Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.

This study investigates venture capital (VC) reputation impact on the pre-IPO performance of the entrepreneurial firms backed by three kinds of VCs. This study employs backward stepwise regression models following prior theoretical frameworks to examine the research question. Based on a database of the top 50 VC firms ranked during 2016 to 2020 and their portfolio firms. This study shows some contingent contribution to pre-IPO firm performance. Firstly, the reputation of the Chinese government-owned VCs is negatively associated with their portfolio firm performance. Still, there is a positive relationship between foreign and local private VCs. Secondly, entrepreneurial firm performance is significantly associated with industry policy and entrepreneur’s performance than VC reputation. This study has practical implications for entrepreneurs and limited partners regarding their corporation relationships with the Chinese VCs.

本文以金融系统性风险的同步变量构成的中国金融压力指数为被解释变量，以滞后的宏观经济变量、货币信贷变量、资产价格变量和相关经济大国的宏观经济变量为解释变量，运用逐步回归法建立了金融系统性风险最佳预测方程，从而构建起了金融系统性风险预警的合理、实用的指标体系；并用此最佳预测方程对我国2010年金融系统性风险状况进行了预测。预测结果表明，前三季度我国金融系统性风险呈上升态势，且高于2008年的最高值；第四季度开始，金融系统性风险有下降趋势。 his paper treated the Chinese Financial Stress Index which constructed by synchronized variable of the financial systemic risk as the explained variable, and lagged macroeconomic variables, monetary and credit variables, asset price variables and related foreign countries’ macroeconomic variables as explanatory variables. Using stepwise regression, we established the best financial systemic risk forecast equation and built a reasonable and applied financial systemic risk prediction index system. Using the equation we forecasted the financial systemic risk status of China in 2010. The predicted result is that the financial systemic risk rise in the first three quarter, and is higher than the highest value in 2008; the financial systemic risk goes downward in the fourth quarter.