As the largest terrestrial ecosystem covering extensive expanses of the Earth’s surface, forests offer crucial health benefits to humans, both directly and indirectly. Presently, health services derived from forest resources have presented significant opportunities for enhancing human well-being. Nonetheless, the absence of a comprehensive understanding regarding the mechanisms by which forests impact human health jeopardizes the potential gains in health. Regrettably, there remains a dearth of scholarly work elucidating these pathways. This paper aims to furnish a thorough examination of how forests influence human health. We initiate by formulating a conceptual framework upon which we delineate the various pathways through which forests impact human health. These encompass the provisioning of resources, preventive services, and forest therapies. Concurrently, we outline the moderating influence of social, economic, and individual characteristics as mediators within this pathway. These characteristics are classified into two overarching dimensions: accessibility and behavioral choices, which notably affect marginalized demographics such as those with lower socioeconomic status, women, the elderly, individuals with disabilities, and children in developing nations. Consequently, we build upon these foundational insights to propose six strategies aimed at perpetuating the positive impact of forests on human health in the foreseeable future. In the future, the development of forest management policies, the assessment of long-term health benefits, social practices, and international cooperation must be considered holistically to attain the dual objective of sustainable forest management and the advancement of human well-being.

Information of burned areas (BAs) is particularly important for research on forest fires. Mapping BAs contributes to managing and preventing further destruction. BA mapping has long been constrained by the temporal and spatial resolution of satellite data and classification accuracy, while also neglecting the spectral characteristics of optical satellites. To address this issue, a novel method for mapping and extracting BAs using a semantic segmentation model has been proposed herein. Leveraging medium spatial resolution satellites, Landsat-8 and Sentinel-2, and after determining the optimal spectral band combination, a spectral–spatial module was integrated into the U-Net; this results in a new model, known as the SSU-Net. SSU-Net precisely extracted BAs from a single post-fire image. The optimal band combinations for Landsat-8 and Sentinel-2 were bands 7-5-4 and 12-8-4, respectively. SSU-Net achieved an F1 score of 0.9296 and an intersection over union (IoU) of 0.8707 on Landsat-8 data. It achieved slightly higher performance on Sentinel-2 with an F1 score of 0.9314 and IoU of 0.8752. Compared with U-Net, SSU-Net improved the F1 scores by 4.8% and 3.4% for Landsat-8 and Sentinel-2, respectively, and IoU by 7.7% and 5.9%. In terms of generalisation, SSU-Net achieved an F1 score of 0.9151 when trained on Landsat-8 data and tested on Sentinel-2 data, and 0.9313 vice versa, with improvements of 2.1% and 5.1% over U-Net, respectively. SSU-Net also demonstrated higher accuracy and generalisation in BA mapping, supporting the integration of multisource remote sensing data and emphasising its crucial role in sustainable forest management on a global scale.

Degradation of black soil areas is a serious threat to national food security and ecological safety; nevertheless, the current lack of information on the location, size, and condition of black soil farmland productivity is a major obstacle to the development of strategies for the sustainable utilization of black soil resources. We synthesized remote sensing data and geospatial thematic data to construct a farmland productivity assessment indicator system to assess the productivity of black soil cropland at the regional scale. Furthermore, we conducted research on the spatial differentiation patterns and a spatial autocorrelation analysis of the assessment results. We found that farmland productivity within this region exhibited a decline pattern from south to north, with superior productivity in the east as opposed to the west, and the distribution follows a “spindle-shaped” pattern. Notably, the Songnen and Sanjiang typical black soil subregions centrally hosted about 46.17% of high-quality farmland and 53.51% of medium-quality farmland, while the Mondong typical black soil subregion in the west predominantly consisted of relatively low-quality farmland productivity. Additionally, farmland productivity displayed a significant positive spatial correlation and spatial clustering, with more pronounced fluctuations in the northeast–southwest direction. The developed indicator system for farmland productivity can illustrate the spatial differentiation and thereby offer a valuable reference for the sustainable management of farmland resources.

This paper examines how the entrepreneurial vibrancy of a city impacts the relationships between financing constraints and private SMEs’ heterogeneous innovations (i.e. exploration and exploitation). Using the random-sample of Chinese private enterprises, surveyed by the World Bank from December 2011 to February 2013, we find that financing constraints significantly restrain private SMEs’ exploration yet have no significant influence on their exploitation. In addition, in the cities with a high level of entrepreneurial vibrancy, the negative effect of financing constraints on exploration would be weakened, while the effect on exploitation turns negative.

Exposure to PM_2.5 poses severe risks to public health and sustainable development, with exposure inequalities exacerbated by variations in atmospheric activity and uneven regional development. However, the urban-rural inequalities and natural-human driving mechanisms underlying PM_2.5 exposure inequalities within urban agglomerations are poorly understood. Taking the Central Plains Urban Agglomeration (CPUA) in China as an example, this study investigated the spatio-temporal variations of PM_2.5 and considered its future trends. The Theil index was employed to quantify PM_2.5 exposure inequalities. An interpretable machine learning model (RF-SHAP) was applied to identify the raster natural and socioeconomic driving factors. We found that 99.68% of the CPUA exhibited a decreasing trend in ground-level PM_2.5. The overall Theil index decreased from 0.168 to 0.142, with a rural decline from 0.115 to 0.084, suggesting an overall reduction in air pollution inequalities, particularly in rural areas. Conversely, the urban Theil index increased from 0.096 to 0.208, highlighting an increasing inequality in urban PM_2.5 exposure. Resource-based cities, such as Changzhi, Jincheng, and Jiaozuo, exhibited the largest PM_2.5 exposure inequality. Elevation was identified as the dominant factor influencing overall and rural PM_2.5 exposure inequalities, while population density was the primary driver of urban inequalities. This study highlighted the differences in urban−rural PM_2.5 inequalities and their drivers at the city agglomeration scale. The aims were to mitigate PM_2.5 exposure inequalities through socio-environmental systems, provide evidence for the integrated management of PM_2.5 exposure inequalities in city agglomerations, and support regional sustainable development.