Evaluating Potential Sources of Aggregation Bias with a Structural Optimization Model of the U.S. Forest Sector

Abstract

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.

Towards Carbon Neutrality: The Innovation Efficiency of China’s Forestry Green Technology and Its Spatial Spillover Effects

“…Ecological output is related to the forest area and is measured by new afforestation areas in the year. Environmental output is represented by the rate of forest pest and rodent control, as studies have demonstrated that the incidence of forest pests and rodents reflects the forestry production environment [6].…”

“…Innovation is an eternal force for development and progress [4]. Innovation produces solutions for the dilemmas confronting traditional forestry in China under the goal of carbon neutrality and can solve significant issues in the contemporary transformation of traditional forestry, such as factor structure, resource allocation and industrial integration forestry [5][6][7]. Advancing the integration of forestry and technological innovation can address the over-exploitation of forest resources that leads to energy consumption or generate the expansion of factor demand, promote the improvement of forest quality and green and intensive development, and have direct and indirect rebound effects on CO 2 emissions.…”

Insights from adding transportation sector detail into an economy-wide model: The case of the ADAGE CGE model

“…Despite these challenges, enhancing representation of the transportation sector within CGE models may improve understanding of potential economy-wide impacts of future shifts in energy and transportation service consumption. Prior work (e.g., Wade et al, 2019;Zhang, Caron, and Winchester (2018); Britz and van der Mensbrugghe ( 2016)) has shown that aggregation of technological and spatial detail in economic models can mask important sources of heterogeneity, potentially leading to substantially different predicted outcomes between models employing different levels of aggregation. In CGE models, this is sometimes labeled "aggregation bias."…”

Policy Perspective on the Role of Forest Sector Modeling

“…The degree of uncertainty often depends on the quality of data inputs and assumptions used (e.g., Cai et al, 2018), as well as parametric and model structure-based uncertainties. Discourse on the technical differences between modeling approaches-like different perspectives on foresight (e.g., recursive dynamic models with myopic expectations vs. inter temporal optimization models with perfect foresight)-and related uncertainties, strengths and weaknesses can be found in the papers in this special issue and other discussions (e.g., Wade et al, 2019;Lauri et al, 2019;Johnston et al, 2019;Sjølie et al, 2015).…”

“…However, these models rely on assumptions about future environmental as well as macroeconomic and specific forest market conditions which adds uncertainty. Model structures can also affect results which adds another level of uncertainty (e.g., Wade et al, 2019;Sjølie et al, 2015).…”