Search Results (900)

Community-scale sustainable built-environment practices are increasingly important for linking urban sustainability policy with everyday spatial transformation, yet the evidence base remains fragmented across nature-based solutions, housing retrofit, settlement upgrading, public-space transformation, digital mapping, circular infrastructure, and governance-oriented planning. This scoping review, reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR), maps and synthesizes peer-reviewed evidence on community-scale sustainable built-environment practices, with publication years from 2015 to 2026 and indexed by the final search date of 13 June 2026. Searches were conducted in Scopus and the Web of Science Core Collection, yielding 3506 records. After duplicate removal, title-and-abstract screening, and full-text eligibility assessment, 130 studies were included for Spatial–Social–Governance (SSG) coding and thematic synthesis. This review identified eight thematic clusters, led by nature-based and green–blue infrastructure practices, followed by housing, retrofit, and settlement upgrading; neighborhood sustainability assessment and planning; public-space transformation and placemaking; digital tools and mapping; circular neighborhood infrastructures; mobility-related practices; and heritage-led regeneration. The largest concentration of evidence was found in nature-based and green–blue infrastructure practices, followed by housing, retrofit, and settlement upgrading; smaller clusters extended the field towards neighborhood assessment, public-space transformation, digital evidence systems, circular infrastructures, mobility, and heritage-led regeneration. As a review-derived synthesis output, the Spatial–Social–Governance Mechanism Framework does not introduce spatial, social, and governance as new analytical dimensions. Instead, it operationalizes these established concerns as a common comparison structure for heterogeneous community-scale built-environment practices, linking spatial intervention, social embedding, and governance enablement. The framework is neither a general theory nor an effectiveness model; its contribution is an integrative and review-specific analytical instrument. Full article

Urban sustainability governance has long been dominated by growth-oriented metrics that measure aggregate improvement without establishing whether neighborhoods provide enough for all residents without continuous territorial expansion or resource-intensive redevelopment. Drawing on the post-growth planning, sufficiency theory, spatial justice, land governance, and the adaptive planning literature, this paper proposes a Spatial Sufficiency Multifunctionality Framework (SSMF). SSMF is a provisional conceptual framework requiring empirical validation and aiming to reorient neighborhood land assessment from growth optimization to sufficiency thresholds. The framework organizes planning around five interconnected dimensions: social sufficiency, economic resilience without growth dependence, ecological resilience, spatial governance and land-use sufficiency, and institutional and participatory capacity. Each is operationalized through illustrative indicators, thresholds, and linked governance instruments. SSMF asks whether per-capita land use remains within ecological capacities and whether access to services, green space, and affordable space is equitably distributed. The framework is developed through a systematic scoping review of the literature on urban sustainability, land governance, and post-growth planning, and provides a diagnostic logic and a menu of governance instruments. By shifting the evaluative focus from “more” to “enough, equity distributed within neighborhood capacities,” SSMF offers a provisional architecture for understanding post-growth principles at the neighborhood scale. Full article

Sustainable urban development is increasingly shaped by the joint evolution of social development, ecological conditions, and technological capacity. Understanding how these dimensions are associated with green economic efficiency (GEE) is therefore important for advancing urban green transition. This study examines GEE from a social–ecological–technological (S–E–T) systems perspective and incorporates spatial dependence into the empirical framework. Using panel data for 281 prefecture-level cities in China from 2011 to 2022, this study measures GEE with a Super-SBM model, characterizes its spatiotemporal evolution, and estimates a two-way fixed-effects spatial Durbin model to distinguish local and cross-city associations. The results show that: (1) urban GEE improved overall, while maintaining a clear southeast-high and northwest-low spatial gradient; (2) regional disparities widened over time, and efficiency transitions exhibited strong path dependence and spatial neighborhood effects; (3) the spatial Durbin model is preferred over the SAR and SEM alternatives, indicating that both local conditions and geographically connected cities should be considered; (4) green patenting shows the most stable positive association with GEE, both locally and across connected cities; and (5) population density and ecological conditions display differentiated local and cross-city associations. These findings show that combining the S–E–T framework with spatial panel analysis provides a systematic approach for understanding urban GEE and offers evidence for coordinated green development policies across connected cities. Full article

This study investigates the collaborative green vehicle routing problem with time-dependent travel speeds (CGVRP-TD), which integrates horizontal collaboration among multiple depots with time-dependent traffic conditions. The problem jointly optimizes customer allocation, vehicle routing, and departure-time decisions to minimize transportation-related carbon emissions subject to vehicle capacity and customer time-window constraints. We formulate the CGVRP-TD as a mixed-integer programming model and develop a two-phase adaptive large neighborhood search algorithm with embedded departure-time optimization. The first phase explores routing and customer-assignment decisions using problem-specific operators, including two speed-related removal operators, while the second phase applies exact departure-time optimization to fixed routes. Computational experiments show that the proposed algorithm obtains high-quality solutions efficiently and that both departure-time optimization and speed-related operators contribute to emission reduction. The results further demonstrate that combining horizontal collaboration with time-dependent travel-speed information can substantially reduce transportation emissions while preserving on-time service. We also discuss emission-savings allocation mechanisms for sustaining collaboration among participating depots. Full article

This article examines the conceptual and practical relationships between UNESCO’s 2011 Historic Urban Landscape (HUL) Recommendation and the European Commission’s 2020 Circular Economy (CE) Action Plan, bringing together cultural heritage conservation and sustainable resource management. A comparative analysis was conducted using directed qualitative content analysis across four analytical themes: value systems, resource management, governance models, and sustainability perspectives. Building upon these results, this article proposes the circular cultural landscape (CCL) model, which positions cultural heritage as an active component of circular systems through four interconnected layers. To illustrate its potential applicability, the model is translated into a preliminary assessment framework through the proposed CCL index and conceptually demonstrated using a hypothetical scenario in the Balat neighborhood of Istanbul. Rather than providing empirical validation, the scenario illustrates how the framework may support the integrated assessment of cultural, environmental, social, and economic sustainability in historic urban landscapes. The proposed CCL model offers a conceptual and methodological foundation for future research and decision support in sustainable heritage management. Full article

Urban forests are expected to cool cities, sustain biodiversity, and distribute access to nature; however, evidence is often produced at scales that do not align with implementation. This structured evidence map characterizes English-language articles and reviews indexed in OpenAlex from 2000 to 15 July 2026 and interprets the patterns through three implementation cases. Five title-and-abstract searches returned 1201 records; 1103 remained after DOI/OpenAlex-ID deduplication, and a precision-oriented deterministic screen retained a 140-record core set. Metadata and abstracts were coded for outcome, scale, method, and management/governance language. Management/governance-related language occurred in 110 records, thermal outcomes in 74, biodiversity in 59, and explicit equity terms in 26. Thirty-three records were coded at two or more specified spatial scales and seven at all three; 14 jointly contained thermal and equity codes. These are record-level co-occurrences, not evidence of causal integration. Melbourne, Singapore, and Barcelona illustrate, without serving as controlled comparisons, three ways to translate citywide ambition into precinct stewardship, connected networks, and heat-health priorities. Within the defined corpus, canopy cover alone is an incomplete performance measure. The review proposes a non-aggregated decision chain and indicator portfolio linking network configuration, neighborhood vulnerability, site-level forest quality, and funded stewardship. Because screening and coding used one index and title/abstract metadata, the numerical patterns describe explicit framing rather than evidence quality, independent studies, or pooled effects. Full article

The United Nations, in developing the Sustainable Development Goals, pays particular attention to cities, as they are crucial to achieving established benchmarks. Globally, many cities have high levels of food insecurity, and food access is a major component of the problem. Cities have increasingly complex and interdependent food environments. Yet, urban food systems are not well understood with respect to the impacts of redlining, residential segregation, and food store closures on food outlet distribution. Limited work is also being done on the relationship between urban sustainability efforts and food access. Here, we analyze 2229 open and closed food outlets in the Greater Grand Rapids area of southwest Michigan to examine their distribution and whether disparities are evident. We examined the impact of historic redlining on the distribution of food outlets. We also described sustainability efforts in the study area and the way county and city plans connect food access with sustainability. Roughly half of the food outlets are restaurants, 13.3% are small grocery and convenience stores, and 6.3% are pharmacies, dollar stores, and variety stores. Supermarkets and large grocery stores, which are the subject of most food access studies, account for 5% of food outlets. The most reliable predictors of food outlet distribution in the fully adjusted regression models are HOLC classification grade, median household income, educational attainment, and population density. The racial composition of the census tract was significant in only one model. The study leads us to urge more researchers to compare cities and adjacent municipalities, as income dynamics, neighborhood characteristics, population density, and educational attainment vary across urban, suburban, and rural areas. Full article

With the acceleration of global urbanization, neighborhood green spaces have emerged as important carbon sinks. However, current urban carbon inventories frequently neglect the understory shrub layer owing to morphological heterogeneity and a lack of standardized allometric models. To address this limitation, we analyzed 13 major shrub species (n = 665) through whole-plant excavation. Hierarchical cluster analysis and linear discriminant analysis classified the 13 species into three functional morphological groups based on intrinsic morphological traits (basal stem density, root-to-shoot biomass allocation, and secondary radial growth capacity) (p < 0.001): small shrubs (Type I), large shrubs with high root-to-shoot allocation (Type II), and multi-stemmed sprouting shrubs (Type III). Standard models accurately estimated biomass for Type I species, whereas symbolic regression improved the prediction of the complex non-linear biomass allocation of Type II species. For Type III species, characterized by multi-stemmed growth and anthropogenic management, robust regression provided stable biomass estimates. Gompertz growth models predicted carbon sequestration trajectories, indicating that urban shrubs function as rapid carbon sinks during the early establishment stage. To facilitate practical application, we developed the Urban Forest Carbon Storage Calculator, which integrates Monte Carlo simulation and bootstrapping to generate 95% confidence intervals for species-specific biomass estimation. This study quantifies the overlooked carbon value of the urban shrub layer and provides a morphology-driven methodological approach and a practical tool for sustainable urban forest management. Full article

Extreme heatwaves intensify urban heat islands and pose increasing risks to urban sustainability and human health. However, how urban morphology is associated with daytime and nighttime land surface temperature (LST) across urban functional zones (UFZs), particularly under heatwave conditions, remains insufficiently understood. To address this gap, this study integrates daytime and nighttime LST data derived from SDGSAT-1, multi-dimensional urban morphology indicators, and two interpretable ensemble models (XGBoost and GWRF) to investigate overall sample-level nonlinear model-based associations between urban morphology and LST and to explore spatial variation in local predictor importance within Beijing and Nanjing, China. Because the daytime and nighttime scenes were not always paired within the same heatwave episode, the analysis focuses on selected heatwave-condition observations. The results show marked contrasts between the selected daytime and nighttime observations in UFZ-level thermal patterns. Industrial zones generally exhibited the highest daytime LST, whereas residential zones showed the highest nighttime LST. Building density was identified as the primary model-based predictor of daytime LST in both cities, although its association with LST was nonlinear and varied across density ranges. In contrast, nighttime LST was characterized by more heterogeneous predictor associations, involving vegetation structure, sky openness, building form, anthropogenic indicators, and material-related variables, with their relative importance differing across cities and UFZ types. Local predictor-importance patterns also varied across neighborhoods, cities, and observation times, indicating that model-identified locally important predictors were not spatially uniform within each city. These findings highlight the potential of SDGSAT-1 daytime and nighttime thermal observations and interpretable machine learning for screening candidate local thermal priority areas and key morphology-related factors under heatwave conditions. Full article

Urban vegetation provides essential ecosystem services, including temperature regulation, air purification, noise reduction, and carbon storage. However, urban densification and climate-induced stresses increasingly threaten these ecosystems. Accurate classification of urban vegetation is critical for sustainable urban planning, yet remains challenging due to the structural complexity and high data density of urban LiDAR (Light Detection and Ranging) point clouds. To address current research gaps, including insufficient model interpretability, high computational demands, and limited generalization capabilities, we introduce transmissivity, a novel feature that combines echo-based LiDAR properties with spatial context to more effectively characterize urban vegetation structures. This feature enhances vegetation classification by estimating whether laser beams tend to traverse or terminate within a local neighborhood, independent of the specific return order of individual beams, thereby characterizing vegetation’s volumetric permeability. This makes transmissivity highly interpretable, unlike other echo-based statistical features. Transmissivity was evaluated alongside 52 conventional features using three different feature-importance measures across two distinct urban LiDAR datasets from Berlin and Hessigheim 3D (both datasets are from Germany). Transmissivity consistently ranked among the most influential features in both datasets across multiple scales and achieved the highest average gain (Berlin: 0.465; Hessigheim: 0.286) and the second highest mean absolute Shapley value (Berlin: 2.066; Hessigheim: 0.824). Permutation importance confirmed that transmissivity has the strongest impact on the mean decrease in F1-score (Berlin: 0.53; Hessigheim: 0.34) if used in uncorrelated feature subsets. Our findings support that echo-enriched point clouds allow efficient and accurate monitoring of urban vegetation. The feature is simple to compute and does not require additional data beyond standard multi-return LiDAR attributes. Full article

Against the backdrop of China’s advancing strategy for ecological protection and high-quality development in the Yellow River Basin, improving urban land green-use efficiency is critical to reconciling ecological conservation, resource efficiency, and coordinated regional development. Using data for 64 cities across seven urban agglomerations, this study applies a Super-SBM model to estimate urban land green-use efficiency from 2012 to 2024 and combines Dagum Gini decomposition, kernel density estimation, and conventional and spatial Markov chains to examine its spatial disparities and dynamic evolution. Four principal findings emerge. First, efficiency increased overall but exhibited a clear spatial gradient, with higher levels in the upper and middle reaches and lower levels downstream. Second, overall disparities initially widened, subsequently narrowed, and rebounded slightly toward the end of the study period. Between-agglomeration disparities were the largest component on average and during most of the study period; however, their contribution declined, and transvariation density became the largest component in 2023–2024, indicating greater overlap among the efficiency distributions of urban agglomerations. Third, both within- and between-agglomeration disparities exhibited marked heterogeneity, with distinct trajectories across and within urban agglomerations. Fourth, the conventional Markov-chain analysis revealed strong state persistence, a pronounced tendency for high-efficiency states to persist, and transitions occurring predominantly between adjacent classes. The spatial Markov results further showed that local transition probabilities varied across neighborhood efficiency conditions. Overall, efficiency disparities did not exhibit sustained unidirectional convergence; instead, they were characterized by phased adjustment, increasing cross-agglomeration overlap, and neighborhood-conditioned state transitions. By establishing a sequential framework of “efficiency measurement–disparity decomposition–distributional evolution–state transition,” this study provides empirical evidence for understanding the spatial disparities and dynamic evolution of urban land green-use efficiency in the Yellow River Basin. Full article

In outbound China–Europe Railway Express (CR Express) operations, dispersed cargo access, transshipment-node activation, order consolidation path assignment, block train line selection, and departure-time matching are strongly coupled, directly affecting the economic performance, timeliness, and block train operation efficiency of transport plans. Existing studies have mainly focused on the local optimization of consolidation paths, train routes, or departure times. At the same time, insufficient attention has been paid to the bilevel combinatorial structure between node activation and order path assignment. To address this issue, this study proposes an outbound path optimization model for the CR Express considering cargo consolidation, and develops a graph-guided adaptive large neighborhood search (Gra-ALNS) algorithm. The algorithm is organized around a bilevel search structure of node activation and order path assignment. At the upper level, a transport organization graph is constructed, and graph convolutional network (GCN) embeddings are integrated with cargo-access reachability to generate H-node scores, which guide the search for promising transshipment-node activation combinations. At the lower level, outbound generalized paths are used as the basic search units, and upper confidence bound (UCB)-based operator selection, local exact refinement, and cross-$\epsilon$-grid elite-solution warm starts are combined to optimize order consolidation paths, block train line selection, and departure-time matching. Numerical results show that the proposed Gra-ALNS outperforms conventional adaptive large neighborhood search (ALNS) in computational efficiency and multi-objective solution-set quality. Activating transshipment nodes can reduce the transport cost and in-transit time cost of some orders, but may increase fixed train operating cost and reduce average load factors. The findings provide methodological support for outbound cargo organization optimization, transshipment-node configuration, and block train operation planning of the CR Express. By improving cargo consolidation efficiency, coordinating departure-time matching, and revealing the trade-off between temporal accessibility and train-loading efficiency, this study provides a decision-support reference for a more efficient and sustainable CR Express freight organization. Full article

To achieve the European Union’s zero-emission building targets and overcome the limitations of slow, costly on-site construction, off-site prefabrication is vital. However, existing research often neglects real-world constructability and circularity constraints. This study presents a systematic technology supply mapping and a performance-oriented taxonomy—single-function envelopes, multi-functional integrated systems, and stand-alone installations—of prefabricated renovation solutions, focusing predominantly on Southern European contexts. Applying a structured three-step screening protocol across the literature, industry databases, and European project repositories, technologies were evaluated through a multi-dimensional framework capturing market readiness, functional integration, structural constraints, and circularity indicators. Results reveal a strongly polarized market where passive envelope systems dominate commercial availability, while multi-functional active solutions remain largely confined to experimental prototype stages. Furthermore, most systems target low-to-mid-rise residential buildings, show limited compatibility with complex architectural geometries, and exhibit low integration of circular criteria due to conventional material reliance. This study concludes that bridging the research-to-market gap requires interface standardization, demand aggregation through Green Public Procurement, and localized regulatory adaptations for envelope thickness. Ultimately, this taxonomy establishes a rigorous diagnostic framework and a solid foundation for a future quantitative Prefabrication Readiness Index (PRI). Full article

With growing emphasis on green and low-carbon development and rising urban delivery demand, electric vehicles (EVs) have been increasingly adopted in logistics distribution systems. However, their limited driving range, relatively long charging durations, and the limited capacity of charging stations pose substantial challenges to real-world electric delivery operations. When multiple vehicles arrive at a station with a limited number of chargers, queueing delays may disrupt subsequent customer service and increase total operating costs. To address this issue, this study investigates an electric vehicle routing problem with capacitated charging stations and queueing delays. A mixed-integer linear programming model is formulated, and an enhanced adaptive large neighborhood search (ALNS) algorithm is developed to efficiently solve medium- and large-scale instances. In the proposed model, each vehicle visit to a charging station is represented as a charging event, while finite station capacity is enforced through charging-event assignment and temporal non-overlap constraints. Computational results show that the enhanced ALNS matches the proven optimal solution for the 10-customer instance. For the 15- and 20-customer instances, the best objective values obtained by the enhanced ALNS were 0.39% and 4.84% lower than the corresponding time-limited Gurobi incumbents, respectively. For the 30-, 50-, and 100-customer instances, the enhanced ALNS consistently generates feasible solutions within the prescribed computational budget, whereas Gurobi does not obtain a feasible incumbent within substantially longer time limits. Compared with the baseline ALNS, the enhanced version generally achieves lower mean objective values and more favorable convergence behavior. Sensitivity analyses further show that increasing the number of chargers and improving the charging rate can reduce queueing delays and total charging duration. The proposed approach provides practical decision support for reliable and sustainable urban electric freight operations. Full article

With the growing emphasis on sustainable manufacturing, the recovery and reuse of end-of-life products have become increasingly important. Disassembly plays a critical role in this process by facilitating the recovery of reusable and recyclable components, thereby reducing resource consumption, waste generation, and associated environmental impacts. This work addresses disassembly line balancing problems with a fixed number of workstations for minimizing the maximum completion time (makespan). First, a mathematical model is developed that satisfies the priority and time interference constraints. Second, four meta-heuristics with a Q-learning strategy are proposed, including Q-learning-based variable neighborhood search (QVNS), Q-learning-based artificial bee colony algorithm (QABC), Q-learning-based genetic algorithm (QGA), and Q-learning-based particle swarm optimization (QPSO). In the iterations of meta-heuristics, Q-learning is employed to select competitive local search strategies. Finally, experiments are conducted for solving five large-scale instances, and the proposed algorithms are compared with existing algorithms. The statistical results and comparisons show that the meta-heuristics with Q-learning strategy have competitive performance on the tested instances. Among the proposed algorithms, QPSO achieves the best average performance on the tested instances. Full article