Economic impact of growth effects in mixed stands of Norway spruce and European beech – A simulation based study

Stefan Friedrich, Carola Paul, Susanne Brandl, Peter Biber, Katharina Messerer, Thomas Knoke

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Productivity in mixed forest stands is often higher than that in pure forest stands. Economic analyses usually exclude this phenomenon. In our study, we assess the consequences of an increased productivity in mixed forests for the economically optimal proportions of tree species in forest stands. The economic model applied centers around the Modern Portfolio Theory with the Value-at-Risk as the objective function. The methodological approach encompasses a structured Monte Carlo simulation to generate distributions of returns for different scenarios. Risks include price fluctuations for raw wood as well as natural hazards. The study's major novelty is that it combines well-researched productivity relations in mixed forests with recent empirical survival models for mixed and pure stands to address site variability across Bavaria. We parametrized the model with simulated growth data for Norway spruce and European beech for various climatic and geological conditions. From the results, we conclude that decision makers can increase the Value-at-Risk of forest portfolios when overyielding occurs, by choosing higher shares of beech. Climatic conditions and the extent of overyielding strongly influence optimal stand composition. Using Value-at-Risk as a risk measure that regards both expected returns and their variation, however, leads to the observation that increased risk aversion rather favors higher shares of spruce in forest stands, despite its higher biophysical risks.

Original languageEnglish
Pages (from-to)65-80
Number of pages16
JournalForest Policy and Economics
Volume104
DOIs
StatePublished - Jul 2019

Keywords

  • Overyielding
  • Portfolio analysis
  • Survival probability
  • Tree species mixture
  • Value-at-risk

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