Search Results (7,729)

Methane emissions from coal mines pose significant environmental and operational challenges. Methane can be released from coal seams and surrounding rock layers as a result of mining operations. These emissions pose environmental risks and can lead to fire and explosion hazards. Therefore, reducing coal mine methane emissions is essential for both protecting miners’ safety and cutting greenhouse gas emissions. Additionally, capturing methane before it escapes into the atmosphere can be economically beneficial and used as a valuable energy source. This study proposes an integrated approach that combines advanced methane capture and hydrogen production technologies to enhance both environmental performance and energy recovery in coal mining operations. By combining methane capture with hydrogen production, the study presents a practical solution for lowering greenhouse gas emissions in the coal sector. This strategy promotes the adoption of low-carbon energy sources and offers a sustainable path forward for coal-dependent regions facing decarbonization challenges. A scenario-based techno-economic analysis is presented, including investment and operating costs, hydrogen yield, energy generation potential, and greenhouse gas mitigation. Further research should focus on process optimization, the integration of carbon capture technologies, and the valorization of by-products to further reduce the environmental footprint. Full article

This article examines the integration of Building Information Modeling (BIM) and Life Cycle Assessment (LCA) to support sustainable revitalization of post-industrial areas, using the “Pralfa” Factory in Tarnów, Poland, as a case study. Under increasing environmental and investment pressures, demolition decisions require comprehensive assessment of their environmental consequences. BIM enables the generation and comparison of design scenarios at the conceptual stage, supporting informed decision-making. LCA of the “Pralfa” Factory showed that demolition would generate approximately 195,100 kg CO₂e. Two revitalization scenarios were evaluated: (1) construction using reclaimed materials recovered from demolished buildings and (2) construction using only new materials. The results demonstrated that demolition and the production of new materials substantially increase environmental impacts, while material reuse significantly reduces embodied emissions. The study confirms that BIM–LCA integration effectively supports sustainable design decisions and circular economy strategies. Its scientific contribution lies in extending BIM–LCA analysis to the end-of-life stages (C1–C4) of non-listed post-industrial buildings, addressing a gap in previous research focused mainly on new construction and stages A1–B7. The proposed workflow provides a transferable decision-support framework for circular revitalization and selective demolition planning. Full article

Despite a growing body of research on economic growth, updated empirical evidence on the relative contributions of factor accumulation and productivity to Jordan’s long-run growth remains limited. This study investigates the sources of economic growth in Jordan over the period 1980–2024 by integrating production function estimation with a long-run growth accounting methodology. Annual macroeconomic data are used to estimate the production function through the ordinary least squares (OLS) method. The estimated parameters are then applied in a growth accounting methodology to decompose output growth into the contributions of labor, physical capital, and total factor productivity (TFP). The estimation results indicate that capital per worker has a positive and statistically significant impact on real output per worker. The growth accounting analysis shows that labor has been the primary driver of economic growth, contributing an average of 48.94% of total growth over the study period. Physical capital follows with a contribution of 40.16%, while total factor productivity (TFP) accounts for only 11.13%. These results suggest that Jordan’s long-run economic growth has been driven mainly by the accumulation of labor and capital rather than by sustained improvements in productivity. By integrating production function estimation with long-term growth accounting over more than four decades, this study provides updated empirical evidence on the evolution of Jordan’s growth drivers and offers a comprehensive country-specific assessment. The findings also underscore the importance of policies that foster innovation, improve resource allocation and production efficiency, accelerate digital transformation, strengthen the business environment, and encourage investment in high-value-added activities to support more sustainable long-run economic growth. Full article

This study investigates the misalignment between ESG environmental (E) scores and actual sustainable investment activities, addressing a critical gap in the literature regarding the reliability of ESG metrics. While prior research has highlighted concerns about greenwashing, few studies have systematically linked ESG ratings to EU Taxonomy-based capital expenditure (CapEx) indicators. This study aims to bridge this gap by developing a novel firm-level typology that captures discrepancies between reported environmental performance and real investment commitments. The empirical analysis is based on Bloomberg data for European firms over the 2022–2024 period and employs cluster analysis alongside non-parametric statistical testing (Kruskal–Wallis) to assess intergroup differences. The findings reveal substantial heterogeneity across firms, including cases where high ESG ‘E’ scores are not supported by aligned sustainable investments and instances of under-recognised investment activity. These inconsistencies suggest potential distortions in ESG signalling and indicate limitations in current rating methodologies. The study contributes to the literature by integrating ESG evaluation with EU Taxonomy metrics and proposing a refined analytical framework for detecting greenwashing risks. From a practical perspective, the results provide valuable insights for investors, regulators and policymakers seeking to enhance the credibility, comparability and transparency of sustainability disclosures. Full article

Against the rapid expansion of the digital economy in Southeast Asia, improving firms’ digital capability has become increasingly important for sustaining employee innovation in the IT services sector. However, existing research has mainly emphasized organizational or performance outcomes and has paid less attention to the internal mechanisms through which corporate digital capability shapes employees’ sustainable innovation behavior, especially in the heterogeneous institutional and cultural context of ASEAN. Drawing on Conservation of Resources theory and Social Information Processing Theory, this study examines the relationship between corporate digital capability and sustainable employee innovation behavior, with innovation atmosphere and knowledge sharing as mediating mechanisms. Using cross-sectional survey data collected from 257 employees in IT services firms across five ASEAN countries, and employing hierarchical regression analysis with bootstrap mediation testing, this study finds that corporate digital capability positively promotes sustainable employee innovation behavior (β = 0.38, p < 0.01). The results further show that innovation atmosphere and knowledge sharing serve as important mediating pathways through which digital capability is translated into employee-level innovation outcomes (indirect effects: 0.20, 95% CI [0.10, 0.31] for IA; 0.19, 95% CI [0.11, 0.28] for KS). In addition, innovation atmosphere strengthens knowledge sharing, forming a sequential mechanism (indirect effect = 0.26, 95% CI [0.15, 0.37]) that further supports sustainable innovation behavior. This study contributes to the literature by clarifying how digital capability affects sustainable employee innovation at the micro level, by distinguishing the organizational climate and knowledge exchange mechanisms involved in this process, and by providing context-sensitive evidence from ASEAN’s diverse digital transformation environment. The findings also provide practical insights for firms and policymakers seeking to strengthen sustainable innovation capacity through digital capability building, supportive organizational climates, and stronger knowledge-sharing practices. Specifically, policymakers should align DEFA-funded digital skill programs with organizational climate interventions that foster psychological safety and cross-border collaboration, while managers in resource-constrained SMEs can leverage low-cost measures such as monthly idea recognition schemes, public endorsement of reasonable failure, and designated cross-border knowledge brokers to amplify the innovation returns of digital investments. Full article

New energy vehicles constitute a crucial component of low-carbon economic systems and green development initiatives. Supported by government subsidy policies, the new energy vehicle industry has achieved remarkable development in recent years. This study conducts an empirical analysis based on panel data of 93 listed new energy vehicle enterprises from 2012 to 2022 to explore the impacts of government subsidies on corporate R&D investment. Using Stata 17.0, we use return on assets, debt-to-asset ratio, enterprise size and operating efficiency as control variables. A two-way fixed-effect model is selected via the Hausman test, followed by linear regression analysis. Furthermore, a dynamic panel vector autoregression (PVAR) model is employed to examine the dynamic interaction between government subsidies and corporate R&D investment. This research perspective overcomes the limitations of traditional static innovation policy research, effectively supplements the empirical evidence on long-term policy incentive effects in the new energy vehicle industry, and enriches the theoretical and empirical literature on the intrinsic dynamic correlation between government subsidies and corporate innovation investment. The empirical results show that government subsidies exert a significantly positive effect on firms’ R&D investment, and that there exists a stable long-term two-way positive interaction and dynamic equilibrium between the two. However, such mutual promotion effects are economically weak in magnitude, and the long-term evolutionary trends of both variables are predominantly dominated by their respective internal self-reinforcing inertia. In view of the limited incentive contributions of existing subsidy policies, the results of this study suggest the need to optimize the precision and targeting of government subsidy mechanisms to amplify policy incentive efficiency, while enterprises should fully leverage their endogenous R&D inertia to strengthen their independent innovation capabilities. The presented findings provide empirical evidence and policy guidance for the promotion of stable R&D innovation and high-quality development of the new energy vehicle industry. Full article

Photovoltaic (PV) enhancement technologies, including cooling systems, reflectors, and tracking mechanisms, are widely employed to improve the electrical performance of PV systems. However, the effectiveness of these technologies should be evaluated not only in terms of performance improvement but also by considering the associated implementation costs. To address this need, previous studies introduced the Cost Effectiveness Factor (${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$), which integrates power output and manufacturing cost into a single performance indicator. While ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$ is useful for short-term and experimental assessments, it is based on instantaneous power output and does not account for the cumulative energy generated over extended operating periods. Furthermore, many experimental and field studies on PV enhancement technologies, particularly PV cooling systems, report their performance in terms of energy generation (kWh) rather than instantaneous power output (W), creating a need for an energy-based assessment methodology. Therefore, this study proposes a new Cost–Energy Effectiveness Factor (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$) that extends the concept of ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$ by incorporating energy output instead of power output, thereby enabling a more comprehensive evaluation of long-term techno-economic performance. The proposed indicator integrates the output energy of PV systems with and without enhancers, the manufacturing cost of PV enhancers, and the unit cost of PV electricity into a single dimensionless factor. In addition, a theoretical minimum value (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$) is introduced to establish a benchmark for performance evaluation. Comprehensive sensitivity analyses were performed to investigate the influence of key technical and economic parameters, including the output energy of the enhanced and unenhanced PV systems, manufacturing cost, the unit PV electricity cost, and maximum output power under standard test conditions. The results indicate that ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ decreases with increasing enhanced PV energy output and electricity value; however, it increases with higher manufacturing costs and greater energy production from the reference PV system. In contrast, variations in the maximum output power affect only the benchmark value (${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E},\mathrm{m}\mathrm{i}\mathrm{n}}$) without influencing the actual ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ values. The proposed indicator was further validated using data obtained from real photovoltaic cooling systems, demonstrating its applicability under practical operating conditions and confirming its suitability for real-world PV enhancement scenarios. Compared with ${\mathrm{F}}_{\mathrm{C}\mathrm{E}}$, the proposed ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ provides a more realistic representation of the long-term benefits of PV enhancement technologies because it evaluates accumulated energy generation rather than instantaneous power output. The indicator successfully differentiates between effective, neutral, and ineffective PV enhancers and offers a practical tool for researchers, designers, manufacturers, and investors seeking to compare PV enhancement technologies from both energy and economic perspectives. Consequently, ${\mathrm{F}}_{\mathrm{C}\mathrm{E}\mathrm{E}}$ can serve as an effective preliminary screening and comparative assessment tool for PV enhancement technologies, thereby promoting the efficient utilization of sustainable energy resources, while detailed investment decisions should be supported by comprehensive techno-economic analyses that consider lifecycle costs, discount rates, financing conditions, and other project-specific economic factors. Full article

The essay rereads the literary field of the mid-twentieth century as structured by “the art of the insult”—a game the literati, exemplified by James Baldwin and Norman Mailer, are invested in. In essays and interviews, these writers engage in ad hominem verbal attacks to test the limits of the socially sayable, which are also the limits of personal friendship. Existing scholarship has shown how the fraught relation between Baldwin and Mailer was marked by gender, race, and sexuality. Drawing on under-researched and newly accessible material, the article makes the case that these differences are superseded by the shared identity as literati who ground literary innovation in social provocation. The alliance culminates in their closing ranks to censor and legitimize the underlying scandal: Mailer’s physical assault against his wife Adele, whose autobiography the article considers in closing. Full article

To address three major industry pain points—the lack of quantified profit-sharing standards in the two-stage pricing under the separation model of military research and production, the absence of stable Nash equilibrium in single-layer synchronous optimization, and insufficient incentives for full-cycle process optimization in design units—this paper constructs a two-level Stackelberg leader–follower game model with the general contracting unit as the leader and design and general contracting units as followers. This aligns with current prototype incentives and phased pricing policies for production rewards and penalties. At the theoretical level, it improves the complete proof system for the two-stage concave profit two-level Stackelberg Nash equilibrium, distinguishes the mathematical differences in equilibrium existence between sequential decision-making and synchronous optimization, and extracts general rules for phased differentiated profit sharing: high-innovation segments should be allocated more profit weight; simply maximizing total alliance profit may cause imbalanced interests, while introducing a minimum net profit-weighted objective can achieve Pareto improvements without profit loss. This conclusion can be applied to multi-stage general contracting scenarios across industries, such as EPC and military–civil collaborative innovation, enriching the basic theory of profit sharing and hierarchical games. Theoretically, the existence of the lower-level Nash equilibrium is proven using Brouwer’s fixed-point theorem, and combining it with the strictly monotonically decreasing feature of the best response function, uniqueness of the equilibrium is derived. Multiple sets of differentiated initial values are simulated to rule out multi-equilibrium bifurcation risk. The model incorporates the military’s reward and penalty policies as rigid exogenous constraints, sets dual individual rationality constraints of ‘cooperative profit greater than baseline profit with no allocation, and both parties’ net profit non-negative,’ and introduces differentiated cost-reduction efficiency and quadratic increasing effort costs to characterize the heterogeneous input of the two types of development entities. For models with piecewise nonlinearity and multi-constraint nonconvex structures, this paper modifies the standard PSO into a Bi-PSO solving framework through hierarchical temporal adaptation. It does not innovate the underlying particle update mechanism and is only used to match the sequential decision order of the leader–follower game. By comparing five algorithms—IPM, GA, SA, DE, and adaptive PSO—through 20 repeated simulations: gradient-based interior point methods easily get stuck in locally invalid solutions that violate cooperation thresholds; differential evolution has the best numerical global search performance, but all general evolutionary algorithms optimize allocation and effort variables simultaneously, disrupting the Stackelberg hierarchical timing. Only Bi-PSO maintains consistent game logic. Using a pricing case for a certain type of equipment and jointly calibrating all parameters with policy documents, three simulation scenarios were set up: no allocation, equal 50/50 split, and single-layer profit maximization. Under the no-allocation mode, R&D investment from the design unit drops to zero and alliance benefits plummet; a blanket equal split ignores differences in technical contributions across two stages, leading to clear efficiency losses; single-layer optimization only pursues total profit maximization, causing a severe imbalance in profit distribution. The two-layer basic framework can achieve the upper limit of alliance benefits, and by adding a weighted optimization goal that considers both total profit and cooperation fairness, it can achieve equal net profits for both parties without reducing overall profit. Through single-parameter sweeps and two-factor heatmap simulations, the study further revealed the coupled effects of main party efficiency and mass production rewards and penalties on equilibrium input and optimal sharing ranges. A robust check was performed by replacing the logarithmic concave output function, producing a standardized allocation range resilient to parameter perturbations: optimal split for the prototype stage is 0.4–0.6 for the design unit, and for mass production stage 0.7–0.9. The findings suggest that high-contribution stages in multi-phase collaboration contracts should receive more benefits, and a weighted fairness objective can achieve Pareto improvements. These conclusions can extend to multi-stage collaboration scenarios such as EPC and military–civilian cooperation. Theoretically, this research further completes the equilibrium proof system for two-party concave payoff two-layer games, providing a new reference for the theory of phased differentiated benefit-sharing contracts in the military sector. Methodologically, it proposes a two-layer intelligent solving tool adapted to leader–follower sequential decisions, effectively mitigating issues where single-layer model equilibria fail or analytical algorithms struggle with multi-constraint nonconvex games. The results can provide quantitative support for the military, general contracting unit, and design unit in drafting equipment incentive pricing contracts and managing full-cycle cost collaboration. Full article

High-altitude regions are known as corridors assisting exchange among communities, and the implementation of economic corridors in these regions provides connections and networks based on two geographical territories. For example, the China–Pakistan Economic Corridor (CPEC), established as an infrastructural development project with wide-reaching socioeconomic and environmental implications, is regarded as a significant driver of regional transformation, with the potential to generate several businesses, build institutional resilience, reduce poverty, foster a circular economy, and create job opportunities. The current study explores the perceptions and attitudes of educated adult residents in selected mountain districts regarding CPEC, which is vital to ensure wide-ranging and sustainable infrastructure development in alignment with the SDG goals, explicitly SDG 4 (Quality Education), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 15 (Environmental Protection). Grounded in Social Exchange Theory, Structural Equation Modeling (SEM) was used to assess local perceptions concerning the costs and benefits linked with infrastructural development projects and concentrating on its socioeconomic and environmental outcomes in Gilgit-Baltistan, Pakistan. The questionnaire was self-administered by respondents, who documented their own responses to all items. The researcher remained present throughout data collection to distribute the questionnaires, provide verbal explanations or translations into the local language for specific terms upon request, and collect completed forms; in total, 384 respondents completed the survey. Findings revealed that surveyed educated adult respondents perceived sustainable infrastructural development favorably, particularly regarding anticipated improvements in quality of life, job creation, alleviating poverty, and the current education system, with 59.9%, 53.1%, 53.1% and 41.7% agreement, respectively. Respondents also expressed concerns about environmental protection, such as health and safety, environmental risks, and construction/transportation-related pollution at 39.8%, 37.5% and 36.7%, respectively. Backed up by SEM direct path analysis, which showed significant positive relationships between infrastructural development and educational development (H1: β = 0.439), quality of life (H2: β = 0.638), job opportunities (H3: β = 0.603), poverty alleviation (H4: β = 0.534), and environmental protection (H5: β = 0.267). Thus, all hypotheses H1-H5 were supported. This study recommends that officials and policymakers promote stakeholder investment for regional sustainability transitions, formulate regional environmental policies and governance for sustainable development to enhance the benefits of CPEC. Full article

Escalating energy demand and the evolving landscape of power transmission have intensified the operational pressures on electrical grids, specifically during N-1 contingency events. The unexpected loss of a single transmission circuit often precipitates critical system instabilities, including power flow congestion, voltage degradation, and heightened transmission losses. While conventional grid expansion remains the standard for maintaining reliability, its implementation is frequently hindered by lengthy permitting processes, significant capital investment, and regulatory complexities. This research offers an alternative by presenting a robust optimization-based framework for the strategic placement and sizing of a battery energy storage system (BESS) within the Electricity Generating Authority of Thailand (EGAT) transmission network. Focusing on the N-1 contingency resulting from the 115 kV Thatako Substation (TTKS)–Bueng Sam Phan Substation (BGSS) circuit outage during peak load hours, this study introduces the musk ox optimizer (MOO) to solve the complex, non-linear allocation problem. The objective function is formulated to minimize cumulative system costs while enforcing strict network operational envelopes. Simulation results indicate that the optimized BESS configuration achieves compliant grid performance, successfully restoring the post-fault bus voltage, enhancing voltage profile, reducing transmission losses, and mitigating peak line loading. These findings demonstrate that the proposed MOO-based strategy provides a physically compliant, flexible, and robust non-wire alternative for transmission constraint management, confirming its viability as a sustainable, low-environmental-impact framework for enhancing modern grid resilience and utility development. Full article

Background: Technology like ultrasonography (US) has revolutionised the process of measuring cervical dilatation, cervical elasticity, station, and position of the foetal head to assess progress of labour. Ultrasound (US) is deemed non-traumatic, accurate, and user-friendly, offering an objective alternative to traditional vaginal examination (VE). Alternative methods such as position-tracking systems with fingertip sensors have shown limited precision, despite their widespread use. Additionally, US technologies have not been shown to measure effacement, caput, and moulding, as compared to VE. There is a lack of strong evidence supporting US effectiveness in improving outcomes for women and babies. Further research is required to enable respectful care in monitoring of women during labour and birth while eradicating preventable morbidity and mortality by utilising US. Objective: This scoping review aimed to map intrusive and non-intrusive ultrasound technologies and devices used in vaginal examinations during intrapartum. Methods: This scoping review followed Arksey and O’Malley’s five-step framework and the population, concepts, and contexts (PCC) model. A comprehensive search was conducted across seven databases using refined keywords. The protocol for this scoping review has been registered on the open science framework. The data were extracted, charted, synthesised, and summarised. Result: This scoping review included 47 original articles with a combined sample size of over 9000 women in labour or delivery. Most studies focused on ultrasound-based labour monitoring methods such as transabdominal, transperineal, 2D, 3D, and automated approaches—compared to traditional vaginal examination (VE). These ultrasound techniques were consistently praised for their accuracy, reliability, and feasibility in assessing cervical dilation, foetal head station, and angle of progression. Ultrasound was particularly effective in determining foetal head engagement during the second stage of labour, which can influence clinical decision-making and outcomes. Additionally, the review identified emerging intrapartum technologies, including non-invasive purple line observation and low-intensity light imaging probes. These innovations reflect a growing shift toward objective and less invasive labour monitoring methods. Tools like ultrasound and automated tracking algorithms demonstrated superior sensitivity, specificity, and patient comfort compared to VE. However, challenges remain in clinical applicability, standardisation, and implementation, particularly in resource-limited settings. Overall, the findings suggest a transition toward digital and ultrasound-based technologies as central components of modern labour monitoring, with further research needed to validate newer devices and ensure equitable integration into clinical practice. Conclusions: This scoping review highlights a shift toward ultrasound-based technologies transabdominal, transperineal with 2D/3D, and automated as more accurate and patient-friendly alternatives to vaginal examination. These methods improve assessment of cervical dilation, foetal head station, and progression angle. Emerging tools like the purple line and light imaging probes show promise but require further validation. Policymakers should support investment in affordable digital tools; clinical practice must prioritise training and integration; and research should focus on standardisation, long-term outcomes, and feasibility to ensure equitable and effective implementation of these technologies in labour monitoring. Full article

Despite substantial AI technology investment, many firms fail to translate isolated AI applications into integrated capabilities that deliver strategic returns and drive business model changes. Grounded in service-dominant logic (SDL), this study proposes and empirically tests a theoretical framework that positions AI capability (AIC) as a key antecedent in the nomological network of business model innovation (BMI). Drawing on a three-wave, two-week-interval longitudinal survey of 193 Chinese digital-intensive firms across IT, technical services, and digital leasing industries, and employing PLS-SEM, we examine associations among focal constructs, specifically, the mediating role of customer responsiveness (CR) and the moderating effect of digital organizational culture (DOC). This design mitigates common method bias and establishes temporal causal ordering. Empirical results indicate that AIC positively relates to BMI both directly and indirectly through CR, and that DOC significantly enhances the indirect effect of AIC on BMI via CR, particularly under high levels of AI-enabled sensing and interpretation. However, causal inference is limited by the cross-sectional nature of the data and self-reported measures. This study makes three key theoretical contributions. First, we identify CR as a market-oriented mechanism linking AIC to BMI, shifting focus from prior internal efficiency-focused mechanisms to customer-centric value co-creation. Second, we extend SDL to the AI context by clarifying how DOC shapes the strategic transformation of ambiguous probabilistic AI outputs into market-oriented actions. Third, we introduce DOC as an internal boundary condition for AIC, complementing prior research on external environmental moderators. These findings provide actionable guidance for managers seeking to unlock the strategic value of AI investments. Findings reflect statistical associations rather than confirmed causal effects, and results are based on perceptual survey data from Chinese digital firms. Full article

The Levelized Cost of Energy (LCOE) is the standard metric for evaluating renewable energy project economics; however, conventional formulations inadequately represent the dynamic effects of logistics performance, supply chain disruptions, geopolitical risk, and institutional constraints on project costs. This study addresses this gap by proposing the Integrated Levelized Cost of Energy (I-LCOE), a conceptual framework designed for macro-level renewable energy planning and policy analysis. An interpretivist qualitative research design was adopted, combining a systematic literature review with twelve semi-structured interviews involving renewable energy, logistics, regulatory, and academic experts from the MENA and GCC regions. Thematic analysis identified five recurring challenges: limited knowledge management, weak integration of logistics within conventional LCOE models, fragmented sustainability metrics, stakeholder coordination inefficiencies, and reliance on tacit knowledge. The findings indicate that transportation delays, customs bottlenecks, infrastructure limitations, and geopolitical disruptions generate dynamic risk premiums that are insufficiently reflected in existing macro-level cost assessment approaches. In response, the study develops the four-layer I-LCOE framework, supported by an operational proxy variable mapping framework, a comparative assessment against established uncertainty methods, and an integrated digital knowledge management platform. The proposed framework provides a structured approach for incorporating logistics-induced uncertainty into renewable energy cost assessment, supporting more informed strategic planning, investment prioritization, and policy development aligned with Sustainable Development Goal 7 and the Paris Agreement. Full article

Reliable preconstruction cost estimation (PCE) is a fundamental stage to project planning and investment decision-making. However, the early-stage uncertainty and limited information make early-stage cost estimation challenging. Artificial intelligence has attracted growing interest as a way out of this impasse, producing a substantial empirical literature worth systematic examination. This study synthesizes the findings of 30 empirical studies published up to December 2025 using PRISMA protocols and examines the AI techniques, project types, dataset characteristics, validation practices, and model interpretability. The results show that artificial neural networks (ANNs) and hybrid approaches dominate the literature, with applications concentrated in building and transportation projects. Reported model performance is generally strong across commonly used evaluation metrics. However, several structural limitations persist: poor generalizability across project contexts, inconsistent validation procedures, limited adoption of explainable AI, and minimal integration of domain expertise. These factors, together, limit the transferability of existing models to real-world practice. This review contributes a structured methodological synthesis, maps the gaps that most limit progress, and proposes a conceptual AI–Expert Integration Framework to support estimation approaches that are more robust, interpretable, and decision-oriented. The findings offer both a current assessment of the field and a practical roadmap for advancing AI-driven PCE research. Full article