Search Results (10,656)

Balancing economic growth with environmental sustainability remains a serious challenge for many transition economies. This study analyzes the relationship between economic growth, service sector expansion, urbanization, and environmental degradation in Uzbekistan. Employing yearly temporal series data from 1991 to 2024, the research implements an autoregressive distributed lag (ARDL) bounds testing methodology to investigate the short-run and long-run relationships among the variables. Carbon dioxide (CO₂) emissions are used as proxies for environmental degradation, while GDP per capita, service sector value added, and urban population growth represent key economic and structural factors. The empirical results show that there is a stable long-run cointegration relationship between the variables. The findings show that economic growth significantly reduces CO₂ emissions in the long run, which supports the Environmental Kuznets Curve (EKC) hypothesis for Uzbekistan. Conversely, the expansion of the service sector and rapid urbanization are found to increase CO₂ emissions and contribute to environmental degradation. The error correction model shows a rapid adjustment rate of approximately 66.5% to the long-run equilibrium after short-term shocks. Through these results, we attempted to reveal the importance of sustainable urban development, green economic policies, and the development of an environmentally friendly service sector in Uzbekistan. Full article

Urban areas contribute a dominant share of global carbon dioxide (CO₂) emissions, yet city-scale emission estimates remain highly uncertain due to the limited spatial and temporal resolution of existing observation methods. This review paper examined the potential of horizontally scanning Differential Absorption LiDAR (DIAL) systems to address these gaps in urban CO₂ monitoring. We reviewed current top-down and bottom-up approaches, identified their limitations in resolving intra-urban variability, and outlined a conceptual framework for deploying compact, eye-safe CO₂ DIAL systems on elevated urban structures. We reviewed methods that combined horizontal CO₂ scanning with wind measurements to enable high-resolution mapping of CO₂ distributions, assessment of boundary-layer effects, and estimation of urban carbon fluxes using established mass-balance and inversion techniques. Conceptual deployment scenarios, illustrated using Boston as an example, demonstrated how a single elevated LiDAR unit could provide near-city-wide coverage and capture neighborhood-scale emission patterns. We further discussed calibration strategies, safety considerations, and integration with existing sensor networks and column observations. This review concluded horizontal CO₂ LiDAR represents a promising pathway toward high-resolution, observation-based urban carbon monitoring, offering a complementary tool to existing methods and supporting future efforts to verify emission inventories, evaluate mitigation policies, and advance city-scale carbon management. Full article

Sustainability is recognized as a key driver for the formation and evolution of sustainable urban forms, serving as a critical approach for guiding and assessing urban sustainability. In the context of global warming, climate adaptation has become a pressing concern in shaping sustainable urban forms. However, the complex mechanisms linking sustainability, urban form, and climate adaptation are difficult to identify and track due to variations in data precision, research scales, and stakeholder needs. This study first re-views the relevant literature to clarify the current state and trends in this field. It then employs locally weighted regression to analyze the relationships between sustainability principles, urban form, and climate adaptation from 2005 to 2024. Based on the “mitigation-adaptation” framework, sparsity-responsiveness indicators are constructed to define four types of climate adaptation. These types are used to classify 31 representative cities in China. Considering the cities’ developmental stages, the study proposes design strategies that prioritize sustainability. Full article

Disaster risk perception in informal urban settlements remains insufficiently understood despite its importance for climate adaptation and urban resilience policies. This study evaluated disaster risk perception among residents of nine informal settlements in Greater Santo Domingo, Dominican Republic, focusing on the relationship between the subjective risk of disaster, poverty, and socio-environmental vulnerability. A mixed-methods approach was applied using the RISKPERCEP algorithm and a structured survey composed of 98 questions grouped into key perception variables. A total of 198 surveys were conducted across four municipalities, supported by expert consultation, probabilistic sample-size calculation, and statistical analyses including chi-square tests and Cramér’s V coefficients. The results revealed a generalized moderate underestimation of climate-related risks, mainly associated with limited understanding of hazards, weak institutional trust, low personal involvement, poor memory of past disasters, and reduced concern about consequences. Socioeconomic deprivation, particularly among women, retirees, and low-income groups, was strongly associated with higher perceived vulnerability. Statistical analyses demonstrated a dense and multidimensional network of interdependent social, economic, and spatial variables shaping vulnerability. The study concludes that low disaster risk perception constitutes a major barrier to climate adaptation in informal settlements and highlights the need for integrated public policies combining social protection, climate education, urban resilience, and community-based adaptation strategies. The implications of this study point to the use of a disaster risk perception inquiry method at the community level, structured around specific variables that allow the identification of underlying causes of the widespread underestimation of disaster risk. This approach enables the design of adaptive policies—both structural and non-structural—that explicitly incorporate these aspects when formulating resilience measures. Future research should aim to generalize the study to other communities across the country, preceded by addressing the limitations identified. Full article

Greening urban spaces is one of the fundamental elements in strengthening urban resilience to climate change and introducing sustainable urban lifestyles. The subject of this research is innovative urban solutions, situated in the context of cities’ aspirations to attain the title of being the greenest and most sustainable city in Europe. The conducted research is based on a comparative analysis of greening initiatives in 41 cities in 13 European countries, united in the international project Green Cities Europe, carried out from 2020 to 2023. The aim of the research is to evaluate urban solutions implemented in the context of social, economic, environmental, and spatial values, as well as the use of blue–green infrastructure, renewable energy sources, and new technologies. The study employed a mixed-methods approach combining qualitative and quantitative analyses. The research included a review of planning and strategic documents, a comparative analysis of 131 urban greening projects, and an evaluation of 45 projects nominated for the Green Cities Europe Award (GCEA). The findings were synthesised using comparative statistical analyses and descriptive evaluation. Based on the conducted analysis and assessment, we demonstrate that the studied cities, which are pursuing development and climate policies, implement urban greening actions with varying, often limited, degrees of innovation. We show that the implemented solutions place significant emphasis on environmental and social values, but only minimally incorporate elements of renewable energy sources and new technologies. This study indicates the need for cities to pursue a coherent development policy that fosters the planning and implementation of innovative urban solutions. Full article

Urban Heat Island (UHI) effects are increasingly acknowledged as a critical urban climate challenge with far-reaching consequences for human health and overall well-being. However, the conceptual structure, temporal evolution, and intellectual landscape of studies examining the relationships between UHI and human well-being remain fragmented, thereby constraining the development of integrated knowledge frameworks needed to guide future research, urban adaptation strategies, and evidence-based policy interventions. To this end, a total of 4857 studies were retrieved from the Scopus and Web of Science databases and screened following the PRISMA guidelines. These studies were then analyzed using Bibliometrix and VOSviewer. The results reveal a rapid and exponential growth in scientific output, particularly after 2010, with the output reaching its highest level in recent years. These outputs were shaped mostly in China and the United States with a well-established international collaboration network, while the Global South remain significantly underrepresented in scientific productions. We also found that studies are primarily structured around four dominant research clusters: urban heat island, thermal comfort, land surface temperature, and climate change. Furthermore, early studies predominantly focused on urban surface properties and built-environment characteristics, while recent research has increasingly shifted toward human health impacts, thermal stress, heat vulnerability, and well-being. Emerging research directions further highlight growing interest in nature-based solutions for mitigating UHI effects, alongside the application of advanced technologies such as machine learning and remote sensing for high-resolution urban climate assessment. Overall, our findings indicate a transition toward a more integrated urban climate–health–well-being research framework, while simultaneously revealing persistent geographical and conceptual gaps, particularly across the Global South. We therefore advocate for increased empirical research, stronger international collaboration, and context-specific urban adaptation strategies to better safeguard human well-being under intensifying urban heat conditions. Full article

The COVID-19 pandemic triggered an unprecedented collective reflection on everyday urban life, reshaping how citizens perceived proximity, mobility patterns, and the sustainability of everyday transport systems. This article examines whether these pandemic-induced reflections resonated with the core principles of the 15-Minute City, understood not as a policy blueprint but as an emergent social imaginary. Drawing on a large-scale comparative survey conducted during the strictest lockdown phase in April–May 2020 (n = 9575), the study analyses survey respondents’ desires and expectations regarding post-pandemic urban change across nine Ibero-American countries: Argentina, Brazil, Chile, Colombia, Ecuador, Mexico, Peru, Portugal, and Spain. Using one-way ANOVA and Tukey post hoc tests, the results reveal a strong and widely shared normative orientation toward greener, more walkable, and more localized urban environments, all of which are key dimensions of sustainable urban mobility transitions, including reduced car dependence and a greater reliance on active transport modes such as walking and cycling. However, these aspirations are consistently accompanied by significantly lower expectations of implementation, resulting in a pronounced desire–expectation gap across all countries. This gap displays clear geographical variation and appears closely associated with perceived governance capacity and institutional trust, with European countries showing greater alignment between aspiration and expectation than their Latin American counterparts. The findings suggest that, even in the absence of explicit policy discourse, respondents expressed aspirational urban imaginaries displaying affinities with principles commonly associated with the 15-Minute City framework. By empirically capturing these bottom-up imaginaries, the study contributes to debates on sustainable urban transport and mobility transitions by highlighting the social foundations and governance constraints of proximity-based planning across diverse institutional contexts. Full article

Advanced metering infrastructure (AMI) is the sensing backbone of the smart grid, and its reliability underpins urban energy services such as state estimation, demand response, and distributed-energy integration. When AMI uses cellular spectrum leased through a cognitive mobile virtual network operator (C-MVNO), allocating channels to data aggregation points (DAPs) each frame is difficult because three uncertainties interact: imperfect spectrum sensing, time-varying and cross-channel-correlated primary-user activity, and stochastic urban propagation. Classical Hungarian assignment is optimal per frame but blind to primary-user dynamics, while cognitive-radio heuristics ignore queue state and cross-channel structure. We propose a two-timescale hierarchy that couples these established tools in a new way: a Proximal Policy Optimization (PPO) agent decides, once per epoch, which opportunistic channels to expose, and an exact Hungarian solver performs the per-frame DAP-to-channel assignment. To our knowledge this is the first coupling of a learned cognitive layer with exact Hungarian assignment for cognitive-radio resource allocation. On a 3GPP TR 38.901-compliant simulator, PPO significantly outperforms a Bayesian-belief baseline and the Hungarian-only configuration in delivery ratio, latency, and a strict per-meter satisfaction metric, and is robust across independent seeds and sensitivity sweeps. An architectural ablation shows the DAP tier is a precondition for viability, not merely an optimization. Full article

Against the backdrop of tightening ecological constraints and the accelerating transition toward high-quality development, the coordinated development of technological innovation and the ecological environment has become an important topic in regional sustainability research. Using 111 cities in the Yangtze River Economic Belt from 2013 to 2023 as the study sample, this study applied principal component analysis and a coupling coordination model to measure the coupling coordination degree between technological innovation and the ecological environment. It further employed the XGBoost-SHAP model to investigate the factors associated with their coordinated development. The results showed that: (1) During the study period, both the comprehensive development level of technological innovation and that of the ecological environment level increased, although a pronounced spatial mismatch existed between them. The former exhibited an east-to-west declining gradient, whereas the latter followed a spatial pattern of the middle reaches > upper reaches > lower reaches. (2) The overall coupling coordination level transitioned from the low coordination stage to the primary coordination stage. Significant regional disparities were observed, with the coupling coordination degree decreasing progressively from east to west. (3) The XGBoost-SHAP results indicated that the urbanization level and fiscal autonomy were important factors associated with the coordinated development of technological innovation and the ecological environment. Furthermore, the marginal contributions of the individual factors exhibited complex nonlinear patterns. These findings enhance the understanding of the coordinated development of technological innovation and the ecological environment and provide empirical evidence for differentiated governance and policy design across the Yangtze River Economic Belt. Full article

Mitigating population exposure to fine particulate matter (PM_2.5) is a core prerequisite for advancing Sustainable Development Goal 11.6.2 and global urban sustainability. This study integrated 0.1° × 0.1° gridded PM_2.5 reanalysis data, WorldPop high-resolution population datasets, and official SDG 11.6.2 scoring records spanning 2000–2019, and adopted multi-scale spatial statistics and population-weighted exposure models to systematically explore the spatiotemporal differentiation of global PM_2.5 concentrations and associated population exposure risks, as well as their coupling relationship with SDG 11.6.2 implementation progress. This study employs ArcGIS 10.6 software to harmonize the spatial scales of multi-source heterogeneous data through spatial statistics and resampling methods, and applies the SDSN (Sustainable Development Solutions Network) standardized scoring framework to quantify long-term progress across regions in meeting urban air quality targets. The results reveal significant latitudinal spatial heterogeneity in global PM_2.5 concentrations, with values ranging from 0.95 to 262.15 μg/m³; severe pollution hotspots exceeding 35 μg/m³ were agglomerated across Asia, Africa and South America, while Canada, Greenland, the Tibetan Plateau and eastern Russia maintained ultra-low PM_2.5 levels below 5 μg/m³. Global SDG 11.6.2 standardized scores rose steadily from 71.05 in 2000 to 76.64 in 2019, with Asia and Africa achieving the most remarkable score growth despite low initial baselines. Regional gaps in target realization remained stark: North America, Oceania and Northern Europe basically met the PM_2.5 sustainability standards, whereas densely populated regions of Asia and Africa faced critical governance challenges. Among nine typical countries, only China and India recorded population-weighted PM_2.5 concentrations above 25 μg/m³ in 2019; China’s air pollution control policies delivered continuous emission reductions after 2013, while India sustained extremely high exposure risks. From 2010 to 2019, the global range of urban population-weighted PM_2.5 concentrations narrowed, yet Afghanistan, Tajikistan and North Korea remained the most severely exposed nations. This study verifies the severe cross-regional inequity of PM_2.5 exposure risks under the SDG framework, and provides multi-scale empirical evidence for differentiated air quality governance and international collaborative interventions to accelerate the delivery of the 2030 sustainable development agenda. Full article

Global food security remains under severe strain, and the conversion of cultivated land to non-grain uses poses a significant threat to the stability of food production capacity. Driven by mineral development and urbanization, the risk of non-grain conversion in resource-based counties has increased. This study selected Qian’an City as the study area to accurately identify the spatiotemporal evolution characteristics of non-grain production at the county scale. GF-1 satellite (China Centre for Resources Satellite Data and Application, Beijing, China) imagery from 2016, 2020, and 2024 was employed, integrated with a vegetation index, multi-scale segmentation, and a decision tree model. Non-grain conversion rate measurement, spatial trend surface analysis, and kernel density estimation were then applied for analysis. The results were as follows. (1) The high-precision identification system for the non-grain conversion of cultivated land accurately distinguished grain crops from non-grain land uses, achieving an overall accuracy of over 91%. (2) The process of non-grain conversion of cultivated land in Qian’an City showed significant stage characteristics. The non-grain conversion area first increased and then decreased, with the non-grain rate fluctuating from 49.38 to 53.48% before declining to 46.62%. (3) The driving mechanism exhibited a phased evolution: from natural economy-led, to policy-driven strong intervention, and finally to market location-led. In 2016, the terrain undulation (q = 0.63) and GDP (q = 0.551) were dominant. In 2020, the proportion of ecological protection red line area (q = 0.539) took the lead. By 2024, GDP (q = 0.66) and the distance from the main road (q = 0.544) were dominant. This reflected the coupling effect of market and location within the policy baseline and interpreted the dynamic evolution of cultivated land use management. The spatial and temporal evolution characteristics of non-grain conversion for cultivated land were quantitatively revealed. The findings provide a scientific basis and decision support for regional cultivated land protection, food security, and optimal allocation of land resources. Full article

This paper demonstrates how detailed acoustic micro-segmentation can serve as a vital decision-support tool, providing localized, evidence-based data required to effectively integrate environmental acoustics and foster community co-design in urban rail modernization planning. Furthermore, while physical noise barriers are widely praised as standard mitigation products in transport engineering, our analysis highlights their severe inherent limitations, including spatial constraints, high costs, and visual pollution, making them highly unsuitable for dense historical fabrics. As viable alternatives, we propose the implementation of active, source-targeted engineering technologies (e.g., rail absorbers, modernized track beds) alongside dynamic operational governance. To be effective, these alternative solutions must be sourced and embedded directly into the earliest stages of infrastructural project documentation. Ultimately, this paper demonstrates that evidence-based acoustic governance is an essential institutional pillar for long-term resilience, providing interdisciplinary perspectives that inform future sustainable transport policy and practice. Full article

Green total factor productivity (GTFP) is an important indicator for assessing urban green development under resource and environmental constraints. Using panel data from 110 prefecture-level cities in the Yangtze River Economic Belt from 2007 to 2023, this study examines changes in urban green total factor productivity. A super-efficiency slack-based measure model that includes undesirable outputs is adopted to measure GTFP, while the Malmquist–Luenberger index is used to decompose its dynamic changes. Spatial variation is then analyzed through trend surface analysis, center-of-gravity migration analysis, spatial pattern analysis, and the geographical detector model. The results indicate that GTFP in the Yangtze River Economic Belt improved on the whole, but its growth did not follow a smooth upward path. Among the decomposed effects, technological progress (TC) was the main source of improvement. Clear spatial differences were also observed. Cities in the middle and lower reaches generally had higher GTFP levels than those in the upper reaches, although this gap became less marked over time. The center of gravity of GTFP stayed mainly in the middle reaches and shifted gradually toward the northeast. The driving factors behind spatial differentiation were not constant. In the early stage, energy intensity and economic development level had stronger effects, whereas technological innovation, human capital, and industrial structure upgrading became more influential in the later stage. These findings provide empirical support for differentiated green development policies and coordinated regional governance in the Yangtze River Economic Belt. Full article

Currently, access to safe drinking water in developing countries remains a structural challenge. Despite progress, many households still lack access to clean and safe water, leading to serious consequences for health, human dignity, and the economy. This research aims to evaluate the impact of drinking water and sanitation investment projects on monetary expenditures for water consumption. Using data from the 2017 and 2023 National Household Survey (ENAHO-INEI) in Peru, a longitudinal study with a retrospective cohort design was conducted. The complex, probabilistic sampling method allowed for modeling both periods using ordered multiple response logit regression. The results show that household connections to potable water positively impacted water consumption expenditures in 2017 (n = 21,251) and 2023 (n = 19,482). Similarly, connections to sanitation facilities positively influenced expenditures in 2017, although no significant evidence was found in 2023. Having access to safe water (adequate chlorine) versus unsafe water (inadequate chlorine) showed a positive effect on consumption expenditures in both years, reflecting that families must bear a double expense to access alternatives such as bottled water. A higher monthly income of the head of household, associated with greater age and years of education, increases expenditures on potable water, excluding households in monetary poverty. A differentiated design of water policies is recommended, based on natural region (coast, highlands, and rainforest) and area (urban or rural), considering the different water access alternatives according to the geographic location of the dwelling. Full article

Public transit systems play an important role in reducing traffic congestion, energy consumption, and greenhouse gas emissions in smart urban transportation environments. However, large-scale transit operations still suffer from inefficient routing, scheduling, and resource management decisions under highly dynamic traffic and passenger demand conditions. Furthermore, existing intelligent transportation approaches often ignore communication unreliability, anomalous transportation observations, computation offloading overhead, and network congestion in low-altitude intelligent transportation systems. To address these challenges, this paper proposes a secure and resilient UAV-assisted edge-enabled transit offloading framework based on Security-Aware Multi-Agent Proximal Policy Optimization (SA-MAGPPO). In the proposed framework, UAVs operate as low-altitude communication assistants that enhance V2I and V2V connectivity. These UAVs provide aerial traffic observations and reduce communication congestion in dense urban transportation networks. The proposed framework jointly optimizes transit routing, charging scheduling, fleet management, and computation offloading decisions between onboard units and edge servers. Unlike conventional MAGPPO, the proposed framework augments the agent state representation with communication reliability and anomaly information and incorporates security-aware policy learning to improve robustness against unreliable traffic observations, communication disruptions, and unstable network conditions. Extensive simulations under multiple operational scenarios demonstrate that the proposed framework consistently outperforms RP, GS, RBH, SPPO, and conventional MAGPPO approaches in terms of energy, operational efficiency, offloading reliability, and robustness against communication anomalies. Full article