Table 1.
Attributes and levels used to create hypothetical carbon credit programs.
Table 2.
Selection of potential average, per acre, per year revenue generation scenarios.
Fig 1.
Choice scenario presented to respondents in mail survey.
Table 3.
Effects coding for analysis of best-worst scaling data.
Fig 2.
Percent respondents, non-respondents, and undeliverable surveys by state of residence of landowner in survey sample.
Table 4.
Conditional logit level-scale impacts.
Table 5.
Conditional logit attribute impacts.
Table 6.
Results from binary-choice model: Random effects logit model.
Fig 3.
Willingness to accept carbon credit program feature from random effects logit model binary choice (USD per acre per year/choice).