Data quality and uncertainty in LCI

It has recently been acknowledged that the quality of data used in Life Cycle Assessment (LCA) is one of the most important limiting factors to the application of the methodology. Early approaches dealing with this problem solely based on Data Quality Indicators (DQI) have revealed their limitations, and stochastic models are increasingly proposed as an alternative. Although facing methodological and practical difficulties, for instance the characterization of the distribution of input data, these stochastic models can significantly enhance decision-making in LCA. Uncertainty and data quality, however, are two distinct attributes. No matter how sophisticated the stochastic models are, they do not address the issue of the adequacy of the data used with regard to the goal of the study. Actual data on the distribution of SO emissions for US coal fired power plants for instance, would be of low quality for a European study. It is therefore believed that mixed approaches DQI/stochastic models should be developed in the future.     

Experiences with the critical review process of aluminium LCI data

Background, aim, and scope  

This paper summarises the critical review process according to ISO 14040/44 performed for the European Aluminium Association (EAA), Brussels. Scope of the review was a life cycle inventory (LCI) project, aiming at providing the life cycle assessment (LCA) community with reliable generic data relevant for the European aluminium market, including the production of aluminium ingot either from primary aluminium or from recycled aluminium and the fabrication of semi-finished products, i.e. sheet, foil or extrusion fabrication from aluminium ingots.     

Influence of derived operation-specific tractor emission data on results from an LCI on wheat production

The shortage of data for emissions from agricultural tractors contributes to LCA results on environmental load from modern crop production possibly having high error levels and high uncertainties. The first part of this work describes measurements and calculations made in order to obtain operation-specific agricultural emission data. Calculations are based on emission data measured on a standard 70 kW tractor of a widely available make. In the second part, results from an LCI on wheat production based on traditionally used emission data are calculated and compared with results obtained when using the emission data for specific working operations derived in part one. One conclusion of the study is that the emission values, when related to the energy in the used fuel, show very large variations between different driving operations. Another conclusion is that the use of the new data results in a marked reduction of the total air emissions produced in the wheat production chain, especially for CO and HC, but also for NO_x and SO₂.     

Comparison of LCI studies in South Africa and Germany

This article investigates the possibilities for and potential applications of Life Cycle Assessments (LCA’s) and specifically of Life Cycle Inventories (LCI’s) in developing countries (e.g. South Africa). The situation in South Africa is compared to that prevailing in Germany, a highly developed country. Although South Africa is unique concerning the different degrees of development within the industry, most of the principles discussed in this article can be applied similarly to other developing countries. No significant full LCI studies have yet been performed in South Africa. Although the immediate local needs for the South Africa enonomy are solving labour problems, creating jobs, building houses, the industries should seriously consider performing LCA and related studies for their products. The concept of quality should be extended to include the environmental performance of a product, process or service.     

Using fuzzy numbers to propagate uncertainty in matrix-based LCI

Background, aim, and scope  Analysis of uncertainties plays a vital role in the interpretation of life cycle assessment findings. Some of these uncertainties arise from parametric data variability in life cycle inventory analysis. For instance, the efficiencies of manufacturing processes may vary among different industrial sites or geographic regions; or, in the case of new and unproven technologies, it is possible that prospective performance levels can only be estimated. Although such data variability is usually treated using a probabilistic framework, some recent work on the use of fuzzy sets or possibility theory has appeared in the literature. The latter school of thought is based on the notion that not all data variability can be properly described in terms of frequency of occurrence. In many cases, it is necessary to model the uncertainty associated with the subjective degree of plausibility of parameter values. Fuzzy set theory is appropriate for such uncertainties. However, the computations required for handling fuzzy quantities has not been fully integrated with the formal matrix-based life cycle inventory analysis (LCI) described by Heijungs and Suh (2002). Materials and methods  This paper integrates computations with fuzzy numbers into the matrix-based LCI computational model described in the literature. The approach uses fuzzy numbers to propagate the data variability in LCI calculations, and results in fuzzy distributions of the inventory results. The approach is developed based on similarities with the fuzzy economic input–output (EIO) model proposed by Buckley (Eur J Oper Res 39:54–60, 1989). Results  The matrix-based fuzzy LCI model is illustrated using three simple case studies. The first case shows how fuzzy inventory results arise in simple systems with variability in industrial efficiency and emissions data. The second case study illustrates how the model applies for life cycle systems with co-products, and thus requires the inclusion of displaced processes. The third case study demonstrates the use of the method in the context of comparing different carbon sequestration technologies. Discussion  These simple case studies illustrate the important features of the model, including possible computational issues that can arise with larger and more complex life cycle systems. Conclusions  A fuzzy matrix-based LCI model has been proposed. The model extends the conventional matrix-based LCI model to allow for computations with parametric data variability represented as fuzzy numbers. This approach is an alternative or complementary approach to interval analysis, probabilistic or Monte Carlo techniques. Recommendations and perspectives  Potential further work in this area includes extension of the fuzzy model to EIO-LCA models and to life cycle impact assessment (LCIA); development of hybrid fuzzy-probabilistic approaches; and integration with life cycle-based optimization or decision analysis. Additional theoretical work is needed for modeling correlations of the variability of parameters using interacting or correlated fuzzy numbers, which remains an unresolved computational issue. Furthermore, integration of the fuzzy model into LCA software can also be investigated.     

Application of LCI: Environmental profile of LAB and detergent alcohols

LAB (Linear Alkyl Benzene) and long chain linear alcohols are the intermediates for the production of the most important surfactants used in the field of the world detergents. In the last two decades, as a consequence of the oil crisis and of the increasing environmental issues, technological development has been targeting the reduction of resources consumption and emissions related to these production activities. The results of the application of the principles of Life Cycle Inventory (LCI) to the production paths of Oxo-alcohols and LAB from kerosene are shown here, taking into account the improvements made within our production technologies. The case studies detailed in this study show the most relevant achievements, regarding our processes, in terms of environmental quality and demonstrate that the correct application of LCI methodology is decisive to obtain considerable advantages, as far as the environmental quality of our products is concerned.     

Rigorous proof of fuzzy error propagation with matrix-based LCI

Background, aim, and scope  

Propagation of parametric uncertainty in life cycle inventory (LCI) models is usually performed based on probabilistic Monte Carlo techniques. However, alternative approaches using interval or fuzzy numbers have been proposed based on the argument that these provide a better reflection of epistemological uncertainties inherent in some process data. Recent progress has been made to integrate fuzzy arithmetic into matrix-based LCI using decomposition into α-cut intervals. However, the proposed technique implicitly assumes that the lower bounds of the technology matrix elements give the highest inventory results, and vice versa, without providing rigorous proof.     

Statistical analysis of algal and water quality data

Data on cyanobacteria (blue-green algae) are generallycollected on a reactive basis, frequently in responseto bloom events. Such data presents a biased andincomplete snapshot of water quality. This paper looksat two typical data sets for UK waters showing thatwhile statistics may be used to describe the data theyare of limited use in forecasting. Suggestions ofappropriate tests for small and sparse data sets aremade.     

Processing and quality control of DNA array hybridization data

MOTIVATION: The technology of hybridization to DNA arrays is used to obtain the expression levels of many different genes simultaneously. It enables searching for genes that are expressed specifically under certain conditions. However, the technology produces large amounts of data demanding computational methods for their analysis. It is necessary to find ways to compare data from different experiments and to consider the quality and reproducibility of the data. RESULTS: Data analyzed in this paper have been generated by hybridization of radioactively labeled targets to DNA arrays spotted on nylon membranes. We introduce methods to compare the intensity values of several hybridization experiments. This is essential to find differentially expressed genes or to do pattern analysis. We also discuss possibilities for quality control of the acquired data. AVAILABILITY: http://www.dkfz.de/tbi CONTACT: M.Vingron@dkfz-heidelberg.de     

Quick and simple: quality control of microarray data

MOTIVATION: Microarrays are high-throughput tools for parallel miniaturized detection of biomolecules. In contrast to experiments using ratios of signals in two channels, experiments with only one fluorescent dye cause special problems for data analysis. The present work compares algorithms for quality filtering on spot level as well as array/slide level. RESULTS: Methods for quantitative spot filtering are discussed and new sets of quality scores for data preprocessing are designed. As measures of spot quality also reflect the quality of protocols, they were employed to find the optimal print buffer in an optimization experiment. In order to determine problematic arrays within a set of replicates we tested methods of outlier detection which can suitably replace the visual inspection of slides. CONTACT: Ursula.Sauer@arcs.ac.at.     

Application of robust optimization in matrix-based LCI for decision making under uncertainty

Purpose

When product systems are optimized to minimize environmental impacts, uncertainty in the process data may impact optimal decisions. The purpose of this article is to propose a mathematical method for life cycle assessment (LCA) optimization that protects decisions against uncertainty at the life cycle inventory (LCI) stage.

Methods

A robust optimization approach is proposed for decision making under uncertainty in the LCI stage. The proposed approach incorporates data uncertainty into an optimization problem in which the matrix-based LCI model appears as a constraint. The level of protection against data uncertainty in the technology and intervention matrices can be controlled to reflect varying degrees of conservatism.

Results and discussion

A simple numerical example on an electricity generation product system is used to illustrate the main features of this methodology. A comparison is made between a robust optimization approach, and decision making using a Monte Carlo analysis. Challenges to implement the robust optimization approach on common uncertainty distributions found in LCA and on large product systems are discussed. Supporting source code is available for download at https://github.com/renwang/Robust_Optimization_LCI_Uncertainty.

Conclusions

A robust optimization approach for matrix-based LCI is proposed. The approach incorporates data uncertainties into an optimization framework for LCI and provides a mechanism to control the level of protection against uncertainty. The tool computes optimal decisions that protects against worst-case realizations of data uncertainty. The robust optimal solution is conservative and is able to avoid the negative consequences of uncertainty in decision making.     

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.

     

Statistical quality control of variety trial data

I propose detection criteria for identifying an abnormal or erroneous data vector provided by a single variety trial in a longer series of variety trials. The test criteria are based on the linear effects estimated separately for each studied trial using global variance components estimated from the whole series of variety trials. The criteria comprise three mutually independent test statistics. The first one is a quadratic form in the estimated fixed effects, the second one is a quadratic form in the estimated realized linear random effects not including the residual effects, and the third one is a quadratic form in the estimated realized residual effects. Under the null hypothesis defining a valid data vector, the three quadratics have independent chi2 distributions. Under natural alternative hypotheses, they have noncentral chi2 distributions. Decomposing the total variation of the data vector studied into quadratic forms due to different types of the realized linear effects intuitively justifies the resulting test criteria. The decomposition may also be used to show that the resulting tests are likelihood ratio tests. I further present computational procedures that allow us to dispense with the need for prior estimation of the linear effects.