Stumpage Price Impacts on Carbon Accumulation

California’s Cap-and-Trade program, created by the California Air Resources Board (CARB), allows large-scale polluters in the state to partially offset their greenhouse gas emissions by purchasing carbon offset credits. Although there are several types of allowable credits, by far the most common are credits generated by forest carbon projects. These projects require landowners to demonstrate that their forests are storing carbon at levels that exceed what CARB has determined to be the Common Practice Baseline for the associated ecological region and forest type. CARB does not disclose its procedure for calculating these baselines, but it is known to use data collected by the U.S. Forest Service’s Forest Inventory and Analysis (FIA) Program. Forest carbon offset developers like our client, Bluesource LLC, rely on the CARB baselines and data on forest carbon stocking levels to identify potential development opportunities. In this report, we seek to determine if CARB baselines and FIA carbon levels can be predicted by prices received by forest owners for their timber, otherwise known as stumpage prices. Previous studies have demonstrated a theoretical effect of stumpage prices on timber harvests, hence on aboveground forest carbon levels, but none has modeled the empirical relationship between stumpage prices and either the CARB baselines or FIA carbon levels. The first section of this report provides an overview of the carbon market in California, focusing on how forest carbon offsets are developed by third-party organizations like Bluesource. This section also states our three research questions:

1. Can stumpage prices be used to explain CARB baselines?
2. Can stumpage prices be used to explain FIA aboveground carbon levels?
3. If the answers to questions 1 and 2 are affirmative, can the associated statistical models be used to predict future CARB baselines and FIA carbon levels?

The second section describes the work done to collect data on stumpage prices from Maine, Michigan, and West Virginia, the CARB baseline report from 2011 and 2015, and the FIA carbon data from the same three states. There were significant differences in the reporting styles of the various datasets, so this section also explains how the data were cleaned and reformatted in preparation for our analyses. It provides as well some descriptive statistics to orient the reader on data characteristics.

The third section describes the methods undertaken in our analyses. We used regression models to examine the relationship between stumpage prices and both the CARB baselines and the FIA carbon levels. Because the CARB and FIA data differ in the years in which they are reported, so to do the analysis methods. The CARB analysis utilized a cross-sectional regression, whereas the FIA analysis utilized a panel analysis. Beyond stumpage prices, we tested the effects of a number of other explanatory variables in each analysis and ultimately selected two models to be used to project future CARB baselines and FIA carbon levels. The model results and the results of their projections are also included in this section and are shown using maps of the expected percentage increase for each geographic region of our study.

The fourth and final section highlights the implications of our findings both to our client and to the relevant literature. We also acknowledge limitations of our models and offer suggestions of how the models could be improved and adjusted for use in other geographic regions. Although stumpage prices influence forest owner harvesting behavior and thus forest carbon levels and CARB baselines too, they likely do not capture all forces influencing landowners’ forest management decisions. A more sophisticated model with more complete data may have greater predictive capability.

This report makes several key points and recommendations: • Stumpage prices can be used as predictors of both CARB baselines and FIA carbon levels. • The models we developed could be improved by including more complete stumpage price data from the study region, including sales from both public and private forests instead of only one or the other. • The effects of other variables should be investigated, particularly interest rates and price trends (vs. price levels). • Analyzing the effect of FIA carbon levels on CARB baselines may provide a more direct way to project future CARB baselines. • These models are applicable only to Maine, Michigan, and West Virginia, but the methods used can be replicated for any region with similar data.