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%.

Quantified Uncertainties in Comparative Life Cycle Assessment: What Can Be Concluded?

Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs-discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST-and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics' branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.

Displaying geographic variability of peri-urban agriculture environmental impacts in the Metropolitan Area of Barcelona: A regionalized life cycle assessment.

Peri urban agriculture (peri-UA) can supply food locally and potentially more sustainably than far-away conventional agricultural systems. It can also introduce significant environmental impacts depending on the local biophysical conditions and resources required to implement it and, on the crops managing practices, which could vary widely among growers. Sophisticated methods to account for such variability while assessing direct (on-site) and indirect (up/down stream) environmental impacts of peri-UA implementation are thus needed. We implemented an attributional, regionalized, cradle-to-gate life cycle assessment (LCA) for which we derive spatially explicit inventories and calculate 14 impacts due to peri-UA using the ReCiPe method. Further, to show the importance of impact assessment regionalization for the environmental assessment of peri-UA, we regionalize eutrophication impacts characterization. We use the Metropolitan Area of Barcelona (AMB) to illustrate these methodological developments. Vegetables and greenhouses, the prevalent peri-UA land uses, had the largest impacts assessed, of all peri-UA land uses. European NPK mineral fertilizer production to cover N demand of these crops drives all impacts. For fruit crops, on-site N emissions drive marine eutrophication impacts and for irrigated herbaceous crops, phosphate runoff drives freshwater eutrophication impacts. Geographic variability of peri-UA metabolic flows and impacts was displayed. Management practices at the plots, which are linked the land use, are responsible for impacts variability. Regionalization of eutrophication impacts highlights the importance of accounting for the biophysical aspects at the geographic scale at which peri-UA takes place, which is a much finer scale than those implemented in current regionalization of impact assessment methods in LCA. This study provides a fundamental baseline needed to assess transition scenarios of peri-UA at an appropriate geographic level of analysis and gives essential knowledge to guide appropriate circular and sustainability strategies for the sector.

Mapping direct N2O emissions from peri-urban agriculture: The case of the Metropolitan Area of Barcelona.

Geographically explicit datasets reflecting local management of crops are needed to help improve direct nitrous oxide (N2O) emission inventories. Yet, the lack of geographically explicit datasets of relevant factors influencing the emissions make it difficult to estimate them in such way. Particularly, for local peri-urban agriculture, spatially explicit datasets of crop type, fertilizer use, irrigation, and emission factors (EFs) are hard to find, yet necessary for evaluating and promoting urban self-sufficiency, resilience, and circularity. We spatially distribute these factors for the peri-urban agriculture in the Metropolitan Area of Barcelona (AMB) and create N2O emissions maps using crop-specific EFs as well as Tier 1 IPCC EFs for comparison. Further, the role of the soil types is qualitatively assessed. When compared to Tier 1 IPCC EFs, we find 15% more emissions (i.e. 7718 kg N2O-N year-1) than those estimated with the crop-specific EFs (i.e. 6533 kg N2O-N year-1) for the entire AMB. Emissions for most rainfed crop areas like cereals (e.g. oat and barley) and non-citric fruits (e.g. cherries and peaches), which cover 24% and 13% of AMB's peri-urban agricultural area respectively, are higher with Tier 1 EF. Conversely, crop-specific EFs estimate higher emissions for irrigated horticultural crops (e.g. tomato, artichoke) which cover 33% of AMB's peri-urban agricultural area and make up 70% of the total N2O emissions (4588 kg N2O-N year-1 using crop-specific EFs). Mapping the emissions helps evaluate spatial variability of key factors such as fertilizer use and irrigation of crops but carry uncertainties due to downscaling regional data to represent urban level data gaps. It also highlighted core emitting areas. Further the usefulness of the outputs on mitigation, sustainability and circularity studies are briefly discussed.

Estimating greenhouse gas emissions from direct land use change due to crop production in multiple countries.

Greenhouse gas (GHG) emissions from direct land use change (LUC) in GHG footprint studies of crops are often estimated using national land use change statistics, as in many cases the exact location of crop cultivation and land use history is unknown. As such, these studies neglect spatial variability in land use change (amount and configuration) at the sub-national level as well as spatial variability in natural carbon stocks. For this reason, a spatial approach that enables consistent implementation of LUC emissions of crop production at different locations is developed and applied in this study. The dataset of crop production covers 69 crops cultivated on 1885 farms in 33 countries, spanning North and South America, Asia, Australia and Oceania, Europe and Africa, in the year 2014. Of the 1885 farms, 33% (619 farms) were identified to have LUC emissions when estimated at the local scale. LUC emissions of farms, derived using local scale location information, were found to have little correlation with those estimated at coarser spatial scales (such as the province or country level) using the spatial approach in this study or estimated using accounting approaches based on national statistics. Analysis at coarser spatial scales typically overestimated the LUC emissions of crops, as LUC in other regions can heavily influence these estimates. Therefore, it is concluded that local scale LUC emissions better represent local LUC dynamics, thereby improving the reliability of GHG footprint studies.