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.