Reconstructing Dynamic Material Stock–Flow Systems Under Data Scarcity: A Methodological Framework Demonstrated in Vietnam

Dynamic material stock–flow analysis is widely used to assess long-lived built-environment systems, but data scarcity makes its application challenging because stock, inflow, and outflow evidence often do not form a coherent accounting structure. This study develops a reconstruction framework for dynamic material stock–flow modeling under fragmented and incomplete data conditions. The framework integrates data structure diagnosis, harmonization, historical inflow reconstruction, stock anchoring through calibration, dynamic reconstruction, and credibility assessment via empirical consistency checking, uncertainty propagation, and sensitivity analysis. The study uses Vietnam’s residential metabolism case to formalize and test a reconstruction methodology with incomplete historical inflow records, intermittent benchmark stock observations, heterogeneous classifications, and limited evidence on demolition outflows. Comparative results show that omitting historical inflow reconstruction distorts cohort depth and delayed outflow behavior, while omitting stock anchoring leads to persistent underestimation of stock levels relative to benchmarks. The analysis further shows that credible interpretation depends not only on internal stock–flow consistency, but also on compatibility with benchmarks and robustness under plausible parameter variation. The study concludes that, under data scarcity, dynamic stock–flow modeling should be approached as system reconstruction, providing a transparent and reproducible methodological basis for long-lived material systems.