCF, several scenarios were developed covering fuel and carbon prices for identifying the marginal supply mix of electricity, as well as the likely production routes for sodium carbonate in the context of sodium hydroxide substitution.

Results and discussion

cCF results of sodium hypochlorite (13 % w/w) and ferric chloride (42 % w/w) are presented using simple and complex marginal technologies, and the implications of choosing one marginal technology over the other in the context of water treatment chemicals are presented. For the simple marginal technology approach, the global warming potential (GWP) per megagram of chemical varied from 68 to 429 kg CO₂-eq for sodium hypochlorite (13 % w/w) and 59–1,020 kg CO₂-eq for ferric chloride (42 % w/w). For the complex marginal technology approach, the GWP per megagram of chemical varied from 266 to 332 kg CO₂-eq for sodium hypochlorite (13 % w/w) and 214–629 kg CO₂-eq for ferric chloride (42 % w/w). Insights are given in relation to the impact of the price of fossil fuels, the carbon price, and the different substitution routes.

Conclusions

The use of a partial equilibrium model (PEM) has enabled a better understanding of the variability of the results in this study. For example, the use of PEM for the identification of the complex marginal source of electricity shows that, for the case of Australia, any benefit from a carbon price is lost with high prices of natural gas due to the incentive to use cheaper fuels such as black and brown coal. Likewise, the use of explorative scenarios was decisive to manage the inherent uncertainty of the parameters included in the model. In relation to substitution, the case of ferric chloride (42 % w/w) indicated that using only one substitution route was not enough to fully understand the potential continuum of cCF results. The simple marginal approach, where an exclusive marginal source of electricity or substitution route is considered, presents significant risks for the modelling accuracy of the cCF as shown here for sodium hypochlorite (13 % w/w) and ferric chloride (42 % w/w), therefore, it is not recommended.     

Data for the Carbon Footprinting of Rendering Operations

This article presents a tool and data for calculation of the carbon footprint of rendering operations in North America, quantifying Scope 1 (direct) and Scope 2 (indirect) greenhouse gas emissions. Scope 3 (life cycle) emissions are not included. According to the sample data, in one year an average‐size rendering plant in North America processes 100,000 tonnes (t) of meat by‐products, fallen animals, and restaurant grease and produces 40,000 t of marketable fats and proteins. A plant of this size emits directly about 20,000 t of carbon dioxide (CO₂), mostly by burning fuels to operate cookers that destroy pathogens, drive off moisture, and separate the fat and protein. Another 4,000 t of CO₂ is emitted by utility companies to provide electricity for the rendering process. These direct and indirect emissions are equivalent to about 30% of the CO₂ that would be released if all of the carbon in the rendered raw material were decomposed into CO₂.     

Carbon Footprint Assessment of a Paperback Book

This study presents the carbon footprint of a paperback book for which the cover and inside papers were produced in the United States and printed in Canada. The choice of paper mills for both cover and page papers was based on criteria such as percentage of recycled content in the pulp mix, transport distances (pulp mill to paper mill, paper mill to print), and technologies. The cradle‐to‐gate assessment of greenhouse gas (GHG) emissions follows recognized guidelines for carbon footprint assessment. The results show that the production of 400,000 books, mainly distributed in North America, would generate 1,084 tonnes carbon dioxide equivalent (CO₂‐eq), or 2.71 kilograms (kg) CO₂‐eq per book. The impact of using deinked market pulp (DMP) is shown here to be detrimental, accounting for 54% of total GHG emissions and being 32% higher than reference virgin Kraft pulp. This supports findings that DMP mill GHG emissions strongly correlate with the carbon intensity of the power grid supplying the pulp mill and that virgin Kraft mills that reuse wood residue and black liquor to produce heat and electricity can achieve lower GHG emissions per tonne of pulp produced. Although contrary to common thinking, this is consistent with the Paper Task Force 2002 conclusion for office paper (the closest paper grade to writing paper or fine paper) (EDF 2002a). To get a cradle‐to‐grave perspective, three different end‐of‐life (EOL) scenarios were analyzed, all of which included a harvested wood product (HWP) carbon storage benefit for 25 years. The GHG offset concept within the context of the book editor's “carbon‐neutral” paper claims is also discussed.     

Evaluating environmental impacts of contrasting pig farming systems with life cycle assessment

Environmental impacts of 15 European pig farming systems were evaluated in the European Union Q-PorkChains project using life cycle assessment. One conventional and two non-conventional systems were evaluated from each of the five countries: Denmark, The Netherlands, Spain, France and Germany. The data needed for calculations were obtained from surveys of 5 to 10 farms from each system. The systems studied were categorised into conventional (C), adapted conventional (AC), traditional (T) and organic (O). Compared with C systems, AC systems differed little, with only minor changes to improve meat quality, animal welfare or environmental impacts, depending on the system. The difference was much larger for T systems, using very fat, slow-growing traditional breeds and generally outdoor raising of fattening pigs. Environmental impacts were calculated at the farm gate and expressed per kg of pig live weight and per ha of land used. For C systems, impacts per kg LW for climate change, acidification, eutrophication, energy use and land occupation were 2.3 kg CO₂-eq, 44.0 g SO₂-eq, 18.5 g PO₄-eq, 16.2 MJ and 4.1 m², respectively. Compared with C, differences in corresponding mean values were +13%, +5%, 0%, +2% and +16% higher for AC; +54%, +79%, +23%, +50% and +156% for T, and +4%, −16%, +29%, +11% and +121% for O. Conversely, when expressed per ha of land use, mean impacts were 10% to 60% lower for T and O systems, depending on the impact category. This was mainly because of higher land occupation per kg of pig produced, owing to feed production and the outdoor raising of sows and/or fattening pigs. The use of straw bedding tended to increase climate change impact per kg LW. The use of traditional local breeds, with reduced productivity and feed efficiency, resulted in higher impacts per kg LW for all impact categories. T systems with extensive outdoor raising of pigs resulted in markedly lower impact per ha of land used. Eutrophication potential per ha was substantially lower for O systems. Conventional systems had lower global impacts (global warming, energy use, land use), expressed per kg LW, whereas differentiated systems had lower local impacts (eutrophication, acidification), expressed per ha of land use.     

A methodology for separating uncertainty and variability in the life cycle greenhouse gas emissions of coal-fueled power generation in the USA

Purpose

Results of life cycle assessments (LCAs) of power generation technologies are increasingly reported in terms of typical values and possible ranges. Extents of these ranges result from both variability and uncertainty. Uncertainty may be reduced via additional research. However, variability is a characteristic of supply chains as they exist; as such, it cannot be reduced without modifying existing systems. The goal of this study is to separately quantify uncertainty and variability in LCA.

Methods

In this paper, we present a novel method for differentiating uncertainty from variability in life cycle assessments of coal-fueled power generation, with a specific focus on greenhouse gas emissions. Individual coal supply chains were analyzed for 364 US coal power plants. Uncertainty in CO₂ and CH₄ emissions throughout these supply chains was quantified via Monte Carlo simulation. The method may be used to identify key factors that drive the range of life cycle emissions as well as the limits of precision of an LCA.

Results and discussion

Using this method, we statistically characterized the carbon footprint of coal power in the USA in 2009. Our method reveals that the average carbon footprint of coal power (100 year time horizon) ranges from 0.97 to 1.69 kg CO₂eq/kWh of generated electricity (95 % confidence interval), primarily due to variability in plant efficiency. Uncertainty in the carbon footprints of individual plants spans a factor of 1.04 for the least uncertain plant footprint to a factor of 1.2 for the most uncertain plant footprint (95 % uncertainty intervals). The uncertainty in the total carbon footprint of all US coal power plants spans a factor of 1.05.

Conclusions

We have developed and successfully implemented a framework for separating uncertainty and variability in the carbon footprint of coal-fired power plants. Reduction of uncertainty will not substantially reduce the range of predicted emissions. The range can only be reduced via substantial changes to the US coal power infrastructure. The finding that variability is larger than uncertainty can obviously not be generalized to other product systems and impact categories. Our framework can, however, be used to assess the relative influence of uncertainty and variability for a whole range of product systems and environmental impacts.     

The Carbon Footprint of Bioinformatics

Bioinformatic research relies on large-scale computational infrastructures which have a nonzero carbon footprint but so far, no study has quantified the environmental costs of bioinformatic tools and commonly run analyses. In this work, we estimate the carbon footprint of bioinformatics (in kilograms of CO₂ equivalent units, kgCO₂e) using the freely available Green Algorithms calculator (www.green-algorithms.org, last accessed 2022). We assessed 1) bioinformatic approaches in genome-wide association studies (GWAS), RNA sequencing, genome assembly, metagenomics, phylogenetics, and molecular simulations, as well as 2) computation strategies, such as parallelization, CPU (central processing unit) versus GPU (graphics processing unit), cloud versus local computing infrastructure, and geography. In particular, we found that biobank-scale GWAS emitted substantial kgCO₂e and simple software upgrades could make it greener, for example, upgrading from BOLT-LMM v1 to v2.3 reduced carbon footprint by 73%. Moreover, switching from the average data center to a more efficient one can reduce carbon footprint by approximately 34%. Memory over-allocation can also be a substantial contributor to an algorithm’s greenhouse gas emissions. The use of faster processors or greater parallelization reduces running time but can lead to greater carbon footprint. Finally, we provide guidance on how researchers can reduce power consumption and minimize kgCO₂e. Overall, this work elucidates the carbon footprint of common analyses in bioinformatics and provides solutions which empower a move toward greener research.     

庐山风景区碳源、碳汇的测度及均衡

旅游目的地系统碳源、碳汇的计算与分析,不仅是旅游业节能减排政策制定的重要依据,也是旅游与环境相互关系研究的一个新的科学命题.以庐山风景区为例,计算并分析了2010年的碳源及碳汇.结果表明:(1)2010年庐山风景区包括本地居民和旅游者的总碳排放为108 697 t.其中,本地居民占碳排放总量的19.52％,旅游者占碳排放总量的80.48％.在旅游者碳排放中,旅游交通碳排放占50.24％,旅游住宿碳排放占38.04％,旅游食物消费碳排放占10.65％,旅游活动碳排放仅占1.07％;(2)2010年庐山风景区内陆地生态系统碳吸收为9447 t;(3)从碳源、碳汇均衡角度看,庐山陆地生态系统的固碳量吸收了区内碳排放的23.47％.但由于旅游者的区际流动和旅游业的产业关联性强,陆地生态系统的碳吸收仅占区内和区外碳排放总量的8.69％,旅游业使庐山成为一个显著的碳源.