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To introduce the Securities Law of the People's Republic of China (the “Securities Law 2019”) revised on 28 December 2019, and provide a detailed analysis on its key implications to the securities regulatory regime and market activities, especially securities issuance and trading activities in China.

This article starts from a historical overview of the Securities Law and its several revisions and amendments, highlights the notable core revisions in the Securities Law 2019, analyzes the key legal and regulatory impacts to the securities-related activities and market players, and finally, provides an outlook to the future developments of securities regulatory regime in conformity with the Securities Law 2019.

This article concludes that the revisions made to the Securities Law 2019 cover a broad range of issues including the issuance and trading of securities, acquisition of a listed company, information disclosure, securities registration and settlement, etc. Such revisions to the Securities Law will lead to far-reaching and profound implications on the securities regulatory system and industry practice in China.

The Securities Law 2019 attracts broad attention from securities market players as well as relevant professionals of the industry, including securities lawyers. As this is a novel and hot topic within the industry, it is important for securities lawyers to keep on track.

High-level guidance from experienced lawyers in the Capital Markets and Financial Regulation practices.

In confronting the challenge of climate change and progressing towards dual carbon goals, China is actively implementing low-carbon city pilot policy. This paper aims to focus on the potential impact of this policy on enterprise green governance, aiming to promote the reduction and balance of carbon emissions.

Based on the panel data of China's large-scale industrial enterprises from 2007 to 2013, this paper uses the Difference-in-differences (DID) method to study the impact and path mechanism of the implementation of low-carbon city pilot policy on enterprise green governance. Heterogeneity analysis is used to compare the effects of low-carbon city pilot policy in different regions, different enterprises and different industries.

The low-carbon pilot can indeed effectively enhance corporate green governance, a conclusion that still holds after a series of robustness tests. The low-carbon city pilot policy mainly enhances enterprise green governance through two paths: an industrial structure upgrade and enterprise energy consumption, and it improves green governance by reducing enterprise energy consumption through industrial structure upgrade. The impact of low-carbon city pilot policy on enterprise green governance shows significant differences across different regions, different enterprises and different industries.

This paper examines the impact of low-carbon city pilot policy on enterprise green governance. However, due to availability of data, there are still some limitations to be further tackled. The parallel trend test in this paper shows that the pilot policy has a significant positive effect on the green governance of enterprises. However, due to serious lack of data in some years, the authors only selected the enterprise data of a shorter period as our experimental data, which leads the results to still have certain deficiencies. For the verification of the impact mechanism, the conclusions obtained in this paper are relatively limited. Although all the mechanism tests are passed, the reliability of the results still needs to be further tested through future data samples. In addition, as the pilot policy of low-carbon cities is still in progress, the policy can be tracked and analysed in the future as more data are disclosed, and further research can be carried out through dimensional expansion.

Low-carbon city pilot policy plays an important role in inducing the green governance of enterprises. Therefore, policy makers can continue to strengthen the construction of low-carbon city pilots by refining pilot experience, building typical cases, actively promoting pilot policy experience, expanding pilot scope and enhancing the implementation efficiency of pilot policy nationwide, which will contribute to the optimization and upgrading of the regional industrial structure at the urban level and will provide experience and reference for the synergistic implementation plan of pollution reduction and carbon reduction.

The impact of the low-carbon city pilot policy on enterprise green governance not only exists in two separate paths of urban industrial upgrading and enterprise energy consumption but also exists in a chain transmission path from macro to micro. The authors find that the effect value of each influence path is different, and there is an obvious leading influence path for the role of enterprise green governance. Therefore, in the process of implementing a low-carbon city pilot policy, policies should be designed specifically for different mechanisms. Moreover, complementing and coordinating several paths should be advocated to give full play to the green governance effect of enterprises brought by different paths and to further expand the scope of industries and enterprises where policies play a role.

To the best of the authors’ knowledge, for the first time, this paper connects macro mechanisms with micro mechanisms, discovering a macro-to-micro transmission mechanism in the process of low-carbon city pilot policy affecting enterprise green governance. That is, the low-carbon city pilot policy can facilitate industrial structure upgrading, resulting in reduced enterprise energy consumption, ultimately enhancing enterprise green governance.

This study aims to contrastively investigate the effects of biodiesel and diesel on the power, economy and combustion characteristics of a compression ignition aviation piston engine for unmanned aerial vehicles.

Biodiesel used as alternative fuel will not be mixed with diesel during experimental study. Pure diesel fuel is used for the comparative test. Same fuel injection strategies, including pilot and main injection, are guaranteed for two fuels in same test points.

The engine-rated power of biodiesel is lower than diesel, which results in higher specific fuel combustion (SFC) and effective thermal efficiency (ETE). Biodiesel has the faster burning rate, shorter combustion duration. The crank angle of 50% mass fraction burned (CA50) is earlier than diesel. The ignition delay angle of biodiesel and diesel in the pilot injection stage is almost the same at high engine speed. As the speed and load decrease, the ignition delay angle of biodiesel in the pilot injection stage is smaller than diesel. At 100% high load conditions, the fuel-burning fraction of biodiesel in the pilot injection is the same as diesel. The peak heat release rate (HRR) of biodiesel is slightly lower than diesel. At 20% part load conditions, the fuel-burning fraction of biodiesel in the pilot injection stage is lower than diesel. Because of the combustion participation of unburned pilot injected fuel, the peak HRR of biodiesel in the main injection is equal to or even higher than diesel.

The application feasibility of alternative fuel and its effects on aviation engine power, economy and combustion characteristics will be evaluated according to the “drop-in“ requirements and on the low-cost premise without changing the aviation engine structure and parameters.

In this article we compare the performance of the traditional CAPM with the multi factor model of Fama and French (1996) for equities listed in the Shanghai Stock Exchange. We also investigate the explanatory power of idiosyncratic volatility and respond to the claim that multi factor model findings can be explained by the turn of the year effect. Our results show that firm size, book to market equity and idiosyncratic volatility are priced risk factors in addition to the theoretically well specified market factor. As far as the turn of the year effect is concerned we reject the claim that the findings are driven by seasonal factors.

In most portfolio performance studies, a reference portfolio is used to assess the performance of a portfolio manager. The choice of an appropriate reference portfolio is essential to yield a fair and unbiased evaluation of the manager. In the following analyses, category‐based benchmarks are assessed against established benchmarks to evaluate, which alternative accurately evaluates a portfolio manager's performance. The results indicate that the category‐based benchmarks are more appropriate comparison reference for evaluating the systematic risk of equity portfolios and equity security returns.

The purpose of this paper is to observe listed firms in China during the 2008 financial crisis and investigates how group affiliation affects firm value when the economy turns down. The paper focusses the study on answering the following questions: during the crisis, do affiliated firms have higher or lower stock returns than independent firms? Does corporate governance relate to the value of group firms? How does group affiliation influence firm value? Does performance of affiliated entrepreneurial firms differ from affiliated state-owned enterprises (SOEs)?

The paper uses non-parametric tests and regression analysis on a sample of 1,469 Chinese listed companies to investigate the research questions.

Affiliated firms have lower stock returns than independent firms by 1.91 percent during September to December of 2008. This poor performance is even worse for firms seriously shocked by the crisis. Good corporate governance can mitigate the negative effects of group affiliation on firm value. The lower valuation of affiliated firms lies in the fact that controlling shareholders undertake more related party transactions at the expense of minority shareholders. Finally, although business groups can provide internal financing for entrepreneurial firms in China, affiliated entrepreneurial firms experience a larger value decrease than affiliated SOEs due to the conflict interest between controlling and minority shareholders.

This research provides unique evidence about the performance of group-affiliated firms during the 2008 financial crisis and documents the mechanisms through which group affiliation influences firm value.

This study aims to explore and compare the approach and avoidance factors in motivating students to study using three different learning methods: face-to-face learning, online learning and hybrid learning.

This research uses in-depth online interviews to gain insights from students. Purposive sampling was applied to recruit 33 informants from two private universities in Indonesia. The verbatim data were analyzed using a thematic content analysis to identify motivational factors.

This study revealed four motivational factors regarding the approach to face-to-face learning/avoidance of online learning and five motivational factors regarding the approach to online learning/avoidance of face-to-face learning. Most of the motivational factors (i.e. learning effectiveness, social interaction, campus life experiences, physical wellness, flexibility and technological learning) are also found in the approach to hybrid learning.

The existing qualitative research suffers from generalizability, as does this study. Future research can investigate other contexts or use quantitative research to validate the findings.

By identifying the approach and avoidance motivational factors, higher education institutions can enhance the approach (positive) factors and minimize or eliminate the avoidance (negative) factors that motivate their students to study using different learning methods.

This research complements the existing literature using new perspectives, namely, the approach and avoidance factors that motivate students to study through face-to-face learning, online learning and hybrid learning post-COVID-19.

Dynamic movement primitives (DMPs) is a general robotic skill learning from demonstration method, but it is usually used for single robotic manipulation. For cloud-based robotic skill learning, the authors consider trajectories/skills changed by the environment, rebuild the DMPs model and propose a new DMPs-based skill learning framework removing the influence of the changing environment.

The authors proposed methods for two obstacle avoidance scenes: point obstacle and non-point obstacle. For the case with point obstacles, an accelerating term is added to the original DMPs function. The unknown parameters in this term are estimated by interactive identification and fitting step of the forcing function. Then a pure skill despising the influence of obstacles is achieved. Using identified parameters, the skill can be applied to new tasks with obstacles. For the non-point obstacle case, a space matching method is proposed by building a matching function from the universal space without obstacle to the space condensed by obstacles. Then the original trajectory will change along with transformation of the space to get a general trajectory for the new environment.

The proposed two methods are certified by two experiments, one of which is taken based on Omni joystick to record operator’s manipulation motions. Results show that the learned skills allow robots to execute tasks such as autonomous assembling in a new environment.

This is a new innovation for DMPs-based cloud robotic skill learning from multi-scene tasks and generalizing new skills following the changes of the environment.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.