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This paper investigates the relationship between climate finance and climate ergodicity. More specifically the paper examines how climate ergodicity as measured by a mean-reverting Ornstein–Uhlenbeck process affects the value of climate-linked bonds.

Bond valuation is evaluated using Monte Carlo methods of the Ornstein–Uhlenbeck process. The paper describes climate risk in terms of the Hurst coefficient and derives a direct linkage between the Ornstein–Uhlenbeck process and the Hurst measure.

We use the Ornstein–Uhlenbeck mean reversion relationship in its OLS form to estimate Hurst coefficients for 5 × 5° grids across the US for monthly temperature and precipitation. We find that the ergodic property holds with Hurst coefficients between 0.025 and 0.01 which implies increases in climate standard deviation in the range of 25%–50%.

The approach provides a means to stress-test the bond prices to uncover the probability distribution about the issue value of bonds. The methods can be used to price or stress-test bonds issued by firms in climate sensitive industries. This will be of particular interest to the Farm Credit System and the Farm Credit Funding Corporation with agricultural loan portfolios subject to spatial climate risks.

This paper examines bond issues under conditions of rising climate risks using Hurst coefficients derived from an Ornstein–Uhlenbeck process.

Testing several approaches for implied volatility modeling and forecasting.

Comparative empirical study with four traded options.

Non-parametric higher-order spline is better than parametric stochastic volatility inspired (SVI) in China.

Our results imply that even though popular on Wall Street, SVI seems not to be utilized by traders and market-makers in China.

Traders may consider higher-order spline as a better method for implied volatility modeling and forecasting.

Propose to model and forecast implied volatility via the fifth-order spline interpolation as a first; initiates studies of the empirical performance of SVI and the fifth-order spline models in implied volatility modeling and forecasting.

Existing research suggests a positive relationship between firms’ research and development investments (RDIs) and their patenting performance (PP) according to assumptions of linear productivity and homogeneous behavior. This study unravels the RDI–PP relationship by taking a strategic view to reveal its underlying mechanisms.

We study the effects of firms’ RDI on PP in the context of China’s listed firms in 16 patent-intensive industries, including the pharmaceutical, computer communication, electronic equipment and electrical machinery and equipment manufacturing industries. To test our hypotheses, we use panel data from 2010 to 2017. We apply generalized estimating equations to estimate our models.

The study finds an inverted U-shaped relationship between RDI and PP that arises from the transition of innovation portfolios and the strategic balancing of patenting costs and benefits. The study further examines two contingencies: (1) top management team (TMT) education level and (2) TMT compensation. It shows the turning point of the inverted U-shape shifts to the right when TMT education level is high; the curve flattens when TMT education level and TMT compensation are high.

We contribute to literature on innovation and appropriability strategy in three ways: First, we reveal the underlying mechanisms of the inverted U-shaped relationship between RDI and PP. Second, because previous research on appropriability strategies pays little attention to how innovation portfolios influence patenting decisions at the firm level, we provide evidence and insights on how the tension between exploitative and explorative innovations affects appropriability strategies. Third, we connect appropriability strategy literature with two streams of literature: corporate governance and upper-echelon theory.

The purpose of this paper is to characterize and understand the effects of polymer binder, thixotropic agent, solvent and organic medium content on the rheological properties of silver pastes for screen printing front electrode films of solar cells.

Dispersions of silver particles (surface modified with oleic acid) in ethyl cellulose (EC) polymer solutions with and without thixotropic agent were prepared, and yield stress values were measured by setting shear stress to characterize the inter-particle interaction strength of pastes. Steady-state flow, three interval thixotropy shear test and oscillatory measurements were conducted to study the effect of EC polymer and thixotropic agent on viscosity, structure rebuilding and viscoelastic properties of electrode pastes. The effect of solvent was studied by investigating the steady viscosity of cellulose acetate butyrate (CAB) polymer solutions and Ag dispersions.

Weak flocculation network of silver particles was produced because of depletion flocculation. Besides the interaction between thixotropic agent micelles, EC polymer also has a significant interaction with thixotropic agent. Merely increasing EC polymer or thixotropic agent content is not the best way to prevent the layer printed from laying down. The effect of solvent on the viscosity of paste is mainly attributed to the difference of hydromechanics radius and configuration of CAB polymer in solvents. With the increase of organic medium content, the properties of electrode pastes were converted from rigidity to flexibility.

It is still a challenge to obtain high-quality front electrode films for crystalline silicon solar cells by screen printing, because of the difficulty in reducing shadowing losses while ensuring a low series resistance and high filling factor. The paste rheological properties are the key properties related to the paste’s passing ability through the meshes and resistance of paste spreading on the substrate. Organic medium as an important component of the paste is acknowledged to be used to tailor the paste’s rheological properties and have a great role in screen printing.

We are the first to investigate the relationship between seasoned equity offerings (SEOs) and anchoring on historical high prices in China.

We use the ratio of the recent closing price to its historical high in the previous 12–60 months (anchoring-high-price ratio) to study its impact on the market timing of SEOs.

Empirical results show that the anchoring-high-price ratio significantly and positively affects the probability of additional stock issuances. Contrary to the USA market, the Chinese stock market reacts negatively to the SEOs at historical highs. Moreover, the anchoring-high-price ratio exacerbates the negative effect of announcements and leads to long-term underperformance. Finally, we investigate the impact of the anchoring-high-price ratio on a company’s capital structure, showing that the additional issuance anchoring on historical highs reduces the company’s leverage ratio in the long run. Overall, our findings support the anchoring theory and can help understand better the anchoring behavior of managers and the company’s decision on additional stock issuances.

We are the first to use the anchoring-high-price ratio to study the timing of SEOs. We find that the anchoring-high-price ratio positively affects the probability of SEOs. Unlike the USA, the Chinese stock market reacts negatively to SEOs at high prices. SEOs anchoring on historical highs reduce a firm’s leverage ratio in the long run. Finally, our results support the anchoring theory.

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

The intelligent Central Traffic Control (CTC) system plays a vital role in establishing an intelligent high-speed railway (HSR) system. As the core of HSR transportation command, the intelligent CTC system is a new HSR dispatching command system that integrates the widely used CTC in China with the practical service requirements of intelligent dispatching. This paper aims to propose key technologies and applications for intelligent dispatching command in HSR in China.

This paper first briefly introduces the functions and configuration of the intelligent CTC system. Some new servers, terminals and interfaces are introduced, which are plan adjustment server/terminal, interface for automatic train operation (ATO), interface for Dynamic Monitoring System of Train Control Equipment (DMS), interface for Power Supervisory Control and Data Acquisition (PSCADA), interface for Disaster Monitoring, etc.

The key technologies applied in the intelligent CTC system include automatic adjustment of train operation plans, safety control of train routes and commands, traffic information data platform, integrated simulation of traffic dispatching and ATO function. These technologies have been applied in the Beijing-Zhangjiakou HSR, which commenced operations at the end of 2019. Implementing these key intelligent functions has improved the train dispatching command capacity, ensured the safe operation of intelligent HSR, reduced the labor intensity of dispatching operators and enhanced the intelligence level of China's dispatching system.

This paper provides further challenges and research directions for the intelligent dispatching command of HSR. To achieve the objectives, new measures need to be conducted, including the development of advanced technologies for intelligent dispatching command, coping with new requirements with the development of China's railway signaling system, the integration of traffic dispatching and train control and the application of AI and data-driven modeling and methods.

This paper aims to propose a train timetable rescheduling (TTR) approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.

Firstly, a single-train trajectory optimization (STTO) model is constructed based on train dynamics and operating conditions. The train kinematics parameters, including acceleration, speed and time at each position, are calculated to predict the arrival times in the train timetable. A STTO algorithm is developed to optimize a single-train time-efficient driving strategy. Then, a TTR approach based on multi-train tracking optimization (TTR-MTTO) is proposed with mutual information. The constraints of temporary speed restriction (TSR) and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train. The multi-train trajectories at each position are optimized to generate a time-efficient train timetable.

The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF. The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay (TTD). As for the TSR scenario, the proposed TTR-MTTO can reduce TTD by 60.60% compared with the traditional TTR approach with dispatchers’ experience. Moreover, TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.

With the cooperative relationship and mutual information between train rescheduling and control, the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

Climate change has been universally recognized as a major threat to human well-being, necessitating a comprehensive transformation of people's activities. Various measures have been proposed to contain climate change among which the green transformation grabs special attention, thanks to its desirable properties. Within the green transformation process, green tourism comes to prominence with huge potential. As one of the largest carbon emitters, the transition towards green tourism may offer substantial benefits not only for tourism companies but also for the whole economy. Yet, most studies tend to focus on the adverse effects of tourism on climate change while overlooking the potential impact of climate change on tourism. This chapter clarifies the feedback relationship between climate change and tourism and makes some recommendations.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.