Search Results (4,102)

Coastal areas are under intense pressure worldwide because diverse stakeholders rely on coastal resources, and the supply of land is highly limited. Coast-dependent economic activities like transportation and logistics infrastructure in the Jade Bay, Germany, have experienced extensive demand for land. The situation is more interesting because national parks encircle the seaport. Understanding the complex seaside–landside dynamics following the development of Jade Weser Port is crucial for promoting sustainability, as massive development exceeds existing spatial capacity. However, a comprehensive framework to assess land use conflicts when dealing with infrastructure development in sensitive coastal areas is often missing. We analyze the origin of land use developments and the planning process at different administrative levels by retracing land use changes from 1970 to 2015 using a time series of satellite images, analyzing planning documents, and examining realized activities. We look for an embedding of transport infrastructure development and its feedback on land use. As a consequence of land use conflicts, these land system dynamics create winners and losers across multidisciplinary aspects. Our findings reflect interdisciplinary aspects which discuss both societal changes and the constellation of inadequate planning approaches to address the complexity of coastal land use. The degree to which these activities cause land use conflicts depends on institutional settings, especially the consistency of ICZM and infrastructure planning. Full article

Sustainability is gaining relevance across organizations, yet significant challenges remain in how it is implemented and translated into daily operations. This paper examines how Lean Six Sigma can be used to address operational challenges while also supporting the integration of sustainability objectives in industrial contexts. The study is based on a project conducted in a fish processing plant, aiming to increase production capacity and reduce delays. Using the DMAIC framework, the team addressed key bottlenecks through demand-based workload leveling, earlier production planning, and targeted maintenance to improve equipment performance. These actions led to measurable gains in throughput, resource use, and schedule reliability. In parallel, they contributed to sustainability outcomes, including reduced rework, lower waste, and improved working conditions. The results suggest that Lean Six Sigma, typically focused on performance, can also act as a platform for embedding sustainability into existing routines. The findings offer insight into how performance-driven approaches can support sustainability transitions in process-intensive industries. Full article

This study aimed to implement an effective power prediction method to support the optimal management of the 30 MW Nagréongo solar photovoltaic (PV) plant in Burkina Faso. Initially, the performance of the PV plant was assessed by an external consultant based on data recorded in 2023 and 2024, revealing efficiency with a performance ratio (PR) of 73.73% in 2023, which improved to 77.43% in 2024. To forecast the plant’s power output, several deep learning models—namely LSTM, a GRU, LSTM-GRU, and an RNN—were applied using historical power data recorded at five-minute intervals during the 2024 periods of January–February; March–April; and July–August. All the deep learning models achieved accurate short-term forecasting for the 30 MW Nagréongo PV plant, with the seasonal performance shaped by the Sahelian weather regimes. The GRU performed best during the dry season (nRMSE ≈ 4%) and LSTM excelled in the hot months (nRMSE ≈ 2%), while the hybrid LSTM-GRU model proved most robust under rainy-season variability. Overall, the forecasting errors remained within 2–5% of plant capacity, demonstrating the suitability of these architectures for grid integration and operational planning in Sahel PV systems. Full article

The study evaluates the impact of massive electric vehicle (EV) penetration on Quito’s 138 kV distribution system in Ecuador, employing a probabilistic approach to support a sustainable energy transition. The rapid adoption of EVs, as projected by Ecuador’s National Electromobility Strategy, poses significant challenges to the capacity and reliability of the city’s electrical infrastructure. The objective is to analyze the system’s response to increased EV load and assess its readiness for this scenario. A methodology integrating dynamic battery modeling, Monte Carlo simulations, and power flow analysis was employed, evaluating two penetration levels: 800 and 25,000 EVs, under homogeneous and non-homogeneous distribution scenarios. The results indicate that while the system can handle moderate penetration, high penetration levels lead to overloads in critical lines, such as L10–15 and L11–5, compromising normal system operation. It is concluded that specific infrastructure upgrades and the implementation of smart charging strategies are necessary to mitigate operational risks. This approach provides a robust framework for effective planning of EV integration into the system, contributing key insights for a transition toward sustainable mobility. Full article

Urbanization, characterized by rapid construction land expansion, has transformed natural landscapes and significantly altered river networks in emerging metropolitan areas. Understanding the historical and current conditions of river networks is crucial for policy-making in sustainable urban development planning. Based on the topographic maps and remote sensing images, this study employs a multi-metric framework to investigate river network variations in the Suzhou-Wuxi-Changzhou metropolitan area, a rapidly urbanized plain with high-density river networks in the Yangtze River Delta, China. The results indicate a significant decline in the quantity of rivers, with the average river density in built-up areas falling from 2.70 km·km⁻² in the 1960s to 1.95 km·km⁻² in the 2010s, along with notable variations in the river network’s structure, complexity and its storage and regulation capacity. Moreover, shifts in the structural characteristics of river networks reveal that urbanization has a weaker impact on main streams but plays a dominant role in altering tributaries. The analysis demonstrates the extensive burial and modification of rivers across the metropolitan plains. These findings underscore the essence of incorporating river network protection and restoration into sustainable urban planning, providing insights for water resource management and resilient city development in rapidly urbanizing regions. Full article

This article presents an integrated assessment of pasture ecosystem degradation under conditions of extreme aridity in the Atyrau Region, where high livestock density, limited grazing capacity, and institutional fragmentation of land tenure exacerbate degradation risks. The study aimed to conduct a spatio-temporal analysis of pasture conditions and identify critical load zones to support sustainable management strategies. The methodology was based on a multi-factor Anthropogenic Load (AL) model integrating (1) calculation of pasture load (PL) using 2023 agricultural statistics with livestock numbers converted into livestock units; (2) spatial analysis of grazing concentration through Kernel Density Estimation in ArcGIS 10.8; (3) assessment of infrastructural accessibility (Accessibility Index, A_i); and (4) quantitative evaluation of institutional land use organization (Institutional Index, I_i). This integrative approach enabled the identification of stable, transitional, and critically overloaded zones and provided a cartographic basis for sustainable management. Results revealed persistent degradation hotspots within 3–5 km of water sources and settlements, while up to 40% of productive pastures remain excluded from use. The proposed AL model demonstrated high reproducibility and applicability for environmental monitoring and regional land use planning in arid regions of Central Asia. Full article

Climate change continues to threaten global biodiversity, making it essential to assess how keystone species may shift their distributions and to use these findings to inform conservation planning. This study evaluated the current and future habitat suitability of D. macropodum, an important tree species within subtropical evergreen broad-leaved forests in China, using 354 occurrence records and a suite of environmental variables. A parameter-optimized MaxEnt model (calibrated with ENMeval; RM = 4, FC = QHPT) was applied to simulate the species’ present distribution and projected changes under three climate scenarios (SSP126, SSP245, SSP585). The main factors influencing distribution were determined to be moisture and temperature seasonality, with the precipitation of the coldest quarter (Bio19, 36.3%), the mean diurnal range (Bio2, 37.5%), and the precipitation of the warmest quarter (Bio18, 14.2%) jointly contributing 88.0% of the total influence. The model projections indicated a 40.1% reduction in the total number of suitable habitats under high-emission scenarios (SSP585) by the 2090s, including a loss of over 80% of highly suitable areas. Centroid movements also diverged across the scenarios: a southwestern shift under SSP126 and SSP245 contrasted with a southeastern shift under SSP585, with each accompanied by significant habitat fragmentation. Key climate refugia were identified primarily in central Taiwan Province and the mountainous zones of Zhejiang and Fujian Provinces, which should be prioritized for conservation activities. These insights offer a foundational understanding for the conservation of D. macropodum and other ecologically similar subtropical evergreen species. However, direct extrapolation to other taxa should be made cautiously, as specific responses may vary based on differing ecological tolerances and dispersal capacities. Further research is needed to test the generalizability of these patterns across diverse plant functional types. Full article

Amid the global energy transition, rapidly expanding wind and solar installations challenge power grids with variability and uncertainty. We propose an adaptive framework for renewable energy accommodation assessment under high-dimensional uncertainties, integrating three innovations: (1) Response Surface Methodology (RSM) is adopted for the first time to construct a closed-form polynomial of renewable energy accommodation in terms of resource hours, load, installed capacity, and transmission limits, enabling millisecond-level evaluation; (2) LASSO-regularized RSM suppresses high-dimensional overfitting by automatically selecting key interaction terms while preserving interpretability; (3) a Bayesian kernel density extension yields full posterior distributions and confidence intervals for renewable energy accommodation in small-sample scenarios, quantifying risk. A case study on a renewable-rich grid in Northwest China validates the framework: two-factor response surface models achieve R² > 90% with < 0.5% mean absolute error across ten random historical cases; LASSO regression keeps errors below 1.5% in multidimensional space; Bayesian density intervals encompass all observed values. The framework flexibly switches between deterministic, sparse, or probabilistic modes according to data availability, offering efficient and reliable decision support for generation-transmission planning and market clearing under multidimensional uncertainty. Full article

Urban climate readiness requires multi-dimensional implementation strategies that operate effectively across both spatial scales and time horizons. This article introduces a multi-scale, phased model designed to support integrated climate action by distinguishing between metropolitan and building levels and addressing three core domains: physical resilience, decarbonization, and social/community engagement. The framework conceptualizes metropolitan and building scales as analytically distinct but operationally linked, allowing strategies to reflect the different systems, stakeholders, and capacities at each level. It also outlines a three-phase progression—Initial (assessment and goal setting), Readiness (planning and implementation), and Steady-State (monitoring and iterative adjustment)—to support staged, adaptive deployment. Each phase includes sample metrics and SMART goals that can be tailored to local context and tracked over time. By integrating theoretical insights with practical implementation tools, the framework offers a flexible yet rigorous approach for advancing urban sustainability. It emphasizes the importance of aligning technical interventions with institutional capacity and community participation to enhance effectiveness and equity. This model contributes to both planning theory and applied sustainability efforts by providing a structured pathway for cities to enhance climate readiness across systems and scales. Full article

The Copernicus Atmosphere Monitoring Service (CAMS) offers a broad portfolio of global and regional atmospheric products that support environmental monitoring, air quality assessment, health applications and climate policy. Under the CAMS National Collaboration Programme (NCP), the ERATOSTHENES Centre of Excellence (ECoE) serves as the national coordinator for Cyprus, working to bridge the gap between CAMS outputs and local end-user needs. This paper presents the strategy and implementation framework adopted by ECoE to facilitate CAMS uptake in Cyprus. Efforts focus on integrating CAMS data into national systems, developing tailored applications (e.g., UV forecasting, dust event alerts), building stakeholder capacity, and supporting regulatory reporting. Outcomes also include the deployment of the AirData Hub platform and initial steps toward institutionalizing CAMS-derived workflows in public health and environmental planning. The work highlights both the opportunities and technical challenges of customizing CAMS products for small-island contexts. Full article

Household food waste remains a persistent challenge despite widespread pro-environmental intentions. Drawing on Brehm’s Motivational Intensity Theory, this study examined how perceived difficulty and perceived ability interact with motivation to predict self-reported food waste. We surveyed 939 participants in Flanders and Spain, measuring motivation to avoid waste, self-rated perceived ability to manage food, meal planning perceived difficulty, and food waste. Moderated moderation analyses revealed that motivation and perceived ability each independently predicted lower waste. Crucially, a significant three-way interaction showed that motivation most effectively reduced waste when perceived difficulty was low and perceived ability was high; when perceived difficulty exceeded perceived ability, motivation had no mitigating effect. These findings underscore that effort mobilization influenced by both individual capacity and situational demands is key to closing the intention–behavior gap in food waste. Practically, interventions should go beyond raising awareness to simplify tasks and bolster consumers’ skills, aligning action demands with realistic effort levels. Full article

With the increasing frequency and intensity of forest fires driven by climate change and human activities, efficient detection and rapid response have become critical for forest fire prevention. Effective fire detection, swift response, and timely rescue are vital for forest firefighting efforts. This paper proposes an unmanned aerial vehicle (UAV)-based forest fire inspection system that integrates a ground support system (GSS), aiming to enhance automation and flexibility in inspection tasks. A three-layer mixed-integer linear programming model is developed: the first layer focuses on the site selection and capacity planning of the GSS; the second layer defines the coverage scope of different GSS units; and the third layer plans the inspection routes of UAVs and coordinates multi-UAV collaborative tasks. For planning UAV patrol routes and collaborative tasks, a goal-driven greedy algorithm (GDGA) based on traditional greedy methods is proposed. Simulation experiments based on a real forest fire case in Turkey demonstrate that the proposed model reduces the total annual costs by 28.1% and 16.1% compared to task-only and renewable-only models, respectively, with a renewable energy penetration rate of 68.71%. The goal-driven greedy algorithm also shortens UAV patrol distances by 7.0% to 12.5% across different rotation angles. These results validate the effectiveness of the integrated model in improving inspection efficiency and economic benefits, thereby providing critical support for forest fire prevention. Full article

Gastronomic tourism is a strategic tool for territorial development, as it promotes cultural heritage, supports local economies and encourages environmentally responsible consumption. This study attempts to analyse the evolution of key gastronomic products through digital marketing tools, reflecting on the need to know this real data in order to carry out sustainable territorial and tourism planning. To do so, it uses a methodology based on the analysis of data obtained through Google Trends, taking as a reference a set of terms related to seafood, traditional meats and wines with designation of origin. The study examines the seasonal patterns and geographical distribution of interest in these terms, evaluating their impact both inside and outside Galicia as a replicable methodological case. The results show significant differences between categories. In addition, there is a generalised decrease in the search for gastronomic terms, which may indicate a reduction in the relative weight of this element as a factor in the creation of the image of the territories. In conclusion, the article demonstrates the capacity of this methodology to propose more sustainable tourism, territorial and economic planning strategies based on the transformation of qualitative imaginaries into quantitative data and trends. Full article

The application of time series models for forecasting environmental variables such as precipitation is essential for understanding climatic patterns and supporting sustainable urban planning in environments characterized by high or moderate levels of risk. This study aims to evaluate and optimize time series forecasting models for rainfall prediction in Barranquilla, Colombia. To this end, five models were applied, namely, Simple Moving Average (SMA), Weighted Moving Average (WMA), Exponential Smoothing (ES), and multiplicative and additive Holt–Winters models, using 139 monthly precipitation records from the IDEAM database covering the period 2013–2025. Model accuracy was evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE), and nonlinear optimization techniques were applied to estimate smoothing and weighting parameters for improved accuracy. The results showed that optimization significantly enhances model performance, particularly in the multiplicative Holt–Winters model, which achieved the lowest errors, with a minimum MAE of 75.33 mm and an MSE of 9647.07. The comparative analysis with previous studies demonstrated that even simple models can yield substantial improvements when properly optimized. Furthermore, forecasts optimized using MAE were more stable and consistent, whereas those optimized with MSE were more sensitive to extreme variations. Overall, the findings confirm that seasonal models with optimized parameters offer superior predictive capacity, making them valuable tools for hydrological risk management. Full article

In contemporary spatial planning, territorial resilience is rapidly gaining relevance, referring to a territory’s capacity to withstand, adapt to, recover from, and transform in response to environmental, social, and economic pressures. However, several constraints limit its operationalisation in planning. A key element to addressing this gap is to investigate where and which interventions are most urgently needed to tackle the impact of hazards on territories. This can be achieved by understanding and localising the vulnerabilities of territorial systems, thereby enabling the definition of appropriate mitigation and adaptation measures. This paper presents the application of R3C-GeoResilience, an open-source GIS tool and its methodological framework, which allows mapping territorial vulnerabilities across different geographical contexts and spatial scales. The methodology is applied to the Italian case of the Union of Bassa Romagna (UBR), aiming to build capacity for local practitioners to implement resilience thinking in decision-making processes. Findings underscore the potential of R3C-GeoResilience to enhance evidence-based planning and policymaking, supporting adaptive and transformative strategies to address territorial vulnerabilities. The application of the research demonstrates the replicability and adaptability of the methodological framework for integrating participatory vulnerability mapping into local governance and urban planning strategies, thereby enhancing the resilience of territories. Full article