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Abstract

Forest planning is vital for ensuring objective fulfilment for decision-makers. Forest-owning companies often organise their planning in a hierarchy of separate stages (i.e., strategic, tactical, and operational planning). The objectives for the strategic stage are generally to maximise net present value and long-term harvest levels without threatening the environmental integrity of the forests. However, in the subsequent stages of the planning hierarchy, with a shorter-term focus, the objective is often to minimise costs due to budgetary constraints. These misaligned objectives introduce a dilemma, especially when considering that decisions are typically made using uncertain data. We examined the suboptimality caused by using low-quality forest data in a long-term harvesting planning problem and how this suboptimality is affected by misaligned objectives between the strategic and tactical planning stages. The low-quality forest data were simulated in a Monte Carlo simulation that maintained a real-world structure of errors. The results show that uncertainty in forest data impacts objective fulfilment more than the level of alignment of objectives. However, a high degree of objective alignment performs better than the opposite, regardless of the level of quality of data.

Keywords

forest management; data uncertainty; Monte Carlo simulation; objective alignment; optimisation under uncertainty

Published in

Canadian Journal of Forest Research
2025, volume: 55, article number: 0118

SLU Authors

UKÄ Subject classification

Forest Science

Publication identifier

  • DOI: https://doi.org/10.1139/cjfr-2025-0118

Permanent link to this page (URI)

https://res.slu.se/id/publ/143046