China’s large-scale tree planting programs are critical for achieving its carbon neutrality by 2060, but determining where and how to plant trees for maximum carbon sequestration has not been rigorously assessed. Here, we developed a comprehensive machine learning framework that integrates diverse environmental variables to quantify tree growth suitability and its relationship with tree numbers. Then, their correlations with biomass carbon stocks were robustly established. Carbon sink potentials were mapped in distinct tree-planting scenarios. Under one of them aligned with China’s ecosystem management policy, 44.7 billion trees could be planted, increasing forest stock by 9.6 ± 0.8 billion m³ and sequestering 5.9 ± 0.5 PgC equivalent to double China’s 2020 industrial CO₂ emissions. We found that tree densification within existing forests is an economically viable and effective strategy and so it should be a priority in future large-scale planting programs.

Accelerating energy transition towards renewables is central to net-zero emissions. However, building a global power system dominated by solar and wind energy presents immense challenges. Here, we demonstrate the potential of a globally interconnected solar-wind system to meet future electricity demands. We estimate that such a system could generate ~3.1 times the projected 2050 global electricity demand. By optimizing solar-wind deployment, storage capacity, and trans-regional transmission, the solar-wind penetration could be achieved using only 29.4% of the highest potential, with a 15.6% reduction in initial investment compared to a strategy without interconnection. Global interconnection improves energy efficiency, mitigates the variability of renewable energy, promotes energy availability, and eases the economic burden of decarbonization. Importantly, this interconnected system shows remarkable resilience to climate extremes, generation outages, transmission disruptions, and geopolitical conflicts. Our findings underscore the potential of global interconnection in enabling high renewable penetration and guiding sustainable energy transitions.

Industrialization production with high quality and effect on winter is an important measure for accelerating the shift from increasing agricultural production to improving quality in terms of grain protein content (GPC). Remote sensing technology achieved the GPC prediction. However, large deviations in interannual expansion and regional transfer still exist. The present experiment was carried out in wheat producing areas of Beijing (BJ), Renqiu (RQ), Quzhou, and Jinzhou in Hebei Province. First, the spectral consistency of Landsat 8 Operational Land Imager (LS8) and RapidEye (RE) was compared with Sentinel-2 (S2) satellites at the same ground point in the same period. The GPC prediction model was constructed by coupling the vegetation index with the meteorological data obtained by the European Center for Medium-range Weather Forecasts using hierarchical linear model (HLM) method. The prediction and spatial expansion of regional GPC were validated. Results were as follows: (1) Spectral information calculated from S2 imagery were highly consistent with LS8 (R² = 1.00) and RE (R² = 0.99) imagery, which could be jointly used for GPC modeling. (2) The predicted GPC by using the HLM method (R² = 0.524) demonstrated higher accuracy than the empirical linear model (R² = 0.286) and showed higher improvements across inter-annual and regional scales. (3) The GPC prediction results of the verification samples in RQ, BJ, Xiaotangshan (XTS) in 2018, and XTS in 2019 were ideal with root mean square errors of 0.61%, 1.13%, 0.91%, and 0.38%, and relative root mean square error of 4.11%, 6.83%, 6.41%, and 2.58%, respectively. This study has great application potential for regional and inter-annual quality prediction.

Precipitation nowcasting is pivotal for disaster prevention, urban planning, and various societal applications. Although deep learning-based radar echo extrapolation methods are widely adopted, their effectiveness is limited by challenges in handling sparse echo sequences with low pixel proportions. This limitation hinders accurate predictions of low-frequency heavy rainfall and localized precipitation events. To address these issues, a novel Patch-wise (PW) mechanism is proposed in this study. Specifically, radar echo frames are divided into patches, which are then encoded into sequences and processed using a local attention mechanism to enhance critical regional information extraction. Furthermore, multi-scale convolutions tailored to the patch scale are employed to expand the receptive field, and a convolutional block attention module is introduced to capture the spatiotemporal dynamics of sparse echoes and intense rainfall. These improvements enable extraction of sparse spatiotemporal features in a patch-wise manner, enhancing prediction accuracy for regional sparse precipitation. Experiments across Chinese regions demonstrate the effectiveness of the PW mechanism. Notably, integrating the PW into PredRNN model yields improvements in the Critical Success Index by 1.24%, 1.37%, and 12.59% for precipitation intensity thresholds of 20 dBZ, 30 dBZ, and 45 dBZ, respectively. Spatial visualizations of radar echoes reveal PW's superior in predicting localized and intense precipitation. This study is expected to advance regional precipitation nowcasting by offering both practical insights and methodological innovations.

Tree density is a significant parameter for forest. However, it has always been challenging to use remote-sensing techniques to acquire it efficiently and effectively on a large spatial scale. As a matter of fact, tree counting can be regarded as end-to-end density regression, which uses a remote-sensing image as an input and generates a tree density map as an output. Therefore, the tree number can be easily obtained by the summation of density values in an area. To this end, a deep neural network has been constructed to aggregate multiple decoding paths to extract hierarchical features at different encoding stages, in order to merge tree features of multiple scales in remote-sensing images. At the same time, a hybrid loss function has been proposed to effectively guide the network training and enhance the model ability. A tree count dataset consisting of 2400 sample pairs has been constructed for training and validation. When compared with other popular counting networks, it has been found that the proposed network achieved the best results with a relative mean absolute error (rMAE) of 16.72%, a root mean squared error (RMSE) of 77.96, and an $R^{2}$ of 0.96, evidencing that this is a promising method for estimating tree numbers on a large spatial scale.