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Heavy trucks contribute significantly to climate change, and in 2020 were responsible for 7% of total Swedish GHG emissions and 5% of total global CO2 emissions. Here we study the full lifecycle of cargo trucks powered by different energy pathways, comparing their biomass feedstock use, primary energy use, net biogenic and fossil CO2 emission, and cumulative radiative forcing. We analyse battery electric trucks with bioelectricity from standalone or combined heat and power (CHP) plants, and pathways where bioelectricity is integrated with wind and solar electricity. We analyse trucks operated on fossil diesel fuel and on dimethyl ether (DME). All energy pathways are analysed with and without carbon capture and storage (CCS). Bioelectricity and DME are produced from forest harvest residues. Forest biomass is a limited resource, so in a scenario analysis we allocate a fixed amount of biomass to power Swedish truck transport. Battery lifespan and chemistry, the technology level of energy supply, and the biomass source and transport distance are all varied to understand how sensitive the results are to these parameters. The scenario spans 100 years into the future. We find that pathways using electricity to power battery electric trucks have much lower climate impacts and primary energy use, compared to diesel and DME based pathways. The pathways using bioelectricity with CCS result in negative emissions leading to global cooling of the earth. The pathways using diesel and DME have significant and very similar climate impact, even with CCS. The robust results show that truck electrification and increased renewable electricity production is a much better strategy to reduce the climate impact of cargo transport and much more primary energy efficient than the adoption of DME trucks. This climate impact analysis includes all fossil and net biogenic CO2 emissions as well as the timing of these emissions. Considering only fossil emissions is incomplete and could be misleading. This dataset contains data on 4 metrics (primary energy use, biomass feedstock use, cumulative CO2 emissions, and cumulative radiative forcing) resulting from scenario modeling of cargo truck use in Sweden powered by different energy pathways. The energy pathways include battery electric trucks powered by bioelectricity, solar photovoltaic electricity and wind electricity, and internal combustion trucks powered by fossil diesel and dimethyl ether. The scenario spans 100 years into the future. The Excel sheet "tables" contains input data for the scenario modeling, with sources listed where applicable. The remaining sheets contains the modeled results and generated figures that are also a published in the associated article Sathre & Gustavsson (2023). Refer to the method description and reference list in the included documentation files for details. Tunga lastbilar bidrar kraftigt till klimatförändringarna och stod 2020 för 7% av de totala svenska växthusgasutsläppen och 5% av de totala globala CO2-utsläppen. Här studerar vi hela livscykeln för lastbilar som drivs av olika energivägar, jämför deras användning av biomassaråvaror, primär energianvändning, biogena och fossila CO2-utsläpp netto och kumulativ strålningstvingning. Vi analyserar batterielektriska lastbilar med bioel från fristående eller kraftvärmeverk och vägar där bioel integreras med vind- och solkraft. Vi analyserar lastbilar som drivs med fossilt dieselbränsle och med dimetyleter (DME). Alla energivägar analyseras med och utan avskiljning och lagring av koldioxid (CCS). Bioelektricitet och DME produceras av skogsavverkningsrester. Skogsbiomassa är en begränsad resurs, så i en scenarioanalys avsätter vi en fast mängd biomassa för att driva svenska lastbilstransporter. Batteriets livslängd och kemi, tekniknivån för energiförsörjning och biomassakällan och transportavståndet varierar alla för att förstå hur känsliga resultaten är för dessa parametrar. Scenariot sträcker sig 100 år in i framtiden. Vi finner att vägar som använder el för att driva batterielektriska lastbilar har mycket lägre klimatpåverkan och primär energianvändning, jämfört med diesel- och DME-baserade vägar. De vägar som använder bioelektricitet med CCS resulterar i negativa utsläpp som leder till global kylning av jorden. Vägarna med diesel och DME har betydande och mycket liknande klimatpåverkan, även med CCS. De robusta resultaten visar att elektrifiering av lastbilar och ökad förnybar elproduktion är en mycket bättre strategi för att minska godstransporternas klimatpåverkan än införandet av DME-lastbilar, och mycket mer primärenergieffektiv. Denna klimatkonsekvensanalys omfattar alla fossila och biogena CO2-utsläpp samt tidpunkten för dessa utsläpp. Att bara ta hänsyn till fossila utsläpp är ofullständigt och kan vara missvisande. Detta dataset innehåller data om 4 mätvärden (primär energianvändning, biomassaråvara, kumulativa CO2-utsläpp och kumulativ strålkraftspåverkan) som härrör från scenariomodellering av lastbilsanvändning i Sverige som drivs av olika energivägar. Energivägarna inkluderar batterielektriska lastbilar som drivs av bioelektricitet, solcellselektricitet och vindkraft samt förbränningsbilar som drivs av fossil diesel och dimetyleter. Scenariot sträcker sig 100 år in i framtiden. På arket "tables" i Excelfilen återfinns den indata som använts i modelleringen med angivna källor där detta är tillämpligt. Övriga ark innehåller resultat samt figurer som också publiceras i den samhörande artikeln Sathre & Gustavsson (2023). Se metodbeskrivning samt referenslista i tillhörande dokumentationsfiler för detaljer.

We present a multi-temporal, multi-modal remote-sensing dataset for predicting how active wildfires will spread at a resolution of 24 hours. The dataset consists of 13.607 images across 607 fire events in the United States from January 2018 to October 2021. For each fire event, the dataset contains a full time series of daily observations, containing detected active fires and variables related to fuel, topography and weather conditions. Documentation WildfireSpreadTS_Documentation.pdf includes further details about the dataset, following Gebru et al.'s "Datasheets for Datasets" framework. This documentation is similar to the supplementary material of the associated NeurIPS paper, excluding only information about experimental setup and results. For full details, please refer to the associated paper. Code: Getting started Get started working with the dataset at https://github.com/SebastianGer/WildfireSpreadTS. The code includes a PyTorch Dataset and Lightning DataModule to allow for easy access. We recommend converting the GeoTIFF files provided here to HDF5 files (bigger files, but much faster). The necessary code is also available in the repository. This work is funded by Digital Futures in the project EO-AI4GlobalChange. The computations were enabled by resources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) at C3SE partially funded by the Swedish Research Council through grant agreement no. 2022-06725.

Production of biodiesel from yellow grease (waste cooking oil and waste animal fats) is fast emerging as a promising alternative to address the twin challenges before the biodiesel industry today-fluctuation in prices of vegetable oil and the food versus fuel debate. Yellow grease has a high percentage of free fatty acids (FFA) and proves to be an unsuitable feedstock for biodiesel production from commercially viable alkali-catalyzed production systems due to saponification problems.“Green” methodologies based on heterogeneous solid acid catalyzed reactions have the potential to simultaneously promote esterification and transesterification reactions of yellow grease to produce biodiesel without soap formation and offer easy catalyst separation without generation of toxic streams. This paper presents kinetic studies for the conversion of model yellow grease feeds to biodiesel using a heteropolyacid supported on alumina (HSiW/Al2O3) using a batch autoclave. Three model yellow grease feeds were prepared using canola oil with added FFA such as palmitic, oleic and linoleic acid. A pseudo homogeneous kinetic model for the parallel esterification and transesterification was developed. The rate constants and activation parameters for esterification and transesterification reactions for the model yellow grease feeds were determined. The rate constants for esterification are higher than the transesterification rate constants. The kinetic model was validated using the experimental biodiesel data obtained from processing a commercial yellow grease feed. The kinetic model could be used to design novel processes to convert various low-value waste oils, fats and non-food grade oils to sustainable biodiesel. Keywords: Yellow grease, Canola oil, Free fatty acids, Heteropolyacid, Kinetics for esterification and transesterification

The aim of this paper is to contribute to the establishment of a robust framework for the assessment of Sustainable Development Goals (SDGs) in businesses, using the construction industry as an example and with the primary focus on combating climate change (SDG 13). We provide a critical analysis of a selection of relatively widely used SDG impact assessment tools, combined with a case study from the construction industry to explore how a meaningful SDG assessment can be framed with linkages between SDG 13 and other related SDGs. Our analysis points towards the importance of framing SDG assessments in a way that discourages "Greenwashing". Any SDG assessment that relates to climate targets in line with the Paris Agreement should identify the processes and activities that can be expected to be particularly challenging in terms of their abatement. In our road construction work case, we identify four such hard-to-abate activities: 1) introducing biomass for renewable transportation fuels for use in construction equipment and heavy transport; 2) electrification of transport and industrial processes; 3) substitution as part of transitioning from fossil fuel use; and 4) applying carbon capture and storage technologies in the production of basic materials, such as cement and steel. The approach applied will avoid that businesses only focus on SDGs in situations where they are already performing well or can apply low-cost measures or that they only relate to the part of the supply chain that pertains to their own business (Scope 1 emissions). For an SDG assessment to provide basis for informed decisions regarding real change towards more sustainable and equitable corporate practices it should: (i) identify and include concrete measures to align with the terms of the Paris Agreement; (ii) include relevant value chains; and (iii) consider both the short-term and long-term effects of strategic choices.

AbstractShipbuilding is an energy-intensive industrial sector that produces a significant amount of waste, pollution and air emissions. However, the International Maritime Organization concentrates only on reducing emissions during the operational phase. In order to completely phase out emissions from the shipping industry, a life-cycle approach must be taken. The study implemented the proposed transdisciplinary energy management framework in a Bangladeshi shipyard. The framework aims to support shipyard decision makers in making rational and optimized decisions to make shipyards sustainable, while maintaining good product quality and reducing relative cost. This is achieved by applying the Fuzzy Analytical Hierarchy Process and Fuzzy Order of Preference by Similarity to Ideal Solution methods to identify optimal solutions. In addition to making shipyards more sustainable, the framework can enhance both the business and socio-economic prospects of the shipyard and promote the reputation of the shipyard and improve its competitiveness and, in line with this, lead to the promotion of nationally determined contributions under the Paris Agreement for States. The implementation of the framework shows that the political and legal discipline, the social criteria and the implementation of ISO 14001 and cyber security were the most important criteria and options for the yard's decision makers.

Abstract. Reliable quantification of the sources and sinks of greenhouse gases, together with trends and uncertainties, is essential to monitoring the progress in mitigating anthropogenic emissions under the Paris Agreement. This study provides a consolidated synthesis of CH4 and N2O emissions with consistently derived state-of-the-art bottom-up (BU) and top-down (TD) data sources for the European Union and UK (EU27+UK). We integrate recent emission inventory data, ecosystem process-based model results, and inverse modelling estimates over the period 1990–2018. BU and TD products are compared with European National GHG Inventories (NGHGI) reported to the UN climate convention secretariat UNFCCC in 2019. For uncertainties, we used for NGHGI the standard deviation obtained by varying parameters of inventory calculations, reported by the Member States following the IPCC guidelines recommendations. For atmospheric inversion models (TD) or other inventory datasets (BU), we defined uncertainties from the spread between different model estimates or model specific uncertainties when reported. In comparing NGHGI with other approaches, a key source of bias is the activities included, e.g. anthropogenic versus anthropogenic plus natural fluxes. In inversions, the separation between anthropogenic and natural emissions is sensitive to the geospatial prior distribution of emissions. Over the 2011–2015 period, which is the common denominator of data availability between all sources, the anthropogenic BU approaches are directly comparable, reporting mean emissions of 20.8 Tg CH4 yr−1 (EDGAR v5.0) and 19.0 Tg CH4 yr−1 (GAINS), consistent with the NGHGI estimates of 18.9 ± 1.7 Tg CH4 yr−1. TD total inversions estimates give higher emission estimates, as they also include natural emissions. Over the same period regional TD inversions with higher resolution atmospheric transport models give a mean emission of 28.8 Tg CH4 yr−1. Coarser resolution global TD inversions are consistent with regional TD inversions, for global inversions with GOSAT satellite data (23.3 Tg CH4yr−1) and surface network (24.4 Tg CH4 yr−1). The magnitude of natural peatland emissions from the JSBACH-HIMMELI model, natural rivers and lakes emissions and geological sources together account for the gap between NGHGI and inversions and account for 5.2 Tg CH4 yr−1. For N2O emissions, over the 2011–2015 period, both BU approaches (EDGAR v5.0 and GAINS) give a mean value of anthropogenic emissions of 0.8 and 0.9 Tg N2O yr−1 respectively, agreeing with the NGHGI data (0.9 ± 0.6 Tg N2O yr−1). Over the same period, the average of the three total TD global and regional inversions was 1.3 ± 0.4 and 1.3 ± 0.1 Tg N2O yr−1 respectively, compared to 0.9 Tg N2O yr−1 from the BU data. The TU and BU comparison method defined in this study can be operationalized for future yearly updates for the calculation of CH4 and N2O budgets both at EU+UK scale and at national scale. The referenced datasets related to figures are visualized at https://doi.org/10.5281/zenodo.4288969 (Petrescu et al., 2020).

Society faces the double challenge of increasing biomass production to meet the future demands for food, materials and bioenergy, while addressing negative impacts of current (and future) land use. In the discourse, land use change (LUC) has often been considered as negative, referring to impacts of deforestation and expansion of biomass plantations. However, strategic establishment of suitable perennial production systems in agricultural landscapes can mitigate environmental impacts of current crop production, while providing biomass for the bioeconomy. Here, we explore the potential for such “beneficial LUC” in EU28. First, we map and quantify the degree of accumulated soil organic carbon losses, soil loss by wind and water erosion, nitrogen emissions to water, and recurring floods, in ∼81.000 individual landscapes in EU28. We then estimate the effectiveness in mitigating these impacts through establishment of perennial plants, in each landscape. The results indicate that there is a substantial potential for effective impact mitigation. Depending on criteria selection, 10–46% of the land used for annual crop production in EU28 is located in landscapes that could be considered priority areas for beneficial LUC. These areas are scattered all over Europe, but there are notable “hot-spots” where priority areas are concentrated, e.g., large parts of Denmark, western UK, The Po valley in Italy, and the Danube basin. While some policy developments support beneficial LUC, implementation could benefit from attempts to realize synergies between different Sustainable Development Goals, e.g., “Zero hunger”, “Clean water and sanitation”, “Affordable and Clean Energy”, “Climate Action”, and “Life on Land”. I studien har vi utforskat potentialen för fördelaktig markanvändningsförändring genom strategisk perennialisering i Europa. Miljöproblematiken i fler än 81,000 individuella landskap har kvantifierats och potentialen att lindra miljöproblematik med hjälp av strategisk etablering av perenna grödor har uppskattats i varje enskilt landskap. För mer information, se engelsk beskrivning.

Abstract Forests can help mitigate climate change in different ways, such as by storing carbon in forest ecosystems, and by producing a renewable supply of material and energy products. We analyse the climate implications of different scenarios for forestry, bioenergy and wood construction. We consider three main forestry scenarios for Kronoberg County in Sweden, over a 201-year period. The Business-as-usual scenario mirrors today's forestry while in the Production scenario the forest productivity is increased by 40% through more intensive forestry. In the Set-aside scenario 50% of forest land is set-aside for conservation. The Production scenario results in less net carbon dioxide emissions and cumulative radiative forcing compared to the other scenarios, after an initial period of 30–35 years during which the Set-aside scenario has less emissions. In the end of the analysed period, the Production scenario yields strong emission reductions, about ten times greater than the initial reduction in the Set-aside scenario. Also, the Set-aside scenario has higher emissions than Business-as-usual after about 80 years. Increasing the harvest level of slash and stumps results in climate benefits, due to replacement of more fossil fuel. Greatest emission reduction is achieved when biomass replaces coal, and when modular timber buildings are used. In the long run, active forestry with high harvest and efficient utilisation of biomass for replacement of carbon-intensive non-wood products and fuels provides significant climate mitigation, in contrast to setting aside forest land to store more carbon in the forest and reduce the harvest of biomass.