Land use of drained peatlands: Greenhouse gas fluxes, plant production, and economics.

Drained peatlands are hotspots for greenhouse gas (GHG) emissions, which could be mitigated by rewetting and land use change. We performed an ecological/economic analysis of rewetting drained fertile peatlands in a hemiboreal climate using different land use strategies over 80 years. Vegetation, soil processes, and total GHG emissions were modeled using the CoupModel for four scenarios: (1) business as usual-Norway spruce with average soil water table of -40 cm; (2) willow with groundwater at -20 cm; (3) reed canary grass with groundwater at -10 cm; and (4) a fully rewetted peatland. The predictions were based on previous model calibrations with several high-resolution datasets consisting of water, heat, carbon, and nitrogen cycling. Spruce growth was calibrated by tree-ring data that extended the time period covered. The GHG balance of four scenarios, including vegetation and soil, were 4.7, 7.1, 9.1, and 6.2 Mg CO2 eq ha-1  year-1 , respectively. The total soil emissions (including litter and peat respiration CO2 + N2 O + CH4 ) were 33.1, 19.3, 15.3, and 11.0 Mg CO2 eq ha-1  year-1 , respectively, of which the peat loss contributed 35%, 24%, and 7% of the soil emissions for the three drained scenarios, respectively. No peat was lost for the wet peatland. It was also found that draining increases vegetation growth, but not as drastically as peat respiration does. The cost-benefit analysis (CBA) is sensitive to time frame, discount rate, and carbon price. Our results indicate that the net benefit was greater with a somewhat higher soil water table and when the peatland was vegetated with willow and reed canary grass (Scenarios 2 and 3). We conclude that saving peat and avoiding methane release using fairly wet conditions can significantly reduce GHG emissions, and that this strategy should be considered for land use planning and policy-making.

Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms.

Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups-algae, aquatic invertebrates and fish-and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC50/EC10), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.

Economic interests cloud hazard reductions in the European regulation of substances of very high concern.

Here we investigate how the conflicts between hazard reduction and economic interests have shaped the regulation of substances of very high concern (SVHCs) under the Authorization program of the European chemical regulation Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH). Since regulation starts with listing SVHCs on the Candidate List, we analyze the relative importance of toxicological properties, economic motivations, and available scientific knowledge on the probability of inclusion on the Candidate List. We find that the most important factor in whether a substance is listed is whether it is being produced in, or imported into, the European Economic Area (EEA), with the regulators less likely to place a substance on the list if it is currently being produced or imported in the EEA. This evidence suggests that regulators have listed chemicals of secondary importance leading to lesser than anticipated hazard reductions, either because production and imports had already ceased before the listing, or because the compound has never been produced or imported in the EEA at all.

To trade or not to trade: firm-level analysis of emissions trading in Santiago, Chile.

Whether tradable permits are appropriate for use in transition and developing economies--given special social and cultural circumstances, such as the lack of institutions and lack of expertise with market-based policies--is much debated. We conducted interviews and surveyed a sample of firms subject to emissions trading programs in Santiago, Chile, one of the first cities outside the OECD that has implemented such trading. The information gathered allows us to study what factors affect the performance of the trading programs in practice and the challenges and advantages of applying tradable permits in less developed countries.