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The public’s co-production behaviors are influenced by individual and organizational factors. This article analyzes these factors using 2110 questionnaire data from He Ping, Tianjin. Results show that self-efficacy, solidarity incentives, spiritual incentives and inclusive administrative culture have a statistically significant positive correlation with public coproduction behaviors, while the impact of material incentives is limited. In addition to the nonlinear effect of age, middle-aged women, homeowners, middle-income people, and the public with bachelor’s degree are more likely to participate in community regeneration coproduction. The findings provide theoretical and practical implications for the existing co-production theory and public administration practitioners.

Based on the data obtained on carbon emissions in Guangdong Province, China, from 1997 to 2019, this study focused on the relationship between energy consumption and population development in Guangdong Province. This study quantitatively analyzed the impact of different population structures and technological progress on carbon emissions in Guangdong Province by establishing an extended model of Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT). The results showed that the population size factor was not as good as the population structure factor for carbon emissions. In addition, different demographic factors affected carbon emissions differently with both positive and negative effects. Finally, relevant policy suggestions were proposed from the perspectives of encouraging the childbearing of appropriate-age residents, optimizing the population structure, reducing the separation of people and households, guiding residents to return to the traditional family model, guiding residents to live a low-carbon life, optimizing industrial institutions, and adjusting the energy consumption structure.

This paper, using the singular spectrum analysis (SSA), decomposes the stock price into terms of the trend, the market fluctuation, and the noise with different economic features over different time horizons, and then introduce these features into the support vector machine (SVM) to make price predictions. The empirical evidence shows that, compared with the SVM without these price features, the combination predictive methods-the EEMD-SVM and the SSA-SVM, which combine the price features into the SVMs perform better, with the best prediction to the SSA-SVM.

In order to research the situation of the stock market extreme gains and extreme volatility how to have great influence on investor sentiment. This article selects the SaP 500 weekly closing price of American market and extracts yield and volatility sequences. Linear regression model is established on the basis of the difference between extreme and not extreme case to study the emotional change brought by extreme income and extreme volatility. The empirical results show: Except for the extreme negative earnings, other yield sequence can significantly stir up investor's mood, among which the not extreme gains place the first in causing the investor's emotional change in the United States; Extreme positive yield have more significant effect on investor sentiment than extreme negative yield; Volatility cannot affect all investor's sentiment effectively and steadily, thus it has limited explanation on investor sentiment.