Green and low-carbon rural development (GLRD) is becoming an important way to explore sustainable development in rural areas of China. It is significant for the sustainable development of the rural economy and of society to build a rural carbon sink system, advocate low-carbon emissions in rural areas, and promote the development of rural green industries and rural transformation. In this study, the existing knowledge system of GLRD was analyzed using CiteSpace. Keywords related to GLRD and their occurrence frequency were identified using keyword co-occurrence analysis. The knowledge evolution stages of GLRD were explored using citation burst analysis. Thus, the evolution of the research related to GLRD was revealed. The summary of Chinese GLRD-related research literature shows that: (1) according to the CiteSpace analysis, the GLRD research can be divided into three stages: starting, rising, and expanding and deepening; (2) GLRD research has focused on low-carbon development, green development, and then green and low-carbon integrated development, thus forming three major research content systems; (3) GLRD’s research content gradually became rich, as over time, the research focus became increasingly prominent and research integration was gradually strengthened; (4) the GLRD literature still has some limitations, e.g., nonunified measurement standards, insufficient research depth and practical significance, and relatively weak innovative policy research; (5) future research should strengthen the scientific evaluation and prediction of rural green and low-carbon functions. The industrial development pathways and regional characteristic modes of GLRD should be further studied. Innovative GLRD policies should be proposed to provide a theoretical basis and decision-making reference for GLRD and construction.

Investigating the coupling coordination between urban scale and vitality is critical for enhancing holistic urban development quality and advancing sustainability. Taking the Changsha-Zhuzhou-Xiangtan (ChangZhuTtan) metropolitan area as a case study, this research integrates multi-source raster and vector data to: (1) analyze spatial patterns of urban scale and virtual–substantive vitality; (2) delineate a “scale-vitality” hierarchical zonal structure; (3) quantify coupling relationships across subzones; and (4) propose synergistic spatial optimization strategies. Key findings reveal that, distinct core-periphery structure characterizes urban scale and vitality, with Changsha’s central districts dominating population, land use, and economic metrics, while Zhuzhou and Xiangtan exhibit moderate concentrations. Significant positive correlations exist between urban scale and dual vitality types, with scale-driven vitality enhancement being most pronounced in core agglomeration zones. Furthermore, in the metropolitan core, where both urban scale and vitality values are high, they exhibit a high-value coupling state. As they expanded outward, both metrics gradually decreased, resulting in a low-value coupling state. However, zonal comparisons (core agglomeration circle–peripheral expansion circle) reveal that the proportion of spatially coupled units progressively increases. By elucidating scale-vitality coupling in the ChangZhuTtan metropolitan area, this study provides actionable insights for spatial planning and sustainable urban transition. The methodology framework is replicable for similar metropolitan regions globally.

This study aims to address the complexities in the calculation of the tangent stiffness matrix and the issues of divergence in iterative calculations in the shape-finding process of existing suspension bridge main cables. The research investigates the factors influencing the computational errors of existing cable element theories and the convergence or divergence of the main cable shape-finding calculations. First, a nonlinear equation for calculating the height of the cable element is constructed. Subsequently, a formula for cable height calculation is established according to the differential equations of the deformed cable element. Finally, considering the mass conservation principle before and after the cable deformation, a nonlinear system of equations for the configuration of the cable element is derived. Given the symmetric nature of the mid-span structure and loading in most suspension bridges, it is inferred that the point of the lowest slope of the main cable in the completed bridge state serves as the symmetry center of the structure. Consequently, a symmetric main cable shape-finding method is developed. A comparative analysis between the proposed method and existing iterative methods was conducted in terms of calculation accuracy and convergence behavior. The results indicate that the difference in horizontal cable force at the IP point between the two methods is 1.9 kN, and the difference in unstressed length is 2.5 mm. The calculation efficiency of the symmetric main cable shape-finding method is more than twice that of traditional iterative algorithms, with the number of iterations required for convergence generally being lower than that of traditional methods. For initial values that cause divergence in traditional iterative methods, the symmetric main cable shape-finding method achieved convergence within 10 iterations. The derived cable element theory and the symmetric main cable shape-finding calculation method can lay a theoretical foundation for the refined and efficient calculation of the main cable shape-finding process.

The United Nations 2030 Agenda for Sustainable Development (UN SDGs) frames sustainable agriculture as a core pathway to advance zero hunger (SDG 2) and protect life on land (SDG 15). As critical platforms for the green transition of agriculture, innovative perception among new-type agricultural business entities inside national modern agricultural industrial parks constitutes a prerequisite cognitive factor shaping sustainable farming practices. Taking Anhua National Modern Agricultural Industrial Park—China’s sole industrial park specialized in dark tea—as the research case, this study develops a four-dimensional measurement framework for innovative perception covering innovation cognition, motivation, demand, and intention, grounded in 363 field micro-survey questionnaires. Factor analysis is adopted to quantify township-level composite innovative perception scores, and the Geodetector model is integrated to detect driving factors as well as their interactive coupling effects behind spatial disparities. The results indicate the following: (1) Marked spatial heterogeneity characterizes innovative perception levels of new-type agricultural business entities across the park, which maintain an overall high standard. The core zone registers an average score of 4.209 versus 3.726 for the demonstration zone; innovative perception in the core zone is statistically higher, presenting a typical core–periphery gradient pattern across townships. (2) Individual endogenous attributes, including age, educational attainment, and farming experience, show strong explanatory capacity. The interaction between age and innovation applicability yields a q-statistic of 0.961, the strongest pairwise spatial association observed within the present sample; nevertheless, q-statistics from the Geographical Detector are sample-specific descriptive metrics for spatially stratified explained variance and do not represent causal effect sizes. This research uncovers the core–periphery polarization rule and multi-factor spatial coupling patterns underlying innovative perception of operators within specialized agricultural industrial zones. The empirical micro-evidence generated in this work informs the design of tiered, differentiated governance policies aimed at mitigating intra-park spatial disparities, delivering empirical insights into the cognitive foundations of the park’s green transition. This analysis targets innovative perception as the cognitive prerequisite for sustainable farming practices instead of direct sustainability outcomes; empirical tracing of the perception-to-behavior-to-outcome causal chain would require longitudinal outcome-focused datasets, which constitutes a key avenue for future inquiry.

Driven by the goal of carbon neutrality, low-carbon development in township spaces is essential for sustainable urban–rural growth. This paper employs a carbon accounting methodology, taking Fuqiushan Town in the Dongting Lake Ecological Economic Zone as a case study to develop a detailed carbon measurement inventory at the township scale. Using spatial analysis techniques, it synthesizes multi-source data—including land use, agricultural inputs, and population—to estimate emissions from key sources such as crop cultivation, livestock and poultry breeding, industrial production, and residential activities. The study also evaluates the carbon sequestration capacity of sinks such as woodlands and water bodies, enabling the spatial visualization of both carbon emissions and carbon sinks. Key findings include: (1) Fuqiushan Town exhibits a carbon emission profile characterized by “industrial activities as the primary source, supplemented by agriculture, with additional contributions from residential and transportation sectors,” while forested areas and water bodies serve as core carbon sink zones. (2) An innovative multidimensional indicator system for low-carbon development efficiency was established, consisting of the Low-Carbon Development Efficiency Index in Production, the Daily Life Carbon Responsibility Efficiency Index, and the Ecological Carbon Sink Efficiency Index, which together form a Comprehensive Efficiency Index for Low-Carbon Development. (3) Analysis reveals significant spatial coupling relationships and efficiency differentiation patterns among carbon emissions, industrial structure, energy dependence, and ecological background. Based on dominant carbon emission types, low-carbon efficiency thresholds, and spatial factor interactions, the 17 villages and one forest farm in the township are classified into five zones: “Industrial High-Carbon Transition Zone,” “Agricultural Pollution Reduction and Carbon Emission Reduction Synergy Zone,” “Ecological Low-Carbon Conservation Zone,” “Human Settlements Balanced Development Zone,” and “Ecological Core Zone.” Tailored low-carbon spatial planning strategies for material resources are proposed for each zone. These results offer quantitative support and spatially targeted insights for low-carbon spatial planning in ecologically sensitive townships, contributing to the achievement of objectives such as “carbon reduction and sink increase” and “rural revitalization.”