Global forest management data for 2015 at a 100 m resolution

Spatially-explicit land use change emissions and carbon payback times of biofuels under the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)

The Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) requires airlines to offset their greenhouse gas (GHG) emissions above 2019 levels by either buying carbon offsets or using Sustainable Aviation Fuels (SAFs). These are drop-in jet fuels made from biomass or other renewable resources that reduce GHG emissions by at least 10 % compared to kerosene and meet certain sustainability criteria. This study assesses the direct land use change (DLUC) emissions of SAF, i.e., GHG emissions from on-site land conversion from previous uses (excluding primary forests, peatlands, wetlands, and protected and biodiversity-rich areas) into alternative feedstocks, considering spatial variability in global yields and land carbon stocks. The results provide DLUC values and carbon payback times at 0.5-degree resolution for six SAF pathways, with and without irrigation and a medium-input intensity, according to CORSIA sustainability criteria. When excluding CORSIA non-compliant areas, soybean SAF shows the highest mean DLUC factor (31.9 ± 20.7 gCO2/MJ), followed by reed canary grass and maize. Jatropha SAF shows the lowest mean DLUC factor (3.6 ± 31.4 gCO2/MJ), followed by miscanthus and switchgrass. The latter feedstocks show potential for reducing GHG emissions over large areas but with relatively greater variability. Country-average DLUC values are higher than accepted ILUC ones for all pathways except for maize. To ensure the GHG benefits of CORSIA, feedstocks must be produced in areas where not only carbon stocks are relatively low but also where attainable yields are sufficiently high. The results help identify locations where the combination of these two factors may be favourable for low-DLUC SAF production. Irrigated miscanthus offers the highest SAF production potential (2.75 EJ globally) if grown on CORSIA-compliant cropland and grassland areas, accounting for ∼1/5 of the total kerosene used in 2019. Quantifying other environmental impacts of SAFs is desirable to understand sustainability trade-offs and financial constraints that may further limit production potentials.

Landscape-Scale Biodiversity Impacts Analysis of Côte d'Ivoire's Cocoa Cultivation along Export Supply Chains

Agricultural land use for export commodities leads to significant biodiversity impacts. A spatially detailed assessment of these impacts is crucial for implementing effective mitigation policies. Using cocoa cultivation and exports in Côte d'Ivoire as an example, we present a novel framework that combines earth observations, enhanced landscape-scale biodiversity models, and subnational export supply chain data sets to track the tele-connected potential biodiversity impacts of export groups and importing countries. We found that cocoa cultivation accounts for ∼44% of the biodiversity impacts in Côte d'Ivoire's cocoa cultivation areas, with >90% attributable to cocoa exports. The top 10 importing countries account for ∼84% of these impacts. Our method offers improved spatial detail compared to the existing approaches, facilitating the identification of biodiversity impact hotspots. Additionally, the biodiversity impacts of agroforestry cocoa are not always lower compared to full-sun cocoa, especially when agroforestry systems are established in regions of high biodiversity importance. Our transferable framework provides a comprehensive understanding of biodiversity footprint and promotes informed decision-making for sustainable agricultural production, processing, and trade. Our framework's application is currently constrained by the scarcity of detailed supply chain data sets; we underscore the urgent need for improved supply chain transparency to fully unlock the framework's potential.

Land-use intensification dominates China's land provisioning services: From the perspective of land system science

A pressing challenge to global sustainability is meeting the escalating needs of a growing population while safeguarding land resources from degradation. In recent decades, China's rapid growth, expanding population, urban sprawl, and diminishing high-quality farmland have presented a compelling case suitable for exploring solutions and challenges related to this critical issue. Therefore, there is an urgent need for comprehensive and detailed information regarding land systems. Here, we developed the first fine-scale dataset of the China Land System at a spatial resolution of 1 km, covering the period from 2000 to 2015. By leveraging this comprehensive land information, we identified five primary types of land systems and their respective subsystems, thereby delineating distinct patterns of human-environmental interaction. Land system dynamics followed diverse developmental trajectories characterized by incremental shifts toward more functionally centralized systems. Land use intensification played a significant role in increasing the population capacity and food production in China, contributing nearly 93.94% and 84.99%, respectively. In contrast, land cover changes accounted for only 4.69% and 11.43%, respectively. These findings underscore the tendency of previous studies to overestimate the impact of land cover change and underestimate the influence of land use intensification in meeting the growing demands of land-based production. This study emphasizes the importance of transcending traditional land cover-based approaches and integrating land systems into land representation and global land change scenario simulations to promote sustainability.

A global clustering of terrestrial food production systems

Food production is at the heart of global sustainability challenges, with unsustainable practices being a major driver of biodiversity loss, emissions and land degradation. The concept of foodscapes, defined as the characteristics of food production along biophysical and socio-economic gradients, could be a way addressing those challenges. By identifying homologues foodscapes classes possible interventions and leverage points for more sustainable agriculture could be identified. Here we provide a globally consistent approximation of the world's foodscape classes. We integrate global data on biophysical and socio-economic factors to identify a minimum set of emergent clusters and evaluate their characteristics, vulnerabilities and risks with regards to global change factors. Overall, we find food production globally to be highly concentrated in a few areas. Worryingly, we find particularly intensively cultivated or irrigated foodscape classes to be under considerable climatic and degradation risks. Our work can serve as baseline for global-scale zoning and gap analyses, while also revealing homologous areas for possible agricultural interventions.

Biodiversity Impact Assessment Considering Land Use Intensities and Fragmentation

Land use is a major threat to terrestrial biodiversity. Life cycle assessment is a tool that can assess such threats and thereby support environmental decision-making. Within the Global Guidance for Life Cycle Impact Assessment (GLAM) project, the Life Cycle Initiative hosted by UN Environment aims to create a life cycle impact assessment method across multiple impact categories, including land use impacts on ecosystem quality represented by regional and global species richness. A working group of the GLAM project focused on such land use impacts and developed new characterization factors to combine the strengths of two separate recent advancements in the field: the consideration of land use intensities and land fragmentation. The data sets to parametrize the underlying model are also updated from previous models. The new characterization factors cover five species groups (plants, amphibians, birds, mammals, and reptiles) and five broad land use types (cropland, pasture, plantations, managed forests, and urban land) at three intensity levels (minimal, light, and intense). They are available at the level of terrestrial ecoregions and countries. This paper documents the development of the characterization factors, provides practical guidance for their use, and critically assesses the strengths and remaining shortcomings.

Improving climate and biodiversity outcomes through restoration of forest integrity

Targeting degraded areas in forested landscapes for restoration could deliver rapid climate mitigation and biodiversity conservation, improve resilience of forested lands to future climate change, and potentially reduce the trade-offs between nature recovery and agriculture. Although the importance of forest restoration for climate mitigation is acknowledged, current estimates of its climate mitigation potential may be underestimated because they focus predominantly on reforesting cleared areas. We built on recent analyses of forest integrity and unrealized forest biomass potential to examine the potential for restoring the integrity of degraded forests. There are over 1.5 billion ha of forests worldwide that retain 50-80% of their potential biomass. Prioritizing restoration in these areas could deliver rapid biodiversity and climate mitigation benefits, relative to restoring forest on cleared land. We applied a spatial planning approach to demonstrate how restoration interventions can be targeted to support the conservation of high-integrity forest, a potential pathway to the delivery of the 30×30 goal of the Convention on Biodiversity's Global Biodiversity Framework.

The impact of the German timber footprint on potential species loss in supply regions

Previous own assessments have shown that a) Germany has a wood consumption above global average, b) is strongly dependent on imports and c) has a domestic roundwood production that is at the limit of the sustainable harvest potential. Thereby Germany further increases the pressures on global forests which are already stressed by climate-change related impacts and a continuously growing global demand for wood. This leads to negative impacts on the biodiversity in the areas where wood is harvested. This paper aims to show the connection between Germany's timber consumption footprint and the impact on the biodiversity in the regions where the roundwood is sourced. A two-step process is used. In the first step, high-resolution land cover and land use maps are used as a basis for the countryside species-area relationship model, assessing the projected loss of the four taxa amphibians, birds, mammals and reptiles in relation to undisturbed natural ecosystems due to forests occupied for roundwood production. In the second step, roundwood equivalents consumed in Germany in 2015 are traced back to the region of origin using an environmentally-extended input-output analysis and the thereby induced potential species loss is calculated. We show that the highest impact on projected species richness loss caused by roundwood logging is taking place in Oceania (3.34E-03 species/m3), Carribean (1.56E-04 species/m3), and East Asia (1.43E-04 species/m3). German roundwood consumption has the highest projected species loss in the United States (7.4 species), followed by China (7.3 species) and Brazil (4.8 species). From a biodiversity impact perspective, Germany could theoretically reduce its impact by relocating imports to European countries. In view of the planetary boundary of sustainable roundwood consumption, which has already been exceeded, reducing consumption appears to be the only viable long-term option for high-consumption countries such as Germany to reduce negative impacts on global biodiversity.

Spatial predictions and uncertainties of forest carbon fluxes for carbon accounting

Countries have pledged to different national and international environmental agreements, most prominently the climate change mitigation targets of the Paris Agreement. Accounting for carbon stocks and flows (fluxes) is essential for countries that have recently adopted the United Nations System of Environmental-Economic Accounting - ecosystem accounting framework (UNSEEA) as a global statistical standard. In this paper, we analyze how spatial carbon fluxes can be used in support of the UNSEEA carbon accounts in five case countries with available in-situ data. Using global multi-date biomass map products and other remotely sensed data, we mapped the 2010-2018 carbon fluxes in Brazil, the Netherlands, the Philippines, Sweden and the USA using National Forest Inventory (NFI) and local biomass maps from airborne LiDAR as reference data. We identified areas that are unsupported by the reference data within environmental feature space (6-47% of vegetated country area); cross-validated an ensemble machine learning (RMSE=9-39 Mg C [Formula: see text] and [Formula: see text]=0.16-0.71) used to map carbon fluxes with prediction intervals; and assessed spatially correlated residuals (<5 km) before aggregating carbon fluxes from 1-ha pixels to UNSEEA forest classes. The resulting carbon accounting tables revealed the net carbon sequestration in natural broadleaved forests. Both in plantations and in other woody vegetation ecosystems, emissions exceeded sequestration. Overall, our estimates align with FAO-Forest Resource Assessment and national studies with the largest deviations in Brazil and USA. These two countries used highly clustered reference data, where clustering caused uncertainty given the need to extrapolate to under-sampled areas. We finally provide recommendations to mitigate the effect of under-sampling and to better account for the uncertainties once carbon stocks and flows need to be aggregated in relatively smaller countries. These actions are timely given the global initiatives that aim to upscale UNSEEA carbon accounting.

Spatial database of planted forests in East Asia

Planted forests are critical to climate change mitigation and constitute a major supplier of timber/non-timber products and other ecosystem services. Globally, approximately 36% of planted forest area is located in East Asia. However, reliable records of the geographic distribution and tree species composition of these planted forests remain very limited. Here, based on extensive in situ and remote sensing data, as well as an ensemble modeling approach, we present the first spatial database of planted forests for East Asia, which consists of maps of the geographic distribution of planted forests and associated dominant tree genera. Of the predicted planted forest areas in East Asia (948,863 km2), China contributed 87%, most of which is located in the lowland tropical/subtropical regions, and Sichuan Basin. With 95% accuracy and an F1 score of 0.77, our spatially-continuous maps of planted forests enable accurate quantification of the role of planted forests in climate change mitigation. Our findings inform effective decision-making in forest conservation, management, and global restoration projects.