Pekka Leskinen

Improving the quality of landscape ecological forest planning by utilising advanced decision-support tools

Abstract The quality of landscape ecological analyses and their integration with the multi-objective comparison of forest plans can be improved by making use of the decision-support methods, techniques, and tools produced by recent research on forest planning, as demonstrated in this study. Special attention is given to strengthening the ecological grounds of calculations through modelling expert knowledge, quantification of ecological evaluations, integration of different objectives and different phases of the planning process, and analysing the effects of uncertainty in ecological judgments on the final results. The planning process is illustrated by a case study. The landscape ecological approach is finding increasing application in practical forest planning, especially in boreal forestry. Unfortunately, gaps in the available ecological knowledge, and the inefficiency of the planning methods and tools used often lead to vague planning processes. In many cases, only methods originally developed for wood-production planning are still applied, and planning advances (e.g. multi-objective optimisation, Geographical Information Systems (GIS) tools, and modelling expert knowledge) are under-utilised. In this study, HERO heuristic multi-objective optimisation, GIS operations, pairwise comparisons techniques, and Bayesian analysis are applied in an integrated planning process. Efficient forest plan alternatives are generated for further consideration by utilising heuristic optimization and GIS. Given the multi-objective choice situation, the plans generated are holistically evaluated by means of multiple decision-support tools and techniques.

Measurement scales and scale independence in the analytic hierarchy process

One approach to evaluate the relative performance of decision alternatives with respect to multiple criteria is provided by the analytic hierarchy process. The method is based on pairwise comparisons between attributes, and several numerical measurement scales for the ratio statements have been proposed. The choice of measurement scale is re-examined, and new arguments supporting the measurement scale of geometric progression are derived. Separately from the measurement scale considerations, the effects of the scale parameter in geometric measurement scale are also studied. By using a regression model for pairwise comparisons data, it is shown that the statistical inference does not depend on the value of the scale parameter in the case of a single pairwise comparison matrix. It is also shown when the scale independence of statistical inference can be achieved in a decision hierarchy. This requires the use of the geometric-mean aggregation rule instead of the traditional arithmetic-mean aggregation. The results of the case study demonstrate that the measurement scale and the aggregation rule have potentially large impacts on decision support. Copyright

Assessing ecological values with dependent explanatory variables in multi-criteria forest ecosystem management

Multi-criteria forest ecosystem management considers the overall evaluation of alternative management plans with respect to objectives, which typically deal with both ecological and economic issues. One approach is to make the evaluations quantitatively so that analyses based on trade-offs between decision criteria can be carried out. This requires that the ecological objectives should also be assessed numerically. The present paper studies the numerical assessment of ecological values of forest ecosystem management in the context of multi-criteria planning. In a case study concerning planning applied to a forest area in Kainuu, eastern Finland, the ecological values were described by the amount of old forest, the amount of dead wood and the amount of deciduous trees. Compared to previous models, recently developed statistical modelling techniques were applied to handle the assessment process. The advantages of the statistical approach included that the potential interactions between the ecological variables can be taken into account. The case study results showed statistically significant interactions between the amount of old forest and the amount of dead wood suggesting that the proposed model is of practical importance for multi-criteria forest ecosystem management.

Impact of normalisation, elicitation technique and background information on panel weighting results in life cycle assessment

PurposeWeighting in Life Cycle Assessment (LCA) is a much-debated topic. Various tools have been used for weighting in LCA, Multi-Criteria Decision Analysis (MCDA) being one of the most common. However, it has not been thoroughly assessed how weight elicitation techniques of MCDA with different scales (interval and ratio) along with external and internal normalisation affect weighting and subsequent results. The aim of this survey is to compare different techniques in an illustrative example in the building sector.MethodsA panel of Nordic LCA experts accomplished six weighting exercises. The different weight elicitation techniques are SWING which is based on the interval scale; Simple Multi-Attribute Rating Technique (SMART) and Analytic Hierarchy Process (AHP) which is based on the ratio scale. Information on the case study was provided for the panellists, along with characterised or normalised impact assessment scores. However, in the first weighting exercise, the panellists were not provided with any scores or background information, but they had to complete the weighting at a more general level. With the weights provided by the panel, the environmental impacts of three alternative house types were aggregated. The calculations were based on three well-grounded aggregation rules, which are commonly used in the field of LCA or decision analysis.Results and discussionIn the illustrative construction example, the different aggregation rules had the biggest impact on the results. The results were different in the six calculation methods: when externally normalised scores were applied, house type A was superior in most of the calculations, but when internal normalisation was accomplished, house type C was superior. By using equal weights, similar results were obtained. None of the panellists intuitively considered A as the superior house type, but in some of the calculations, this was indeed the case. Furthermore, the results refer to the fact that the panellists completed the weighting on the basis of their general knowledge, without taking the features of different weight elicitation techniques into account.ConclusionsExternal normalisation provides information on a magnitude of impacts, and in some cases, external normalisation may be a more influential factor than weighting. Based on the results, it cannot be stated which different weight elicitation technique is the most suitable for LCA. However, the method should be selected based on the aims and purpose of the study. Moreover, the elicitation questions should be explained with care to experts so that they interpret the questions as intended.

Rank reversals in multi-criteria decision analysis with statistical modelling of ratio-scale pairwise comparisons

In multi-criteria decision analysis, the overall performance of decision alternatives is evaluated with respect to several, generally conflicting decision criteria. One approach to perform the multi-criteria decision analysis is to use ratio-scale pairwise comparisons concerning the performance of decision alternatives and the importance of decision criteria. In this approach, a classical problem has been the phenomenon of rank reversals. In particular, when a new decision alternative is added to a decision problem, and while the assessments concerning the original decision alternatives remain unchanged, the new alternative may cause rank reversals between the utility estimates of the original decision alternatives. This paper studies the connections between rank reversals and the potential inconsistency of the utility assessments in the case of ratio-scale pairwise comparisons data. The analysis was carried out by recently developed statistical modelling techniques so that the inconsistency of the assessments was measured according to statistical estimation theory. Several type of decision problems were analysed and the results showed that rank reversals caused by inconsistency are natural and acceptable. On the other hand, rank reversals caused by the traditional arithmetic-mean aggregation rule are not in line with the ratio-scale measurement of utilities, whereas geometric-mean aggregation does not cause undesired rank reversals.

A Spatial Approach to Participatory Planning in Forestry Decision Making

A method is introduced in which qualitative information obtained in a public participation process is transformed into quantitative spatial decision support for forest management planning. In the first phase, qualitative analysis is implemented by using the tools of qualitative research analysis. Then, a method is developed whereby it is possible to connect the expressed opinions to certain locations, to weight these opinions in a sensible way, and then to combine them in the form of a score map. In this phase, geographic information system (GIS) and preference analysis tools were used. The case study, carried out in the area managed by the Finnish Forest and Park Service, illustrates how to pack huge amounts of unstructured public feedback as decision support. The result of the analysis was a score map ranking pixels in the study area according to the aggregated preferences and norms expressed by public. Based on the case study experiences, it was concluded that most of this kind of public feedback can be illustrated for decision makers by using a few score maps.

Modelling and simulation of timber prices for forest planning calculations

The variation in timber prices is one of the main sources of uncertainty in forest planning. Timber prices in Finland are modelled in this study for the purposes of forest planning calculations. The model constructed consists of two different processes, one for price peaks, and the other for the rest of the time series. Modelling of price peaks, i.e. the exceptionally high timber prices in the early 1950s and mid‐1970s, is important especially when studying the adaptive behaviour of forestry decision‐makers. The AR(1) model is used in modelling the rest of the time series. The model can be used to simulate realistic future timber price scenarios, thus enabling the study of uncertainty in forest planning caused by timber prices. Although timber prices in Finland are used, similar methods could be applied in many other countries, too. The modelling approach presented could also be applied in simulating the prices of other raw materials, e.g. oil and some metals.

Rank-based modelling of preferences in multi-criteria decision making

Abstract In multi-criteria decision making, one approach to utilise mixed data consisting of both ordinal and ratio scale information is to consider ratio scale random samples fulfilling the existing ordinal data. However, some of the distributional assumptions proposed in the literature for the phase of transforming the ordinal data into ratio scale may lead to over-interpretation of the available information. In this paper, we propose a statistical model for overcoming these difficulties. In the cardinal part of the model, the ratio scale measurements are analysed by a regression model developed for pairwise comparisons data. The model for ordinal measurements is proposed by taking the regression model as a background model, and by assuming that instead of the ratio scale, only the ranks of the items are observed.

Regression Methods for Pairwise Comparison Data

Multi-objective decision making often requires the comparison of qualitatively different entities. For example, a forest owner has to assess the aesthetic and recreation values of the forest in addition to the income from selling wood. Pairwise comparisons can be used to elicit relative preferences concerning such entities. Eigenvalue techniques introduced by Saaty (1977) are one way to analyse pairwise comparisons data. A weak point of the original methodology has been that it does not allow a statistical analysis of uncertainties in judgements. The eigenvalue technique also requires that all entities have been compared with each other. In many applications, this is impracticable because of the large number of pairs. The number of judges can also be large, and there can be missing observations. Moreover, it is frequently of interest to analyse how different attributes of the entities, or different attributes of the judges, influence the relative preference. In this paper, we first review our previous work with an alternative methodology based on regression analysis. Then, we show how explanatory variables can be incorporated. The construction of the design matrix is detailed and the interpretation of the results is discussed.

Forestry Organization Network in Northern Finland

This study is a network analysis of the regional network of forest-related organizations in northern Finland. The study shows the position of the organizations in the network by grouping the organizations into clusters in terms of co-operation and mutual appreciation. The results are based on a questionnaire mailed to 1400 representatives of the main forestry organizations. Freemans centrality degree measures, multidimensional scaling, factor analysis and hierarchical clustering were used to synthesize three main subgroups, named the Private-forestry-orientated group, the Environment- and nature-orientated group and the Background group. The results were quite predictable. It was a surprise to note how consistently the respondents answered the questions concerning mutual co-operation and appreciation. The attitudes, on the one hand, towards economical forestry and, on the other hand, towards environmental aims, defined the enduring tendency in the responses.