Search Results (692)

Offshore oil and gas exploitation is one of the riskiest businesses to invest in and is dominated by various uncertainties: high deepwater pressure, low temperatures, remote operation, long-distance tiebacks and transportation, as well as environmental factors such as wind, waves and ocean currents. Serving as a profitability threshold, the minimum economic field size is defined as the economic recoverable reserve level that an oilfield must exceed to achieve economic returns. This paper develops an approach for determining the minimum economic field size of offshore oil and gas reservoirs. It categorizes the capital expenditure into four major components: drilling and completion costs, platform costs, pipeline costs, and subsea production system costs. The regression models of drilling costs and subsea production costs are developed respectively, with water depth and recoverable reserves as key influencing factors. The pipeline costs are estimated using the unit pipeline cost per mile and pipeline length. A profit model for the offshore field is established under the constraints of the contract, which allocates the oilfield’s production profits between the contractor and the government according to the contractual fiscal terms. Finally, taking the Lucius oilfield in the Gulf of Mexico as a case study, the paper simulates its investment, operating costs, and oilfield revenues. The minimum economic field size is calculated, accompanied by the derivation of the sensitivity boundaries for the primary parameters. Full article

Aiming to mitigate renewable energy curtailment and curb the carbon emissions of traditional thermal power units (TPUs), this paper proposes a stochastic optimal scheduling of a multi-energy complementary base considering multi-resource reserve and TPU doped with ammonia-concentrated solar power coordination. Firstly, the proton exchange membrane (PEM) electrolyzer (EL) and coal-to-hydrogen (C2H) technology are combined to produce hydrogen, and a mixed-hydrogen-source ammonia production model is constructed. The low-carbon characteristics of ammonia gas are used for thermal power mixed ammonia combustion. Secondly, to alleviate the operational burden on TPUs, a collaborative operating framework integrating a concentrating solar power (CSP) plant, an electric heater (EH), and an ammonia-coal co-fired power unit (ACCPU) is introduced. Furthermore, its low-carbon mechanisms during both peak and off-peak load intervals are thoroughly investigated. Thirdly, the ‘electricity–hydrogen–ammonia’ conversion link inside the deep excavation base and the reserve potential of the CSP plant are constructed, and a variety of flexible resource collaborative reserve models are constructed. Building upon this foundation, to account for the diverse uncertainties associated with load demand, wind, and PV generation, a fuzzy chance-constrained programming method is formulated. Seeking to enhance economic efficiency, the framework focuses on lowering the aggregate operational expenditures. Ultimately, the example results demonstrate that the presented approach effectively expands the accommodation capacity for renewable energy, lowers the base’s carbon emission, and alleviates the operational strain on TPUs. Full article

This article addresses the operational challenges facing maritime vessels in the context of decarbonization, with a focus on developing staged recommendations for the integration of power limitation systems and alternative fuels. The systematisation of existing decarbonization problems in the maritime sector and the establishment of their interrelationships constitute the framework for developing coherent decarbonization strategies for the industry. The analysis of alternative fuels identifies the key factors that drive fuel selection in practice. The analysis of contemporary energy consumption regulation technologies has shown that power limitation systems operating through controllable pitch propellers (CPP), integrated with electronic remote-control systems, provide the highest flexibility in managing propulsion characteristics without altering engine rotational speed. The comparative analysis of the engine power limitation (EPL) and shaft power limitation (SHaPoLi) systems has confirmed that SHaPoLi offers a greater potential for reducing fuel consumption and carbon dioxide (CO₂) emissions; however, it comes at higher capital expenditure at the implementation stage. Pairing power limitation with alternative fuels shows that deep cuts in the sector’s carbon footprint are within reach. The economic analysis of power limitation system deployment has revealed the potential for achieving considerable operational cost savings, with a balanced consideration of capital investments and operational benefits. Future research should target the optimisation of EPL and SHaPoLi systems and their integration with other energy-saving technologies. Transitioning to alternative fuels in parallel offers the greatest cumulative reduction in the sector’s carbon footprint. Full article

Population aging weakens the research base for growth in Organisation for Economic Co-operation and Development (OECD) economies. This paper develops a balanced-growth benchmark with semi-endogenous knowledge production, human-capital deepening, and artificial intelligence (AI) research capital to derive in closed form the minimum AI-investment share consistent with non-negative per capita growth. Calibrated to an illustrative 15-country OECD sample spanning contrasting demographic regimes and gross expenditure on research and development (GERD)-intensity profiles, using United Nations World Population Prospects 2024 and OECD Main Science and Technology Indicators data, the formula yields midpoint thresholds of 0.236–0.275% of gross domestic product (GDP) when 10% of GERD is assumed to be AI-designated. The midpoint normalization is anchored to the best currently available OECD/European Commission (EC) measurement evidence, which places the AI-designated share of aggregate research and development (R&D) at 8.8% for the EU27, 9.9% for the United States, and 7.9% for Japan—all within the 5–15% window used here. Although this range is narrow in GDP-point terms, it implies research requirements from about 5–7% of GERD in South Korea and the United States to about 18–20% in Italy, Poland, and Spain. The common normalization shifts levels but not the cross-country ranking. These results favor demographically adjusted, country-specific AI-investment benchmarks over an OECD-wide target and imply that migration and research-base expansion can partly substitute for higher AI spending in high-pressure economies. Full article

Regional electricity interconnections are increasingly recognised as enablers of cost-effective power system expansion, resilience and energy security in emerging economies. In East Africa, Kenya and neighbouring countries, namely Tanzania, Ethiopia, and Uganda, operate relatively low-carbon electricity systems; however, rapidly growing electricity demand and expanding thermal generation are placing upward pressure on grid emissions intensity. This study examines whether planned cross-border interconnections can mitigate this trajectory using OSeMOSYS Global v1.0.0, an open-source least-cost capacity expansion model, comparing stand-alone national power systems against an interconnected regional grid over 2022–2045. Results show that interconnection enables access to low-cost renewable electricity and facilitates surplus generation exports, maintaining system-wide carbon intensity within climate finance eligibility thresholds of 100 gCO₂/kWh. Outcomes are heterogeneous: Ethiopia and Kenya incur cost increases (+USD 481 million and +USD 568 million, respectively) attributable to transmission capital expenditure, whereas Tanzania and Uganda achieve net cost savings (−USD 590 million and −USD 891 million) alongside substantial emissions intensity reductions of 141.9 and 280.5 gCO₂/kWh, respectively. Regional emissions equity is preserved, with modest intensity increases in Ethiopia and Kenya offset by large reductions elsewhere. These findings strengthen the case for climate-financed regional transmission as a scalable and equitable mitigation strategy in East Africa. Full article

The occurrence of short-wave corrugation with wavelengths of 32–44 mm on curved sections of urban express railway lines is particularly pronounced, yet the underlying initiation mechanisms have remained insufficiently understood. Furthermore, conventional mitigation strategies—including the installation of rail dampers and passive grinding—entail substantial maintenance expenditures, thereby hindering their large-scale application. To elucidate the initiation mechanisms of rail corrugation and to formulate effective control measures, the characteristic corrugation parameters under various track structure configurations across an entire alignment were first measured and systematically analyzed. Dynamic interaction models between vehicles and three distinct track typologies were subsequently developed, together with a comprehensive analytical framework for corrugation evolution. The wheel–rail dynamic response characteristics and corrugation growth rates corresponding to each track type were examined, and the wheel–rail coupled vibration modes that exacerbate corrugation propagation in urban express lines were identified. The instantaneous wear behavior of the rail under differing creep regimes was also investigated, leading to the proposal of a novel self-mitigating approach for rail corrugation. The results demonstrate that the excitation frequency of rail corrugation is predominantly confined to the 600–700 Hz range, exhibiting a fixed-frequency characteristic that remains invariant with respect to curve radius, track structure type, and operational speed. An interesting finding is that, although the intrinsic vibration properties of different track structures diverge significantly, the third-order bending resonance of the rail segment situated between bogie wheels is largely unaffected by track-borne vibrations and manifests as a localized wheel–rail resonance within the vehicle–track coupled system. This particular resonance markedly accelerates corrugation development and is identified as the critical governing factor for corrugation initiation in urban express lines, regardless of the underlying track configuration. Furthermore, rail instantaneous wear displays a substantial phase shift under varying creep conditions, with the wear profiles under creep saturation (full sliding) and low creep (rolling–sliding) exhibiting a distinct anti-phase relationship. This insight underpins a novel self-wear suppression strategy: by intentionally mixing rolling–sliding and full-sliding operational regimes, destructive interference between the out-of-phase wear contributions is achieved, resulting in a considerably attenuated corrugation growth rate compared with exclusive rolling–sliding operation. This methodology thus offers a promising and fundamentally new alternative for the long-term management of rail corrugation through intrinsic wheel–rail interaction. Full article

At the Second Water Intake Pump Station of the Chenhang Reservoir in Shanghai, suboptimal pump scheduling resulted in electricity consumption cost attributable to pump-motor equipment accounting for an exceptionally large proportion of the total power expenditure. In response to the economical operation issues, a mathematical model of power consumption cost for the pump station was established by introducing time-of-use electricity pricing and constraint suppression terms. Taking the minimum cost as the research objective, the differential evolution (DE) algorithm was employed to establish a fitness function for electricity cost, aiming to find the most economical and reliable scheduling strategy. However, owing to its low computational speed and high complexity, machine learning was introduced to establish neural network surrogate models of the DE algorithm. By comparing three surrogate models, the Multilayer Perceptron (MLP) neural network model was adopted as the most appropriate surrogate model. It was optimized for robustness improvement and verified on site. The results demonstrate that implementing the surrogate model achieves over 25% savings in electricity cost per thousand cubic meters of water, while slashing the solution time by 88.53% compared to the standard DE algorithm. Furthermore, the overall power consumption is reduced by 2.20% under a cost-priority strategy and by 15.89% under a power-priority strategy, thereby directly mitigating the carbon footprint of the pump station. The proposed hybrid computational framework in this study bridges the gap between the computationally expensive heuristic optimization and the strict real-time control requirements in engineering, highlighting its significant contribution to the sustainable and low-carbon operation of water infrastructure. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

The escalating financial burden of deteriorating civil infrastructure worldwide necessitates a shift from conventional repair methods towards more durable and economically efficient long-term solutions. This paper presents a comprehensive techno-economic review of using carbon fibre-reinforced polymer (CFRP) fabrics for structural strengthening. Moving beyond a simple first-cost comparison, this review utilizes a life-cycle cost analysis (LCCA) framework to evaluate the total cost of ownership. The analysis deconstructs the complete cost profile, demonstrating that while CFRP systems have a high initial material cost, this is frequently offset by substantial savings in labour, equipment, and, critically, the indirect costs associated with reduced construction time and operational disruption. Furthermore, the inherent corrosion immunity of CFRP virtually eliminates future maintenance and repair expenditures, leading to a lower total life-cycle cost compared to traditional steel or concrete-based methods in a wide range of applications. Specifically, the conducted LCCA case study demonstrates that the CFRP alternative can reduce total life-cycle costs by nearly 25% relative to conventional steel sheet bonding, overwhelmingly driven by minimized operational downtime and related indirect costs. The value proposition is shown to be context-dependent, driven by minimizing user delay costs in bridges, mitigating catastrophic risk in seismic retrofitting, preserving cultural value in heritage structures, and maximizing revenue uptime in industrial facilities. The review also examines market dynamics, including the roles of standardization and government policy in driving adoption, and explores future trends such as inorganic matrix composites (TRM/FRCM), integrated structural health monitoring (SHM), and the push towards a circular economy. The findings conclude that a holistic, life-cycle-based economic assessment establishes CFRP strengthening as a cornerstone technology for the sustainable and resilient management of modern civil infrastructure. Full article

This study investigates the key determinants of firm profitability in the global automotive sector, examining whether superior returns on assets (ROA) stem from operational efficiency, strategic leverage, or innovation intensity, and highlighting the potential trade-off between efficiency and investment in capital-intensive industries. Analysing a global panel dataset of 192 automotive firms from 38 countries/regions over 2010–2024, a fixed effects regression model with Driscoll–Kraay standard errors was applied to control for unobserved heterogeneity, heteroskedasticity, and cross-sectional dependence across 11 financial and strategic variables. The findings reveal that firm size and inventory turnover are significant positive drivers of profitability, while research and development (R&D) intensity exerts a strong negative impact. The positive association with the effective tax rate reflects reverse causality, where more profitable firms incur higher tax burdens, rather than a causal effect of taxation on performance. Notably, working capital management, leverage, sales growth, and capital expenditure showed no statistically significant effects after controlling for firm and time effects. Temporal fluctuations, including a marked profitability decline in 2024, underscore the sector’s sensitivity to macroeconomic shocks. This study contributes robust, large-scale empirical evidence on the short-term profitability trade-off associated with R&D intensity in a globally integrated industry, addressing cross-sectional dependence through its methodological approach. Full article

Sustainable urban development depends not only on efficient design and construction but also on the long-term governance of built assets during their operational phase. However, Building Information Modeling (BIM) is still predominantly applied to design and delivery processes, with limited integration into structured maintenance and renewal planning. This study develops a BIM-enabled lifecycle governance methodology that integrates lifecycle cost modeling, service-life estimation, and time-based renewal scheduling into a unified digital asset environment. Rather than proposing a new theoretical model, the study focuses on the systematic integration and operationalization of these components into a reproducible and auditable workflow. The methodology is validated through an anonymized multi-asset industrial portfolio comprising buildings, technical infrastructure, and external works, modeled over a 30-year planning horizon using structured maintenance and renewal data. Comparative scenario analysis between reactive and planned lifecycle strategies evaluates expenditure distribution, capital concentration, and intervention synchronization. The results demonstrate that embedding structured lifecycle parameters within BIM improves the predictability of annual expenditures, reduces cost concentration in peak renewal years, and enhances transparency of long-term asset planning without significantly altering cumulative lifecycle costs. These outcomes support more structured financial planning and coordination of maintenance and renewal activities at the portfolio level. The study does not quantify environmental or social sustainability impacts; its contribution lies in providing a governance-oriented methodology that transforms BIM-based asset data into decision-support outputs for long-term lifecycle planning. Full article

This study examines whether fiscal decentralization operates as a strategic macroeconomic risk-management instrument and whether its effectiveness depends on institutional quality. Using a balanced panel of 27 European Union member states over 2008–2023, a composite fiscal decentralization index combining expenditure and revenue autonomy is constructed, and a dynamic specification is estimated using a two-step System-GMM estimator. Output volatility is measured as a five-year rolling standard deviation of real GDP growth. The results indicate that fiscal decentralization exhibits a statistically significant effect on volatility whose direction depends on governance quality. Institutional quality directly reduces volatility, and the interaction between decentralization and institutional quality is negative and highly significant. A critical institutional threshold of 1.865 (WGI estimate scale), above which decentralization reduces output volatility, is identified. These findings indicate that decentralization functions as a conditional risk-management mechanism embedded within institutional capacity. The results provide policy-relevant insights into EU fiscal architecture design in an era of recurrent macroeconomic shocks. Full article

Background/Objectives: The COVID-19 pandemic intensified concerns regarding the resilience and financing architecture of mental health services, yet it remains unclear whether crisis-induced adjustments fundamentally altered hospital case-mix complexity or merely exposed pre-existing structural configurations. This study examines the relationship between financing regimes and case-mix complexity in psychiatric hospitals in Romania, a Central and Eastern European health system characterized by mixed financing arrangements and pronounced interregional heterogeneity. Methods: Using administrative data comprising 752 hospital section–year observations (2019–2024), we identify structural financing–organization regimes through a two-step clustering procedure (hierarchical Ward method followed by K-means refinement) based on revenue composition, expenditure allocation, workforce structure, and operational pressure indicators. Results: Three distinct regimes emerge, reflecting persistent institutional configurations rather than temporary crisis-induced groupings. Chi-square tests confirm that regime membership is statistically independent of pandemic timing. A multivariate regression model controlling for financing composition and expenditure structure shows that structural variables (particularly the share of contract-based revenues and the allocation of expenditures) exert systematic and economically meaningful effects on the case-mix index (CMI). Pandemic and post-pandemic indicators do not retain robust explanatory power once structural determinants are accounted for. Regional robustness analyses further demonstrate that financing architecture consistently outweighs temporal shock effects in explaining territorial variation in clinical complexity. Conclusions: The findings suggest that psychiatric hospital case-mix dynamics are structurally embedded within differentiated financing regimes whose influence persists beyond crisis periods. By integrating regime identification with outcome modeling in a Central and Eastern European context, this study contributes to the international literature on health system resilience and highlights the primacy of institutional financing architecture over episodic shock effects in shaping hospital complexity. Full article

Empirical parameter settings in steam-based soil disinfestation for Panax notoginseng (a valuable medicinal plant) often hinder the simultaneous optimization of pathogen control and energy efficiency. To address this limitation, this study aims to develop a parameter regulation framework that integrates multi-output regression with scenario-oriented intelligent decision-making. Initially, a comprehensive dataset comprising critical parameters—steam pressure (P_steam), soil compaction (C_soil), and heating time (t_heat)—was established. A random search (RS) hyperparameter optimization scheme was employed to comparatively evaluate the multi-output predictive performance of Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP) for the joint estimation of soil temperature (T_soil) and root-rot pathogen kill rate (Kill_rate). Subsequently, by integrating total energy consumption (E_total) and operating electricity cost models, a constrained search algorithm was implemented to develop three objective-oriented decision modes: “maximize Kill_rate”, “minimize C_electricity”, and “maximize Efficiency”. Results demonstrate that the RS-optimized SVR yielded superior multi-output performance, achieving R² of 0.968 for T_soil (MAE = 2.44 °C) and 0.808 for Kill_rate (MAE = 7.85%). Compared to conventional empirical configurations, the proposed decision modes exhibited significant advantages across diverse scenarios. In the “maximize Kill_rate” mode, dynamic extensions of t_heat facilitated theoretical complete inactivation even under challenging heating conditions, effectively eliminating disinfection “blind spots” inherent in fixed-duration strategies. Under the “minimize C_electricity” mode, precise regulation of P_steam reduced operational electricity costs by 18.2% while satisfying the constraint of Kill_rate ≥ 95%. Furthermore, the “maximize Efficiency” mode identified an optimal operating point at C_soil = 64 kPa (P_steam = 0.4 MPa, t_heat = 13 min), thereby mitigating performance degradation associated with excessive tillage or high media rigidity and achieving an optimized cost–benefit ratio. By synthesizing high-fidelity multi-output regression with a flexible multi-mode decision-making framework, this study provides an intelligent solution for soil disinfestation in protected agriculture, facilitating the coordinated optimization of phytosanitary efficacy, energy expenditure, and economic viability. Full article

Digital technologies have fundamentally changed how public administration operates, moving it from traditional bureaucratic structures toward more efficient and responsive systems. This study analyzes the links between e-government usage (measured as the percentage of individuals who interact with public authorities via online platforms), governance quality, and fiscal performance across ten Central and Eastern European countries from 2010 to 2023. Using a fixed-effects panel data model, we investigate whether higher e-government usage is associated with stronger government effectiveness, improved budget balances, and more sustainable public debt levels, while controlling for key macroeconomic and structural factors. Employing a fixed-effects panel data model, we examine whether greater use of e-government services is associated with stronger government effectiveness, improved budget balances, and more sustainable public debt levels, while accounting for key macroeconomic and structural factors. The findings show a positive and statistically significant association between e-government usage and government effectiveness. The links to fiscal outcomes are more nuanced: e-government usage is associated with better budget balances, mainly through indirect channels such as higher tax compliance and tighter expenditure control. In contrast, its association with public debt levels is weaker and appears to depend more strongly on broader macroeconomic conditions. Overall, the findings suggest that greater e-government usage is associated with improvements in governance quality in the CEE region, although its contribution to long-term fiscal sustainability remains conditional on the quality of existing institutions. Full article