Search Results (6,500)

Buildings account for a significant share of final energy consumption, with space heating representing one of the major energy uses in residential buildings. Therefore, improving the thermal performance of building envelopes is an important strategy for reducing energy demand. Green roofs can contribute to this objective by modifying roof thermal properties and reducing heat losses through the building envelope. This study investigates the use of machine learning to predict annual heating demand and potential heating energy savings associated with replacing conventional roof configurations with a selected green roof assembly in a representative stock of Macedonian buildings. A representative dataset comprising 2934 building cases based on post-2013 buildings designed in accordance with the national energy-performance regulations was assembled. The dataset covers a wide range of building typologies, envelope thermal properties, climatic conditions and heating schedules. Three supervised learning models, Random Forest, Artificial Neural Network and Extreme Gradient Boosting (XGBoost), were developed and compared. The results show that XGBoost achieved the highest predictive accuracy and the best computational efficiency, with test coefficients of determination of 0.9901 for the heating demand of conventional roof buildings and 0.9956 for green-roof-related heating energy savings. Most simulated buildings showed heating energy savings of up to 10% following green roof implementation, while only a limited number of cases exhibited increases in heating demand of up to 3%. The feature importance analysis identified heated floor area, heating duration and wall area as the major drivers of heating demand in conventional roof buildings, whereas roof thermal transmittance was the most influential factor governing green-roof-related heating energy savings. The findings demonstrate that machine learning can reliably reproduce the results of the established energy performance assessment methodology and provide rapid estimates of the potential heating energy savings associated with replacing conventional roofs with a selected green roof system across a representative building stock. The proposed approach can support engineers, urban planners and architects in the early-stage assessment of green roofs as an energy-efficient measure. Full article

Economic well-being is essential for assessing sustainability of human settlement in urbanizing regions; however, the geographic factors linking settlement characteristics to residents’ well-being remain underexplored, particularly in counties in China undergoing urban–rural transformation. In this study, six representative Chinese counties (Yanshou, Wafangdian, Bazhou, Yugan, Yongsheng, and Raoping) with varying urbanization levels are investigated to establish a multidimensional evaluation framework and reveal the geographic factors underlying economic well-being. Through original household surveys conducted across these six geographically and economically diverse counties, we collected primary data from 1659 households; these data provide unique insights into residents’ lived experiences. By integrating these original survey data with objective indicators from statistical yearbooks and geographic features from multisource spatial data, key drivers were identified using Pearson correlation and random forest models. The results show the following trends: (1) significant county-level variation in subjective well-being, with Wafangdian ranking the highest and Bazhou ranking the lowest, while well-being aligned more closely with economic development levels; (2) income and happiness were the dominant determinants of subjective well-being, with work-related factors also contributing substantially, whereas nighttime light intensity, building density, and construction land area drove fusion well-being; and (3) multifactor modeling demonstrated strong explanatory power for fusion well-being (training set R² = 0.8313; validation set R² = 0.7531), indicating generalizability. The primary data collection across varied settlement settings provides strong empirical grounding. The findings reveal the spatial differentiation of economic well-being in urbanizing settlements, offering empirical support for targeted settlement planning and urban governance policies to improve sustainability and residents’ well-being in developing countries. Full article

Improving regional economic resilience is a point addressed in the sustainable development goals (SDGs; i.e., SDG 8 and SDG 11). The Yangtze River Delta (YRD) has demonstrated excellent economic resilience during the COVID-19 pandemic, largely due to the persistent green transition of the YRD in the past two decades. This paper uses a single-case method combined with the perspective of evolutionary economic geography to systematically investigate the process of green transition in the YRD (2000–2023) at both vertical and horizontal levels and proposes an integrated multi-dimensional system framework to reveal the collaborative logic of the overall green transition action and the internal mechanism of enhancing economic resilience in the YRD. The findings indicate that the combination of external factors such as contradiction change, magnifying crises, economic stabilization, and policy steering has driven the historical inevitability of green transition in China. Under such conditions, the YRD not only completed development in terms of primitive accumulation of space (coordinated development, i.e., chassis), industry (orderly upgrade, i.e., engine), and governance (equal supply, i.e., lubricant) earlier but also ensured the stability of this triangle, injecting sustained strong momentum into the rapid recovery of the economy under the impact. The solidification of green concepts further enhances the sustainability and strength of the YRD’s economic resilience. These findings provide beneficial experience on how to resume production after the pandemic or lay out cities in developing countries that are still in rapid urbanization in advance. Full article

This study examines the governance challenges posed by new forms of employment in the context of China’s new urbanization, focusing particularly on the widespread phenomenon of “residing without settling” among on-demand platform workers in megacities. Based on a survey of 1627 respondents in Guangzhou, the findings reveal a pronounced gradient stratification in on-demand platform workers’ residence intention. Specifically, 57.3% of respondents demonstrate positive short-term residence intention, whereas long-term residence intention is predominantly characterized by neutral attitudes (73.3%), and settlement intention exhibits a clearly negative tendency (67.8%). Structural Equation Modeling indicates that residential environment exerts the strongest positive effect on sense of place (β = 0.47), followed by economic foundation (β = 0.24), while social capital demonstrates the weakest promoting effect (β = 0.22). Scenario analysis using Bayesian Network further reveals that when place attachment reaches a high level, the probability of positive long-term residence intention among on-demand platform workers increases by 22.1%, whereas improvements in residential environment reduce the probability of negative settlement intention by 5.2 percentage points. The results demonstrate that the residence intention of on-demand platform workers arises from the interplay of socioeconomic conditions, material space and emotional embedding. Notably, the sense of place forms a critical emotional attachment mechanism through a mediating effect. Economic deprivation acts as the primary constraint, while environmental quality serves as a fundamental limiting factor. Conversely, social capital accumulation partially mitigates these barriers. The findings contribute to a nuanced understanding of urban inclusiveness and labor force stability in the digital economy. Moreover, this study provides policy insights for optimizing urban population structures and fostering socially sustainable development. Full article

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations. Full article

Long-standing studies of historical cultural spaces have primarily focused on the preservation of heritage objects and landscapes, while insufficient attention has been paid to the structural relationships, land-use transformations, and cultural–ecological processes that sustain their long-term continuity. Taking the World Heritage site of Lijiang as a case, this study develops a three-dimensional structural decoding framework composed of spatial base, spatial network, and spatial entity, together with an analytical pathway of “Identification–Interpretation–Evaluation–Synthesis–Practice.” By integrating qualitative and quantitative approaches with multi-source data, the study establishes an evidence chain linking historical processes and contemporary conditions to examine the formation mechanisms, continuity, and contemporary deviations of Lijiang’s historical cultural space. The results show that terrain–habitat adaptability, water system coupling, and environmental risk avoidance shaped environmental adaptation; historical corridors, landscape perception, and core node associations organized spatial networks; and functional diversity, cultural capital agglomeration, and spatial-scale compatibility supported entity-based spatial practices. Although tourism development, urban expansion, and land-use transformation have not completely dismantled these historical relationships, they have caused localized deviations in ecological boundaries, path continuity, visual connections, functional vitality, and spatial scale. This study argues that the governance of historical cultural spaces should shift from preserving isolated heritage objects to sustaining cultural–ecological relationships that support memory, identity, spatial practice, and adaptive land governance. Full article

Conventional static approaches to cultivated land quality (CLQ) assessment often fail to capture the rapid spatial restructuring of cultivated land in urbanizing regions. This study takes Chengdu, China, as the study area and employs multi-source and multi-indicator datasets from 2010, 2017, and 2023 to assess CLQ using an integrated AHP-CRITIC weighting approach, combined with obstacle degree and constraint factor analyses. The results show that the mean CLQ score increased from 0.520 in 2010 to 0.695 in 2023, reflecting the continuous improvement in stable cultivated land quality. Constraint factors also shifted from natural endowment limitations to engineering- and management-related disturbances: converted land was mainly constrained by climatic and topographic conditions, newly added land by soil moisture and fertility after land-use conversion, and stable land by compound soil-water and terrain constraints. These findings provide scientific evidence and practical references for high-standard farmland construction and refined cultivated land governance in rapidly urbanizing grain-producing regions. Full article

Urban ecological resilience (UER) has become a central concern in rapidly urbanizing regions where development pressures increasingly interact with ecological constraints. Focusing on the Guanzhong Plain Urban Agglomeration (GPUA), a semi-arid urban agglomeration in western China, this study examines the non-static and locally heterogeneous drivers of UER across 11 prefecture-level cities from 2000 to 2023. UER is measured through resistance, adaptability, and recovery. An extended STIRPAT model, Elastic Net with stability selection, two-way fixed-effects period interactions, and Geographically and Temporally Weighted Regression (GTWR) are integrated to identify robust drivers, test post-2011 shifts, and estimate city-year local associations. Residual Moran’s I diagnostics and Spatial Lag GTWR (SLM-GTWR) are used as supplementary checks. The results show that UER remains relatively stable at the aggregate regional level but becomes increasingly divergent across cities. Ten robust drivers are retained, with fiscal investment intensity, human capital, medical and health level, and total energy consumption emerging as key variables. Period heterogeneity results indicate that fiscal investment becomes more favorably associated with UER after 2011, while the marginal association of energy consumption weakens. GTWR reveals clear local heterogeneity: human capital shows the most stable positive association, medical and health level remains generally negative, fiscal investment is positive but context-dependent, and energy consumption is predominantly negative but locally differentiated. Supplementary spatial diagnostics suggest that the GTWR specification captures the main spatiotemporal structure of UER, while spatial-lag checks broadly support the robustness of the local coefficient patterns, although estimates of spatial interaction remain sensitive to how inter-city linkages are defined. These findings indicate that UER drivers are dynamic rather than fixed, with resilience formation shaped mainly by governance-regime shifts and localized heterogeneity. The study contributes a sequential screening–heterogeneity framework for identifying non-static resilience drivers and suggests that resilience governance should combine stage-sensitive policy adjustment, place-based intervention, and regional coordination where ecological functions and environmental risks cross administrative boundaries. Full article

Rapid electric-vehicle adoption is reshaping urban energy and mobility systems, especially in multi-unit dwellings (MUDs), where concentrated charging in shared parking areas simultaneously stresses distribution transformers and amplifies the consequences of charger faults, battery thermal events, smoke spread, and emergency-access constraints. The central argument of this paper is that grid stress, resident-facing service quality, lifecycle cost, and fire-risk exposure in enclosed residential parking should be governed jointly rather than as four separate problems. To make that argument concrete, we develop an integrated framework that couples stochastic EV adoption, residential charging-behavior simulation, XGBoost demand forecasting, and linear-programming-based optimization for coordinated control, and we evaluate it through 1000 Monte Carlo trials on representative Turkish MUDs. Unmanaged charging triggers transformer overload at about 30% EV penetration, whereas coordinated control reduces peak demand by 44.7% (405 kW to 224 kW) and raises load factor from 0.40 to 0.68. Strict capacity protection exposes a sharp service–quality trade-off, with only 8.9% of users reaching 80% state of charge (SOC) by departure. Smart charging lowers upfront cost by about 55% ($200 vs. $439 per dwelling unit) and yields roughly $306 net present value per unit over ten years. Building on these results, we propose a five-pillar fire-risk-aware governance architecture—coordinated control, interoperability standards, time-of-use pricing, building–utility coordination, and monitoring—that turns coordinated charging into a preventive governance layer for reducing hazardous congestion in enclosed residential charging environments. Full article

This exploratory study examines the heterogeneous spatial evolution of cities in a fragmented non-metropolitan region of Southern Brazil and develops an original functional-symbolic typological framework that integrates functional performance and symbolic production in the classification of cities. Grounded in the theoretical contributions of Lefebvre, Santos, and Corrêa, the framework was designed by the authors to simultaneously incorporate economic, territorial, cultural, and identity-related dimensions that are typically analysed separately in conventional urban typologies. The research adopts a qualitative and inductive approach to analyse secondary data from municipalities in the state of Santa Catarina. Rather than treating urbanisation as a homogeneous process, the study conceptualises urban typologies as analytical devices capable of revealing differentiated urban trajectories, uneven capacities of territorial articulation, and distinct modes of governance in non-metropolitan contexts. The findings show that cities with similar demographic scales perform diverse social, cultural, and economic roles shaped by historically and symbolically produced spatial relations. Five urban typologies were identified: Multifunctional Metropolises, Industrial Regional Capitals, Agroindustrial Cities, Cultural Tourist Cities, and Local Centres of Basic Function. The results demonstrate that urban centrality in non-metropolitan regions is not determined solely by economic performance or demographic scale, but also by symbolic attributes such as cultural heritage, territorial identities, festivals, and religious functions. By integrating material and symbolic dimensions within a single analytical structure, the proposed framework advances the understanding of fragmented urban systems, contributes to contemporary debates on non-metropolitan urbanisation and territorial governance, and offers a transferable approach for the analysis of urban diversity beyond the Brazilian context. The findings also provide practical implications for regional planning and public policy by highlighting the role of symbolic production in shaping territorial organisation and regional influence. Full article

Existing studies of China’s overarching ideological framework for national development, “Ecological Civilization”, have focused narrowly on environmental governance; how it reshapes sustainable rural development remains underexplored. This paper pursues two analytically distinct tasks. First, it reconstructs the policy history of how President Xi Jinping’s “Two Mountains” theory was incorporated into the Eco-civilization framework and how Zhejiang Province’s “Green Rural Revival” (GRR) program, as a lived example of Eco-civilization, was elevated as the national template for rural development in 2024. Second, drawing on three cases from a sample of 21 villages in Zhejiang, it identifies three core practices of GRR and conceptualizes it as a post-productivist project: withdrawal from extractivist production, restoration of ecological resources, and development of facilities catering to urban consumption. We argue that GRR has directed rural development in Zhejiang toward a post-productivist transition and question the model’s replicability and sustainability as the central government promotes it nationwide. Full article

Urban parks, as essential urban green infrastructure, contribute significantly to public health, psychological restoration, and socially sustainable urban living. However, existing research has primarily emphasized landscape aesthetics while paying comparatively limited attention to routine landscape maintenance as an important component of sustainable urban park governance. Drawing on Stress Recovery Theory (SRT), this study examines how landscape maintenance quality influences perceived well-being through perceived safety and restorative experience. Survey data were collected from 278 urban park users in Wangjianglou Park, Chengdu, China, and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that maintenance quality positively affects perceived well-being both directly and indirectly through perceived safety and restorative experience, which serve as significant mediators. Multi-group analysis further reveals demographic differences, with female users demonstrating stronger safety-related responses and older users exhibiting stronger restorative and perceived well-being benefits associated with maintenance conditions. The findings highlight the importance of routine park maintenance in supporting perceived safety, psychological restoration, inclusiveness, and the long-term usability of urban public spaces. The study advances understanding of how maintenance practices shape psychological restoration and urban perceived well-being while providing empirical support for sustainable urban green space management and the achievement of Sustainable Development Goal 11. Full article

Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limited network coverage and insufficient support for both low- and high-throughput applications, which hinder the deployment of conventional and broadband-intensive Internet of Things solutions. A central challenge is the lack of adequate field-level network infrastructure, with connectivity often unavailable or unreliable. This article presents a comprehensive survey of Broadband-based IoT (B-IoT) as a solution for supporting both low- and high-data-rate digital agriculture applications, including UAVs, computer vision, and extended reality, even in settings without continuous internet connectivity. Using a structured narrative-review approach, this survey synthesizes relevant peer-reviewed and technical literature on B-IoT-enabled digital agriculture and organizes the evidence around communication key performance indicators (KPIs), deployment constraints, and four technology domains: sensing, connectivity, intelligence/compute, and control/application. It examines how technologies such as 5G/6G, dynamic spectrum access, non-terrestrial networks, and edge computing can help address connectivity and infrastructure gaps in underserved agricultural areas. Furthermore, we introduce and analyze the concept of Evolved-Variety Technologies, which combines modified state-of-the-art modules with next-generation networks to create flexible, modular, and scalable system designs adaptable to diverse topographical and operational conditions. Beyond technical evaluations, the article examines economic feasibility, environmental sustainability, and policy implications, emphasizing the need for coordinated roles among governments, telecom providers, and agribusiness stakeholders. Our findings advocate for hybrid telecom architectures that integrate terrestrial and non-terrestrial components, leveraging emerging technologies to reduce the rural–urban digital divide and enable scalable, data-driven agriculture in underserved regions. Full article

Public lighting can account for nearly 40% of municipal energy consumption in some European cities and plays a vital role in road safety, mobility, and the quality of public spaces. Despite notable efficiency gains from the widespread adoption of light-emitting diode (LED) technologies, the technical outputs of standards-based and installation-level assessment methods are not usually simple and communicable energy-performance labels for municipal decision-making. This study addresses this issue by introducing an algorithm-based framework for classifying energy performance in public lighting at the road-segment level. This approach translates existing lighting standards and efficiency indicators into a straightforward and understandable energy label, adapting the energy labelling concept, commonly used for buildings and appliances, to public space infrastructure. This framework is implemented through a national digital platform for public lighting classification, which has already attracted formal interest from more than 100 municipalities, indicating strong institutional uptake. The results indicate that road-segment-level energy classification is feasible and scalable as a voluntary tool to enhance municipal accountability and support informed decision-making. This study concludes that algorithmic energy labels for public lighting can support sustainable urban governance transparency, comparability and decision-making capacity, with future research aimed at building capacity for large-scale implementation and incorporating environmental, human health, and ecological impact considerations into the classification system. Full article

Over the past fifty years, urban and rural spaces have been reshaped by global sustainability policies, digital innovation, and emerging socio-ecological needs. This article investigates the convergence of agro-urban planning strategies, Smart City infrastructures, and adaptive governance models, proposing an integrated agro-urban paradigm for sustainable territorial transformation. Drawing on a literature review and comparative analysis of international case studies—including Toronto, Milan, and Woven City—the research develops a triadic interpretative framework based on worldview, program, and faith. The study identifies AgroCities as systems centered on food sovereignty and ecological resilience, Smart Cities as efficiency-driven digital ecosystems, and Adaptive Cities as flexible, human-centered responses to complexity. Findings suggest that integrating food systems, technological innovation, and participatory governance enhances urban resilience and sustainability across scales. The article concludes by advocating for multi-scalar planning tools, cross-sectoral policies, and civic engagement to support the transition toward inclusive and regenerative cities. This framework offers a theoretical and operational contribution to reimagining urban planning in line with the principles of Smart Land and adaptive urbanism. Full article