Goaf restoration is an important part of urban space management. With mining of coal resources, appearance of goaf and subsidence areas causes serious geological disasters and environmental and ecological problems, which significantly affect urban safety, development, and construction. Therefore, repair of goafs is crucial. In this study, the goaf of Jingmen Garden Expo Park was taken as an example. Through acquisition of engineering geological condition parameters and data on the goaf combined with the mechanical parameters selected for the site, the deformation mechanism of the overlying strata of the goaf was analyzed, and a numerical model of the goaf was established. On this basis, FLAC(3D) was used for numerical simulation to evaluate the stability of the goaf; the suitability of the site was evaluated and divided, and the ecological restoration model of the goaf in Jingmen Garden Expo Park was studied. The results showed that different degrees of ecological restoration and construction of various facilities and buildings could be carried out in the goaf. Based on the varying degrees of stability in the goaf, an appropriate restoration path is suggested according to the suitability of these different degrees. The green, innovative, and sustainable restoration design of the goaf can be carried out according to these restoration paths in order to establish a green ecological system in Jingmen Garden Expo Park.

Carbon emissions from global warming pose significant threats to both regional ecology and sustainable development. Understanding the factors affecting emissions is critical to developing effective carbon neutral strategies. This study constructed a precise 1 km resolution net carbon emissions map of Hubei Province, China (2000–2020), and compared the ten distinct machine learning models to identify the most effective model for revealing the relationship between carbon emissions and their influencing factors. The random forest regressor (RFR) demonstrates optimal performance, achieving root mean square error (RMSE) and mean absolute error (MAE) values that are nearly 10 times lower on average than the other models. The results are interpreted using Shapley additive explanation (SHAP), revealing dynamic factor impacts. Our findings include the following. (1) Between 2000 and 2020, net carbon emissions in Hubei increased threefold, with emissions from construction land rising by approximately 7.5 times over the past two decades. Woodland, a major carbon sink, experienced a downward trend. (2) Six key factors are population, the normalized difference vegetation index (NDVI), road density, PM_2.5, the degree of urbanization, and the industrial scale, with only the NDVI reducing emissions. (3) Net carbon emissions displayed significant spatial differences and aggregation and are mainly concentrated in the central urban areas of Hubei Province. Overall, this study evaluates various regression models and identifies the primary factors influencing net carbon emissions. The net carbon emission map we have developed can visually identify and locate high-emission hotspots and vulnerable carbon sink areas, thereby providing a direct basis for provincial land use planning.

Amidst the increasing urgency of global climate change, achieving carbon neutrality has become a critical objective for rapidly developing economies like China. This study presents an innovative carbon emission forecasting framework for the Yangtze River Middle Reaches-comprising Hubei, Hunan, and Jiangxi provinces-by integrating an extended STIRPAT model with partial least squares (PLS) regression. Distinct from existing provincial-level research, our approach incorporates a broader set of socio-economic and environmental drivers, utilizes variable importance analysis, and employs scenario-based projections to systematically compare emission trajectories and driving mechanisms across multiple provinces. By simulating carbon emission pathways from 2001 to 2021 and projecting future trends to 2080 under three differentiated scenarios, the study reveals pronounced regional heterogeneity in emission peaks, neutrality timelines, and driver effects. Results indicate that while all three provinces are likely to achieve peak emissions around 2030, the path to carbon neutrality by 2060 remains highly challenging due to persistent technological and structural constraints, particularly in provinces with slower industrial transformation. The findings underscore the necessity of region-specific, adaptive mitigation strategies-balancing economic growth, industrial upgrading, and energy structure optimization-to ensure practical progress toward China's dual-carbon goals. This work not only advances carbon forecasting methodology by quantifying the interactive effects of multiple drivers at a subnational scale, but also offers empirical evidence to inform targeted, differentiated policy interventions.

As a core tenet of Green Urbanism, fostering social sustainability through restorative urban environments is essential for enhancing the psychological resilience of active urban generations. While urban parks are recognized as critical blue-green infrastructure, the micro-mechanisms through which their morphological configurations influence perceived restoration remain insufficiently understood. The aim of this study is to investigate how specific landscape element types and proportions in urban parks modulate the visual behavior and psychological restorative outcomes of young urban populations through a multimodal experimental approach. This study employs a novel assessment framework, integrating VR-based eye-tracking and physiological monitoring (HRV, EDA, EEG), with a sample of 77 young adults (aged 18–30) to investigate how landscape element types and proportions modulate visual behavior and restorative outcomes. The findings indicate that landscape components drive restoration through divergent visual cognitive pathways: natural elements promote recovery by fostering sustained visual engagement and exploratory saccades, whereas artificial elements function as cognitive stressors that fragment visual continuity. Mediation analysis further reveals a “quality-over-quantity” effect, demonstrating that restorative efficacy is governed by specific morphological configurations rather than mere green coverage. We identify critical restorative thresholds where the systematic reduction in artificial visibility, combined with the strategic prioritization of multi-layered vegetation and optimized sky openness, significantly maximizes restorative fascination and physiological relaxation. These evidence-based design strategies offer a precise toolkit for sustainable urban renewal, allowing urban planners to optimize the restorative quality of public spaces. By aligning micro-scale visual perception with macro-scale social sustainability goals, this research contributes to the development of resilient and health-promoting cities under the principles of Green Urbanism.