Search Results (229)

Urban forests, as a form of green infrastructure, play a vital role in enhancing urban resilience, environmental health, and quality of life. However, users perceive and utilize these spaces in diverse ways. This study aims to identify latent perception types among urban forest visitors and analyze their behavioral, demographic, and policy-related characteristics in Incheon Metropolitan City (Republic of Korea). Using latent class analysis, four distinct visitor types were identified: multipurpose recreationists, balanced relaxation seekers, casual forest users, and passive forest visitors. Multipurpose recreationists preferred active physical use and sports facilities, while balanced relaxation seekers emphasized emotional well-being and cultural experiences. Casual users engaged lightly with forest settings, and passive forest visitors exhibited minimal recreational interest. Satisfaction with forest elements such as vegetation, facilities, and management conditions varied across visitor types and age groups, especially among older adults. These findings highlight the need for perception-based green infrastructure planning. Policy recommendations include expanding accessible neighborhood green spaces for aging populations, promoting community-oriented events, and offering participatory forest programs for youth engagement. By integrating user segmentation into urban forest planning and governance, this study contributes to more inclusive, adaptive, and sustainable management of urban green infrastructure. Full article

The impacts of land use transition on ecological environment quality (EEQ) during China’s rapid urbanization have attracted growing concern. However, existing studies predominantly focus on single-scale analyses, neglecting scale effects and driving mechanisms of EEQ changes under the coupling of administrative units and grid scales. Therefore, this study selects Zhejiang Province—a representative rapidly transforming region in China—to establish a “type-process-ecological effect” analytical framework. Utilizing four-period (2005–2020) 30 m resolution land use data alongside natural and socio-economic factors, four spatial scales (city, county, township, and 5 km grid) were selected to systematically evaluate multi-scale impacts of land use transition on EEQ and their driving mechanisms. The research reveals that the spatial distribution, changing trends, and driving factors of EEQ all exhibit significant scale dependence. The county scale demonstrates the strongest spatial agglomeration and heterogeneity, making it the most appropriate core unit for EEQ management and planning. City and county scales generally show degradation trends, while township and grid scales reveal heterogeneous patterns of local improvement, reflecting micro-scale changes obscured at coarse resolutions. Expansive land transition including conversions of forest ecological land (FEL), water ecological land (WEL), and agricultural production land (APL) to industrial and mining land (IML) primarily drove EEQ degradation, whereas restorative ecological transition such as transformation of WEL and IML to grassland ecological land (GEL) significantly enhanced EEQ. Regarding driving mechanisms, natural factors (particularly NDVI and precipitation) dominate across all scales with significant interactive effects, while socio-economic factors primarily operate at macro scales. This study elucidates the scale complexity of land use transition impacts on ecological environments, providing theoretical and empirical support for developing scale-specific, typology-differentiated ecological governance and spatial planning policies. Full article

The intersection of sports and natural forests and green spaces represents an emerging interdisciplinary field with implications for public health, environmental science, and sustainable land management and refers to the variety of cultural ecosystem services demanded by people from ecosystems. This manuscript presents a systematic bibliometric and thematic analysis of 148 publications for the period 1993–2024 identified through Web of Science and Scopus, aiming to evaluate the current state of research on sports activities conducted in natural forest environments. Findings indicated a marked increase in scientific interest of this topic over the past two decades, with key contributions from countries such as England, Germany, China, and the United States. Researchers most frequently examined sports such as hiking, trail running, mountain biking, and orienteering for their capacity to provide physiological and psychological benefits, reduce stress, and enhance mental well-being. The literature analysis highlights ecological concerns, particularly those associated with habitat disturbance, biodiversity loss, and conflicts between recreation and conservation. Six principal research themes were identified: sports in urban forests, sports tourism, hunting and fishing, recreational sports, health benefits, and environmental impacts. Keyword and co-authorship analyses revealed a multidisciplinary knowledge base with evolving thematic focuses. In conclusion, the need for integrated approaches that incorporate ecological impact assessment, stakeholder perspectives, and adaptive forest governance to ensure sustainable recreational use of natural forest ecosystems is underlined. Full article

With rapid socio-economic development, excessive anthropogenic consumption and the exploitation of natural resources have impaired the self-healing, supply, and carrying capacities of ecosystems. The assessment and prediction of ecosystem service values (ESVs) are crucial for the coordinated development of ecology and economy. This research examines the Xiangjiang River Basin and combines land use data from 1995 to 2020, Landsat images, meteorological data, and socio-economic data. These data are incorporated into the PLUS model to simulate land use patterns in 2035 under the following five scenarios: natural development, economic development, farmland protection, ecological protection, and coordinated development. Additionally, this research analyzes the dynamics of land use and changes in ESVs in the Xiangjiang River Basin. The results show that between 1995 and 2020 in the Xiangjiang River Basin, urbanization accelerated, human activities intensified, and the construction land area expanded significantly, while the areas of forest, farmland, and grassland decreased continuously. Based on multi-scenario simulations, the ESV showed the largest and smallest declines under economic development and ecological protection scenarios, respectively. This results from the economic development scenario inducing a rapid expansion in construction land. In contrast, construction land expansion was restricted under the ecological protection scenario, because the ecological functions of forests and water bodies were prioritized. This research proposes land use strategies to coordinate ecological protection and economic development to provide a basis for sustainable development in the Xiangjiang River Basin and constructing a national ecological security barrier, as well as offer Chinese experience and local cases for global ecological environment governance. Full article

As a crucial manifestation of ecosystem water regulation and supply functions, water conservation plays a vital role in regional ecosystem development and sustainable water resource management. This study investigates nine major Chinese river basins (Songliao, Haihe, Huaihe, Yellow, Yangtze, Pearl, Southeast Rivers, Southwest Rivers, and Inland Rivers) through integrated application of the InVEST model and geographical detector model. We systematically examine the spatiotemporal heterogeneity of water conservation capacity and its driving mechanisms from 1990 to 2020. The results reveal a distinct northwest–southeast spatial gradient in water conservation across China, with lower values predominating in northwestern regions. Minimum conservation values were recorded in the Inland River Basin (15.88 mm), Haihe River Basin (42.07 mm), and Yellow River Basin (43.55 mm), while maximum capacities occurred in the Pearl River Basin (483.68 mm) and Southeast Rivers Basin (517.21 mm). Temporal analysis showed interannual fluctuations, peaking in 2020 at 130.98 mm and reaching its lowest point in 2015 at 113.04 mm. Precipitation emerged as the dominant factor governing spatial patterns, with higher rainfall correlating strongly with enhanced conservation capacity. Land cover analysis revealed superior water retention in vegetated areas (forests, grasslands, and cultivated land) compared to urbanized and bare land surfaces. Our findings demonstrate that water conservation dynamics result from synergistic interactions among multiple factors rather than single-variable influences. Accordingly, we propose that future water resource policies adopt an integrated management approach addressing climate patterns, land use optimization, and socioeconomic factors to develop targeted conservation strategies. Full article

China’s accelerated urbanization has instigated construction land expansion and ecological land attrition, aggravating the carbon emission disequilibrium. Notably, the “land carbon emission elasticity coefficient” in urban agglomerations far exceeds international benchmarks, underscoring the contradiction between spatial expansion and low-carbon goals. Existing research predominantly centers on single-spatial-type or static-model analyses, lacking cross-scale mechanism exploration, policy heterogeneity consideration, and differentiated carbon metabolism assessment across functional spaces. This study takes Xiong’an New Area as a case, delineating the spatiotemporal evolution of land use and carbon emissions during 2017–2023. Construction land expanded by 26.8%, propelling an 11-fold escalation in carbon emissions, while emission intensity decreased by 11.4% due to energy efficiency improvements and renewable energy adoption. Cultivated land reduction (31.8%) caused a 73.4% decline in agricultural emissions, and ecological land network restructuring (65.3% forest expansion and wetland restoration) significantly enhanced carbon sequestration. This research validates a governance paradigm prioritizing “structural optimization” over “scale expansion”—synergizing construction land intensification with ecological restoration to decelerate emission growth and strengthen carbon sink systems. Full article

Amid intensifying global climate anomalies and accelerating urban expansion, land use systems have become increasingly dynamic, complex, and uncertain. Accurately predicting and scientifically evaluating the evolution of land use patterns is essential to advancing territorial spatial governance and achieving ecological security goals. However, most existing land use models emphasize quantity forecasting and spatial allocation, while overlooking the third critical dimension—structural complexity, which is essential for understanding the nonlinear, fragmented evolution of urban systems, thus limiting their ability to fully capture the evolutionary characteristics of urban land systems. To address this gap, this study proposes an integrated SD-PLUS-FD model, which combines System Dynamics, Patch-based Land Use Simulation, and Fractal Dimension analysis to construct a comprehensive three-dimensional framework for simulating and evaluating land use patterns in terms of quantity, spatial distribution, and structural complexity. Wuhan is selected as the case study area, with simulations conducted under three IPCC-aligned climate scenarios—SSP1-2.6, SSP2-4.5, and SSP5-8.5—to project land use changes by 2030. The SD model demonstrates robust predictive performance, with an overall error of less than ±5%, while the PLUS model achieves high spatial accuracy (average Kappa >0.7996; average overall accuracy >0.8856). Fractal dimension analysis further reveals that since 2000, the spatial boundary complexity of all land use types—except forest land—has generally shown an upward trend across multiple scenarios, highlighting the increasingly nonlinear and fragmented nature of urban expansion. The FD values for construction land and cultivated land declined to their historical low in 2005, then gradually increased, reaching their peak under the SSP1-2.6 scenario. Notably, the increase in FD for construction land was significantly greater than that for cultivated land, indicating a stronger dynamic response in spatial structural evolution. In contrast, forest land exhibited pronounced scenario-dependent variations in FD. Its structural complexity remained generally stable under all scenarios except SSP5-8.5, reflecting higher structural resilience and boundary adaptability under diverse socioclimatic conditions. The SD-PLUS-FD model effectively reveals how land systems respond to different socioclimatic drivers in both spatial and structural dimensions. This three-dimensional framework reveals how land systems respond to socioclimatic drivers across temporal, spatial, and structural scales, offering strategic insights for climate-resilient planning and optimized land resource management in rapidly urbanizing regions. Full article

The construction of transition rules is the core and difficulty faced by the cellular automata (CA) model. Dynamic mining of transition rules can more accurately simulate urban land use change. By introducing a graph attention network (GAT) to mine CA model transition rules, the temporal and spatial dynamics of the model are increased based on the construction of a real-time dynamic graph structure. At the same time, by adding an agent-based model (ABM) to the CA model, the simulation evolution of different human decision-making behaviors can be achieved. Based on this, an urban expansion scenario prediction (UESP) model has been proposed: (1) the UESP model employs a multi-head attention mechanism to dynamically capture high-order spatial dependencies, supporting the efficient processing of large-scale datasets with over 50,000 points of interest (POIs); (2) it incorporates the behaviors of agents such as residents, governments, and transportation systems to more realistically reflect human micro-level decision-making; and (3) by integrating macro-structural learning with micro-behavioral modeling, it effectively addresses the existing limitations in representing high-order spatial relationships and human decision-making processes in urban expansion simulations. Based on the policy context of the Outline of the Beijing–Tianjin–Hebei (BTH) Coordinated Development Plan, four development scenarios were designed to simulate construction land change by 2030. The results show that (1) the UESP model achieved an overall accuracy of 0.925, a Kappa coefficient of 0.878, and a FoM index of 0.048, outperforming traditional models, with the FoM being 3.5% higher; (2) through multi-scenario simulation prediction, it is found that under the scenario of ecological conservation and farmland protection, forest and grassland increase by 3142 km², and cultivated land increases by 896 km², with construction land showing a concentrated growth trend; and (3) the expansion of construction land will mainly occur at the expense of farmland, concentrated around Beijing, Tianjin, Tangshan, Shijiazhuang, and southern core cities in Hebei, forming a “core-driven, axis-extended, and cluster-expanded” spatial pattern. Full article

This study investigates the coupling coordination between carbon emission efficiency (CEE) and carbon balance (CB) in the Yellow River Basin (YRB), aiming to support high-quality regional development and the realization of China’s “dual carbon” goals. Based on panel data from 74 cities in the YRB between 2006 and 2022, the Super-SBM model, Ecological Support Coefficient (ESC), and coupling coordination degree (CCD) model are applied to evaluate the synergy between CEE and CB. Spatiotemporal patterns and driving mechanisms are analyzed using kernel density estimation, Moran’s I index, the Dagum Gini coefficient, Markov chains, and the XGBoost algorithm. The results reveal a generally low and declining level of CCD, with the upstream and midstream regions performing better than the downstream. Spatial clustering is evident, characterized by significant positive autocorrelation and high-high or low-low clusters. Although regional disparities in CCD have narrowed slightly over time, interregional differences remain the primary source of variation. The likelihood of leapfrog development in CCD is limited, and high-CCD regions exhibit weak spillover effects. Forest coverage is identified as the most critical driver, significantly promoting CCD. Conversely, population density, urbanization, energy structure, and energy intensity negatively affect coordination. Economic development demonstrates a U-shaped relationship with CCD. Moreover, nonlinear interactions among forest coverage, population density, energy structure, and industrial enterprise scale further intensify the complexity of CCD. These findings provide important implications for enhancing regional carbon governance and achieving balanced ecological-economic development in the YRB. Full article

Although China has achieved substantial reductions in particulate matter pollution, continually rising ground-level ozone now constitutes the primary challenge to further air-quality improvements. A systematic assessment of the long-term spatiotemporal behavior of ozone (O₃) and its links to meteorology and emissions is still lacking. Here, we develop a novel framework that couples Kolmogorov–Zurbenko (KZ) filtering with an optimized random forest (RF) regression model to examine daily maximum 8 h average ozone (O₃-8h) in 372 Chinese cities from 2013 to 2023. The approach quantitatively disentangles meteorological and emission contributions at the national scale, overcoming the limitations of traditional linear methods in capturing non-linear processes. Long-term components explain, in general, <40% of total O₃ variance. In eastern urban agglomerations, long-term meteorological factors—particularly temperature and surface ultraviolet radiation—account for up to 80% of the trend, whereas long-term emission contributions remain modest (≈5–6%), with pronounced signals in the Beijing–Tianjin–Hebei and Fenwei Plain regions. Empirical orthogonal function analysis further reveals distinct spatial patterns of emission influence: long-term O₃ trends in mega-cities such as Beijing, Tianjin, and Shanghai are driven mainly by local emissions (1.5–3% contribution), while key transport hubs including Xi’an, Tangshan, and Langfang are markedly affected by traffic-related emissions (>2%). These findings clarify the heterogeneous mechanisms governing O₃ formation across China and provide a scientific basis for designing and implementing the next phase of region-specific, joint prevention-and-control policies. Full article

This study examines the establishment of a Hub for Circular Economy and Industrial Symbiosis (HUB-CEIS) centred on a forest biomass waste plant in Fundão, Portugal, presenting an innovative model for rural industrial symbiosis, circular economy governance, and sustainable waste management. Designed as a strategic node within a reverse supply chain, the hub facilitates the conversion of solid waste into renewable energy and high-value co-products, including green hydrogen, tailored for industrial and agricultural applications, with an estimated 120 ktCO₂/year reduction and 60 direct jobs. Aligned with the United Nations (UN) Sustainable Development Goals (SDGs) and the Paris Agreement, this initiative addresses global challenges such as decarbonization, resource efficiency, and the energy transition. Employing a mixed research methodology, this study integrates a comprehensive literature review, in-depth stakeholder interviews, and comparative case study analysis to formulate a governance framework fostering regional partnerships between industry, government, and local communities. The findings highlight Fundão’s potential to become a benchmark for rural industrial symbiosis, offering a replicable model for circularity in non-urban contexts, with a projected investment of USD 60 M. Special emphasis is placed on the green hydrogen value chain, positioning it as a key enabler for regional sustainability. This research underscores the importance of cross-sectoral collaboration in achieving scalable and efficient waste recovery processes. By delivering practical insights and a robust governance structure, the study contributes to the circular economy literature, providing actionable strategies for implementing rural reverse supply chains. Beyond validating waste valorization and renewable energy production, the proposed hub establishes a blueprint for sustainable rural industrial development, promoting long-term industrial symbiosis integration. Full article

Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R² = 0.612), outperforming the global GWRF (R² = 0.59) and local GWRF (R² = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article

Rapid urbanization enhances the necessity of exploring sustainable development paths to achieve ecological and carbon storage protection. This study takes Zhengzhou, one of the national central cities in China, as a case to investigate the dynamic correlation between urbanization (UR) and carbon storage (CS). The PLUS and InVEST models were employed to simulate land use and carbon storage dynamics under natural development, cultivated land protection, and ecological protection scenarios for 2030 and 2040. This was also complemented by elasticity analysis of UR, construction land expansion (CEL), and CS. The results show that from 2000 to 2020, cultivated land declined by 15.33%, while construction land expanded by 13.31%. By 2030, construction land growth is expected to be 7.34%, 2.87%, and 4.96% across scenarios, with cultivated land of −6.96%, −2.36%, and −4.78%, respectively. Carbon storage in 2030 decreases under all scenarios (5.181 × 10⁷ t, 5.235 × 10⁷ t, 5.209 × 10⁷ t) but stabilizes by 2040, with ecological protection enhancing forest/water bodies and mitigating losses. Elasticity analysis reveals that unconstrained elasticity coefficient significantly exacerbates carbon losses, while policy interventions reduce losses through expansion control and carbon sequestration. Integrating land use policies to balance farmland protection, ecological restoration, and low-carbon urban expansion is critical for sustainable megacity development and a scalable framework for carbon governance. Full article

Intensive farming in urban suburbs often causes habitat loss, soil erosion, wastewater discharge, and agricultural productivity decline, threatening long-term benefits for the local community. We developed a nature-based solution for sustainable land restoration by establishing “Green Treasure Island” (GTI). The aim of this study is to evaluate the ecological restoration effectiveness of GTI and explore its feasibility and replicability for future applications. The core eco-functional zone of GTI—a 7 hm² forested wetland—embedded a closed-loop framework that integrates land consolidation, ecological restoration, and sustainable land utilization. The forested wetland efficiently removed 65% and 74% of dissolved inorganic nitrogen and phosphorus from agricultural runoff, raised flood control capacity by 22%, and attracted 48 bird species. Additionally, this biophilic recreational space attracted over 3400 visitors in 2022, created green jobs, and promoted local green agricultural product sales. Through adaptive management and nature education activities, GTI evolved into a landmark that represents local natural–social characteristics and serves as a publicly accessible natural park for both rural and urban residents. This study demonstrates the feasibility of creating GTI for improving ecosystem services, providing a practical, low-cost template that governments and local managers can replicate in metropolitan rural areas worldwide to meet both ecological and development goals. Full article

In the context of digital transformation, examining the differences in commercial site selection and the factors influencing these decisions holds significant practical value for understanding market adaptation strategies across varying business models and predicting future industry trends. This study divides the research area into 100 m × 100 m grids and employs a random forest model and related interpretability methods to conduct an empirical analysis of the site selection and influencing factors of Luckin Coffee and Starbucks stores in Shanghai. By integrating the prediction results with existing planning documents, this study achieves a coupling between urban spatial structure and location strategies. The findings indicate the following: (1) The random forest model demonstrates high accuracy in predicting new retail store locations, with an accuracy rate of 90.0% for Luckin Coffee and 92.2% for Starbucks. (2) The influence of traditional factors on the expansion of new retail coffee stores is declining, while Luckin Coffee’s layout demonstrates a stronger reliance on urban functional zones. (3) Relative suitability is derived by calculating the difference between the predicted probability values and the normalized kernel density values. In the central activity areas of the city, the relationship between site selection probability and suitability exhibits an inverse correlation, with Starbucks generally showing higher relative suitability overall. (4) Suitable areas for both brands’ site selections are spatially contiguous and integrated within the urban fabric, which suggests significant growth potential for both brands in the main urban areas. This study not only focuses on commercial optimization but also offers theoretical and methodological insights by exploring how different retail models interact with urban spatial structures, thereby contributing to the fields of retail geography and spatial governance. Full article