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Unions, fossil fuel workers, and the energy transition: learning from plant closures in Finland and the U.S.
Full text linkEnergy-related emissions account for nearly 85 percent of global carbon dioxide emissions. Consequently, the energy sector is a primary focus of climate policies aimed at mitigating global warming, and a transition to low-carbon and net-zero energy systems and economies is at the focus of many national and international decarbonization policies. Like other economic restructuring projects, the energy transition is not a singular process but instead is unfolding as multiple interdependent processes across geographical and temporal scales. In these processes, transitions to renewable and low-carbon energy sources also translate to transitioning away from hydrocarbons and their attendant socio-economic and socio-cultural production systems. In their most tangible form, these low-carbon transitions manifest themselves in local contexts as plant-level and industry closures that – unless carefully managed – have the potential to not only upend local socioeconomic realities but also to fuel discontent against the broader societal programme of the low-carbon transition. In this article, we take a focus on two such local low-carbon transitions – the closure of coal-fired Hanasaari power plant in Helsinki, Finland and the sudden closure of the Marathon Petroleum oil refinery in Contra Costa County, California, US – as case studies of plant-level closures that reflect several experienced transition injustices. However, our analysis goes beyond merely documenting and comparing injustices across these transition contexts. Against the backdrop of literatures on radical energy justice and the role of labour unions as just transition actors, our analysis sheds light on both the interpretations of justice labour unions work to advance and different ways in which union responses in individual plant closure contexts have broadened the unions’ scope of interest and agency in just transition related broader political and societal agendas
Lehmän liha on tärkeä osa suomalaista naudanlihantuotantoa
Full text linkLehtiartikkeli (Rinnakkaistallennusluvan asianumero: 526/12 05 01 02/2025
Digital mapping of peat thickness and extent in Finland using remote sensing and machine learning
Full text linkAccurate data on peat extent and thickness is essential for managing drained peatlands and reducing greenhouse gas emissions. Machine learning-based digital soil mapping offers an effective approach for large-scale peat occurrence prediction. In this study, we present a workflow for producing peat occurrence maps for the whole of Finland. For this, we used random forest classification to map areas with peat thicknesses of ≥ 10 cm, ≥30 cm, ≥40 cm, and > 60 cm. The input data consisted of 3.5 million point observations and 188 feature rasters from various sources. We carefully split the reference data into training and test sets, allowing for independent and robust model validation. Feature selection included an initial screening for multicollinearity using correlation-based feature pruning, followed by final selection using a genetic algorithm. Feature importance was evaluated using permutation importance and SHAP values. The resulting models utilized 26–33 features, achieving overall accuracies and F1-scores between 86–95 % and 0.82–0.95, respectively. The most important features included soil wetness indices, terrain roughness indices, and natural gamma radiation. Additionally, we provided an approach for evaluating spatial prediction uncertainty based on the models’ internal prediction agreement. Compared to existing superficial deposit maps, our peat predictions significantly improve the spatial detail of peatlands at the national level, offering new opportunities for land use planning and emission mitigation. Our exceptionally comprehensive approach is broadly applicable, offering new insights into optimizing machine learning-based digital peatland mapping, particularly through refining feature selection to account for local conditions and enhance prediction accuracy
A method for phenotyping lettuce volume and structure from 3D images
Full text linkMonitoring plant growth is crucial for effective crop management, and using color and depth (RGBD) cameras to model lettuce has emerged as one of the most convenient and non-invasive methods. In recent years, deep learning techniques, particularly neural networks, have become popular for estimating lettuce fresh weight. However, these models are typically specific to particular datasets, lack domain adaptation, and are often limited by the availability of open-access datasets. In this study, we propose a method based on plant geometric features for estimating the rosette structure and volume of lettuce. This new approach was compared to existing methods that reconstruct surfaces from point clouds, such as Ball Pivoting and Alpha Shapes. The proposed method creates a tight hull around the plant's point cloud, preserving high detail of the rosette structure while filling in surface holes in areas not visible to 3D cameras. Using a linear regression model, we estimated fresh weight for this dataset, achieving a root mean square error (RMSE) of 18.2 g when using only the estimated plant volume, and 17.3 g when both volume and geometric features were included. Additionally, we introduced new geometric features that characterize leaf density, which could be useful for breeding applications. A dataset of 402 point clouds of lettuce plants, captured before harvest, was compiled using one top-down and three side-view 3D cameras
Economic feasibility of biochar for carbon stock enhancement in Finnish agricultural soils
Full text linkBiochar is a promising climate mitigation measure that can safely capture and store atmospheric carbon dioxide in soil for many years. We conduct an economic analysis to assess the economic feasibility of increasing Finnish mineral agricultural soil carbon stock with biochar in an increasingly dry and warm climate scenario. The Monte Carlo simulations showed that it is challenging to achieve economic feasibility with current carbon prices and biochar costs. To make biochar application economically feasible with a carbon subsidy at the level of the European Union Emissions Trading System (EU ETS) carbon price of 88 EUR/t CO2eq, the cost of biochar material would need to be reduced to less than one-third of its current average price. Alternatively, economic viability could be achieved if the subsidy paid to the farmers was between two to nine times larger than the EU ETS carbon price for the current range of biochar market prices. Lastly, the feasibility can be achieved by simultaneous doubling of the carbon price and halving average biochar cost. Currently, the biochar market is thin and a decrease in biochar cost level is needed to make biochar competitive with other climate change mitigation measures
Impact of the EU biodiversity strategy for 2030 on the EU wood-based bioeconomy
Full text linkThe EU Biodiversity Strategy (EUBDS) for 2030 aims to conserve and restore biodiversity by protecting large areas throughout the European Union. A target of the EUBDS is to protect 30 % of the EU’s land area by 2030, with 10 % being strictly protected (including all primary and old growth forests) and 20 % being managed ‘closer to nature’. Even though this will have a positive impact on biodiversity, it may negatively impact the EU’s wood-based bioeconomy. In this study, we analyze how alternative interpretations and distributions of the EU’s protection targets may affect future woody biomass harvest levels, exports of wood commodities, and the spatial distribution of managed areas under wood demands aligned with SSP2-RCP1.9. Using the model GLOBIOM-Forest, we simulate scenarios representing a variety of interpretations and geographic distributions of the EUBDS targets. The EUBDS targets would have a limited impact on EU harvest levels since the EU can still increase its wood harvest between 21 % and 24 % by 2100. With strict protection of 30 % of the area, the EU harvest level can still be increased by 10 %. Moreover, the most likely scenario (10 %/20 % protection within each MS) will result in increased net exports in the coming decades, but a slight decline after 2050. However, if protection is intended to also represent site productivity or to re-establish a green infrastructure, then EU net exports will also decline before 2050. With the decreased EU roundwood harvest, increased harvest will occur in other biomes and mostly leaking into boreal regions
Wood Biomolecules as Agricultural Adjuvants for Effective Suppression of Droplet Rebound from Plant Foliage
Full text linkThe agrochemical run-off associated with crop control is an unintended consequence of droplet rebound from plant foliage, which negatively affects crop performance and the environment. This is most critical in water-based formulations delivered on plant surfaces that are typically waxy and nonwetting. This study introduces an alternative to synthetic surfactants and high molecular weight polymers that are used as spreading agents for agrochemicals. Specifically, biopolymeric adjuvants (hemicelluloses and oligomeric lignin) extracted from wood by pressurized hot water are shown for their synergistic pinning capacity and surface activity that can effectively suppress droplet rebound from hydrophobic surfaces. Hemicellulose and lignin mixtures, alongside several model compounds, are investigated for understanding the dynamics of droplet impact and its correlation with biomacromolecule formations. The benefit of utilizing lean solutions (0.1 wt.% concentration) is highlighted for reducing droplet rebounding from leaves, outperforming synthetic systems in current use. For instance, a tenfold deposition improvement is demonstrated on citrus leaves, because of a significantly suppressed droplet roll-off. These results establish the excellent prospects of wood extracts to improve crop performance
Assessing climate impacts of agroforestry system in LCA: case study in Zambia
Full text linkPurpose: Agroforestry systems have the potential to reduce the carbon footprint (CF) of food production. One of the advantages of these systems is carbon removal from the atmosphere to biogenic carbon of trees. Nevertheless, there is not a common agreement on the method to include the climatic benefits of agroforestry systems in life cycle assessment (LCA). This study aims to evaluate methods for including biogenic carbon in the LCA of agroforestry systems.
Methods: We studied three different maize production systems in Zambia: Low-input, High-input, and Agroforestry scenario. In the Agroforestry scenario, we studied a maize–Faidherbia albida system by investigating three methodological approaches with a functional unit (FU) of 1 ha. In Methodological Approach 1, biogenic carbon in the above- and below-ground biomass of trees in the agroforestry system was considered as a temporary carbon dioxide (CO2) storage via correction flow. In Methodological Approach 2, the biomass of trees was used as an energy source. In Methodological Approach 3, the mass balance principle was employed. The system expansion method was utilized to make each production system comparable. Three sensitivity analyses with a FU of 1000 kg of dry matter maize grain were also conducted following the methodological approaches.
Results and discussion: The Agroforestry scenario had the lowest CF in all methodological approaches and in all sensitivity analyses when the maize grain yield level was the same as reported in the literature. Yet, uncertainty levels were high, as an IPCC tier 1 method was used. It was found that the biogenic carbon of the trees in agroforestry systems can be included in LCA by the currently available methods. The mass balance principle was a practical method for including the biogenic carbon of the trees in LCA. System expansion was another feasible method, wherein processes are added to the compared systems until they include the provision of the same functions.
Conclusions: Biogenic carbon in the biomass of trees in agroforestry systems can greatly contribute to decreasing the CF if it is considered in LCA. If the FU and allocation methods are selected accordingly, the biogenic carbon can be effectively included in LCA. Based on the results of this case study, the maize–F. albida agroforestry system has the potential for decreasing the CF of maize production in Zambia.202
Exploring the potential of SAR and terrestrial and airborne LiDAR in predicting forest floor spectral properties in temperate and boreal forests
Full text linkForest floor vegetation plays a crucial role in ecosystem processes of temperate and boreal forests. Remote sensing offers a valuable tool to characterize the forest floor through reflectance spectra. While passive optical airborne and satellite data have been used to map spectral properties of forest understory, these sensors are limited by cloud cover, especially in high latitudes. To date, LiDAR and SAR have not been explored for this application even though their data are less dependent on illumination conditions and provide information on tree canopy structure and tree distribution which is connected to forest floor properties. We investigated active remote sensing techniques to establish links between forest structure and spectral properties of forest floor across European temperate, hemiboreal and boreal forest ecosystems. First, in the exploratory part, the research question was : Which forest structure metrics are connected to the spectral properties of the forest floor? Next, our predictive part focused on: What is the potential of (1) terrestrial laser scanning (TLS) data, (2) airborne laser scanning data, (3) satellite-borne SAR data, and (4) these data sources combined to predict forest floor spectral properties? Our results revealed that nine forest structure metrics were potentially associated with forest floor reflectance. We identified TLS-derived clumping index and SAR-derived VV backscatter coefficient and VH/VV ratio as significantly connected to forest floor reflectance in certain Sentinel-2 spectral bands. Overall, the active remote sensors achieved the best predictions for forest floor reflectance in red-edge, near-infrared and shortwave infrared regions. Using data from all three sensors together to predict the forest floor spectra yielded better results than using any of the sensors alone. When data from a single sensor were used, the highest prediction accuracies for forest floor reflectance in the red-edge and near-infrared regions were achieved with SAR data, and in the shortwave infrared region with either SAR or TLS data. In the future, the accuracy of predicting forest floor characteristics in temperate and boreal forests could benefit from a synergy of passive and active technologies