The International Journal of Life Cycle Assessment

The research presented here was motivated by an interest in understanding the magnitude of sampling error in crop production unit process data developed for life cycle assessments (LCAs) of food, biofuel, and bioproduct production. More broadly, uncertainty data are placed within the context of conclusive interpretations of comparative bioproduct LCA results. Data from the US Department of Agriculture's Agricultural Resource Management Survey were parameterized for 466 crop–state–year combinations, using 146 variables representing the previous crop, tillage and seed operations, irrigation, and applications of synthetic fertilizer, lime, nitrogen inhibitor, organic fertilizer, and pesticides. Data are described by Student's t distributions representing sampling error through the relative standard error (RSE) and are organized by the magnitude of the RSE by data point. Also, instances in which the bounds of the 95 % confidence intervals are less than zero or exceed actual limits are identified. Although the vast majority of the data have a RSE less than 100 %, values range from 0 to 1,600 %. The least precision was found in data collected between 2001 and 2002, in the production of corn and soybeans and in synthetic and pesticide applications and irrigation data. The highest precision was seen in the production of durum wheat, rice, oats, and peanuts and in data representing previous crops and till and seed technology use. Additionally, upwards of 20 % of the unit process, data had 95 % confidence intervals that are less than or exceed actual limits, such as an estimation of a negative area or a portion exceeding a total area, as a consequence of using a jackknife on subsets of data for which the weights are not calibrated explicitly and a low presence of certain practices. High RSE values arise from the RSE representing a biased distribution, a jackknife estimate being nearly zero, or error propagation using low-precision data. As error propagates to the final unit process data, care is required when interpreting an inventory, e.g., Monte Carlo simulation should only be sampled within the appropriate bounds. At high levels of sampling error such as those described here, comparisons of LCA bioproduct results must be made with caution and must be tested to ensure mean values are different to a desired level of significance.

Quantitative uncertainties are a direct consequence of averaging, a common procedure when building life cycle inventories (LCIs). This averaging can be amongst locations, times, products, scales or production technologies. To date, however, quantified uncertainties at the unit process level have largely been generated using a Numerical Unit Spread Assessment Pedigree (NUSAP) approach and often disregard inherent uncertainties (inaccurate measurements) and spread (variability around means). A decision tree for primary and secondary data at the unit process level was initially created. Around this decision tree, a protocol was developed with the recognition that dispersions can be either results of inherent uncertainty, spread amongst data points or products of unrepresentative data. In order to estimate the characteristics of uncertainties for secondary data, a method for weighting means amongst studies is proposed. As for unrepresentativeness, the origin and adaptation of NUSAP to the field of life cycle assessment are discussed, and recommendations are given. By using the proposed protocol, cross-referencing of outdated data is avoided, and user influence on results is reduced. In the meantime, more accurate estimates can be made for horizontally averaged data with accompanying spread and inherent uncertainties, as these deviations often contribute substantially towards the overall dispersion. In this article, we highlight the importance of including inherent uncertainties and spread alongside the NUSAP pedigree. As uncertainty data often are missing in LCI literature, we here describe a method for evaluating these by taking several reported values into account. While this protocol presents a practical way towards estimating overall dispersion, better reporting in literature is promoted in order to determine real uncertainty parameters.

Although LCA is traditionally a site-independent tool, there is currently a trend towards making LCA more site-dependent if not site-specific. For Europe, site-dependent impact factors have been calculated on a country basis for acidification, terrestrial eutrophication and toxicological impacts. It is, however, an open question whether this is the optimum level for site-dependent factors. The aim of this paper is to develop site-dependent characterisation factors for different parts of Sweden for air emissions of NOx, SOx and particulates regarding ecosystem and human health impacts. Based on experiences from a case-study, the usability of the site-dependent factors for LCA are discussed, as well as the appropriate level of site-dependency for ecosystem and human health impacts. logy. The Ecosense model is used for calculating site-dependent factors for some atmospheric pollutants. Characterisation factors are calculated for four different places in Sweden with two different stack heights. The characterisation factors for ecosystem impacts show fairly small differences between different parts of Sweden (within a factor of two). For health impacts, the differences between different parts of the country were larger and more significant (up to one order of magnitude). Also the difference between low and high stack heights may be relevant, especially in densely populated areas. These results suggest that for ecosystems, site-dependent characterisation factors for the considered atmospheric pollutants on a country level may be sufficient for most applications. However, for health impacts, site-dependent factors on a country level may be inappropriate. Beside LCA, the calculated factors and the methodology used should also be useful for other environmental system analysis tools, such as Strategic Environmental Assessment, Cost-Benefit Analysis and Environmental Management Systems.

The water footprint (WF) method is widely applied to quantify water use along the life cycle of products and organizations and to evaluate the resulting impacts on human health. This study analyzes the cause-effect chains for the human health damage related to the water use on a local scale in the Province Punjab of Pakistan, evaluates their consistency with existing WF models, and provides recommendations for future model development. Locally occurring cause-effect chains are analyzed based on site observations in Punjab and a literature review. Then, existing WF models are compared to the findings in the study area including their comprehensiveness (covered cause-effect chains), relevance (contribution of the modeled cause-effect chain to the total health damage), and representativeness (correspondence with the local cause-effect chain). Finally, recommendations for the development of new characterization models describing the local cause-effect chains are provided. The cause-effect chains for the agricultural water deprivation include malnutrition due to reduced food availability and income loss as well as diseases resulting from the use of wastewater for irrigation, out of which only the first one is addressed by existing WF models. The cause-effect chain for the infectious diseases due to domestic water deprivation is associated primarily with the absence of water supply systems, while the linkage to the water consumption of a product system was not identified. The cause-effect chains related to the water pollution include the exposure via agricultural products, fish, and drinking water, all of which are reflected in existing impact assessment models. Including the groundwater compartment may increase the relevance of the model for the study area. Most cause-effect chains identified on the local scale are consistent with existing WF models. Modeling currently missing cause-effect chains for the impacts related to the income loss and wastewater usage for irrigation can enhance the assessment of the human health damage in water footprinting.

If the aim of an LCA is to support decisions or to generate and evaluate ideas for future decisions, the allocation procedure should generally be effect-oriented rather than cause-oriented. It is important that the procedure be acceptable to decision makers expected to use the LCA results. It is also an advantage if the procedure is easy to apply. Applicability appears to be in conflict with accurate reflection of effect-oriented causalities. To make LCA a more efficient tool for decision support, a range of feasible allocation procedures that reflect the consequences of inflows and outflows of cascade materials is required.

The evaluation of packaging’s environmental performance usually concentrates on a comparison of different packaging materials or designs. Another important aspect in life cycle assessment (LCA) studies on packaging is the recycling or treatment of packaging wastes. LCA studies of packed food include the packaging with specific focus on the contribution of the packaging to the total results. The consumption behaviour is often assessed only roughly. Packaging is facilitating the distribution of goods to the society. Broader approaches, which focus on the life cycle of packed goods, including the entire supply system and the consumption of goods, are necessary to get an environmental footprint of the system with respect to sustainable production and consumption. A full LCA study has been conducted for two food products: coffee and butter packed in flexible packaging systems. The aim was to investigate the environmental performance of packaging with respect to its function within the life cycle of goods. The study looks at the environmental relevance of stages and interdependencies within the life cycle of goods whilst taking consumers’ behaviour and portion sizes into consideration. The impact assessment is based on the following impact categories: non-renewable cumulative energy demand (CED), climate change, ozone layer depletion (ODP), acidification, and eutrophication. The study shows that the most relevant environmental aspects for a cup of coffee are brewing (i.e. the heating of water) and coffee production. Transport and retail packaging are of minor importance. Brewing and coffee production have an impact share between 40% (ODP, white instant coffee) and 99% (eutrophication, black coffee). Milk added for white coffee is relevant for this type of preparation. The instant coffee in the one-portion stick-pack needs more packaging material per cup of coffee and is prepared by a kettle with lower energy demand, such as a coffee machine, thus leading to higher shares of the retail packaging in all indicators. A one-portion stick-pack can prevent wastage and resources related to coffee production can be saved. The most relevant aspect regarding the life cycle of butter is butter production, dominated by the provision of milk. Over 80% of the burdens in butter production stem from the provision of milk for all indicators discussed. Regarding climate change, methane and dinitrogen monoxide, emissions of milk cows and fodder production are most relevant. Fertilisation during livestock husbandry is responsible for most burdens regarding acidification and eutrophication. The distribution and selling stage influences the indicators CED and ODP distinctly. The reasons are, on the one hand, the relatively energy-intensive storage in supermarkets and, on the other hand, the use of refrigerants for chilled storage and transportation. The storage of butter in a refrigerator for 30 days is responsible for about 10% of the CED. Several aspects have been modelled in a sensitivity analysis. The influence of coffee packaging disposal is very small due to the general low influence of packaging. In contrast, the brewing behaviour is highly relevant for the environmental impact of a cup of coffee. That applies similarly to the type of heating device—i.e. using a kettle or an automatic coffee machine. Wastage leads to a significant increase of all indicators. Under the wastage scenario, the coffee from one-portion stick-packs has a considerable better environmental performance concerning all indicators because, in case of instant coffee wastage of hot water and in case of ground coffee wastage of prepared coffee, has been predicted. Regardless of urban or countryside distances, grocery shopping has a low impact. The storage time of butter is relevant for the results in the indicator non-renewable CED. This is mainly the case when butter is stored as stock in the freezer. The end of life treatment of the packaging system has practically no influence on the results. Grocery shopping is of limited importance no matter which means of transport are used or which distances are regarded. Spoilage or wastage is of great importance: a spoilage/wastage of one third results in about 49% increased impacts compared to the standard case for all indicators calculated. The most important factors concerning the environmental impact from the whole supply chain of a cup of coffee are the brewing of coffee, its cultivation and production and the milk production in case of white coffee. The study highlights consumer behaviour- and packaging-related measures to reduce the environmental impact of a cup of coffee. The most relevant measures reducing the environmental impacts of butter consumption are the optimisation of the milk and butter production. Another important factor is the consumers’ behaviour, i.e. the reduction of leftovers. The consumer can influence impacts of domestic storage using efficient and size-adequate appliances. The impacts of packaging in the life cycle of butter are not of primary importance. This study shows that, in the case of packaging industry, a reduction of relevant environmental impacts can only be achieved if aspects indirectly influenced by the packaging are also taken into account. Thus, the packaging industry should not only aim to improve the production process of their packages, but also provide packages whose functionality helps to reduce other more relevant environmental impacts in the life cycle such as, for example, losses. Depending on the product, tailor-made packaging may also help to increase overall resource efficiency.