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This article analyzes the implementation of anti-corruption reforms in Taiwan by two former presidents, Chen Shui-bian (2000-2008) and Ma Ying-jeou (2008-2016).

A three-tier research approach, including in-depth interviews, questionnaire survey, and descriptive and inferential analysis, is adopted. In addition to Chen and Ma, 11 senior anti-corruption officials who served under both presidents were interviewed by the author. The survey questions cover 12 factors which are later grouped into the five dimensions of ethical leadership, considerate leadership, delegating leadership, participatory leadership, and performance to illustrate the causal relationship between these dimensions and performance.

Ma’s personal ethics are distinct from Chen’s in kind but Ma’s overall leadership and performance outshine Chen’s in degree rather than in kind. While the gap of ethical leadership between two Taiwan presidents is significantly wider than other dimensions of leadership, the outcome of the national integrity is not proportionate to the ethical leadership gap. In other words, personal ethics are not automatically transformed into political will for enhancing anti-corruption effectiveness. Three popular forms of corruption, red envelopes (bribing), influence-peddling, and vote-buying are analyzed in the context of changing political culture through national leadership. Unfortunately, both Chen and Ma produce insignificant contributions based on public surveys.

Policy-makers and scholars can use these research findings to further explore how ethical leadership can enhance a country’s anti-corruption performance.

This study aims to propose an enhanced eco-driving strategy based on reinforcement learning (RL) to alleviate the mileage anxiety of electric vehicles (EVs) in the connected environment.

In this paper, an enhanced eco-driving control strategy based on an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed for connected EVs. The EEDC-HRL simultaneously controls longitudinal velocity and lateral lane-changing maneuvers to achieve more potential eco-driving. Moreover, this study redesigns an all-purpose and efficient-training reward function with the aim to achieve energy-saving on the premise of ensuring other driving performance.

To illustrate the performance for the EEDC-HRL, the controlled EV was trained and tested in various traffic flow states. The experimental results demonstrate that the proposed technique can effectively improve energy efficiency, without sacrificing travel efficiency, comfort, safety and lane-changing performance in different traffic flow states.

In light of the aforementioned discussion, the contributions of this paper are two-fold. An enhanced eco-driving strategy based an advanced RL algorithm in hybrid action space (EEDC-HRL) is proposed to jointly optimize longitudinal velocity and lateral lane-changing for connected EVs. A full-scale reward function consisting of multiple sub-rewards with a safety control constraint is redesigned to achieve eco-driving while ensuring other driving performance.

This paper examines the relationship between transport connectivity and regional economic development in China. It develops measurements appropriate for transport connectivity based on a set of evaluation models. This model is used to analyze the logistic connectivity of China’s 31 provinces by focusing on 11 variables, including some new factors (Density of road network, Density of railway network, Number of Internet Users) not used in previous studies, over the 13-year period from 2002 to 2014. Using panel data regression analysis, the empirical results show a statistically significant and positive impact of transport connectivity (factors like Density of road network, Density of railway network and Number of Internet Users) on economic development in China. In particular, the Number of internet users is a key factor reflecting information connectivity in all the variables. Comparative analysis regarding economic development is conducted to benchmark between coastal provinces and interior provinces. Like most previous research, this study yields the same finding of higher impact of transport connectivity on economic development in eastern provinces than in western provinces. This study suggests that decentralized decision-making will be significantly more efficient for analyzing regional infrastructure development. It also shows that the influence of transport connectivity on economic development is dependent on a certain developmental stage. This suggests that an economic region should adopt different development strategies for transport connectivity during different stages of development.

The development of China's foreign exchange market and the reform of Chinese yuan (hereinafter “CNY”) exchange rate are closely linked with each other. Their respective journey through the past 70 years can both be divided into three historical periods; as follows: China's foreign exchange market underwent a difficult exploration period, a formation and development period and an innovative development period; in the meanwhile, the formation mechanism of CNY exchange rate also witnessed three periods marked successively by a single exchange rate system with administrative pricing, an explorative formation mechanism of CNY exchange rate and a reformed, marketized CNY exchange rate mechanism.

In the present world, the development of almost every country is closely linked to the international community, which is the result of the heterogeneity in system, market, humanity and history, in addition to the differences in natural resource endowments and the diversity in technology, administration, information, experience and diplomacy. International economic exchanges require foreign exchange, which gives rise to the existence and development of the foreign exchange market.

The 70-year history of China's foreign exchange market has proven the need to continue safeguarding national sovereignty and interests of the people, stick to the general direction of serving economic development, adhere to the strategy of steadily and orderly promoting the construction of the foreign exchange market, keep on making innovation in monetary policy operation and unbendingly stay away from any systemic financial risks.

During the 70-year history of the new China, as an indispensable economic resource in China's economic development, the foreign exchange mechanism bolstered each stage of economic development and was always an important manifestation of China's economic sovereignty. It is argued that during the 30-year planned economy that preceded reform and opening-up, China pursued a closed-door policy with few international economic exchanges. The subtext of such argument is that China did not have (or hardly had much of) a foreign exchange mechanism during this period, which is clearly in conflict with historical evidence. In fact, although China did not have an open foreign exchange market before the reform and opening-up, it had a clear foreign exchange management system and exchange rate system.

In an era marked by heightened geopolitical uncertainties, such as international conflicts and economic instability, the dynamics of energy markets assume paramount importance. Our study delves into this complex backdrop, focusing on the intricate interplay the between traditional and emerging energy sectors.

This study analyzes the interconnections among green financial assets, renewable energy markets, the geopolitical risk index and cryptocurrency carbon emissions from December 19, 2017 to February 15, 2023. We investigate these relationships using a novel time-frequency connectedness approach and machine learning methodology.

Our findings reveal that green energy stocks, except the PBW, exhibit the highest net transmission of volatility, followed by COAL. In contrast, CARBON emerges as the primary net recipient of volatility, followed by fuel energy assets. The frequency decomposition results also indicate that the long-term components serve as the primary source of directional volatility spillover, suggesting that volatility transmission among green stocks and energy assets tends to occur over a more extended period. The SHapley additive exPlanations (SHAP) results show that the green and fuel energy markets are negatively connected with geopolitical risks (GPRs). The results obtained through the SHAP analysis confirm the novel time-varying parameter vector autoregressive (TVP-VAR) frequency connectedness findings. The CARBON and PBW markets consistently experience spillover shocks from other markets in short and long-term horizons. The role of crude oil as a receiver or transmitter of shocks varies over time.

Green financial assets and clean energy play significant roles in the financial markets and reduce geopolitical risk. Our study employs a time-frequency connectedness approach to assess the interconnections among four markets' families: fuel, renewable energy, green stocks and carbon markets. We utilize the novel TVP-VAR approach, which allows for flexibility and enables us to measure net pairwise connectedness in both short and long-term horizons.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.