Search Results (50)

The willingness to pay (WTP) for green energy consumption not only indicates the public’s green energy consumption practices, but also affects the realization of China’s “dual carbon” goals and global green development. Based on data from the 2018 Chinese General Social Survey (CGSS), this study describes the WTP for green energy consumption of Chinese urban and rural residents in the context of “dual carbon”. Moreover, it provides an in-depth interpretation from the perspectives of capital endowment and energy cognition, guided by social practice theory (SPT). This study found that, firstly, the public’s WTP for green energy consumption needs to be strengthened urgently, and the percentage of the refusal to participate group reaches 41.44%, and shows significant urban–rural differences. Compared with rural residents, the proportion and amount of WTP for urban residents are 7.5% and 4.016 CNY/month higher, respectively. Secondly, capital endowment and energy cognition are important influencing factors. Among them, economic capital (β = 0.647, p < 0.01) and cultural capital (β = 0.358, p < 0.05) play a significant role for urban residents, while rural residents depend on the government support cognition of energy (β = 7.678, p < 0.001). Finally, the urban–rural divergence in WTP for green energy consumption mainly stems from the gap in capital endowment, which contributes 29.08%, significantly higher than the contribution of energy cognition (8.34%). Therefore, efforts should be made to enhance the capital endowment levels of urban and rural residents, implement a targeted energy knowledge dissemination system, build a comprehensive government support system, and break down institutional barriers through urban–rural integration to guard against the disadvantages of rural residents. Full article

With the acceleration of global urbanization, cropland loss and fragmentation due to construction land expansion have become critical threats to food security and ecological sustainability, particularly in rapidly developing coastal regions. Understanding urban–rural differences in these processes is essential as divergent governance policies, socioeconomic pressures, and land use transition pathways may lead to uneven impacts on agricultural systems. However, past comparisons of urban–rural differences regarding this issue have been insufficient. Therefore, this study takes Jiangsu Province, China, as an example. Based on 30 m-resolution land use data, Geographic Information System (GIS) spatial analysis, and landscape pattern indices, it delves into the urban–rural differences in cropland loss and fragmentation caused by construction land expansion from 1990 to 2020. The results show that cropland in urban and rural areas decreased by 44.14% and 5.97%, respectively, while the area of construction land increased by 2.61 times and 90.14%, respectively. 94.36% of the newly added construction land originated from cropland, with the conversion of rural cropland to construction land being particularly prominent in northern Jiangsu, while the conversion of urban cropland to construction land is more pronounced in southern Jiangsu. The expansion of construction land has led to the continuous fragmentation of cropland, which is more severe in urban areas than in rural areas, while construction land is becoming increasingly agglomerated. There are significant differences in the degree of land use change between urban and rural areas, necessitating the formulation of differentiated land management policies to balance economic development with agricultural sustainability. Full article

This study investigates the integration of ChatGPT in Philippine higher education institutions (HEIs) through narrative inquiry, employing Clandinin and Connelly’s three-dimensional framework (temporality, sociality, place) to explore the lived experiences of 18 participants (10 students, 8 faculty). The research identifies three global themes: (1) the need for strong ethical guidelines amid widespread but tacit “silent acceptance” of AI use, (2) faculty efforts to adapt traditional pedagogy while addressing concerns about critical thinking erosion, and (3) strategies to optimize ChatGPT’s utility without exacerbating inequities. Participant narratives reveal divergent adoption patterns: urban stakeholders leverage ChatGPT for efficiency and learning augmentation, while rural counterparts face infrastructural barriers that deepen the urban–rural divide. Students report evolving ethical engagement, from initial dependency to reflective use, whereas faculty grapple with academic integrity and assessment redesign. The findings underscore how cultural resistance, institutional policy gaps, and technological disparities shape ChatGPT’s uneven adoption, reinforcing existing educational inequalities. This study contributes to the literature on AI in education by proposing context-sensitive strategies for equitable integration, including offline AI tools for rural areas, faculty training programs, and transparent policy frameworks. By centering stakeholder narratives, the research advocates for culturally grounded AI adoption that balances innovation with pedagogical integrity, offering a model for Global South contexts facing similar challenges. Full article

Urban green spaces face increasing pressure to reconcile ecological conservation with rising public demand as urbanization accelerates. Yet the spatial coupling of human needs and natural provisioning in the urban–rural fringe remains insufficiently understood. Focusing on the country park cluster in Beijing’s Huitian region, this study develops an integrated assessment framework—combining ecological importance, ecological connectivity potential, construction suitability, and recreational demand—to evaluate nature suitability and pinpoint spatial mismatches in human–nature interactions. Ecological importance is quantified through ecological sensitivity analysis and InVEST-based habitat-quality modeling, while ecological connectivity potential is estimated via normalized least-cost corridor analysis. Construction suitability is derived from the proportion of artificial surfaces and road network density, and recreational demand is measured by population concentration, facility density, and transport accessibility. These metrics are synthesized to map nature-suitability patterns and reveal divergences between human demand and ecological provisioning. The results show a pronounced north–south gradient in ecological importance, with Dongxiaokou Ditch and Dongxiaokou Forest Park emerging as ecological hotspots. Nineteen ecological source patches are identified, with the strongest connectivity in the southern sector of Dongxiaokou Forest Park. Areas of high construction suitability coincide with well-developed infrastructure, whereas recreational demand clusters around Tiantongyuan and Beiqing Road. Natural and artificial elements intertwine to form an ecological core of 870.74 hm², yet 13.29% of the study area exhibits mismatches—particularly residential zones lacking green-space access. Over-development within Dongxiaokou Urban Recreation Park likewise falls short of ecological expectations. The study offers targeted recommendations—ecological protection, facility optimization, green-space enhancement, and the integration of multifunctional green infrastructure—and provides a transferable framework for coordinating human and natural systems in other urban–rural fringe contexts. Full article

The Belt and Road Initiative’s infrastructure development faces significant challenges in understanding and addressing the divergent perceptions between urban and rural populations, particularly regarding high-speed rail projects. This study investigates the behavioral intentions and sustainability perspectives regarding the China–Laos High-Speed Rail Service among Thai people, with particular focus on urban–rural differences. While the China–Laos railway became operational in December 2021, it is important to note that the high-speed rail extension into Thailand is not yet in operation and remains in the planning and development stage. Using survey data from 2866 respondents (1301 urban and 1565 rural) across 22 Thai provinces, this study employs structural equation modeling to examine relationships between perceived benefits, service quality, cultural factors, emotional aspects, and behavioral intentions. The findings reveal significant urban–rural disparities in infrastructure acceptance patterns. Urban residents demonstrate stronger relationships between perceived benefits and attitudes (β = 0.260) compared to rural residents (β = 0.170), while rural populations show substantially stronger responses to cultural factors (β = 0.365 vs. β = 0.309). Service quality more strongly influences behavioral intentions in rural areas (β = 0.154 vs. β = 0.138), suggesting varying priorities across geographical contexts. The study recommends implementing differentiated development strategies that address these urban–rural differences, including culturally sensitive rural engagement approaches and comprehensive service quality management systems. This research contributes to infrastructure development literature by empirically validating spatial heterogeneity in acceptance factors, extending theoretical frameworks on sustainability perceptions, and providing evidence-based guidance for managing urban–rural disparities in major infrastructure projects. Full article

Landslides pose a significant threat to the safety and stability of settlements in karst regions worldwide. The long-standing tight balance state of settlement funding and infrastructure makes it difficult to allocate disaster prevention resources effectively against landslide impacts. There is an urgent need to fully leverage the landscape resources of karst settlements and develop landslide risk prevention strategies that balance economic viability with local landscape adaptability. However, limited research has explored the differential resilience characteristics and patterns of landslide disaster risk and settlement landscapes from a spatial coupling perspective. This study, based on landslide disaster and disaster-adaptive landscape data from a typical karst province in China, employs the frequency ratio-random forest model and weighted variance method to construct landslide disaster risk (LDR) and disaster-adaptive landscape (DAL) base maps. The spatial characteristics of urban, urban–rural transition zones, and rural settlements were analyzed, and the resilience differentiation and driving factors of the LDR–DAL coupling relationship were assessed using bivariate spatial autocorrelation and geographical detector models. The key findings are as follows: (1) Urban and peri-urban settlements exhibit a high degree of spatial congruence in the differentiation of LDR and DAL, whereas rural settlements exhibit distinct divergence; (2) the Moran’s I index for LDR and DAL is 0.0818, indicating that urban and peri-urban settlements predominantly cluster in H-L and L-L types, whereas rural settlements primarily exhibit H-H and L-H patterns; (3) slope, soil organic matter, and profile curvature are key determinants of LDR–DAL coupling, with respective influence strengths of 0.568, 0.555, and 0.384; (4) in karst settlement development, augmenting local vegetation in residual mountain areas and parks can help maintain forest ecosystem stability, effectively mitigating landslide risks and enhancing disaster-adaptive capacity by 6.77%. This study helps alleviate the contradiction between high LDR and weak disaster-adaptive resources in the karst region of Southwest China, providing strategic references for global karst settlements to enhance localized landscape adaptation to landslide disasters. Full article

China’s rapidly aging population has intensified demand for long-term services and supports (LTSSs), yet geographic disparities in accessibility persist despite policy reforms like long-term care insurance (LTCI). This study evaluates spatial inequities in Chengdu, a megacity piloting LTCI, using an enhanced two-step floating catchment area (2SFCA) method with demand intensity coefficients and a spatial mismatch index (SMI). Results reveal critically low average accessibility: 0.126 LTSS beds and 0.019 formal caregivers per thousand recipients within a 60 min travel threshold. Accessibility declines sharply along urbanization gradients, with urban cores (“first loop”) exceeding suburban “second” and “third loop” by ratios of 1.5–2.1 and 2.0–8.0, respectively. Strong correlations with impervious surface ratios (R² = 0.513–0.643) highlight systemic urban bias in resource allocation. The SMI analysis uncovers divergent spatial mismatches: home care accessibility predominates in western suburbs due to decentralized small-scale providers, while institutional care clusters in eastern suburbs, reflecting government prioritization of facility-based services. Despite LTCI’s broad coverage (67% of Chengdu’s population), rural and peri-urban older adults face compounded barriers, including sparse LTSS facilities, inadequate transportation infrastructure, and reimbursement policies favoring urban institutional care. To address these inequities, this study proposes a multi-stakeholder framework: (1) strategic expansion of LTSS facilities in underserved suburban zones, prioritizing institutional care in the “third loop”; (2) road network optimization to reduce travel barriers in mountainous regions; (3) financial incentives (e.g., subsidies, tax breaks) to attract formal caregivers to suburban areas; (4) cross-regional LTCI coverage to enable access to adjacent district facilities; and (5) integration of informal caregivers into reimbursement systems through training and telehealth support. These interventions aim to reconcile spatial mismatches, align resource distribution with Chengdu’s urban–rural integration goals, and provide scalable insights for aging megacities in developing contexts. By bridging geospatial analytics with policy design, this study underscores the imperative of data-driven governance to ensure equitable aging-in-place for vulnerable populations. Full article

This study explores the importance of considering regional aspects and different calculation approaches when assessing the environmental impact of passenger cars in Germany. The transportation sector, in general, needs to improve its transition to comply with national and international goals, and more efficient measures are necessary. To achieve this, the spatial heterogeneity of underlying data, such as vehicle stocks, cubic capacity classes as a proxy for consumption values, and annual mileage, is investigated with respect to regional differences. Using data samples for the year 2017, the average emission values per car and year are calculated as well as Germany’s total emission values from the utilization of passenger cars. Conducting a spatially informed allocation algorithm, battery electric vehicles (BEVs) are added to certain regional fleets, replacing cars with internal combustion engines (ICEs). The results show significant regional differences in the underlying data, with a divergence between rural and urban areas as well as northern and southern regions, while the spread in mileage values is higher than that in consumption values. Comparing the tank-to-wheel (TtW) and well-to-wheel (WtW) approaches reveals different values with an increased spread as more BEVs are introduced to the fleet. Using the presented concept to allocate BEVs, emissions can be reduced by 1.66% to 1.35%, depending on the calculation perspective, compared to the extrapolation of historical values. Furthermore, rural areas benefit more from optimized allocation compared to urban ones. The findings suggest that regional distribution strategies could lead to more efficient reductions in GHG emissions within the transportation sector while incorporating both TtW and WtW approaches, leading to more comparable and precise analyses. Full article

Exploring the phenological divergences in vegetation caused by global climate change is of great significance for gaining a deeper understanding of the carbon cycling process in natural ecosystems. However, in many existing studies, the response of the start of the growing season (SOS) and the end of the growing season (EOS) to temperature exhibited multi-scale inconsistencies. In view of this, we took 259 Chinese urban agglomerations and their rural regions as the study areas, using MODIS phenological products (MCD12Q2), land surface temperature (LST) datasets, altitude, and latitude as data, and explored the phenological divergences in vegetation with LST changes in different geographical zones through box plots, linear regression models, and Spearman’s correlation analysis. The mean SOS and EOS in urban areas were both the earliest on approximately the 100.06th day and 307.39th day, respectively, and were then gradually delayed and advanced separately along an urban–rural gradient of 0–25 km. The divergences in vegetation phenology were no longer significant in rural areas 10 km away from urban boundaries, with change amplitudes of less than 0.4 days. In high latitude (40–50° N) regions, the correlation coefficients between the SOS and EOS of various urban agglomerations and LST were −0.627 and 0.588, respectively, whereas in low latitude (18–25° N) regions, the correlation coefficients appeared to be the opposite, being 0.424 and −0.426, respectively. In mid- to high-altitude (150–400 m) areas, LST had a strong advanced effect on SOS, while in high-altitude (above 1200 m) areas, LST had a strong delayed effect on EOS, with the R² values all being above 0.7. In summary, our study has revealed that within the context of varying geographical zones, the effects of LST on phenology exhibited significant spatial heterogeneity. This may provide strong evidence for the inconsistencies in the trends of phenology observed across previous studies and more relevant constraints for improving vegetation phenology prediction models. Full article

Capital plays a crucial role in driving rural sustainable development. Some rural areas have achieved revitalization through capital intervention, while others have experienced failure. It is possible that the purposes of capital intervention initiated by different parties (such as government, enterprises, and individual investors) in rural areas differ, which leads to the divergence of development routes and effectiveness. Yet, the questions of why and how this phenomenon occurs have not been well studied. Based on observation and an in-depth interview conducted in two suburban villages near Wuhan, we have established an analytical framework with which to compare the route and effectiveness of rural developments driven by capital intervention. The results are as follows: (1) The sources of capital and the embedded purposes determine the modes of rural resource reconfiguration and the arrangement of the relevant industrial sectors. The answer to the question of how to allocate capital gains among different interest groups engaged in rural development determines whether a community of shared interests with respect to sustainable rural development can be established and operate effectively. (2) As the profit-making process differs among capital originating from different sources, it is necessary to evaluate this process such that the pursuit of capital gains and its influence upon rural sustainable development can be clarified and coordinated. (3) Throughout the capital intervention process, villagers’ participation is crucial as it is the prerequisite for the establishment of a mutually beneficial win–win relationship between external capital investors and local villagers. This comparative study of the two villages can provide insights into policy formulation for the purpose of rural revitalization in China and other countries in the Global South undergoing rapid urbanization. Full article

This study investigates the comparative analysis of the divergent pathways of sustainable energy development in Thailand and Japan. It offers a nuanced analysis of their policy frameworks, technological advancements, and socioeconomic contexts. This study elucidates the distinct strategies of the two nations by leveraging a robust dataset from sources including the Electricity Generating Authority of Thailand (EGAT) and Japan’s Agency for Natural Resources and Energy (ANRE) toward renewable energy. The key findings indicate that Thailand has capitalized on policy instruments such as the Alternative Energy Development Plan 2018 (AEDP 2018) to augment its renewable energy capacity, particularly in the solar and biomass sectors. This policy-driven approach addresses the rural–urban energy divide and enhances energy access nationwide. Conversely, Japan’s trajectory is characterized by integrating technological innovations like smart grids and the Feed-in Tariff (FiT) system, which have catalyzed significant increases in solar energy adoption and efficiency. Japan places great emphasis on technological solutions that underscore its strategy to mitigate the legacy constraints of energy infrastructure post-Fukushima. The implications of these findings are extended beyond national borders, offering critical insights into the complex interplay between policy, technology, and social engagement in the renewable energy transition. This study highlights the potential for community-based renewable energy projects in Thailand to drive economic growth and social equity. At the same time, Japan’s experience illustrates the importance of regulatory reforms and technological leadership in overcoming structural barriers to energy innovation. These insights are particularly relevant for policymakers and stakeholders aiming to balance the imperatives of energy security, economic development, and environmental sustainability. Finally, this study emphasizes the need for tailored strategies that align renewable energy adoption with the unique contexts of each country, thereby enhancing global efforts against climate change. Full article

The river and lake chief system offers a valuable policy toolkit to mitigate the degradation of water ecology, thereby bolstering water resource management for sustainable water development in China. To evaluate the effects of implementation and improve policy, this study took Beijing as a typical case and conducted a quantitative assessment using multidimensional data. The findings suggest that while the river and lake chief system in Beijing is effective and has significantly contributed to the ecological management of rivers and lakes, there are also notable regional disparities and urban–rural divergences. In addition, human activities are the main sources of environmental pollution in rivers and lakes, which should be the focus of the river and lake chief system. The river and lake chief system needs to embed more public participation and cooperative governance. This research aids in better understanding China’s river and lake chief system for both researchers and practitioners, facilitating the advancement of the knowledge body of global water policy and governance. Full article

Human migration triggered by flooding will create sociodemographic, economic, and cultural challenges in coastal communities, and adaptation to these challenges will primarily occur at the municipal level. However, existing migration models at larger spatial scales do not necessarily capture relevant social responses to flooding at the local and municipal levels. Furthermore, projecting migration dynamics into the future becomes difficult due to uncertainties in human–environment interactions, particularly when historic observations are used for model calibration. This study proposes a stochastic agent-based model (ABM) designed for the long-term projection of municipal-scale migration due to repeated flood events. A baseline model is demonstrated initially, capable of using stochastic bottom-up decision rules to replicate county-level population. This approach is then combined with physical flood-exposure data to simulate how population projections diverge under different flooding assumptions. The methodology is applied to a study area comprising 16 counties in coastal Virginia and Maryland, U.S., and include rural areas which are often overlooked in adaptation research. The results show that incorporating flood impacts results in divergent population growth patterns in both urban and rural locations, demonstrating potential municipal-level migration response to coastal flooding. Full article

Although functional identifiability represents a key aspect for promoting visual connotation and sustainable usability in historic building groups, there is still no consensus on how to quantitatively describe its identification basis at a large scale. The recent emergence of the potentiality of deep learning and computer vision has provided an alternative to traditional empirical-based judgment, which is limited by its subjective bias and high traversal costs. To address these challenges, this study aims to build a workflow for a visual analysis of function and facade to extract the different contributions that facade elements provide to functional expression. The approach is demonstrated with an experiment on a section of the Chinese Eastern Railway (CER) where large-scale historical buildings images were categorized to identify functions using deep learning, together with activation and substance for visual calculations. First, the dataset aggregated with images of historic buildings along the CER was used to identify functional categories using SE-DenseNet merging channel attention. The results of the model visualized using t-SNE and Grad-CAM were then used to analyze the relationships of facade features across functional categories and differences in elemental feature representation across functional prototypes. The results show the following: (1) SE-Densenet can more efficiently identify building functions from the closely linked facade images of historic building groups, with the average accuracy reaching 85.84%. (2) Urban–rural differences exist not only in the count of spatial distributions among the CER’s historic building groups, but also in a significant visual divergence between functions related to urban life and those involved in the military, industry, and railways. (3) Windows and walls occupy areas with more characteristics, but their decorative elements have a higher intensity of features. The findings could enhance the objective understanding and deeper characteristics of the historical building group system, contributing to integrated conservation and characteristic sustainability. Full article

Clarifying urban-rural spatial explicit structure changes is of great significance for understanding the urban-rural relationship evolution. Previous studies have mostly focused on urban internal spatial structure evolutions and less on the regional scale when it comes to exploring urban and rural evolutions. Nighttime light can timely reflect the human activities in regions and provides great potential for investigating the evolutions of urban and rural spatial explicit structures. Here, taking Dezhou City, a rapidly urbanizing city in China, as a case study, we employed the local contour tree method and nighttime light data to map urban and active rural extents from 2012 to 2020 and further explored their respective development processes. This study showed that unlike in rural regions, the internally explicit structures of urban regions were more complex, and there were often multiple hotspots inside them. The area of the urban-rural region increased significantly by 39.3% from 2012 to 2020 (p < 0.05). Populations were greatly responsible for the spatial explicit structure changes of urban and active rural regions. The urban and rural region rankings of the identified counties were basically consistent with the urban and rural population rankings. Unlike the perspectives of earlier land use (i.e., built-up land or impervious surface), this study underlined urban and active rural regions in view of the scope of active human activities. These results can likely help policymakers understand current active human activity extents and provide a data-based reference for future public services and infrastructure planning. Full article