Road transport systems are central to sustainable mobility and the energy transition because they account for a large share of final energy use and remain heavily dependent on fossil fuels. With more than 90% of transport energy still supplied by petroleum-based fuels, improving energy efficiency and reducing emissions in road networks has become a strategic priority. This review compares Australia, Hong Kong, and the United Kingdom to examine how road-design standards and emerging digital technologies can improve energy performance across planning, design, operations, and maintenance. Using Australia’s Austroads Guide to Road Design, Hong Kong’s Transport Planning and Design Manual (TPDM), and the UK’s Design Manual for Roads and Bridges (DMRB) as core reference frameworks, we apply a rubric-based document analysis that codes provisions by mechanism type (direct, indirect, or emergent), life-cycle stage, and energy relevance. The findings show that energy-relevant outcomes are embedded through different pathways: TPDM most strongly supports urban operational efficiency via coordinated/adaptive signal control and public-transport prioritization; DMRB emphasizes strategic-network flow stability and whole-life carbon governance through managed motorway operations and life-cycle assessment requirements; and Austroads provides context-sensitive, performance-based guidance that supports smoother operations and active travel, with implementation varying by jurisdiction. Building on these results, the paper proposes an AI-enabled benchmarking overlay that links manual provisions to comparable energy and carbon indicators to support cross-jurisdictional learning, investment prioritization, and future manual revisions toward safer, more efficient, and low-carbon road transport systems.

This study examines the relationship between public electric-vehicle charging-infrastructure deployment and the credit risk of rural commercial banks in China. Using an unbalanced panel dataset of 180 RCBs across 10 provinces from 2016 to 2022, this paper employs a two-way fixed-effects model and uses the provincial number of public charging piles as a proxy for the deployment scale of public charging infrastructure. The results show that the rapid expansion of public charging infrastructure is statistically associated with higher non-performing loan ratios among local RCBs and this association remains under alternative standard-error specifications and robustness tests. The theoretical analysis suggests that this association may be related to policy uncertainty, technological upgrading pressure, utilization uncertainty, maturity mismatch, and market competition in the new energy vehicle charging sector. Heterogeneity results indicate that the positive association is weaker in economically stronger regions, while higher provision coverage is associated with a stronger positive relationship, possibly reflecting risk-taking incentives, historical asset-quality differences, or delayed non-performing asset disposal. By connecting sustainable transport infrastructure, green finance, infrastructure governance, and local banking risk, this study contributes to debates on how low-carbon mobility transitions can be financed without weakening regional financial stability. The findings highlight the need for coordinated transport–energy planning, clearer regulatory standards, regionally differentiated risk-sharing mechanisms, and stronger financial governance frameworks for public charging infrastructure. Because the analysis relies on provincial-level infrastructure data rather than project-bank matched loan-level data, the documented findings should be interpreted as statistical associations rather than definitive causal relationships.

Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees.