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Where and When Carbon Storage can be Bought Cost Effectively from Private Forest Owners

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Abstract

The role of time in estimating the cost of forest carbon is often ignored in the literature, nor does the literature address the issues of where and when the purchase of forest carbon storage becomes socially beneficial. In our study, we identify the spatial and temporal allocations of forest carbon investments that are socially beneficial based on empirical analysis. We use the Central and Southern Appalachian region in the Eastern United States as a case study over three periods (i.e., 1992–2001, 2001–2006, and 2006–2011) that are roughly in line with moderate, upturn, and downturn market conditions. The areas from which it is socially beneficial to buy carbon storage are mainly in flat terrain and further away from urban boundaries, hence facing lower development pressure and lower urban net returns. These areas also have less urban land and more forestland. The mapping of carbon cost over the three market conditions in our case study also indicates that the socially beneficial carbon area shrinks as the opportunity cost increases when the real-estate market evolves from a moderately growing to a booming market. The socially beneficial carbon area shrinks further as the demand from urban development on forestland collapses when the real-estate market enters a downturn stage.

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Notes

  1. Forest management practices can provide substantial carbon storage (Khanal et al., 2016; Galik et al., 2013; Latta et al., 2011). We assume fixed forest management practices in this paper as we focus on carbon storage benefits from incentive payments that affect decisions at the extensive margin.

  2. In addition, the 1992 NLCD retrofit data still may not be seamless with NLCD data in later years. Although the retrofit data minimize discrepancies between the original 1992 NLCD and the NLCD data in later years, the discrepancies are not eliminated completely. Around 37,000 grid cells in our sample have losses of urban land between 1992 and 2001. We drop the grid cells with discrepancies in forestland shares greater than or equal to 1%.

  3. If we rewrite Eq. (1) as \(\log \left( {\frac{{Sh_{ft \,+\, 1}}}{{Sh_{ft}}}} \right) = f(x)\), where \(f(x)\) is the right-hand side of Eq. (1) that comprises the independent variables and their coefficients plus the error term. Reorganizing the above equation gives \(Sh_{ft + 1} = Sh_{ft} \ast e^{f(x)}\). The \(e^{f(x)}\) is a growth rate of forestland.

  4. To check for robustness, we used different cut-off values. When we increased the cut-off value to four or more, the rural dummy was no longer statistically significant.

  5. The time periods in 1992–2001 are 10 years and 5 years for 2001–2006 and 2006–2011. In order to keep the time window consistent, we also carry out the analysis by pooling 2001–2011 in a single regression instead of in separate regressions for 2001–2006 and 2006–2011. This model specification, however, aggregates the data between a fast-growing real-estate market and a downturn market. The estimated coefficients are not statistically significant for some of the key variables during 2001–2006. The regression results are presented in Appendix A.

    We also did another robustness check on the cut-off time that defines the moderate and upturn market by pooling 1992–2006. The regression results for this check are presented in Appendix B. The estimation lead to a marginal cost of carbon ($2.22 per ton) that is between the estimates from 1992–2001 ($1.84 per ton) to 2001–2006 ($12.09 per ton).

  6. To test whether the market conditions have different impacts on land-use decisions, we conducted a joint F test on whether the coefficients are equal among the three periods and also a Chow test to test the same. Both the joint F tests and the Chow test lead to rejection of the null hypothesis that coefficients are equal across the three time periods at a 1% significance level. These results indicate that the coefficients are different in the three time periods, justifying our decision to run separate regressions for the different time periods.

  7. The DWH test comprises two-stage regressions. The first stage regresses \(DNR_t\) on a set of instrumental variables (IV) and exogenous variables. Out of the first-stage regression, we calculate the residuals and then combine these residuals as regressors into the second-stage regression. In the second-stage regression, the dependent variable is \(\log \left( {\frac{{Sh_{ft \,+\, 1}}}{{Sh_{ft}}}} \right)\) and independent variables include the residuals and the same set of variables specified in Eq. (1). If the estimated coefficients of the residual are statistically significant, then the DNR is endogenous, otherwise, it is not. Given that we have interactions of DNRs and other exogenous variables in our forestland change model, we adopt a nonlinear functional form specified in Kelejian (1971), including the squared term of the regressors and their interactions.

  8. The net present value (NPV) of the social cost of CO2 in 2015 at 3% discount rate is $36/ton in 2007 dollars. Using a conversion factor of 3.67 between CO2 and carbon, we calculate the NPV of the social cost of carbon as $132.12. This NPV is based on a 40-year period from 2010 to 2050, hence the annuity value is $5.72 or $6.01 in 2010 dollars. Social costs of CO2 estimated based on impacts to particular local communities will be different across different areas. However, the focus of this study is on potential for carbon payments that would be deployed at large scales (across this region or nationally). As a result, we use a single, aggregate, social cost to compare targeted areas across different time periods (market conditions) on the same basis, rather than considering local variation in these costs.

  9. The unobservable component of the model will come from a range of sources, including individual variation in decision-making over conversion decisions as well as limitations on our ability to resolve some aspects of the economic data to the grid-cell level.

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Acknowledgements

We gratefully acknowledge the Agriculture and Food Research Initiative Competitive Grant no. 11401442 and Multistate Project no. TEN00507 (Multistate no. W4133) from the USDA National Institute of Food and Agriculture through the project “Developing a Cost-Effective Payment System for Forest Carbon Sequestration” and “Costs and Benefits of Natural Resources on Public and Private Lands: Management, Economic, Valuation, and Integrated Decision-Making,” respectively. We also gratefully acknowledge G. Chen for generating carbon model outputs and K. Clark, B. Wilson, J. Menard, L. Lambert, T. Kim, and S. Kwon for helpful discussion and data support.

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Authors and Affiliations

  1. Department of Agriculture Science, Texas State University, San Marcos, TX, USA

    Xiangping Liu

  2. Department of Agricultural and Resource Economics, University of Tennessee, Knoxville, TN, USA

    Seong-Hoon Cho

  3. Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA

    Paul R. Armsworth

  4. School of Forest Resources, University of Maine, Orono, ME, USA

    Daniel J. Hayes

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  1. Xiangping Liu
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Appendices

Appendix A

Table 4 Regression results for periods 1992–2001 and 2001–2011
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Appendix B

Table 5 Regression results for periods 1992–2006
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Appendix C

Table 6 Instrumental variable (IV) regression results for period 2001–2006
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Liu, X., Cho, SH., Armsworth, P.R. et al. Where and When Carbon Storage can be Bought Cost Effectively from Private Forest Owners. Environmental Management 67, 930–948 (2021). https://doi.org/10.1007/s00267-021-01427-4

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