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Extensive forest restoration is a key strategy to meet nature-based sustainable development goals and provide multiple social and environmental benefits 1 . Yet achieving forest restoration at scale requires cost-effective methods 2 . Tree planting in degraded landscapes is a popular but costly forest restoration method that often results in less biodiverse forests when compared to natural regeneration techniques under similar conditions 3 . Here we assess the current spatial distribution of pantropical natural forest (from 2000 to 2016) and use this to present a model of the potential for natural regeneration across tropical forested countries and biomes at a spatial resolution of 30 m. We estimate that an area of 215 million hectares-an area greater than the entire country of Mexico-has potential for natural forest regeneration, representing an above-ground carbon sequestration potential of 23.4 Gt C (range, 21.1-25.7 Gt) over 30 years. Five countries (Brazil, Indonesia, China, Mexico and Colombia) account for 52% of this estimated potential, showcasing the need for targeting restoration initiatives that leverage natural regeneration potential. Our results facilitate broader equitable decision-making processes that capitalize on the widespread opportunity for natural regeneration to help achieve national and global environmental agendas., (© 2024. The Author(s).)

Protected area (PA) assessments rarely evaluate socio-economic and environmental impacts relative to competing land uses, limiting understanding of socio-environmental trade-offs from efforts to protect 30% of the globe by 2030. Here we assess deforestation and poverty outcomes (fiscal income, income inequality, sanitation and literacy) between 2000 and 2010 of strict PAs (SPAs), sustainable-use PAs (SUPAs) and Indigenous territories (ITs) compared with different land uses (agriculture and mining concessions) across ~5,500 census tracts in the Brazilian Legal Amazon. ITs reduced deforestation relative to all alternative land uses (48-83%) but had smaller socio-economic benefits compared with other protection types and land uses (18-36% depending on outcome), indicating that Indigenous communities experience socio-economic trade-offs. By contrast, SUPAs, and potentially SPAs, did not reduce deforestation relative to small-scale agriculture (landholdings <10 ha) but did so relative to larger agricultural landholdings (70-82%). Critically, these reductions in deforestation frequently occurred without negative socio-economic outcomes. By contrast, ITs and SUPAs protected against deforestation from mining, but at the cost of smaller improvements in income and inequality. Our results suggest that although PAs in the Brazilian Legal Amazon substantially reduced deforestation without compromising local socio-economic development, efforts to secure Indigenous rights need additional interventions to ensure these communities are not further disadvantaged., (© 2024. The Author(s).)

In an industrial environment, the quality assurance of weld seams requires extensive efforts. The most commonly used methods for that are expensive and time-consuming destructive tests, since quality assurance procedures are difficult to integrate into production processes. Beyond that, available test methods allow only the assessment of a very limited set of characteristics. They are either suitable for determining selected geometric features or for locating and evaluating internal seam defects. The presented work describes an evaluation methodology based on microfocus X-ray computed tomography scans (µCT scans) which enable the 3D characterization of weld seams, including internal defects such as cracks and pores. A 3D representation of the weld contour, i.e., the complete geometry of the joint area in the component with all quality-relevant geometric criteria, is an unprecedented novelty. Both the dimensions of the weld seam and internal defects can be revealed, quantified with a resolution down to a few micrometers and precisely assigned to the welded component. On the basis of the methodology developed within the framework of this study, the results of the scans performed on the alloy AA 2219 can be transferred to other aluminum alloys. In this way, the data evaluation framework can be used to obtain extensive reference data for the calibration and validation of inline process monitoring systems employing Deep Learning-based data processing in the scope of subsequent work.