A spatially explicit life cycle inventory of the global textile chain

Background, aim, and scope  Life cycle analyses (LCA) approaches require adaptation to reflect the increasing delocalization of production to emerging countries. This work addresses this challenge by establishing a country-level, spatially explicit life cycle inventory (LCI). This study comprises three separate dimensions. The first dimension is spatial: processes and emissions are allocated to the country in which they take place and modeled to take into account local factors. Emerging economies China and India are the location of production, the consumption occurs in Germany, an Organisation for Economic Cooperation and Development country. The second dimension is the product level: we consider two distinct textile garments, a cotton T-shirt and a polyester jacket, in order to highlight potential differences in the production and use phases. The third dimension is the inventory composition: we track CO₂, SO₂, NO _x , and particulates, four major atmospheric pollutants, as well as energy use. This third dimension enriches the analysis of the spatial differentiation (first dimension) and distinct products (second dimension). Materials and methods  We describe the textile production and use processes and define a functional unit for a garment. We then model important processes using a hierarchy of preferential data sources. We place special emphasis on the modeling of the principal local energy processes: electricity and transport in emerging countries. Results  The spatially explicit inventory is disaggregated by country of location of the emissions and analyzed according to the dimensions of the study: location, product, and pollutant. The inventory shows striking differences between the two products considered as well as between the different pollutants considered. For the T-shirt, over 70% of the energy use and CO₂ emissions occur in the consuming country, whereas for the jacket, more than 70% occur in the producing country. This reversal of proportions is due to differences in the use phase of the garments. For SO₂, in contrast, over two thirds of the emissions occur in the country of production for both T-shirt and jacket. The difference in emission patterns between CO₂ and SO₂ is due to local electricity processes, justifying our emphasis on local energy infrastructure. Discussion  The complexity of considering differences in location, product, and pollutant is rewarded by a much richer understanding of a global production–consumption chain. The inclusion of two different products in the LCI highlights the importance of the definition of a product's functional unit in the analysis and implications of results. Several use-phase scenarios demonstrate the importance of consumer behavior over equipment efficiency. The spatial emission patterns of the different pollutants allow us to understand the role of various energy infrastructure elements. The emission patterns furthermore inform the debate on the Environmental Kuznets Curve, which applies only to pollutants which can be easily filtered and does not take into account the effects of production displacement. We also discuss the appropriateness and limitations of applying the LCA methodology in a global context, especially in developing countries. Conclusions  Our spatial LCI method yields important insights in the quantity and pattern of emissions due to different product life cycle stages, dependent on the local technology, emphasizing the importance of consumer behavior. From a life cycle perspective, consumer education promoting air-drying and cool washing is more important than efficient appliances. Recommendations and perspectives  Spatial LCI with country-specific data is a promising method, necessary for the challenges of globalized production–consumption chains. We recommend inventory reporting of final energy forms, such as electricity, and modular LCA databases, which would allow the easy modification of underlying energy infrastructure. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Framework for modelling data uncertainty in life cycle inventories

Modelling data uncertainty is not common practice in life cycle inventories (LCI), although different techniques are available for estimating and expressing uncertainties, and for propagating the uncertainties to the final model results. To clarify and stimulate the use of data uncertainty assessments in common LCI practice, the SETAC working group ‘Data Availability and Quality’ presents a framework for data uncertainty assessment in LCI. Data uncertainty is divided in two categories: (1) lack of data, further specified as complete lack of data (data gaps) and a lack of representative data, and (2) data inaccuracy. Filling data gaps can be done by input-output modelling, using information for similar products or the main ingredients of a product, and applying the law of mass conservation. Lack of temporal, geographical and further technological correlation between the data used and needed may be accounted for by applying uncertainty factors to the non-representative data. Stochastic modelling, which can be performed by Monte Carlo simulation, is a promising technique to deal with data inaccuracy in LCIs.     

Abiotic Resource Depletion Different perceptions of the problem with mineral deposits

Goal, Scope and Background The goal of the present paper is to demonstrate how environmental product declarations (EPDs) are developed based on a set of product category rules (PCRs) in accordance with the requirements in the ISO 14025-standard. This is demonstrated by examples from the furniture industry in Norway, where several case models are evaluated. To ease the capability of developing EPDs in this industry, a database with specific environmental data for materials in furniture is developed. The database is used to produce the LCA for selected furniture models, and further, the database is the backbone of a data-assistance tool used to create the EPDs. Methods The LCA-data are produced based on traditional LCA-methodology. The PCR is based on a stakeholder analysis and the proposed methodology in the ISO 14025-standard. The EPDs developed so far, are results of close collaboration between companies and research centres in the Nordic countries. For the verification of the EPDs, auditing methodologies are used as a part of the audit of the companies' environmental management systems (EMS). Results and Conclusion Based on a hearing of a set of suggested PCRs, a consensus document for seating accommodation is developed. This is further the model for how to develop PCR-documents for all types of furniture, for example sleeping accommodations. Likewise, the database shall contain the most important data for the parts of a furniture model. Within the goal of the project period, EPDs will be developed for 80% of Norwegian furniture. The verification of the EPDs is done as a part of the certification procedures of EMS in accordance with the ISO 14001. Recommendation and Perspective The results presented in the paper are mainly for the pilot models in the project. However, the results will be further tested and the data-tool will be developed as a part of a product design tool where environmental requirements will be combined with quality requirements. The product design tool will be implemented in the furniture industry. Information on how to use EPDs in public purchasing will also be a part of future work.     

Environmental performance of a municipal wastewater treatment plant

Goal, Scope and Background  Nowadays, every strategy must be developed taking into account the global impact on the environment; if this aspect is forgotten, a change of environmental loads or their effect will be caused and no reduction will be attained. For instance, a wastewater treatment plant (WWIP), which is considereda priori as an ecological treatment system, gives rise to an environmental impact due to its energy consumption, use of chemical compounds, emissions to the atmosphere and sludge production, the post-treatment of which will also have diverse environmental effects. The goal of this study is to evaluate the potential environmental impact corresponding to a municipal WW1P and to identify the hot spots associated with the process. Methods  In this study, the Centre of Environmental Science (CML) of Leiden University methodology has been considered to quantify the potential environmental impact associated with the system under study. A comprehensive analysis of the WWTP was evaluated for the physico-chemical characterisation of the wastewaters as well as the inventory of all the inputs (energy, chemical compounds, ...) and outputs (emissions to air, water, soil and solid waste generation) associated with the global process. Regarding Life Cycle Inventory Assessment, SimaPro 5.0 was used and in particular CML factors (updated in 2002) were chosen for characterisation and normalisation stages. Results and Discussion  A comprehensive inventory of empirical data from water, sludge and gas flows during 2000 and 2001 was obtained. Two impact categories arise due to their significance: eutrophication and terrestrial ecotoxicity. Consequently, the aspects to be minimised in order to reduce the environmental impact of the system are the pollutant load at the watercourse discharge (mainly NH₃, PO₄ ^[3- and COD, even when all of them are below legal limits) and the emissions to soil (mainly Cr, Hg and Zn, even when they are present in low concentrations) when the sludge is used for agricultural application. Conclusions  As far as the environmental impact is concerned, differentiation between humid and dry season is not required as results are practically equal for both situations. Water discharge and sludge application to land have turned out to be the main contributors in the environmental performance of a WWTP. Regarding the former, the removal of nitrogen by means of a nitrification-denitrification system coupled to conventional biological aerobic treatment implies a high environmental impact reduction and, as for the latter, bearing in mind the proposed legislation, heavy metals as well as pathogens are supposed to be the key parameters to define the most adequate treatment strategies for the generated sludge. Recommendations and Outlook  This study can serve as a basis for future studies that can apply a similar policy to a great number of wastewater facilities. Besides, features such as different treatment systems and capacities can provide additional information with the final aim of including the environmental vector in the decision-making process when the operation of a WWTP is intended to be optimised. Moreover, sludge must also be a focus of attention due to the expected increase and its major contribution to the global environmental impact of a WWTP, which can determine other treatment alternatives.     

Using monte carlo simulation in life cycle assessment for electric and internal combustion vehicles

1 Background

The U.S. Government has encouraged shifting from internal combustion engine vehicles (ICEVs) to alternatively fueled vehicles such as electric vehicles (EVs) for three primary reasons: reducing oil dependence, reducing greenhouse gas emissions, and reducing Clean Air Act criteria pollutant emissions. In comparing these vehicles, there is uncertainty and variability in emission factors and performance variables, which cause wide variation in reported outputs.

2 Objectives

A model was developed to demonstrate the use of Monte Carlo simulation to predict life cycle emissions and energy consumption differences between the ICEV versus the EV on a per kilometer (km) traveled basis. Three EV technologies are considered: lead-acid, nickel-cadmium, and nickel metal hydride batteries.

3 Methods

Variables were identified to build life cycle inventories between the EVs and ICEV. Distributions were selected for each of the variables and input to Monte Carlo Simulation soft-ware called Crystal Ball 2000®.

4 Results and Discussion

All three EV options reduce U.S. oil dependence by shifting to domestic coal. The life cycle energy consumption per kilometer (km) driven for the EVs is comparable to the ICEV; however, there is wide variation in predicted energy values. The model predicts that all three EV technologies will likely increase oxides of sulfur and nitrogen as well as particulate matter emissions on a per km driven basis. The model shows a high probability that volatile organic compounds and carbon monoxide emissions are reduced with the use of EVs. Lead emissions are also predicted to increase for lead-acid battery EVs. The EV will not reduce greenhouse gas emissions substantially and may even increase them based on the current U.S. reliance on coal for electricity generation. The EV may benefit public health by relocating air pollutants from urban centers, where traffic is concentrated, to rural areas where electricity generation and mining generally occur. The use of Monte Carlo simulation in life cycle analysis is demonstrated to be an effective tool to provide further insight on the likelihood of emission outputs and energy consumption.     

Life Cycle Assessment of Advanced Industrial Wastewater Treatment Within an Urban Environment

A dissolved air flotation (DAF) system upgrade was proposed for an urban paper mill to recycle effluent. To understand the influence of operating variables on the environmental impacts of greenhouse gas (GHG) emissions and water consumption, a dynamic supply chain model was linked with life cycle assessment (LCA) to produce an environmental inventory. Water is a critical natural resource, and understanding the environmental impacts of recycling water is paramount in continued development of sustainable supply chains involving water. The methodology used in this study bridged the gap between detailed process models and static LCA modeling so that operating variables beyond discrete scenario analysis could be investigated without creating unnecessarily complex models. The model performed well in evaluating environmental impacts. It was found that there was no single optimum operating regime for all environmental impacts. For a mill discharging 80 cubic meters of effluent per hour (m³/hour), GHGs could be minimized with a DAF capacity of 17.5 m³/hour, while water consumption could be minimized with a DAF capacity of 25 m³/hour, which allowed insight into where environmental trade‐offs would occur. The study shows that more complexity can be achieved in supply chain modeling without requiring a full technical model. It also illustrates the need to consider multiple environmental impacts and highlights the trade‐off of GHG emissions with water consumption in water recycling. The supply chain model used in this water treatment case study was able to identify the environmental trade‐offs from the operating variables selected.     

Life Cycle Inventory of Particleboard: A Case Study in the Wood Sector (8 pp)

Goal, Scope and Background Wood has many applications and it is often in competition with other materials. Chipboard is the most common item of wood-based materials and it has attained the highest economical development in recent years. Relevant up-to-date environmental data are needed to allow the environmental comparison of wood with other materials. There are several examples of Life Cycle Assessment (LCA) evaluations of some wood products and forest-technology systems, but no comprehensive Life Cycle Inventory (LCI) data for particleboard manufacture is available in the literature. The main focus of this study is to generate a comprehensive LCI database for the manufacture of resin-bonded wood particleboards. Methods In this work, International Organization for Standardization (ISO) standards and Ecoindicator 99 methodology were considered to quantify the potential environmental impact associated to the system under study. A Spanish factory considered representative of the 'state of art' was studied in detail. The system boundaries included all the activities taking place inside the factory as well as the activities associated with the production of the main chemicals used in the process, energy inputs and transport. All the data related to the inputs and outputs of the process were obtained by on-site measurements. Results and Discussion LCI methodology was used for the quantification of the impacts of the particleboard manufacture. The inventory data of the three defined subsystems are described: - Wood preparation: a comprehensive inventory of data including storage, debarking, particle production, storage and measurement of particles, drying and combustion of the bark for energy purposes. - Board shaping: data related to particle classification, resin mixing, mattress formation and the pressing stage. - Board finishing: cooling data, finishing, storage and distribution of the final product. The system was characterised with Ecoindicator 99 methodology (hierarchic version) in order to identify the 'hot spots'. Damage to Human Health was mainly produced by the subsystem of Board finishing. The subsystem of Board shaping was the most significant contributor to damage to the Ecosystem Quality and Resources. Conclusions With the final aim of creating a database to identify and characterise the manufacture of particleboard, special attention was paid to the inventory analysis stage of the particleboard industry. A multicriteria approach was applied in order to define the most adequate use of wood wastes. Environmental, economic and social considerations strengthen the hypothesis that the use of forest residues in particleboard manufacture is more sustainable than their use as fuel. Recommendations and Outlook In this work, particleboard was the product analysed, as it is one of the most common wood-based materials. Future work will focus on the study of another key wood board: Medium Density Fibreboard (MDF). Moreover, factors with strong geographical dependence, such as the electricity profile and final transport of the product, will be analysed. In addition, the definition of widespread functional unit to study the use of wood wastes at the end-of-life stage may be another issue of outstanding interest.     

Data implementation matters: Effect of software choice and LCI database evolution on a comparative LCA study of permanent magnets

Life cycle assessment (LCA) databases and software evolve. We analyzed to which extent software and evolving life cycle inventory databases affect the comparison of technology alternatives, using a comparative LCA on permanent magnets as a case study, with two selected software tools: CMLCA and Brightway LCA. We migrated the system models from the CMLCA to Brightway LCA software and alternated between the ecoinvent database versions 2.2 and 3.1 to 3.6 in the system background. When using ecoinvent v3.6 instead of v2.2, the change of the indicator results ranged from $-34\%$ to 283%. The evolution of the ecoinvent database impacted the absolute amounts of the characterized results and the relative performance between alternatives. The impact category with the highest variability was ionizing radiation, which even showed a ranking inversion with ecoinvent v3.4. In contrast, the impact of using CMLCA or Brightway was negligible because the same data and modeling assumptions caused percentage differences below 0.4%. During the semi-automated data migration to Brightway, we identified 23 environmental flows in the CMLCA model that were not paired with their corresponding characterization factors in the published study of reference. This error had led to an underestimation of 63% in the photochemical oxidation indicator of one of the alternatives. This underestimation relates to an interoperability issue regarding the nomenclature of environmental flows in software alternatives and is a matter of data implementation rather than an issue intrinsic to the selected software. Finally, we identified improvement opportunities for the transparency and reusability of LCA models. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges .      

Screening stochastic Life Cycle assessment inventory models

A screening methodology is presented that utilizes the linear structure within the deterministic life cycle inventory (LCI) model. The methodology ranks each input data element based upon the amount it contributes toward the final output. The identified data elements along with their position in the deterministic model are then sorted into descending order based upon their individual contributions. This enables practitioners and model users to identify those input data elements that contribute the most in the inventory stage. Percentages of the top ranked data elements are then selected, and their corresponding data quality index (DQI) value is upgraded in the stochastic LCI model. Monte Carlo computer simulations are obtained and used to compare the output variance of the original stochastic model with modified stochastic model. The methodology is applied to four real-world beverage delivery system LCA inventory models for verification. This research assists LCA practitioners by streamlining the conversion process when converting a deterministic LCI model to a stochastic model form. Model users and decision-makers can benefit from the reduction in output variance and the increase in ability to discriminate between product system alternatives.     

A simple methodology for elaborating the life cycle inventory of agricultural products

Goal, Scope and Background  A methodological approach for representing agricultural products in terms of life cycle inventory is suggested in this paper. This approach was developed during the conduction of an LCA study for two perennial crops of important Brazilian exportation products: green coffee and orange juice, which included tillage cultivation by commercial farms, harvest, as well as product processing when pertinent. The published papers on agricultural products LCA usually discuss the final results in terms of LCIA, being not very clear what methodology or principles were applied on the LCI phase. The aim of this paper is to present a simple methodology that would be employed by different stakeholders as farmers, environment managers and decision makers for evaluating the environmental performance of their products. In recent years, many researchers have tried to make a worldwide effort in order to reach comparable results of LCA studies developed in different countries. So, the proposed methodology has also the aim of isolating the site-dependency of the results that are not strictly related to the agricultural production. The time coverage suggested is the period can be considered as an average for the specific tillage under evaluation, usually two crops, since there is a large variation on the inputs in every other crop, including the higher and subsequent lower productive periods. Method  The functional unit recommended is 1,000 kg of the specific product, being recommended to distinguish the energy used for the cultivation from that used by the processing stage. There are several specific considerations to transform the data collected through the questionnaires in an inventory data set of fertilizers (macro and micro nutrients), correctives, fillers and pesticides further detailed. Water used for chemicals preparation, in the cleaning and processing stages of the harvested crop is also considered. Land use refers to the area used land for cultivation divided by the medium life period of the tillage. The stoichiometric balance is performed based on the elementary composition of the products. An average carbohydrate formula is established for the products considering the relationship among the carbon, hydrogen and oxygen contents of them. The carbohydrate formula (output) is balanced with carbon dioxide and water (inputs) according to the basic principles of the photosynthesis reaction. The differences among the mineral composition of the products and the total content of these elements (N, P, K, Ca, Mg and micronutrients elements) for all the crop inputs (fertilizers, pesticides, correctives) are allocated as outputs of the system. The pesticides is counted in two forms: grouped in classes (herbicide, fungicide, acaricide, bactericide and inseticide) and specified by the chemical name of the active ingredient. Results and Discussion  A simplified inventory useful for different purposes is generated with the principles described in this paper. The exact fate of each pesticide, fertilizer or corrective or assumptions can be further associated to impact categories as nutriphication, human health, natural resources depletion, ecological toxicity, etc. In this approach the mass balance was focused in the grain or fruit growth and not in the plant or tree as a whole, considering basically the elementary composition of the product and the photosynthesis principle. Despite agricultural LCAs performed in different countries have been published, neither of them considers the carbon capture by the agricultural products during their growth. Conclusions  This method is based on well accepted universal principles of stoichiometry applied to the grain or fruit growth. Minimum estimations were introduced in this approach, which produces ‘clean inventories’, with comparable results between different studies. The generated inventory can be gradually improved as the understanding about each emission fate is known, producing a valid methodology for actual and future knowledge about the fate of tillage emissions. The inventory results of this method can be employed by different stakeholders as farmers, environment managers, decision makers and traders, with valuable environmental parameters for evaluating the environmental performance of their products and also for introducing improvements on their systems, without however to exhibit any particular data.