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Ecosystem services and economic competitiveness of perennial energy crops in the modelling of biomass potential – A case study of the Czech Republic

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  • Králík, T.
  • Knápek, J.
  • Vávrová, K.
  • Outrata, D.
  • Romportl, D.
  • Horák, M.
  • Jandera, J.

Abstract

The paper focuses on the design of a methodology that links the modelling of biomass potential from agricultural land using geographic information systems (derivation of biomass yields according to soil, topographical and climatic characteristics) with the growing need to provide non-productive functions contributing to sustainable land use and ensure the economic viability of biomass from energy crops. According to the proposed methodology, perennial energy crops are prioritized for allocation where the land risk index is highest. The land risk index is determined by assessing 5 basic characteristics - landscape connectivity, landscape heterogeneity, threat of drought, threat of water erosion and threat of soil erosion. In order to assess the economic competitiveness of biomass produced, a methodology for determining biomass production price is used, with the aim of matching the economic effect of growing conventional crops. Land best allocated to perennial energy crops is characterized by a high land risk index value and a competitive biomass production price. The application of the methodology is demonstrated using the case study of the Czech Republic. The results of the case study show that when energy crops are selected based on a combination of necessary non-productive functions and economic competitiveness, this significantly influences the allocation of perennial energy crops to individual land blocks and their contribution to biomass energy potential.

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  • Králík, T. & Knápek, J. & Vávrová, K. & Outrata, D. & Romportl, D. & Horák, M. & Jandera, J., 2023. "Ecosystem services and economic competitiveness of perennial energy crops in the modelling of biomass potential – A case study of the Czech Republic," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:rensus:v:173:y:2023:i:c:s1364032122010012
    DOI: 10.1016/j.rser.2022.113120
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