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A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO2 abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM. [ABSTRACT FROM AUTHOR]

An effective dead-time control strategy for the three-phase dual active bridge (3P-DAB) converter of a distribution system is studied to reduce the switching losses of power switches and improve the under-light-load power conversion efficiency. Because of the advantages of a dual-active bridge converter, such as an inherent zero-voltage switching (ZVS) capability without any additional resonant tank and a seamless bi-directional power transition, this is an attractive topology for bi-directional application. The 3P-DAB converter is apt for high-power applications such as aircraft due to an interleaved structure, which can reduce conduction losses. However, the design of the dead time depends on engineering experience and empirical methods. In order to overcome the conventional practicality of the dead-time design method, the effective control of dead time is proposed based on the theoretical analysis. In this paper, the overall explanation of the 3P-DAB converter is shown with operation principles. In addition, the dead-time effect of the 3P-DAB converter is examined and the practical variable dead-time control strategy is studied. Finally, experimental results validate the proposed variable dead-time control strategy using a 25 kW prototype 3P-DAB converter. [ABSTRACT FROM AUTHOR]

Shunt defects are often detected in solar panels intended for photovoltaic applications. However, existing nondestructive detection technologies have certain inherent drawbacks depending on the application scenario. In this context, this paper reports a comprehensive empirical investigation into lock-in thermography (LIT) and its applicability to diagnosing shunt defects in copper indium gallium selenide (CIGS) solar modules. LIT was compared with biased thermography, and its distinctive attributes were elucidated. The comparison results demonstrate the superior capabilities of LIT at enhancing the signal-to-noise ratio, improving the visibility, resolution, and quantification of defects, and highlighting the usefulness of LIT for advanced defect analysis. We explored scenarios in which biased thermography could be appropriate despite its inherent limitations and identified conditions under which it might be preferred. The complex thermal behavior of different types of defects under various voltage conditions was analyzed, contributing to a more nuanced understanding of their behavior. Thus, integrating experimental results and theoretical understanding, we provide valuable insights and scientific guidelines for photovoltaic research. Our findings could help enhance the efficiency of defect detection in CIGS modules, highlighting the critical role of optimized thermographic techniques in developing photovoltaic technologies. [ABSTRACT FROM AUTHOR]

Existing research has insufficiently explored the nexus between the new energy industry and CO2 emissions from the standpoint of export sophistication. This study analyses the implications of the new energy industry's export sophistication on CO2 emissions, regional heterogeneity, and its influencing mechanism by gathering data from 31 major economies throughout the world between 1996 and 2021. The study found that the new energy industry's export sophistication helps reduce carbon dioxide emissions, and this conclusion still holds after robustness testing; the carbon emission reduction effect of the export sophistication of the new energy industry is more significant in developed countries than in developing countries; the new energy industry's export sophistication possesses a crowding-out effect on domestic technological progress, which to a certain extent impedes carbon reduction effect. This paper's findings provide theoretical guidance for the global low-carbon energy transition. [ABSTRACT FROM AUTHOR]

The purpose of the article is to define the risk factors in rail transport and to show that the lack of sufficient identification of risks in individual phases affects the implementation of this type of transport. A literature study has been conducted to identify key risk factors and their impact on rail transport in Poland. For this purpose, a list of railway accidents in 2010–2020 in which people were injured, or there were significant losses in terms of the environment was presented. The realization of the objective focused on research proceedings covering the theoretical and cognitive sphere. This study included an analysis of the existing theoretical heritage in the area of risk management processes in rail transport, as well as a survey of the empirical research, which concentrated on the identification and assessment of key factors that influence the realization of rail transport in Poland. The work is of a utilitarian nature, as the need for conscious risk management has been demonstrated in rail transport, and guidelines for risk management in rail transport have been developed. In addition, the paper presents the possibilities offered by modeling a problem situation with the help of network thinking methodology for solving complex problems, including supporting creativity on the example of railroad transport in Poland. To define problem situations in the studied area, a complex process of a multifaceted approach to the analyzed issue is required. When assessing the effectiveness of risk management in rail transport, one should take into account the adverse factors influencing the studied phenomenon. The main sources used to write this article were the available literature in the field of emergency management and publications on railroad transport. The conclusions have been based on the results of logical analysis, verified empirically with the use of statistical methods. [ABSTRACT FROM AUTHOR]

In recent years, as people's awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers' perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of "Technology Maturity" (A13) which reached 25,329, and which belongs to the "Performance Risk" (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the "Social Risk" (PR5) dimension, the abstract "Social Needs" (A51) received only 3224 comments and "Preference and Trust Rank" (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension's emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the "Time Risk" (PR4), accounting for 54%. On a finer scale, the top three negative emotions are "Charging Time" (A42), "EV Charging Facilities" (A41), and "Maintenance of Value" (A33). Another interesting result is that "Social Needs" (A51)'s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers' perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs' consumption market in China. [ABSTRACT FROM AUTHOR]

This paper empirically examined relevant data on BRICS CO2 emissions, financial development, and economic growth in the past 40 years, and analyzed the correlation between them. Using the cointegration test, it found that there is a clear correlation between the variables in China and South Africa, which show that there is a two-way relationship between CO2 emissions, financial development, and economic growth in both countries. Using the quantile regression method in the analysis, the results demonstrated that at the 0.6th quartile, South Africa's financial development had a negative impact on CO2 emissions, while Brazil's CO2 emissions had a negative impact on financial development. Economic growth was subsequently added as a control variable, and the quantile-on-quantile regression method was used to test the correlation between the financial development of the BRICS countries and their CO2 emissions. Finally, based on empirical conclusions, this paper proposed that BRICS countries should focus on sustainable economic development; when government departments formulate emission-reduction policies, they must reasonably consider the relationship between financial development and emission-reduction policies. [ABSTRACT FROM AUTHOR]