Human interventions, such as farmland management, have long been considered crucial for soil carbon sequestration, but little is known about the exact impact of these interventions on the net carbon flux, represented by net ecosystem productivity (NEP). Here, using multiple long-term, large-scale data and statistical data, we reveal that 75.54% of farmland NEP in China experiences an increase, with northern regions showing the greatest potential for future farmland carbon sequestration. This growth is primarily attributed to the role of farmland management, especially the enhancement of no-tillage, land consolidation and multiple cropping level (17.02%, major grain-producing areas in 2020). Notably, the current unreasonable practices of mechanized straw returning and irrigation have a negative impact on farmland NEP. Our results show that it is imperative to acknowledge the crucial role of human interventions on farmland NEP to strike a balance between food security and farmland carbon sequestration.

Several safe boundaries of critical Earth system processes have already been crossed due to human perturbations; not accounting for their interactions may further narrow the safe operating space for humanity. Using expert knowledge elicitation, we explored interactions among seven variables representing Earth system processes relevant to food production, identifying many interactions little explored in Earth system literature. We found that green water and land system change affect other Earth system processes strongly, while land, freshwater and ocean components of biosphere integrity are the most impacted by other Earth system processes, most notably blue water and biogeochemical flows. We also mapped a complex network of mechanisms mediating these interactions and created a future research prioritization scheme based on interaction strengths and existing knowledge gaps. Our study improves the understanding of Earth system interactions, with sustainability implications including improved Earth system modelling and more explicit biophysical limits for future food production.

Solar energy is an abundant, clean, and renewable source that can mitigate global climate change, environmental pollution, and energy shortage. However, comprehensive datasets and efficient identification models for the spatial distribution of photovoltaic (PV) plants locally and globally over time remain limited. In the present study, a model that combines original spectral features, PV extraction indexes, and terrain features for the identification of PV plants is established based on the pilot energy city Golmud in China, which covers 71,298.7 km² and has the highest density of PV plants in the world. High-performance machine learning algorithms were integrated with PV plant extraction models, and performances of the XGBoost, random forest (RF), and support vector machine (SVM) algorithms were compared. According to results from the investigations, the XGBoost produced the highest accuracy (OA = 99.65%, F1score = 0.9631) using Landsat 8 OLI imagery. The total area occupied by PV plants in Golmud City in 2020 was 10,715.85 ha based on the optimum model. The model also revealed that the area covered by the PV plant park in the east of Golmud City increased by approximately 10% from 2018 (5344.2 ha) to 2020 (5879.34 ha). The proposed approach in this study is one of the first attempts to identify time-series large-scale PV plants based on a pixel-based machine learning algorithm with medium-resolution free images in an efficient way. The study also confirmed the effectiveness of combining original spectral features, PV extraction indexes, and terrain features for the identification of PV plants. It will shed light on larger- and longer-scale identification of PV plants around the world and the evaluation of the associated dynamics of PV plants.

Environmentally Extended Input-Output databases (EEIOs) provide an effective tool for assessing environmental impacts around the world. These databases have yielded many scientific and policy relevant insights, especially through the national accounting of impacts embodied in trade. However, most approaches average out the spatial variation in different factors, usually at the level of the nation, but sometimes at the subnational level. It is a natural next step to connect trade with local environmental impacts and local consumption. Due to investments in earth observation many new datasets are now available, offering a huge potential for coupling environmental datasets with economic models such as Multi-Region Input-Output (MRIO) models. A key tool for linking these scales are Spatially-explicit Input-Output (SIO) models, which provide both demand and supply perspectives by linking producers and consumers. Here we define an SIO model as a model having a resolution greater than the underlying input-output transaction matrix. Given the increasing interest in this approach, we present a timely review of the methods used, insights gained, and limitations of various approaches for integrating spatial data in input-output modelling. We highlight the evolution of these approaches, and review the methodological approaches used in SIO models so far. We investigate the temporal and spatial resolution of such approaches and analyze the general advantages and limitations of the modelling framework. Finally, we make suggestions for the future development of SIO models.

Key biodiversity areas (KBAs) are critical regions for preserving global biodiversity. KBAs are identified by their importance to biodiversity rather than their legal status. As such, KBAs are often under pressure from human activities. KBAs can encompass many different land-use types (e.g., cropland, pastures) and land-use intensities. Here, we combine a global economic model with spatial mapping to estimate the biodiversity impacts of human land use in KBAs. We find that global human land use within KBAs causes disproportionate biodiversity losses. While land use within KBAs accounts for only 7% of total land use, it causes 16% of the potential global plant loss and 12% of the potential global vertebrate loss. The consumption of animal products accounts for more than half of biodiversity loss within KBAs, with housing the second largest at around 10%. Bovine meat is the largest single contributor to this loss, at around 31% of total biodiversity loss. In terms of land use, lightly grazed pasture contributes the most, accounting for around half of all potential species loss. This loss is concentrated mainly in middle- and low-income regions with rich biodiversity. International trade is an important driver of loss, accounting for 22-29% of total potential plant and vertebrate loss. Our comprehensive global, trade-linked analysis provides insights into maintaining the integrity of KBAs and global biodiversity.