Search Results (15,148)

The sultanate of Oman has joined other nations in promoting sustainability, guided by Oman Vision 2040 and the Oman National Spatial Strategy. Oman now focuses on developing more human-centered cities, enhancing community well-being, boosting the local economy, and increasing investments. This study addresses a research gap by examining the social and economic impact of the sustainable neighborhood “Al-Mouj” on the nearby urban area “Al-Mawaleh North” to maximize sustainability benefits. It analyzes how a sustainable neighborhood influences the economy, society, quality of life, and overall well-being. The study also identifies key factors driving the growth of sustainable practices in society and the economy. It has four main objectives in terms of answering the research question, primarily through surveys of community members and business owners, and by analyzing land use development around Al-Mouj. Data collection methods include literature review, case study, questionnaires, and interviews. Data analysis employs spatial, statistical, and thematic techniques. Responses from 515 participants are examined to ensure reliable results. Ethnographic methods are used to gain insights from open-ended questionnaire responses and interviews. The results confirm that Al-Mouj’s mixed-use development and sustainability features positively influence mental and physical health and stimulate economic activity within the local community. This study provides decision-makers and urban planners valuable insights into sustainable neighborhoods’ social and economic impacts when developed as open communities. It highlights the challenges of following international NSAT standards, which do not fully address local concerns. Full article

Urban streetscapes are among the most frequently encountered spatial environments in daily life, and their restorative visual features have a significant impact on well-being. Although existing studies have revealed the relationship between streetscape environments and perceived restorativeness, there remains a lack of scalable, data-driven methods for quantifying such perception at the street level. This study proposes an interpretable and replicable framework for predicting streetscape restorativeness by integrating semantic segmentation, perceptual evaluation, and machine learning techniques. Taking Liwan District of Guangzhou as a case study, street-view images (SVIs) were collected and processed using the Mask2Former model to extract the following five key visual metrics: greenness, openness, enclosure, walkability, and imageability. Based on the Perceived Restorativeness Scale (PRS), an online questionnaire was designed from four dimensions (fascination, being away, compatibility, and extent) to score a random sample of images. A random forest model was then trained to predict the perceptual levels of the full dataset, followed by K-means clustering to identify spatial distribution patterns. The results revealed that there were significant differences in visual characteristics among high, medium, and low restorativeness street types. The proposed framework enables scalable, data-driven evaluation of perceived restorativeness across diverse urban streetscapes. By embedding perceptual metrics into large-scale urban analysis, the framework offers a replicable and efficient approach for identifying streets with low restorative potential—thus providing urban planners and policymakers with a novel tool for prioritizing street-level renewal, improving public well-being, and supporting perception-oriented urban design without the need for labor-intensive fieldwork. Full article

In recent years, there has been growing recognition that reducing environmental pollution, particularly from building emissions, is essential for improving residents’ well-being. Buildings contribute substantially to worldwide greenhouse gas and pollutant emissions, making effective mitigation strategies a priority in achieving Sustainable Development Goals (SDGs). Using data from the 2021 China General Social Survey (CGSS), this study examines the relationship between perceived building environmental pollution and residents’ well-being, as well as the mechanism underlying this relationship, through an ordered probit model. The results indicate that higher levels of building environmental pollution significantly reduce residents’ well-being. To explore heterogeneity, the sample was further divided by urban–rural differences, local environmental protection expenditure level, and geographic region. The research found that residents with lower environmental protection expenditures, residents in rural areas and those in the central region are more likely to be negatively affected by building environmental pollution, with the correlation coefficients being −0.111, −0.104 and −0.101 respectively. Furthermore, the analysis indicates that annual income, the number of children, and type of work have moderating effects on this relationship, with correlation coefficients of 0.047, −0.054, and −0.095 respectively. Overall, this study provides empirical evidence for perceiving the social impact of building pollution in the context of building-related emissions and offers policy-related insights for strengthening environmental protection measures in the construction industry to enhance residents’ well-being. Full article

Pilot policies are key determinants of urban ecological resilience, while the corresponding results are inconsistent. Moreover, existing research on the synergistic effects of policies on ecological resilience remains insufficient. Thus, this study selects low-carbon pilot policies and smart city pilot policies to explore the possible channels through which they affect ecological resilience. Consequently, using the sample data of China’s prefecture-level cities during the period of 2005–2022, we employ a multi-period difference-in-differences approach and two-step regression to examine the relationship between dual pilot policies and ecological resilience. We find that dual pilot policies have a significant positive impact on ecological resilience, and the conclusion is still held after a series of robustness tests. We also find that regional and population size heterogeneity effects exist. Furthermore, the sequences of pilots significantly influence ecological resilience, where the sequence of implementing low-carbon pilot programs earlier than smart city pilot programs has a greater impact on ecological resilience. Finally, the dual pilot policies enhance ecological resilience through channels of technological innovation and industrial structure upgrading. Overall, this study reveals the relationship between policies and ecological resilience, providing policy insights for building resilient cities. Full article

Achieving environmentally sustainable growth is a core challenge for developing economies, yet the welfare consequences of green development policies for vulnerable populations remain understudied. This article investigates the distributional impacts of one of the world’s largest development interventions: China’s energy transition. By integrating provincial-level energy metrics with a decade-long household panel survey (CFPS), we employ a fixed-effects model to provide a holistic assessment of the policy’s effects on household well-being. The analysis reveals a stark trade-off: a 10% increase in clean energy adoption generates significant non-monetary well-being gains, equivalent to a 190,000 CNY annual income rise, primarily through improved environmental quality and cleaner cooking fuel access. However, these benefits are partially offset by rising energy costs. Our heterogeneity analysis reveals a clear regressive burden: the transition significantly increases energy expenditures for rural and low-income households, while having a negligible or even cost-reducing effect on their urban and high-income counterparts. Our findings demonstrate that while the energy transition promotes aggregate welfare, its benefits are unevenly distributed, potentially exacerbating energy poverty and inequality. This underscores a critical development challenge: green growth is not automatically inclusive. We argue that for the energy transition to be truly pro-poor, it must be accompanied by robust social protection mechanisms, such as targeted subsidies, to shield the most vulnerable from the adverse economic shocks of the policy. Full article

Background and Objectives: Sciatica is a common condition associated with significant pain and reduced quality of life (QoL). However, its impact and associated determinants remain underexplored in Saudi Arabia. Therefore, this study aimed to examine determinants of quality of life among Saudi adults with sciatica. Materials and Methods: A cross-sectional study was conducted in Saudi Arabia between February and March 2024 using an online Arabic questionnaire disseminated via social media. Participants (n = 927) aged ≥18 years completed a 50-item questionnaire covering sociodemographic characteristics, clinical history, lifestyle factors, and the Short Form-12 (SF-12) QoL scale. Results: Of the 927 adults (mean age 30 ± 11 years), 76% (n = 706) were female and 24% (n = 221) male; 10% (n = 92) reported sciatica. Overall, 57% (n = 531) reported good QoL. Multivariate analysis revealed that increasing age (Odds ratio (OR) = 1.04; 95% CI: 1.01–1.07), urban residence (OR = 1.49; 95% CI: 1.11–2.00), and higher income (>15,000 Saudi Arabia Riyal (SAR); OR = 1.53; 95% CI: 1.03–2.27) were associated with better QoL. Conversely, diabetes (OR = 0.42; 95% CI: 0.22–0.79), arthritis (OR = 0.52; 95% CI: 0.32–0.83), and sciatica duration >1 year (OR = 0.29; 95% CI: 0.12–0.69) were associated with poorer QoL. Gender, body mass index (BMI), smoking, and physical activity showed no significant associations. Conclusions: Sciatica, prolonged symptom duration, and comorbidities such as diabetes and arthritis negatively affect QoL in Saudi adults, whereas socioeconomic and demographic factors contribute positively. The results underscore the importance of early intervention and tailored support for sciatica patients with long-standing symptoms or specific comorbidities to improve health outcomes. Longitudinal studies are warranted to assess causality and the impact of interventions. Full article

Information and communication technology (ICT) infrastructure can facilitate urban–rural integration. However, few studies have explored the role of virtual agglomeration in the mechanisms underlying this process, which can enable geographically dispersed market participants (both urban and rural) to achieve proximity in network space through digital connectivity provided by ICT. This study uses the PLS-SEM method to empirically analyzes the relationships among ICT infrastructure, virtual agglomeration, and urban–rural integration based on data obtained from 31 provincial-level regions in China from 2012 to 2022. The results indicate that: (1) ICT infrastructure can promote urban–rural integration. (2) Virtual agglomeration plays a significant mediating role in the relationship between ICT infrastructure and urban–rural integration. In relatively developed eastern China, virtual agglomeration fully mediates the impact of ICT infrastructure on urban–rural integration. (3) Other complementary infrastructures—including transport and education—have positive moderating effects on the process of virtual agglomeration facilitated by ICT. This study advances the understanding of ICT’s effects on regional development from the perspective of employing a new form of spatial agglomeration (i.e., virtual agglomeration). Meanwhile, this study indicates that in order to address the global challenge of urban–rural divide, it is necessary to strengthen the development of ICT infrastructure in remote rural areas, while developing complementary infrastructure such as transportation or education in alignment with regional characteristics. Full article

This study adopts a systems perspective to examine the persistent effects of China’s One-Child Policy (OCP) on the subjective well-being of older adults, emphasizing structural persistence, reinforcing feedback, and path-dependent lock-in in complex socio-technical systems. Using nationally representative data from the China Longitudinal Aging Social Survey (CLASS-2014), we exploit the OCP’s formal rollout at the end of 1979—operationalized with a 1980 cutoff—as a quasi-natural experiment. A Fuzzy Regression Discontinuity (FRD) design identifies the Local Average Treatment Effect of being an only-child parent on late-life well-being, mitigating endogeneity from selection and omitted variables. Theoretically, we integrate three lenses—policy durability and lock-in, intergenerational support, and life course dynamics—to construct a cross-level transmission framework: macro-institutional environments shape substitution capacity and constraint sets; meso-level family restructuring reconfigures support network topology and intergenerational resource flows; micro-level life-course processes accumulate policy-induced adaptations through education, savings, occupation, and residence choices, with effects materializing in old age. Empirically, we find that the OCP significantly reduces subjective well-being among the first generation of affected parents decades later (2SLS estimate ≈ −0.23 on a 1–5 scale). The effects are heterogeneous: rural residents experience large negative impacts, urban effects are muted; men are more adversely affected than women; and individuals without spouses exhibit greater declines than those with spouses. Design validity is supported by a discontinuous shift in fertility at the threshold, smooth density and covariate balance around the cutoff, bandwidth insensitivity, “donut” RD robustness, and a placebo test among ethnic minorities exempt from strict enforcement. These results demonstrate how demographic policies generate lasting impacts on elderly well-being through transforming intergenerational support systems. Policy implications include strengthening rural pension and healthcare systems, expanding community-based eldercare services for spouseless elderly, and developing complementary support programs. Full article

This study intends to identify and quantify the individual, perceptual, and contextual factors associated with odor-related perception and to assess the perception of odor sources according to meteorological conditions. Two face-to-face seasonal community surveys were conducted using stratified random sampling with proportional allocation, yielding representative samples of residents in a southern Brazilian city, where mild constant temperatures throughout the year and shifting prevailing wind directions expose residents to different odor sources. Chi-Square tests were applied to assess associations between odor perception and qualitative variables, while logistic regression was used to identify predictors of higher annoyance. Results showed that prevailing wind direction influenced source attribution, with steel industry and sewage-related sites most frequently cited. Proximity to the steel plant increased both source recognition and annoyance levels. Reported impacts included closing windows and reducing outdoor activities. Self-reported respiratory problems consistently predicted higher annoyance levels in both surveys. The statistical methods were effective in analyzing the likelihood of odor-related perception and its relationship with explanatory variables. These findings highlight the value of a data-driven approach—specifically, integrating wind direction, source proximity, and community-based perception—to support urban environmental management and guide odor mitigation strategies. Full article

Digital inclusive finance (DIF) serves as a critical mechanism for sustainable economic development among marginalized populations. However, DIF’s impact on China’s 376 million migrants remains understudied, despite their significance for inclusive growth. This study provides the first comprehensive empirical analysis of DIF–migrant entrepreneurship relationships using nationally representative data and multiple analytical approaches. Three key findings emerge: First, DIF significantly reduces entrepreneurship likelihood among migrant populations (marginal effect: −0.449, p < 0.01). Second, heterogeneity analysis reveals differential impacts across entrepreneurial motivations—specifically, inhibiting necessity-driven entrepreneurship (marginal effect: −0.426) while showing no significant impact on opportunity-driven entrepreneurship. Third, while DIF reduces overall entrepreneurial participation, it substantially enhances income levels and the employment scale of existing migrant entrepreneurs. Mechanism analysis reveals that DIF operates through expanding urban employment demand and raising wage levels, thereby increasing entrepreneurship’s opportunity cost for migrants. These findings provide evidence for leveraging digital finance to achieve SDG 8 (Decent Work) and SDG 10 (Reduced Inequalities) while ensuring equitable access to digital dividends for vulnerable populations. Full article

Against the background of global warming, the carbon emission of cities accounts for more than 70%, and its carbon sink increase and emission reduction have become the research focus. The water bodies and green spaces in the urban blue–green space have a synergistic carbon sequestration effect, but current research pays less attention to the small and medium scales. Therefore, taking the waterfront green space on both sides of Qinhuai New River in Nanjing as the research object, this paper explores the impact of the synergy between water and greenery on the carbon sequestration efficiency of green space. The study first estimates the carbon sequestration efficiency of green spaces by integrating measured Leaf Area Index (LAI) data with the mean carbon sequestration rate per unit leaf area for typical tree and shrub species. It then constructs a set of water–green spatial relationship indicators and applies a random forest regression model to identify the key factors influencing carbon sequestration efficiency. Finally, multiple scenario models are developed to simulate the effects of green spaces on CO₂ reduction, thereby validating the roles of the identified influencing factors. The study found that waterfront green spaces tended to exhibit slightly higher carbon sequestration efficiency compared with non-waterfront green spaces. The proportion of 10 m forest land area and the proportion of 10–20 m forest land area had a higher impact on the carbon sequestration capacity of waterfront green space; that is, the closer the distance between the green space and the water, the better the carbon sequestration capacity. In order to improve the carbon sequestration efficiency of the waterfront area, the green space should be arranged along the water bank as much as possible, the depth of the green space should be increased, the proportion of the forest land area should be increased, the arbor and shrub should be planted evenly, and ribbon planting should be avoided. The study confirmed the synergistic effect of water and greenery in carbon sequestration benefits, providing data support and theoretical reference for the optimization and renewal of urban waterfront green space, and contributing to the realization of urban waterfront green space planning, design, and renewal with the goal of a high carbon sink. Full article

To address the global challenge of sustainable energy transition in cities, there is a growing demand for innovative solutions to provide flexible, low-carbon, and socio-economically profitable energy systems. In this context, there is a need for holistic evaluation frameworks for the prioritization and economic optimization of interventions. This paper provides a literature review on sustainable planning and economic impact assessment of innovative urban areas, such as Positive Energy Districts (PEDs), to analyze research trends in terms of evaluation methods, impacts, system boundaries, and identify conceptual and methodological gaps. A dedicated search was conducted in the Scopus database using several query strings to conduct a systematic review. At the end, 57 documents were collected and categorized by analysis approach, indicators, project interventions, and other factors. The review shows that the Cost–Benefit Analysis (CBA) is the most frequently adopted method, while Life Cycle Costing and Multi-Criteria Analysis result in a more limited application. Only in a few cases is the reduction in GHG emissions and disposal costs a part of the economic model. Furthermore, cost assessments usually do not consider the integration of the district into the wider energy network, such as the interaction with energy markets. From a more holistic perspective, additional costs and benefits should be included in the analysis and monetized, such as the co-impact on the social and environmental dimensions (e.g., social well-being, thermal comfort improvement, and biodiversity preservation) and other operational benefits (e.g., increase in property value, revenues from Demand Response, and Peer-To-Peer schemes) and disposal costs, considering specific discount rates. By adopting this multi-criteria thinking, future research should also deepen the synergies between urban sectors by focusing more attention on mobility, urban waste and green management, and the integration of district heating networks. According to this vision, investments in PEDs can generate a better social return and favour the development of shared interdisciplinary solutions. Full article

Accessibility is a key dimension for sustainable transport network management and planning. However, conventional location-based accessibility measures typically rely on average travel times as the sole temporal metric, neglecting detailed travel time distributions. Consequently, these methods yield identical accessibility values for study zones with the same mean travel time but different travel time variations. To overcome this limitation, we developed a novel approach that explicitly integrates the probability density distributions of travel times, modelling the impact of travel time variability on accessibility. We applied the proposed method using GPS data collected from taxis in Harbin, China, and compared its outcomes with those from existing potential accessibility calculations. Across all 103 study zones in Harbin, the existing method underestimated the accessibility by 6–28%, with an average underestimation of 17% when benchmarked against the new method. These inaccuracies also impaired the identification of urban areas with the lowest accessibility levels, leading to the misclassification of 20% of problematic zones. The findings highlight the limitations of existing methods, which produce biassed accessibility estimations and misleading results. In contrast, the proposed travel time variability-integrated accessibility measure demonstrates greater sensitivity to actual traffic conditions, providing a more accurate and objective assessment of network performance. Full article

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

Traditional villages (TVLGS) have significantly declined as a result of China’s fast urbanization, especially in Fujian Province, where efficient conservation efforts are hampered by a lack of thorough study. The geographical and temporal distribution features of Fujian’s traditional villages (FTVLGS) are investigated using ArcGIS 10.8 and GeoDa software. Additionally, it identifies 18 driving factors to investigate the primary influences and interaction mechanisms through a combination of Python 3.7 and GeoDa 1.16. The results show that: (1) FTVLGS are distributed both spatially and temporally in a pattern that is oriented from northeast to southwest to east. Over time, the distribution center of gravity moves from north to southeast, increasing directional tendencies and broadening the distribution area. (2) The impact of each driving factor on the spatial distribution of TVLGS varies, with the strongest influence being the interaction between average annual precipitation and the straight-line distance from provincial highways. The straight-line distance between TVLGS and provincial highways is found to be the most significant factor affecting their distribution. This study clarifies the intricate dynamics associated with the distribution of TVLGS and the factors that influence them, providing evidence-based recommendations for the future preservation and advancement of these TVLGS. It also aims to enhance the connectivity of developmental elements at a regional scale and to foster the advancement of global tourism within TVLGS. Full article