Regionalized LCA in practice: the need for a universal shapefile to match LCI and LCIA

The International Journal of Life Cycle Assessment -     

Temporal differentiation of background systems in LCA: relevance of adding temporal information in LCI databases

Purpose

Because the potential impacts of emissions and extractions can be sensitive to timing, the temporal aggregation of life cycle inventory (LCI) data has often been cited as a limitation in life cycle assessment (LCA). Until now, examples of temporal emission and extraction distributions were restricted to the foreground processes of product systems. The objective of this paper is to evaluate the relevance of considering the temporal distribution of the background system inventory.

Methods

The paper focuses on the global warming impact category for which so-called dynamic characterization factors (CFs) were developed and uses the ecoinvent v2.2 database as both an example database to which temporal information can be added and a source of product systems to test the relevance of adding temporal information to the background system. Temporal information was added to the elementary and intermediate exchanges of 22 % of the unit processes in the database. Using the enhanced structure path analysis (ESPA) method to generate temporally differentiated LCIs in conjunction with time-dependent global warming characterization factors, potential impacts were calculated for all 4,034 product systems in the ecoinvent database.

Results and discussion

Each time, the results were calculated for (1) systems in which temporal information was only added to the first two tiers, representing studies in which only the foreground system is temporally differentiated, and (2) systems in which temporal information was also added to the background system. For 8.6 % of the database product systems, adding temporal differentiation to background unit processes affected the global warming impact scores by more than 10 %. For most of the affected product systems, considering temporal information in the background unit processes decreased the global warming impact scores. The sectors that show most sensitivity to the temporal differentiation of background unit processes are associated with wood and biofuel sectors.

Conclusions

Even though the addition of temporal information to unit processes in LCI databases would not benefit every LCA study, the enhancement can be relevant. It allows for a more accurate global warming impact assessment, especially for LCAs in which products of biomass are present in substantial amounts. Relevance for other impact categories could be discussed in further work.     

Flaws in the interpretation phase of bioenergy LCA fuel the debate and mislead policymakers

The International Journal of Life Cycle Assessment - We hypothesize that the current heated scientific debate on bioenergy sustainability is fuelled by flaws in the interpretation phase of...     

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 .      

Next-generation sequencing data interpretation: enhancing reproducibility and accessibility

Areas of life sciences research that were previously distant from each other in ideology, analysis practices and toolkits, such as microbial ecology and personalized medicine, have all embraced techniques that rely on next-generation sequencing instruments. Yet the capacity to generate the data greatly outpaces our ability to analyse it. Existing sequencing technologies are more mature and accessible than the methodologies that are available for individual researchers to move, store, analyse and present data in a fashion that is transparent and reproducible. Here we discuss currently pressing issues with analysis, interpretation, reproducibility and accessibility of these data, and we present promising solutions and venture into potential future developments.     

Template mixing: a method of enhancing detection and interpretation of codominant RAPD markers

Ten codominant RAPD markers, ranging in size from about 300 to about 1350 bp, were identified in mapping populations of chickpea (Cicer arietinum L.) and diploid strawberry (Fragaria vesca L.). A distinguishing feature of all ten markers, and perhaps of codominant RAPD markers in general, was the presence in heterozygous individuals of a non-parental, heteroduplex band migrating more slowly than either of the respective parental bands. This non-parental band could also be generated by mixing parental DNAs before PCR (template mixing). As a means of identifying primers likely to detect codominant RAPD markers, parental and mixed-template (parent-parent) PCR-product gel lanes were compared for 20 previously untested RAPD primers (10-base oligomers). Four primers that produced a total of five non-parental, heteroduplex bands in mixed-template reactions were selected, and then used to detect a total of five segregating, codominant markers and nine dominant markers in the respective F₂ mapping population, a codominant marker frequency of 35.7%. When closely migrating fast and slow bands of codominant RAPDs were difficult to differentiate, parent-progeny template mixing was used to deliberately generate heteroduplex bands in fast- or slow-band F₂ homozygotes, respectively, allowing confirmation of marker phenotype.     

Including the use phase in LCA of floor coverings

The results from two previously published case studies were used to assess the importance of use-related emissions from building materials in a life cycle perspective. The first study was an LCA study of linoleum, vinyl flooring, and solid wood flooring, while the second study examined the Volatile Organic Compounds (VOCs) emitted by these floorings. For linoleum and vinyl flooring, the emitted amounts for the use phase are of much the same magnitude as those emitted in the rest of the life cycle, but in the case of solid wood flooring the emissions of the use phase far exceed those of the remaining life cycle. The ranking of the selected floorings in the LCA study did not change when the impact of the use phase was also considered. This study recommends that LCAs should not neglect flooring-related emissions in the use phase when assessing regional and global environmental effects.     

Assessment of lignocellulosic biorefineries in Germany using a hybrid LCA multi‐objective optimization model

In this study a tiered hybrid life cycle assessment (LCA) multi‐objective optimization model is developed and applied to determine the optimal choice of new biorefinery technologies in Germany. Thereby, several aspects can be explicitly addressed, including a regionally differentiated accountability of sustainable feedstock availability, identification of environmental impacts along global value chains, and identification of trade‐offs between different sustainability goals. The model is applied to assess the optimal choice between two lignocellulosic biorefinery concepts. Two optimization objectives are taken into account: maximizing the investor's profit and minimizing global impacts on climate change related to a specified demand for products. In terms of environmental impacts, the model also takes into account the comparison of new biorefineries with current available technologies producing the specified final demand. The results of the case study show that the biorefinery concept including the ethylene production is more beneficial in terms of reducing climate impacts, while on the other hand the biorefinery including the ethanol production is more cost‐effective. Depending on the decision‐maker's preference on weighting the two objectives, different capacities of biorefineries and optimal locations in Germany are identified. Furthermore, regions in Germany providing the necessary biomass feedstock can be identified on a county level. Finally, we argue that the extension of LCA by multi‐objective optimization is well suited guiding the way toward well‐informed decision‐making in the field of technological choices.     

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.

     

HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems

In heterogeneous distributed computing systems like cloud computing, the problem of mapping tasks to resources is a major issue which can have much impact on system performance. For some reasons such as heterogeneous and dynamic features and the dependencies among requests, task scheduling is known to be a NP-complete problem. In this paper, we proposed a hybrid heuristic method (HSGA) to find a suitable scheduling for workflow graph, based on genetic algorithm in order to obtain the response quickly moreover optimizes makespan, load balancing on resources and speedup ratio. At first, the HSGA algorithm makes tasks prioritization in complex graph considering their impact on others, based on graph topology. This technique is efficient to reduction of completion time of application. Then, it merges Best-Fit and Round Robin methods to make an optimal initial population to obtain a good solution quickly, and apply some suitable operations such as mutation to control and lead the algorithm to optimized solution. This algorithm evaluates the solutions by considering efficient parameters in cloud environment. Finally, the proposed algorithm presents the better results with increasing number of tasks in application graph in contrast with other studied algorithms.     

Perceived uncertainties of characterization in LCA: a survey

Purpose

Uncertainty analyses in life cycle assessment (LCA) literature have focused primarily on the life cycle inventory (LCI) phase, but LCA experts generally agree that the life cycle impact assessment (LCIA) phase is likely to contribute even more to the overall uncertainty of an LCA result. The magnitude of perceived uncertainties in characterization relative to that in LCI, however, has not been examined in the literature. Here, we use the pedigree approach to gauge the perceived uncertainty in the characterization phase relative to the LCI phase. In addition, we evaluate the level of approval on the pedigree approach as a means to characterize uncertainty in LCA.

Methods

Applying the Numeral Unit Spread Assessment Pedigree (NUSAP) approach to environmental risk assessment literature, we extracted the criteria for evaluating the uncertainty in the characterization phase. We used expert elicitation to identify a pool of experts and conducted a survey, to which 47 LCA practitioners from 12 countries responded. In order to reduce personal biases in perceived geometric standard deviation (GSD) values, we used two reference questions on weight and life expectancy at birth for calibration.

Results

Nearly half (49%) of respondents expressed their approval to the pedigree matrix approach as a means of characterizing uncertainties in LCA, and responses were highly sensitive to the respondent’s familiarity with the pedigree matrix. For instance, respondents who are highly familiar with the pedigree matrix were more polarized, with 15% and 19% of them expressing either strong approval or strong disapproval, respectively. Respondents less familiar with the pedigree approach were generally more favorable to its use. Compared with LCI, variability in characterization factors was influenced more strongly by geographical correlation and reliability of the underlying model, which showed 11 to 16% larger average GSDs when compared with the comparable criteria for LCI. Conversely, temporal correlation criterion was a less significant factor in characterization than in LCI.

Conclusions and discussion

Overall, survey respondents viewed LCIA characterization as only marginally more uncertain than LCI, but with a wider variability in responses on characterization than LCI. This finding indicates the need for additional research to develop more thorough methods for characterizing uncertainties in life cycle impact assessment that are compatible with the uncertainty measures in LCI.

     

Improving the integrated hybrid LCA in the upstream scope 3 emissions inventory analysis

Purpose

The protocols of carbon footprints generally define three scopes for different greenhouse gas (GHG) emissions levels. The most important carbon footprint emissions source comes from upstream indirect emissions of scope 3 for products that do not consume energy during their use phase. Upstream scope 3 GHG inventory can usually be analyzed through input–output or hybrid LCA analysis. The economic input–output life cycle analysis (EIO-LCA) and the hybrid LCA model have been widely used for this purpose. However, a cutoff error exists in the hybrid model, and the lack of a truncation criterion between process and IO inventory may lead to a high level of uncertainty in the hybrid model. This study attempts to improve the problem of cutoff uncertainty in hybrid LCA and proposes a method to minimize the cutoff uncertainty.

Methods

The way to improve the cutoff uncertainty could follow two steps. First, through the IO inventory analysis of EIO-LCA, we can define the emissions by various tiers of product components. The IO inventory indicator can provide a definitive criterion for the process inventory of the hybrid model. Second, we connect the process- and IO-LCI according to the IO inventory result. The advantage of the process inventory is that it provides detailed manufacturing information on the target while the IO encompasses a complete system boundary. For improvements, the process inventory can catch the most important process of the GHG emissions, and the IO inventory could compensate for the remainder of the incomplete system inventory.

Results and discussion

In this case study, the printed circuit board production process is used to evaluate the efficiency of the improved method. The threshold M was set to 70 in this case study, and the IO inventory provides the remaining 30 %. For the integrated hybrid model, the tier 3 process inventory takes only 64 % while the incorporation of the proposed method can include 92 % of the total emissions, which shows the cutoff uncertainty can be reduced through the improvement.

Conclusions

This study provides a clear guideline for process and IO cutoff criteria, which can help the truncation uncertainty. When higher precision is required, process LCI will need to play an important role, and thus, a higher M value should be set. In this situation, the emissions from IO-LCI would be smaller than the emissions from the process LCI. The appropriate solution would attain a comfortable balance between data accuracy and time and labor consumption.     

NJML: a hybrid algorithm for the neighbor-joining and maximum-likelihood methods

In the reconstruction of a large phylogenetic tree, the most difficult part is usually the problem of how to explore the topology space to find the optimal topology. We have developed a "divide-and-conquer" heuristic algorithm in which an initial neighbor-joining (NJ) tree is divided into subtrees at internal branches having bootstrap values higher than a threshold. The topology search is then conducted by using the maximum-likelihood method to reevaluate all branches with a bootstrap value lower than the threshold while keeping the other branches intact. Extensive simulation showed that our simple method, the neighbor-joining maximum-likelihood (NJML) method, is highly efficient in improving NJ trees. Furthermore, the performance of the NJML method is nearly equal to or better than existing time-consuming heuristic maximum-likelihood methods. Our method is suitable for reconstructing relatively large molecular phylogenetic trees (number of taxa >/= 16).     

A two-stage pruning algorithm for likelihood computation for a population tree

We have developed a pruning algorithm for likelihood estimation of a tree of populations. This algorithm enables us to compute the likelihood for large trees. Thus, it gives an efficient way of obtaining the maximum-likelihood estimate (MLE) for a given tree topology. Our method utilizes the differences accumulated by random genetic drift in allele count data from single-nucleotide polymorphisms (SNPs), ignoring the effect of mutation after divergence from the common ancestral population. The computation of the maximum-likelihood tree involves both maximizing likelihood over branch lengths of a given topology and comparing the maximum-likelihood across topologies. Here our focus is the maximization of likelihood over branch lengths of a given topology. The pruning algorithm computes arrays of probabilities at the root of the tree from the data at the tips of the tree; at the root, the arrays determine the likelihood. The arrays consist of probabilities related to the number of coalescences and allele counts for the partially coalesced lineages. Computing these probabilities requires an unusual two-stage algorithm. Our computation is exact and avoids time-consuming Monte Carlo methods. We can also correct for ascertainment bias.     

Ascertainment correction for a population tree via a pruning algorithm for likelihood computation

We present a method for correcting ascertainment-bias in a coalescent-based likelihood for population trees. Our method is computationally simple and fast. To correct for the bias we compute the probability of allele-counts conditioned on the locus being included. This conditional probability is simply the uncorrected likelihood divided by the inclusion probability. A modification of a pruning algorithm is introduced so that the inclusion probability can be computed with a single run of the algorithm. Our computation is exact and avoids Monte-Carlo based methods.