A triple bottom line evaluation of solid waste management strategies: a case study for an arid Gulf State,Kuwait

Purpose

We extend a life cycle assessment (LCA) embracing both economic and social perspectives to develop an integrated solid waste management system for Kuwait. This study considers the domestic waste generated by households and waste generated commercially. Six municipal solid waste (MSW) scenarios (SR₁, SR₂, …, SR₆) are evaluated using a triple bottom line (TBL) approach that incorporates environmental, financial, and social bottom lines (social BLs).

Methods

Within the TBL framework, the environmental BL employs LCA in accordance with ISO 14044. The financial BL is calculated using capital and operational costs as well as the associated recycling revenues. The social BL applies macro-economic indicators that examine the effects of a given MSW scenario (SR) on the inhabitants. To integrate the TBLs, we apply an analytic hierarchy process (AHP) because of its advantage of pairwise unit-free rescaling. The relative importance of each BL is determined by considering the political, legal, socio-cultural, and economic climates of the country. The relative weights are cross-multiplied with indicators from each BL to calculate a composite sustainability index (CSI) for the proposed MSW SR.

Results and discussion

The environmental BL (LCA) indicates that global warming, acidification, and human toxicity are the most adversely affected impact categories, considering the local conditions and waste composition. Environmentally, SR₁ (landfilling) scored the worst in almost all impact categories and, thus, was labeled the worst-case scenario environmentally. SR₆ (composting, recycling, and incineration) performed the best from an environmental perspective. Financially, landfilling (SR₁) is the most economical scenario. Any SR that focused on incineration (SR₂ and SR₅) was financially unfavorable. The scenarios that involved composting were scored as financially reasonable (SR₃, SR₄, and SR₆). From a social acceptability perspective, SR₂ (incineration) scored the highest, while SR₁ (landfills) scored the lowest. Finally, across the TBL framework, SR₄ (composting and incineration) had the highest CSI based on the relative importance scheme adopted for each BL.

Conclusions

Although they are often overlooked in most LCA studies, the financial and social aspects are indispensable to proving feasibility and credibility at a strategic level. The complexity of financial and social formulations in LCA is inherited from the difficulty in quantifying emissions and other impacts. In addition, from a social perspective, the contingent risks and associated uncertainty vary widely across cultures, ideologies, and degrees of development and are further complicated because of the scarcity and uncertainty of the data.

     

Uncertainty analysis in LCA using precalculated aggregated datasets

Purpose

Some LCA software tools use precalculated aggregated datasets because they make LCA calculations much quicker. However, these datasets pose problems for uncertainty analysis. Even when aggregated dataset parameters are expressed as probability distributions, each dataset is sampled independently. This paper explores why independent sampling is incorrect and proposes two techniques to account for dependence in uncertainty analysis. The first is based on an analytical approach, while the other uses precalculated results sampled dependently.

Methods

The algorithm for generating arrays of dependently presampled aggregated inventories and their LCA scores is described. These arrays are used to calculate the correlation across all pairs of aggregated datasets in two ecoinvent LCI databases (2.2, 3.3 cutoff). The arrays are also used in the dependently presampled approach. The uncertainty of LCA results is calculated under different assumptions and using four different techniques and compared for two case studies: a simple water bottle LCA and an LCA of burger recipes.

Results and discussion

The meta-analysis of two LCI databases shows that there is no single correct approximation of correlation between aggregated datasets. The case studies show that the uncertainty of single-product LCA using aggregated datasets is usually underestimated when the correlation across datasets is ignored and that the magnitude of the underestimation is dependent on the system being analysed and the LCIA method chosen. Comparative LCA results show that independent sampling of aggregated datasets drastically overestimates the uncertainty of comparative metrics. The approach based on dependently presampled results yields results functionally identical to those obtained by Monte Carlo analysis using unit process datasets with a negligible computation time.

Conclusions

Independent sampling should not be used for comparative LCA. Moreover, the use of a one-size-fits-all correction factor to correct the calculated variability under independent sampling, as proposed elsewhere, is generally inadequate. The proposed approximate analytical approach is useful to estimate the importance of the covariance of aggregated datasets but not for comparative LCA. The approach based on dependently presampled results provides quick and correct results and has been implemented in EcodEX, a streamlined LCA software used by Nestlé. Dependently presampled results can be used for streamlined LCA software tools. Both presampling and analytical solutions require a preliminary one-time calculation of dependent samples for all aggregated datasets, which could be centrally done by database providers. The dependent presampling approach can be applied to other aspects of the LCA calculation chain.

     

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).

     

Geographic variability of agriculture requires sector-specific uncertainty characterization

Purpose

Regionalization in life cycle assessment (LCA) has focused on spatially differentiated environmental variables for regional impact assessment models. Relatively less attention has been paid to spatial disparities in intermediate flows for life cycle inventory (LCI).

Methods

First, we compiled state-specific LCIs for four major crops in the USA and evaluated their geographic variability in the characterized results due to the differences in intermediate inputs. Second, we evaluated the consequence of choosing average or region-specific LCIs in understanding the life cycle environmental implications of land use change from cotton to corn or soybean. Finally, we analyzed the implications of our findings in characterizing the uncertainties associated with geographic variability under the conventional pedigree approach.

Results and discussion

Our results show that spatial disparities in LCI alone lead to two to fourfold differences in characterized results for most impact categories. The differences, however, increase to over an order of magnitude for freshwater ecotoxicity and human health non-cancer. Among the crops analyzed, winter wheat shows higher variability partly due to a larger difference in yield. As a result, the use of national average data derived from top corn and soybean producing states significantly underestimates the characterized impacts of corn and soybean in the states where land conversion from cotton to corn or soybean actually took place. The results also show that the conventional pedigree approach to uncertainty characterization in LCA substantially underestimates uncertainties arising from geographic variability of agriculture. Compared to the highest geometric standard deviation (GSD) value of 1.11 under the pedigree approach, the GSDs that we derived are as high as 7.1, with the median around 1.9.

Conclusions

The results highlight the importance of building regional life cycle inventory for understanding the environmental impacts of crops at the regional level. The high geographic variability of crops also indicates the need for sector-specific approaches to uncertainty characterization. Our results also suggest that the uncertainty values in the existing LCI databases might have been signficantly underestimated especially for those products with high geographic variability, demanding a cautious interpretation of the results derived from them. 

     

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

Purpose

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

Methods

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

Results and discussion

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

Conclusions

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

     

Attributional and consequential LCA in the ILCD handbook

Purpose

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

Methods

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

Results and discussion

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

Conclusions

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

     

What distribution function do life cycle inventories follow?

Purpose

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

Methods

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

Results and discussion

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

Conclusions

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

     

Streamlining scenario analysis and optimization of key choices in value chains using a modular LCA approach

Purpose

The environmental performance of products or services is often a result of a number of key decisions that shape their life cycles (e.g., techology choices). This paper introduces a modular LCA approach that is capable of reducing the effort involved in performing scenario analyses and optimization when several key choices along a product’s value chain lead to many alternative life cycles.

Methods

The main idea is that the value chain of a product can be divided into interconnected but exchangeable modules, which together represent a full life cycle. A module is comprised of unit processes from the practitioner’s LCI database. The inputs, outputs, and system boundaries of each module can be tailored to the context of the studied system. Alternatives arise whenever multiple modules produce substitutable products. Unlike in conventional LCI databases, no copies are necessary to represent the same process with different inputs. A module-product matrix is used to store this information. It can be used as a basis for an automated scenario analysis of all alternatives or as an input to an optimization model.

Results and discussion

Our approach is illustrated in two case studies: (1) Passenger car fuel choices are modeled by 15 modules representing 33 alternative value chains for diesel, petrol, natural gas and electric cars. The automated comparison of LCA results indicates that electric mobility is often the preferable option from a climate perspective, but impacts depend strongly on the electricity source. (2) A dynamic optimization model including stocks is built from eight modules to analyze the optimal use of wood for material and energy applications. Results indicate that although direct substitution benefits are higher for energy applications, cascading use of wood can maximize environmental performance over the entire life cycle.

Conclusions

The modular LCA approach permits an efficient modeling and comparison of alternative product life cycles, enabling practitioners to focus on key decisions. It can be applied to exploit a potential that is hidden in LCI databases, which is that they contain many specific inventories but not all useful combinations in the context of scenario analyses. The user-defined level of abstraction that is introduced through modules can be helpful in the communication of LCA results. The modular approach also facilitates the integration of LCA and optimization as well as other industrial ecology methods. An open source software is provided to enable others to apply and further develop our implementation of a modular LCA approach.

     

Inclusion of uncertainty in the LCA comparison of different cherry tomato production scenarios

Purpose

Knowledge regarding environmental impacts of agricultural systems is required. Consideration of uncertainty in life cycle assessment (LCA) provides additional scientific information for decision making. The aims of this study were to compare the environmental impacts of different growing cherry tomato cultivation scenarios under Mediterranean conditions and to assess the uncertainty associated to the different agricultural production scenarios.

Materials and methods

The burdens associated to cherry tomato production were calculated and evaluated by the LCA methodology. The functional unit (FU) chosen for this study was the mass unit of 1 t of commercial loose cherry tomatoes. This study included the quantitative uncertainty analysis through Monte Carlo simulation. Three scenarios were considered: greenhouse (GH), screenhouse (SH), and open field (OF). The flows and processes of the product scenario were structured in several sections: structure, auxiliary equipment, fertilizers, crop management, pesticides, and waste management. Six midpoint impact categories were selected for their relevance: climate change, terrestrial acidification, marine eutrophication, metal depletion, and fossil depletion using the impact evaluation method Recipe Midpoint and ecotoxicity using USEtox.

Results and discussion

The structure, auxiliary equipment, and fertilizers produced the largest environmental impacts in cherry tomato production. The greatest impact in these stages was found in the manufacture and drawing of the steel structures, manufacture of perlite, the amount of HDPE plastics used, and the electricity consumed by the irrigation system and the manufacture and application of fertilizers. GH was the cropping scenario with the largest environmental impact in most categories (varying from 18 and 37% higher than SH and OF, respectively, in metal depletion, to 96% higher than SH and OF, in eutrophication). OF showed the highest uncertainty in ecotoxicity, with a bandwidth of 60 CTUe and a probability of 100 and 99.4% to be higher than GH and SH, respectively.

Conclusions

The LCA was used to improve the identification and evaluation of the environmental burdens for cherry tomato production in the Mediterranean area. This study demonstrates the significance of conducting uncertainty analyses for comparative LCAs used in comparative relative product environmental impacts.

     

A flexible parametric model for a balanced account of forest carbon fluxes in LCA

Purpose

Despite a mature debate on the importance of a time-dependent account of carbon fluxes in life cycle assessments (LCA) of forestry products, static accounts of fluxes are still common. Time-explicit inventory of carbon fluxes is not available to LCA practitioners, since the most commonly used life cycle inventory (LCI) databases use a static approach. Existing forest models are typically applied to specific study fields for which the detailed input parameters required are available. This paper presents a simplified parametric model to obtain a time-explicit balanced account of the carbon fluxes in a forest for use in LCA. The model was applied to the case of spruce as an example.

Methods

The model calculated endogenous and exogenous carbon fluxes in tons of carbon per hectare. It was designed to allow users to choose (a) the carbon pools to be included in the analysis (aboveground and belowground carbon pools, only aboveground carbon or only carbon in stem); (b) a linear or sigmoidal dynamic function describing biomass growth; (c) a sigmoidal, negative exponential or linear dynamic function describing independently the decomposition of aboveground and belowground biomass; and (d) the forest management features such as stand type, rotation time, thinning frequency and intensity.

Results and discussion

The parametric model provides a time-dependent LCI of forest carbon fluxes per unit of product, taking into account the typically limited data available to LCA practitioners, while providing consistent and robust outcomes. The results obtained for the case study were validated with the more complex CO2FIX. The model ensures carbon balance within spatial and time delimitation defined by the user by accounting for the annual biomass degradation and production in each carbon pool. The inventory can be used in LCA studies and coupled with classic indicators (e.g. global warming potential) to accurately determine the climate impacts over time. The model is applicable globally and to any forest management practice.

Conclusions

This paper proposes a simplified and flexible forest model, which facilitates the implementation in LCA of time-dependent assessments of bio-based products.

     

A trade-based method for modelling supply markets in consequential LCA exemplified with Portland cement and bananas

Purpose

This study proposes a method based on the analysis of trade networks over time for modelling the marginal supply of products in consequential life cycle assessment (LCA). It aims at increasing the geographical granularity of markets, accuracy of transport distances and modes and material losses during transit by creating country-specific markets, instead of region-based supply-origin markets as currently proposed by ecoinvent. It leads to a better consideration of the environmental weight of trade following a change in demand on a local market and may serve as an inspirational basis for future releases of consequential life cycle inventory (LCI) databases.

Methods

The method uses ecoinvent v.3.3 as a support LCI database and two distinct traded products: bananas and grey Portland cement. Each country involved in the trade of a said product has a corresponding market created in the LCI database. The behavior of market to a marginal change in internal demand is modelled after its marginal trading preferences: it can either affect local production, imports, exports or a mix of the first two. Markets are linked to one another based on the linear regression analysis of their historical trade relations. The inventories that follow an increase in demand of 1000 kg of bananas and grey Portland cement are calculated for each market involved in their trade and are environmentally characterized and compared to the generic region-based market datasets provided by ecoinvent to assess the gains in accuracy through a higher geographical granularity. Furthermore, the characterized inventories of the markets for bananas are compared to a parallel scenario where transport distances are kept to a minimum using the shortest path method. It isolates the environmental burden associated to the utility maximization of the demand.

Results and discussion

When comparing the characterized impacts of country-specific markets with the generic ecoinvent market datasets, disparities in results appear. They highlight the importance of transport induced by demand displacement and losses of material during transport, both being the consequences of the extent a given market decides to be supplied directly from producing markets at the margin. These are aspects that may go unaccounted for when using generic regional markets. Second, optimizing transport distances for each market decreases the environmental impacts for most categories by more than 70%.

Conclusions

This study shows there is a need for modelling and understanding market relations to more accurately define the role of trade, supply chain efficiency and import policies in LCA.

     

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.

     

Environmental evaluation of aluminium cans for beverages in the German context

Background

In the years 2000 and 2002, the German Environment Agency in Berlin (UBA) published the results of a comprehensive LCA study on beverage containers comprising aluminium cans with volumes of 330 ml and 500 ml. Starting with the aluminium can scenarios and the respective results obtained during the UBA study, additional analyses were performed by IFEU in 2003, a German consultant having been a member of the project team working on the UBA study. The objective was to examine the influence of selected parameters on the LCA profile of carbonated soft drink containers. Data and method were in complete analogy with the LCI and LCA part of the UBA study.

Materials

In 2006, the aluminium industry commissioned a study on further influential factors that help determine the sale of certain types of beer, studying the effects of two selected parameter settings on the comparative results of the aluminium can against the refillable glass bottle. In this scenario, special attention was given to two influential factors, the distribution distance—distinguished by regional and nationwide distribution—and trippage rate.

Results and discussion

The results of the initial LCA from the years 2000 and 2002 showed, for the examined parameters container weight, rate of post-consumer recovery of used containers, degree of recycled content and quality of the recycling routes, that each had a considerable influence on the environmental impact profile of the aluminium can within the given framework. Can weight and recycling rate were sensitive factors in the impact categories of climate change, fossil resources, summer smog (POCP), acidification and terrestrial eutrophication. Can volume affected virtually all impact categories examined.

Conclusions

By now, individual improvement options have already been put into practice in Germany. The environmental profile of the average 330 ml aluminium can on the German market can be expected to be ahead of that of the aluminium can at the time of the UBA study. The introduction of a 500-ml can on the market denotes a fundamental step forward in improving LCA results of the aluminium can as a container for beverages.

     

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.

     

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

Purpose

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

Methods

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

Results and discussion

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

Conclusions

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

     

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

Purpose

Life cycle inventories (LCI) of electricity generation and supply are among the main determining factors regarding life cycle assessment (LCA) results. Therefore, consistency and representativeness of these data are crucial. The electricity sector has been updated and substantially extended for ecoinvent version 3 (v3). This article provides an overview of the electricity production datasets and insights into key aspects of these v3 inventories, highlights changes and describes new features.

Methods

Methods involved extraction of data and analysis from several publically accessible databases and statistics, as well as from the LCA literature. Depending on the power generation technology, either plant-specific or region-specific average data have been used for creating the new power generation inventories representing specific geographies. Whenever possible, the parent–child relationship was used between global and local activities. All datasets include a specific technology level in order to support marginal mixes used in the consequential version of ecoinvent. The use of parameters, variables and mathematical relations enhances transparency. The article focuses on documentation of LCI data on the unlinked unit process level and presents direct emission data of the electricity-generating activities.

Results and discussion

Datasets for electricity production in 71 geographic regions (geographies) covering 50 countries are available in ecoinvent v3. The number of geographies exceeds the number of countries due to partitioning of power generation in the USA and Canada into several regions. All important technologies representing fossil, renewable and nuclear power are modelled for all geographies. The new inventory data show significant geography-specific variations: thermal power plant efficiencies, direct air pollutant emissions as well as annual yields of photovoltaic and wind power plants will have significant impacts on cumulative inventories. In general, the power plants operating in the 18 newly implemented countries (compared to ecoinvent v2) are on a lower technology level with lower efficiencies and higher emissions. The importance of local datasets is once more highlighted.

Conclusions

Inventories for average technology-specific electricity production in all globally important economies are now available with geography-specific technology datasets. This improved coverage of power generation representing 83 % of global electricity production in 2008 will increase the quality of and reduce uncertainties in LCA studies worldwide and contribute to a more accurate estimation of environmental burdens from global production chains. Future work on LCI of electricity production should focus on updates of the fuel chain and infrastructure datasets, on including new technologies as well as on refining of the local data.

     

The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA

Purpose

Pesticides are applied to agricultural fields to optimise crop yield and their global use is substantial. Their consideration in life cycle assessment (LCA) is affected by important inconsistencies between the emission inventory and impact assessment phases of LCA. A clear definition of the delineation between the product system model (life cycle inventory—LCI, technosphere) and the natural environment (life cycle impact assessment—LCIA, ecosphere) is missing and could be established via consensus building.

Methods

A workshop held in 2013 in Glasgow, UK, had the goal of establishing consensus and creating clear guidelines in the following topics: (1) boundary between emission inventory and impact characterisation model, (2) spatial dimensions and the time periods assumed for the application of substances to open agricultural fields or in greenhouses and (3) emissions to the natural environment and their potential impacts. More than 30 specialists in agrifood LCI, LCIA, risk assessment and ecotoxicology, representing industry, government and academia from 15 countries and four continents, met to discuss and reach consensus. The resulting guidelines target LCA practitioners, data (base) and characterisation method developers, and decision makers.

Results and discussion

The focus was on defining a clear interface between LCI and LCIA, capable of supporting any goal and scope requirements while avoiding double counting or exclusion of important emission flows/impacts. Consensus was reached accordingly on distinct sets of recommendations for LCI and LCIA, respectively, recommending, for example, that buffer zones should be considered as part of the crop production system and the change in yield be considered. While the spatial dimensions of the field were not fixed, the temporal boundary between dynamic LCI fate modelling and steady-state LCIA fate modelling needs to be defined.

Conclusions and recommendations

For pesticide application, the inventory should report pesticide identification, crop, mass applied per active ingredient, application method or formulation type, presence of buffer zones, location/country, application time before harvest and crop growth stage during application, adherence with Good Agricultural Practice, and whether the field is considered part of the technosphere or the ecosphere. Additionally, emission fractions to environmental media on-field and off-field should be reported. For LCIA, the directly concerned impact categories and a list of relevant fate and exposure processes were identified. Next steps were identified: (1) establishing default emission fractions to environmental media for integration into LCI databases and (2) interaction among impact model developers to extend current methods with new elements/processes mentioned in the recommendations.