Search

This study investigates the correlation between the environmental legal practices of different countries and their environmental performance. It entails an empirical analysis of cross-sectional environmental data collected from 34 countries, including members of the Organization for Economic Cooperation and Development (OECD) and the BRICs nations (Brazil, Russia, India, China, and South Africa). Then the study explores the correlation between a country’s environmental performance and both the environmental policy stringency and regulatory enforcement. The findings from this global assessment are subsequently corroborated through an examination of China’s environmental time series data spanning a decade, revealing a significant relationship between a country’s environmental performance and regulatory enforcement. These results validate the Incomplete Law Theory within the field of environmental law. Moreover, as the second most populous and the third-largest country in terms of land area globally, China’s environmental protection strategies and performance play a pivotal role in influencing international environmental outcomes. Consequently, the study conducts a case study on China’s environmental legal practices and provides suggestions for enhancing China’s allocation strategies of residual legislative power. The study advocates for the optimization of residual legislative power allocation within local environmental law enforcement agencies and a balanced distribution of public and private residual legislative power. This approach reinforces the government’s role in strategic formulation.

The imbalanced problem is common in the real world,and the highly-skewed distribution of imbalanced data seriously affects the performance of the model.In general,the imbalanced data affects the model performance from two aspects.On the one hand,the imbalance in sample size leads to more updates of parameters in majority classes,which leads to the model biased to majority classes.On the other hand,the sample size of minority classes is too small,and the diversity is insufficient,which leads to the insufficient representation ability of the model.To solve these problems,this paper proposes a feature fusion framework combining attention mechanism and geometric information.Specifically,in the first stage,the model learns the semantic information and discriminative information of the data through pre-training,and combines the attention mechanism to discover where the mo-del pays more attention.In the second stage,the model uses geometric information to mine boundary features,and combines the attention weight obtained in the first stage to fuse the boundary features,so as to supplement minority classes.Experimental results on long tail CIFAR10,CIFAR100 and KDD Cup99 datasets show that the proposed feature fusion framework combining attention mechanism and geometric information can effectively improve the classification performance of imbalanced data,and can effectively improve the classification performance for different types of data,including image data and structured data.