This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

We investigate the impact of strategic alliances on corporate green innovation by collecting and collating announcements of Chinese A-share listed enterprises related to their participation in strategic alliances from 2007 to 2022, and yields three main findings: Strategic alliances significantly improve corporate green innovation, and it contributes more to symbolic green innovation than to substantive green innovation, using the proportion of other enterprises in the same industry and same province that participated in strategic alliances in the previous year as an instrumental variable, we find that this positive impact is likely to be causal. Mechanism analysis indicates that public attention and productivity play an important moderating role in this process. The research results also show that the impact of strategic alliances on corporate green innovation is heterogeneous in terms of industry competitiveness, enterprise categories and strategic alliances types. Our research sheds mechanisms of strategic alliances on corporate green innovation, and provides a theoretical reference for enterprises to improve their green innovation level and enhance their core competitiveness.

In recent years, as global climate change intensifies and ESG investment concepts gain prominence, climate risk has become a crucial factor for investors and financial markets. Investigating how climate risk is priced in the Chinese capital market is both theoretically and practically important for promoting the sustainable development of China's capital markets and advancing green finance. This study employs natural language processing (NLP) to quantify the proportion of climate risk-related content in the annual reports of listed companies, creating an indicator to assess their climate risk level. The study uses climate risk of listed companies as the explanatory variable and annual stock returns as the dependent variable, applying a fixed-effects model to empirically examine the impact of climate risk on investment returns. The study finds that: first, the climate risk of listed companies shows a clear trend of change over time; second, there is a significant positive correlation between the climate risk of listed companies and investment returns.