Structural economic optimization models of the forestry and land use sectors can be used to develop baseline projections of future forest carbon stocks and annual fluxes, which inform policy dialog and investment in programs that maintain or enhance forest carbon stocks. Such analyses vary in terms of the degree of spatial, temporal, and activity-level aggregation used to represent forest resources, land cover, and markets. While the statistical and econometric modeling communities widely discuss the effects of aggregation bias and have developed correction techniques, there is limited prior research investigating how aggregation bias may affect structural optimization models. This paper explores potential aggregation bias using the Land Use and Resource Allocation model (LURA), a detailed spatial allocation partial equilibrium model of the U.S. forest sector. We ran a series of projections representing alternative aggregation approaches including averaging forest stocks at plot, county, state, and regional levels, across one-, five, or ten-year age classes, and by two or fourteen forest types. We compared the resulting projections of forest carbon stocks and harvesting activities across each aggregation scenario. This allows us to isolate the effect of aggregation on key variables of interest (e.g., GHG emissions and supply costs), while holding all other structural characteristics of the modeling framework constant. We find that age-class and forest type aggregations have the greatest impact on modeling results, with the potential to substantially impact market and greenhouse gas projections. On the other hand, spatial aggregation has a small impact on national carbon stock projections. Importantly, regional results are greatly impacted by different aggregation approaches, with projected regional cumulative carbon stocks differing by more than 25% across scenarios.

林业和土地利用部门的结构经济优化模型可用于制定未来森林碳储量和年度通量的基线预测，为政策对话和对维持或增强森林碳储量计划的投资提供信息。此类分析在用于代表森林资源、土地覆盖和市场的空间、时间和活动级别聚合的程度方面有所不同。虽然统计和计量经济学建模社区广泛讨论了聚合偏差的影响并开发了校正技术，但之前研究聚合偏差如何影响结构优化模型的研究有限。本文使用土地利用和资源分配模型 (LURA) 探索潜在的聚集偏差，这是美国的详细空间分配部分均衡模型 林业部门。我们进行了一系列代表替代聚合方法的预测，包括在地块、县、州和区域级别，跨一年、五年或十年年龄组以及两种或十四种森林类型的平均森林库存。我们比较了每种聚合情景中森林碳储量和采伐活动的预测结果。这使我们能够隔离聚合对感兴趣的关键变量（例如温室气体排放和供应成本）的影响，同时保持建模框架的所有其他结构特征不变。我们发现年龄等级和森林类型聚合对建模结果的影响最大，有可能对市场和温室气体预测产生重大影响。另一方面，空间聚合对国家碳储量预测的影响很小。