农产品水足迹研究进展

水是人类生产生活的重要资源,科学合理地评价人类活动对水资源的影响是实现水资源可持续利用的重要保障.水足迹概念的提出创新性地将人类活动消耗的水资源区分为绿水、蓝水和灰水,拓展了水资源可持续利用的评价思路.基于虚拟水(VW)的水足迹理论和基于生命周期(LCA)的水足迹理论将水质与水量的概念相结合,成为了农业水资源管理研究的热点内容.基于VW的水足迹理论主要包括绿水足迹、蓝水足迹和灰水足迹的计算,以及水环境可持续性评价,而基于LCA的水足迹理论体现了水资源的消耗和污染及其对环境造成的综合影响.本文详细介绍了这两种水足迹理论的计算方法与环境可持续评价的研究进展,对比分析两种水足迹理论在描述农产品生产用水及其环境影响方面的差异性,并对其研究前景进行了展望.     

基于ISO 14046的工业产品水足迹评价研究——以电缆为例

水足迹国际标准(ISO 14046)于2014年发布,基于生命周期评价(LCA)的思想,水足迹被定义为量化与水相关潜在环境影响的指标。在ISO14046的原则、要求和方法学框架基础上,介绍了工业产品水足迹的计算和评价方法,并以铜电缆和铝合金电缆为例进行研究,分别评价了两类电缆生命周期过程产生的与水相关环境影响。与水足迹网络(WFN)的方法侧重于计算生产产品所需要的水资源总量不同,ISO的方法更关注于产品全生命周期过程的环境影响评价。案例研究表明:铜电缆生命周期全过程耗水量与铝合金电缆相比少24.8%,水短缺足迹相比则少97.9%。这是因为铜电缆生产地江苏的水压力指数(WSI)小于铝合金电缆生产地河北的WSI。由此,在江苏地区生产电缆使用的水资源对当地水环境压力造成的影响远小于在河北地区生产电缆造成的影响。采用科学合理的水足迹评价方法,量化工业产品全生命周期带来的环境影响,能为我国实现工业布局的合理规划和水资源的可持续利用提供科学依据。     

工业水足迹评价与应用

大规模的工业生产不但消耗了大量的水资源,同时产生了大量的工业废水,这是造成和加剧全球或区域水资源危机和水环境问题最重要的原因之一。对工业生产过程的水资源综合影响进行科学有效评估是目前资源环境管理领域研究的重要问题之一,是采取有效措施、提升工业生产的水资源利用效率和管理水平的重要前提和基础。以发展和完善工业生产过程的水资源综合影响的评价系统和方法体系为目标,基于水足迹理论,提出了工业水足迹的概念,即用以测量由某一产品、工业企业、行业或区域的工业生产过程引发的水资源利用增量的一个多维指标。以此概念为理论基础,进而首先分析了工业水足迹的内涵,包括直接工业水足迹与间接工业水足迹两部分。另外,区别于全生命周期的水足迹,工业水足迹评价是针对工业生产过程中由于工业原料、辅料、能源和水的输入,以及废水、废物与产品的输出产生的水足迹,其系统边界不包括分销零售、消费使用与废物处理等环节。其次,在确定的系统边界内构建了工业水足迹的核算框架和基础方法体系,即从生产、公共及运输3个方面收集数据、处理并核算。最后,从产品、企业、行业和区域4个层面对工业水足迹的应用及其意义进行了展望。     

水足迹研究进展

水为生命之源,水资源的合理分配与科学管理是区域可持续发展与流域综合管理的核心环节;水足迹作为一种全面核算人类活动对水资源真实占用的综合指标,将人类消费终端与水资源利用密切关联,为维护流域水资源安全、提高区域水资源利用效率提供了重要的科学依据,已成为当前国际水资源管理的前沿研究领域。在明确水足迹及水资源生态足迹相关概念的基础上,对比分析了水足迹与生态足迹、水资源生态足迹模型的异同,明晰了过程、产品及区域等不同研究对象的水足迹核算方法,系统梳理了产品和区域水足迹评价、基于水足迹的区域水资源安全研究、区域水足迹可持续性分析等水足迹主要研究内容的近今进展,并展望了进一步的重点研究方向,即水足迹综合研究、水足迹评价不确定性分析、水足迹与物质流核算的关联研究,以及基于足迹整合的可持续发展多维测度等。     

足迹家族:概念、类型、理论框架与整合模式

足迹研究是当前生态经济学和可持续发展领域的热点与前沿课题。探讨了足迹类指标的内涵,将其定义为一类评估人类资源消费和废弃物排放等活动环境影响的指标;介绍了生态足迹、碳足迹、水足迹、能源足迹、化学足迹、氮足迹和生物多样性足迹7类典型足迹指标的概念与研究进展;在此基础上提出了普适性的足迹家族概念,总结了足迹家族的选择性、开放性、系统性和不确定性特征,并根据足迹类指标的一般运算流程构建了足迹家族的理论框架;基于大量文献成果系统比较了生态足迹、碳足迹和水足迹3类关键足迹的特征差异,提出了在足迹家族层面增强指标兼容性的措施;通过逐一测试各关键足迹与27项环境问题的相关程度,从决策相关性的角度初步探索了该足迹家族的整合模式;展望了未来足迹(家族)研究的重点方向。     

基于生命周期评价的本土化水足迹影响评价模型构建

水足迹作为评价水资源消耗和污染情况的综合性指标,能够对水环境面临的环境风险进行科学系统的量化、评估和管理。针对传统水足迹影响方法未进行环境影响评估等问题,研究构建了一个符合ISO 14046国际标准的基于生命周期评价的通用型本土化水足迹影响评价模型。研究通过多介质逸度模型模拟了多介质污染物排放在环境中的迁移转换,从而剔除最终未进入到水介质的部分,同时仅考虑了与水环境有关的经口摄入途径,首创了集水稀缺影响、水污染生态与健康风险量化为一体的且适用于我国国情的水足迹评价模型。模型的构建可帮助实现水系统优化,有效控制二次污染及污染转移,实现精准管控。同时由于模型具有普适性,其也可为其他国家或区域开展生命周期水足迹影响评价提供理论支持和实践经验。此外,研究以某企业镍铁合金生产的水足迹影响评价为例,对模型的应用进行了示例研究。研究发现为该企业镍铁合金生产的水足迹影响主要来源于交通运输、焦炭生产、发电、压缩空气以及电极糊制备等间接过程。同时,为降低其环境负荷,需控制氮、磷、二氧化硫及铬、砷、汞、铜等重金属的排放。     

基于环境污染账户核算的生态足迹模型优化——以珠江三角洲城市群为例

针对传统的生态足迹分析模型在账户设置中忽略对环境污染核算的缺陷,本文提出了涵盖污染账户的生态足迹模型,将污染物进行合理转化进而纳入核算账户,在生态足迹核算中增加了污染足迹的计算,在生态承载力核算中增加了环境承载力的计算,并通过2005年珠江三角洲城市群的生态足迹对优化的生态足迹模型进行了验证.结果表明,通过优化模型计算得出的2005年珠江三角洲城市群的生态足迹结果与该区的实际发展特征和空间结构具有较高的吻合度.说明优化的模型在内涵上为环境污染在生态足迹模型中进行了较准确的定位,使结果能更全面地反映人类活动对生态环境的影响,在核算的完整性和客观性上均优于传统的生态足迹核算体系.     

长株潭地区生态可持续性

基于长株潭地区被批准为“全国资源节约型和环境友好型社会建设综合配套改革试验区”的背景,针对生态足迹方法的产量因子参数进行改进,利用区域产量因子代替全球产量因子,对长株潭地区1986-2005年生态足迹和生态承载力进行核算,在此基础上,着重采用两种预测方法对该地区2007-2015年生态足迹和生态容量进行预测.两种预测方法分别是二项式曲线预测模型和灰色GM(1,1)模型,对长株潭地区1986-2005年20a的人均生态足迹与时间关系进行了拟合,得出二项式曲线预测模型具有更高的预测精度;用两种预测模型预测了长株潭地区的人均生态容量,GM(1,1)模型的预测精度更高.选取精度最高的模型分别预测研究区未来10a人均生态足迹和生态容量.未来10a人均生态容量增长平缓(年平均增长率1.8％),人均生态足迹增长快(年平均增长率达16％),相应的人均生态赤字增长快.     

基于宏观贸易调整方法的国家生态足迹模型

针对生态足迹分析模型传统贸易调整方法中仅仅考虑生物产品和能源直接贸易而没有涵盖各类产品间接贸易所产生的贸易足迹的缺陷,分别提出了包含两类贸易足迹核算在内的生物产品贸易调整系数和能源贸易调整系数,并在此基础上构建了基于宏观贸易调整方法的国家生态足迹分析模型.将该模型用于中国1986～2005年的实证研究表明,模型结果虽然没有对中国生态超载形势逐渐恶化的结论形成颠覆性影响,但却揭示了人均生态足迹具有间隔为5a左右的周期性下降现象.生物产品的贸易足迹变化趋势与我国目前面临的农业乃至粮食生产形式较为一致;能源贸易足迹变化趋势则真实地反映了我国现行经济发展模式、产业结构乃至国际贸易发展的特点.实证研究成果还充分证明,加工产品的贸易对国家生态足迹的影响不容忽略,特别是能源贸易足迹的合理估算,可以作为国家层面全球环境责任分工的依据之一.     

生命周期评价方法在城市生活垃圾管理中的应用研究述评

结合城市生活垃圾管理系统特征,系统归纳基于生命周期评价(Life cycle assessment,LCA)方法的城市生活垃圾管理模型的发展现状,并对LCA方法在城市生活垃圾管理中的实践以及在我国开展城市生活垃圾管理LCA研究的应用前景进行评述。分析表明,LCA是城市生活垃圾管理领域的重要工具之一,基于LCA方法的城市生活垃圾管理模型在全生命周期环境影响评价与识别、处置工艺选择与改进、可持续生活垃圾管理决策支持等方面具有十分重要的应用价值。中国在本地化生活垃圾管理系统LCA模型开发、清单数据库和评价指标体系构建以及与其他研究方法集成等方面面临挑战。     

中国34个省级行政区三维生态足迹动态研究

当前中国正处于生态文明建设的关键时期,东西部环境资源不均衡的格局日益加剧,限制了区域社会经济的可持续发展。基于资源、社会经济统计数据,利用三维生态足迹模型对中国34个省级行政区进行动态研究,细化不同类型土地的均衡因子与产量因子,刻画2009—2016年人均生态足迹广度、生态足迹深度的时空特征,并探讨分析省域间的资本利用格局及其社会经济影响因素。主要结论如下：人均生态足迹广度在省域间差异较大,且生态足迹深度均已超过自然原长1.00;足迹广度在研究期间呈先降后升趋势,最高值为西藏的10.87—12.35 hm2/cap,最小值为澳门的0.02 hm2/cap;且资本流量占用率并不充足,广西与上海分别仅为47.68%—67.34%、66.31%—68.88%。香港、澳门、宁夏的足迹深度远高于中国均值,分别为19.24—26.02、8.60—10.88、4.60—7.46,最小值为西藏（1.64—1.79）,其中耕地、化石能源用地的生态赤字贡献率最高,而林地则为生态盈余状态。将各省自然资本利用格局聚类分为4类并通过ArcGIS进行空间分析。基于改进的三维生态足迹模型并系统性地应用于中国34个省域,本研究定量化各省的自然资本利用格局,可为中国区域间资源配置、政策制定提供基础数据支撑及环境决策参考。     

Assessment of economic and ecological carrying capacity of agricultural crops in Nicaragua

The relationships between, and usefulness of, three different analysis methods: (1) economic cost and return estimation (CAR), (2) ecological footprint (EF) and (3) emergy analysis (EA) in assessing economic viability, ecological carrying capacity and sustainability in tropical crop production was the focus for this study. The analyses were conducted on six agricultural crop production systems in Nicaragua: common bean (Phaseolus vulgaris L.), tomato (Lycopersicum esculentum L. Mill), cabbage (Brassica oleraceae L. var. capitata), maize (Zea mays L.), pineapple (Ananas comosus L. Merr.) and coffee (Coffea arabica L.). The economic indices studied were revenues and profitability. The ecological footprint indices were ecological footprint per hectare of crop (EF_crop), ecological footprint per 1000 USD revenues (EF_rev) and ecological footprint per gigacalorie of food energy produced (EF_Gcal). The emergy analysis indices used were emergy-based profitability (EA_prof) and emergy-based ecological footprint (EA_EF). The study indicated that cabbage and tomato were the most profitable crops, both in economic and emergy terms, and that coffee was the least profitable crop to grow. On the other hand, beans, coffee and maize were most sustainable when sustainability was measured as ecological carrying capacity, assessed by EF or emergy-based EF, while cabbage and tomato were the least sustainable. Moreover, maize turned out to be the crop with the lowest area demand per production of gigacalorie. Profitability assessed in economic terms or in relation to emergy use (EA_prof) or to ecological footprint showed similar patterns and gave the same rankings between the crops. However, profitability assessed by CAR was higher than when assessed by EA_prof, due to the fact that no environmental appropriation is included in the CAR. Area appropriation assessed with emergy or with ordinary ecological footprint also resulted in mainly the same rankings between the crops, while the actual size of the areas was at most 10 times larger when assessed in emergy than with plain ecological footprint. Our results add to the body of knowledge on the poor coherence between economic profitability and ecological sustainability. However, we argue that these evaluations may be used as methods for quantitatively assessing different production systems, leading to indices weighting together economic and environmental aspects that may be used to make decisions.     

An effect factor approach for quantifying the impact of plastic additives on aquatic biota in life cycle assessment

Purpose

Plastic pervades now almost every aspect of our daily lives, but this prosperity has led to an increasing amount of plastic debris, which is now widespread in the oceans and represents a serious threat to biota. However, there is a general lack of consideration regarding marine plastic impacts in life cycle assessment (LCA). This paper presents a preliminary approach to facilitate the characterization of chemical impacts related to marine plastic within the LCA framework.

Methods

A literature review was carried out first to summarize the current state of research on the impact assessment of marine plastic. In recent years, efforts have been made to develop LCA-compliant indicators and models that address the impact of marine littering, entanglement, and ingestion. The toxicity of plastic additives to marine biota is currently a less understood impact pathway and also the focus of this study. Relevant ecotoxicity data were collected from scientific literature for a subsequent additive-specific effect factor (EF) development, which was conducted based on the USEtox approach. Extrapolation factors used for the data conversion were also extracted from reliable sources.

Results and discussion

EFs were calculated for six commonly used additives to quantify their toxicity impacts on aquatic species. Triclosan shows an extremely high level of toxicity, while bisphenol A and bisphenol F are considered less toxic according to the results. Apart from additive-specific EFs, a generic EF was also generated, along with the species sensitivity distribution (SSD) illustrating the gathered data used to calculate this EF. Further ecotoxicity data are expected to expand the coverage of additives and species for deriving more robust EFs. In addition, a better understanding of the interactive effect between polymers and additives needs to be developed.

Conclusions

This preliminary work provides a first step towards including the impact of plastic-associated chemicals in LCA. Although the toxicity of different additives to aquatic biota may vary significantly, it is recommended to consider additives within the impact assessment of marine plastic. The generic EF can be used, together with a future EF for adsorbed environmental pollutants, to fill a gap in the characterization of plastic-related impacts in LCA.

     

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.     

Sensitivity of the carbon footprint of New Zealand milk to greenhouse gas metrics

Milk production is responsible for emitting a range of greenhouse gases (GHGs), mainly carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄). In Life Cycle Assessments (LCA), the Global Warming Potential with a time horizon of 100 years (GWP100) is used almost universally to aggregate emissions of individual gases into so-called CO₂-equivalent emissions that are used to calculate the overall carbon footprint of milk production. However, there is growing awareness that, depending on the purpose of the LCA, metrics other than GWP100 could be justified and some would give a very different weighting for the short-lived gas CH₄ relative to the long-lived gases CO₂ and N₂O when calculating the carbon footprint. Pastoral dairy production systems at different levels of intensification differ in the balance of short- and long-lived GHGs associated with on- and off-farm emissions. Differences in the carbon footprint of different production systems could therefore be highly sensitive to the choice of GHG metric. Here we explore the extent to which alternative GHG metric choices would alter the carbon footprint of New Zealand milk production at different levels of intensification at national, regional and individual farm scales and compared to the carbon footprint of milk of selected European countries. We find that the ranking of different production systems and individual farms in terms of their carbon footprint is relatively robust against the choice of GHG metric, despite significant differences in their utilisation of pastures versus supplementary off-farm feed, fertiliser use and energy consumption at various stages of farm operations. However, there are instances where alternative GHG metric choices would fundamentally change the conclusions of LCA of different production systems, including whether a move towards higher or lower input systems would increase or decrease the average carbon footprint of milk production in New Zealand. Greater transparency about the implications of alternative GHG metrics for LCA, and the often inadvertent and implicit value judgements embedded in these metrics, would help ensure that policy decisions and consumer choices based on LCA indeed deliver the climate outcomes intended by end-users.     

Weighting of environmental trade-offs in CCS—an LCA case study of electricity from a fossil gas power plant with post-combustion CO2 capture, transport and storage

Purpose

Carbon capture and storage (CCS) is increasingly acknowledged as a potent global warming abatement option. It is demonstrated that whilst the global warming potential (GWP) decreases, the other environmental impact category potentials often increase in a life cycle perspective. Despite this, only a few studies clearly address this trade-off or use weighting to compare the positive and negative effects of CCS. The present life cycle assessment (LCA) study focuses, therefore, on presenting several environmental indicators and on weighting the inventory results in order to ascertain which of the analysed systems is to be preferred.

Method

The case studied is a projected gas power plant at Tjeldbergodden (Norway), where it is proposed to include post-combustion CCS. Four main scenarios have been analysed, one without and three with CCS. The principal variation between the CCS scenarios is that the steam required for amine regeneration is produced in three different ways: in a separate gas fuelled steam boiler; in a separate biomass fuelled steam boiler; and delivered from the low-pressure steam turbine in the power plant. Design information and technical specifications have been available. The study has used LCA methodology based on the ISO standard 14044, SimaPro 7.3.2.4 software and the Ecoinvent 2.0 database. The functional unit is 1?TWh electricity delivered to the grid. The following environmental impact categories have been included: GWP, acidification potential, eutrophication potential, photochemical ozone creation potential (POCP) and cumulative energy demand (CED). Three weighting methods have been used to ascertain the robustness of the weighting results: ReCiPe, EPS 2000 and IMPACT 2002+.

Results and discussion

The characterisation results show that the CCS scenarios have reduced impacts only in the case of GWP. The weighting demonstrates that in the ReCiPe model, climate change is strongly in focus, whilst in EPS 2000, human health and depletion of reserves are dominant. Climate change is also an important factor in IMPACT 2002+, together with effects on human health (respiratory inorganics). The process integration scenario has, however, the best result for all three weighting models. This contrasts with the results from the impact analysis where four of the five analysed impact categories rated the CCS-3 scenario as worse than the reference scenario. One possible option for improving the biofuel boiler scenario is to capture the CO₂ from the combustion of biomass in the external steam boiler. This would not, in all probability, affect the acidification, eutrophication, POCP and CED to any significant degree, but the GWP, and hence the ReCiPe and the IMPACT 2002+, weighting results could be expected to improve.

Conclusions

The weighting exercise has identified toxicity as a concern with regard to the biofuel boiler scenarios (CCS-2) and human health issues as having importance for the CCS-3 scenario. It would seem that process integration is a better CCS option than that of CCS providing steam from a separate steam boiler (without CCS), even where this boiler is biomass-fuelled. Any future analysis should focus both on the process integration scenario and the biofuel boiler scenario with CCS of biological CO₂.     

Product Carbon Footprints and Their Uncertainties in Comparative Decision Contexts

In response to growing awareness of climate change, requests to establish product carbon footprints have been increasing. Product carbon footprints are life cycle assessments restricted to just one impact category, global warming. Product carbon footprint studies generate life cycle inventory results, listing the environmental emissions of greenhouse gases from a product’s lifecycle, and characterize these by their global warming potentials, producing product carbon footprints that are commonly communicated as point values. In the present research we show that the uncertainties surrounding these point values necessitate more sophisticated ways of communicating product carbon footprints, using different sizes of catfish (Pangasius spp.) farms in Vietnam as a case study. As most product carbon footprint studies only have a comparative meaning, we used dependent sampling to produce relative results in order to increase the power for identifying environmentally superior products. We therefore argue that product carbon footprints, supported by quantitative uncertainty estimates, should be used to test hypotheses, rather than to provide point value estimates or plain confidence intervals of products’ environmental performance.     

Environmental and resource burdens associated with world biofuel production out to 2050: footprint components from carbon emissions and land use to waste arisings and water consumption

Environmental or ‘ecological’ footprints have been widely used in recent years as indicators of resource consumption and waste absorption presented in terms of biologically productive land area [in global hectares (gha)] required per capita with prevailing technology. In contrast, ‘carbon footprints’ are the amount of carbon (or carbon dioxide equivalent) emissions for such activities in units of mass or weight (like kilograms per functional unit), but can be translated into a component of the environmental footprint (on a gha basis). The carbon and environmental footprints associated with the world production of liquid biofuels have been computed for the period 2010–2050. Estimates of future global biofuel production were adopted from the 2011 International Energy Agency (IEA) ‘technology roadmap’ for transport biofuels. This suggests that, although first generation biofuels will dominate the market up to 2020, advanced or second generation biofuels might constitute some 75% of biofuel production by 2050. The overall environmental footprint was estimated to be 0.29 billion (bn) gha in 2010 and is likely to grow to around 2.57 bn gha by 2050. It was then disaggregated into various components: bioproductive land, built land, carbon emissions, embodied energy, materials and waste, transport, and water consumption. This component‐based approach has enabled the examination of the Manufactured and Natural Capital elements of the ‘four capitals’ model of sustainability quite broadly, along with specific issues (such as the linkages associated with the so‐called energy–land–water nexus). Bioproductive land use was found to exhibit the largest footprint component (a 48% share in 2050), followed by the carbon footprint (23%), embodied energy (16%), and then the water footprint (9%). Footprint components related to built land, transport and waste arisings were all found to account for an insignificant proportion to the overall environmental footprint, together amounting to only about 2%     

Using the product environmental footprint for supply chain management: lessons learned from a case study on pork

Purpose

The purpose of this study was to test the chain-organization environmental footprint (chain-OEF) approach by applying it to part of a pork production chain in Belgium. The approach is supposed to provide insight into the environmental impact of a specific production chain in an efficient manner by applying pragmatic data collection throughout the chain. This is achieved by allocating the environmental impact of each of the production sites to the product of interest using straightforward allocation rules.

Methods

The cradle-to-gate (up to retail) environmental impact of pork was determined by life cycle assessment (LCA), in line with the product and organisation environmental footprint guidelines (PEF and OEF; European Commission 2013b). Foreground data was gathered at a feed production site, two farmers, a slaughterhouse and a meat processing site. All foreground operations are part of the same pork production chain in Belgium. The chain was completed using background data from Ecoinvent v3.01 (Wernet et al. 2016), Agri-Footprint v1.0 (Blonk 2014), European Life Cycle Database v3.0, LCA Food Database (Nielsen et al. 2003) and OEF Sector Rules Retail (Humbert et al. 2015b). The impact was quantified using the international reference life cycle data system (ILCD) midpoint method for 14 impact categories, but focussing on climate change.

Results and discussion

The total carbon footprint of the cradle-to-gate pork production system equals 0.46 kg CO₂-eq. (100 g pork)^?1. This result is quite similar to that of earlier studies analysing the pork production chain: 0.58 and 0.57 kg CO₂-eq. (100 g pork)^?1 (Bracquené et al. 2011, Agri-Footprint 2014). Most of the carbon footprint was caused by feed production and more specifically, by the feed ingredients and their transport. Grains, soy and palm oil have the largest impact contributions. The farms are responsible for most of the remaining impact. N₂O and CH₄ emissions are the largest cause of greenhouse gas emissions at the farms. Also, in the other 13 considered impact categories, feed production and farming are responsible for more than half of the total impact, mostly followed by meat processing.

Conclusions

Applying the chain-OEF approach in this study has shown that a chain LCA can be performed successfully and pragmatic data collection allows obtaining LCA results relatively fast, especially for small or medium-sized enterprises (SMEs). Whereas data availability was not such an issue, the main bottlenecks identified are data management and the link of LCA to other disciplines such as engineering, policy, etc. which could increase the added value of LCA studies.