Reduction of carbon dioxide (CO₂) emissions is one of the biggest challenges for global sustainable development, in which economic growth characterized by industrialization plays a formidable role. We innovatively adopted the input and output (I-O) table of 41 countries released by World I-O Database to determine the industrial structure change and analyze its impact on CO₂ emission evolution by developing a cross-country panel model. The empirical results show that industrial structure change has a significantly negative effect on CO₂ emissions; to be specific, 0.1 unit increase in the linkage of manufacturing sector and service sector will lead to a decrease of 0.94 metric tons per capita CO₂ emissions, indicating that upgrading industrial structure contributes to carbon mitigation and sustainable development. Further, urbanization, technology and trade openness have significantly negative impact on CO₂ emissions, while economy growth and energy use take positive impacts. In particular, a 1% increase in per capita income will contribute to an increase of 8.6 metric tons per capita CO₂ emissions. However, the effect of industrial structure on environment degradation is moderated by technology level. These findings fill the gaps of previous literature and provide valuable references for effective policies to mitigate CO₂ emissions and achieve sustainable development.

In recent years, the population growth rate has been gradually declining in China. As the population problem becomes increasingly significant, the accurate prediction of population development trends has become a top priority, used to facilitate national scientific planning and effective decision making. Based on historical data spanning a period of 20 years (1999–2018), this article presents predictions of the populations of 210 prefecture-level cities using the Malthusian model, Unary linear regression model, Logistic model, and Gray prediction model. Furthermore, because the gray prediction model exhibited the highest degree of accuracy in formulating predictions, this study uses the model to predict and analyze future population development trends. The results reveal that the population gap between cities is gradually widening, and the total urban population shows a pattern of rising in middle-tier cities (second-tier cities and third-tier cities) and declining in high-tier cities (first-tier cities and new first-tier cities) and low-tier cities (fourth-tier cities and fifth-tier cities). From the viewpoint of geographical distribution, the population growth rate is basically balanced between the northern part and the southern part of China. In addition, the population growth of the high-tier cities is gradually slowing while the low-tier cities are experiencing a negative growth of population, but middle-tier cities are experiencing skyrocketing population growth. From the viewpoint of regional development, although the development of regional integration has been strengthened over the years, the radiative driving effect of large urban agglomerations and metropolitan areas is relatively limited.

Using a database of more than 1.1 million comments from the largest Chinese stock discussion forum Eastmoney, we explore the value of “brand” in the Chinese stock market by empirically investigating the impact of brand attention on stock performance and the moderation role of investor sentiment, as well as the heterogeneity of these effects across companies with different brand values. It finds that brand attention has a positive impact on stock returns and stock trading volume. Investor sentiment has a positive impact on stock returns while having a negative impact on stock trading volume. Investor sentiment positively moderates the impact of brand attention on stock returns, but negatively moderates the impact of brand attention on stock trading volume. The impact of brand attention and investor sentiment on stock performance varies across companies with different brand values. It is more pronounced in companies with high brand value than in companies with a relatively low brand value, while the negative effect of investor sentiment on stock trading volume is lower in companies with high brand value, which highlights the importance of brand-building to improve stock performance.

Could the corporate carbon information disclosure strategy influence a firm’s brand value, and how does corporate carbon information affect it? Previous research mainly examines the impact of ESG information disclosure on firm value and other financial indicators, but little research has focused on the effect of carbon information on brand value. This paper focuses on the influence of corporate carbon information disclosure on brand value, and we find that it positively impacts corporate brand value. In addition, when a company chooses to adopt a more quantitative and diverse carbon information strategy, it increases its brand value. We also examine the potential mechanisms involved in how corporate carbon information disclosure influences brand value. We focus on three types of factor: analyst rating, customer attitude, and corporate financial performance, and find that higher analyst forecasts and positive customer attitudes have a positive impact on the association between the carbon information strategy and corporate brand value. In contrast, corporate financial performance provides only weak evidence. These results are consistent with demands by users for more precise guidelines from regulators and standard-setters for measuring and disclosing carbon-related information.

Environmental regulation affects the financial performance of firms, while the findings are mixed. This paper quantitatively analyzes the current and lagged effect of environmental regulation (ER) on financial performance (FP), based on the data of 361 highly polluting A-shares firms and 936 mildly polluting A-shares firms in China. It is proved that ER exerts a negative effect on the FP of polluting firms in the short term and a positive effect in the long term, which unifies the ‘Porter Hypothesis’ (PH) and the ‘Costly Regulation Hypothesis’ (CRH) on the temporal dimension. Mechanism analysis reveals that ER negatively affects current FP of highly polluting firms by improving their green innovation investment. In addition, ER has a significant positive lagged effect on the FP of polluting firms by improving their operating efficiency, rather than reducing production costs. Furthermore, we find that ER significantly improves the FP of highly polluting firms, especially state-owned firms, as opposed to mildly polluting firms and privately-owned firms. The conclusions imply that government should make subsidies for green firms or firms going green, and firms should pay more attention to green innovation investment and green development.