Due to the rapid development of urbanization, land-use types have changed greatly, which has led to many ecological problems. Therefore, the current research objective is to solve the problems in existence in Jinan, so as to determine the existing landscape ecological risks and optimize the landscape structure. Using 2 m high-resolution remote sensing images and related natural economic data, this study evaluated the landscape ecological risk and constructed a full-factor ecological network in Jinan with a landscape ecological risk assessment method (ERI) and a minimum cumulative resistance model (MCR) based on landscape ecology theory. The results showed that: (1) The ERI in Jinan presented a spatial concentration of high value areas in the central and central–eastern regions, while other levels in ERI areas presented a spatial distribution around the ecological regions with high risk. (2) The important corridors were mainly distributed in the south of Jinan, which were stable and not easily destroyed. The corridors in other areas were secondary, mainly passing through cultivated land and urban greenways, which were unstable and susceptible to interference.

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.

Accurately evaluating territorial space use efficiency is a prerequisite for promoting the realization of high-quality development. Existing efficiency evaluation models all treat decision making units (DMUs) as independent individuals, ignoring geospatial effects between geographical spaces, which leads to unreliable results. This study proposes a geographic data envelopment analysis (GeoDEA) model, integrating a spatially constrained multivariate clustering model with generalized data envelopment analysis (DEA). The GeoDEA model reconstructs evaluation and reference sets considering spatial adjacency, cluster numbers, and socio-economic indicators and then applies a slack-based measure (SBM) super-efficient formula. It is verified that the efficiency value evaluated using the GeoDEA model is higher than that of the traditional DEA model, but it is also more consistent with cognition and more reliable. This is mainly explained by the fact that the GeoDEA model takes into account the geospatial effect and selects DMUs with relatively close geographic distance and higher levels of development as the reference frontier for efficiency evaluation. The GeoDEA model optimizes the traditional DEA model and avoids the problem that the efficiency of DMU is underestimated when the geographical background and development mode of DMU are very different from the reference frontier. It enhances the reliability of the evaluation of territorial space use efficiency.

Against the backdrop of rapid global economic development, the Beijing-Tianjin-Hebei (BTH) region, a pivotal economic hub and environmentally sensitive area in China, faces significant challenges in sustaining its landscape ecosystem. Given the region’s strategic importance and vulnerability to environmental pressures, this study investigated the intricate relationships between landscape ecological risk, urban expansion, and economic growth (EG) in the BTH region. Utilizing the landscape as the focal point, we constructed a decoupling model at the grid scale to explore the decoupling relationship between the landscape ecological risk index (ERI), construction area growth (CAG), and EG. The results showed that (1) distinct stages and regional disparities were observed in the trends of ERI, CAG, and EG within the BTH region. The hot and cold spot patterns for these factors did not align consistently. (2) From 1995 to 2019, the coupling relationship between ERI, CAG, and EG in the BTH region underwent a fluctuating transition, initially moving from an undesirable state to an ideal state, and subsequently reverting to an undesirable state. Although the overall trends in these relationships showed some convergence, there were notable spatial distribution differences. (3) The spatial heterogeneity of the two decoupling relationships in the BTH region was relatively poor. Further analysis revealed that the evolution of these decoupling relationships was closely intertwined with regional policy shifts and adjustments.

Territorial space use efficiency (TSUE) serves as a core quantitative metric for evaluating the sustainability of space use, which constitutes a vital component of regional sustainable development. Nevertheless, existing studies have not established a systematic quantitative system for assessing the status of the sustainability of space use based on TSUE. From the perspective of production–living–ecological spaces, this study constructs a multi-dimensional efficiency evaluation framework for sustainable territorial management. This study adopts geographic data envelopment analysis (GeoDEA), spatial autocorrelation analysis and multi-scale geographically weighted regression (MGWR). It calculates TSUE, applies efficiency levels to characterize the status of the sustainability of space use, and further reveals its spatiotemporal evolution and driving mechanisms. The results indicate that the level of the sustainability of space use generally exhibits a fluctuating evolution, with distinct phase transitions emerging around 2010. Only 20% of cities attain medium-high or higher levels of sustainability in space use, with pronounced spatial disparities across the country. Areas with high-efficiency production and living spaces are mainly clustered in eastern and central China, while high-efficiency ecological spaces are concentrated in western and northeastern China. In addition, driving factors exert markedly heterogeneous effects on the three types of territorial functional spaces. This study provides scientific support for implementing differentiated territorial spatial governance and advancing regional sustainable development.

Territorial space use efficiency (TSUE) is a core indicator characterizing regional sustainable development. Nevertheless, existing studies have not established a systematic quantitative system to assess sustainable development status based on TSUE. From the perspective of production-living-ecological spaces, this study constructs a multi-dimensional efficiency evaluation framework for sustainable territorial management. This study adopts geographic data envelopment analysis (GeoDEA), spatial autocorrelation analysis and multi-scale geographically weighted regression (MGWR). It calculates TSUE, uses efficiency levels to characterize and identify territorial sustainable development status, and further reveals the spatiotemporal evolution characteristics and driving mechanisms of regional sustainable development. The results indicate that the regional sustainable development level reflected by TSUE generally shows fluctuating evolution, with distinct phase transitions emerging around 2010. Merely 20% of cities attain medium-high or higher sustainability levels, and spatial imbalance is prominent nationwide. Areas with high-efficiency production and living spaces are mainly clustered in eastern and central China, while highly sustainable ecological spaces concentrate in western and northeastern China. In addition, driving factors exert significantly heterogeneous effects on the three types of territorial functional spaces. This study provides scientific references for implementing differentiated territorial spatial governance and advancing sustainable territorial development.