Understanding demand for wood products in New Zealand’s major log markets. (2015)
Type of ContentTheses / Dissertations
Degree NameBachelor of Forestry Science
PublisherUniversity of Canterbury
AuthorsDrummond, Ryan C.M.show all
New Zealand’s forestry sector is largely reliant on the presence of a strong export market with 57% of the volume harvested being exported of which 99% goes to Japan, the Republic of Korea, China and India. This identifies the need to analyse demand in these countries to better understand their needs in the future. Consumption of wood products per capita is a commonly used metric for estimating demand and was used in this research. Volumes of imports, exports and production were collected from the Food and Agricultural Organisation of the United Nations (FAO) and data for a range of explanatory variables was collected from a variety of official sources. Historical trends in consumption identified that as countries develop socially and economically their consumption shifts from largely solid wood products such as sawn timber to more processed products such as wood-based panels and paper and paperboard. Consumption was modelled using linear regression techniques to develop models which could be used to forecast consumption in the future. A wide variety of potential explanatory variables were considered and the models presented represent the most effective of these. GDP per capita was found to be the single most effective explanatory variable being highly significant (p<0.01) in all models. Price was also found to be a strong determinant of consumption, understandable as price is a major component of supply and demand dynamics. Measures of construction activity were found to be related to consumption of sawn timber in all studied countries and for wood-based panels in Japan. Forecasts produced for consumption in Japan should be used as only an example of the capability of the models presented herein. More work is required to develop these equations into a form where they can be used to more accurately estimate future consumption.