Assessment of forest carbon (C) stock and sequestration and the influence of forest harvesting and climatic variations are important issues in global forest ecology. Quantitative studies of the C balance of tropical forests, such as those in Papua New Guinea (PNG), are also required for forest-based climate change mitigation initiatives. We develop a hierarchical Bayesian model (HBM) of aboveground forest C stock and sequestration in primary, selectively harvested, and El Niño SouthernOscillation (ENSO)- effected lowland tropical forest from 15 years of Permanent Sample Plot (PSP) census data for PNG consisting of 121 plots in selectively harvested forest, and 35 plots in primary forest. Model parameters indicated: C stock in aboveground live biomass (AGLB) of 137 ± 9 (95% confidence interval (CI)) MgC ha-1 in primary forest, compared with 62 ± 18 MgC ha-1 for selectively harvested forest (55% difference); C sequestration in primary forest of 0.23 ± 1.70 MgC ha-1 y-1, which was lower than in selectively harvested forest, 1.12 ± 3.41 MgC ha-1 y-1; ENSO-induced fire resulted in significant C emissions (-6.87 ± 3.94 MgC ha-1 y-1). High variability between PSPs in C stock and C sequestration rates necessitated random plot effects for both stock and sequestration. The HBM approach allowed inclusion of hierarchical autocorrelation, providing valid CIs on model parameters and efficient estimation. The HBM model has provided quantitative insights on the C balance of PNG's forests that can be used as inputs for climate change mitigation initiatives.