Industrial Development and Economic Impacts of Forest Biomass for Bioenergy: A Data-Driven Holistic Analysis Framework

Abstract

A data-driven holistic analysis framework was developed to aid the industrial development of forest biomass for bioenergy to promote the regional bioeconomy. Leveraging the existing but fragmented multi-source data, four components of industrial bioenergy development were integrated into the framework including spatial statistical analysis of biomass feedstock and bioenergy production, machine learning-based suitability assessment, bioenergy plant sites identification and ranking, and socio-economic impacts assessment. A case study was conducted for forest biomass to pellet fuel in the U.S. Mid-Atlantic region. Our results indicate that the great potential of forest biomass with high variation at the county level is primarily clustered in western Pennsylvania and eastern North Carolina. Integrating the datasets of biomass feedstock, road conditions, employment status, income status, population, and current bioenergy production, the machine-learning model demonstrates good performance for bioenergy industry suitability assessment, with the high-suitable areas accounting for 19.76%, medium-suitable areas for 34.74%, and low-suitable areas for 54.49% in the region. Forest biomass availability and distance to major roads are the two top factors affecting bioenergy industry development. We identified 65 industrial sites within the suitable areas and their rankings were derived as a reference of the bioenergy development priority. The socio-economic impacts assessment indicates that the one-year construction of a medium-size pellet fuel facility (75,000 dry tons/year) could create 127 jobs, $8.78 million of labor income, while the operation could create 202 jobs, $10.52 million of labor income, $14.66 million of value-added, and $33.61 million of output in total per year for the state-level economy.