Emissions trading is a cost-effective climate policy for reducing greenhouse gas emissions. It could also be useful for addressing road transport emissions, especially given that this sector is the largest CO₂ emitter in the transportation sector and its emissions continue to increase. However, emissions trading for road transport (ETS-RT) has rarely been implemented due to its complexity. This paper designs a novel and practical policy framework for an ETS-RT based on advanced blockchain technology, including all related entities upstream, midstream and downstream of the road transport sector. First, the government determines the cap and allocates the initial permits. Then, fuel producers, vehicle manufacturers, and vehicle users are involved as regulated entities with tradable emission permits. They are responsible for the three determinants of CO₂ emissions in the road transport sector: fuel emission factors, vehicle fuel economy, and vehicle miles travelled, respectively. With all the regulated entities collaborating on compliance, the three determinants can be synergistically optimized so that the efficiency of the emissions abatement can be maximized. In addition, all trading, monitoring, reporting, and verification of the emission permits are automatically executed and recorded via a smart contract deployed on a decentralized blockchain. This approach can dramatically reduce administrative costs, improve transparency and traceability, and eliminate double counting and fraud. Finally, the proposed policy was evaluated using a multicriteria analysis method compared with other possible ETS-RT approaches.

To enhance the application of building information modeling (BIM) in schedule management for engineering projects, improve spatial planning capabilities, and minimize subjective factors in schedule preparation, this paper presents a framework and method for automatic construction schedule planning based on BIM and data-driven approaches under multi-dimensional spatial resource constraints. The method includes data acquisition, construction process reasoning, time parameter calculation, and schedule generation and scheduling. Initially, the construction data from the BIM model and manual input are imported into the reasoning system to establish the logical relationships of the construction process. Construction time parameters and spatial resource requirements are then automatically calculated using MySQL. Subsequently, the spatial layout and resource allocation data exported from the BIM model are compared to assess whether the space meets the project requirements. Finally, a construction schedule that satisfies the space constraints is generated. The method was validated through a case study, demonstrating its effectiveness in automatic schedule planning under human-computer interaction. This study introduces an innovative approach that considers spatial conflicts in the automatic scheduling process, improving planning efficiency and reducing the risk of spatial conflicts on construction sites.

Under limited technological resources, optimising financing channel to R&D investment has important theoretical and practical implications. This paper analyzed the threshold effects of internal financing channels and external financing channels on the R&D investment using 692 manufacturing listed companies’ data from 2008 to 2015. And five threshold factors including the debt level, profit level, operational capacity, company’s size, and bank loan ratio were used to estimate the threshold effects of different financing channels. The results show that internal financing has a positive threshold effect on R&D investment when the debt level is lower than the threshold value; equity financing is negatively related to R&D investment; debt financing has a significant negative effect on the R&D investment under certain threshold levels; government subsidies promote the R&D investment extremely for the companies with certain size and scale effect. Besides, this paper proposed corresponding suggestions for different threshold values and financing channels.

The flood losses caused by storm surges are very severe in coastal cities, which highlights the importance of quantitative assessment of disaster risks. This study estimates the potential life and vehicle damage in the coastal city of Macau based on flood scenarios and storm surge probabilities. The hydrodynamic model considering surface flow and drainage system is applied to simulate floods caused by storm surges. Based on the flood results of the model and the vulnerability, a distribution map of mortality and car damage in the study area was obtained. The proportion and total loss of flood life and cars corresponding to different probabilities are calculated. We found that about 20% of the study area has a flood mortality exceeding 0.003 and cars are damaged when the exceedance probability of flood is below 0.005. When the exceedance probability of flood is less than 0.001, it may result in about 5 fatalities and car losses exceeding 13 million. This study provides a valuable reference for improving flood management and mitigation.