NARA is tasked with helping stakeholders identify depot and biorefinery site locations suitable for a developing industry that uses forest residuals to produce bio-jet fuel and valued co-products. To provide impartial, science-based recommendations, NARA researchers devise ways to quantify and score important factors that impact a production facility’s economic sustainability, and then rank locations according to their combined scores.
In a recently published paper funded by NARA and titled “Integrating biogeophysical and social assets into biomass-to-biofuel supply chain siting decisions”, the authors describe NARA’s approach used to rank counties for their suitability as depot or biorefinery locations. The measures used to rank these counties look at biogeophysical capacity and social assets. The authors then demonstrate their methodology to rank counties within the NARA pilot supply region, the western Montana corridor (WMC), for both biogeophysical capacity and social assets.
You can view the article Integrating biogeophysical and social assets into biomass-to-biofuel supply chain siting decisions here.
Information regarding the western Montana corridor (WMC) supply chain study can be found here.
Scoring biogeophysical capabilities
To measure a county’s biogeophysical capabilities, the authors recorded for each county, and county cluster, the amount of unused forest residuals available within a given distance and the approximate distance to a petroleum refinery that would process the isobutanol produced. Additional considerations were given for rail and road access. Combining all of these factors produced a single score termed “Weighted Overlay Score” (WOS) that ranged from 1-10, with 10 representing the most favorable score for a facility location.
Eleven counties within the WMC received WOS scores of eight or greater. Of the 11 counties, eight had population centers greater than 1000 citizens and road and rail access close by. These eight counties are Bonner, Kootenai, and Boundary Counties in Idaho; Spokane County in Washington; and Lincoln, Lake, Flathead and Missoula Counties in Montana.
Determining a Social Asset Factor
Ranking refinery site locations based on biogeophysical capabilities satisfies logistical considerations but neglects a community’s willingness or cultural ability to positively engage with a new industry. The authors point out case studies that describe collaborative efforts occurring within the NARA region that succeed in one community yet fail in others. The conclusion made to explain success and failure in these cases is that communities differ in their “social assets”.
Measures used in this study to characterize a community’s social assets are 1) presence of rent-seeking groups, 2) presence of an arts related workforce, and 3) the health status construct. The presence of rent seeking-groups is a good indicator of a community that values networking between individuals and groups and reflects a willingness to collaborate. The presence of an arts related or creative workforce is indicative of a community that would show innovative approaches to handling change and overcoming challenges. The physical and mental health of a community will impact a workforce’s commitment and ability to run a facility. All of the datasets used to score these measures are available at the county level. The authors combine these three measurements into a single score designated as a Social Asset Factor (SAF). The higher the SAF score, the greater likelihood that a community will be socially inclined to embrace a new bio-facility. The authors in this study describe a high SAF as,
“… indicative of healthy communities that have trust of their government and each other, support for new ideas, and strong leadership to turn ideas into reality.”
The authors go on to say that,
“…a low SAF score may point toward additional investor challenges when trying to mobilize communities to support biomass-to-biofuel economic development opportunities.”
WMC county rankings combining biogeophysical and social asset scoring.
A social asset factor was generated for the eight counties identified with high biogeophysical capabilities, and the five counties with the highest SAF score are Missoula, MT (1.69 SAF), Spokane, WA (0.79), Flathead, MT (0.75), Bonner, ID (0.32) and Kootenai, ID (0.13). Based on these results, it is anticipated that engaging with stakeholders in Missoula, Spokane and Flathead Counties will provide a positive collaborative experience.
Additional refinements to be done
The authors intend to apply the SAF scoring method to other arenas (natural resources, health and human services) where a new policy or facility was introduced to communities and experienced success in some and failure in others. Their intent is to see if SAF scoring could have predicted the outcome. This retroactive analysis will test the method’s predictive capability to determining social acceptance. Meanwhile, the two-tiered scoring system described in this paper will be applied to other sub-regions within NARA’s four-state region (ID, MT, WA and OR) in order to provide facility recommendations for those sub-regions. Currently locations are being evaluated in western Oregon and Washington in a NARA pilot supply region called the Mid-Pacific to Cascade (MC2P) region.