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.     

Provision of LCI data in the European aluminium industry Methods and examples

Background, aim, and scope

The development of robust and up-to-date generic life cycle inventory data for materials is absolutely crucial for the LCA community since many LCA studies rely on these generic data about materials. LCA databases and software usually include within their package such generic LCI datasets. However, in many cases, the quality of these data is poor while the methodology and the models used for their development are rarely accessible or transparent. This paper presents the development of robust European LCI datasets for the production of primary and recycled aluminium ingots and for the transformation of aluminium ingot into semi-finished products, i.e. sheet, foil and extrusion.

Materials and methods

The environmental data have been collected through an extensive environmental survey, organised among the European aluminium industry, focusing on the year 2005 and covering EU27 countries as well as EFTA countries (Norway, Iceland and Switzerland). From this survey, European averages, i.e. foreground data, have been calculated for the direct inputs and outputs of the various aluminium processes. Using the GaBi software, the foreground data have been combined within LCI models integrating background LCI data on energy supply systems, ancillary processes and materials. For the primary aluminium production (smelters), a specific model for the electricity production has been developed. The methodology for the data consolidation and for the development of the various models is explained as well as the main differences between the new modelling approach and LCI models used in the past. An independent expert has critically reviewed the entire LCI project including data collection, models development, calculation of LCI data and associated environmental indicators.

Results

As confirmed by the critical review, the new LCI datasets for aluminium ingot production and transformation into semi-finished products have been developed though a robust methodology in full accordance with ISO 14040 and 14044. Most significant environmental data and LCI results are reported in this paper with an emphasis on energy use and the major emissions to air. The full environmental report, including the critical review report and the calculation of environmental indicators for a pre-set of impact categories, is available on the website of the European Aluminium Association (EAA 2008). Whenever possible, the updated European averages and the new LCI data are compared with previous results developed from two past European surveys covering respectively the years 2002 and 1998. For the aluminium processes related to primary production, European averages are also benchmarked against global averages calculated from two worldwide surveys covering the years 2000 and 2005.

Discussion

While some data evolutions are directly attributed to the variation of foreground data, e.g. raw materials consumption or energy use within the aluminium processes, modifications related to the system boundaries, the background data and the modelling hypotheses can also influence significantly the LCI results. For primary aluminium production, the evolution of the foreground data is dominated by the strong decrease of PFC (perfluorocarbon) emissions (about 70% since 1998). In addition, the electricity structure calculated from the refined electricity model shows significant differences compared to previous models. In the 2005 electricity model, the hydropower share reaches 58% while coal contributes to 15% only of the electricity production. In 1998, the respective share of coal-based and hydro-electricity were respectively calculated to 25% and 52%. As a result, the electricity background LCI data are then significantly affected and influence also positively the environmental profile of primary aluminium in Europe. For the semi-production processes, the reduction of process scrap production, especially for extrusion and foil, demonstrates the increase of process efficiency from 1998. In parallel, a significant reduction of energy use is observed between 1998 and 2005. However, this positive trend is not fully reflected within LCI data due to the significant contribution of the background electricity data. The choice of the electricity model plays also a critical role for these transformation processes since electricity production contributes to about 2/3 of the consumption of the non-renewable energy and to about the same level of the air emissions. In such a case, the move from the UCPTE electricity model used in the past towards the EU25 electricity model used for the development of the updated LCI data has a detrimental effect on the environmental profile of the three LCI datasets respectively related to sheet, foil and extrusion. In addition to energy and process scrap reduction, the reduction of the VOC (volatile organic compounds) emission is also a major trend in foil production. Finally, for old aluminium scrap recycling, the new LCI data show a dramatic improvement regarding energy efficiency, reinforcing the environmental soundness of promoting and supporting aluminium recycling within the aluminium product life cycles.

Conclusions

This paper shows the development of generic LCI data about aluminium production and transformation processes which are based on robust data, methodologies and models in full accordance with ISO 14040 and 14044 standards, as confirmed by the critical review. The publishing of these LCI datasets definitely shows the commitment of the European aluminium industry to contribute in a transparent, fair and scientifically sound manner to product sustainability in a life cycle thinking perspective.

Recommendations and perspectives

Software houses and LCA practitioners are invited to update their generic European data on aluminium with the herewith datasets. Even if the quality and the completeness of these LCI data reach a high standard, some areas for data improvements have been identified, as described within the review report. Land use, water use and solid waste treatment appear as three priority areas for data refining and improvement. The land use dimension, particularly meaningful for bauxite mining, is not covered in the current LCI data while it is now integrated within many LCA studies. Up to now, the reporting of meaningful and robust data on water origins and use have not been possible due to the huge discrepancies between the surveyed sites combined with the difficulty to report coherent input and output water mass flows. The development of water data, only focussing on water-stressed areas, will most probably make more sense in the future. Finally, collecting more qualitative information about solid waste processing and treatment will help to include such operations within the system boundaries and to model their associated air, water and soil emissions.

     

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.     

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.

Main features

Critical reviewing according to ISO 14040 and 14044, although described formally in the standards, evolved essentially via ‘learning by doing’. This special review has been conducted as a critical review by one external expert. Since no comparative assertions are to be expected from the results obtained, a critical review according to the panel method (at least three reviewers) was deemed not to be necessary. The review process was interactive and took about a year (March 2007 to April 2008). The full review report is printed in full length at the end of the published LCI data report.

Results

The report continues the tradition of the former reports but offers new aspects. The main change refers to the use of new software for data handling (GaBi 4.0 replacing the formerly used LCA-2 based on BUWAL data), including generic data for ancillary processes and inputs for the energy model. The LCI results, therefore, cannot be compared exactly with the data of the previous reports. There is no disconnection, however, so that trends can be observed and discussed with some precaution. The main trend with respect to energy and emissions is one of slow but steady improvement. A main methodological improvement with regard to the former projects is the new energy model, especially with regard to imported primary aluminium.

Discussion

There was some discussion about the term ‘waste’ when it is put outside the system boundary together with the resulting emissions. According to the author’s opinion, there are at least three types of waste: (1) waste to be reused or recycled—this waste stays within the technosphere and, thus, within the system boundaries of a typical LCA; (2) waste to be collected and removed legally by incineration, controlled landfilling or composting—this waste stays within the technosphere, too; only the emissions of the waste removal processes (CO₂, CH₄, organic contaminants to ground water, leached metal ions to ground water, etc.) escape into the environment if not collected properly; (3) waste thrown away, e.g. by littering, illegal dumping, burning, etc.; this waste ends up in the environment if not collected. There was a time when solid waste in LCA (if landfilled) was considered as an ‘emission into soil’. This is only true for illegal, uncontrolled land filling. Controlled landfilling is a kind of process and belongs to the technosphere as long as it is controlled. EAA envisages to include appropriate data in future updates (incineration is already included).

Conclusions

According to ISO 14040, “The critical review process shall ensure that: the methods used to carry out the LCA are consistent with the international Standard; the methods used to carry out the LCA are scientifically and technically valid, the data used are appropriate and reasonable in relation to the goal of the study; the interpretations reflect the limitations identified and the goal of the study; the study report is transparent and consistent.” These five points can be confirmed with a few restrictions. With regard to the first item, consistency with ISO 14040/44, there is a formal lack of a section ‘interpretation’. It was also discussed that the study is not a full LCA, but the standard allows for LCI studies. As such, the study conforms to ISO. The methods used in data collection and modelling are described clearly and correspond to the state of the art. They should be published and become standard for generic data collection.

Perspectives

It is assumed and recommended that the process of continuous improvement (both technological and relating to data collection and modelling) will continue in the following years. However, since raw aluminium production is faced with thermodynamic limits, it is proposed to rethink the whole aluminium system, which is based on a century-old technology and to conceive bold new routes, especially aiming at a further increase of renewable energy use and further improving recycling in countries with deficient waste collecting systems. The use of heavy fuel oil in alumina production should be discouraged.

     

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.     

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 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.