Modeling structural breaks and socioeconomic drivers of deforestation in Tanzania to inform sustainable forest management

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Abstract Deforestation reporting discontinuities pose significant challenges to understanding socioeconomic drivers and achieving global conservation targets. Using Tanzania as a case study, this paper develops a regression model incorporating structural breaks to quantify the interplay between economic influences and methodological transitions in deforestation data (1994–2014). The key innovation is the explicit modeling of the 2010 NAFORMA methodological transition as a binary structural break, combined with a formal comparison between Ordinary Least Squares (OLS) and Bayesian inference using conjugate priors, an approach that distinguishes data artifacts from true environmental change. The structural break model demonstrated a superior fit (lower AIC and BIC values), and the findings reveal that the 2010 methodological transition was the most influential factor, introducing a statistically distinct positive shift in reported deforestation rates post-implementation. This shift reflects improved measurement under NAFORMA, leading to higher reported deforestation rates than would be expected from socioeconomic drivers alone. Socioeconomic analysis shows that purchasing power and poverty rates correlate positively with deforestation, while per capita income and inflation exhibit negative relationships. These results are critically important because unresolved reporting biases can lead to misguided policies; by identifying true drivers and correcting for data artifacts, this study provides a robust, discontinuity‑aware framework for evidence‑based policy in forest‑dependent economies. The findings directly support the implementation of Sustainable Development Goal (SDG) 15 by ensuring that forest management strategies are grounded in accurate environmental reporting.