中国湿地温室气体清单编制研究进展

湿地是重要的土地利用类型之一，在陆地生态系统碳循环中起重要作用。在缔约国向《联合国气候变化框架公约》提交的温室气体国家清单报告中，湿地作为"农业、林业及其他土地利用（AFOLU）"的一部分，因其不确定性较高而备受关注。自2006年以来，IPCC先后发布了《2006 IPCC国家温室气体清单指南》、《2013 IPCC 2006国家温室气体清单指南的增补：湿地》和《IPCC 2006年国家温室气体清单指南2019精细化》，为缔约国提供了清单编制的参考方法学。然而，IPCC指南中对湿地的定义和分类与中国现行的土地利用类型和并不统一，其提供的缺省参数对中国的研究亦未充分整合。因此，亟需在IPCC框架下开发适合中国的湿地温室气体清单方法学及参数库，以降低清单编制的不确定性。综述了IPCC湿地清单编制的方法学与中国湿地清单的研究进展，主要包括（1）比较了IPCC三部指南中的湿地清单的方法学，梳理了后两部对《2006 IPCC国家温室气体清单指南》在湿地类型、评估方法和缺省参数的更新内容；（2）比较了中国湿地清单编制与其他湿地温室气体研究结果的差异并探讨原因；（3）梳理了IPCC三部清单指南中湿地温室气体排放/清除因子数据库对来自中国的研究数据收录情况；（4）归纳了湿地的温室气体清单高层级方法学在中国的应用。在上述内容的归纳总结的基础上，基于现阶段我国在湿地温室气体清单编制方面存在活动水平数据难以获取、若干领域的排放因子缺失和评估方法不完善等问题，提出未来从提高活动水平数据透明度、加强人类活动影响评估、发展高层级方法学等方面改进。本文有助于提升我国编制湿地温室气体清单的能力，从而降低AFOLU领域对实现"碳中和"贡献的不确定性。     

大型丝状藻类对城市河流甲烷排放的影响研究进展

温室气体排放导致的全球变暖受到广泛关注.近期研究发现,经由河流系统排放的二氧化碳（CO₂）和甲烷（CH₄）可部分抵消陆地生态系统的碳固定量,从而使人们开始重新思考河流对于全球碳平衡和温室气体排放清单的影响.作为城市河流系统中重要的初级生产者,大型丝状藻类通过改变水-沉积物界面物理、化学以及生物等环境因子,深刻影响着河流生态系统的碳循环过程.本文从3个方面阐述大型丝状藻类对于城市河流中CH₄排放的影响:城市化对河流生态系统及其CH₄排放通量的影响;大型丝状藻类对自然河流系统中CH₄产生与排放过程的影响;大型丝状藻类对城市河流系统初级生产力及CH₄产生过程的影响.最后对目前存在的问题和今后的研究方向进行了展望.     

西安市温室气体排放的动态分析及等级评估

为了解西安市温室气体排放的动态规律和排放水平,基于全球标杆的温室气体排放等级评价方法,并采用国际公认的《2006年IPCC国家温室气体清单指南》和基于IPCC的《省级温室气体编制指南》推荐的方法对西安市的温室气体排放进行了动态分析和排放等级评估。结果表明,从1995年到2011年,西安市温室气体排放呈快速上升趋势,16年间温室气体排放量从1207.16×104t上升为3934.17×104t,年均增高7.66%。增幅最高的是水泥温室气体(年均增高11.75%)、废弃物(8.77%)和能源(7.63%),农业年均降低1.74%,林业固碳年圴增加3.56%。从温室气体构成看,能源占80.13%—90.55%,水泥占1.75%—7.49%,农业占1.86%—8.01%,林业固碳占-2.58%—-5.22%,废物处理占7.52%—16.38%。可见能源消费的增加是导致西安市温室气体排放增长的主要原因,林业碳汇能力有待提高。万元GDP温室气体排放不断降低,说明西安市碳减排方面的科技进步在不断提高。人均、单位面积温室气体排放量和排放指数增速很快,年均增幅分别达5.84%、7.66%和6.84%。西安市温室气体排放等级持续增高,16年间从较低等级(Ⅰc)上升为中下等级(Ⅱa),目前距应对气候变暖目标(Ⅰb)已高出两个亚级,温室气体排放增高的趋势不容忽视。     

广东省土地利用变化和林业温室气体清单变化规律分析与预测

根据联合国气候变化框架协议的要求, 土地利用变化和林业(LUCF)温室气体清单是需要编制的五个部门(能 源、工业、农业、土地利用变化和林业、废弃物)的清单之一。研究省级 LUCF 动态变化对掌握该区域生态固碳的能力和潜力有重要意义。该研究选取广东省森林资源第六期(2002 年)、第七期(2007 年)、第八期(2012 年)一类清查数据, 采取省级 LUCF 温室气体清单编制的方法结合广东省实际情况, 在获取和测算广东省 2005 年、2010 年、2015 年LUCF 温室气体的活动水平、排放因子的基础上, 得出全省范围 LUCF 温室气体清单。并设置了 3 种情景, 对广东省未来 LUCF 领域碳储量和温室气体减排进行了预测。研究结果表明广东省 LUCF 净碳汇量呈增长的趋势, 增长的主要原因是乔木林碳吸收的增加量高于活立木消耗碳排放的变化量。期间, 活立木蓄积生长率从 7.98%(2005)增加到了9.61%(2015); 而采伐消耗率从 6.94%(2005)下降到了 5.54%(2015)。这些得益于 2005 至 2015 十年间广东省土地利用变化和林业领域在应对气候变化方面的政策、措施的实施。情景模拟结果表明如果未来措施有利于广东省活力木碳储量的增加, 广东省森林还将发挥较长时间持续的增汇作用。     

农田生态系统温室气体减排技术评价指标

目前,国内外学者很少从评价的角度去研究农田温室气体减排技术,缺乏完整性和统一性的评价指标,不利于对农田管理技术进行科学的判断.本研究汇总了当前可作为农田减排技术评价的指标,遵循代表性、客观性、完整性、主导性和可操作性原则,对各项指标的合理性进行了分析,依据我国农业生产实际情况,确定了农田生态系统温室气体减排技术的评价指标.以单位面积粮食产量作为约束性指标,温室气体排放强度(单位产量下的温室气体排放总量)作为综合指标,并将粮食产量、土壤有机碳变化、N2O直接排放、水田CH4排放与农田投入直接和间接排放作为后者的分项指标;依据温室气体排放强度计算公式,能够科学、系统地评价农田减排技术的温室效应,可为我国农田减排技术的提出和推广提供科学依据.     

草原畜牧业温室气体排放现状、问题及展望

草原畜牧业生产系统是一个涉及环境、经济、社会多层面、且系统内部气候-土壤-草地-家畜-管理之间相互作用的复杂的社会生态系统。草原不仅为人类提供所需要的肉奶,也提供了多种生态系统服务。然而,草原畜牧业也是主要的温室气体排放源之一。减缓畜牧业温室气体排放的研究已成为当前气候变化科学研究关注的焦点。综述了国内外草原畜牧业温室气体排放研究现状,指出现有研究的不足主要集中在以下3个方面：（1）虽然生命周期评价方法广泛应用于草原畜牧业温室气体排放研究,但是存在诸多问题,导致目前的研究框架体系尚不完善,特别体现在以下几方面：是否考虑外部输入、是否考虑土壤有机碳、畜牧业温室气体排放强度指标的选择等;（2）缺乏单一环节减缓措施对草原畜牧业整体温室气体减排效果的研究;（3）目前对影响草原畜牧业温室气体排放强度的因素主要集中在生态系统层面的分析,忽略了社会系统的作用,无法反映社会系统与生态系统的相互反馈机制,导致机制阐释不完善。综上所述,未来仍需从以下三方面开展研究：（1）完善草原畜牧业研究框架体系及提升研究方法;（2）加强对单一环节减缓措施对草原畜牧业温室气体整体减排效果的综合评价;（3）基于社会生态系统的角度深入研究影响草原畜牧业温室气体排放强度差异的机制。一方面,这有助于深入理解草原畜牧业温室气体排放强度情况,也为低碳型草原畜牧业发展政策的制定提供思路借鉴;另一方面对于科学合理的可持续利用草场和恢复草地生态环境均具有重要意义。     

基于DNDC模型的稻田温室气体排放通量模拟

理解土地利用方式转变过程影响生态系统生物地球化学循环及温室气体排放的机理,并利用模型模拟土地利用方式转变过程对温室气体通量的影响是一项长期、艰巨的科学任务。本研究基于国际上广泛应用的生物地球化学过程模型(DNDC模型),结合气象、土壤和管理措施等数据,模拟了旱田转水田土地利用方式转变后稻田CH_4、CO_2和N_2O三种温室气体的通量和常年种植水稻的稻田温室气体通量,并将模拟值与观测值进行比较。结果表明:DNDC模型能够较好地模拟新、老稻田温室气体通量的季节变化,但对老稻田温室气体的排放通量模拟效果(R~20. 89,n=40,P0. 01)优于新转稻田(R~20.79,n=265,P0.01),且对CH_4和CO_2的模拟效果优于对N_2O的模拟效果;根据田间观测数据,改变模型模拟土地利用方式转换前后土壤SOC浓度和p H值,并不能完全模拟土地利用变化对温室气体的影响,微生物群落在土地利用方式转变过程中可能发生较大变化,需要在模型中进一步体现。通过模型模拟土地利用方式改变对温室气体排放的影响,可为国家温室气体、碳排放清单的编制及管理政策的制定提供参考依据。     

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。     

中国农业系统近40年温室气体排放核算

基于排放因子法构建了包含种植业和牲畜养殖业的农业系统温室气体排放核算体系,系统核算了1980-2020年我国全国尺度上的农业系统温室气体排放总量和变化趋势,并在区县级尺度下对1980、2000、2011年的中国农业系统的温室气体排放量进行核算,对比不同阶段农业系统温室气体排放变化的时空异质性规律。研究发现：1980-2020年我国农业系统温室气体排放量呈波动增长趋势,增长了近46%。CH₄是农业系统排放贡献最大的温室气体,占总排放量的47.33%。我国农业系统温室气体排放与不同地区农业生产方式有关,CH₄排放量高的地区主要位于我国主要水稻产区以及旱地作物产区。CO₂排放量高的地区主要位于东北、西北等地区以及华东地区。N₂O排放量较高地区主要位于西北的主要畜牧养殖地区,以及我国农业经济发展水平高的中南部地区。研究有助于揭示我国农业温室气体排放的动态特征,现状规律,以及空间差异性特征,从农业减排角度为实现双碳目标提供科学参考。     

剔除杂草对山核桃林地土壤温室气体排放的影响

森林土壤是CO2、N2O和CH4等温室气体的重要排放源,山核桃是中国特有的高档干果和木本油料树种,林下杂草管理对山核桃林地温室气体排放具有重要影响.采用静态箱-气相色谱法在浙江临安山核桃主产区进行了为期1年的原位试验,研究剔除林下杂草对山核桃林地土壤温室气体排放的影响.结果表明:剔除杂草和留养杂草山核桃林地土壤CO2排放通量呈现一致的季节变化规律:夏秋季高、冬春季低;N2O排放在夏季较高,其他季节变化平稳;CH4的排放无明显季节变化规律.剔除杂草显著降低了土壤CO2排放,促进了N2O排放和CH4吸收.剔除杂草对土壤水溶性有机碳和微生物生物量碳没有显著影响.剔除杂草的山核桃林地土壤排放温室气体的综合增温潜势为15.12 t CO2-e·hm-2·a-1,显著低于留养杂草处理(17.04 t CO2-e·hm-2·a-1).     

长三角城市群碳排放与城市用地增长及形态的关系

城市是一种重要的碳源,城市扩张过程中的用地面积增长和空间特征变化均会影响城市碳排放。分析1995—2015年长三角城市群碳排放重心转移,查明碳排放和城市用地增长的脱钩状态时空变化,并通过构建面板数据模型探究城市形态对碳排放的影响,得出以下结论:(1) 1995—2015年长三角城市群碳排放重心经历了西南向-西北向-东南向-西北向的转移过程,这种转移过程与其相应时期内部分城市的工业发展与产业结构调整有关;(2) 1995—2015年,长三角城市群碳排放与城市用地增长的脱钩状态存在着显著的时空异质性。研究区由以扩张负脱钩为主变化为以弱脱钩为主,2005年以后,区域之间的脱钩差异开始缩小,总体来看研究区脱钩状态趋向于同质。至2015年,近70%的城市已达到了脱钩,其中上海等城市实现了强脱钩;(3)连续完整的地块在区域内的主导程度会对城市碳排放产生负向的影响,而城市用地斑块的破碎化程度和聚集程度对碳排放有着正向的影响,且相对而言,聚集程度的正向影响更为显著。     

能源活动CO2排放不同核算方法比较和减排策略选择

能源活动CO₂排放是温室气体排放的最重要部分,这部分CO₂排放量的核算是温室气体清单编制和减排方案制定的关键和基础。采用直接法、电热终端法和隐含终端法核算了2009年中国能源消费的CO₂排放量,对不同核算法的CO₂排放部门分布、部门排放强度进行了比较,明确不同核算方法的差异和适用范围。采用电热终端法的核算结果定量分析了各产业部门和工业行业的经济增长和排放强度变化对中国能源活动CO₂排放增长的影响。结果表明,中国2009年隐含终端CO₂排放量为65.6亿t,略高于直接和电热终端CO₂排放量62.2亿t。3种核算方法的CO₂排放部门分布和排放强度有明显的差异:电、热力生产与供应业的直接排放占比为45.2%,而电热终端CO₂排放仅占4.5%;制造业的直接法、电热终端法和隐含终端法核算的CO₂排放占比分别为35.3% 、61.1%和65.5%,是终端能源消费CO₂排放最主要的部门;制造业、电热力生产与供应业和交通运输业的电热终端CO₂排放强度分别为2.166、1.72和1.622 t CO₂/万元GDP,是排放强度较高的部门。在产业部门中,制造业的色金属冶炼及压延加工业、非金属矿物制品业等5个行业以9.8%的经济增长贡献,排放了52.4%的CO₂,是产业结构调整、技术和工程减排的重点;服务业以7.2%的CO₂排放,贡献了38.4%的经济增长,应作为中国低碳经济优先发展的产业。     

Effect of improved nitrogen management on greenhouse gas emissions from intensive dairy systems in the Netherlands

Dairy systems in Europe contribute to the emissions of the greenhouse gases (GHGs) nitrous oxide (N₂O), methane (CH₄) and carbon dioxide (CO₂). In this paper, the effects of improved nitrogen (N) management on GHG emissions from Dutch dairy farms are determined. The GHG emissions are calculated using the panel on climate change (IPCC) methodology for the Netherlands, an updated and refined IPCC methodology, and a full accounting approach. The changes in dairy farming over the last 20 years, and the consequences for N management are described using detailed farm‐level data, collected in 1985, 1997 and 2002. The selected years represent distinct stages in the implementation of N policies. The changes in N management have reduced the GHG emissions. A reduction of the N surplus per kilogram milk with 1 g N reduced the GHG emissions per kilogram milk with approximately 29 g CO₂‐equivalents. The reduction of the N surpluses was mainly brought about by reduced fertilizer use and reduced grazing time. The use of updated and refined emission factors resulted in higher CH₄ emissions and lower N₂O emissions. On average, the overall emission was 36% higher with the refined method. Full accounting, including all direct and indirect emissions of CH₄, N₂O and CO₂, increased the emission with 36% compared with the refined IPCC methodology. We conclude that the N surplus at farm level is a useful indicator of GHG emissions. A full accounting system as presented in this study may effectively enable farmers to address the issue of emissions of GHGs in their operational management decisions. Both approaches serve their own specific objectives: full accounting at the farm level to explore mitigation options, and the IPCC methods to report changes in GHG emissions at the national level.     

Increasing Precision in Greenhouse Gas Accounting Using Real‐Time Emission Factors

For many companies, the greenhouse gas (GHG) emissions associated with their purchased and consumed electricity form one of the largest contributions to the GHG emissions that result from their activities. Currently, hourly variations in electricity grid emissions are not considered by standard GHG accounting protocols, which apply a national grid emission factor (EF), potentially resulting in erred estimates for the GHG emissions. In this study, a method is developed that calculates GHG emissions based on real‐time data, and it is shown that the use of hourly electricity grid EFs can significantly improve the accuracy of the GHG emissions that are attributed to the purchased and consumed electricity of a company. A model analysis for the electricity delivered to the Spanish grid in 2012 reveals that, for companies operating during the day, GHG emissions calculated by the real‐time method are estimated to be up to 5% higher (and in some special cases up to 9% higher) than the emissions calculated by the conventional method in which a national grid EF is applied, whereas for companies operating during nightly hours, GHG emissions are estimated to be as low as 3% below the GHG emissions determined by the conventional method. A significant error can therefore occur in the organizational carbon footprint (CF) of a company and, consequently, also in the product CF. It is recommended that hourly EFs be developed for other countries and power grids.     

我国典型城市化石能源消费CO2排放及其影响因素比较研究

城市是化石能源消费和CO_2排放的主要区域。分析典型城市化石能源消费CO_2排放特征,明确不同城市CO_2排放动态及主要影响因素的差异,是开展城市减排行动的重要科学依据。采用IPCC推荐方法及中国的排放参数核算11个典型城市2006—2015年间化石能源消费产生的CO_2排放量。根据各城市经济发展和CO_2排放特征将之分为四类:经济高度发达城市(北京、上海、广州)、高碳排放城市(重庆、乌鲁木齐、唐山)、低排放低增长城市(哈尔滨、呼和浩特和大庆)和低排放高增长城市(贵阳、合肥),并运用对数平均迪氏指数法(Logarithmic Mean Divisia Index,即LMDI分解法)对比分析了四类城市CO_2排放量的影响因素。结果表明:(1)研究期内大部分城市CO_2排放总量有所增加,仅北京和广州呈下降趋势,工业部门CO_2排放在城市排放总量及其变化中占据主导地位;四类城市的人均CO_2排放量表现出与排放总量相似的变化趋势;CO_2排放强度整体上表现为经济高度发达城市(均值为0.88 t CO_2/万元)低排放低增长城市(均值为2.82 t CO_2/万元)低排放高增长城市(均值为3.05 t CO_2/万元)高碳排放城市(均值为6.62 t CO_2/万元)。(2)在城市CO_2排放的影响因素中,经济发展和人口规模均是4类城市CO_2排放增长的促进因素,但经济发展效应的累积贡献值大于人口规模效应;能源强度降低是4类城市CO_2排放最主要的抑制因素,且经济高度发达和高碳排放城市的抑制作用强于其他两类城市;对第三产业GDP年平均增速高于第二产业的6个城市来说,产业结构是CO_2排放的抑制因素;能源结构的变化仅对煤炭消费比重较低且降幅较大的北京和广州的CO_2排放是抑制作用,累积贡献值分别为-21.73Mt和-0.03Mt,而对其他城市,特别是高碳排放城市的CO_2排放具有明显的促进作用。     

Evaluation of the effect of accounting method,IPCC v. LCA,on grass-based and confinement dairy systems’ greenhouse gas emissions

Life cycle assessment (LCA) and the Intergovernmental Panel on Climate Change (IPCC) guideline methodology, which are the principal greenhouse gas (GHG) quantification methods, were evaluated in this study using a dairy farm GHG model. The model was applied to estimate GHG emissions from two contrasting dairy systems: a seasonal calving pasture-based dairy farm and a total confinement dairy system. Data used to quantify emissions from these systems originated from a research study carried out over a 1-year period in Ireland. The genetic merit of cows modelled was similar for both systems. Total mixed ration was fed in the Confinement system, whereas grazed grass was mainly fed in the grass-based system. GHG emissions from these systems were quantified per unit of product and area. The results of both methods showed that the dairy system that emitted the lowest GHG emissions per unit area did not necessarily emit the lowest GHG emissions possible for a given level of product. Consequently, a recommendation from this study is that GHG emissions be evaluated per unit of product given the growing affluent human population and increasing demand for dairy products. The IPCC and LCA methods ranked dairy systems’ GHG emissions differently. For instance, the IPCC method quantified that the Confinement system reduced GHG emissions per unit of product by 8% compared with the grass-based system, but the LCA approach calculated that the Confinement system increased emissions by 16% when off-farm emissions associated with primary dairy production were included. Thus, GHG emissions should be quantified using approaches that quantify the total GHG emissions associated with the production system, so as to determine whether the dairy system was causing emissions displacement. The IPCC and LCA methods were also used in this study to simulate, through a dairy farm GHG model, what effect management changes within both production systems have on GHG emissions. The findings suggest that single changes have a small mitigating effect on GHG emissions (<5%), except for strategies used to control emissions from manure storage in the Confinement system (14% to 24%). However, when several management strategies were combined, GHG emissions per unit of product could be reduced significantly (15% to 30%). The LCA method was identified as the preferred approach to assess the effect of management changes on GHG emissions, but the analysis indicated that further standardisation of the approach is needed given the sensitivity of the approach to allocation decisions regarding milk and meat.     

Estimating annual N2O emissions from agricultural soils in temperate climates

The Kyoto protocol requires countries to provide national inventories for a list of greenhouse gases including N₂O. A standard methodology proposed by the Intergovernmental Panel on Climate Change (IPCC) estimates direct N₂O emissions from soils as a constant fraction (1.25%) of the nitrogen input. This approach is insensitive to environmental variability. A more dynamic approach is needed to establish reliable N₂O emission inventories and to propose efficient mitigation strategies. The objective of this paper is to develop a model that allows the spatial and temporal variation in environmental conditions to be taken into account in national inventories of direct N₂O emissions. Observed annual N₂O emission rates are used to establish statistical relationships between N₂O emissions, seasonal climate and nitrogen‐fertilization rate. Two empirical models, MCROPS and MGRASS, were developed for croplands and grasslands. Validated with an independent data set, MCROPS shows that spring temperature and summer precipitation explain 35% of the variance in annual N₂O emissions from croplands. In MGRASS, nitrogen‐fertilization rate and winter temperature explain 48% of the variance in annual N₂O emissions from grasslands. Using long‐term climate observations (1900–2000), the sensitivity of the models with climate variability is estimated by comparing the year‐to‐year prediction of the model to the precision obtained during the validation process. MCROPS is able to capture interannual variability of N₂O emissions from croplands. However, grassland emissions show very small interannual variations, which are too small to be detectable by MGRASS. MCROPS and MGRASS improve the statistical reliability of direct N₂O emissions compared with the IPCC default methodology. Furthermore, the models can be used to estimate the effects of interannual variation in climate, climate change on direct N₂O emissions from soils at the regional scale.     

Updated and harmonised greenhouse gas emissions for crop inventories

Purpose

The emission of greenhouse gases (GHG) is a key criterion in the environmental assessment of biofuels. Life cycle inventories taking into account the latest methodological developments are an essential prerequisite for this assessment. In the last years, substantial progresses in the modelling of nitrogen emissions relevant for the climate as well as in modelling the emissions from land use change (LUC) have been achieved. Therefore, the biomass production inventories in the ecoinvent database were revised to take into account these developments.

Methods

The IPCC method tier 1 has been used for the assessment of N₂O emissions. Induced emissions from NH₃ and NO₃ were included as well. Due to the importance of the latter emissions for N₂O formation, these emissions have also been updated and harmonised. The Agrammon model was used for the NH₃ emissions. The SALCA-NO₃ model has been applied in the European inventories to estimate nitrate leaching, whilst in non-European inventories the SQCB-NO₃ model has been used. The quantification of the land use change areas has been based on annualized, retrospective data of the last 20 years. All carbon pools (from aboveground biomass to soil organic carbon) were considered and differentiated on a regional level for all of the natural vegetation categories affected. Whenever possible, default values and methods from the IPCC 2006 were applied.

Results and discussion

The changes for ammonia emissions were generally very small (?5 % on average). The nitrate emissions increased on average by +13 %, but this slight trend is the result of important downward and upward changes, whilst the average N₂O emissions decreased by ?26 %. For the existing inventories of soybean, palm oil and sugarcane production, significant increases of GHG emissions resulted from LUC modelling. This was mainly due to the consistent inclusion of all carbon stocks according to the IPCC guidelines. The calculation method can also result in important C sequestration effects in certain cases like African Jatropha production.

Conclusions

The changes in greenhouse gas emissions due to the updated methodology were significant. This shows that life cycle assessment studies for biofuels using older methodological bases need to be revised and could lead to different conclusions. The implemented and cultivated superstructure for LUC modelling is modular and flexible and can be easily extended to other important crop activities. The new parameterisation functionality applied for the activities provides powerful means for the simple generation of site-specific activities.