Electric passenger car transport and passenger car life cycle inventories in ecoinvent version 3

Purpose

Due to the large environmental challenges posed by the transport sector, reliable and state-of-the art data for its life cycle assessment is essential for enabling a successful transition towards more sustainable systems. In this paper, the new electric passenger car transport and vehicle datasets, which have been developed for ecoinvent version 3, are presented.

Methods

The new datasets have been developed with a strong modular approach, defining a hierarchy of datasets corresponding to various technical components in the vehicle. A vehicle is therefore modelled by linking together the various component datasets. Also, parameters and mathematical formulas have been introduced in order to define the amount of exchanges in the datasets through common transport and vehicle characteristics. This supports users in the choice of the amount of exchanges and enhances the transparency of the dataset.

Results

The new transport dataset describes the transport over 1 km with a battery electric passenger car taking into account the vehicle production and end of life, the energy consumption due to the use phase, non-exhaust emissions, maintenance and road infrastructure. The dataset has been developed and is suitable for a compact class vehicle.

Conclusions

A new electric passenger car transport dataset has been developed for version 3 of the ecoinvent database which exploits modularisation and parameters with the aim of facilitating users in adapting the data to their specific needs. Apart from the direct use of the transport dataset for background data, the various datasets for the different components can also be used as building blocks for virtual vehicles.

     

Well-to-wheel GHG emissions and mitigation potential from light-duty vehicles in Macau

Purpose

The rapid growth of vehicle sales and usage has highlighted the need for greenhouse gas (GHG) emission reduction in Macau, a special administrative region (SAR) of China. As the most primary vehicle type, light-duty vehicles (LDV, including light-duty gasoline vehicles (LDGVs) and light-duty diesel vehicles (LDDVs)) play a key role in promoting the GHG reduction and development of green transportation system in Macau.

Methods

This study, on the basis of real-world tested and statistical data, firstly performed a streamlined life-cycle assessment (SLCA) on LDVs, to evaluate the potential GHG emissions and reduction through shifting to hybrid electric vehicles (HEVs) and electric vehicles (EVs).

Results and discussion

The results show that the mean GHG emissions from the LDGVs, LDDVs, and HEVs per 100 km were 25.16, 20.30, and 15.00 kg CO₂ eq, respectively. Under the current electricity mix in Macau, EVs with the emissions of 12.39 kg CO₂ eq/100 km can achieve a significant GHG emission reduction of LDVs in Macau. The total GHG emissions from LDVs increased from 124.99 to 247.82 thousand metric tons over the periods 2001–2014, with a 5.42% annual growth rate. A scenario analysis indicated that the development of HEVs and EVs—especially EVs—has the potential to control the GHG emissions from LDVs. Under the electricity mix of natural gas (NG) and solar energy (SE), the GHG emissions from EVs would drop by about 22 and 28%, respectively, by 2030.

Conclusions

This study develops a useful approach to evaluate the potential GHG emissions and its reduction strategies in Macau. All the obtained results could be useful for decision makers, providing robust support for drawing up an appropriate plan for improving green transportation systems in Macau.

     

A new hybrid method for reducing the gap between WTW and LCA in the carbon footprint assessment of electric vehicles

Purpose

The well-to-wheel (WTW) methodology is widely used for policy support in road transport. It can be seen as a simplified life cycle assessment (LCA) that focuses on the energy consumption and CO₂ emissions only for the fuel being consumed, ignoring other stages of a vehicle’s life cycle. WTW results are therefore different from LCA results. In order to close this gap, the authors propose a hybrid WTW+LCA methodology useful to assess the greenhouse gas (GHG) profiles of road vehicles.

Methods

The proposed method (hybrid WTW+LCA) keeps the main hypotheses of the WTW methodology, but integrates them with LCA data restricted to the global warming potential (GWP) occurring during the manufacturing of the battery pack. WTW data are used for the GHG intensity of the EU electric mix, after a consistency check with the main life cycle impact (LCI) sources available in literature.

Results and discussion

A numerical example is provided, comparing GHG emissions due to the use of a battery electric vehicle (BEV) with emissions from an internal combustion engine vehicle. This comparison is done both according to the WTW approach (namely the JEC WTW version 4) and the proposed hybrid WTW+LCA method. The GHG savings due to the use of BEVs calculated with the WTW-4 range between 44 and 56 %, while according to the hybrid method the savings are lower (31–46 %). This difference is due to the GWP which arises as a result of the manufacturing of the battery pack for the electric vehicles.

Conclusions

The WTW methodology used in policy support to quantify energy content and GHG emissions of fuels and powertrains can produce results closer to the LCA methodology by adopting a hybrid WTW+LCA approach. While evaluating GHG savings due to the use of BEVs, it is important that this method considers the GWP due to the manufacturing of the battery pack.

     

Integrated environmental and economic assessment of current and future fuel cell vehicles

Purpose

Light-duty vehicles contribute considerably to global greenhouse gas emissions. Fuel cell vehicles (FCVs) may play a key role in mitigating these emissions without facing the same limitations in range and refueling time as battery electric vehicles (BEVs). In this study, we assess the environmental impacts and costs of a polymer electrolyte membrane fuel cell system (FCS) for use in light-duty FCVs and integrate these results into a comparative evaluation between FCVs, BEVs, and internal combustion engine vehicles (ICEVs).

Methods

We conduct a detailed life cycle assessment (LCA) and cost assessment for the current state of the technology and two future scenarios for technological development. We compile a detailed and consistent inventory for the FCS by systematically disassembling and integrating information found in cost studies. For the vehicle-level comparison, we use models to ensure that vehicle size, performance, and fuel consumption are unbiased between vehicle types and consistent with the scenarios for technological development.

Results and discussion

Our results show that FCVs can decrease life cycle greenhouse gas emissions by 50 % compared to gasoline ICEVs if hydrogen is produced from renewable electricity, thus exhibiting similar emission levels as BEVs that are charged with the same electricity mix. If hydrogen is produced by natural gas reforming, FCVs are found to offer no greenhouse gas reductions, along with higher impacts in several other environmental impact categories. A major contributor to these impacts is the FCS, in particular the platinum in the catalyst and the carbon fiber in the hydrogen tank. The large amount of carbon fiber used in the tank was also the reason why we found that FCVs may not become fully cost competitive with ICEVs or BEVs, even when substantial technological development and mass production of all components is assumed.

Conclusions

We conclude that FCVs only lead to lower greenhouse gas emissions than ICEVs if their fuel is sourced from renewable energy, as is the case with BEVs. FCVs are an attractive alternative to ICEVs in terms of vehicle performance criteria such as range and refueling time. However, the technological challenges associated with reducing other environmental impacts and costs of FCVs seem to be as large, if not larger, than those associated with the capacity and costs of batteries for BEVs—even when not taking into account the efforts required to build a hydrogen infrastructure network for road transportation.

     

Estimating the global warming emissions of the LCAXVII conference: connecting flights matter

Purpose

Conferences are an important element of scientific activity but can also be a major cause of environmental burden. With this in mind, we analysed the global warming emissions of the 2017 annual conference of the American Center for Life Cycle Assessment (ACLCA), in order to estimate the carbon footprint and identify potential ways to reduce it.

Methods

We used survey data from participants as well as literature sources to complete an attributional assessment of the greenhouse gas emissions per participant. A method to calculate the ‘ideal’ location is proposed, which can be used to identify ‘unreasonably’ distant conference locations.

Results and discussion

The average emissions per participant were found to be 952 kg CO_2eq, but with a large variability due to differences in travelled distance. Connecting flights were found to increase emissions up to 32% compared to direct flights, due to the increased number of take-offs and landings.

Conclusions

Results indicate that future studies should use distance-dependent flight emissions to increase the accuracy of the assessment. Some measures, such as meat-free menus, had a relatively minor contribution to emission reductions, but could be important as scientists advocating for the reduction of environmental burden should lead by example.

     

Analysis of greenhouse gas emissions related to aluminium transport applications

Background, aim and scope

Climate change is a subject of growing global concern. Based on International Energy Agency (IEA 2004) research, about 19% of the greenhouse gas emissions from fuel combustion are generated by the transportation sector, and its share is likely to grow. Significant increases in the vehicles fleets are expected in particular in China, India, the Middle East and Latin America. As a result, reducing vehicle fuel consumption is most essential for the future. The reduction of the vehicle weight, the introduction of improved engine technologies, lower air friction, better lubricants, etc. are established methods of improving fuel efficiency, reducing energy consumption and greenhouse gas emissions. Continued progress will be required along all these fronts with light-weighting being one of the most promising options for the global transport sector. This paper quantifies greenhouse gas savings realised from light-weighting cars with aluminium based on life cycle assessment methodology. The study uses a pragmatic approach to assess mass reduction by comparing specific examples of components meeting identical performance criteria. The four examples presented in this analysis come from practical applications of aluminium. For each case study, the vehicle manufacturer has supplied the respective masses of the aluminium and the alternative component.

Material and methods

A full life cycle assessment with regards to greenhouse gas emissions and savings has been carried out for different aluminium applications in cars as compared to the same applications in steel or cast iron. The case studies reference real cases, where aluminium is actually used in series production. The studies are based on a greenhouse gas lifecycle model, which has been developed following the ISO standard 14040 framework. For each component, sensitivity analysis is applied to determine the impact of lifetime driving distance, driving characteristics (impact of air friction) and recycling rate.

Results

Life cycle results show that in automotive applications, each kilogram of aluminium replacing mild steel, cast iron or high strength steel saves, depending on the specific case (bumper and motor block of a compact car, front hood of a large family car, body-in white of a luxury car), between 13 and 20 kg of greenhouse gas emissions.

Discussion

The performed sensitivity analysis finds that even with ‘worst case’ scenarios savings are still significant.

Conclusions

The results not only demonstrate significant benefits of aluminium with regard to greenhouse gas savings but also show that these are very sensitive to variations of the recycling rate, the life-time driving distance and the driving behaviour.

Recommendations and perspectives

Good care is needed to gather life-cycle data and to make informed estimates, where no data are available. Furthermore, greenhouse gas savings for additional components should be calculated using this life cycle model to sustain the findings.

     

Iridoid and phenylethanoid/phenylpropanoid metabolite profiles of <Emphasis Type="Italic">Scrophularia</Emphasis> and <Emphasis Type="Italic">Verbascum</Emphasis> species used medicinally in North America

Introduction

Botanicals containing iridoid and phenylethanoid/phenylpropanoid glycosides are used worldwide for the treatment of inflammatory musculoskeletal conditions that are primary causes of human years lived with disability, such as arthritis and lower back pain.

Objectives

We report the analysis of candidate anti-inflammatory metabolites of several endemic Scrophularia species and Verbascum thapsus used medicinally by peoples of North America.

Methods

Leaves, stems, and roots were analyzed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and partial least squares-discriminant analysis (PLS-DA) was performed in MetaboAnalyst 3.0 after processing the datasets in Progenesis QI.

Results

Comparison of the datasets revealed significant and differential accumulation of iridoid and phenylethanoid/phenylpropanoid glycosides in the tissues of the endemic Scrophularia species and Verbascum thapsus.

Conclusions

Our investigation identified several species of pharmacological interest as good sources for harpagoside and other important anti-inflammatory metabolites.

     

Lightweighting in light commercial vehicles: cradle-to-grave life cycle assessment of a safety-relevant component

Purpose

Currently, the reduction of weight in automotive is a very important topic in order to lower the air pollution. In this context, the purpose of the present paper was to analyze a real case study through a comparison of the environmental sustainability between a conventional steel crossbeam for light commercial vehicles and an innovative lightweight aluminum one.

Methods

For both scenarios, a cradle-to-grave life cycle assessment methodology and a sensitivity analysis has been used through the study of the following phases: mineral extraction, component manufacturing, use on vehicle, and end of life. In particular, many primary data and a complete vehicle model simulation with three different European driving cycles have been used in order to reach the highest possible level of accuracy during the analysis.

Results and discussion

Regarding the manufacturing phase, the aluminum component’s production gave the highest impact because of the high energy required in the mineral reduction. Anyway, this stage of the analysis had a low effect on the entire LCA, because the benefit of weight reduction during vehicle use showed a strongly higher contribution. The urban driving cycle had the most relevant impact, as a consequence of the frequent start and stop operations and the longest time with engine at idle speed, while the extra-urban cycle is the less demanding due to its higher average speed and no start and stop.

Conclusions

In conclusion, the present research demonstrated the environmental importance of the lightweight for an actual case study in the commercial vehicles field.

     

Identification of cancer subtypes from single-cell RNA-seq data using a consensus clustering method

Background

Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.

Methods

In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.

Results

Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.

Conclusions

Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.

     

<Emphasis Type="Italic">massPix</Emphasis>: an R package for annotation and interpretation of mass spectrometry imaging data for lipidomics

Introduction

Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.

Objectives

We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.

Methods

massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.

Results

Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.

Conclusion

massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.

     

A novel approach to minimize false discovery rate in genome-wide data analysis

Background

High-throughput technologies, such as DNA microarray, have significantly advanced biological and biomedical research by enabling researchers to carry out genome-wide screens. One critical task in analyzing genome-wide datasets is to control the false discovery rate (FDR) so that the proportion of false positive features among those called significant is restrained. Recently a number of FDR control methods have been proposed and widely practiced, such as the Benjamini-Hochberg approach, the Storey approach and Significant Analysis of Microarrays (SAM).

Methods

This paper presents a straight-forward yet powerful FDR control method termed miFDR, which aims to minimize FDR when calling a fixed number of significant features. We theoretically proved that the strategy used by miFDR is able to find the optimal number of significant features when the desired FDR is fixed.

Results

We compared miFDR with the BH approach, the Storey approach and SAM on both simulated datasets and public DNA microarray datasets. The results demonstrated that miFDR outperforms others by identifying more significant features under the same FDR cut-offs. Literature search showed that many genes called only by miFDR are indeed relevant to the underlying biology of interest.

Conclusions

FDR has been widely applied to analyzing high-throughput datasets allowed for rapid discoveries. Under the same FDR threshold, miFDR is capable to identify more significant features than its competitors at a compatible level of complexity. Therefore, it can potentially generate great impacts on biological and biomedical research.

Availability

If interested, please contact the authors for getting miFDR.

     

Bridge processes: a solution for LCI datasets independent of background databases

Introduction

New platforms are emerging that enable more data providers to publish life cycle inventory data.

Background

Providing datasets that are not complete LCA models results in fragments that are difficult for practitioners to integrate and use for LCA modeling. Additionally, when proxies are used to provide a technosphere input to a process that was not originally intended by the process authors, in most LCA software, this requires modifying the original process.

Results

The use of a bridge process, which is a process created to link two existing processes, is proposed as a solution.

Discussion

Benefits to bridge processes include increasing model transparency, facilitating dataset sharing and integration without compromising original dataset integrity and independence, providing a structure with which to make the data quality associated with process linkages explicit, and increasing model flexibility in the case that multiple bridges are provided. A drawback is that they add additional processes to existing LCA models which will increase their size.

Conclusions

Bridge processes can be an enabler in allowing users to integrate new datasets without modifying them to link to background databases or other processes they have available. They may not be the ideal long-term solution but provide a solution that works within the existing LCA data model.

     

Potential emission savings from refrigeration and air conditioning systems by using low GWP refrigerants

Purpose

Refrigeration and air conditioning systems have high, negative environmental impacts due to refrigerant charge leaks from the system and their corresponding high global warming potential. Thus, many efforts are in progress to obtain suitable low GWP alternative refrigerants and more environmentally friendly systems for the future. The system’s life cycle climate performance (LCCP) is a widespread metric proposed for the evaluation of the system’s environmental impact.

Methods

In this paper, the potential emission reductions in the commercial refrigeration and residential air conditioning systems, made possible by shifting towards more environmentally friendly refrigerants in the US, are presented. First, the current LCCP of the most common commercial refrigeration and residential air conditioning systems is calculated. Then, the LCCP of the baseline systems, when using the potential low GWP alternative refrigerants, is presented. This helps to determine the systems which have the highest potential for emission reductions.

Results and discussion

By shifting from the baseline refrigerants, R-404A and R-410A, to the suggested low GWP refrigerants, N-40 and L-41a, in the commercial refrigeration and residential HVAC systems, respectively, a combined drop of 30.43 % in the total emissions (i.e., total equivalent mass of emissions in kg CO_2eq) is obtained. This results from a 50.5 and 28.01 % drop in total emissions from supermarket refrigeration systems and residential air conditioning, respectively.

Conclusions

Shifting to lower GWP refrigerants in the refrigeration and air conditioning systems helps to reduce the total emissions and negative environmental impacts of these systems. Shifting to a secondary circuit commercial refrigeration system using N-40/L-40 and residential air conditioning and heat pump systems using L-41a helps in reducing the total emissions.

     

rDolphin: a GUI R package for proficient automatic profiling of 1D <Superscript>1</Superscript>H-NMR spectra of study datasets

Introduction

Adoption of automatic profiling tools for ¹H-NMR-based metabolomic studies still lags behind other approaches in the absence of the flexibility and interactivity necessary to adapt to the properties of study data sets of complex matrices.

Objectives

To provide an open source tool that fully integrates these needs and enables the reproducibility of the profiling process.

Methods

rDolphin incorporates novel techniques to optimize exploratory analysis, metabolite identification, and validation of profiling output quality.

Results

The information and quality achieved in two public datasets of complex matrices are maximized.

Conclusion

rDolphin is an open-source R package (http://github.com/danielcanueto/rDolphin) able to provide the best balance between accuracy, reproducibility and ease of use.

     

Vehicle lightweighting through the use of molybdenum-bearing advanced high-strength steels (AHSS)

Purpose

The past two decades have seen growing pressure on vehicle manufacturers to reduce the environmental impact of their vehicles. One effective way to improve fuel efficiency and lower tailpipe emissions is to use advanced high-strength steels (AHSS) that offer equal strength and crash resistance at lower mass. The present study assesses the life cycle environmental impacts of two steel grades considered for the B-pillar in the Ford Fusion: A press-hardened boron steel design as used in the previous model of the vehicle and a hydroformed component made from a mix of the molybdenum-bearing dual phase steels DP800 and DP1000.

Methods

Information related to the component masses and grades was provided by Ford. Process models for the steelmaking process, finishing, forming, vehicle use and end of life were created in the GaBi LCA software tool. Sensitivity analyses were conducted on the impact of the hydroforming process for the new component, for which only proxy data were available and on the mix of DP800 and DP1000 in the B-pillar. Results have been presented for the environmental impact categories deemed most relevant to vehicle use.

Results and discussion

The life cycle assessment showed that the new DP800/DP1000 B-pillar design has a lower impact for the environmental impact categories assessed. Overall, the global warming potential (GWP) of the new DP800/DP1000 design was 29 % lower than the boron steel design over the full life cycle of the vehicle. The use phase was found to be the major source of environmental impacts, accounting for 93 % of the life cycle GWP impact. The 4 kg weight saving accounts for the majority of the difference in impacts between the two B-pillar designs. Impacts from manufacturing were also lower for the new design for all of the impact categories assessed despite the higher alloy content of the steel. A sensitivity analysis of the hydroforming process showed that even if impacts from forming were 100 % greater than for press hardening, the GWP from production of the new B-pillar design would still be lower than the boron steel version.

Conclusions and recommendations

The molybdenum-bearing DP1000/DP800 B-pillar was found to have lower life cycle and production impacts than the previous boron steel design. The assessment indicates that significant improvements in the environmental impacts associated with the body structure of vehicles could be made through the increased use of AHSS in vehicles without compromising crash performance.

     

Life cycle assessment of the construction of an unpaved road in an undisturbed tropical rainforest area in the vicinity of Manu National Park,Peru

Purpose

The main goal of this study is to provide a thorough environmental sustainability analysis of the construction, traffic, and maintenance of a 45.6-km section of the ‘Manu Road’, an unpaved tropical road that is currently being built in the vicinity of Manu National Park, in the region of Madre de Dios, Peru.

Methods

Life cycle assessment (LCA) using a set of 18 different impact categories was selected to conduct the environmental analysis. Modelling of machinery and vehicle emissions, as well as dust emissions, was performed to account for site-specific characteristics in terms of road construction and traffic. Similarly, direct land use changes were modelled with a particular emphasis on the decay of deforested biomass during construction. A set of different scenarios for the production system were considered to account for uncertainty regarding vehicle transit, amount of deforested biomass, and emission standards.

Results and discussion

Construction, maintenance, and traffic of the Manu Road varied considerably depending on methodological assumptions. Deforestation due to direct land use changes appears to be the main environmental hotspot in terms of climate change, whereas in the remaining impact categories, traffic was the main carrier of environmental burdens.

Conclusions

To the best of our knowledge, this study is the first LCA that focuses on the construction, maintenance, and traffic in a tropical rainforest environment. Despite the low requirements in terms of materials and technology to build this road, its derived environmental impacts are relevant in terms of climate change and particulate matter formation due to deforestation and dust emissions, respectively. Unpaved roads represent a relevant proportion of the entire road network worldwide, especially in developing tropical countries, playing a crucial role in the transportation of raw materials. Furthermore, road infrastructure is expected to expand explosively in the decades to come. Therefore, we suggest that LCA studies can and should improve the planning of road infrastructure in terms of life cycle inventories.

     

Deep learning for DNase I hypersensitive sites identification

Background

The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood.

Methods

Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens.

Results

Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens.

Conclusions

Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning.

     

NS-kNN: a modified <Emphasis Type="Italic">k</Emphasis>-nearest neighbors approach for imputing metabolomics data

Introduction

A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection of the analytical instrumentation.

Objectives

Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations.

Methods

We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data.

Results

Our results show that NS-kNN typically outperforms kNN when at least 20–30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR.

Conclusion

Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

     

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).