Large-area hyperspectral remote sensing monitoring of physiological parameters of reclaimed vegetation is a crucial means of environmental supervision in rare earth (RE) mining areas. The variation analysis of reclaimed vegetation characteristics under environmental stress in mining areas provides the necessary foundation for accurate dynamic monitoring of ecological restoration. In this article, the original spectral reflectance of six typical reclaimed vegetations in the RE mining area and their corresponding vegetations in the normal environment are measured. The spectral variations between reclaimed and normal vegetations are then compared and analyzed. To extract more hidden information in spectral features, the original spectrum is transformed using common derivative transform. In addition, wavelet transform (WT) and fractional differential (FD) transform are used to strengthen the detailed information of the original spectrum and explore the spectral features of reclaimed vegetation under environmental stress in RE mining areas. It can be deduced that in the first derivative spectrum, the reclaimed vegetations show a blueshift in the red edge position except for Wetland pine . This indicates that reclaimed vegetation is affected by external factors, such as environmental stresses to different degrees in the mining area. WT can extract the original spectral features with a high degree of influence, highlighting the detailed information with obvious changes and sharp fluctuations in spectral information features, in which the reclaimed vegetations and normal vegetations had obvious differences at the d5 scale. These results provide technical support for process management and formulation of reclamation measures of reclaimed vegetation in RE mining areas, and therefore contribute to the ecological reconstruction of RE reclamation mining areas.

The urban thermal environment poses a significant challenge to public health and sustainable urban development. Conventional pre-defined classification schemes, such as the Local Climate Zone (LCZ) system, often fail to capture the highly heterogeneous structure of complex urban areas, thus limiting their applicability. This study introduces a novel framework for urban thermal environment analysis, leveraging multi-source data and eXplainable Artificial Intelligence to investigate the driving mechanisms of Land Surface Temperature (LST) across various urban form types. Focusing on the area within Beijing’s 5th Ring Road, this study employs a K-Means clustering algorithm to classify urban blocks into nine distinct types based on their building morphology. Subsequently, an eXtreme Gradient Boosting (XGBoost) model, coupled with the SHapley Additive exPlanations (SHAP) method, is utilized to analyze the non-linear impacts of ten selected driving factors on LST. The findings reveal that: (1) The Compact Mid-rise type exhibits the highest annual average LST at 296.59 K, with a substantial difference of 11.29 K observed between the hottest and coldest block types. (2) SHAP analysis identifies the Normalized Difference Built-up Index (NDBI) as the most significant warming factor across all types, while the Sky View Factor (SVF) plays a crucial cooling role in high-rise areas. Conversely, road density (RD) shows a negative correlation with LST in Open Low-rise areas. (3) The influence of urban form is twofold: increased building height (BH) can induce warming by trapping heat while simultaneously providing a cooling effect through shading. (4) The impact of land use functional zones on LST is significantly modulated by urban form, with temperature differences of up to 2 K observed between different functional zones within compact block types. The analytical framework proposed herein holds significant theoretical and practical implications for achieving fine-grained thermal environment governance and fostering sustainable development in the context of global urbanization.

Carbon emission inequality has become a critical factor constraining the coordinated development of socio-economic systems and the natural environment. This inequality exacerbates the disparity in carbon emissions across regions, hindering efforts to achieve sustainable development and environmental justice. Previous research has primarily focused on the structure of carbon footprints and their influencing factors, but there has been limited quantitative research on carbon emission inequality, particularly from a multi-scale perspective. This study constructs a 250 m-high-resolution consumption-based carbon footprint grid for China and uses the Theil index to reveal significant spatial inequalities in carbon footprints. The results indicate that smaller-scale analyses better reveal the spatiotemporal heterogeneity of carbon footprints within regions. At the county level, carbon footprints exhibit significant inequalities, with hotspots concentrated in regions such as Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Pearl River Delta. The top 5% of areas with the highest carbon footprints (139 cities) contributed 19.6% of the national total, indicating a concentration in a few large cities. The decomposition of the Theil index shows that county-level cities contributed 55% of the national carbon inequality. The study also reveals the complex relationship between carbon footprints and income, as well as urban-rural disparities. The underdeveloped central and western regions exhibit a pronounced spatial lag effect, with the growth rate of carbon footprints in rural areas surpassing that of urban areas. Carbon footprints in impoverished areas and inter-provincial marginal areas overlap significantly with low-emission zones, demonstrating characteristics of “low-carbon growth”. To achieve carbon peak and carbon neutrality targets, China must adopt comprehensive measures to reduce carbon footprints and their inequalities, including strengthening multi-scale carbon inequality monitoring, implementing differentiated carbon reduction policies, and promoting coordinated emission reduction development at the county level.

As nations worldwide commit to carbon neutrality targets in response to accelerating climate change, the spatial modeling of carbon emissions has emerged as an indispensable tool for policy implementation and assessment. This paper presents a systematic review of the field from bibliometric and methodological perspectives. We synthesize key developments in spatial allocation techniques, data-driven models, and emission characterization methods. A central focus is the transformative role of geospatial big data in improving model accuracy and applicability, particularly how fine-grained, high-resolution modeling enhances the efficacy of emission reduction strategies. Our analysis reveals several key conclusions. First, the literature on carbon emission spatial modeling is expanding rapidly, with a discernible shift in focus from coarse, large-scale assessments toward more granular analyses that are sector-specific, high-resolution, and multidimensional. Second, hybrid models that integrate top-down and bottom-up approaches are now the predominant strategy for enhancing both accuracy and applicability; coupling mechanistic models with machine learning techniques effectively reconcile macro-scale data consistency with micro-scale heterogeneity. Third, the integration of geospatial big data is revolutionizing the field by providing the high-resolution, multidimensional, and dynamic inputs necessary to transition from macro- to micro-scale analysis. This is particularly evident in fine-grained assessments of urban systems—including spatial functions, morphology, and transportation networks—where such data dramatically improve the characterization of emission sources, intensities, and their spatiotemporal heterogeneity. This study ultimately elucidates the critical role of fine-grained modeling in advancing the quantitative understanding of carbon emission drivers, enabling robust scenario simulations for carbon neutrality, and informing effective low-carbon spatial planning. The synthesis presented here aims to provide a firm theoretical and technical foundation to support the ambitious carbon reduction targets set by nations worldwide.