Innovation Ecosystems, Cultural Context, and Climate Performance: A Cross‐National Analysis Using Panel Regression and Machine Learning

This study investigates how national cultural values moderate the relationship between innovation ecosystems and climate change performance across 28 countries from 2013 to 2022. While innovation infrastructure, comprising regulatory quality, education, ICT, investment, and knowledge diffusion, is widely regarded as a key driver of climate action, countries with similar innovation capacities often demonstrate stark differences in environmental outcomes. Drawing on Hofstede's six cultural dimensions and the technology–structure–behavior (TSB) framework, this paper theorizes that cultural norms act as informal institutions that shape how societies internalize and operationalize innovation for climate impact. Using a dual‐method approach, we employ panel regressions to test six interaction hypotheses and apply ensemble machine learning models (Bagging, Random Forest, and Boosting) with SHAP and Partial Dependence Plots to uncover nonlinearities and contextual effects. Results reveal that cultural traits such as masculinity, indulgence, and long‐term orientation significantly moderate the effectiveness of innovation drivers, whereas others like power distance and individualism show limited influence. These findings highlight that climate innovation strategies must be culturally contextualized to enhance their effectiveness. The study contributes theoretically by advancing a culture‐sensitive institutional model of innovation‐led sustainability and offers actionable insights for tailoring climate policy to national value systems.