Search Results (166)

The rapid urbanization of Pleiku City, Vietnam, has led to a sharp increase in the demand for and prices of residential land, creating challenges for urban management and land valuation. This study aims to identify and quantify the key factors influencing residential land prices in Pleiku to provide a scientific basis for land use planning and smart urban development. Data were collected through surveys of 30 state officials involved in land valuation and 250 households living along major streets in Pleiku. Cronbach’s alpha was used to test the reliability of the collected data, and exploratory factor analysis (EFA) was used to identify influencing factor groups. The results show that residential land prices are strongly influenced by multiple factors, with location and infrastructure playing the most decisive roles. Market land prices were found to be approximately 1.5–2 times higher than state-regulated prices. Among the identified factor groups, location and infrastructure had the strongest influence, followed by economic, social, legal, and specific land use factors. Price differences between land plots mainly reflect variations in location, street characteristics, accessibility, and commercial potential. The study concludes that location and infrastructure development are the dominant drivers of residential land prices in Pleiku. These findings have important implications for land valuation, urban planning, and the implementation of smart urban construction policies in rapidly developing cities in Vietnam. Full article

Sustainability in the agri-food sector has become a cornerstone of global efforts to combat climate change, ensure food security through climate-smart agriculture, and strengthen economic resilience. Sustainability reporting within agri-food systems has gained increasing regulatory significance with the introduction of mandatory frameworks such as the Turkish Sustainability Reporting Standards (TSRSs). This article searches for the sustainability reports of agri-business firms listed in BIST in Turkey. Although TSRS reporting is not yet mandatory for the agribusiness sector, this study examines the first TSRS-aligned sustainability reports published by eight agri-food companies, excluding the retail sector. The analysis assesses how effectively these reports address sector-specific environmental and social challenges defined in the GRI 13 Agriculture, Aquaculture and Fishing Sector Standard and their alignment with the United Nations Sustainable Development Goals (SDGs). Using a structured content analysis approach, disclosure patterns were examined at both thematic and company levels. The findings indicate that TSRS-aligned reports place strong emphasis on environmental and climate-related disclosures, particularly emissions, climate adaptation and resilience, water management, and waste. In contrast, agro-ecological and land-based impacts—such as soil health, pesticide use, and ecosystem conversion—are weakly addressed. Economic disclosures are predominantly framed around climate-related financial risks and supply chain traceability, while social reporting focuses mainly on occupational health and safety, employment practices, and food safety, with limited attention to labor and equity issues across the broader value chain. Company-level results reveal marked heterogeneity, with internationally active firms demonstrating deeper alignment with GRI 13 requirements. From an SDG alignment perspective, high levels of coverage are observed across all companies for SDG 13 (Climate Action), SDG 12 (Responsible Consumption and Production), and SDG 6 (Clean Water and Sanitation). By contrast, SDGs critical to agro-ecological integrity and social equity—namely SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 10 (Reduced Inequalities), and SDG 15 (Life on Land)—are weakly represented or entirely absent. Overall, the results suggest that while TSRS-aligned reporting enhances transparency in climate-related domains, it achieves only selective alignment with the SDG agenda. This underscores the need for a stronger integration of sector-specific sustainability priorities into mandatory sustainability reporting frameworks. Full article

The global shipping industry is surging ahead, and with it, a quiet revolution is taking place on the water: marine lithium-ion batteries have emerged as a crucial clean energy carrier, powering everything from ferries to container ships. When these vessels dock, they increasingly rely on shore power charging systems to refuel—essentially, plugging in instead of idling on diesel. But predicting how much power they will need is not straightforward. Think about it: different ships, varying battery sizes, mixed charging technologies, and unpredictable port stays all come into play, creating a load profile that is random, uneven, and often concentrated—a real headache for grid planners. So how do you forecast something so inherently variable? This study turned to the Monte Carlo method, a probabilistic technique that thrives on uncertainty. Instead of seeking a single fixed answer, the model embraces randomness, feeding in real-world data on supply modes, vessel types, battery capacity, and operational hours. Through repeated random sampling and load simulation, it builds up a realistic picture of potential charging demand. We ran the numbers for a simulated fleet of 400 vessels, and the results speak for themselves: load factors landed at 0.35 for conventional AC shore power, 0.39 for high-voltage DC, 0.33 for renewable-based systems, 0.64 for smart microgrids, and 0.76 when energy storage joined the mix. Notice how storage and microgrids really smooth things out? What does this mean in practice? Well, it turns out that Monte Carlo is not just academically elegant, it is practically useful. By quantifying uncertainty and delivering load factors within confidence intervals, the method offers port operators something precious: a data-backed foundation for decision-making. Whether it is sizing infrastructure, designing tariff incentives, or weighing the grid impact of different shore power setups, this approach adds clarity. In the bigger picture, that kind of insight matters. As ports worldwide strive to support cleaner shipping and align with climate goals—China’s “dual carbon” ambition being a case in point—achieving a reliable handle on charging demand is not just technical; it is strategic. Here, probabilistic modeling shifts from a simulation exercise to a tangible tool for greener, more resilient port energy management. Full article

Spatial inequalities and land abandonment remain key obstacles to balanced territorial development in Cyprus. This study analyzes how digital agriculture can act as an instrument of territorial restructuring and spatial integration in rural areas. Using statistical and spatial data on land use, agricultural productivity and the degree of digital application, this research examines the ability of local agricultural communities to revitalize unused land through models of smart spatial management. The findings show that municipalities that implement precision agriculture, digital advisory systems and local water resource management technologies realize greater resilience of rural communities and better spatial connectivity. Digital agriculture is thus recognized as a technological and management tool that connects community-based decision-making with decentralized land management. The paper concludes with a proposal for a framework for “smart territorial restructuring”, emphasizing how digital transitions in agriculture can contribute to reducing rural differences, strengthening localism and aligning with EU goals for inclusive and spatially balanced development. Full article

This study presents an efficient and reproducible framework for estimating wind power density (WPD) across Mexico using a Dense Neural Network (DNN) trained exclusively on ERA5 and ERA5-Land reanalysis data. The model is designed as a computationally efficient surrogate that reproduces the statistical behavior of the ERA5 benchmark while enabling national-scale WPD mapping and short-term projections at minimal computational cost. Meteorological variables—including wind components at 10 m and 100 m, surface temperature, pressure, and terrain elevation—were harmonized on a 0.25° grid for the 1971–2024 period. A chronological dataset split (70-20-10%) was applied to realistically evaluate forecasting capability. The optimized DNN architecture (512-256-128 neurons) achieved high predictive performance (R² ≈ 0.91, RMSE ≈ 6.2 W/m²) and accurately reproduced spatial patterns and seasonal variability, particularly in high-resource regions such as Oaxaca and Baja California. Compared with deeper neural architectures, the proposed model reduced training time by more than 60% and energy consumption by approximately 40%, supporting principles of sustainable computing and Industry 5.0. The resulting WPD fields, delivered in interoperable NetCDF formats, can be directly integrated into decision-support tools for wind-farm planning, smart-grid management, and long-term renewable-energy strategies in data-scarce environments. Full article

Smart cities rely on digital infrastructures and utilize data-driven frameworks to enhance quality of life, optimizing public services by promoting transparency in urban and heritage management. Based on the ArchTerr project for archeological heritage protection, this study introduces an integrated framework uniting two components: GIS-based land mapping and blockchain-enabled document management. The system supports urban planning, land administration, and governance by combining spatial intelligence with secure data handling. The GIS module enables precise land mapping using geographic coordinates, facilitating spatial analysis, land use monitoring, and infrastructure planning. The document management system employs blockchain storage functionalities to ensure the immutability, transparency, and traceability of records such as land ownership documents, permits, and regulatory filings. Developed using the Design Science Research methodology, the framework translates abstract principles of data immutability and interoperability into a functional architecture that addresses persistent issues of fragmented datasets, insecure records, and limited institutional accountability and improves scalability, efficiency, and transparency in a variety of urban situations. We explored its implications for policy and governance, illustrating how interdisciplinary technology serves as a basis for transparent, accountable, and resilient urban management. This study advances theoretical understanding of how the convergence of spatial and trust-based technologies can foster geo-trusted governance and contribute to more transparent and resilient heritage management. Full article

Understanding how carbon is stored and stabilized in temperate agricultural soils is central to addressing one of the defining environmental challenges of our time—climate change. In this review, we bridge quantitative bibliometric insights with a qualitative synthesis of the mechanisms, regional differences, management practices, and models governing soil organic carbon (SOC) dynamics. We systematically analyzed 481 peer-reviewed publications published between 1990 and 2024, retrieved from Scopus and Web of Science, using bibliometric tools such as VOSviewer to map research trends, collaboration networks, and thematic evolution. The bibliometric analysis revealed a marked increase in publications after 2010, coinciding with growing global interest in climate-smart agriculture and carbon sequestration policies. Comparative synthesis across temperate sub-regions—such as the humid temperate plains of Europe, the semi-arid temperate zones, and the temperate black soil region of Northeast China—reveals that the effectiveness of common practices varies with soil mineralogy, texture, moisture regimes, and historical land-use. Reduced tillage (average SOC gain of 0.25 Mg C ha⁻¹ yr⁻¹), cover cropping (0.32 Mg C ha⁻¹ yr⁻¹), and organic amendments such as compost and biochar (up to 1.1 Mg C ha⁻¹ yr⁻¹) consistently enhance SOC accumulation, but with region-specific outcomes driven by these contextual factors. Recognizing such heterogeneity is essential for developing regionally actionable management recommendations. Recent advances in machine learning, remote sensing, and process-based modeling are enabling more accurate and scalable monitoring of SOC stocks, yet challenges remain in integrating micro-scale stabilization processes with regional and global assessments. To address these gaps, this review highlights a multi-method integration pathway—combining field measurements, mechanistic modeling, data-driven approaches, and policy instruments that incentivize adoption of evidence-based practices. By combining quantitative bibliometric analysis with regionally informed mechanistic synthesis, this review provides a holistic understanding of how knowledge about SOC in temperate agroecosystems has evolved and where future opportunities lie. The findings underscore that temperate agricultural soils, when supported by appropriate scientific practices and enabling policy frameworks, represent one of the most accessible natural climate solutions for advancing climate-resilient and sustainable food systems. Full article

Climate change threatens global food security, highlighting the necessity for Climate-Smart Agriculture (CSA) to enhance agricultural resilience and sustainability. Yet low adoption among farmers highlights gaps in understanding adoption barriers. Existing models often overlook the dynamic, multi-layered nature of farmers’ decisions. This study introduces the Multidimensional Dynamic Decision Analysis Framework (MDDAF), which integrates Sustainable Livelihoods Framework, Diffusion of Innovations, and Behavioral Economics, and applies it to conservation agriculture in Northeast China’s black soil region. We conducted 125 semi-structured interviews (100 farmers, stage-mapped into six groups; 20 leaders of agricultural socialized service organizations; 5 technical experts) and analyzed transcripts in NVivo using a hybrid deductive–inductive approach. Findings show stage-specific barriers: superficial knowledge and fragmented perceptions in awareness; traditional norms and social stigmatization in evaluation; biosecurity risks, ecological mismatches, and land tenure disputes during decision-making; economic constraints and policy inconsistencies during implementation; and operational failures, incomplete practices, and climate-driven volatility at confirmation. Priority implications are as follows: professionalize service provision; safeguard bundle fidelity and manage climate risk; reduce context and tenure risks; and counter misbeliefs via complement-focused demonstrations, diverse opinion leaders, and targeted training. MDDAF thus links dynamic, stage-specific barriers to actionable interventions, supporting more effective CSA scale-up. Full article

Urban gardening, particularly through food-producing green spaces, is increasingly recognized as a key strategy for addressing the complex challenges of climate change, food insecurity, biodiversity loss, and social inequity in African cities. This systematic review synthesizes evidence from 47 peer-reviewed studies across sub-Saharan Africa between 2000–2025 to analyze how urban home gardens, rooftop farms, and agroforestry systems contribute to sustainable urban development. The protocol follows PRISMA guidelines and focuses on (i) plant species selection for ecological resilience, (ii) integration of modern technologies in urban gardens, and (iii) socio-economic benefits to communities. The findings emphasize the ecological multifunctionality of urban gardens, which support services such as pollination, soil fertility, and microclimate regulation. Biodiversity services are shaped by both ecological and socio-economic factors, highlighting the importance of mechanisms such as polyculture, shared labour and management of urban gardens, pollinator activity and socio-economic status, reflected in sub-Saharan urban gardens. Socioeconomically, urban gardening plays a crucial role in enhancing household food security, income generation, and psychosocial resilience, particularly benefiting women and low-income communities. However, barriers exist, including insecure land tenure, water scarcity, weak technical support, and limited policy integration. Although technologies such as climate-smart practices and digital tools for irrigation are emerging, their adoption remains uneven. Research gaps include regional underrepresentation, a lack of longitudinal data, and limited focus on governance and gender dynamics. To unlock urban gardening’s full potential, future research and policy must adopt participatory, equity-driven approaches that bridge ecological knowledge with socio-political realities. Full article

The Lake Velence watershed faces increasing challenges driven by local and global factors, including the impacts of climate change, energy resource limitations, and greenhouse gas emissions. These issues, particularly acute in water management, are exacerbated by prolonged droughts, growing population pressures, and shifting land use patterns. Such dynamics strain the region’s scarce water resources, negatively affecting the environment, tourism, recreation, agriculture, and economic prospects. Nadap, a hilly settlement within the watershed, experiences frequent flooding and poor water retention, yet it also boasts the highest solar panel capacity per property in Hungary. This research addresses these interconnected challenges by designing a solar-hydrodynamic network comprising four multi-purpose water reservoirs. By leveraging the settlement’s solar capacity and geographical features, the reservoirs provide numerous benefits to local stakeholders and extend their impact far beyond their borders. These include stormwater management with flash flood mitigation, seasonal green energy storage, water security for agriculture and irrigation, wildlife conservation, recreational opportunities, carbon-smart winery developments, and the creation of sustainable blue-green settlements. Reservoir locations and dimensions were determined by analyzing geographical characteristics, stormwater volume, energy demand, solar panel performance, and rainfall data. The hydrodynamic system, modeled in Matlab, was optimized to ensure efficient water usage for irrigation, animal hydration, and other needs while minimizing evaporation losses and carbon emissions. This research presents a design framework for low-carbon and cost-effective solutions that address water management and energy storage, promoting environmental, social, and economic sustainability. The multi-purpose use of retained rainwater solves various existing problems/challenges, strengthens a community’s self-sustainability, and fosters regional growth. This integrated approach can serve as a model for other municipalities and for developing cost-effective inter-settlement and cross-catchment solutions, with a short payback period, facing similar challenges. Full article

With the gradual adoption of smart hardware such as the Internet of Things (IoT) in warehousing and logistics, the efficiency bottlenecks and resource wastage inherent in traditional storage management models are now poised for breakthrough through digital and intelligent transformation. This study focuses on the cross-border cold chain storage scenario for Malaysia’s Musang King durians. Influenced by the fruit’s extremely short 3–5-day shelf life and the concentrated harvesting period in primary production areas, the issue of delayed dynamic demand response is particularly acute. Utilizing actual sales order data for Mao Shan Wang durians from Beigang Logistics in Guangxi, this study constructs a demand forecasting model integrating Bayesian optimization with bidirectional long short-term memory networks (BO-BiLSTM). This aims to achieve precise forecasting and optimization of cold chain storage inventory. Experimental results demonstrate that the BO-BiLSTM model achieved an R² of 0.6937 on the test set, with the RMSE reduced to 19.1841. This represents significant improvement over the baseline LSTM model (R² = 0.5630, RMSE = 22.9127). The bidirectional Bayesian optimization mechanism effectively enhances model stability. This study provides a solution for forecasting inventory demand of fresh durians in cold chain storage, offering technical support for optimizing the operation of backbone hub cold storage facilities along the New Western Land–Sea Trade Corridor. Full article

Urban flooding represents a growing concern on a global scale, particularly in regions characterized by rapid urbanization and increased climate variability. This study concentrates on the Rangsit area in Pathum Thani Province, Thailand, an urbanizing peri-urban area north of Bangkok and within the Chao Phraya River Basin where transitions in land use and the intensification of rainfall induced by climate change are elevating flood risks. A physically based hydrodynamic model was developed utilizing PCSWMM to assess current and future flood scenarios that considered future build-out plans and climate change scenarios. The model underwent calibration and validation using a continuous modeling approach that conservatively focused on wet year conditions, based on available rainfall and water level data. In assessing future scenarios, we considered land use projections based on regional development plans and climate projections downscaled under RCP4.5 and RCP8.5 pathways. Results indicate that both urban expansion and intensifying rainfall are likely to increase flood magnitudes, durations, and impacted areas, although in this rapidly developing peri-urban area, land use change was the most important driver. The findings suggest that a physically based modeling approach could support a smart-control framework that could effectively inform evidence-based urban planning and infrastructure investments. These insights are of paramount importance for flood-prone regions in Thailand and Southeast Asia, where dynamic modeling tools must underpin governance, climate adaptation, and risk communication. Furthermore, given the greater impact of future build-out on flood risk, as compared to climate change, there is an opportunity to effectively and proactively improve flood resilience through the implementation of integrated Nature-based Solution and hard engineering approaches, in combination with effective flood management policy. Full article

With agriculture being the second biggest contributor to greenhouse gas (GHG) emissions through the excessive use of fertilizers, machinery, and inefficient farming practices, global efforts to reduce emissions have been intensified, opting for smarter, data-driven solutions. However, while machine learning (ML) offers powerful predictive capabilities, its black-box nature presents a challenge for trust and adoption, particularly when integrated with auditable financial technology (FinTech) principles. To address this gap, this work introduces a novel, explanation-focused GHG emission optimization framework for IoT-enabled smart agriculture that is both transparent and prescriptive, distinguishing itself from macro-level land-use solutions by focusing on optimizable management practices while aligning with core FinTech principles and pollutant stock market mechanisms. The framework employs a two-stage statistical methodology that first identifies distinct agricultural emission profiles from macro-level data, and then models these emissions by developing a cluster-oriented principal component regression (PCR) model, which outperforms simpler variants by approximately 35% on average across all clusters. This interpretable model then serves as the core of a FinTech-aligned optimization framework that combines cluster-oriented modeling knowledge with a sequential least squares quadratic programming (SLSQP) algorithm to minimize emission-related costs under a carbon pricing mechanism, showcasing forecasted cost reductions as high as 43.55%. Full article

Climate change substantially affects water resources and agriculture, highlighting the critical importance of assessing land suitability for surface irrigation. This study was initiated with the objective of assessing the present and future land suitability for surface irrigation in the Gardulla Zone of Southern Ethiopia, utilizing meteorological, topography, soil, land cover, and proximity data. The analytic hierarchy process and weighted overlay analysis were employed to assign factor weights, while future climate projections were downscaled via a statistical downscaling model (SDSM4.2) under the shared socio-economic pathways (i.e., SSP2-4.5 and SSP5-8.5) scenarios. Irrigation suitability mapping was performed via inverse distance-weighted interpolation. The results revealed that 8% of the area is highly suitable, 54.3% is moderately suitable, 30% is marginally suitable, and 2.3% is unsuitable under current climate conditions. In the future periods, under both SSP scenarios, highly suitable land increases (up to 9.7% and 10.3% by 2050s and 10.8% and 13.5% by the 2080s under SSP2-4.5 and SSP5-8.5, respectively), whereas unsuitable land decreases (down to 0.6% by 2080s under SSP5.8.5). In terms of area, highly to moderately suitable land expanded by 1357.6–6867.7 ha, depending on the scenario and timeframe. The study concludes that climate change is expected to affect the suitability of land for surface irrigation potential in the study area and similar hydroclimatic settings, highlighting the need for forward-looking policies and adaptation options. Therefore, it is recommended to promote climate-smart irrigation systems by integrating site-specific suitability mapping into regional land-use planning and prioritizing investment in small-scale, community-managed surface irrigation schemes that reduce water losses and ensure long-term agricultural sustainability. Full article

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article