Surface solar radiation is essential for regional climate and environmental research. The widely used European Centre for Medium-Range Weather Forecasts ERA5 reanalysis products provide long-term spatial estimates of the total and direct solar radiation on an hourly scale; however, its accuracy has not been systematically evaluated for China. In this letter, in situ measurements from 98 sites are used to analyze the deviations in the hourly radiation from ERA5 and to compare the daily variations. The results reveal that estimates from the reanalysis correlate well with ground observations and completely reflect the regional characteristics and daily variations at individual sites. Overall, ERA5 overestimates the hourly total solar radiation and direct radiation by approximately 30.87 and 73.95 W/m², respectively, whereas it underestimates the diffuse radiation by approximately 43.08 W/m². Spatiotemporal differences are found by comparing the errors and time series of different sites. The fact that the accuracy in areas with rainy and cloudy weather is obviously lower indicates that the ERA5 analysis poorly performs when retrieving cloud properties. These conclusions can contribute to guiding the rational use of ERA5 radiation products.

In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8, 0.3, and 0.1 m, which focus on concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline–alkali land, and water surfaces, as well as flat concrete, steel tile, and brick roofs. The dataset is used to examine the model performance of different deep networks on PV segmentation. On average, an intersection over union (IoU) greater than 85 % is achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible and that fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more work on PV technology for greater value, such as developing a PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al., 2021).

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.