Search results

In this paper, the authors take the central environmental protection inspection (CEPI) as an exogenous shock to study the reaction of the stock market in China. Using the event study method, the authors check how the first round of the first batch of CEPI supervision affects the cumulative abnormal return (CAR) of the listed firms on the Shenzhen or Shanghai stock exchange. This paper aims to discuss the aforementioned objective.

In this paper, the authors take the first round of the first batch of CEPI supervision as a clean exogenous shock to study its effects on the capital market. The authors collect daily trading data from the China stock market and accounting research (CSMAR) database, with the sample containing 1,950 Chinese firms listed on either the Shenzhen or Shanghai stock exchanges. And detailed information on CEPI supervision is obtained from the official website of the Ministry of Ecology and Environment of the People's Republic of China. The event study method is adopted to analyze the reaction of the stock market under CEPI supervision. Specifically, the authors constructed the cumulative abnormal return of each firm around the event day of CEPI. To capture the deterrent effects of CEPI supervision, the authors examine the situation of polluting and non-polluting firms in the supervised provinces, adjacent provinces and provinces that are not supervised or close to the supervised provinces, respectively.

This paper throws light on the following: (1) the polluting firms in the supervised provinces were negatively impacted by CEPI within 20 trading days of the event day, and its effects spread to the polluting firms in the neighboring provinces; (2) CEPI had a favorable impact on the non-polluting businesses in the provinces that are neither supervised nor close to the supervised provinces. The authors contend that it is because the investment is being forced out of the polluting sector and into the non-polluting sector, which is more pronounced in the provinces not directly or indirectly targeted by CEPI; (3) by comparison, the “looking back monitoring of the first round” has had no discernible detrimental impact on the firms' CAR, indicating an important role of psychology anticipation of investors in the stock market performance; (4) although not physically located in the supervised provinces, the downstream enterprises of the polluting firms suffer significantly from CEPI shock; (5) the effectiveness of CEPI supervision in the supervised provinces depends on the level of local environmental regulation and the ownership structure of the company. Private firms in the provinces with stronger environmental regulations suffer more from the CEPI shock; (6) the multivariate analysis shows that while enterprises with high ROE and financial leverage may be at risk of CAR loss, older, larger firms are less likely to experience CEPI shock; (7) the study of persistent effect reveals that the strike of CEPI supervision can last for at least 10 months after the event day and deterrent effect can be spread within the whole polluting industry.

In this paper, the authors only concentrate on the market reaction within 20 trading days after the event day. An analysis of long-term effects should be valuable to get a deeper knowledge of the capital market reaction to the CEPI policy. In addition, the paper only focuses on the first round of the first batch of CEPI. Since CEPI has been built as a constant regulation of local environmental performance, further study may need to track both the reaction of listed firms and investment behavior in the capital market.

Policy implications of the paper are as follows: First, for the policymakers, it is important to construct a constant environmental regulation system instead of a campaign movement. Second, for investors, as environmental issues are receiving increasing attention from both the government and the public, investment decisions should take into account firms' environmental performance, which can help reduce the risk from environmental regulations. Third, the firms in the polluting industry should take more action to reduce pollutant releases and adopt green technology, which is essential for sustainable development under environmental protection.

This paper contributes to the existing literature in the following aspects. First, the authors provide new evidence on the effects of environmental regulations as a shock to the stock market, which has been wildly concentrated in the literature about environmental policies evaluation and capital market reaction. Second, the authors supplement the literature on green finance and sustainability transformation, which has got increasing attention in recent years. Theoretically, by guiding investment and affecting the stock market performance, environmental regulations are considered to be an efficient way to stimulate polluting firms to transform into green development. The results of the paper support this intuition by showing that the CAR of the non-polluting firms in non-supervised provinces in fact benefit from the CEPI supervision.

This paper attempts to extend classic absolute degree of grey incidence so that the extended model can be used for grey number sequences.

The classic absolute degree of grey incidence was extended according to basic principles of grey incidence analysis. First, modelling methods and theories of the classic grey incidences were summarized. Then, the zeroing starting operator in grey incidence was extended for grey sequence. Third, the parameters in classic incidence degree were redefined, and an absolute degree of grey incidence for grey number sequences was proposed. The degree can not only be applied to grey number sequence, but also contains the uncertain information of analysis result. Fourth, two non‐linear programming models were constructed to estimate the grey coverage interval of absolute degree of incidences. Finally, the comparison method of grey numbers was introduced for sorting the different absolute degrees of incidences.

A theoretically feasible absolute degree of grey incidence was constructed for grey sequence. A case study showed that the proposed incidence degree was an effective method for grey sequence incidence analysis.

The method exposed in the paper can be used for grey sequences clustering, grey decision making, multi‐attribute decision making theory, uncertain target recognition and other related fields.

The paper succeeded in establishing an incidence analysis model for grey sequences which was still a research gap in grey system theory.

The purpose of this paper is to build a linear time-varying discrete Verhulst model (LTDVM), to realise the convert from continuous forms to discrete forms, and to eliminate traditional grey Verhulst model's error caused by difference equations directly jumping to differential equations.

The methodology of the paper is by the light of discrete thoughts and countdown to the original data sequence.

The research of this model manifests that LTDVM is unbiased on the “s” sequential simulation.

The example analysis shows that LTDVM embodies simulation and prediction with high precision.

This paper is to realise the convert from continuous forms to discrete forms, and to eliminate traditional grey Verhulst model's error caused by difference equations directly jumping to differential equations. Meanwhile, the research of this model manifests that LTDVM is unbiased on the “s” sequential simulation.

The purpose of this paper is to attempt to realize a complete analysis at scenario deduction of unconventional incidents coupling based on the GERTS network method.

Starting from the manifestation of coupling objects, three types of emergency coupling are analyzed according to different rules, which are “events‐events” coupling, “event‐factors” coupling and “factors‐factors” coupling. Then the coupling mechanism for emergency is focused on analyzing, and the concepts of three types of coupling are put forward, at the same time, three quantitative models for coupling mechanisms are present. Also, a case was discussed to verify the analysis of coupling mechanism of emergency.

According to the types of factors rules, the classes of coupling of emergency have been divided into three types. The coupling mechanism of emergency can be used to describe the novel coupling models.

This research provides the method for coupling analysis in the scenario of unconventional incidents and guides the emergency managers to develop contingency strategies.

The paper succeeds in constructing a novel coupling model for emergency, and it could provide an effective tool for a quantitative study on unconventional incidents coupling.

The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set.

To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model.

The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules.

The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers.

The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

The purpose of this paper is to construct a novel GM(0, N) model based on grey number sequences and to solve the problem of cost forecasting of commercial aircraft which puzzled managers, especially the factors of cost with uncertain information.

Based on the definition of traditional GM(0, N) model, the paper considers the limited information and knowledge, and the algorithm of grey numbers with greyness and kernel was designed. A novel GM(0, N) model based on grey number sequences, named the IN‐GM(0, N) model, is proposed according to the definition of the grey numbers algorithm. The steps of the proposed model are then given. Finally, a case of domestic commercial aircrafts is developed as an example, based on information gathering and model calculating.

The results of this research indicate that the IN‐GM(0,N) model is effective in cost calculating, providing reliable technical support for cost estimation of large‐scale complex equipment including commercial aircraft.

Cost forecasting of commercial aircraft can be solved effectively and the model can also be utilized to predicate other products.

The paper succeeds in constructing a novel grey forecasting model. This work contributes significantly to improving grey forecasting theory and to undoubtedly propose more novel grey forecasting models.

This paper attempts to put forward a convincing and flexible grey cluster method that can be used in confirming credibility level of trustworthy software development process.

Determination on credibility level in the software development process is dynamic, as credibility of the results may be different at different times and under different project requirements. Qualitative methods are not entirely convincing, and most quantitative methods are not flexible enough. Grey cluster based on nonlinear grey whitening weight function put forward in this paper is both convincing and flexible. Finally eight projects from the ISBSG database are used for empirical analysis, which confirm that the method put forward in this paper is available and credible.

The results are convincing: not only that grey cluster based on nonlinear grey whitening weight function put forward in this paper is both convincing and flexible, but it can be used in confirming credibility level of trustworthy software development process.

Eight projects from the ISBSG database are used for empirical analysis, which confirms that the method put forward in this paper is available and credible.

Nonlinear grey whitening weight function is derivable except endpoint. Grey cluster based on nonlinear grey whitening weight function put forward in this paper is both convincing and flexible.

Since the present university leading cadre capability evaluations are mainly static, there is a lack of evaluation methods to address dynamic and multi‐scale‐based situations. Some important factors to address people's performances, such as the developing trends and potentialities are considered. The purpose of this paper is to attempt to propose a dynamic evaluation model based on multivariate delay connection number.

First, evaluation results of previous periods are taken into the current period and values of uncertain numbers are revised. Next, a dynamic evaluation model is built for university leading cadre. Last, a numerical example is used to prove its feasibility and effectiveness.

This paper promotes a corrected equal portion value method of uncertain numbers i1(τ), … ,in(τ) from the adjacent two periods to multi periods, and builds a dynamic evaluation model, which proved simple‐structured and easy to apply.

The new model is applied in the evaluation of university leading cadre, and could give a more comprehensive, dynamic, scientific analysis for evaluation by model application.

The paper develops a novel evaluation model based on a multivariate delay connection number, which expands traditional methods from a static situation to a dynamic and multi‐scale‐based situation.

The purpose of this paper is to reveal the pattern of passengers' transferring on occasion of a large crowd being stranded at transportation hubs (such as a bus station, railway station, airport, etc.) in climate disasters, and then propose the proper policy recommendations for the government to evacuate stranded passengers.

A model is established based on Bayesian network and influence diagram to catch the features of a passenger's decision‐making process, and the transition probabilities of passengers are revised on the basis of the theory of herd behaviors in information to describe the influence of group behaviors on passenger individuals. Subsequently, a multi‐agent model is developed in Repast platform in Java language, and simulation and analysis are also made.

The results of simulation show that it is possible to apply the theory of herd behaviors and the multi‐agent method in analyzing the effectiveness of government policies on evacuating stranded passengers in climate disasters.

The research of this paper has important practical significance for the government to developing policies to evacuating stranded passengers in climate disasters, and is a useful exploration to open up new methodologies for emergency management.

The purpose of this paper is to evaluate resource configuration and service ability in hospital on public private partnership (PPP) model (Chaonan Minsheng Hospital of Guangdong Province), supplying decision-making reference for participants of hospital on PPP model.

Four model of grey relational analysis (GRA) (Deng's correlation degree, grey absolute correlation degree, grey relative correlation degree and grey comprehensive correlation degree) are applied to evaluate resource configuration and service ability, a total of 11 indicators of hospital on PPP model public hospital and private hospital from 2007 to 2011.

The paper finds that different GRA models have different results when the paper applied them to evaluate resource configuration and service ability in hospital on PPP model. More than 60 per cent indicators of resource configuration (total six indicators) and service ability (total six indicators) are assessed as “hospital on PPP model ≻ public hospital” or “hospital on PPP model≻ private hospital” from three models of Deng's correlation degree, grey absolute correlation degree and grey comprehensive correlation degree.

Evaluation of resource configuration and service ability for hospital on PPP model with GRA makes results quantified objective and provides reference for decision making and management. GRA makes the comparison of resource configuration and service ability between hospital on PPP model and other model hospitals becoming possible.

The shortcoming for data analysis method of “large sample” is overcome and data analysis method of “small sample” is realized by using GRA, which broaden the method of evaluating hospital on PPP model.