This study presents a realistic hypothetical scenario-based analysis of an airline’s transition from kerosene to hydrogen propulsion between 2030 and 2050, under a Techno-Economic Environmental Risk Assessment (TERA) framework. Two scenarios are modelled: a baseline fleet scenario using only conventional CMRT and CLRT aircraft, and a hydrogen transition scenario that introduces hydrogen-powered Airbus ZEROe and HVLMR aircraft starting in 2035. Using detailed aircraft (Orion from Cranfield) and jet engine (TURBOMATCH from Cranfield) performance simulations across 85 global routes, fuel consumption, energy demand, emissions, and operating costs are assessed. Strategic hydrogen hubs at London Heathrow and Neom Bay enable network feasibility for aircraft with limited range. Key findings show that the hydrogen scenario reduces total fuel mass consumption by approximately 28%, due to hydrogen’s high specific energy, and cuts CO2 emissions by 49%, assuming green hydrogen usage. However, it also results in a 9.6% increase in energy demand and ~15–20% higher cumulative operating costs, driven by greater depreciation, maintenance, and fuel price premiums. While the hydrogen transition introduces higher upfront and operational costs, it offers long-term environmental benefits and compliance with net-zero aviation goals. The study concludes that hydrogen aviation holds strategic promise but faces significant technical challenges, particularly due to the immaturity of hydrogen storage and propulsion systems. Realising this potential will require coordinated investment in infrastructure, policy support, and adaptive route planning.
Photocatalytic O2 reduction to hydrogen peroxide (H2O2) is a promising chemical synthesis pathway with green property. However, the development of efficient and stable photocatalysts that enable high selectivity and activity remains an urgent scientific challenge. Herein, cyano-based covalent organic framework (cyano-COF) photocatalysts modulated by noble metal sites (i.e., Pt, Pd, Au, and Ag), denoted as Pt/cyano-COF, Pd/cyano-COF, Au/cyano-COF, and Ag/cyano-COF, are designed and synthesized. The cyano-group (-C≡N), acting as a strong electron acceptor, interacts with the noble metal sites to establish an efficient electron transfer pathway, which facilitates the separation of photogenerated charges, optimizes the reaction pathway, and thus enables boosted generation of H2O2 via the two-step single electron oxygen reduction reaction (O2→·O2−→H2O2). Under visible irradiation, Pt/cyano-COF, Pd/cyano-COF, Au/cyano-COF, and Ag/cyano-COF deliver superior H2O2 production rates of 903 ± 24, 1073 ± 35, 963 ± 9, and 851 ± 56 μmol·g−1·h−1, respectively, much higher than that of pristine cyano-COF (577 ± 69 μmol·h−1·g−1). This study offers profound insights into the mechanism of noble metal sites in the solar-driven selective reduction of O2 to H2O2 synthesis.
As a key component of industrial machinery, accurate prediction of the degradation trend of rolling bearings is crucial for equipment safety. However, traditional health indicator (HI) extraction methods often suffer from feature redundancy, and prediction models lack the ability to capture spatial dimension features, leading to significant prediction errors. To address these issues, 16 time-frequency domain features were first extracted, and a new HI was constructed by combining the Gaussian Process latent variable model (GPLVM) for non-linear feature fusion and exponentially weighted moving average (EWMA) for smoothing. Additionally, a spatial-temporal convolutional long short-term memory network (ST-CNet) was proposed, which integrates a 3-layer CLSTM, fully connected layers, and batch normalization to effectively capture local and long-term spatiotemporal dependencies. Case studies on IMS bearing datasets show that the constructed HI accurately describes the degradation process, and ST-CNet achieves superior performance with lower MAE and RMSE compared to existing methods.
Confronting a global ecological crisis, this paper argues that conventional anthropocentric governance models, rooted in instrumental rationality, are inadequate. Drawing on Edoardo Ongaro’s concept of an integrative approach to an ontological and political philosophical understanding of public governance and administration, it proposes a relational framework for ecological governance by integrating the Rights of Nature (RoN) movement with classical Chinese philosophical traditions. The study emphasizes the complementary foundations offered by Daoism, specifically its concepts of ziran (natural spontaneity) and wuwei (non-coercive action) which support decentralized governance aligned with ecological self-organization, and Confucianism, particularly tian ren he yi (unity of heaven and humanity), which embeds ecological stewardship within moral self-cultivation (ren) and social duty (li). Comparative case studies highlight cultural complexities in implementing such relational governance. This paper outlines a tripartite pathway for building transformative capacities within this relational framework and discusses policy implications.
This study examines the challenges of green financing in India using Interpretive Structural Modeling (ISM) to identify hierarchical relationships among key factors. The research identifies regulatory deficiencies as the foundational barrier, cascading into secondary challenges such as data gaps, low investor awareness, high costs, and limited access to financial products. These issues, compounded by greenwashing, hinder transparency and the accurate measurement of environmental returns. The structural modeling approach provides a novel contribution by revealing how these interconnected challenges stem from weak regulatory frameworks—an insight not previously mapped in Indian green finance literature. The study underscores the importance of strong legal systems, standardized metrics, technological advancement, and policy harmonization to build investor trust and improve accessibility. For scalable and effective solutions, future research should explore the integration of emerging technologies and conduct cross-regional comparative analysis.
