Search Results (1,177)

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

Upon reaching stabilized production in waterflooded reservoirs, waterflood performance curves are conventionally used to predict technically recoverable reserves (TRRs). However, as reservoirs enter high water-cut stages, the relationship between the relative permeability ratio and saturation becomes nonlinear, causing deflection in waterflood performance curves. This leads to systematic overestimation of both predicted TRR and ultimate recovery factors. To overcome these limitations in conventional TRR prediction methods, this study establishes a novel relative permeability ratio-saturation relationship based on characteristic relative permeability curve behaviors. The proposed model is validated for three distinct fluid-rock interaction types. We further develop a permeability-driven forecasting model for oil production rates and water cuts. Comparative analyses with a conventional waterflood curve methodology demonstrate significant accuracy improvements. The results show that while traditional methods predict TRR ranging from 78.40 to 92.29 million tons, our model yields 70.73 million tons—effectively resolving overestimation issues caused by curve deflection during high water-cut phases. This approach establishes a robust framework for determining critical development parameters, including economic field lifespan, strategy adjustments, and ultimate recovery factor. Full article

This study presents a comprehensive evaluation of miscible gas injection (MGI) strategies for enhanced oil recovery (EOR) in high-salinity reservoirs, with a focus on the Raleigh Oil Field. Using a calibrated Equation of State (EOS) model in CMG WinProp™, eight gas injection scenarios were simulated to assess phase behavior, miscibility, and swelling factors. The results indicate that carbon dioxide (CO₂) and enriched separator gas offer the most technically and economically viable options, with CO₂ demonstrating superior swelling performance and lower miscibility pressure requirements. The findings underscore the potential of CO₂-EOR as a sustainable and effective recovery method in pressure-depleted, high-salinity environments. Full article

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article

Hybridization of solar and wind energy sources is a promising solution to enhance the dispatch capability of renewables. The complementarity of wind and solar radiation, as well as the sharing of transmission lines and other infrastructures, can notably benefit the deployment of renewable power. Mapping of hybrid solar–wind potential can help identify new emplacements or existing power facilities where an extension with a hybrid system might work. This paper presents an analysis of a hybrid solar–wind potential by considering a reference power plant of 40 MW in the Iberian Peninsula and comparing the hybrid and non-hybrid energy generated. The generation of energy is estimated using SAM for a typical meteorological year, using PVGIS and ERA5 meteorological information as input. Modeling the hybrid plant in relation to individual PV and wind power plants minimizes the dependence on technical and economic input data, allowing for the expression of potential hybridization analysis in relative numbers through maps. Correlation coefficient and capacity factor maps are presented here at different time scales, showing the complementarity in most of the spatial domain. In addition, economic analysis in comparison with non-hybrid power plants shows a reduction of around 25–30% in the LCOE in many areas of interest. Finally, a sizing sensitivity analysis is also performed to select the most beneficial sharing between PV and wind. Full article

Renewable energy projects are critical for sustainable development, yet their success often hinges on local community acceptance. This study refines the Community Acceptance Framework to classify and better understand the social and behavioral factors that shape community responses to renewable energy projects. To support the reclassification, we draw selectively on three psychological concepts to refine definition and streamline categories. Based on a systematic review of 212 studies, we identified 29 influencing factors and categorized them into a seven-dimensional framework: social, economic, environmental, political, process, project details, and temporal. The findings reveal that financial capital, which reflects economic gains, emerges as the most frequently cited factor influencing local acceptance. However, when viewed dimensionally, the social dimension encompassing factors such as social capital, cognitive response, and cultural capital accounts for the largest share of influencing factors. Additionally, the often-overlooked political and temporal dimensions highlight the importance of governance quality and timely community engagement. While the framework offers a more robust and context-sensitive tool for analyzing acceptance dynamics, empirical validation is needed to assess its practical applicability. Nevertheless, the refined CAF can guide policymakers, researchers, and practitioners in designing renewable energy initiatives that are both technically sound, economically viable, and socially inclusive. Full article

Incremental energy demand, environmental constraints, restrictions in the availability of energy resources, economic conditions, and political impact prompt the power sector toward deregulation. In addition to these impediments, electric power competition for power quality, reliability, availability, and cost forces utilities to maximize utilization of the existing infrastructure by flowing power on transmission lines near to their thermal limits. All these factors introduce problems related to power network stability, reliability, quality, congestion management, and security in restructured power systems. To overcome these problems, power-electronics-based FACTS devices are one of the beneficial solutions at present. In this review paper, the significant role of FACTS devices in restructured power networks and their technical benefits against various power system problems such as load frequency control, voltage stability, and congestion management will be presented. In addition, an extensive discussion about the comparison between different FACTS devices (series, shunt, and their combination) and comparison between various optimization techniques (classical, analytical, hybrid, and meta-heuristics) that support FACTS devices to achieve their respective benefits is presented in this paper. Generally, it is concluded that third-generation FACTS controllers are more popular to mitigate various power system problems (i.e., load frequency control, voltage stability, and congestion management). Moreover, a combination of multiple FACTS devices, with or without energy storage devices, is more beneficial compared to their individual usage. However, this is not commonly adopted in small power systems due to high installation or maintenance costs. Therefore, there is a trade-off between the selection and cost of FACTS devices to minimize the power system problems. Likewise, meta-heuristics and hybrid optimization techniques are commonly adopted to optimize FACTS devices due to their fast convergence, robustness, higher accuracy, and flexibility. Full article

Energy storage technologies are becoming increasingly important when it comes to maintaining the balance between electricity generation and consumption, especially with the increasing share of variable renewable energy sources (VRES). Pumped storage hydropower plants (PSHs) are currently the largest form of energy storage at the grid level. The aim of this study is to investigate the importance and prospects of using PSHs as part of the energy transition to decarbonize energy sources. A comparison was made between PSHs and battery energy storage systems (BESSs) in terms of technical, economic, and ecological aspects. To identify the key factors influencing the wider adoption of PSHs, a combined approach using SWOT analysis (which assesses strengths, weaknesses, opportunities, and threats) and the Analytical Hierarchy Process (AHP) as a decision support tool was applied. Regulatory and market uncertainties (13.54%) and financial inequality (12.77%) rank first and belong to the “Threats” group, with energy storage capacity (10.11%) as the most important factor from the “Strengths” group and increased demand for energy storage (9.01%) as the most important factor from the “Opportunities” group. Forecasts up to 2050 show that the capacity of PSHs must be doubled to enable the integration of 80% of VRES into the grids. The study concludes that PSHs play a key role in the energy transition, especially for long-term energy storage and grid stabilization, while BESSs offer complementary benefits for short-term storage and fast frequency regulation. Recommendations to policymakers include the development of clear, accelerated project approval procedures, financial incentives, and support for hybrid PSH systems to accelerate the energy transition and meet decarbonization targets. Full article

This study explores modernization strategies for existing district heating (DH) networks to enhance their efficiency and sustainability, focusing on achieving net-zero emissions in urban heating systems. Building upon a literature review and expert interviews, we developed a strategic decision-support framework that outlines distinct strategies for retrofitting district heating grids and includes a portfolio analysis. This framework serves as a tool to guide DH operators and stakeholders in selecting well-founded modernization pathways by considering technical, economic, and social dimensions. The review identifies several promising measures, such as reducing operational temperatures at substations, implementing optimized substations, integrating renewable and waste heat sources, implementing thermal energy storage (TES), deploying smart metering and monitoring infrastructure, and expanding networks while addressing public concerns. Additionally, the review highlights the importance of stakeholder engagement and policy support in successfully implementing these strategies. The developed strategic decision-support framework helps practitioners select a tailored modernization strategy aligned with the local context. Furthermore, the findings show the necessity of adopting a comprehensive approach that combines technical upgrades with robust stakeholder involvement and supportive policy measures to facilitate the transition to sustainable urban heating solutions. For example, the development of decision-support tools enables stakeholders to systematically evaluate and select grid modernization strategies, directly helping to reduce transmission losses and lower greenhouse gas (GHG) emissions contributing to climate goals and enhancing energy security. Indeed, as shown in the reviewed literature, retrofitting high-temperature district heating networks with low-temperature distribution and integrating renewables can lead to near-complete decarbonization of the supplied heat. Additionally, integrating advanced digital technologies, such as smart grid systems, can enhance grid efficiency and enable a greater share of variable renewable energy thus supporting national decarbonization targets. Further investigation could point to the most determining context factors for best choices to improve the sustainability and efficiency of existing DH systems. Full article

Environmental costs (carbon emissions) have come with China’s economic rise, and its forestry sector now faces difficulties in maintaining both its profit and the health of its ecosystems. This study assesses the impact of carbon emissions on forestry efficiency and total factor productivity (TFFP) in China’s 31 provinces between 2001 and 2021. Using the data envelopment analysis (DEA) model through the slack-based measure (SBM framework) and Malmquist–Luenberger index (MLI), we examine the efficiency and productivity growth of forestry, both with and without accounting for carbon emissions. The study reveals that when carbon emissions are not taken into account, traditional measures of productivity tend to overstate both efficiency and total factor forestry productivity (TFFP) growth, resulting in an average of 7.7 percent higher efficiency and 1.6 percent of additional TFFP growth per year. If we compare the regions, coast provinces with stricter technical regulations have improved efficiency in usage, but places like Tibet and Qinghai, with more vulnerable ecosystems, endure harsher consequences. Regardless of incorporating bad output into the TFFP estimation, China’s growth in forestry productivity primarily depends on efficiency change (EC) rather than technological change (TC). Full article

As Chinese firms play pivotal roles in global supply chains, multinational corporations face increasing pressure to ensure ESG accountability across their sourcing networks. Current ESG rating systems lack transparency in incorporating China’s unique industrial, economic, and cultural factors, creating reliability concerns for stakeholders managing supply chain sustainability risks. This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. We analyze comprehensive ESG data of 1608 Chinese listed companies over 13 years (2009–2021), integrating financial and non-financial determinants traditionally examined in isolation. Empirical findings demonstrate that random forest algorithms significantly outperform multivariate linear regression in capturing nonlinear ESG relationships. Key non-financial determinants include patent portfolios, CSR training initiatives, pollutant emissions, and charitable donations, while financial factors such as current assets and gearing ratios prove influential. Sectoral analysis reveals that manufacturing firms are evaluated through pollutant emissions and technical capabilities, whereas non-manufacturing firms are assessed on business taxes and intangible assets. These insights provide essential tools for multinational corporations to anticipate supply chain sustainability conditions. Full article

The effective adoption of quality assurance and traceability systems is increasingly recognised as a critical enabler of circular economy (CE) outcomes in the plastics sector. This study examines the factors that influence the implementation of such systems within Australia’s recycled plastics industry, with a focus on how these factors vary by company size, supply chain role, and adoption of CE strategy. Recycled plastics are defined here as post-consumer or post-industrial polymers that have been reprocessed for reintegration into manufacturing applications. A mixed-methods survey was conducted with 65 stakeholders across the Australian plastics value chain, comprising recyclers, compounders, converters, and end-users. Respondents assessed a structured set of regulatory, technical, economic, and systemic factors, identifying whether each currently operates as an enabler or barrier in their organisational context. The analysis employed a comparative framework adapted from a 2022 European study, enabling a cross-regional interpretation of patterns and a comparison between CE-aligned and non-CE firms. The results show that firms with CE strategies report greater alignment with innovation-oriented enablers such as digital traceability, standardisation, and closed-loop models. However, these firms also express heightened sensitivity to systemic weaknesses, particularly in areas such as infrastructure limitations, inconsistent material quality, and data fragmentation. Small- and medium-sized enterprises (SMEs) highlighted compliance costs and operational uncertainty as primary barriers, while larger firms frequently cited frustration with regulatory inconsistency and infrastructure underperformance. These findings underscore the need for differentiated policy mechanisms that account for sectoral and organisational disparities in capacity, scale, and readiness for traceability. The study also cautions against the direct transfer of European circular economy models into the Australian context without consideration of local structural, regulatory, and geographic complexities. Full article

This paper presents a multi-criteria decision-making (MCDM) approach for optimizing a microgrid system to achieve Plus-Energy Building (PEB) performance at the University of Coimbra’s Electrical Engineering Department. Using Python 3.12.8, Rhino 7, and PVsyst 8.0.1, simulations considered architectural and visual constraints, with economic feasibility assessed through a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis. The system is projected to generate approximately 1 GWh annually, with a 98% probability of exceeding 1076 MWh based on Gaussian estimation. Consumption is estimated at 460 MWh, while a 3.8 MWh battery ensures up to 72 h of autonomy. Rooftop panels and green parking arrays, fixed at 13.5° and 59°, minimize visual impact while contributing a surplus of +160% energy injection (or a net surplus of +60% energy after self-consumption). Assuming a battery cost of EUR 200/kWh, each hour of energy storage for the building requires 61 kWh of extra capacity with a cost of 12,200 (EUR/hr.storage). Recognizing environmental variability, these figures represent cross-validated probabilistic estimates derived from both PVsyst and Monte Carlo simulation using Python, reinforcing confidence in system feasibility. A holistic photovoltaic optimization strategy balances technical, economic, and architectural factors, demonstrating the potential of PEBs as a sustainable energy solution for academic institutions. Full article

Financial forecasting is a research and practical challenge, providing meaningful economic and strategic insights. While Machine Learning (ML) models are employed in various studies to examine the impact of technical and sentiment factors on financial markets forecasting, in this work, macroeconomic indicators are also combined to forecast the Standard & Poor’s (S&P) 500 index. Initially, contextual data are scored using TextBlob and pre-trained DistilBERT-base-uncased models, and then a combined dataset is formed. Followed by preprocessing, feature engineering and selection techniques, three corresponding datasets are generated and their impact on future prices is examined, by employing ML models, such as Linear Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost, and Multi-Layer Perceptron (MLP). LR and MLP show robust results with high R² scores, close to 0.998, and low error MSE and MAE rates, averaging at 350 and 13 points, respectively, across both training and test datasets, with technical indicators contributing the most to the prediction. While other models also perform very well under different dataset combinations, overfitting challenges are evident in the results, even after additional hyperparameter tuning. Potential limitations are highlighted, motivating further exploration and adaptation techniques in financial modeling that enhance predictive capabilities. Full article

The Non-Wire Alternative (NWA) approach has gained attention as a strategy to replace or defer traditional grid infrastructure upgrades by leveraging integrated solutions combining Energy Storage Systems (ESSs) with Distributed Energy Resources (DERs). The overall feasibility and economics of distributed flexibility solutions can be enhanced by leveraging the synergies among various DERs for NWA deployment. This study presents the results of a techno-economic analysis of an NWA portfolio that integrates Photovoltaic (PV) generation and Demand Response (DR) resources with ESSs. Three representative load profiles are analyzed under different load growth scenarios: a balanced mix of industrial, commercial, and residential loads; residential-dominant loads; and commercial/industrial-dominant loads. The analysis shows that the combined deployment of PVs and DRs significantly reduces the required ESS capacity. Furthermore, economic analysis based on Benefit–Cost Analysis (BCA) demonstrated that combining ESSs with either PVs or DRs enhances economic efficiency compared with an NWA portfolio that relies on ESSs alone, particularly under low-capacity factor conditions. However, the effectiveness of a DR or PV varies depending on the load profile. DR is less effective when the peak load durations are prolonged, whereas PV offers limited economic benefits under residential loads with the evening peak demand. These techno-economic results highlight the importance of tailoring NWA portfolios to specific load conditions to maximize both technical performance and economic value. Full article