Attributional and consequential LCA in the ILCD handbook

Purpose

This discussion article aims to highlight two problematic aspects in the International Reference Life Cycle Data System (ILCD) Handbook: its guidance to the choice between attributional and consequential modeling and to the choice between average and marginal data as input to the life cycle inventory (LCI) analysis.

Methods

We analyze the ILCD guidance by comparing different statements in the handbook with each other and with previous research in this area.

Results and discussion

We find that the ILCD handbook is internally inconsistent when it comes to recommendations on how to choose between attributional and consequential modeling. We also find that the handbook is inconsistent with much of previous research in this matter, and also in the recommendations on how to choose between average and marginal data in the LCI.

Conclusions

Because of the inconsistencies in the ILCD handbook, we recommend that the handbook be revised.

     

The EcoSpold 2 format—why a new format?

Purpose

This paper introduces the new EcoSpold data format for life cycle inventory (LCI).

Methods

A short historical retrospect on data formats in the life cycle assessment (LCA) field is given. The guiding principles for the revision and implementation are explained. Some technical basics of the data format are described, and changes to the previous data format are explained.

Results

The EcoSpold 2 data format caters for new requirements that have arisen in the LCA field in recent years.

Conclusions

The new data format is the basis for the Ecoinvent v3 database, but since it is an open data format, it is expected to be adopted by other LCI databases. Several new concepts used in the new EcoSpold 2 data format open the way for new possibilities for the LCA practitioners and to expand the application of the datasets in other fields beyond LCA (e.g., Material Flow Analysis, Energy Balancing).

     

The Quebec Life Cycle Inventory Database Project

Purpose

Life cycle assessment (LCA) in Quebec (Canada) is increasingly important. Yet, studies often still need to rely on foreign life cycle inventory (LCI) data. The Quebec government invested in the creation of a Quebec LCI database. The approach is to work as an ecoinvent “National Database Initiative” (NDI), whereby the Quebec database initiative uses and contributes to the ecoinvent database. The paper clarifies the relationship between ecoinvent and the Quebec NDI and provides details on prioritization and data collection.

Methods

The first steps were to select a partner database provider and to work out the modalities of the partnership. The main criterion for partner selection was database transparency, i.e., availability of unit process data (gate-to-gate), necessary for database adaptation. This and other criteria, such as free access to external reviewers, conservation of dataset copyright, seamless embedding of datasets, and overall database sophistication, pointed to ecoinvent. Once started, the NDI project proceeded as follows: (1) data collection was prioritized based on several criteria; (2) some datasets were “recontextualized,” i.e., existing datasets were duplicated and relocated in Quebec and linked to datasets representing regional suppliers, where relevant; (3) new datasets were created; and (4) Canadian environmentally extended supply-use tables were created for the ecoinvent IO repository.

Results and discussion

Prioritization identified 500 candidate datasets for recontextualization, based on the relative importance of relative contribution of direct electricity consumption to cradle-to-gate impacts, and 12 key sectors from which about 450 data adaptation or collection projects were singled out. Data collection and private sector solicitation are underway. Private sector participation is highly variable. A number of communication tools have been elaborated and a solicitation team formed to palliate this obstacle. The new ecoinvent database protocol (Weidema et al. 2011) increases the amount of information that is required to create a dataset, which can lengthen or, in extreme cases, impede dataset creation. However, this new information is required for the new database functionalities (e.g., providing multiple system models based on the same unit process data and regionalized LCA).

Conclusions

Being an NDI is advantageous for the Quebec LCI database project on multiple levels. By conserving dataset copyright, the NDI remains free to spawn or support other LCI databases. Embedding datasets in ecoinvent enables the generation of LCI results from “day 1.” The costs of IT infrastructure and data review are null. For these reasons, and because every NDI improves the global representativity of ecoinvent, we recommend other regional or national database projects work as NDIs.

     

Life cycle inventories of electricity generation and power supply in version 3 of the ecoinvent database—part II: electricity markets

Purpose

Representative, consistent and up-to-date life cycle inventories (LCI) of electricity supply are key elements of ecoinvent as an LCI background database since these are often among the determining factors with regard to life cycle assessment (LCA) results. ecoinvent version 3 (ev3) offers new LCI data of power supply (electricity markets) in 71 geographies. This article gives an overview of these electricity markets and discusses new ecoinvent features in the context of power supply.

Methods

The annual geography- and technology-specific electricity production for the year 2008 specifies the technology shares on the high-, medium- and low-voltage level electricity markets. Data are based on IEA statistics. Different voltage levels are linked by transformation activities. Region-specific electricity losses due to power transmission and voltage transformation are considered in the market and transformation activities. The majority of the 71 power markets are defined by national boundaries. The attributional ecoinvent system model in ev3 with linking to average current suppliers results in electricity markets supplied by all geography-specific power generation technologies and electricity imports, while the consequential system model generates markets only linked to unconstrained suppliers.

Results and discussion

The availability of LCI data for 71 electricity markets in ev3 covering 50 countries reduces the “Rest-of-the-World” electricity supply not covered by country- or region-specific inventories to 17 % for the year 2008. Specific power supply activities for all countries contributing more than 1 % to global electricity production are available. The electricity markets show large variations concerning contributions from specific technologies and energy carriers. Imports can substantially change the national/regional power mix, especially in small markets. Large differences can also be observed between the electricity markets in the attributional and the consequential database calculation. Region-specific total power losses between production on the high voltage level and consumer on the low voltage level are on the order of 2.5–23 %.

Conclusions

Electricity supply mixes (electricity markets) in the ecoinvent database have been updated and substantially extended for v3. Inventories for electricity supply in all globally important economies are available with geography-specific technology and market datasets which will contribute to increasing quality and reducing uncertainties in LCA studies worldwide and to allow more accurate estimation of environmental burdens from global production chains. Future work should focus on improving the details of country-specific data, implementation of more countries into the database, splitting of large countries into smaller regions and on developing a more sophisticated approach specifying country-specific electricity mixes in consequential system models.

     

A scalable life cycle inventory of an electrical automotive traction machine—Part I: design and composition

Purpose

A scalable life cycle inventory (LCI) model of a permanent magnet electrical machine, containing both design and production data, has been established. The purpose is to contribute with new and easy-to-use data for LCA of electric vehicles by providing a scalable mass estimation and manufacturing inventory for a typical electrical automotive traction machine. The aim of this article (part I of two publications) is to present the machine design, the model structure, and an evaluation of the models’ mass estimations.

Methods

Data for design and production of electrical machines has been compiled from books, scientific papers, benchmarking literature, expert interviews, various specifications, factory records, and a factory site visit. For the design part, one small and one large reference machine were constructed in a software tool, which linked the machines’ maximum ability to deliver torque to the mass of its electromagnetically active parts. Additional data for remaining parts was then gathered separately to make the design complete. The two datasets were combined into one model, which calculates the mass of all motor subparts from an input of maximum power and torque. The range of the model is 20–200 kW and 48–477 Nm. The validity of the model was evaluated through comparison with seven permanent magnet electrical traction machines from established brands.

Results and discussion

The LCI model was successfully implemented to calculate the mass content of 20 different materials in the motor. The models’ mass estimations deviate up to 21% from the examples of real motors, which still falls within expectations for a good result, considering a noticeable variability in design, even for the same machine type and similar requirements. The model results form a rough and reasonable median in comparison to the pattern created by all data points. Also, the reference motors were assessed for performance, showing that the electromagnetic efficiency reaches 96–97%.

Conclusions

The LCI model relies on thorough design data collection and fundamental electromagnetic theory. The selected design has a high efficiency, and the motor is suitable for electric propulsion of vehicles. Furthermore, the LCI model generates representative mass estimations when compared with recently published data for electrical traction machines. Hence, for permanent magnet-type machines, the LCI model may be used as a generic component estimation for LCA of electric vehicles, when specific data is lacking.

     

What distribution function do life cycle inventories follow?

Purpose

Life cycle inventory (LCI) results are often assumed to follow a lognormal distribution, while a systematic study that identifies the distribution function that best describes LCIs has been lacking. This paper aims to find the distribution function that best describes LCIs using Ecoinvent v3.1 database using a statistical approach, called overlapping coefficient analysis.

Methods

Monte Carlo simulation is applied to characterize the distribution of aggregate LCIs. One thousand times of simulated LCI results are generated based on the unit process-level parametric uncertainty information, from each of which 1000 randomly chosen data points are extracted. The 1 million data points extracted undergo statistical analyses including Shapiro-Wilk normality test and the overlapping coefficient analysis. The overlapping coefficient is a measure used to determine the shared area between the distribution of the simulated LCI results and three possible distribution functions that can potentially be used to describe them including lognormal, gamma, and Weibull distributions.

Results and discussion

Shapiro-Wilk normality test for 1000 samples shows that average p value of log-transformed LCI results is 0.18 at 95 % confidence level, accepting the null hypothesis that LCI results are lognormally distributed. The overlapping coefficient analysis shows that lognormal distribution best describes the distribution of LCI results. The average of overlapping coefficient (OVL) for lognormal distribution is 95 %, while that for gamma and Weibull distributions are 92 and 86 %, respectively.

Conclusions

This study represents the first attempt to calculate the stochastic distributions of the aggregate LCIs covering the entire Ecoinvent 3.1 database. This study empirically shows that LCIs of Ecoinvent 3.1 database indeed follow a lognormal distribution. This finding can facilitate more efficient storage and use of uncertainty information in LCIs and can reduce the demand for computational power to run Monte Carlo simulation, which currently relies on unit process-level uncertainty data.

     

Giving a scientific basis for uncertainty factors used in global life cycle inventory databases: an algorithm to update factors using new information

Purpose

Life cycle inventory (LCI) databases provide generic data on exchange values associated with unit processes. The “ecoinvent” LCI database estimates the uncertainty of all exchange values through the application of the so-called pedigree approach. In the first release of the database, the used uncertainty factors were based on experts’ judgments. In 2013, Ciroth et al. derived empirically based factors. These, however, assumed that the same uncertainty factors could be used for all industrial sectors and fell short of providing basic uncertainty factors. The work presented here aims to overcome these limitations.

Methods

The proposed methodological framework is based on the assessment of more than 60 data sources (23,200 data points) and the use of Bayesian inference. Using Bayesian inference allows an update of uncertainty factors by systematically combining experts’ judgments and other information we already have about the uncertainty factors with new data.

Results and discussion

The implementation of the methodology over the data sources results in the definition of new uncertainty factors for all additional uncertainty indicators and for some specific industrial sectors. It also results in the definition of some basic uncertainty factors. In general, the factors obtained are higher than the ones obtained in previous work, which suggests that the experts had initially underestimated uncertainty. Furthermore, the presented methodology can be applied to update uncertainty factors as new data become available.

Conclusions

In practice, these uncertainty factors can systematically be incorporated in LCI databases as estimates of exchange value uncertainty where more formal uncertainty information is not available. The use of Bayesian inference is applied here to update uncertainty factors but can also be used in other life cycle assessment developments in order to improve experts’ judgments or to update parameter values when new data can be accessed.

     

Assessment of local wood species used for the manufacture of cookware and the perception of chemical benefits and chemical hazards associated with their use in Kumasi,Ghana

Background

Historical proven wood species have no reported adverse health effect associated with its past use. Different historical proven species have traditionally been used to manufacture different wooden food contact items. This study uses survey questionnaires to assess suppliers’, manufacturers’, retailers’ and consumers’ (end-users’) preferences for specific wood species, to examine the considerations that inform these preferences and to investigate the extent of awareness of the chemical benefits and chemical hazards associated with wooden food contact material use.

Methods

Through the combined use of a cross sectional approach and a case study design, 25 suppliers, 25 manufacturers, 25 retailers and 125 consumers (end-users) of wooden food contact materials in four suburbs in Kumasi Metropolitan Area (Anloga junction, Ahinsan Bus Stop, Ahwia-Pankrono and Race Course) and Ashanti Akyim Agogo in the Ashanti Akyim North District of the Ashanti Region were administered with closed ended questionnaires. The questionnaires were prepared in English, but local language, Twi, was used to translate and communicate the content of the questionnaire where necessary.

Results

Suppliers’, manufacturers’ and retailers’ preferences for specific wood species for most wooden cookware differed from that of consumers (end-users). But all respondent groups failed to indicate any awareness of chemical benefits or chemical hazards associated with either the choice of specific wood species for specific wooden cookware or with the general use of wooden food contact materials. The lack of appreciation of chemical benefits or hazards associated with active principles of wooden cookware led to heavy reliance of consumers (end-users) on the wood density, price, attractive grain pattern and colour or on the judgement of retailers in their choice of specific species for a wooden cookware.

Conclusion

This study contributes some practical suggestions to guide national policy development on improvement in quality of available wooden food contact materials in Ghana.

     

A lost opportunity for science: journals promote data sharing in metabolomics but do not enforce it

Introduction

Data sharing is being increasingly required by journals and has been heralded as a solution to the ‘replication crisis’.

Objectives

(i) Review data sharing policies of journals publishing the most metabolomics papers associated with open data and (ii) compare these journals’ policies to those that publish the most metabolomics papers.

Methods

A PubMed search was used to identify metabolomics papers. Metabolomics data repositories were manually searched for linked publications.

Results

Journals that support data sharing are not necessarily those with the most papers associated to open metabolomics data.

Conclusion

Further efforts are required to improve data sharing in metabolomics.

     

A study on the environmental aspects of WEEE plastic recycling in a Brazilian company

Purpose

The high consumption of electrical and electronic equipment motivated by the rapid technological advances seen over the years has lead to an increase in the generation of waste electrical and electronic equipment (WEEE). Such residues contain various dangerous substances and therefore deserve special attention. To that end, the Brazilian Policy on Solid Waste has provided guidelines on integrated and solid waste management, such as consumer electronics, aiming at their appropriate disposal and treatment through reverse logistics. In this context, the present work focuses on studying the recycling of some WEEE plastics.

Methods

This study was conducted using the methodological framework presented in the International Standard ISO 14040:2006 and aimed to determine the life cycle inventory (LCI) of a WEEE plastic recycling process in a company in Brazil. Having collected the data, it was possible to identify and quantify the environmental aspects caused by the recycling process of major plastics (acrylonitrile-butadiene-styrene (ABS) and high impact polystyrene (HIPS). The study was conducted in the only company in Brazil that operates WEEE plastic recycling in large scale.

Results and discussion

Some of the environmental aspects caused during the recycling process of the plastics under study were identified and quantified. As a result, besides presenting the inventory, it was also possible to determine a reduction in the consumption of energy and in CO₂ emissions. When compared to the production of virgin ABS and HIPS, the recycling processes for such plastics showed a reduction in energy consumption by approximately 90% for both plastics and a reduction in CO₂ emissions by approximately 84% for HIPS and 87% for ABS. The plastics recycled by the company retain over 90% of their virgin mechanical properties.

Conclusions

The study shows that recycling is highly relevant and that components present in WEEE received appropriate destination and treatment. Recycling avoids environmental impacts as it prevents WEEE from being disposed of in landfills and as the pellets of recycled plastics can re-enter the supply chain as raw materials. Considering the legislation in Brazil, the stage of collection/transport/treatment of WEEE conducted by the company under study presents strong indications of contributions to the environment, society, and economy of the country.

     

Thai national life cycle inventory readiness for product environmental footprint

Purpose

In the near future, the products of Thai industries and companies mainly producing parts and products for export to the European Union (EU) will require the Product Environmental Footprint (PEF) to assess the environmental performance and resource efficiency of products by using a life cycle perspective. The potential generic (often used interchangeably with background data) data have to be modified and improved for mandatory use in the product-specific and country-specific PEF database.

Methods

PEF is used as a tool for assessing the environmental burden of products and services for export to the EU. It requires both specific data from primary sources and generic data to fulfill assessment requirement. Accordingly, the Thai national life cycle inventory (LCI) database plays a key role in generic data that was used to evaluate the environmental performance of products. This paper presents the perspective of Thai data readiness for PEF in which the quality of LCI is the main issue of concern. The current situation of the Thai national LCI database was reviewed. Then, the gaps of data were addressed, and the gaps were also filled. Non-representative data and untreated waste are the selected issues that were presented in this paper.

Results and discussion

Many gaps were revealed for the Thai national LCI database because this database was developed based on ISO 14040/44, which may not be compliant with the PEF guide. The issues that have been selected for improvement are non-representative data and untreated waste because these gaps can offer inaccuracy concerning the environmental burden of products potentially leading to the reliability of products for export to the EU. However, the Thai national LCI database has not achieved the data quality aspects of the PEF, continuously improving the quality of data to meet the requirements of the PEF.

Conclusions

The lessons learned from the real-world situation of data quality development based on PEF requirements were extracted. The practical procedure and recommendations were transparent for drivers and researchers who would like to start with data quality issues and prepare for the EU single market.

     

Untargeted metabolomics suffers from incomplete raw data processing

Introduction

Untargeted metabolomics is a powerful tool for biological discoveries. To analyze the complex raw data, significant advances in computational approaches have been made, yet it is not clear how exhaustive and reliable the data analysis results are.

Objectives

Assessment of the quality of raw data processing in untargeted metabolomics.

Methods

Five published untargeted metabolomics studies, were reanalyzed.

Results

Omissions of at least 50 relevant compounds from the original results as well as examples of representative mistakes were reported for each study.

Conclusion

Incomplete raw data processing shows unexplored potential of current and legacy data.

     

Application of residual polysaccharide-degrading enzymes in dried shiitake mushrooms as an enzyme preparation in food processing

Objective

To examine the activities of residual enzymes in dried shiitake mushrooms, which are a traditional foodstuff in Japanese cuisine, for possible applications in food processing.

Results

Polysaccharide-degrading enzymes remained intact in dried shiitake mushrooms and the activities of amylase, β-glucosidase and pectinase were high. A potato digestion was tested using dried shiitake powder. The enzymes reacted with potato tuber specimens to solubilize sugars even under a heterogeneous solid-state condition and that their reaction modes were different at 38 and 50 °C.

Conclusion

Dried shiitake mushrooms have a potential use in food processing as an enzyme preparation.

     

KniMet: a pipeline for the processing of chromatography–mass spectrometry metabolomics data

Introduction

Data processing is one of the biggest problems in metabolomics, given the high number of samples analyzed and the need of multiple software packages for each step of the processing workflow.

Objectives

Merge in the same platform the steps required for metabolomics data processing.

Methods

KniMet is a workflow for the processing of mass spectrometry-metabolomics data based on the KNIME Analytics platform.

Results

The approach includes key steps to follow in metabolomics data processing: feature filtering, missing value imputation, normalization, batch correction and annotation.

Conclusion

KniMet provides the user with a local, modular and customizable workflow for the processing of both GC–MS and LC–MS open profiling data.

     

Identifying marginal suppliers of construction materials: consistent modeling and sensitivity analysis on a Belgian case

Purpose

The identification of marginal suppliers is a key element of consequential LCA. This study investigates how systematically the identification of marginal suppliers can be performed across different products, while maintaining consistent modeling choices. Some products relevant for the Belgian construction sector are taken as a case study.

Methods

To gain insight in the current practice of identifying marginal suppliers, 30 recent studies have been reviewed. Based on the findings of the review, a method was proposed to identify geographical market boundaries from trade data and sensitive suppliers from production data. Both retrospective and prospective approaches to anticipate the future effect of a change in demand were taken into account. The method was applied to compute both a retrospective and a prospective marginal supplier’s mix per product. Finally, the effect of the modeling choices on the size of geographical market boundaries and marginal mixes was estimated via regression analysis.

Results and discussion

The forecasts and marginal mixes obtained matched with those from the existing literature, although clear differences in results are observed between the retrospective and prospective approach. Deviations from default assumptions in LCA were observed as well, such as large regional geographical markets for cement and aggregates instead of local ones. The statistical sensitivity analysis showed that identifying geographical market boundaries has the largest effect on the final marginal mix and that these markets are relative stable over time.

Conclusions

The proposed method and corresponding sensitivity analysis is an attempt to gain insight into the effect of modeling choices in the context of the identification of marginal suppliers for consequential LCA. It can in principle be applied to any product for which trade and production data are available. The proposed method helps to identify marginal mixes on a consistent and transparent way, to improve the robustness of the results in future consequential LCAs.

     

Pseudo current density maps of electrophysiological heart,nerve or brain function and their physical basis

Background

In recent years the visualization of biomagnetic measurement data by so-called pseudo current density maps or Hosaka-Cohen (HC) transformations became popular.

Methods

The physical basis of these intuitive maps is clarified by means of analytically solvable problems.

Results

Examples in magnetocardiography, magnetoencephalography and magnetoneurography demonstrate the usefulness of this method.

Conclusion

Hardware realizations of the HC-transformation and some similar transformations are discussed which could advantageously support cross-platform comparability of biomagnetic measurements.

     

Identifying marginal supplying countries of wood products via trade network analysis

Purpose

The consequential inventory modeling approach for life cycle assessment implies that an increase in the demand for a specific product is met by the marginal suppliers within the market. The identification of marginal suppliers is however complicated by difficulties in defining appropriate geographical market delimitations. In this study, an advanced system thinking approach is proposed to address this challenge in the identification of marginal supplying countries of wood products.

Methods

Groups of countries which represent geographical markets are identified from trade data by using a network analysis-based clustering technique. Within these markets, marginal supplying countries are selected based on positive historical increments. The analysis covers 12 different products and all countries in the world using trade data for the period 1998–2013.

Results and discussion

Global indices allow differentiating how product-specific trade networks are separated into communities and how interconnected these networks are. Large differences between products and minor differences between trade years are observed. Communities identified for each product tend to overlap with existing geographical regions and seem thus realistic. By combining this information with product-specific production increment rankings, marginal supplying countries of wood products were identified.

Conclusions

The identified geographical market delimitation is a key for proper consequential life cycle assessment (LCA) inventory modeling in areas such as timber-based construction and biomass-based energy production. The method can in principle be applied to any product for which trade network data are available and ideally should be accompanied by a detailed analysis of technological constrains within the identified supplying country.

     

Feed-forward neural network model for hunger and satiety related VAS score prediction

Background

An artificial neural network approach was chosen to model the outcome of the complex signaling pathways in the gastro-intestinal tract and other peripheral organs that eventually produce the satiety feeling in the brain upon feeding.

Methods

A multilayer feed-forward neural network was trained with sets of experimental data relating concentration-time courses of plasma satiety hormones to Visual Analog Scales (VAS) scores. The network successfully predicted VAS responses from sets of satiety hormone data obtained in experiments using different food compositions.

Results

The correlation coefficients for the predicted VAS responses for test sets having i) a full set of three satiety hormones, ii) a set of only two satiety hormones, and iii) a set of only one satiety hormone were 0.96, 0.96, and 0.89, respectively. The predicted VAS responses discriminated the satiety effects of high satiating food types from less satiating food types both in orally fed and ileal infused forms.

Conclusions

From this application of artificial neural networks, one may conclude that neural network models are very suitable to describe situations where behavior is complex and incompletely understood. However, training data sets that fit the experimental conditions need to be available.