Increasing Supply for Woody-Biomass-Based Energy through Wasted Resources: Insights from US Private Landowners

Abstract

Woody biomass is suggested as a substitute for fossil fuels to achieve sustainable development. However, transitioning the land purpose to produce woody biomass entails investment and a tradeoff between wood pellet production and the current utilities created by the land, hindering the willingness of private landowners. To many forest landowners, forest trees and residues considered unprofitable to transport would be left in the forest without other proper use. The wasted woody resources on the land can be a potential source to increase the woody biomass supply. To support the policymakers, logging companies, state agencies, and landowners to better capitalize on these wasted resources, we aimed to identify the characteristics of woody-resource-wasting landowners and examine how to increase their likelihood to contribute to woody-biomass-based energy. By employing Bayesian Mindsponge Framework (BMF) analytics on a dataset of 707 private landowners in the United States (US), we discovered that landowners being male, having higher income, and being a member of a state/national forestry organization were more likely to waste woody resources. Moreover, woody-resource-wasting landowners perceiving woody-biomass-based energy as a substitution for fossil fuel were more likely to sell wood. In contrast, those perceiving environmental costs over the benefits of woody-biomass-based energy were less likely to sell. These findings can be used as insights for policymakers, logging companies, and state agencies to find an additional supply of woody-biomass-based energy from landowners likely to waste woody resources.

Contradicting effects of subjective economic and cultural values on ocean protection willingness: preliminary evidence of 42 countries

“…To examine the objectives, the Mindsponge Theory was employed to construct models exploring factors influencing ocean protection supports (Nguyen et al, 2022b;Vuong et al, 2023). The models were then measured and validated using.…”