Direct air capture (DAC), green ammonia and long-duration energy storage (LDES) projects have attracted large public commitments, yet project cancellations are mounting. This paper argues that deployment fails when several barriers bind at once, chiefly absent demand, uncompetitive cost, policy uncertainty and unbankable finance, and that a technology's prospects are set by its weakest barrier rather than its average strength. That logic is formalised in a Compound Deployment Risk Index, stress-tested for robustness, and paired with a levelised-cost model for each technology, using sourced data through mid-2026. The results show why each technology stalls. Capturing CO2 from air costs 480-1000 USD per tonne, more than any compliance scheme currently pays. Green ammonia becomes competitive as a shipping fuel only at a carbon penalty of 370 to 680 USD per tonne, above the 100 to 380 USD proposed by the International Maritime Organization. Furthermore, in Great Britain's storage auction, new battery chemistries were undercut by lithium-ion and pumped hydro at every procured duration. The policy conclusion is that creating demand is necessary but not sufficient: it must be paired with cost reduction and de-risked finance for a self-sustaining industry to emerge.
This study examines how the learning advantages of newness (LAN) emerge from organizational structures and strategic behaviours, and from entrepreneurs acting as learning agents. Despite extensive research on LAN, research on how formative childhood experiences shape the learning foundations of new ventures is limited. This study draws on imprinting theory to investigate how early experiences of autonomy leave enduring cognitive and behavioural imprints on entrepreneurs, fostering a disposition that supports the development of LANs. The study relies on an exploratory multiple-case study of ten early internationalizing technology firms, supported by 11 narrative-informed interviews with entrepreneurs and an abductive research strategy. Its findings identify childhood autonomy as an important antecedent of LAN development. Autonomy imprints operate through four organizational learning mechanisms: selective attention to novelty, distributed authority routines, psychological safety norms, and boundary-spanning searches. These mechanisms translate early-life experiences of autonomy into venture-level learning capabilities. The analysis further shows that resource slack strengthens those mechanisms by enabling broader exploration, experimentation, and knowledge development. This study extends imprinting theory by demonstrating that childhood autonomy constitutes a formative imprint with enduring consequences for organizational learning and international venture development. It also contributes to the learning advantages of newness literature by identifying a previously unexplored antecedent of LAN and revealing the organizational learning mechanisms through which founder imprints shape its emergence in international new ventures. Finally, this study offers practical insights for entrepreneurs by illustrating how formative experiences can be leveraged to cultivate sustainable learning advantages in global markets.
Large language models (LLMs) are increasingly proposed as automated judges for evaluation, yet their reliability remains unclear. This study examines the inter-rater agreement of three LLM judges (GPT-4o, Claude 3 Sonnet, and Gemini 1.5 Pro) when evaluating fairness, lack of stereotypicality, and diversity in user personas across varying educational risk contexts. We analyzed 60 individual personas and 12 persona sets to assess whether these judges demonstrate sufficient consistency to function as reliable evaluators. Although inter-rater agreement was moderate at the individual persona level, three critical limitations emerged. At the set level, agreement ranged from .39 to .63 across persona generators and therefore did not show a uniform decline relative to individual-level agreement. Mean dimension-support scores were 41% to 61% lower, although the individual- and set-level tasks are not directly comparable. Second, non-compliance rates, where judges could not provide textual justification for their ratings, ranged from 9.6 to 26.7% at the individual level and increased to 34% to 49% at the set level. Third, agreement differed across the three educational scenarios classified by risk: individual-level agreement declined from the lower- to higher-risk scenarios, while set-level agreement showed no monotonic pattern. We conclude that, under the tested protocol, current LLM judges demonstrate poor-to-moderate inter-rater agreement for persona evaluation. These findings contribute a descriptive characterization of LLM evaluation behavior across risk contexts and provide a foundation for future validation studies comparing LLM agreement against human judgment.
Accurate prediction of exhaust noise is increasingly important for integrating acoustic constraints into modelbased development of large-bore marine engines. However, one-dimensional acoustic modeling practices established primarily for automotive engines may not transfer directly to large-bore systems. This study investigates the sensitivity of predicted exhaust-pressure spectra to numerical resolution, downstream exhauststack representation, turbocharger acoustic modeling, and local duct geometry using a one-dimensional model of a Wa & uml;rtsila & uml; 4L20 medium-speed engine. A configuration combining the selected refinements is then evaluated against measured in-duct pressure spectra. Numerical resolution produced the strongest sensitivity, with coupled spatial-temporal refinement reducing the spectral RMSE relative to the finest-mesh reference from 10.7 to 2.3 dB below 600 Hz. Explicit exhaust-stack representation substantially redistributed spectral energy, while an acoustic turbine representation introduced frequency-dependent transmission loss rising to 15-17 dB near 1 kHz. The combined refined configuration produced closer frequency-resolved agreement immediately downstream of the turbine, where the narrowband RMSE decreased from 11.42 to 9.88 dB and the mean absolute error from 9.66 to 7.57 dB, although the mean-level deviation increased from 2.42 to 3.05 dB. Within the plane-wave-valid range, one-third-octave errors also decreased, and a separate comparison at a farther-downstream station gave a mean absolute error of 7.30 dB, although residual narrowband discrepancies of 9-10 dB remained. The results demonstrate that reliable one-dimensional exhaust-acoustic prediction for the investigated large-bore engine requires adequate numerical wave resolution together with representation of the dominant propagation paths and passive turbine acoustics. However, the remaining spectral errors indicate that further model development and validation are required. The quantitative resolution thresholds and relative importance of individual model components remain platform-specific and require validation across additional engines and operating conditions.
Digital Product Passports (DPPs) connect product identity with lifecycle information for compliance, circularity and stakeholder access. Positive energy districts (PEDs) require this information to remain useable as renewable-energy assets undergo maintenance, component replacement and ownership changes. This study synthesises passport and engineering literature, regulations, standards and initiative documents for photovoltaic systems (PV), small wind turbines (SWTs) and asphalt solar collectors (ASCs). It develops eight shared lifecycle categories with extensions for electrical performance, mechanical condition, and thermal-hydraulic and road-infrastructure information. Four layers organise complementary information responsibilities: identity and lifecycle baseline; operational data and connectivity; semantic and derived information; and governance and exchange. Illustrative records for inverter replacement, bearing inspection and road-work handover explain how asset configurations, observations, assessments and permissions remain connected across lifecycle events. Representative mappings connect the field profile to external information models, while district scenarios show how current capability, availability and constraint records can inform energy management alongside storage and demand coordination. Stewardship, skills and incentives sustain these exchanges. A staged pathway connects shared field definitions, operational tasks and longitudinal assessment to guide implementation for district operation, repair, reuse and recovery.
