In recent years, China’s outward foreign direct investment (OFDI) has risen significantly, arousing considerable interest in the motivations and drivers of Chinese overseas investment. This paper selected 27 host country-related indicators and extracted the common factors using the factor analysis method. This paper discusses the determinants of China’s OFDI location choice by using panel data regression method, and focuses on the differences between Belt and Road countries and nonBelt and Road countries. The results show that the favorable institutional environment and strong market demand of host countries have a positive influence on Chinese foreign investment. Besides, China’s investment in Belt and Road Initiative countries is more prone to a country with less developed technology and unreasonable energy utilization. China’s OFDI can promote technological progress in these countries and making full use of their advantage resources for economic development. As China’s economy has entered a “new normal”, its global influence has risen, and the relationship between the host countries and China has also mattered more on China’s OFDI decisions. This effort provides important supports for optimizing the location choices of Chinese enterprises’ foreign investment.

The investment in green technology in the process of product design and production is viewed as a powerful tool for sustainable development and carbon emission reduction. However, the substantial cost and pressure of competition weaken incentives for manufacturers to engage in green technology. In this paper, we consider two competitive manufacturer-retailer supply chains, where each manufacturer sells partially substitutable products through the exclusive retailer, study green technology investment selection by manufacturers, and examine the efficacy of retailer cost sharing scheme. Our analysis shows that a dominant equilibrium strategy for both manufacturers is to invest in green technologies, whether cost sharing is in place or not. Retailer sharing the cost of manufacturer green technology investment can avoid firms’ preference confliction over the green technology investment and improve social welfare simultaneously when both the cost-sharing rate and the degree of product/channel competition are relatively low. We also find that green technology investment by manufacturers does not necessarily curb total carbon emission, and the cost sharing can either strengthen or weaken the carbon emission reduction of green technology investment.

With the rapid development of mobile communication equipment, the significant role of social media platforms is realized in social media marketing. To determine the effect of instant messaging social media platform characteristics on consumers’ purchase intention, we collected WeChat user data and designed an empirical model based on the technology acceptance theory. Analysis of 388 qualified surveys revealed significant positive effects of instant messaging social media platform characteristics, such as social presence, media richness, immediacy of communication, privacy protection, and entertainment on customers’ purchase intention. This study aims to extend the scope of technology acceptance theory, providing practical ideas for firms and highlighting the prominent role of instant messaging social media platforms in marketing activities.

The goal of sentiment analysis is to detect the opinion polarities of people towards specific targets. For fine-grained analysis, aspect-based sentiment analysis (ABSA) is a challenging subtask of sentiment analysis. The goals of most literature are to judge sentiment orientation for a single aspect, but the entities aspects belong to are ignored. Sequence-based methods, such as LSTM, or tagging schemas, such as BIO, always rely on relative distances to target words or accurate positions of targets in sentences. It will require more detailed annotations if the target words do not appear in sentences. In this paper, we discuss a scenario where there are multiple entities and shared aspects in multiple sentences. The task is to predict the sentiment polarities of different pairs, i.e., (entity, aspect) in each sample, and the target entities or aspects are not guaranteed to exist in texts. After converting the long sequences to dependency relation-connected graphs, the dependency distances are embedded automatically to generate contextual representations during iterations. We adopt partly densely connected graph convolutional networks with multi-head attention mechanisms to judge the sentiment polarities for pairs of entities and aspects. The experiments conducted on a Chinese dataset demonstrate the effectiveness of the method. We also explore the influences of different attention mechanisms and the connection manners of sentences on the tasks.

This study selected 1558 literatures on tourism and environmental management from 1981 to 2020, and systematically analyzed the research hotspots and evolution according to the co-citation network and keyword analysis. We find that these documents focus on the sustainable management patterns in marine and coastal tourism, the environmental management practice of tourism and hotel industry, and the attitude of tourism stakeholders towards environmental management policies. We summarize the status quo, implications and suggestions. In the future, waste management in tourism areas and the competitive advantages established through environmental management would become the new research hotspots.

The paper first analyzes price change due to stock splits in Chinese stock markets, which shows stock prices typically go up for stock splits. Then theoretical analyses based on risk theory are presented to explain the reason, where the method comes from a new perspective and obtained theoretical conclusions show that stock splits typically make stock price go up if risk-compensation function is convex, and go down if risk-compensation function is concave. Stock prices typically go up for stock splits because risk-compensation functions are mainly convex. The obtained conclusions are consistent with the known results in the last three decades.

Since firms are the entities of technological innovation and basic research is the source of technological innovation, encouraging firms to conduct basic research has gained increasing attention. Using Huawei Technologies as a case study, this study employs a vector autoregressive (VAR) model and relevant data of the target firm from 2000 to 2019 to investigate the relationships between basic research, technological innovation, and firm performance. According to the findings, basic research has a positive effect on technological innovation but has a significant lag; technological innovation has a positive effect on firm performance and shows significant periodicity; while basic research has a positive effect on firm performance and a procedural nature. On this basis, we put forward some enlightening suggestions for firms to carry out basic research.

With the development of urbanization in China, tons of municipal solid waste have been produced and disposed Incineration is the best way to deal with municipal solid waste in China but this practice often is opposed and resisted by the public who live nearby. This study systematically analyzed the risk responses of the public, in particular factors affecting the public’s resistant behavior We conducted a survey and collected 376 valid questionnaires which we used for the analysis. We used the structural equation model and path analysis for the examination, and the results showed that risk perception was a critical factor predicting the resistant behavior of the public surrounding the wasteto-energy (WTE) plant. Benefit perception had a negative, but insignificant, impact on the public’s resistant behavior. We found a negative correlation between benefit perception and risk perception but the relationship was weakened when we added systematic processing to the path analysis. The impact of system processing on risk perception was greater than that of benefit perception; that is, systematic processing was better in explaining the risk judgment of the public than benefit perception. Problem knowledge was a significant indicator in predicting risk perception and systematic processing and technology knowledge was a significant indicator in predicting risk perception and benefit perception. Systematic processing increased the public’s risk judgment to the WTE plant. Finally, we discussed practical implications and limitations.

Currently, renewable energy generation has received more and more attention. This article focuses on wind energy generation, one of the renewable energy sources. Aiming at the intermittent and unpredictable wind power problems, according to the day ahead bidding mechanism in the power market, this paper introduces the energy storage system to maximize wind power merchants profit based on the newsvendor model. First, this paper focuses on the wind farms combined with storage system to put forward the optimal bidding decision of selling or buying electricity to the market one day in advance and the optimal bidding amount. Then, we analyze the relationship between the optimal bid amount and the market penalty price coefficient, government subsidy, storage system charge and discharge efficiency, and other parameters. Based on the analysis of various parameters, power generation companies can make reasonable bidding decisions and amount to maximize profits.

This paper studies two manufacturer rebate strategies in a green supply chain. The results show that: 1) Irrespective of the type of rebate strategy used, channel members benefit; 2) Rebates for green products improve the green level of green products; 3) The more significant the slippage effects are, the more beneficial the rebate strategies are, for channel members. On this basis, the model is expanded to consider asymmetric potential market demands and asymmetric product substitution rates, respectively. The results show that the product substitution rate does not affect the strategic preference of channel members, but the potential market demand does.

Accurate knowledge of who knows whom in organizations have important benefits for individual work performance and managerial decision making, but people are not very accurate when recalling connections among others in their social networks. The present study investigates how holisticanalytic thinking styles influence the extent people can accurately perceive network relationships and choose the right persons to form a coalition in a fictious persuasive task. We focused on two dimensions of holistic-analytic thinking style, namely, attention to field (as opposed to parts) and interactionist (as opposed to dispositionist) causal theory. Results from 281 participants reveals that while individuals with greater attention to field were more accurate in recalling relationships in a social network, those inclined toward interactionism in causal theory were less accurate. Furthermore, greater attention to field enhanced the effectiveness of coalition member selection, in part through the mediation of accurate network perception; while interactionism, via the full mediation of network perception, indirectly led to less effective coalition choice.

The MGM(1,m,N) model is an effective grey multi-variate forecasting model that considers multiple system characteristic sequences affected by multiple factors. Nevertheless, it is regularly inaccurate in the application. This is because the model requires a strong correlation between the system characteristic sequences. That reduces the applicability of the model. To solve this problem, this paper proposes a novel multi-variate grey model. This model does not require a certain correlation between system characteristic sequences and has higher applicability. Through numerical integration, a two-point trapezoidal formula, and a recursive method, the time-response expressions of the two model forms are obtained. Some properties of the proposed model are further discussed. Finally, the validity of the proposed model is evaluated by using two real cases related to China’s invention patent development. The results show that the novel models outperform other models in both simulation and prediction applications.

Accurately forecasting short-term electricity prices is of great significance to electricity market participants. Compared with the time series forecasting methods, machine learning forecasting methods can consider more external factors. The forecasting accuracy of machine learning models is greatly affected by the parameters, meanwhile, the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting. Therefore, this paper proposes a random forest (RF) electricity price forecasting model based on the grey wolf optimizer (GWO) to improve the accuracy of forecasting. Among them, RF has a good ability to deal with the problem of non-linear and unstable electricity prices. The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters. On this basis, the short-term electricity prices of the PJM power market in four seasons are separately predicted. Experimental results show that the RF algorithm can better predict the short-term electricity price, and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.

The development and utilization of new and renewable resources of energy has become an important layout of the development strategy in China. Photovoltaic industry is an important strategic emerging industry for the development and utilization of new energy in China. Therefore, it is important for the government to make policy to ensure the stable and orderly development of photovoltaic enterprises to accelerate the industrial structure transition in China. This paper collects the policies on photovoltaic industry, and then analyzes the industrial policy with Latent Dirichlet Allocation (LDA). LDA is generally used in document topic label extraction and recommendation system. However, this paper applies it to policy theme analysis to study the impact of policy information flow on the risk of photovoltaic enterprises. Previous studies on photovoltaic enterprise risk examined traditional financial indicators, such as asset-liability Ratio and ROE. However, the textual information in the industrial policy has rarely been studied to quantitatively analyze photovoltaic enterprise risk. In our proposed method, LDA is first used to extract the text features hiding in the text of the industrial policies, and deep neural networks then are trained on the data, which include the text features and traditional numeric features for predict photovoltaic enterprise risk. The experimental results show that the industrial policy of the current quarter has a significant effect on photovoltaic enterprise risk. Compared with this, the industrial policy of last quarter has a weak impact on the photovoltaic enterprise risk. The proposed model is a useful tool for the prediction of the photovoltaic enterprise risk.

Aiming at the dynamic multi-attribute decision making problem where the weight of each decision stage and attribute weight are completely unknown and the attribute value is unknown distributed three-parameter interval grey number, a three-parameter interval grey number dynamic multiattribute grey target decision making method with attribute value following quasi-normal distribution is proposed. Firstly, the position relationship between the “center of gravity” point and the kernel of the three-parameter interval grey number is discussed. According to the characteristic that the attribute value obeys the quasi-normal distribution, a new weight is given to the “center of gravity” point, and a new distance measure formula of the three-parameter interval grey number is defined. Secondly, according to the principle of maximum entropy, the objective programming model is constructed to determine the stage weight and attribute weight. Then, the schemes are sorted according to the size of the comprehensive bull’s-eye-distance. Finally, an example is given to illustrate the effectiveness of the decision model.

Many studies have shown that energy consumption plays an important role in economic growth. The paper researches the influence of energy consumption on economic growth in China’s Yangtze River Economic Zone. The paper divides the energy of Yangtze River Economic Zone into the coal, the oil, the natural gas and the electricity and explores the influences of coal consumption, gas consumption, natural gas consumption and electricity consumption on economic growth quantitatively using an extended production function model. The paper mainly uses two methods. The first method is the output elasticity analysis. The paper calculates the four energy consumption’s output elasticity to economic growth to compares the influences of energy consumption in terms of out output elasticity. The second method is the contribution rate analysis. The paper calculates the contribution rates of four energy consumption to economic growth to compare the influences of four energy consumption on economic growth in terms of contribution rate. The paper makes an empirical analysis on the influence of energy consumption on economic growth in China’s Yangtze River Economic Zone. Analysis results show that oil consumption has the greatest influence on economic growth in China’s Yangtze River Economic Zone, in terms of both output elasticity and contribution rate, followed by natural gas consumption, electricity consumption and coal consumption.

In the DGM(1,1) model modeling process, the influencing factors are uncertain. But the solution of DGM(1,1) model with uncertain information is unique, which conflicts with the nonuniqueness principle of solution in grey theory. In view of this situation, this paper makes an in-depth analysis of the meaning of grey action quantity β2 in DGM(1,1) model and regards β2 as an interval grey number. The maximum possibility whitenization value is given to estimate the kernel of grey number, and the typical possibility function is constructed to describe the possibility of grey number taking different values. A new DGM(1,1) model with a grey parameter is then proposed, whose simulation results are interval grey numbers. The proposed model is compatible with the DGM(1,1) model in model structure and simulation results. Finally, the practical example results show the applicability and effectiveness of the proposed model.

In our daily life, it is nothing strange to see pixelated images that are spoiled artificially to hide certain information for protecting privacy or pixelated deliberately to cover up bad behaviors even crimes. To prevent these phenomena and recover the true information from pixelated images, it is meaningful to research an effective reconstruction method for recovering pixelated images. This paper aims at recovering the artificial partial pixelated images via deep learning (DL). To abstract more abundant features and enhance the repair ability of DL model, we propose a new DL structure, called deeper inception U-Net, to act as the generator of a generative adversarial network. We combine the feature loss with structural similarity index measure loss as the context loss to minimize the distance between feature maps of clear images and the generated images, which helps to improve the quality of repair images. After obtaining inception features, we use fusion layer to adaptively learn features in each inception block. To evaluate the performance of our model, we introduce a new home dataset that contains 10174 clear home images with corresponding pixelated images. A series of experiments show that our model has ability to rebuild pixelated images.