A distributional analysis of the socio-ecological and economic determinants of forest carbon stocks

• Published in: Environmental science & policy Vol. 60; pp. 28 - 37
• Main Authors: Soto, José R., Escobedo, Francisco J., Adams, Damian C., Blanco, German
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.06.2016
• Subjects: Carbon sequestration; Distributional impacts; Ecosystem services; Forest inventory and analysis; Quantile regression; ASW, USA, Florida; Forest inventory and analysis; Ecosystem services; Quantile regression; Carbon sequestration; Distributional impacts
• ISSN: 1462-9011; 1873-6416
• DOI: 10.1016/j.envsci.2016.02.015

•US Forest Inventory and Analysis and US Census data used to model determinants of forest carbon stocks.•Quantile regression findings show nonlinearity in the effects of key determinants.•Results highlight the limitations of conventionally used mean-based regression analyses.•Findings underscore the importance of considering socio-ecological and economic implications of forest policy decisions.•Results can inform policies to comply with US EPA carbon emissions reductions. Forest carbon (C) sequestration is being actively considered by several states as a way to cost-effectively comply with the forthcoming United States (US) Environmental Protection Agency’s rule that will reduce power plant C emissions by 32% of 2005 levels by 2030. However, little is known about the socio-ecological and distributional effects of such a policy. Given that C is heterogeneous across the landscape, understanding how social, economic, and ecological changes affect forest C stocks and sequestration is key for developing forest management policies that offset C emissions. Using Florida US as a case study, we use US National Forest Inventory Analysis and Census Bureau data in both linear regression and quantile regression analyses to examine the socio-ecological and economic determinants of forest C stocks and its relationship with differing communities. Quantile regression findings demonstrate nonlinearity in the effects of key determinants, which highlight the limitations of regularly used mean-based regression analyses. We also found that forest basal area, site quality, stand size, and stand age are significant ecological predictors of carbon stocks, with a positive and increasing effect on upper quantiles where C stocks are greater. The effect of education was generally positive and mostly significant at upper quantiles, while the effects of income and locations with predominantly minority residents (as compared to whites) were negative. Upper quantiles were also affected by population age. Our findings underscore the importance of considering the broader socio-ecological and economic implications of compliance strategies that target the management of forests for carbon sequestration and other ecosystem services.