An innovative method based on Gaussian cloud distribution and sample information richness for eutrophication assessment of Yangtze's lakes and reservoirs under uncertainty

The precise assessment of a water body's eutrophication status is essential for making informed decisions in water environment management. However, conventional approaches frequently fail to consider the randomness, fuzziness, and inherent hidden information of water quality indicators. These would result in an unreliable assessment. An enhanced method was proposed for the eutrophication assessment under uncertainty in this study. The multi-dimension gaussian cloud distribution was introduced to capture the randomness and fuzziness. The Shannon entropy based on various sample size and trophic levels was proposed to maximize valuable information hidden in the datasets. Twenty-seven significant lakes and reservoirs located in the Yangtze River Basin were selected to demonstrate the proposed method. The sensitivity and consistency were used to evaluate the accuracy of the proposed method. Results indicate that the proposed method has the capability to effectively assess the eutrophication status of lakes and reservoirs under uncertainty and that it has a better sensitivity since it can identify more than 33-50% trophic levels compared to the traditional methods. Further scenario experiments analysis revealed that the sample information richness, i.e., sample size and the number of trophic levels is of great significance to the accuracy/robustness of the method. Moreover, a sample size of 60 can offer the most favorable balance between accuracy/robustness and the monitoring expenses. These findings are crucial to optimizing the eutrophication assessment.

Influencing factors of environmental efficiency of strategic emerging industries and their power cooperation mechanism design

We investigate the influencing factors of environmental efficiency of strategic emerging industries (SEIs) and cooperative game mechanism design amongst diversified actors by using China's provincial panel data from 2004 to 2019. Firstly, we find that the following factors improve the environmental efficiency of SEIs: rationalisation of the industrial structure, proportion of the tertiary industry, government's ability to intervene in the economy and fairness and integrity of environmental law enforcement. Conversely, factors, such as intensity of ecological construction and environmental regulation, hamper the environmental efficiency of SEIs. Secondly, evolutionary game analysis indicates that the behavioural strategies of game decision-making subjects depend on the behavioural decisions of the relative actors, social supervision and government regulation, which work together in influencing the environmental efficiency of SEIs. {innovation, supervision} is the optimal equilibrium state of the game. Thirdly, simulation results show that in the absence of government regulation, foreign direct investment (FDI) slows down the speed of firms tending to the equilibrium state of green innovation. The potential gain and loss of social supervision on corporate behaviour is an important factor affecting government behaviour decision making. Governments prefer punishment tools in environmental regulation, therefore influencing noninnovative firms in SEIs. We contribute to prior works by unifying various policy tools into the same econometric model framework based on an evolutionary game model.

Who Are You More Likely to Help? The Effects of Expected Outcomes and Regulatory Focus on Prosocial Performance

Prosocial behavior refers to a broad category of actions that benefit other people or the society. Compared with other factors that affect prosocial performance, prosocial outcomes, consisting of prosocial gains and prosocial non-losses have received less attention up to now. In the current research, we explored the influences of different types of expected outcomes and regulatory focus on prosocial performance. Studies 1a and 1b examined the differences in prosocial performance elicited by prosocial gain (e.g., enhancing others’ access to clean water) and prosocial non-loss outcomes (e.g., protecting others from suffering dirty water). We found that the expected prosocial non-loss outcomes induced greater prosocial performance compared with the expected prosocial gain outcomes. Studies 2a and 2b examined the effects of dispositional and situational regulatory focus on prosocial loss aversion. We found that differences in prosocial performance between two expected prosocial outcomes were reduced when promotion focus was primed; whereas a primed prevention focus did not significantly increase this difference. Additionally, participants displayed a greater prosocial loss aversion in the prevention focus condition than in the promotion focus condition. The reason for the non-significant interaction between regulatory focus and expected prosocial outcome was discussed.