Reducing standard errors by incorporating spatial autocorrelation into a measurement scheme for soil carbon credits |
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| Authors: | Siân Mooney Ken Gerow John Antle Susan Capalbo Keith Paustian |
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| Institution: | (1) Department of Economics, Boise State University, Boise, Idaho 83725, USA;(2) Department of Statistics, University of Wyoming, Laramie, Wyoming 82071, USA;(3) Department of Agricultural Economics and Economics, Montana State University, Bozeman, Montana 59717, USA;(4) Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA |
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| Abstract: | Several studies have suggested that geostatistical techniques could be employed to reduce overall transactions costs associated
with contracting for soil C credits by increasing the efficacy of sampling protocols used to measure C-credits. In this paper,
we show how information about the range of spatial autocorrelation can be used in a measurement scheme to reduce the size
of the confidence intervals that bound estimates of the mean number of C-credits generated per hectare. A tighter confidence
interval around the mean number of C-credits sequestered could increase producer payments for each hectare enrolled in a contract
to supply C-credits. An empirical application to dry land cropping systems in three regions of Montana shows that information
about the spatial autocorrelation exhibited by soil C could be extremely valuable for reducing transactions costs associated
with contracts for C-credits but the benefits are not uniform across all regions or cropping systems. Accounting for spatial
autocorrelation greatly reduced the standard errors and narrowed the confidence intervals associated with sample estimates
of the mean number of C-credits produced per hectare. For the payment mechanism considered in this paper, tighter confidence
intervals around the mean number of C-credits created per hectare enrolled could increase producer payments by more than 100
percent under a C-contract. |
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