Current issue: 58(4)

Scopus CiteScore 2023: 3.5
Scopus ranking of open access forestry journals: 17th
PlanS compliant
Select issue
Silva Fennica 1926-1997
1990-1997
1980-1989
1970-1979
1960-1969
Acta Forestalia Fennica
1953-1968
1933-1952
1913-1932

Articles containing the keyword 'non-industrial private forest landowners,'

Category : Article

article id 5555, category Article
Mauno Pesonen, Arto Kettunen, Petri Räsänen. (1995). Modelling non-industrial private forest landowners’ strategic decision making by using logistic regression and neural networks: Case of predicting the choice of forest taxation basis. Silva Fennica vol. 29 no. 2 article id 5555. https://doi.org/10.14214/sf.a9206
Keywords: logistic regression; Finland; Neural Networks; forest owners; forest taxation; non-industrial private forest landowners,; timber management strategies
Abstract | View details | Full text in PDF | Author Info

In this study, logistic regression and neural networks were used to predict non-industrial private forests (NIPF) landowners’ choice of forest taxation basis. The main frame of reference of the study was the Finnish capital taxation reform of 1993. As a consequence of the reform, landowners were required to choose whether to be taxed according to site-productivity or realized-income during the coming transition period of thirteen years.

The most important factor affecting the landowners’ choice of taxation basis was the harvest rate during the transition period, i.e. the chosen timber management strategy. Furthermore, the estimated personal marginal tax rate and the intention to cut timber during next three years affected the choice. The descriptive landowner variables did not have any marked effect on the choice of forest taxation basis.

On average, logistic regression predicted 71% of the choices correctly; the corresponding figure for neural networks was 63%. In both methods, the choice of site-productivity taxation was predicted more accurately than the choice of realized-income taxation. An increase in the number of model variable did not significantly improve the results of neural networks and logistic regression.

  • Pesonen, E-mail: mp@mm.unknown (email)
  • Kettunen, E-mail: ak@mm.unknown
  • Räsänen, E-mail: pr@mm.unknown

Register
Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content
Your selected articles