Wood harvesting efficiency analysis of regional forest directorates in Turkey: k-means clustering and data envelopment analysis approach

Abstract

ABSTRACT Wood harvesting is a crucial element of sustainable forest management. Harvesting of wood and non-wood forest products has gained importance for increasing environmental services, economic development and sustainability of forests. The aim of this study was to reveal the efficiency values of regional forest directorates (RFD) for wood harvesting between 2015 and 2020 in Turkey using six input and two output variables. Data envelopment analysis (DEA) and k-means clustering analysis were used. From the results obtained, 35.7% of the 28 RFDs were efficient using the results of the input- and output-oriented Charnes, Cooper, and Rhodes (CCR) model. In the input-oriented CCR model, for inefficient decision-making units (DMUs) to become efficient, on average the variable that needs to be reduced the most is forest area, and the variable that needs to be reduced the least is allowable cutting. Conversely, in the output-oriented CCR model, an average potential increase of 23.84% in the harvest of industrial wood, and of 29.48% for fuelwood, was determined for the inefficient DMUs. In addition, according to the results of the k-means cluster analysis, it was found that the DMUs in the cluster distribution of inefficient DMUs were similar to each other in terms of location. It will make a positive contribution for inefficient DMUs to consider the efficient DMUs shown as references to reach the targeted input/output values to become efficient. In addition, the results of this study are important for enabling forest managers to make performance evaluations based on efficiency analyses.