Urban parks play an essential role in mitigating the urban heat island (UHI) effect driven by urbanization. A rigorous understanding of the cooling effects of urban parks can support urban planning efforts aimed at mitigating the UHI effect and enhancing urban sustainability. However, previous research has primarily focused on the maximum cooling impact, often overlooking the accumulative effects arising from spatial continuity. The present study fills this gap by investigating 74 urban parks located in the central area of Jinan and constructing a comprehensive cooling evaluation framework through two dimensions: maximum impact (Park Cooling Area, PCA; Park Cooling Efficiency, PCE) and cumulative impact (Park Cooling Intensity, PCI; Park Cooling Gradient, PCG). We further systematically examined the influence of park attributes and the surrounding urban structures on these metrics. The findings indicate that urban parks, as a whole, significantly contribute to lowering the ambient temperatures in their vicinity: 62.3% are located in surface temperature cold spots, reducing ambient temperatures by up to 7.77 °C. However, cooling intensity, range, and efficiency vary significantly across parks, with an average PCI of 0.0280, PCG of 0.99 °C, PCA of 46.00 ha, and PCE of 5.34. For maximum impact, PCA is jointly determined by park area, boundary length, and shape complexity, while smaller parks generally exhibit higher PCE—reflecting diminished cooling efficiency at excessive scales. For cumulative impact, building density and spatial enclosure degree surrounding parks critically regulate PCI and PCG by influencing cool-air aggregation and diffusion. Based on these findings, this study classified urban parks according to their cooling characteristics, clarified the functional differences among different park types, and proposed targeted recommendations.

Understanding the interaction between ecological carrying capacity and high-quality development is essential for reconciling ecological protection with socioeconomic development in the Yellow River Basin. This study, spanning the period from 2000 to 2023, constructed an ecological carrying capacity index across four dimensions, including water resources, energy, food, and ecological regulation and carbon source-sink balance, and a high-quality development index across five dimensions, including ecology, water resources, economy, culture, and livelihood. TOPSIS, the coupling coordination degree model, exploratory spatial data analysis, the standard deviational ellipse, and multiscale geographically weighted regression were used to characterize spatiotemporal patterns and spatially varying associations. The results showed that: (1) Ecological carrying capacity declined slightly from 0.297 in 2000 to 0.292 in 2005 and then rose to 0.307 in 2023, whereas high-quality development increased from 0.199 in 2000 to 0.267 in 2015 and subsequently stabilized. (2) The coupling coordination degree increased from 0.476 in 2000 to 0.516 in 2023 and exhibited significant positive spatial autocorrelation (global Moran’s I = 0.327–0.440, p < 0.01). (3) MGWR outperformed GWR (R² = 0.641–0.785 vs. 0.482–0.624), indicating spatial nonstationarity. The ecosystem quality index showed the most stable positive association with coupling coordination degree, the association with patent grants strengthened markedly by 2023, the positive association with GDP per capita diminished, and population density remained negatively associated. This study proposes targeted, zone-specific regulatory recommendations, providing a decision-making basis for scientifically differentiated regulation in the Yellow River Basin.

Land system multifunctionality is increasingly recognized as a key source of competition among production, living, and ecological functions. However, how these interactions are spatially manifested as functional competition within production–living–ecological space (PLES) through nonlinear relationships remains insufficiently understood. This study develops a land-system-oriented framework to assess four representative PLES functional conflicts within farmland production–ecological (PE) and natural ecological (E) spaces. Using Shandong Province, China, as a case study, we characterize PLES patterns in 2010, 2015, and 2020 and use a coupling-based index (D) as a proxy for functional competition intensity. XGBoost–SHAP is employed to examine the nonlinear spatial associations between selected environmental, socioeconomic, and spatial factors and proxy-based functional competition intensity during the 2010–2020 observation period. Results show that (1) the regional land system underwent marked functional reconfiguration, with urban–rural and fishing production–ecological spaces expanding while farmland production–ecological and natural ecological spaces contracted; (2) the four representative conflict scenarios exhibited pronounced spatial heterogeneity, with grain-production-related conflicts generally showing higher functional competition intensity than ecological-function-related conflicts; and (3) the dominant associated factors varied across conflict scenarios and exhibited distinct nonlinear response patterns. Overall, the findings reveal differentiated spatial associations between land-system multifunctionality and proxy-based functional competition intensity during 2010–2020. The identified response ranges provide empirical reference points for spatial monitoring and functionally differentiated territorial planning.

Regional carbon emission systems evolve through the continuous interaction between local emission attributes and interregional spatial relationships. However, existing studies have largely examined the spatiotemporal evolution of carbon emissions and the formation of spatial association networks separately, leaving the relationship between these two dimensions insufficiently understood. To address this gap, this study develops an attribute–relation analytical framework and applies it to 136 counties in Shandong Province, China. By integrating spatial analysis, social network analysis (SNA), SHAP interpretation, and the quadratic assignment procedure (QAP), the study investigates the evolution of carbon emission attributes, the organization of spatial association networks, and the mechanisms governing both processes. The results show that carbon emissions increased continuously from 2000 to 2020, gradually forming a persistent dual-core agglomeration pattern centered on the Jiaodong Peninsula and the central Shandong industrial corridor. Meanwhile, the spatial association network evolved from a core–periphery structure toward a more interconnected and polycentric configuration. Carbon emission attributes were primarily driven by fixed asset investment, population size, and land-use structure, whereas network formation was more strongly associated with economic development and land-use efficiency. The proposed attribute–relation analytical framework provides a systems perspective for understanding the spatial correspondence between carbon emission attributes and spatial association networks, offering an integrated perspective on the spatial state and organizational structure of regional carbon emission systems. This framework providing theoretical support for differentiated and network-oriented carbon governance.