Christopher L. Mutel

GIS-Based Regionalized Life Cycle Assessment: How Big Is Small Enough? Methodology and Case Study of Electricity Generation

We describe a new methodology for performing regionalized life cycle assessment and systematically choosing the spatial scale of regionalized impact assessment methods. We extend standard matrix-based calculations to include matrices that describe the mapping from inventory to impact assessment spatial supports. Uncertainty in inventory spatial data is modeled using a discrete spatial distribution function, which in a case study is derived from empirical data. The minimization of global spatial autocorrelation is used to choose the optimal spatial scale of impact assessment methods. We demonstrate these techniques on electricity production in the United States, using regionalized impact assessment methods for air emissions and freshwater consumption. Case study results show important differences between site-generic and regionalized calculations, and provide specific guidance for future improvements of inventory data sets and impact assessment methods.

Land Use in Life Cycle Assessment: Global Characterization Factors Based on Regional and Global Potential Species Extinction

Land use is one of the main drivers of biodiversity loss. However, many life cycle assessment studies do not yet assess this effect because of the lack of reliable and operational methods. Here, we present an approach to modeling the impacts of regional land use on plants, mammals, birds, amphibians, and reptiles. Our global analysis calculates the total potential damage caused by all land uses within each WWF ecoregion and allocates this total damage to different types of land use per ecoregion. We use an adapted (matrix-calibrated) species-area relationship to model the potential regional extinction of nonendemic species caused by reversible land use and land use change impacts. The potential global extinction of endemic species is used to assess irreversible, permanent impacts. Model uncertainty is assessed using Monte Carlo simulations. The impacts of land use on biodiversity varied strongly across ecoregions, showing the highest values in regions where most natural habitat had been converted in the past. The approach is thus retrospective and was able to highlight the impacts in highly disturbed regions. However, we also illustrate how it can be applied to prospective assessments using scenarios of future land use. Uncertainties, modeling choices, and validity are discussed.

Solar Energy Demand (SED) of Commodity Life Cycles

The solar energy demand (SED) of the extraction of 232 atmospheric, biotic, fossil, land, metal, mineral, nuclear, and water resources was quantified and compared with other energy- and exergy-based indicators. SED represents the direct and indirect solar energy required by a product or service during its life cycle. SED scores were calculated for 3865 processes, as implemented in the Ecoinvent database, version 2.1. The results showed that nonrenewable resources, and in particular minerals, formed the dominant contribution to SED. This large share is due to the indirect solar energy required to produce these resource inputs. Compared with other energy- and exergy-based indicators, SED assigns higher impact factors to minerals and metals and smaller impact factors to fossil energetic resources, land use, and nuclear energy. The highest differences were observed for biobased and renewable energy generation processes, whose relative contribution of renewable resources such as water, biomass, and land occupation was much lower in SED than in energy- and exergy-based indicators.

Life cycle human toxicity assessment of pesticides: comparing fruit and vegetable diets in Switzerland and the United States.

Food consumption represents the dominant exposure pathway of the general public to pesticides. In this paper, we characterize the lifelong cumulative human health damage from ingestion of pesticides contained in fruits and vegetables in Switzerland and the United States. We evaluated pesticide residues in 62,151 food samples. Chemical specific concentrations were combined with pesticide emission data and information on country-specific diets and chemical toxicity to assess the human health impacts of 51 food commodities and national average diets. Furthermore, a list of characterization factors for pesticide ingestion via food was calculated for use in life cycle impact assessment. On average, the Swiss population takes in via food ingestion 0.41g of every 1kg of pesticide applied during agricultural cultivation. The corresponding value in the United States is 0.51. Intake fractions based on experimental monitoring data were compared with outputs from the USEtox model for life cycle impact assessment of toxic substances. The modeled intake fractions were underestimated by up to two orders of magnitude. However, even when using the monitored residue concentration data, the absolute health damage via fruits and vegetable ingestion was small: The potential lifelong damage of pesticides is estimated to be only 4.2 and 3.2 min of life lost per person in Switzerland and the United States, respectively. The results of this study indicate that pesticide intake due to the ingestion of fruits and vegetables consumed in Switzerland and the United States does not lead to significant human health damages.

Two-step sensitivity testing of parametrized and regionalized life cycle assessments: methodology and case study.

Comprehensive sensitivity analysis is a significant tool to interpret and improve life cycle assessment (LCA) models, but is rarely performed. Sensitivity analysis will increase in importance as inventory databases become regionalized, increasing the number of system parameters, and parametrized, adding complexity through variables and nonlinear formulas. We propose and implement a new two-step approach to sensitivity analysis. First, we identify parameters with high global sensitivities for further examination and analysis with a screening step, the method of elementary effects. Second, the more computationally intensive contribution to variance test is used to quantify the relative importance of these parameters. The two-step sensitivity test is illustrated on a regionalized, nonlinear case study of the biodiversity impacts from land use of cocoa production, including a worldwide cocoa products trade model. Our simplified trade model can be used for transformable commodities where one is assessing market shares that vary over time. In the case study, the highly uncertain characterization factors for the Ivory Coast and Ghana contributed more than 50% of variance for almost all countries and years examined. The two-step sensitivity test allows for the interpretation, understanding, and improvement of large, complex, and nonlinear LCA systems.

The application of the pedigree approach to the distributions foreseen in ecoinvent v3

PurposeData used in life cycle inventories are uncertain (Ciroth et al. Int J Life Cycle Assess 9(4):216–226, 2004). The ecoinvent LCI database considers uncertainty on exchange values. The default approach applied to quantify uncertainty in ecoinvent is a semi-quantitative approach based on the use of a pedigree matrix; it considers two types of uncertainties: the basic uncertainty (the epistemic error) and the additional uncertainty (the uncertainty due to using imperfect data). This approach as implemented in ecoinvent v2 has several weaknesses or limitations, one being that uncertainty is always considered as following a lognormal distribution. The aim of this paper is to show how ecoinvent v3 will apply this approach to all types of distributions allowed by the ecoSpold v2 data format.MethodsA new methodology was developed to apply the semi-quantitative approach to distributions other than the lognormal. This methodology and the consequent formulas were based on (1) how the basic and the additional uncertainties are combined for the lognormal distribution and on (2) the links between the lognormal and the normal distributions. These two points are summarized in four principles. In order to test the robustness of the proposed approach, the resulting parameters for all probability density functions (PDFs) are tested with those obtained through a Monte Carlo simulation. This comparison will validate the proposed approach.Results and discussionIn order to combine the basic and the additional uncertainties for the considered distributions, the coefficient of variation (CV) is used as a relative measure of dispersion. Formulas to express the definition parameters for each distribution modeling a flow with its total uncertainty are given. The obtained results are illustrated with default values; they agree with the results obtained through the Monte Carlo simulation. Some limitations of the proposed approach are cited.ConclusionsProviding formulas to apply the semi-quantitative pedigree approach to distributions other than the lognormal will allow the life cycle assessment (LCA) practitioner to select the appropriate distribution to model a datum with its total uncertainty. These data variability definition technique can be applied on all flow exchanges and also on parameters which play an important role in ecoinvent v3.

Environmental Impact of Buildings—What Matters?

The goal of this study was to identify drivers of environmental impact and quantify their influence on the environmental performance of wooden and massive residential and office buildings. We performed a life cycle assessment and used thermal simulation to quantify operational energy demand and to account for differences in thermal inertia of building mass. Twenty-eight input parameters, affecting operation, design, material, and exogenic building properties were sampled in a Monte Carlo analysis. To determine sensitivity, we calculated the correlation between each parameter and the resulting life cycle inventory and impact assessment scores. Parameters affecting operational energy demand and energy conversion are the most influential for the buildings total environmental performance. For climate change, electricity mix, ventilation rate, heating system, and construction material rank the highest. Thermal inertia results in an average 2-6% difference in heat demand. Nonrenewable cumulative energy demand of wooden buildings is 18% lower, compared to a massive variant. Total cumulative energy demand is comparable. The median climate change impact is 25% lower, including end-of-life material credits and 22% lower, when credits are excluded. The findings are valid for small offices and residential buildings in Switzerland and regions with similar building culture, construction material production, and climate.

The Environmental Importance of Energy Use in Chemical Production

In many cases, policy makers and laymen perceive harmful emissions from chemical plants as the most important source of environmental impacts in chemical production. As a result, regulations and environmental efforts have tended to focus on this area. Concerns about energy use and greenhouse gas emissions, however, are increasing in all industrial sectors. Using a life cycle assessment (LCA) approach, we analyzed the full environmental impacts of producing 99 chemical products in Western Europe from cradle to factory gate. We applied several life cycle impact assessment (LCIA) methods to cover various impact areas. Our analysis shows that for both organic and inorganic chemical production in industrial countries, energy-related impacts often represent more than half and sometimes up to 80% of the total impacts, according to a range of LCIA methods. Resource use for material feedstock is also important, whereas direct emissions from chemical plants may make up only 5% to 10% of the total environmental impacts. Additionally, the energy-related impacts of organic chemical production increase with the complexity of the chemicals. The results of this study offer important information for policy makers and sustainability experts in the chemical industry striving to reduce environmental impacts. We identify more sustainable energy production and use as an important option for improvements in the environmental profile of chemical production in industrial countries, especially for the production of advanced organic and fine chemicals.

Effects of Distribution Choice on the Modeling of Life Cycle Inventory Uncertainty: An Assessment on the Ecoinvent v2.2 Database

Summary Life cycle inventory data have multiple sources of uncertainty. These data uncertainties are often modeled using probability density functions, and in the ecoinvent database the lognormal distribution is used by default to model exchange uncertainty values. The aim of this article is to systematically measure the effect of this default distribution by changing from the lognormal to several other distribution functions and examining how this change affects the uncertainty of life cycle assessment results. Using the ecoinvent 2.2 inventory database, data uncertainty distributions are switched from the lognormal distribution to the normal, triangular, and gamma distributions. The effect of the distribution switching is assessed for both impact assessment results of individual products system, as well as comparisons between product systems. Impact assessment results are generated using 5,000 Monte Carlo iterations for each product system, using the Intergovernmental Panel on Climate Change (IPCC) 2001 (100-year time frame) method. When comparing the lognormal distribution to the alternative default distributions, the difference in the resulting median and standard deviation values range from slight to significant, depending on the distributions used by default. However, the switch shows practically no effect on product system comparisons. Yet, impact assessment results are sensitive to how the data uncertainties are defined. In this article, we followed what we believe to be ecoinvent standard practice and preserved the “most representative” value. Practitioners should recognize that the most representative value can depart from the average of a probability distribution. Consistent default distribution choices are necessary when performing product system comparisons.

Uncertain Environmental Footprint of Current and Future Battery Electric Vehicles

The future environmental impacts of battery electric vehicles (EVs) are very important given their expected dominance in future transport systems. Previous studies have shown these impacts to be highly uncertain, though a detailed treatment of this uncertainty is still lacking. We help to fill this gap by using Monte Carlo and global sensitivity analysis to quantify parametric uncertainty and also consider two additional factors that have not yet been addressed in the field. First, we include changes to driving patterns due to the introduction of autonomous and connected vehicles. Second, we deeply integrate scenario results from the IMAGE integrated assessment model into our life cycle database to include the impacts of changes to the electricity sector on the environmental burdens of producing and recharging future EVs. Future EVs are expected to have 45-78% lower climate change impacts than current EVs. Electricity used for charging is the largest source of variability in results, though vehicle size, lifetime, driving patterns, and battery size also strongly contribute to variability. We also show that it is imperative to consider changes to the electricity sector when calculating upstream impacts of EVs, as without this, results could be overestimated by up to 75%.