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Climate change profoundly affects the survival and development of mankind, and provides a major opportunity for the transformation of economic development mode and the promotion of urbanization. Based on the panel data of 284 prefecture-level cities in China from 2003 to 2018, we systematically investigated the effects of climate change on urban green development, its mechanism and regional differences. We found that climate change may make greater differences in economic green growth between the north and south in China. Relative to the base weather bin (6℃~12℃), rising temperature significantly reduced urban green total factor productivity, and the effects would be more obvious for southern cities; conversely, falling temperature would promote urban green development and the positive effects of falling temperature is mainly reflected in northern cities. The mechanism test shows that the expansion of urban construction area will not aggravate the negative impact of high temperature, but improving the green coverage can significantly reduce the inhibitory effect of high temperature on urban green development. The positive effect of low temperature in winter on urban green development will decrease with the increase of energy consumption. In addition, the effects of climate change on urban green development will continue to deepen in the medium and long term, especially based on the forecasting of high fossil energy consumption development path, the green growth of southern cities will be more impacted. Our research provides new evidence for assessing the economic impact of climate change at the urban scale, and also provides useful policy inspiration for responding to climate change and promoting urban green development.
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The research on the nonlinear dynamic evolution of Cournot duopoly game and its equilibrium state in the economic system has been paid much attention by the academic circles, while the multiple constraints of credit have become the key to the stable development of modern enterprises, and have an important impact on the game behavior and stability of enterprises. In view of this, this article considers the multiple constraints on credit such as downstream enterprises’ credit default, bank loan willingness and bank credit risk appetites, to build the upstream duopolies output decision dynamic Cournot duopoly model under the multiple credit constraints. Combining the nonlinear dynamic theory and Cournot game, the influence of multiple credit constraints on the equilibrium stability of two upstream duopolies’ dynamic output game and the evolution characteristics of Cournot-Nash equilibrium are analyzed systematically. Through theoretical derivation and simulation research, it is found that: 1) The increase of downstream enterprises’ credit default, bank loan willingness and the gap between bank credit risk appetites has a stabilizing effect on the Cournot-Nash equilibrium of output decision, and the degree of influence of the three decreases in turn; 2) The increase of the gap between bank credit risk appetites will result in the oligopoly under the chaotic state; 3) The increase of fluctuation frequency of downstream enterprises’ credit default and bank loan willingness can restrain chaotic behavior, but it is not conducive to the stability of Cournot-Nash equilibrium.
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With the development of the digital economy, coupon cooperation through e-commerce platforms and their merchants has become increasingly popular. This not only brings complexity to the operational decisions of e-commerce platforms and their merchants but also poses challenges for regulators and researchers to analyze its economic consequences. This paper constructs a theoretical model to study the optimal strategy and economic benefits of e-commerce platforms and their merchants when issuing coupons cooperatively. We further compare the above results with the traditional coupon strategy and examine the interaction between coupon strategies and advertising promotion strategies. We found that: 1) settled merchants would be more willing to participate in the cooperative issuance of coupons with i) the decrease in the platform commissions, ii) the cost of merchants when participating in the cooperative issuance of coupons and iii) the expansion of the market scale of merchants’ platform sales channels; 2) the merchants’ incentive to participate in coupon cooperation increases with e-commerce platform’s spending on marketing; 3) there exists a complementarity between participating in the cooperative issuance of coupons and advertising investment; 4) when settled merchants can rely more on advertising and marketing to increase demand, cooperative issuance of coupons will increase the actual price paid by consumers and hence hurt consumer welfare. This article provides a new perspective on understanding the economic benefits of emerging cooperative issuance of coupons.
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For ultra-high-dimensional censored data, feature screening can be performed to remove noise in big data, and classical statistic analysis can be applied after that. This paper proposes a robust partial correlation coefficient for feature screening, and introduces an inverse probability weighting method to deal with censoring. Based on that, a new joint feature screening method is developed. By incorporating the information of the entire conditional distribution of the failure time, our method can depict the relationship between the response and covariates comprehensively. Compared with the traditional Pearson partial correlation coefficient, this measurement is robust to outliers , heavy-tailed distribution and heteroscedasticity. Moreover, the joint feature screening method proposed based on this metric eliminates the interference caused by the correlation between the covariates through the projection effect, so as to reduce the false negative errors, false positive errors and tackle the problem of collinearity of covariates. We establish the sure screening property of our method and give the details of the iterative algorithm. The competence of our method is further confirmed through comprehensive simulation studies and a real data example.
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Accurate forecasting of crude oil prices has always been the focus of government management decision makers, investment entities and academics. However, due to the interaction of various risk factors such as monetary policy and geopolitics, crude oil price exhibit more complex nonlinear characteristics, making crude oil price forecasting an unprecedented challenge. Our paper conducts an empirical study on INE and WTI crude oil futures markets through a crude oil price forecasting model (PVMD-QSBT-ECS) conducted based on data decomposition, reinforcement learning integration strategy and error correction technology. Firstly, the crude oil futures price series are decomposed by variational mode decomposition (VMD) using particle swarm optimization (PSO) via adaptive weights; Secondly, the Q-learning (QL) algorithm is utilized to determine the optimal weight combination of stacked bidirectional long short-term memory (SBiLSTM), bidirectional gated recurrent unit (BiGRU) and temporal convolutional network (TCN) to build an integrated prediction model, and then the dynamic error correction is applied to the prediction outcomes. Finally, the modified Diebold and Mariano (M-DM) test was utilized to further evaluate the forecasting performance of PVMD-QSBT-ECS. The empirical results indicate that the PVMD-QSBT-ECS model proposed in this paper not only has lower prediction error than other comparative models, but also exhibits superior performance in both emerging and developed markets, and also has obvious advantages in forecasting at different step sizes.
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The group consensus reaching process induced by the empirical threshold can be significantly influenced by non-cooperative behavior, reducing the consistency and robustness of decision results. Concentrating on the three core contents of the consensus reaching process: Interaction network structure, consensus driving element, and preference interaction model, we propose the adaptive group consensus model based on the BA-BSO interaction mechanism. Considering the imbalance of interaction relations, the BA free scale interaction network is developed to control the interaction opportunities of non-cooperators adaptively and dynamically. Then, the individual interaction utility function, satisfying supermodular game condition, is designed to induce the spontaneous emergence of group consensus, which consists of proximity degree and consensus cost. Through the interaction process, we propose the BSO algorithm-based preference interaction model to reflect the individual interaction preference and avoid the excessive influence of non-cooperators. Moreover, the optimal alternative selecting rule is proposed based on the “Majority Principle”, enhancing the consistency of group decision results. Finally, the effectiveness and efficiency of the proposed consensus model are illustrated by numerical example analysis and comparative analysis part.
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The rapid development of information technology has profoundly changed the way of communication among employees and various online channels have begun to emerge. In order to explore how to use a variety of online and offline channels to promote the diffusion of creative idea, this paper regards the complex system composed of employees communicating the creative idea through multiple channels in an enterprise as a group of multiplex networks. A multi-channel diffusion model of creative idea with consideration of the interaction between any two channels for diffusion and choice overland from the perspective of multiplex network. Threshold conditions for persistent diffusion of a creative idea through online and offline channels are given based on the dynamic analysis of model. The diffusion process in the multiplex networks composed of multiple channels is numerically simulated. Research shows that companies should not blindly build new online communication platforms for employees. It is easy to waste time and psychological resources in the process of choosing channels and hinder the spread of creative idea when there are too many kinds of available channels. If employees communicate only through online channels and ignore offline channel, it will also reduce diffusion efficiency. Only when employees share ideas through offline channels as much as possible, the efficiency of idea diffusion will be maximized and the scope of diffusion will be widest. The research can provide decisions for companies to rationally use online and offline channels to promote creative idea diffusion.
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Study on variation diagnosis of hydrological series can provide basis and reference for various water conservancy and civil engineering planning and management decisions. It is difficult to detect the sudden change of dynamic structure of hydrological series. This paper presents a new method for detecting the variation of a hydrological sequence based on matrix-based Renyi’s alpha order entropy. First, the matrix-based Renyi’s alpha order entropy is introduced to describe dynamic structure of the hydrologic sequence. Second, a sequence of moving cut matrix-based Renyi’s alpha order entropy is constructed by the moving window technique; it is used to describe the evolution of dynamic structure of hydrological system. Finally, Pettitt test is applied to detect the change point and the level of significance of the moving cut sequence. Taking runoff and sediment sequences in Weihe River, Taohe River, Kuye River and Xiliugou River as examples, researches on detection of change point are performed, and the results of Shannon entropy, Mann Kendell and moving T test are also included for comparison. The results indicate that no mutation occurred in the runoff sequences at Xianyang Station, Zhuangtou Station and Red Li interval, while all the annual runoff sequences changed at Zhangjiashan Station, Huaxian Station, Wenjiachuan Station and Longtouguai Station. Moreover, all the probabilities of mutation are greater than 90%. There are change points in the 1980s for the annual sediment discharge sequences at Xianyang Station, Zhangjiashan Station and Zhuangtou Station, while the change points at other stations are in the late 1990s. All the probabilities of the mutation are greater than 95%. Compared with some researches, the results generated by the matrix-based Renyi’s alpha order entropy are basically consistent with the actual situations, while those generated by the other methods are quite different from the actual situations.
