Current issue: 57(1)
Under compilation: 57(2)
The existence and direction of causal relationships between the time series for the Finnish roundwood market for the period 1960–1994 is tested. Using simple bivariate analysis, we found evidence that for both logs and pulpwood, the lagged prices are helpful in forecasting quantity for the next year, but not vice versa. Saw log stumpage prices have significantly Granger-caused pulpwood prices over the business cycles, but the effect has diminished towards the present time. For quantities traded, the direction of causality was rather from pulpwood to saw logs. The consistency of bivariate test results was checked by the Granger-causality tests within trivariate VAR-models for both markets, and the results were found to be fairly similar to bivariate tests. The price fluctuations in the international markets for forest products have been found to be carried to domestic wood markets dominantly via the pulpwood part of the market.
The study aimed at recognizing the phases of forest succession where dead trees most probably occur. The model simulations showed that the increasing occurrence of dead trees culminated after the canopy closure. Thereafter the occurrence of dead trees decreased representing a pattern where high frequency of dead trees was followed by low frequency of dead trees, the intervals between the peaks in the number of dead trees being in Southern Finland about 15–30 years. Around this long-term variation there was a short-term variation, the interval between the peaks in the number of dead trees being 2–4 years. This pattern was associated with the exhausting and release of resources controlled by the growth and death of trees.
The PDF includes an abstract in Finnish.
The study examines the factor demands of the Finnish pulp and paper industry. In the theoretical part of the study, factor demand equations are derived using neoclassical production theory. In the empirical part, econometric factor demand model is estimated using annual time-series data for the period 1960–86. The relationship of factor demands and their prices are examined in terms of own price, cross price and substitution elasticities.
It is assumed that the ”representative firm” in the pulp and paper industry is minimizing its costs of production at a given output level. In addition, a number of other assumptions are made which enable the production technology to be represented by a cost function, in which the inputs are capital, labour, energy and raw materials. From the cost function, the factor demand equations, i.e., the cost share equations are derived by applying Shephard’s lemma. The equations are transformed to estimable form using translog approximation for the underlying factor share functions.
The study differs from the previous factor demand studies by applying the error correction model based on the Granger Representation Theorem and the results of the cointegration literature to model the dynamics of the factor demand. This approach provides a statistically consistent method for estimating the long-run static factor demand equations and the corresponding short-run equations. In general, the econometrics of integrated processes (e.g. stationarity and cointegration tests) applied in the present study have not been applied before in factor demand systems models.
The empirical results of the study indicate that the error correction approach can be applied to estimations of the factor demands for the pulp and paper industry. In both industry sectors, the adjustment to short run disequlibrium (price shocks) appears to be fairly rapid. The most significant results of the calculated elasticities are that the factor demands of pulp and paper industries clearly react to changes in factor prices and that there are significant substitution possibilities between the different inputs. The absolute values of the elasticities are, on average, somewhat larger than have been obtained in previous studies.
The PDF includes a summary in Finnish.
There is growing interest in the use of Landsat data to enable forest monitoring over large areas. Free and open data access combined with high performance computing have enabled new approaches to Landsat data analysis that use the best observation for any given pixel to generate an annual, cloud-free, gap-free, surface reflectance image composite. Finland has a long history of incorporating Landsat data into its National Forest Inventory to produce forest information in the form of thematic maps and small area statistics on a variety of forest attributes. Herein we explore the spatial and temporal characteristics of the Landsat archive in the context of forest monitoring in Finland. The United States Geological Survey Landsat archive holds a total of 30 076 images (1972–2017) for 66 scenes (each 185 km by 185 km in size) representing the terrestrial area of Finland, of which 93.6% were acquired since 1984 with a spatial resolution of 30 m. Approximately 16.3% of the archived images have desired compositing characteristics (acquired within August 1 ±30 days, <70% cloud cover, 30 m spatial resolution). Data from the Landsat archive can augment forest monitoring efforts in Finland, provide new information for science and applications, and enable retrospective, systematic analyses to characterize the development of Finnish forests over the past three decades. The capacity to monitor trends based upon this multi-decadal record with the addition of new measurements is of benefit to multisource inventories and offers nationally comprehensive spatially-explicit datasets for a wide range of stakeholders and applications.