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With the rapid development of the economy, the problem of population aging has become increasingly prominent. To analyse the key factors affecting population aging effectively and predict the development trend of population aging timely are of great significance for formulating relevant policies scientifically and reasonably, which can mitigate the effects of population aging on society. This paper analyses the current situation of population aging in Wuhan of China and discusses the main factors affecting the population aging quantitatively, and then establishes a combination prediction model to forecast the population aging trend. Firstly, considering the attribute values of the primary influence factors are multi-source heterogeneous data (the real numbers, interval numbers and fuzzy linguistic variables coexist), a two-tuple correlation coefficient analysis method is proposed to rank the importance of the influencing factors and to select the main influencing factors. Secondly, a combination prediction model named Multiple Linear Regression Analysis-Autoregressive Integrated Moving Average is established to predict the number and the proportion of aging population in Wuhan. By using the statistical data of Wuhan in the past 20 years, this combination prediction model is used for empirical analysis, and a prediction result of the number and the proportion of aging people in Wuhan in the future is obtained. Based on these quantitative analysis results, we propose some countermeasures and suggestions on how to alleviate the population aging of Wuhan from aspects of economic development, pension security system design and policy formulation, which provide theoretical basis and method reference for relevant population management departments to make scientific decisions.

Under the background of vigorously promoting the construction of ecological civilization, the importance of constructing the “non-waste cities” becomes increasingly prominent. Taking 39 cities in the Yangtze River Economic Zone as the research objects, this paper establishes a new evaluation index system for “non-waste cities,” and proposes a multi-source, heterogeneous and multi-attribute decision-making method for the comprehensive evaluation of “non-waste cities.” Specifically, the evaluation index system of “non-waste cities” is constructed from four aspects: economic level, environmental pollution, resource consumption, and waste utilization. Considering that there is the characteristic of multi-source heterogeneity for the attribute values (that is, real numbers, interval numbers, and fuzzy linguistic variables coexist), the multi-source heterogeneous data are uniformly converted into two-tuples, and then a new two-tuple entropy weight method is proposed to determine the weights of evaluation attributes. Moreover, combining the traditional grey relational analysis method with TOPSIS, a multi-source, heterogeneous and multi-attribute decision-making method based on two-tuple mixed correlation degree (TTMCD-MSHMADM) is proposed to evaluate the “non-waste cities,” and an empirical analysis is made for the 39 cities in the Yangtze River Economic Zone. The result gives a theoretical basis for the formulation of sustainable economic development policies in the Yangtze River Economic Zone, and provides a decision reference for selecting the demonstration cities of “non-waste cities.”

Constructing scientific evaluation system and evaluation methods to make timely quantitative evaluation for regional industrial operation quality is of great practical significance for expediting the new industrialization process and promoting the improvement of national economic operation quality. Aiming at the problem of evaluating the industrial operation quality, this paper constructs a new evaluation system from the perspective of industrial operation performance and industrial development potential, and then proposes a multi-source heterogeneous multi-attribute decision-making method based on the linguistic 2-tuple model to evaluate the industrial operation quality. In this method, the original multi-source heterogeneous data whereby real numbers, interval numbers, and linguistic fuzzy numbers coexist are all transformed into linguistic 2-tuples, then a new ranking method based on grey relational degree of linguistic 2-tuple matrix is presented to rank the level of industrial operation quality for the given cities. Further, a decision-making example of evaluating the industrial operation quality for 14 cities in Hunan Province of China is provided to highlight the implementation, availability, and feasibility of the proposed evaluation model.

Most carbon Emission Trading Systems (ETS) rely on a centralized system to manage the transactional tasks, and are vulnerable to security threats. This article proposes a Blockchain-enabled Distributed ETS (BD-ETS) to improve the security and efficiency of the system. The BD-ETS transforms the centralized Carbon Emissions Permit (CEP) trading mode to a distributed trading system in which the trading mode is based on a smart contract performed in Hyperledger Fabric. In a smart contract, every transaction considers both the offer price and reputation value of the emitting enterprises. The voting power of the emitting enterprise is determined by its reputation value, which stems from their contributions to carbon emission reduction. To achieve consistency of every node in the CEP transactions, we propose a Delegated Proof of Reputation (DPoR) consensus mechanism. Compared to the enhanced Delegated Proof of Stake, the DPoR decreases the attack intention of malicious enterprises and performs better in finding malicious miners faster, thus improving the security of the BD-ETS. A case study and numerical simulations are developed to illustrate how the CEP trading functions, and to validate the DPoR mechanism.

In the emerging peer-to-peer (P2P) lending industry, risks such as credit risk and default risk will bring huge losses to online lending platforms and investors. Therefore, it is necessary to design a reasonable evaluation index system of credit risk to scientifically evaluate the risk level of borrowers. This paper studies the design of comprehensive evaluation index system for P2P credit risk of “three rural” (i.e., agriculture, rural areas and farmers) borrowers. Concretely, we construct the feature set for P2P credit risk of “three rural” borrowers. Based on the traditional index system, we add the static indexes specific to the agriculture-related borrowers and the dynamic indexes reflect the Internet as the preliminary indexes of the feature set and select the borrowers data of the “Pterosaur loan” platform as the research sample. Then, 35 borrower credit features are extracted as a feature set of credit risk. Then, we present a two-stage feature selection method based on filter and wrapper to select the main features from 35 initial borrower credit features. In the stage of filter, three filter methods are used to calculate the importance of the unbalanced features. In the stage of wrapper, a Lasso-logistic method is proposed to filter the feature subset through heuristic search algorithm. In the end, 21 main independent features are selected according to the classification accuracy, which constitute the evaluation index system of credit risk of “three rural” borrowers.

Based on the research background of the new economic norm and the construction of “non-waste city” in China, this paper studies the coordinated development between the industrial green development system and the regional “non-waste” system of the Yangtze River Economic Zone. First, using the data from 11 provinces from 2013 to 2017 as samples, the comprehensive evaluation index systems for the industrial green development system and the regional “non-waste” system are constructed, respectively. Then, considering the multi-source heterogeneous characteristics of the evaluation index data, the two-tuple linguistic entropy weight method, and gray relational analysis method are applied to filter the evaluation indexes and determine the index weights respectively. Third, combined with the dynamic TOPSIS idea, a new improved coupling coordination degree model is proposed to study the coordination development between the two systems. Finally, an empirical analysis is made, and the result shows that the overall degree of coupling coordination of the two systems shows a fluctuating upward trend. Moreover, the provinces are mainly low-level coupling coordination, and the factors that are hindered in various regions are different.

Considering the two-way spillovers of market information, this paper establishes multivariate GARCH models to study the impact of Shenzhen-Hong Kong Stock Connect (SHSC) on the complex co-movements relation between the stock markets of Shenzhen and Hong Kong from the aspects of dynamic correlation and volatility spillover. On the one hand, a t-Copula DCC-GARCH model which combines the Copula function with the DCC-GARCH model is established to model the return rate series of stock index in different stages, and the characteristic that the dynamic correlation coefficient changes with time is analyzed emphatically. On the other hand, a BEKK-GARCH model is established to measure the changes of the volatility spillover effect between the stock markets in Shenzhen and Hong Kong. The results show that the opening of SHSC has gradually increased the dynamic correlation coefficient of the two stock markets, and the openness degree of the two stock markets has increased. At the same time, the volatility spillovers of stock markets in Shenzhen and Hong Kong have shifted from one-way spillover to two-way spillovers, which indicates that the SHSC mechanism has strengthened the correlation degree and has improved the ability of risk spillover in the two stock markets.

With the rapid growth of economy, the environmental pollution problem is becoming increasingly prominent. How to promote the coordinated and balanced development of economy and environment is a strategic problem of great significance that we face urgently. Taking Wuhan City of China as the research object, this paper selects the key indexes of economic growth and environmental pollution and studies the interactive influence between economic growth and environmental pollution in Wuhan. On the one hand, the impact of Wuhan's economic growth on environmental pollution is analyzed by the proposed time-delay correlation analysis method and the time-delay EKC (Environment Kuznets Curve) models. On the other hand, the impact of Wuhan's environmental pollution on environmental growth is studied. By establishing the LARS-LASSO (least angle regression-least absolute shrinkage and selection operator) regression model and the stepwise regression model, the main factors affecting economic growth in preliminary environmental pollution indexes are analyzed, and then, an interaction model is established to study the impact of the interaction between any two main environmental factors on economic growth. The results of empirical analysis show that the main factors affecting economic growth are industrial wastewater emissions, industrial waste gas emissions, and industrial smoke and dust emissions, and the interaction between industrial waste gas emissions and industrial wastewater emissions restrains economic growth.

The implementation of the Shenzhen-Hong Kong Stock Connect (SHSC) mechanism has realized the largest two-way opening of China's capital market, but also increased the transmission of risks. In order to analyze the impact of SHSC on the volatility of single market in Shenzhen or Hong Kong, this paper establishes the volatility models of stock markets in Shenzhen and Hong Kong based on the GARCH-type models with different perturbation terms. The pre-applicable test is made and the result shows that the return rate series of Shenzhen and Hong Kong stock markets are stable and heteroscedastic, and they meet the conditions of establishing the GARCH-type models. Then, the GARCH model and EGARCH model are established to analyze the volatility of stock markets in Shenzhen and Hong Kong respectively. The results show that the opening of SHSC has increased the short-term volatility of the stock markets in Shenzhen and Hong Kong and improved the efficiency of information transmission between these two stock markets. Moreover, influenced by SHSC, the leverage effect of Shenzhen stock market is increasing, while that of Hong Kong stock market is decreasing.

The research of classical multidimensional knapsack problem always assumes that the weights,the values and the capacities are constant values. However, in the real-life industrial engineering applica-tions, the multidimensional knapsack problem often comes with uncertainty for lacking of the informationabout these parameters. This paper investigates a constrained multidimensional knapsack problem underuncertain environment, in which the relevant parameters are assumed to be uncertain variables. Withinthe framework of uncertainty theory, two types of uncertain programming models with discount con-straints are constructed for the problem with di erent decision criteria, i.e., the expected value criterionand the critical value criterion. Taking full advantage of the operational law for uncertain variables, theproposed models can be transformed into their corresponding deterministic models. After theoreticallyinvestigating the properties of the models, we do some numerical experiments. The numerical resultsillustrate that the proposed models are feasible and e cient for solving the constrained multidimensionalknapsack problem with uncertain parameters.

Supplier selection for a type of divisible goods was investigated.We design a two-stage compound mechanism of selecting suppliers.This mechanism considers both the risk attributes and commercial attributes.The first stage can effectively motivate suppliers to report true information.Method of grey correlation degree of mixed sequence is proposed to rank supplier(s). The quality of the supplier base affects the competitiveness of firms and the attendant supply chain. The supplier selection decision is key to effective supply chain management. This paper investigates the problem of supplier selection under multi-source procurement for a type of divisible goods (such as coal, oil, and natural gas). By considering both the risk attributes and the attributes under a commercial criterion, we design a new two-stage compound mechanism for supplier selection based on multi-attribute auction and supply chain risk management. In the first stage, a multi-auction mechanism is established to determine the shortlist among all qualified suppliers based on four attributes (quality, price, quantity flexibility, and delivery time reliability) under a commercial criterion. In the second stage, seven risk attributes against the shortlisted suppliers are further considered, and a new ranking method based on grey correlation degree of mixed sequence is proposed to rank the finalists and to select the final winners. Moreover, the implementation, availability, and feasibility of the two-stage compound mechanism are highlighted by using an example of the multi-source procurement of electricity coal. This presented compound mechanism may well improve the procurement efficiency of divisible goods and greatly reduce the procurement risk.

Against the backdrop of responsible economic development, sustainable supply chain management (SSCM) is key to achieving the sustainable development for enterprise and industry. In this regard, sustainable supplier selection is crucial in SSCM. By integrating the three dimensions of sustainability, economic, environmental and social, this paper presents a new evaluation system for supplier selection from a sustainability perspective. Specifically, we design a decision mechanism for sustainable supplier selection based on linguistic 2-tuple grey correlation degree. In this proposed mechanism, the hybrid attribute values whereby real numbers, interval numbers and linguistic fuzzy variables coexist are transformed into linguistic 2-tuples. A ranking method based on linguistic 2-tuple grey correlation degree is then presented to rank the suppliers. An application example is presented to highlight the implementation, availability and feasibility of the proposed decision making mechanism.

The green characteristics of the product will not only attract more environmental consumers, but also affect the cost of the enterprise. In order to investigate equilibrium decisions and profit coordination methods of green supply chain, a set of Stackelberg game models considering different pricing strategies, profit coordination modes and information pattens are constructed and analyzed, in which two competitive retailers purchase a type of green product from a manufacturer who commits to green investment. We discover that the pricing strategy has an evident impact on the profit level of supply chain members but has no effect on the green degree of the product, both the manufacturer and the retailer with higher market potential tend to choose consistent wholesale price, while the retailer with lower market potential prefers inconsistent wholesale price. In addition, the green degree which reflects the product’s environmental protection degree of the manufacturer is positively related to environmental consciousness of customers and negatively correlated with green degree cost coefficient. Furthermore, an improved coordination model based on two-part tariff can not only reach the profit level under the centralized scenario, but also make the retailers benefit from the profit increasing. Finally, an asymmetric information model with uncertain market demand and asymmetric information between retailers is studied. The results show that the equilibrium profits of supply chain members are obviously affected by uncertain demand and asymmetric information. The retailer with information advantage gains more profit than the retailer without information advantage when the demand is uncertain. Those conclusions are conducive to improving the cooperation level of members and promoting the sustainable development of green supply chain.

In many auctions, buyers know beforehand little about objects to be sold in the future. Whether and how to reveal information about future objects is an important decision problem for sellers. In this paper, two objects are sold sequentially and each buyer’s valuation for the second object is k times that for the first one, and the true value of k is sellers’ private information. The authors identify three factors which affect sellers’ revelation strategies: The market’s competition intensity which is characterized by the number of buyers, buyers’ prior information about the second object, and the difference degree between two objects which is characterized by k. The authors give not only conditions under which revealing information about the second object in advance benefits the seller, but also the optimal releasing amount of information in the market with two sellers and one seller, respectively.

In this paper, the decision making problem of electricity coal procurement in power industry is investigated and a two-stage compound mechanism based on auction and negotiation is designed for multi-attribute and multi-source procurement of electricity coal. In the first stage of this compound mechanism, a multi-attribute auction mechanism of electricity coal is designed. Concretely, the buyer's utility function and the supplier's utility function are defined, and the scoring rules and bidding rules are given. Moreover, aiming at maximizing the buyer's expected utility, an optimization model of selecting winners in multi-attribute auction of electricity coal is established; then, the suppliers' optimal bidding strategies are discussed and the feasibility of auction mechanism is proved. Based on the winners' scheme and corresponding pre-allocation results of electricity coal supply in the auction stage, a negotiation mechanism, which can further improve the allocation efficiency and optimize the attribute combination, is designed in the second stage of this compound mechanism and the bidding efficiency is calculated by using the method of Data Envelopment Analysis (DEA) for each winner. Finally, the specific implementation steps are given to show how to apply this two-stage compou nd mechanism in the actual procurement of electricity coal.