Sustainable innovation has emerged as a central response to global challenges including climate change, energy transition, circular economy transformation, and socio-economic inequality. While technological and policy dimensions of sustainability are widely discussed, the foundational role of statistical science in enabling measurable, scalable, and accountable sustainable innovation remains under-theorized. This review synthesizes interdisciplinary literature to examine how statistical methodologies—ranging from descriptive indicator systems and inferential modeling to predictive analytics, Bayesian inference, and machine learning—form the structural backbone of sustainability transitions. Drawing upon institutional frameworks developed by the United Nations, World Bank, OECD, and International Energy Agency, the study critically evaluates the statistical architecture underlying sustainable innovation ecosystems. The paper identifies fragmentation in current modeling approaches and proposes an integrated Statistical Sustainability Architecture (SSA) to unify measurement, prediction, optimization, and policy evaluation. The findings establish statistics not as a peripheral tool but as the central engine driving evidence-based sustainable innovation.
- et al. (Mon,) studied this question.