Economic development in the “new era” will require green innovation. To encourage the growth of green technology innovation, it has become fashionable to strengthen environmental regulation. However, the impact of environmental regulation on green technology innovation, as well as the role of government subsidies, needs to be examined. Utilizing fixed-effect models and 2SLS models to explore the impact of environmental regulation on green technology innovation in China from 2003 to 2017, this research sought to examine whether environmental regulations impact green technology innovation, as well as the role of government subsidies in the above-mentioned influence path. The findings support the Porter Hypothesis by demonstrating an inverted “U” relationship between environmental regulation and green technology innovation. The impact of environmental regulation on green technology innovation varies by region. To be specific, there is an inverted “U” relationship between environmental regulation and green technology innovation in China’s central and central coast regions. In comparison, the north area, southern coast, and southwest region exhibit a “U” relationship between the two. The relationship is not significant in the Beijing-Tianjin region. Additionally, government subsidies act as an intermediate in this process, positively influencing firms to pursue green technology innovation during the earliest stages of environmental regulation strengthening. However, government subsidies above a certain level are unproductive and should be used appropriately and phased off in due course.

This paper addresses the issue of sampling from the posterior distribution of exponential random graph (ERG) models and other statistical models with intractable normalizing constants. Existing methods based on exact sampling are either infeasible or require very long computing time. We propose and study a general framework of approximate MCMC sampling from these posterior distributions. We also develop a new Metropolis-Hastings kernel with improved mixing properties, to sample sparse large networks from ERG models. We illustrate the proposed methods on several examples. In particular, we combine both algorithms to fit the Faux Magnolia high school data set of Goodreau et al. (2008), a network data with 1, 461 nodes.

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How to effectively identifying opinion leaders has become a hot research point. It's essence is to identify the nodes with the strong influence in social network. This paper analyzes the behaviors of users in social networks and proposes the impact evaluation algorithm based on multi-angle user behaviors which is User-Activity Rank algorithm. The algorithm uses the basic idea of PageRank algorithm, considers user's creativity, interactivity and content quality and designs the uneven distribution mechanism between the users' UA Rank value, making the computation nodes more accurate. This paper uses Car Home Forum Case for analysis and finds that this algorithm can be more accurate and objective. User-Activity Rank algorithm can help companies improve the accuracy of identifying opinion leaders and promote network marketing with their influence.