Abstract
This research uses an econometric model to analyze the factors affecting non-industrial private forest landowners’ choice of intermediate forest management practices, and to examine how these choices might change in response to incentives for carbon sequestration. We also use parameter estimates to simulate the carbon sequestration potential for different combinations of management practices, and compare the effectiveness and costs of carbon sequestration-based and practice-based incentive payment schemes. Our results suggest that incentive payments increase the probability that desirable combinations of management practices are adopted. Simulation results indicate that incentives targeting fertilization yield the highest carbon sequestration potential, and that a carbon-based payment scheme produces higher carbon sequestration than a practice-based payments scheme.
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Notes
In 2007 private individuals, corporations, and other private groups owned 423 million acres (56 %) out of 751 million acres of forest land in the US (Smith et al. 2009).
Intermediate management practices are silvicultural treatments conducted to improve timber productivity and enhance resistance to potential hazards such as fire and disease while the stands are growing. Intermediate management involves practices that improve site quality such as fertilization, thinning, and fuel treatment, or improve resistance to pests and insects (IPCC 2000; North et al. 2009; McKinley et al. 2011).
Pattanayak et al. (2002) provide empirical evidence confirming joint production of timber and nontimber amenities by private forest owners.
Copies of the questionnaires used in the survey are available at http://www.fia.fs.fed.us/nwos/.
The NWOS sample locations correspond to the plot center of the FIA inventory plots (Butler et al. 2005). A certain percentage of randomly chosen plots is fuzzed within a buffer area of 0.5–1 miles around each plot, and swapped with similar plots in the same county to protect landowner privacy, because FIA has a policy of nondisclosure of exact locations due to confidentiality issues. However, the effect of perturbing and swapping on analyses using FIA data is negligible if the study area is large enough (McRoberts et al. 2005).
More precisely, the no management option refers to a landowner who did not choose fertilization, fuel treatment, or a combination of the two. The landowner may have implemented other management activities not included in the NWOS survey, but this is not relevant for the purposes of this analysis, which focuses on the choice between the listed options.
Because of the lack of identifiable information of each landowner’s harvested and replanted trees, we assume all landowners plant and harvest their trees.
\(SEV_{ik} =\frac{\sum \nolimits _{t=0}^T {(P_{ikt} Q_{ikt} -C_{ikt} )(1+r)^{T-t}} }{(1+r)^{t}-1}\)
The discount rate and time horizon were chosen for comparability with other studies following Stavins and Richards (2005).
We assume 30 bags (80 lb/bag) per trip (Hanley et al. 2006).
The Forest Vegetation Simulator (FVS) is an individual tree growth model widely used in the U.S. to support decision making on various forest management issues such as silvicultural prescriptions, fuel treatment, insect and disease impacts, and wildlife habitat management. The spatial scale of the FVS can vary from a single stand to thousands of stands. The stand growth simulation models can differ depending on the geographic region by applying regionally specific model variants (Crookston and Dixon 2005). The FVS is a very flexible carbon accounting tool, since it can consider the spatial heterogeneity of each forest parcel and can be applied with various silvicultural forest management activities.
To simulate tree growth with different management options using FVS, we need to define a general silvicultural treatment rule for each management practice. In the case of fuel treatment, we followed the US Forest Service guide to fuel treatment in the western US (Johnson et al. 2007). We apply four silvicultural options (thinning from below to 50 trees per acre (tpa), 100 tpa, 200 tpa, and 300 tpa with 18 bdh limit with surface fuel removal) to calculate trends of stand volume and carbon sequestration potential. In the case of fertilization, we use application of 200 pounds of nitrogen per acre, since the FVS supports only this option.
Stand density index (SDI) is a measure of stocking of tree stands based on the number of trees per acre (Avery and Hurkhart 2002).
Average marginal effects are calculated using the following formula: \(\partial \Pr _{ik}/\partial z_{ik}^j =\Pr _{ik} \cdot \left[ \beta _k^j -\sum _k {\left( \Pr _{ik} \cdot \beta _k^j \right) } \right] \), where \(z_{ik}^j\) and \(\beta _k^j\) are the \(j\)th elements of vectors \(z_{ik}\) and \(\beta _k\), respectively.
The semi-elasticities are calculated as the percentage point change in the probability of adopting a certain combination for a 1 % change in the net returns for each choice.
Actual probabilities of management choices weighted by acreage are 12.1 % with fertilization and fuel treatment, 3.9 % with fertilization only, 48.2 % with fuel treatment only, and 35.8 % with no management. We also use Theil’s Inequality Coefficient to validate the model further by comparing actual choices and predicted choices (Langpap and Wu 2008). The coefficient is 0.11 which indicates a good predictive performance (the coefficient ranges between 0 and 1 with a value of 0 indicating a perfect prediction).
The carbon accumulation trend for fertilization and fuel treatment is calculated by combining management rules from fuel treatment and fertilization.
Since the data on landowners’ management choices correspond to the years 2002–2006, our simulation is calibrated to begin in 2006. We use the Carbon Report in the Fire and Fuel Extension of Forest Vegetation Simulator (FFE–FVS) (Hoover and Rebain 2011), which accounts for carbon in above ground live trees, below ground live trees, below ground dead trees, standing dead trees, down dead wood, the forest floor, the understory, wood products in use, products in landfills, and carbon emitted from combustion.
Baseline Carbon Sequestration \(\hbox {(Mt/acre)} = \sum \nolimits _{i=1}^N \left( \bar{C}_{ik} \times Acreage_i /\sum \nolimits _{i=1}^N Acreage_i \right) \), where \(\bar{C}_{ik}\) is the amount of carbon stored per acre for management option \(k\) with the highest predicted probability and \(Acreage_{i}\) is acreage of forestlands owned by each individual landowner \(i\).
Note that this is comparable to removing incentives for the ‘Fuel Treatment’ choice.
The contract length of federal conservation payment programs is 10–15 years for the Conservation Reserve Program (CRP), 5–10 years for the Wildlife Habitat Incentives Program (WHIP), and 6 years for the Environmental Quality Incentives Program (EQIP) (USDA Natural Resources Conservation Service 2008).
It is difficult to choose the appropriate range of incentive payment since there are no comparable examples from previous studies of intermediate forest management choices. Therefore, we chose the range of incentive payments based on the average range of maximum carbon price from US EPA (2005) and the U.S. Agricultural Sector Model (Lewandrowski et al. 2004).
The amount of carbon sequestration achieved in the long-run is uncertain since it is unknown when each plot will be harvested. If we knew the distribution of final harvest schedules of these forestlands, we could calculate the expected amount of carbon sequestered in the long-run. However, since the distribution of the harvest schedule is unknown, we only account the amount of carbon sequestered within the duration of a contract.
Additionally, the adoption rate of the choice fertilization only starts decreasing at payments above $50/acre because the own marginal effect of the annual LTV of fertilization and fuel treatment is higher than that of fertilization only. Results showing the probability of choosing different management options as a function of annualized payments per acre are available upon request.
We do not consider implementation costs for measurement, monitoring, and verification when comparing the cost of carbon between a practice-based scheme and a carbon-based scheme. If they were taken into account, the carbon sequestration potential under a carbon-based approach could be more costly than our estimate because generally measurement and monitoring costs are greater under a carbon-based scheme than under a practice-based scheme.
The exception is no management at higher payment levels. This is because as the payment increases relatively more high cost-high benefit landowners choose this option under a carbon-based payment, while more low cost-low benefit landowners enroll under a practice-based payment. The difference in benefits is small, and the adoption rate increases faster with the payment under a practice-based payment scheme. Hence, for high enough payment levels the practice-based scheme outperforms the carbon-based scheme for no management.
We only consider the carbon sequestration potential within the duration of the contract. These results might change if carbon flows after the termination of the contract are considered.
Stavins and Richards (2005) conclude that after normalization to 1997 dollars, the cost of carbon for afforestation ranges from $28 to $83/Mt for 272 MMt of national scale annual carbon sequestration, and from $33/Mt to $99/Mt for 454 MMt of national scale annual carbon sequestration.
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Acknowledgments
We thank Andrew Plantinga, Claire Montgomery, John Antle, Ralph Alig, participants at the 2012 AERE and AAEA Annual Meetings, and anonymous reviewers for helpful comments on earlier versions of this paper. Any remaining errors are our own. We are also thankful to the U.S. Department of Agriculture Forest Service for providing NWOS data. This research was funded by U.S. Department of Agriculture Forest Service Cooperative Agreement 09-JV-11261955-033. The opinions expressed herein are those of the authors, and do not represent the views of the U.S. Department of Agriculture Forest Service.
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Kim, T., Langpap, C. Incentives for Carbon Sequestration Using Forest Management. Environ Resource Econ 62, 491–520 (2015). https://doi.org/10.1007/s10640-014-9827-3
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DOI: https://doi.org/10.1007/s10640-014-9827-3