The Central Appalachian region, where family forest landowners (FFLs) control much of the carbon sequestration potential, holds the potential for forest-based climate solutions. Despite this, participation in carbon offset programs remains low, largely due to the disconnect between small landowner needs and program structure. This study examines FFL preferences for carbon programs in Central Appalachia. Utilizing a panel-data mixed logit model, we evaluated the effects of contract length, payment amount, harvest requirements, and program administration on participation decisions. Our results indicate that higher payments significantly increase program participation. Furthermore, contract lengths of at least 15 years and restrictive harvests negatively influence program participation. Program administration played a significant role, with government-administered programs being less preferred, with odds 48% lower than privately administered programs. Landowner characteristics such as carbon-oriented ownership, education, and income also influence participation. The willingness-to-accept analysis shows high compensation demands for less favorable terms: $107–$397/ha/year for longer contracts and $104–$173/ha/year for harvest restrictions. Additionally, landowners require an extra $66/ha/year for government administration. The findings underscore the importance of designing carbon offset programs that are flexible, offer adequate compensation, and foster trust, while aligning with landowners’ management objectives.

The labor force participation rate (LFPR) in Midwestern counties declined from 2019 to 2021, following national trends but less sharply. Early in the pandemic, declines in LFPR were associated with reduced participation among older workers, in metro areas, and in counties highly vulnerable to the pandemic, while higher participation among Whites, female, and educated workers was associated with partially offsetting these losses. Metro areas with higher LFPR among individuals with children under age 6 and greater in-migration experienced smaller initial declines. By 2020–2021, patterns shifted: younger workers, manufacturing-dependent employment, and human capital were linked to sharper drops. Meanwhile, mid-career workers, educated labor, individuals with young children, and in-migration in metro areas helped moderate declines, highlighting complex regional and demographic dynamics.