Research on Storm-Tide Disaster Losses in China Using a New Grey Relational Analysis Model with the Dispersion of Panel Data

A novel grey spatial extension relational model and its application to identify the drivers for ambient air quality in Shandong Province, China

The ambient air quality is a complex dynamical system that is shocked by a number of subsystems, such as government policies, industry regulation adjustment and internationalization. To identify the drivers for ambient air quality, a grey spatial extension relational analysis model is proposed. Firstly, a spatial extension method for one-dimensional time series of complex systems is introduced, and the two key parameters are obtained based on the grey similarity and proximity relational analysis models. Secondly, grey relational coefficient is calculated by the difference of the three-dimensional vector, and a grey spatial extension relational analysis model is presented. Furthermore, the properties of the proposed model were investigated. Finally, the model is used to identify the drivers of the ambient air quality in eastern coastal Shandong Province, China. Results suggest that the drivers of the ambient air quality vary among cities, but with some common ones. Therefore, this paper provides an important reference for the improvement of ambient air quality.

A Method for Prediction of Waterlogging Economic Losses in a Subway Station Project

In order to effectively solve the problems of low prediction accuracy and calculation efficiency of existing methods for estimating economic loss in a subway station engineering project due to rainstorm flooding, a new intelligent prediction model is developed using the sparrow search algorithm (SSA), the least-squares support vector machine (LSSVM) and the mean impact value (MIV) method. First, in this study, 11 input variables are determined from the disaster loss rate and asset value, and a complete method is provided for acquiring and processing data of all variables. Then, the SSA method, with strong optimization ability, fast convergence and few parameters, is used to optimize the kernel function and the penalty factor parameters of the LSSVM. Finally, the MIV is used to identify the important input variables, so as to reduce the predicted input variables and achieve higher calculation accuracy. In addition, 45 station projects in China were selected for empirical analysis. The empirical results revealed that the linear correlation between the 11 input variables and output variables was weak, which demonstrated the necessity of adopting nonlinear analysis methods such as the LSSVM. Compared with other forecasting methods, such as the multiple regression analysis, the backpropagation neural network (BPNN), the BPNN optimized by the particle swarm optimization, the BPNN optimized by the SSA, the LSSVM, the LSSVM optimized by the genetic algorithm, the PSO-LSSVM and the LSSVM optimized by the Grey Wolf Optimizer, the model proposed in this paper had higher accuracy and stability and was effectively used for forecasting economic loss in subway station engineering projects due to rainstorms.

Direct and Indirect Loss Evaluation of Storm Surge Disaster Based on Static and Dynamic Input-Output Models

Storm surge disaster is one of the biggest threats to coastal areas. Over the years, it has brought serious losses to the economy and environment of China’s coastal areas. In this paper, Guangdong Province is taken as the research object to evaluate the damage caused by storm surge disasters. First of all, regarding the three-industry classification standards of the National Bureau of Statistics, combined with the storm surge disaster assessment index system, the 10-sector storm surge disaster loss input-output table is compiled and analyzed. Secondly, the indirect economic losses of storm surge disasters between 2007–2017 are determined by calculating the direct and indirect consumption coefficients. Thirdly, based on the static input-output model, considering the time factor, the dynamic input-output model of storm surge disaster assessment is established to calculate the cumulative output loss under different recovery periods (30 days, 90 days, 120 days, 180 days, 360 days). The results indicate that: (1) the losses, after a storm surge, in the agricultural economy have the greatest impact on the manufacturing sector, and conversely, they have less effect on the science, education and health service sectors; as well as the construction sector; (2) taking the industry with the biggest loss ratio as an example, the recovery of damaged industries is relatively rapid in the early stage and tends to be stable in the later stage of recovery; (3) the total output loss calculated using the static input-output model is greater than that computed using the dynamic input-output model. Researching the assessment of the direct and indirect loss due to storm surge disasters is of great value and practical significance for the scientific and rational planning of the country’s production layout, the maintenance of social and economic stability and the protection of life and property.

A New Grey Relational Analysis Model Based on the Characteristic of Inscribed Core (IC-GRA) and Its Application on Seven-Pilot Carbon Trading Markets of China

In recent years, the study of the factors affecting the carbon trading price plays an important role in promoting the carbon trading markets and the sustainable development of green economy. However, due to the short establishment time of China’s carbon trading market, the carbon trading price data of the pilot markets were not complete and have the typical characteristics of poor information. The traditional grey correlation model cannot effectively identify the volatility and the grey correlation coefficient of trading data. In this paper, an inscribed cored grey relational analysis model (IC-GRA) is constructed by extracting the values of the triangle inscribed center of the time series sample. Through numerical examples and empirical analysis, it is verified that IC-GRA not only satisfies the four axioms of traditional grey correlation but also avoids the influence of outliers of time series fluctuation and improves the discriminability of the grey correlation coefficient. The empirical results of the IC-GRA model in China’s seven pilot carbon trading markets show that: 1. among international carbon trade factor, the biggest influence factor carbon trade price is different in pilot markets. The price of natural gas has a greater correlation with the carbon price of carbon trading markets in Shenzhen, Guangzhou, and Chongqing. The futures price of Certified Emission Reduction (CER) has a strong correlation with the carbon price of Shanghai and Beijing carbon trading markets; the price of Hubei carbon trading market is the largest related to crude oil future price in the New York Mercantile Exchange ( NYMEX). 2. Air Quality Index (AQI) is most relevant to the market carbon price of carbon trading, followed by the trading turnover and trading volume of the carbon trading market. Therefore, studying the carbon trading price of the carbon trading market plays a positive role in improving the sustainable development in those areas.

Evaluation of Loss Due to Storm Surge Disasters in China Based on Econometric Model Groups

Storm surge has become an important factor restricting the economic and social development of China's coastal regions. In order to improve the scientific judgment of future storm surge damage, a method of model groups is proposed to refine the evaluation of the loss due to storm surges. Due to the relative dispersion and poor regularity of the natural property data (login center air pressure, maximum wind speed, maximum storm water, super warning water level, etc.), storm surge disaster is divided based on eight kinds of storm surge disaster grade division methods combined with storm surge water, hypervigilance tide level, and disaster loss. The storm surge disaster loss measurement model groups consist of eight equations, and six major modules are constructed: storm surge disaster in agricultural loss, fishery loss, human resource loss, engineering facility loss, living facility loss, and direct economic loss. Finally, the support vector machine (SVM) model is used to evaluate the loss and the intra-sample prediction. It is indicated that the equations of the model groups can reflect in detail the relationship between the damage of storm surges and other related variables. Based on a comparison of the original value and the predicted value error, the model groups pass the test, providing scientific support and a decision basis for the early layout of disaster prevention and mitigation.

Multiple Attribute Group Decision-Making Methods Based on Trapezoidal Fuzzy Two-Dimensional Linguistic Partitioned Bonferroni Mean Aggregation Operators

In this paper, we investigate multiple attribute group decision making (MAGDM) problems where decision makers represent their evaluation of alternatives by trapezoidal fuzzy two-dimensional uncertain linguistic variable. To begin with, we introduce the definition, properties, expectation, operational laws of trapezoidal fuzzy two-dimensional linguistic information. Then, to improve the accuracy of decision making in some case where there are a sort of interrelationship among the attributes, we analyze partition Bonferroni mean (PBM) operator in trapezoidal fuzzy two-dimensional variable environment and develop two operators: trapezoidal fuzzy two-dimensional linguistic partitioned Bonferroni mean (TF2DLPBM) aggregation operator and trapezoidal fuzzy two-dimensional linguistic weighted partitioned Bonferroni mean (TF2DLWPBM) aggregation operator. Furthermore, we develop a novel method to solve MAGDM problems based on TF2DLWPBM aggregation operator. Finally, a practical example is presented to illustrate the effectiveness of this method and analyses the impact of different parameters on the results of decision-making.

The Multi-Attribute Group Decision-Making Method Based on Interval Grey Trapezoid Fuzzy Linguistic Variables

With respect to multi-attribute group decision-making (MAGDM) problems, where attribute values take the form of interval grey trapezoid fuzzy linguistic variables (IGTFLVs) and the weights (including expert and attribute weight) are unknown, improved grey relational MAGDM methods are proposed. First, the concept of IGTFLV, the operational rules, the distance between IGTFLVs, and the projection formula between the two IGTFLV vectors are defined. Second, the expert weights are determined by using the maximum proximity method based on the projection values between the IGTFLV vectors. The attribute weights are determined by the maximum deviation method and the priorities of alternatives are determined by improved grey relational analysis. Finally, an example is given to prove the effectiveness of the proposed method and the flexibility of IGTFLV.