How environment and technology affect the regional manufacturing industry development.

To help the manufacturing industry achieve high-quality development, it is urgent to identify the factors that affect the development of regional manufacturing. Compared to previous regression models, this article attempts to discover the nonlinear effects of different factors on regional manufacturing industry development (RMID) and their future impact trends. Based on the theory of new structural economics, we used order parameter analysis to examine the impact of environmental pollution and technology on RMID. The results indicate that: (1) The half of the cities promote industrial growth, but there are still three other situations: development slow down (3/21), a slight downward trend (5/21), and recession (2/21). (2) The two-thirds of cities adopt green development to promote industrial growth, while the development of other cities slows down (3/21), and some cities have a slight downward trend (4/21). The conclusion is as follows: (1) Through comparison, it is found that the impact of environment and technology on the RMID remains roughly synchronous, but currently the environmental promotion effect is greater. (2) We have found four technological development paths and can extend three green development models, effectively promoting RMID's green technology development. These suggestions will lay the foundation for promoting RMID.

Spatiotemporal distribution and dynamics evolution of artificial intelligence development in China.

The quantified measurement and comprehensive analysis of artificial intelligence development (AIDEV) are vital for countries to form AI industrial ecology and promote the long-term development of regional AI technology. Based on the innovation ecosystems (IE) theory, this paper constructs an evaluation system to measure and analyze the spatiotemporal distribution and dynamic evolution of the AIDEV in China from 2011 to 2020. The results show that the AIDEV of China presents an overall upward trend and an obvious unbalance in the spatial distribution which is "eastern > central > western". Meanwhile, the provinces of low-level AIDEV are catching up with the high-level provinces, which leads to the regional difference of AIDEV narrowing. Moreover, the concentration and polarization phenomenon of AIDEV in China has been weakening and the AIDEV will continue to increase in the next three years. Further, there is a significantly positive spatial autocorrelation of AIDEV. Finally, high AIDEV provinces will increase the probability of surrounding provinces' AIDEV to develop. This paper expands the research stream in the field of AI research, extends the application scenarios of IE theory, and puts forward some relevant policy recommendations.

Exploring the influencing factors of carbon neutralization in Chinese manufacturing enterprises.

One of the key issues facing the government in achieving carbon neutrality is what methods can be used to effectively reduce carbon emissions. Taking manufacturing enterprises as an example, this paper studies the carbon emission reduction effects of green technology innovation subsidy (GIS), carbon tax (CT), and carbon emission trading (CET). Under the background of social welfare and carbon emission reduction efficiency, we get the results of optimal carbon emission reduction measures in different environments. The results are as follows: (1) In the initial and mature stage of green technology innovation, GIS is the best choice to improve the degree of green manufacturing and maximize social welfare. CT and CET are the best choice to obtain the highest SE (carbon emission reduction efficiency). (2) In the transitional stage, CET and CT can promote the maturity of green technology. However, with the maturity of green technology, the promotion of green technology has weakened. CT is the best choice to achieve the highest SE. (3) When the carbon tax or carbon trading price is at a high or low level, raising the tax rate or carbon trading price can increase the income of enterprises. Therefore, the government should take measures according to the objectives of different stages. When the goal is to maximize social benefits, GIS is the best choice in the initial stage and transition stage, and CET or CT is the best choice in the transition stage. In the initial stage and fertilization stage, when the highest SE, CT, or CET is the best choice, while in the transition stage, CT is the best choice.

Short-Text Classification Detector: A Bert-Based Mental Approach.

With the continuous development of the Internet, social media based on short text has become popular. However, the sparsity and shortness of essays will restrict the accuracy of text classification. Therefore, based on the Bert model, we capture the mental feature of reviewers and apply them for short text classification to improve its classification accuracy. Specifically, we construct a model text at the language level and fine tune the model to better embed mental features. To verify the accuracy of this method, we compare a variety of machine learning methods, such as support vector machine, convolution neural networks, and recurrent neural networks. The results show the following: (1) Through feature comparison, it is found that mental features can significantly improve the accuracy of short text classification. (2) Combining mental features and text as input vectors can provide more classification accuracy than separating them as two independent vectors. (3) Through model comparison, it can be found that Bert model can integrate mental features and short text. Bert can better capture mental features to improve the accuracy of classification results. This will help to promote the development of short text classification.

Exploring Regional Advanced Manufacturing and Its Driving Factors: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area.

This study aims to analyze the development trend of the manufacturing industry of the Guangdong-Hong Kong-Macao Greater Bay Area (from 2008 to 2018) by constructing an evaluation system. On the basis of push-pull-mooring theory, we analyze these factors by using an entropy and cluster model. The results show the following: (1) Technological development had an obvious spatial distribution pattern of core regional radiation, while others did not. (2) Economic development was based on the city's existing industrial development system, while environmental development depended on governmental policies. (3) Compared with the environmental factor, the development trends of the economic and technological factors were more similar. Lastly, we provide four strategies for the development of the manufacturing industry in different cities.