Search Results (1,622)

Camera and LiDAR provide highly complementary information, and effective fusion of both modalities is desirable for 3D object detection. However, existing decision-level fusion methods mainly rely on the confidence of objects while neglecting the object uncertainty. To address this, we propose UDF-3D, an uncertainty-driven camera–LiDAR decision-level fusion method based on Dempster–Shafer evidence theory. First, object uncertainty is quantified by introducing the theory of subjective logic, where subjective opinions incorporate category belief masses and an uncertainty mass. Second, a cost matrix is designed for object matching, where each element is a weighted combination of geometric and semantic information from both sensors, and the weights are determined by the uncertainty parameters. Third, we construct a view-frustum constraint to re-evaluate unmatched objects, thereby reducing the false-negative rate. Finally, we design a novel evidence discounting factor within the Dempster–Shafer framework for matched objects, thereby mitigating cross-modal object conflicts during fusion and improving detection accuracy. Experiments on the KITTI dataset demonstrate that the proposed method outperforms existing decision-level fusion approaches, yielding improved detection accuracy. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

Institutional shuttle fleets with fixed routes and predictable terminal parking are well-suited to charging photovoltaic–battery energy storage system (PV–BESS) charging for sustainable campus mobility. However, siting and sizing are often solved numerically without identifying the physical constraints that determine the optimum. This study develops a sustainability-oriented framework for converting a 10-van diesel shuttle fleet at Rajamangala University of Technology Lanna into an electric fleet supported by distributed PV–BESS charging stations. A centralized one-station layout is compared with a distributed two-station layout, and a closed-form active-constraint sizing rule is derived using Karush–Kuhn–Tucker (KKT) analysis. Results show that the distributed configuration eliminates dead-run travel and provides higher lifecycle value than the centralized case. KKT analysis identifies two binding constraints: the PV rooftop-area limit and the BESS one-day autonomy requirement. Under base-case assumptions, the transition achieves positive lifecycle value and substantial CO₂ reduction relative to the diesel baseline. Monte Carlo analysis confirms financial robustness within the uncertainty ranges, while deterministic stress tests show sensitivity to diesel prices, PV electricity credit values, discount rate, and fleet utilization. The framework provides an interpretable decision-support method for institutional fleet electrification in solar-rich campus settings, contributing to SDGs 7, 11, and 13 through clean-energy adoption, sustainable transportation, and CO₂-emission reduction. Full article

Electric vehicle (EV) charging behaviors exhibit significant heterogeneity in terms of price sensitivity, time-of-day preference, and weekend charging habits, creating challenges for charging demand prediction and service management. To address this issue, this paper proposes a three-variable charging response framework that jointly considers electricity price, time-of-day preference, and weekend preference. Using real charging-order data from a public charging platform, four behavioral parameters, namely baseline charging demand (Q₀), price sensitivity (α), time preference (β), and weekend preference (γ), are estimated through nonlinear least squares (NLS). Based on the extracted parameter vectors, K-means clustering is employed to identify five representative user groups: Commuting-Dominant, elastic energy-saving, Weekend-Switching, Night-Preferential, and discount-sensitive users. The results reveal substantial behavioral heterogeneity among users. To validate the proposed framework, both parameter interpretability analysis and benchmark comparisons are conducted. Compared with the best baseline model, the proposed method reduces the test RMSE from 11.5 kWh to 8.3 kWh (27.8%), decreases the test MAPE from 25.3% to 18.7% (26.1%), and improves the test R² from 0.70 to 0.80. The proposed framework provides an interpretable approach for EV charging behavior modeling and user segmentation, offering practical support for differentiated pricing, charging demand management, and intelligent charging service operation. Full article

Decarbonising the residential building sector requires not only technical solutions, but also financially viable delivery models. This paper examines the economic performance of Energy Plus Service (EPS) schemes applied to deep renovation projects under uncertainty, with particular attention to the role of project scale and market conditions. The analysis is based on a portfolio of 21 residential buildings in Northern Italy and combines a Discounted Cash Flow (DCF) model with Monte Carlo simulation. Key sources of uncertainty include renovation costs, post-retrofit energy performance, rental values, and electricity prices, allowing for the estimation of probabilistic Net Present Value (NPV) outcomes. The results show a clear impact of residential asset spatial scale on financial outcomes. Small projects are generally unprofitable, while medium-sized assets are highly sensitive to uncertainty. Larger projects, instead, display a much higher likelihood of positive financial outcomes. Sensitivity analysis indicates that financial performance is driven mainly by investment costs and rental income, while energy-related variables play a more limited role. The findings suggest that the viability of EPS models depends as much on market conditions as on technical performance, pointing to a potential misalignment between energy policy objectives and private investment incentives. Results suggest that projects approaching 160 m² are more likely to achieve a 50% probability of a positive NPV, indicating a potential scale threshold beyond which EPS schemes become significantly more bankable and below which aggregation or additional de-risking measures are likely to be required. Full article

Behavioral interventions are widely used to promote sustainable consumption, but their effectiveness under high-friction real-world conditions remains uncertain, especially when multiple tools are combined. We report a quasi-experimental natural field study conducted in a busy urban café in Tel Aviv, Israel, examining the isolated and combined effects of a localized identity-based social-norm cue and a small financial incentive on reusable cup adoption. Across four consecutive weeks and 9414 hot-beverage transactions, a baseline week was followed by a norm condition, a 1 NIS discount condition, and a combined condition. Reusable cup use increased from 3.33% at baseline to 3.59% in the norm week, 4.19% in the incentive week, and 3.72% in the combined week, but none of these changes reached statistical significance. The financial incentive produced the largest descriptive increase, whereas the combined intervention did not outperform the incentive alone. Across the intervention period, reusable cup use exceeded the number expected under the baseline rate by approximately 35 purchases. These bounded null findings suggest that low-cost behavioral tools may yield only modest gains in convenience-driven consumption settings and that combining policy tools does not necessarily generate additive effects. The study contributes ecologically grounded evidence on the boundary conditions of sustainable behavior change and highlights the importance of testing behavioral policies under realistic implementation constraints. Full article

This study proposes a comprehensive techno-economic methodology to assess the economic viability and optimal sizing of grid-connected residential photovoltaic (PV) self-consumption systems without storage in emerging economies. The model uses net present value (NPV) as the optimization criterion and estimates internal rate of return (IRR) and discounted payback time (DPBT) as complementary profitability indicators. It integrates hourly PV generation, synthesized hourly demand profiles, local tariff structures, surplus-energy remuneration, investment and operating costs, inflation, performance losses, and discount-rate assumptions, while explicitly accounting for context-specific limitations related to data availability, storage-free operation, and financing assumptions. The methodology is applied to 30 Colombian residential scenarios, covering five cities and six socioeconomic strata, and is complemented with a replicability case in Jaén, Spain. In Colombia, PV self-consumption is economically viable in all cases, but profitability is highly uneven: maximized NPV ranges from 2.8 € in the least favorable low-income case to 2816 € in the best high-income case, IRR ranges from 5.0% to 14.7%, and DPBT ranges from 8 to 24 years. From an energy-justice perspective, tariff subsidies improve affordability but may reduce PV attractiveness for low-income users, highlighting the need for capital grants, low-interest loans, or community solar schemes. Full article

Hydropower-dependent electricity systems, such as Ecuador’s, face critical supply disruptions during droughts: a vulnerability exemplified by the 2024 power outages. This study assesses the technical, economic and environmental feasibility of a 200.84 MWp grid-connected solar photovoltaic (PV) plant proposed for the Pacific Refinery site in Manabi, Ecuador, as a strategy to diversify the energy matrix and reduce hydrological risk. Using site-specific solar resource data (4.65 kWh/m²/day) and PVSyst simulations, the plant achieves an annual energy production of 295 GWh with a performance ratio (PR) of 85.3%. A discounted cash flow analysis over 25 years, assuming a 7% discount rate and an electricity price of 60 USD/MWh, yields a net present value (NPV) of 104.9 MUSD, an internal rate of return (IRR) of 62.2%, and a levelized cost of energy (LCOE) of 14.5 USD/MWh, well below current industrial tariffs in Ecuador. Sensitivity analysis confirms project viability under ±15% variations in investment cost, energy price, and solar resource. Over its lifetime, the plant avoids 1.83 Mt of CO₂ emissions, supporting national decarbonization goals. The results demonstrate that large-scale PV deployment in high-radiation, low-latitude regions can be highly profitable and contribute to energy sovereignty in hydropower-dependent systems. Furthermore, this study provides a replicable model for repurposing unused industrial land for renewable energy generation, offering actionable insights for policymakers and investors in developing economies. Full article

The DC/AC ratio is a critical design variable in utility-scale photovoltaic (PV) systems because it governs inverter loading, clipping behavior, energy yield, and long-term economic performance. However, conventional sizing approaches often rely on heuristic rules or deterministic annual yield optimization without explicitly accounting for the thermodynamic, optical, and stochastic mechanisms that reshape the DC power envelope. This study develops a physics-informed and bankability-oriented PVsyst-based framework for optimal DC/AC sizing by integrating irradiance transposition, incidence-angle modifier losses, temperature-dependent semiconductor behavior, inverter clipping dynamics, degradation, and discounted lifetime levelized cost of electricity (LCOE). A 10 MWp fixed-tilt PV plant located in Western Türkiye under Mediterranean climatic conditions is analyzed. The base-case simulation yields 15.20 GWh/year with a specific yield of 1519 kWh/kWp/year and a performance ratio of 87.5%, while temperature losses are identified as the dominant loss mechanism, accounting for 6.21% of the annual energy reduction. A regression-based thermal sensitivity analysis shows that monthly PR decreases by approximately $4.9\times {10}^{-3}$ per °C increase in ambient temperature. The DC/AC sweep identifies an optimum range of 1.35–1.40, where improved inverter utilization balances nonlinear clipping growth. A temporal clipping analysis confirms that clipping is concentrated during summer midday periods and is sensitive to sub-hourly irradiance variability. Correlated Monte Carlo simulations and LCOE cost-sensitivity analyses demonstrate that the optimum remains structurally robust under uncertainty, degradation, and inverter cost assumptions. The results show that DC/AC sizing should be treated as a coupled thermodynamic–optical–electrical–economic optimization problem rather than a simple capacity-matching decision. Full article

The integration of blockchain technology into the Internet of Things (IoT) offers a decentralized paradigm for data integrity. However, the emergence of 51% attacks—driven by hashrate concentration—threatens the foundational trust of these resource-constrained networks. In resource-constrained IoT-enabled blockchain environments, mining-power asymmetry and limited governance capability may amplify the impact of strategic attacks. These characteristics motivate the need to analyze long-term adversarial behavior and governance effectiveness under repeated interactions. This paper develops a threshold-based analytical framework that integrates a single-stage decision model and a multi-stage discounted decision model to analyze 51% attack decisions and governance effects in asymmetric blockchain mining environments. We characterize the interaction between competing mining pools as a multi-stage game, integrating key parameters such as the discount factor of future utility and recovery penalty cycles. Our analysis demonstrates that a multi-stage framework creates a “long-term deterrent effect” where the net present value of potential future losses outweighs the immediate gains of hashrate abuse. analytical results indicate that the strategic threshold for launching an attack is highly sensitive to the duration of punitive measures and the accuracy of IoT-based anomaly detection. The results provide useful insights into the design of governance and incentive mechanisms for blockchain systems deployed in resource-constrained and heterogeneous environments. Full article

Grid-connected photovoltaic–energy storage systems (PV-ESSs) enhance electricity reliability and lower energy costs in emerging markets. However, their commercial viability under multi-objective optimization remains under-quantified. This study offers a techno-economic and financial analysis of PV-ESS setups optimized with the Non-Dominated Sorting Genetic Algorithm II across Nigeria, South Africa, and India. The best systems feature 1.3–1.5 MW of solar capacity and 2.5–2.9 MWh of lithium-ion batteries. Results show unsubsidized levelized energy costs of USD 0.061–USD 0.064/kWh, achieving 27–35% savings compared to grid tariffs. Battery storage accounts for 67–76% of total capital costs, making battery expenses the key economic factor. Financial analysis reports net present values of USD 238,000–USD 522,000, internal rates of return of 13.7–15.8%, and discounted payback periods of 7.9–9.2 years. Monte Carlo simulations indicate an 83.4–100% probability of a positive net present value. Sensitivity analysis highlights grid tariffs and battery costs as major influences. Revenue diversification through grid services, capacity credits, and demand response can boost net present value by up to 35%. Overall, optimized PV-ESS projects can be commercially viable in emerging markets with suitable tariffs, financing, and revenue strategies. Full article

Valuation methodologies vary across industries because firms differ in capital intensity, asset life, earnings stability, and exposure to risk. This study examines the valuation approaches used by South African equity analysts across the diversified mining, platinum group metals mining, gold mining, retail, and banking sectors over the 2018–2026 period, with non-financial firm coverage extending to 2024 and banking sector coverage extending to 2026. Using qualitative document analysis of 201 equity research reports covering 24 Johannesburg Stock Exchange-listed companies, including 19 non-financial firms and the five largest South African banks, the study identifies clear clustering of valuation methods by industry. The findings show that resource-based sectors are predominantly valued using intrinsic approaches such as life-of-mine discounted cash flow (DCF) and risk-adjusted net present value (NPV), while retail firms are primarily valued using earnings-based multiples. Gold mining exhibits a hybrid valuation pattern, and banking institutions are valued using balance-sheet- and profitability-based approaches anchored on book value, return on equity, and dividend flows. Overall, the results suggest that valuation practices in the sampled equity research reports are strongly industry-specific and broadly aligned with the underlying economic characteristics of the sectors analysed. The study contributes to the limited empirical literature on professional valuation practice in African capital markets and provides insights relevant to analysts, investors, and regulators. Full article

CO₂ enhanced coalbed methane recovery (CO₂-ECBM) using huff-and-puff technology has attracted increasing attention as a promising approach to enhance the productivity of low-productivity coalbed methane (CBM) wells while simultaneously enabling CO₂ storage. However, the economic feasibility of this method and the optimal soaking time remains unclear. In this study, an economic evaluation model for CO₂ huff-and-puff CBM projects was developed based on the discounted cash flow method, incorporating key factors such as CBM price, government subsidies, carbon trading price, CH₄ separation cost, and CO₂ purchase and injection costs. Three representative scenarios were designed to evaluate the impacts of policy support and market conditions. The effects of CO₂ injection volume and soaking time on net cash flow (NCF), net present value (NPV), and dynamic payback period (DPP) were systematically investigated. The results indicate that CO₂ huff-and-puff is economically viable for enhancing CBM recovery. Increasing CO₂ injection volume markedly improves CH₄ production and project revenue, but also leads to higher initial investment and a longer payback period. The economic performance exhibits a non-monotonic dependence on soaking time, with an optimal range that maximizes NPV. Specifically, the optimal soaking time ranges from 30 to 60 days for injection volumes up to 2000 t, and from 60 to 90 days for higher injection volumes. External economic factors exert a strong influence on project performance: higher carbon trading prices and lower CO₂ purchase costs markedly improve profitability, while government subsidies effectively increase net returns and shorten the payback period. In the absence of subsidies, higher injection volumes are required to maintain economic viability. Overall, this study provides a comprehensive economic evaluation framework for CO₂ huff-and-puff CBM projects, identifies key operational and economic parameters for optimizing project performance, and offers theoretical support for field-scale applications. Nevertheless, the economic evaluation is based on deterministic numerical simulation results and simplified market assumptions. Long-term operational risks and geological heterogeneity are not explicitly considered, which may limit the applicability of the results under field conditions. Full article

Carbon taxes and credits (CT&C) accelerate global deployment of carbon capture, utilization and storage (CCUS) technologies to enable energy transition. This study investigates the economic performance and resilience of floating gas-to-wire with CCUS (f-GTW-CCUS), deployed at the wellhead of stranded CO₂-rich offshore oil and gas reservoirs. The f-GTW-CCUS platform integrates a natural gas combined cycle power plant with monoethanolamine post-combustion capture (PCC-MEA), producing low-carbon electricity (23 kgCO_2e/MWh, competitive with renewables) while monetizing captured CO₂ via enhanced oil recovery (EOR). The mass and energy balance data from the proposed process configuration were obtained in the literature. Critically, f-GTW-CCUS operates on wellhead-sourced in situ-associated gas, eliminating exposure to volatile natural gas markets, and achieves a levelized cost of electricity (LCOE) of USD 67.15/MWh. Monte Carlo analysis (10,000 Gaussian iterations, 30-year lifetime, 10% discount rate, three CT&C scenarios, namely, low/medium/high) is used to quantify economic feasibility across three stochastic variables: oil, natural gas, and electricity prices, starting in the 5th year. The results demonstrate the following: (1) Case A (f-GTW without CCUS) remains economically infeasible (NPV < 0) under all price volatility scenarios due to insufficient electricity-only revenue and carbon taxation penalties; (2) Case B (f-GTW-CCUS with immediate CCUS deployment) maintains positive NPV across all scenarios, with EOR monetization contributing 43% of total revenue; (3) the critical CCUS deployment-delay threshold is 6 years under high carbon taxation, extending to 10 years when carbon credits are included. Gate-to-gate environmental assessment (carbon intensity, water footprint, land transformation) shows f-GTW-CCUS superiority versus alternative power systems, with minimal water–land nexuses due to offshore desalination. An empirical consistency assessment based on the 2026 geopolitical energy crisis demonstrates the structural resilience of the f-GTW-CCUS plant: the wellhead sourcing provides resilience to global natural gas price shocks, while the concurrent crude price escalation amplifies EOR revenues by 43–57%, improving project feasibility during commodity disruptions. These findings position f-GTW-CCUS as a critical decarbonization pathway for O&G producers exploiting stranded gas reserves. The technology combines carbon intensity reduction with economic resilience under volatile energy market conditions and mandatory climate policies. Full article

Formally stipulating rental terms in farmland transfer contracts is essential to safeguarding transacting parties’ rights, anchoring market price signals, and underpinning the rule-based governance of rural land markets. Drawing on survey data from 1496 rural households across three Chinese provinces, this study empirically examines how connection strength between transacting parties shapes the decision to formally stipulate rental terms in farmland transfer contracts. Baseline estimates show that greater connection strength is significantly and negatively associated with the probability of formal rental term stipulation, a pattern robust to alternative model specifications and variable operationalizations. Mechanism analysis reveals that stronger connections inhibit formal stipulation by concurrently heightening reputational constraints among parties suppressing demand for formal enforcement mechanisms and attenuating perceived transactional risk, which erodes the perceived value of the risk-bounding function that written clauses provide. Heterogeneity analysis further shows that this inhibitory effect is concentrated among ordinary farm household transfers and disappears among new-type agricultural business entities, where institutional rationality crowds out connection-based governance logic. Beyond its direct effect on contract formalization, greater connection strength indirectly undermines the price-anchoring function of written agreements, exposing realized rents to systematic connection-based discounting. These findings carry direct implications for the demand-side redesign of contract formalization policy and the development of county-level rental price guidance systems in rural China. Full article