Earth and Environmental Sciences

This study develops and validates the Smart Destination Perception Scale (SDPS), a tool designed to assess perceptions of smart destinations among tourists and residents. Grounded in a comprehensive literature review and refined through expert evaluation and pretesting, the SDPS identifies four main dimensions: Innovation, Connectivity, Market Intelligence, and Social and Environmental Responsibility. Market Intelligence integrates Intelligence Systems and Information and Promotion Systems, while Social and Environmental Responsibility integrates Accessibility and Sustainability. Data were collected from 1200 participants (600 tourists and 600 residents), and exploratory and confirmatory factor analyses confirmed the scale's reliability, validity, and dimensional structure. Madrid was selected as the empirical context due to its consolidated status as a leading smart destination in Spain and its relevance as a major European urban tourism hub, underscoring the real-world applicability of the SDPS within a mature smart destination. Findings identify Market Intelligence as the most influential dimension, highlighting the importance of intelligence systems and information and promotion mechanisms in shaping effective smart destination strategies. The Social and Environmental Responsibility dimension also emerges as pivotal by integrating accessibility and sustainability, emphasizing the need for inclusive and environmentally conscious practices. This study contributes to smart destination research by introducing a robust, multidimensional 29-item scale aligned with stakeholder theory and supportive of both strategic planning and practical application. By providing standardized measurements, the SDPS enables authorities to compare destinations and evaluate perceptions from both tourists and residents, fostering a comprehensive approach to smart destination management.

Global cities are increasingly developing large transport infrastructure projects to maintain their competitive advantage, but funding these projects is challenging. Land value capture (LVC), which harvests financial contributions from property development, has become one means of providing some of this funding. LVC is however a complicated proposition to utilise and requires careful decision making to ensure sufficient revenue is appropriately raised without hampering efforts by the development industry to bring forward new schemes, or local, borough authorities can extract revenue to pay for their own infrastructure needs like affordable housing. London's Crossrail has made extensive use of LVC funding through MCIL (the Mayoral Community Infrastructure Levy) which has to date raised over £1b of funds. In this paper, we critically identify the extent to which MCIL has been successful, drawing on Marsh and McConnell's (2010) framework on the different ways projects can be considered a success.

Testate amoebae are known among the proxies for paleolimnological investigations but, despite their recognized importance and recent findings, they have not been sufficiently studied in alpine lakes, particularly in Europe. The present study analyzes fossil testate amoeba assemblages from the Upper Balma Lake (2216 m a.s.l., Western Alps, Italy) to reconstruct ecological changes over the past 1200 years. 21 species belonging to 5 genera were identified, with Centropyxis and Difflugia being the most abundant. Multivariate statistical analyses, combined with sedimentological and geochemical data, revealed five paleoenvironmental phases corresponding to known climatic events, including the Medieval Climate Anomaly, the Little Ice Age, and the Recent Warming period. Assemblage composition and diversity were related to grain size variations, nutrient input, and trace element concentrations (e.g., As, Mn, Mo) and periods of climatic stability were associated with higher biodiversity, while cold or disturbed intervals, marked by floods or sediment reworking, resulted in reduced abundances and diversity. In recent decades, a decline was observed for the genus Centropyxis , possibly indicating reduced oxygenation and changes in contaminant levels. Comparative analysis with the Lower Balma Lake, located downstream in the same area, highlighted the influence of morphological/hydrological differences between lakes on species distributions. The findings underscore the value of testate amoebae as sensitive bioindicators and their applicability in paleoecological reconstructions of alpine lakes. This study contributes to understanding long-term ecological responses to climate change and provides insights into flood regime variability in high-mountain environments.

Human modifications to rivers, marshlands, and coastlines can dramatically reorganize sediment sources, leaving distinct lithological and geochemical signatures in the sedimentary record. Kuwait Bay (KB), a semi-enclosed, hypersaline coastal embayment in the northern Arabian Gulf (AG), is particularly sensitive to anthropogenic manipulation due to heightened coastal industrial development and watershed reconstruction of the Tigris and Euphrates Rivers from the mid-20th century to the present day. This study reconstructs the recent stratigraphic evolution of the northeastern margin of KB from two sediment cores collected adjacent to Ras Al-Subiyah in response to upstream and local anthropogenic forcing using a multi-proxy approach that combines downcore X-Ray Fluorescence (XRF), grain-size, lithology-corrected Total Mercury (T-Hg) concentrations, and geochronology by radioisotope 137 Cs. Both cores reveal three distinct facies and an overall transition from carbonate-rich muds at depth to siliciclastic sands near the surface. The deposit at depth is characterized by elevated Ca/Ti and Al/Si and low Si/Ti XRF ratios, consistent with a mixture of terrigenous, diagenetic, and marine carbonate deposition under low-energy conditions. A middle transitional facies marks a shift from solely fine-grained carbonate sedimentation to a near equal mix of siliciclastic sands and carbonate mud deposition. XRF ratios reveal a decrease in Ca/Ti and Al/Si and elevated Si/Ti ratios, dated to the mid-1960s to early 1970s, which coincide with a major reduction in the Euphrates River's flow rate, likely reducing fluvial muds to the northern AG and KB. Despite grain size coarsening, T-Hg concentration is elevated in this section, suggesting a localized input unrelated to lithology. The uppermost facies, deposited post-2000, is siliciclastic sand with low Ca/Ti and Al/Si and elevated Si/Ti ratios. Corrected T-Hg remains elevated in this section, suggesting anthropogenic input from the late 1960's to the present day. The transition from the middle to the upper lithofacies is dated to 2000, coinciding with the completion of the Subiya Thermal Power Plant, which shifted local hydrodynamics, increased turbulence, and limited fine-grained accumulation. Our findings suggest that both upstream and local coastal development jointly reorganize sediment provenance and depositional textures in northeastern KB, with T-Hg enrichment not solely controlled by fine-grained sediment accumulation.

Mixed carbonate-siliciclastic margins such as the Belize Barrier Reef provide sensitive archives of glacio-eustatic variability because reef accretion, erosion, and sediment supply respond directly to sea-level fluctuations. This study investigates the geomorphological imprints of relative sea-level change along more than 100 km of the Belize Barrier Reef margin using high-resolution multibeam bathymetry. The new bathymetric dataset provides deeper insight into reef-margin morphology and identifies constructional and erosional paleo-sea-level indicators. Ridge-crest lineaments and reef-wall notches were used for statistic depth distributions from ten along-strike sectors, which reveal laterally persistent elevation clusters that correspond to distinct paleo-sea-level positions. Ridge-crest lineaments show a dominant concentration near ~20 m water depth, interpreted as a period of reduced early Holocene sea-level rise, or even possible MIS 5 stillstands, whereas widespread notch horizons at ~80–85 m and ~ 100–105 m record prolonged late Pleistocene stillstands preceding and approaching the Last Glacial Maximum. The absence of systematic vertical offsets between sectors indicates negligible neotectonic deformation along the margin. Results demonstrate that reef-margin morphology preferentially preserves intervals of slow or stable sea-level change, while phases of rapid transgression leave limited geomorphic imprint. High-resolution bathymetry thus provides a powerful tool for reconstructing sea-level histories from steep carbonate margins and establishes the Belize Barrier Reef as a key reference archive for late Quaternary sea-level dynamics.

Shoreline change assessments are central to coastal management and adaptation planning, yet they commonly rely on a limited number of indicators used as proxies for complex coastal behaviour. Because interpretations of shoreline dynamics and vulnerability depend strongly on measurement methods and observational scales, this study examines how different monitoring approaches influence interpretations of soft coastline shoreline change along the energetic Atlantic coast of southern Ireland. A three-year multi-method monitoring programme (2023–2025) was conducted across five embayed unconsolidated sandy beach systems, integrating long-term aerial imagery and vegetation-line change, Sustainable Coastal Vulnerability Index outputs, repeated seasonal RTK-GNSS cross-shore profiles, high-resolution UAV-derived orthophotos and digital surface models, and targeted post-storm field assessments. Results show that different monitoring methods produce distinct but complementary narratives of shoreline behaviour. Long-term vegetation lines and derived change rates emphasise boundary translation but can mask shorter-term accelerations, regime shifts, or localised erosion hotspots. SCVI classifications provide effective broad-scale screening of exposure and receptors but may misrepresent physical susceptibility where local sediment dynamics or recovery processes are not captured. In contrast, field surveys and UAV-derived topographic datasets reveal spatially variable sediment dynamics, with erosion and accretion occurring simultaneously across different sectors of the same beach systems. These differences highlight the need for monitoring strategies that combine complementary datasets. Based on these findings, an evidence-based tiered monitoring strategy is proposed and distinguishes proxy-based screening, routine cross-shore profile monitoring, and targeted UAV surveys in spatially complex settings, reducing interpretive bias in assessments of shoreline change and coastal vulnerability.

The global maritime sector is undergoing rapid transformation, creating an urgent need to align digital port technologies with a sustainable development framework. However, existing research on smart ports and the blue economy is fragmented and predominantly driven by deterministic approaches that overlook systemic complexity and uncertainty. This study develops a smart port system model grounded in blue economy principles, using a Bayesian network to analyze causal relationships among operational, environmental, and governance variables under uncertainty. The model incorporates key factors including port operational efficiency, logistics reliability, environmental compliance systems, coastal employment, and regulatory enforcement. The findings indicate that operational and logistical factors are the primary drivers of the system, while environmental and socioeconomic variables strongly shape sustainability outcomes. Scenario analysis shows that coordinated interventions targeting these key variables generate the greatest improvements in Smart Port–Blue Economy integration. Sensitivity analysis further identifies coastal economic output, regional competitiveness, and marine ecosystem health as the most responsive outcome variables. The research offers lessons for policymakers to enhance port management by integrating logistics and technological considerations with blue economy principles to design adaptive and resilient policies, particularly in island regions.

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development.

Urban experiments are increasingly embraced for their potential to transform incumbent socio-technical systems by offering multifaceted, ‘high-quality’ learning. The early literature on sustainability transitions painted an optimistic picture of the impact of experiments, prescribing their role in managing transitions. More recently, scholars have elaborated on the different purposes and functions of experiments; however, they generally stress that, as of yet, there is scarce evidence for their effectiveness concerning transformation in practice. This paper develops a tool for more effective follow-ups after an experiment in practice, by anticipating contextual constraints on upscaling innovations. The tool has been developed through a design science research method by first doing action research on sustainable mobility innovations in four European cities and subsequently testing the prototype of the tool in five other places. Our findings suggest that this new tool improves conditions for wider implementation of the innovation being experimented with, and associated transformation. This is one key starting point for increasing the impact of experiments and accelerating urban sustainability transformation.

Water level prediction for the backwater reaches of large reservoirs is a critical step for many tasks of reservoir operation and flood control, directly affecting the sustainability of water–energy–ecosystem balance. The problem is very challenging due to arbitrarily complicated hydrodynamic mechanisms and various types of influencing factors. This paper proposes a method based on time series decomposition for feature extraction from data samples by a novel neural architecture. To accurately quantify the complex hydraulic conditions of large reservoirs, we investigate a type of neural basis expansion to incorporate exogenous variables (e.g., reservoir regulation and storage, upstream confluence, and flow travel time). Unlike the traditional LSTM-based methods, our method is free from recurrent architecture. It can exploit backward and forward residual links as a backbone to ensure the validity and structural distribution of the information during the model training. Extensive experiments on real data of the Three Gorges Reservoir are implemented to evaluate the performance of the proposed method. The results show that the proposed method shows state-of-the-art performance on all evaluation metrics and can provide reliable technical support for the refined and sustainable operation of large reservoirs.

Rapid urbanization has significantly altered urban landscape composition and configuration, making it a key driver exacerbating the urban heat island (UHI) effect. As a rapidly expanding inland city in Central China, Zhengzhou is highly sensitive to changes in landscape composition and spatial configuration. Therefore, clarifying the nonlinear relationship between landscape patterns and the urban thermal environment is of great significance for sustainable urban planning and thermal environment regulation. Taking the main urban area of Zhengzhou as the study area, this paper retrieves land surface temperature (LST) using the radiative transfer equation method based on Landsat 8 remote sensing images from August 2015 to August 2024, and constructs the surface urban heat island intensity (SUHII) index. By integrating multi-dimensional landscape pattern indices, the XGBoost machine learning model, and the SHAP interpretability method, this study systematically analyzes the nonlinear response mechanisms of landscape composition and configuration to SUHII, key regulatory thresholds, and their changes between 2015 and 2024. The results show that: (1) The SUHII in Zhengzhou was substantially higher in 2024 than in 2015. The area proportions of strong and extremely strong heat islands were higher in 2024 (26.16% and 2.34%) than in 2015 (2.22% and 0.12%), and the thermal environment differed between 2015 and 2024, shifting from a localized patch pattern to a more continuously expanding pattern. (2) Landscape area-related indices are the key factors. The areas of green space and water bodies, along with the landscape diversity index, show significant negative correlations, while built-up area and aggregation index show significant positive correlations. (3) SHAP feature importance indicates that water body area is the primary cooling factor, whereas built-up area is the primary warming factor, jointly dominating the spatial pattern of the thermal environment in Zhengzhou. (4) Landscape composition and configuration exhibit significant nonlinear responses to SUHII with region-specific thresholds, and these thresholds were higher/lower in 2024 than in 2015, suggesting a possible association with urban expansion. Specifically, stable cooling effects occurred when the water body area exceeded 3.5 km2 in 2015, with the threshold rising to 4.2 km2 in 2024. The warming threshold for built-up area decreased from 18.8 km2 to 8.5 km2, suggesting a higher sensitivity of the thermal environment to built-up area expansion in 2024 compared to 2015, characterized by a regulation pattern of “dominant scale effect and weakened configuration effect”. This study identifies thresholds specific to Zhengzhou’s main urban area at two time points (2015 and 2024), providing quantitative support and scientific basis for blue–green space optimization, precise heat island mitigation, and territorial spatial planning in Zhengzhou. These findings are based on a comparison of two time points (2015 and 2024) and do not directly capture continuous temporal dynamics.

This paper examines whether the persistent difficulty in addressing the eco-social crisis may partly stem from an inadequate representation of human decision-making within mainstream economic models. Although pro-environmental behaviors (PEBs) and sustainable consumption are increasingly recognized as essential for sustainability transitions, neoclassical economics still largely relies on the homo oeconomicus paradigm, which assumes fully rational and utility-maximizing decision-making. Building on contributions from psychology, behavioral economics, neuroscience, and sustainability studies, this integrative narrative review examines how cognitive biases challenge the foundational assumptions of homo oeconomicus and explores the potential role of emotional intelligence in sustainability-related decision-making. Adopting the integrative narrative review approach, this paper integrates literature on (1) cognitive biases and bounded rationality; (2) emotional intelligence and judgment bias; and (3) emotional intelligence, pro-environmental behaviors, and sustainable consumption. The evidence reviewed suggests that sustainability-related decisions are strongly shaped by cognitive and emotional processes operating under uncertainty and socially embedded consumption patterns. Within this framework, EI may represent a psychological resource capable of influence of cognitive biases by supporting emotional regulation, impulse control, self-awareness, and long-term orientation. Overall, the paper proposes a conceptual framework linking cognitive biases, emotional intelligence, and sustainable behavior beyond the traditional homo oeconomicus paradigm.

Urban green space (UGS) with multidimensional attributes provides crucial ecosystem services and public health benefits. However, there is no systemic knowledge on how these diverse UGS attributes meet needs with different socio-economic and demographic characteristics. This study conducts a meta-analysis of the existing UGS case studies from the Web of Science database to reveal the knowledge breadth, depth and applicability for future research and practice in UGS. We evaluated how 75 socio-economic and demographic indicators shape 73 UGS indicators across 48 global case studies using a combination of relational analysis, meta-analytical effect and spatiotemporal distribution. The relation analysis reveals significant imbalances of UGS knowledge across attributes (Accessibility, security and amenities) and users’ characteristics (e.g., age), the meta-analytical effect size analysis indicates that many associations have not yet received consistent and conclusive empirical support for direct application to UGS planning and design, and the spatiotemporal analysis indicates that the concentration of the sampled studies in China and the United States further constrains the external applicability of the synthesized evidence. These findings recognize the multidimensional nature of UGS and the need for context-sensitive consideration and highlight the urgency of developing a unified assessment framework to improve knowledge discovery and applicability in UGS planning for urban sustainability.

Flood risk is a wicked problem, characterized by non-linear dynamics, cross-scale interdependencies, and contested responsibilities. Disentangling who or what has the capacity to drive change in flood risk management (FRM) systems is a critical step for designing inclusive and sustainable risk-reducing interventions. Given the diverse range of actors often involved in FRM decision-making, it is necessary to consider different interpretations of what system features are important. Using participatory visualization methods this study applied a systems-thinking lens to examine how experts visualize the FRM system and its drivers of change in British Columbia (BC), Canada. Workshop one participants undertook group timeline mapping to visualize their understanding of the events and processes that have driven BC FRM system development. Workshop two participants completed open-ended concept maps, producing personal mental models of the features and relationships that make up the present-day FRM system. Data from both workshops were synthesized into a ‘master’ timeline and concept map, and the experts’ perceived drivers of system change were identified. Our results revealed decentralizing governance shifts and past flood events as drivers of historic system development in the minds of our experts. Our results also indicated leverage points that experts had included as drivers of system improvement in their concept maps, with frequently named and connected features, such as the most interconnected feature: ‘flood mapping’, offering potential opportunities for cross-sector collaboration and cascading risk-reduction action. Possible gaps in the FRM system were also revealed by system features that were acknowledged as important by participants but were represented as disconnected, including themes of ‘climate change impact’ and ‘reconciliation’. Participatory visualization methods, especially when used in combination, offer a practical approach for representing experts’ mental models of FRM systems, revealing expert-identified leverage points for practical FRM improvement that can contribute to sustainable flood risk reduction goals.

This study applies a screening, prospective social life cycle assessment, integrating process-simulation-derived inventory data with worker-hour-based impact metrics and country–sector social risk profiles from the SOCA/PSILCA database to estimate potential risk and opportunity exposure across the avocado oil supply chain in Colombia. For the local community and societal categories, indicators such as unemployment and contribution to economic development are interpreted based on national statistics, while worker-related indicators, including fair salary and working time, are assessed in relation to sectoral wage structures and living wage benchmarks. Results suggest that potential social risk exposure is primarily associated with worker-related indicators, with contributions concentrated in refining and upstream agricultural stages, reflecting underlying labor conditions and sectoral characteristics. In comparison with a palm oil baseline scenario, increases of up to 62% in medium-risk hours for fair salary and 49% for working time are observed in the avocado oil case. Other indicators show more moderate variation, with unemployment indicator impact increasing by 24% and contribution to economic development decreasing by 7%. However, these findings largely reflect the redistribution of pre-existing country–sector risk profiles rather than system-specific or empirically validated social impacts. Consequently, the results should be interpreted as indicative of potential exposure under screening conditions, rather than definitive measures of social performance.

The bioconversion of agro-industrial waste represents a promising strategy for the valorisation of residual biomass. However, the chemical complexity of these matrices and the presence of potentially inhibitory compounds limit their direct use in several bioprocesses. In this study, a quantitative, time-resolved method was used to select bacteria for bioconverting agro-industrial by-products. The growth dynamics of bacterial strains were screened using olive and grape pomace at different compositions (up to 15%) and formulations. An integrated scoring approach (0–1) was used to compare strain behaviour across experimental conditions. The results revealed strain-dependent variability, with a matrix concentration of 10% defined as the growth-limiting concentration, with approximately 50% positive results. Among the tested strains, Bacillus subtilis BL showed a consistent and reproducible response across different by-products and formulations, maintaining stable spore viability over time, particularly in formulations supplemented with calcium carbonate (on average 109 UFC/mL after 144 h). Mixed agro-industrial matrices promoted a more homogeneous and stable microbial response than individual components (reaching 109 CFU/mL after approximately 100 h), supporting their direct use in a real operating environment. Overall, this work proposes a transferable quantitative approach to selecting microorganisms suitable for bioconversion of agro-industrial by-products, providing a methodological basis for developing more reliable and reproducible formulations.