This study presents a spatially explicit life cycle assessment (LCA) of short-rotation coppice (SRC) willow cultivation across 21 modeled biorefinery supply zones on land identified as suitable for willow production in New York State (NYS). Integrating biomass productivity, road-network transportation, soil organic carbon (SOC) change, and cultivation-stage processes at 30 m resolution, the analysis evaluated cradle-to-biorefinery-gate greenhouse gas (GHG) impacts. A fully linear weighted least squares (FL-WLS) model estimated an area-weighted statewide SOC change of 0.241 Mg C ha −1 yr −1 , with supply-zone means ranging from 0.095 to 0.419 Mg C ha −1 yr −1 . Across 11,822,968 valid pixels, the statewide mean GHG impact was −147.87 kg CO 2 -eq Mg −1 of oven-dry willow biomass, with 99.74% of pixels exhibiting net-negative impacts. Supply-zone mean impacts ranged from −98.81 kg CO 2 -eq Mg −1 at P1 to −184.00 kg CO 2 -eq Mg −1 at P7. Under the modeled full-production scenario, the production-weighted statewide GHG intensity was −148.25 kg CO 2 -eq Mg −1 , corresponding to an annual net GHG balance of −2.058 Mt CO 2 -eq yr −1 . Carbon retained in standing stools and coarse roots provided the largest modeled carbon-stock credit (−150.86 kg CO 2 -eq Mg −1 ), followed by SOC change. Transportation (25.65 kg CO 2 -eq Mg −1 ) was the largest positive-emission component, followed by site management (16.80 kg CO 2 -eq Mg −1 ), harvesting and loading (16.18 kg CO 2 -eq Mg −1 ), and leaf-derived N 2 O (11.29 kg CO 2 -eq Mg −1 ). SOC change strongly explained variation in net GHG intensity among supply zones (r = 0.935, p < 0.001), while transportation strongly explained variation in combined positive-emission intensity (r = 0.972, p < 0.001). Biomass productivity was also associated with GHG performance, emphasizing the importance of jointly considering productivity, SOC response, and transportation logistics. These findings demonstrate substantial spatial heterogeneity in modeled willow cultivation impacts and show how spatially explicit GIS–LCA can inform targeted siting and feedstock-sourcing decisions for low-carbon bioenergy systems. The modeled carbon-stock credits are conditional on the adopted assessment boundary and should not be interpreted as permanent carbon sequestration.
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Hossain et al. (2026) studied this question.
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