4.7 Article

A mixed-integer optimization model for the economic and environmental analysis of biomass production

Journal

BIOMASS & BIOENERGY
Volume 67, Issue -, Pages 8-23

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.biombioe.2014.03.025

Keywords

Switchgrass; Biofuel and biomass production; Mixed-integer linear optimization; Sustainability and biodiversity; Carbon emissions and sequestration; Soil erosion prevention

Funding

  1. National Science Foundation [EPS-0903806]
  2. state of Kansas through the Kansas Board of Regents
  3. Office of Integrative Activities
  4. Office Of The Director [0903806] Funding Source: National Science Foundation

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Biofuel production from second-generation feedstock has become critical due to environmental concerns and the need for sustainable energy supply. This paper provides a unique optimization approach of quantifying and formulating the economic and environmental benefits of switchgrass production at the farm level. In particular, we propose a multi-objective mixed-integer programming model, which maximizes the revenue from harvested switchgrass biomass and the economic value obtained from the positive environmental impacts of switchgrass yield during a ten-year planning horizon. Environmental impacts include soil erosion prevention, sustainability of bird populations, carbon sequestration, and carbon emissions, while economic impacts are analyzed under various budget, yield, and sustainability scenarios. The proposed model is then applied to a case study in the state of Kansas. Results show that given the current market prices, switchgrass cultivation on grassland and cropland is highly profitable. The model results also suggest that if utilized by the government, conservation reserve program (CRP) incentives could make marginal land more favorable over cropland. We perform sensitivity analysis to address the uncertainty in budget, yield, and utilization of cropland, and present insights into the economic and environmental impacts of switchgrass production. This model can also be extended to biomass production from any other types of energy crops to identify the most efficient production planning strategies under various management scenarios. Published by Elsevier Ltd.

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