Abstract China is a country with the most water consumption, so it is lack of water resources. Industry has brought serious water pollution while driving economic development, which leads to the destruction of ecological environment. With the improvement of environmental awareness, many scholars have shifted their research direction to how to improve the ecological environment. Most studies consider the whole system as a “black box”, regardless of its internal structure. Therefore, a method to identify inefficiency is necessary and some suggestions for optimization are given. In this paper, a two-stage undesirable fixed-sum output data envelopment analysis (DEA) model is proposed. The industrial chemical oxygen demand (COD) emission during 2011–2015 are adjusted, and the efficiency values are calculated by heuristic search algorithm. The efficiency of 30 provinces and cities is divided into eastern, central and western regions. The model can identify the inefficient stage in industrial system, and find the source of low efficiency in the system. The analysis shows that the efficiency of eastern region is the highest, while the overall efficiency is inclined to the pollutant treatment stage. Finally, the paper puts forward some suggestions for the low efficiency areas, which can save water while ensuring economic benefits, and provide new direction for water pollution reduction and improve the ecological environment.

Abstract In recent years, China’s industrialization and urbanization have deepened, and the economy has grown considerably. But at the same time, they have also brought about many environmental problems. As a pillar of the national economy, the iron and steel (IS) industry is one of the most emitting and energy-consuming sub-sectors of the industrial sector. It is also one of the industries with the most severe overcapacity problem in China. In this paper, we explore the impact of capacity utilization on carbon dioxide emission based on panel data of China’s iron and steel industry from 2005 to 2014. We also tested the heterogeneity in different regions and different sub-samples. Results show that capacity utilization and carbon dioxide emission are positively correlated. However, the impact of capacity utilization on carbon dioxide emission differs when considering regional heterogeneity. Results in all three regions show a positive relationship between capacity utilization and carbon dioxide emission, but the impact intensity is strongest in the western region, followed by the eastern and central regions. Moreover, capacity utilization impacts carbon dioxide emission by influencing firm numbers in the iron and steel industry and energy consumption. Further analysis shows that there exists a threshold effect in different stages of energy consumption and energy structure. Finally, some findings and practical policy recommendations are provided.

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.

This paper sheds light on the demand-side impact of China’s emergence on the world economy from a value chain perspective. A measure linking final demand with value-added in the context of global value chains is developed. Data from the World Input-Output Database is used to analyze foreign value-added induced by final demand in China. It is found that foreign value-added to meet final demand in China increases over time except during 2008-2009, proving that China has a significant power to shape the world economy through its final demand. Besides, it is found that about 88% of foreign value-added is absorbed in China through intermediate imports. In addition, how and to what extend the final demand in China contributes to other countries’ GDP growth is investigated. Results show that continuous increase in foreign value-added absorbed in China becomes a ballooning source of economic growth, and final demand in China is rather a remedy in aftermath of the global recession during 2008-2009. Finally, it is found that the increase in foreign value-added absorbed in China is due not only to the surge in final demand but also to bullwhip effect along global value chains.

We explore the association between the number of critical audit matters and auditing opinion based on the Chinese capital market data. In addition, we investigate the effect of audit risk on the relationship between the number of critical audit matters and auditing opinion. Using 6,662 firm-year observations listed in the Chinese capital market from 2017-2019, we find that the number of critical audit matters has significantly positive association with clear audit opinion. Specially, the greater the number of critical audit matters, the more likely getting clear audit opinion. We also find that the audit risk restricts the effect of the critical audit matters on the audit opinion. Lastly, we find that Big4 accounting firms are less likely to be influenced by the number of critical audit matters; the audit complexity restricts the effect of the critical audit matters on the audit opinion; the more board members, the less effect of the number of critical audit matters on the audit opinion. We also use several robustness tests to strengthen the conclusion. Our research may contribute to the understanding of the new audit standard.

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.

Abstract This paper investigates a simple step-stress accelerated lifetime test (SSALT) model for the inferential analysis of exponential competing risks data. A generalized type-I hybrid censoring scheme is employed to improve the efficiency and controllability of the test. Firstly, the MLEs for parameters are established based on the cumulative exposure model (CEM). Then the conditional moment generating function (MGF) for unknown parameters is set up using conditional expectation and multiple integral techniques. Thirdly, confidence intervals (CIs) are constructed by the exact MGF-based method, the approximate normality-based method, and the bias-corrected and accelerated (BCa) percentile bootstrap method. Finally, we present simulation studies and an illustrative example to compare the performances of different methods.

Abstract The influence of taxation on the economy and society is everywhere. Given the fact that CO2 emission is closely connected with human activities, the question naturally arises whether the tax burden affects the carbon emissions. To address this issue, based on the data of 21 OECD countries over the period from 1991–2014, we use the ratio of tax revenue to GDP as a proxy of the tax burden and employ a panel quantile model with the non-additive fixed effects for analysis. The results suggest that the impact of the tax burden on carbon emissions is heterogeneous across countries based on the level of carbon emissions. Moreover, we find a stable U-shaped relationship between the tax burden and carbon emissions whether for countries with a high or low level of carbon emissions. Our findings suggest that moderate taxes could help reduce carbon emissions, but the effect of excessive taxation is the opposite.

Abstract Crowdsourcing task assignment has become an important task assignment model in the Internet economy era. In this paper, we study the crowdsourcing task assignment problem based on employer net profit and employee satisfaction. First, the reliability and interest of employees are modeled, based on which the mathematical expressions for employer net profit and employee satisfaction are given. Then, a multi-objective optimization problem is formulated to maximize employer net profit and employee satisfaction by jointly optimizing the task assignment matrix and task offer vector. Since the considered problem contains discrete variables, it cannot be solved directly by traditional optimization methods. Therefore, two low-complexity high-performance algorithms are proposed. The first algorithm is based on a fast non-dominated ranking genetic algorithm with an elite, which is able to explore the Pareto bound of the considered problem. The second algorithm is based on a reinforcement learning framework, which is able to maximize the weighted sum of employer net profit and employee satisfaction. Numerical results show that the number of tasks assigned to employees affects both employee satisfaction and employer net profit. The Pareto bounds and Pareto optimal solutions based on the solutions of the two proposed algorithms are also presented numerically, which quantitatively characterize the tradeoff between employer net profit and employee satisfaction.

Abstract Bitcoin is the most famous and the most used cryptocurrency in the world, such that it has received extreme popularity in recent years. However the Bitcoin system is accompanied by different attacks, including the block withholding (BWH) attack. When a miner plays the BWH attack, it will withhold all the blocks newly discovered in the attack pool, damaging the honest miners’ right to obtain the fair reward. In this paper, we consider a setting in which two miners may honestly mine or perform the BWH attack in a mining pool. Different strategy profiles will bring different payoffs, in addition influence the selection of the strategies. Therefore, we establish an evolutionary game model to study the behavior tendency of the miners and the evolutionary stable strategies under different conditions, by formulating the replicator dynamic equations. Through numerical simulations, we further verify the theoretical results on evolutionary stable solutions and discuss the impact of the factors on miners’ strategic choice. Based on these simulation results, we also make some recommendations for the manager and the miners to mitigate the BWH attack and to promote the cooperation between miners in a mining pool.