Soil carbon modelling as a tool for carbon balance studies in forestry

Soils represent a remarkable stock of carbon, and forest soils are estimated to hold half of the global stock of soil carbon. Topical concern about the effects of climate change and forest management on soil carbon as well as practical reporting requirements set by climate conventions have created a need to assess soil carbon stock changes reliably and transparently. The large spatial variability of soil carbon commensurate with relatively slow changes in stocks hinders the assessment of soil carbon stocks and their changes by direct measurements. Models therefore widely serve to estimate carbon stocks and stock changes in soils. This dissertation aimed to develop the soil carbon model YASSO for upland forest soils. The model was aimed to take into account the most important processes controlling the decomposition in soils, yet remain simple enough to ensure its practical applicability in different applications. The model structure and assumptions were presented and the model parameters were defined with empirical measurements. The model was evaluated by studying the sensitivities of the model results to parameter values, by estimating the precision of the results with an uncertainty analysis, and by assessing the accuracy of the model by comparing the predictions against measured data and to the results of an alternative model. The model was applied to study the effects of intensified biomass extraction on the forest carbon balance and to estimate the effects of soil carbon deficit on net greenhouse gas emissions of energy use of forest residues. The model was also applied in an inventory based method to assess the national scale forest carbon balance for Finland’s forests from 1922 to 2004. YASSO managed to describe sufficiently the effects of both the variable litter and climatic conditions on decomposition. When combined with the stand models or other systems providing litter information, the dynamic approach of the model proved to be powerful for estimating changes in soil carbon stocks on different scales. The climate dependency of the model, the effects of nitrogen on decomposition and forest growth as well as the effects of soil texture on soil carbon stock dynamics are areas for development when considering the applicability of the model to different research questions, different land use types and wider geographic regions. Intensified biomass extraction affects soil carbon stocks, and these changes in stocks should be taken into account when considering the net effects of forest residue utilisation as energy. On a national scale, soil carbon stocks play an important role in forest carbon balances.

Country-scale carbon accounting of the vegetation and mineral soils of Finland

The increase in global temperature has been attributed to increased atmospheric concentrations of greenhouse gases (GHG), mainly that of CO2. The threat of severe and complex socio-economic and ecological implications of climate change have initiated an international process that aims to reduce emissions, to increase C sinks, and to protect existing C reservoirs. The famous Kyoto protocol is an offspring of this process. The Kyoto protocol and its accords state that signatory countries need to monitor their forest C pools, and to follow the guidelines set by the IPCC in the preparation, reporting and quality assessment of the C pool change estimates. The aims of this thesis were i) to estimate the changes in carbon stocks vegetation and soil in the forests in Finnish forests from 1922 to 2004, ii) to evaluate the applied methodology by using empirical data, iii) to assess the reliability of the estimates by means of uncertainty analysis, iv) to assess the effect of forest C sinks on the reliability of the entire national GHG inventory, and finally, v) to present an application of model-based stratification to a large-scale sampling design of soil C stock changes. The applied methodology builds on the forest inventory measured data (or modelled stand data), and uses statistical modelling to predict biomasses and litter productions, as well as a dynamic soil C model to predict the decomposition of litter. The mean vegetation C sink of Finnish forests from 1922 to 2004 was 3.3 Tg C a-1, and in soil was 0.7 Tg C a-1. Soil is slowly accumulating C as a consequence of increased growing stock and unsaturated soil C stocks in relation to current detritus input to soil that is higher than in the beginning of the period. Annual estimates of vegetation and soil C stock changes fluctuated considerably during the period, were frequently opposite (e.g. vegetation was a sink but soil was a source). The inclusion of vegetation sinks into the national GHG inventory of 2003 increased its uncertainty from between -4% and 9% to ± 19% (95% CI), and further inclusion of upland mineral soils increased it to ± 24%. The uncertainties of annual sinks can be reduced most efficiently by concentrating on the quality of the model input data. Despite the decreased precision of the national GHG inventory, the inclusion of uncertain sinks improves its accuracy due to the larger sectoral coverage of the inventory. If the national soil sink estimates were prepared by repeated soil sampling of model-stratified sample plots, the uncertainties would be accounted for in the stratum formation and sample allocation. Otherwise, the increases of sampling efficiency by stratification remain smaller. The highly variable and frequently opposite annual changes in ecosystem C pools imply the importance of full ecosystem C accounting. If forest C sink estimates will be used in practice average sink estimates seem a more reasonable basis than the annual estimates. This is due to the fact that annual forest sinks vary considerably and annual estimates are uncertain, and they have severe consequences for the reliability of the total national GHG balance. The estimation of average sinks should still be based on annual or even more frequent data due to the non-linear decomposition process that is influenced by the annual climate. The methodology used in this study to predict forest C sinks can be transferred to other countries with some modifications. The ultimate verification of sink estimates should be based on comparison to empirical data, in which case the model-based stratification presented in this study can serve to improve the efficiency of the sampling design.

Uncertainty in forest simulators and forest planning systems

The forest simulator is a computerized model for predicting forest growth and future development as well as effects of forest harvests and treatments. The forest planning system is a decision support tool, usually including a forest simulator and an optimisation model, for finding the optimal forest management actions. The information produced by forest simulators and forest planning systems is used for various analytical purposes and in support of decision making. However, the quality and reliability of this information can often be questioned. Natural variation in forest growth and estimation errors in forest inventory, among other things, cause uncertainty in predictions of forest growth and development. This uncertainty stemming from different sources has various undesirable effects. In many cases outcomes of decisions based on uncertain information are something else than desired. The objective of this thesis was to study various sources of uncertainty and their effects in forest simulators and forest planning systems. The study focused on three notable sources of uncertainty: errors in forest growth predictions, errors in forest inventory data, and stochastic fluctuation of timber assortment prices. Effects of uncertainty were studied using two types of forest growth models, individual tree-level models and stand-level models, and with various error simulation methods. New method for simulating more realistic forest inventory errors was introduced and tested. Also, three notable sources of uncertainty were combined and their joint effects on stand-level net present value estimates were simulated. According to the results, the various sources of uncertainty can have distinct effects in different forest growth simulators. The new forest inventory error simulation method proved to produce more realistic errors. The analysis on the joint effects of various sources of uncertainty provided interesting knowledge about uncertainty in forest simulators.

Valuation of reduced eutrophication in the Gulf of Finland : Choice experiment with attention to heterogeneous and discontinuous preferences and respondent uncertainty

Eutrophication of the Baltic Sea is a serious problem. This thesis estimates the benefit to Finns from reduced eutrophication in the Gulf of Finland, the most eutrophied part of the Baltic Sea, by applying the choice experiment method, which belongs to the family of stated preference methods. Because stated preference methods have been subject to criticism, e.g., due to their hypothetical survey context, this thesis contributes to the discussion by studying two anomalies that may lead to biased welfare estimates: respondent uncertainty and preference discontinuity. The former refers to the difficulty of stating one s preferences for an environmental good in a hypothetical context. The latter implies a departure from the continuity assumption of conventional consumer theory, which forms the basis for the method and the analysis. In the three essays of the thesis, discrete choice data are analyzed with the multinomial logit and mixed logit models. On average, Finns are willing to contribute to the water quality improvement. The probability for willingness increases with residential or recreational contact with the gulf, higher than average income, younger than average age, and the absence of dependent children in the household. On average, for Finns the relatively most important characteristic of water quality is water clarity followed by the desire for fewer occurrences of blue-green algae. For future nutrient reduction scenarios, the annual mean household willingness to pay estimates range from 271 to 448 and the aggregate welfare estimates for Finns range from 28 billion to 54 billion euros, depending on the model and the intensity of the reduction. Out of the respondents (N=726), 72.1% state in a follow-up question that they are either Certain or Quite certain about their answer when choosing the preferred alternative in the experiment. Based on the analysis of other follow-up questions and another sample (N=307), 10.4% of the respondents are identified as potentially having discontinuous preferences. In relation to both anomalies, the respondent- and questionnaire-specific variables are found among the underlying causes and a departure from standard analysis may improve the model fit and the efficiency of estimates, depending on the chosen modeling approach. The introduction of uncertainty about the future state of the Gulf increases the acceptance of the valuation scenario which may indicate an increased credibility of a proposed scenario. In conclusion, modeling preference heterogeneity is an essential part of the analysis of discrete choice data. The results regarding uncertainty in stating one s preferences and non-standard choice behavior are promising: accounting for these anomalies in the analysis may improve the precision of the estimates of benefit from reduced eutrophication in the Gulf of Finland.

Digital elevation model error in terrain analysis

Digital elevation models (DEMs) have been an important topic in geography and surveying sciences for decades due to their geomorphological importance as the reference surface for gravita-tion-driven material flow, as well as the wide range of uses and applications. When DEM is used in terrain analysis, for example in automatic drainage basin delineation, errors of the model collect in the analysis results. Investigation of this phenomenon is known as error propagation analysis, which has a direct influence on the decision-making process based on interpretations and applications of terrain analysis. Additionally, it may have an indirect influence on data acquisition and the DEM generation. The focus of the thesis was on the fine toposcale DEMs, which are typically represented in a 5-50m grid and used in the application scale 1:10 000-1:50 000. The thesis presents a three-step framework for investigating error propagation in DEM-based terrain analysis. The framework includes methods for visualising the morphological gross errors of DEMs, exploring the statistical and spatial characteristics of the DEM error, making analytical and simulation-based error propagation analysis and interpreting the error propagation analysis results. The DEM error model was built using geostatistical methods. The results show that appropriate and exhaustive reporting of various aspects of fine toposcale DEM error is a complex task. This is due to the high number of outliers in the error distribution and morphological gross errors, which are detectable with presented visualisation methods. In ad-dition, the use of global characterisation of DEM error is a gross generalisation of reality due to the small extent of the areas in which the decision of stationarity is not violated. This was shown using exhaustive high-quality reference DEM based on airborne laser scanning and local semivariogram analysis. The error propagation analysis revealed that, as expected, an increase in the DEM vertical error will increase the error in surface derivatives. However, contrary to expectations, the spatial au-tocorrelation of the model appears to have varying effects on the error propagation analysis depend-ing on the application. The use of a spatially uncorrelated DEM error model has been considered as a 'worst-case scenario', but this opinion is now challenged because none of the DEM derivatives investigated in the study had maximum variation with spatially uncorrelated random error. Sig-nificant performance improvement was achieved in simulation-based error propagation analysis by applying process convolution in generating realisations of the DEM error model. In addition, typology of uncertainty in drainage basin delineations is presented.

Elite Bargaining and the Evolution of Centre-Periphery Relations in Post-Soviet Russia : A Comparative Analysis

This work is concerned with presenting a modified theoretical approach to the study of centre-periphery relations in the Russian Federation. In the widely accepted scientific discourse, the Russian federal system under the Yeltsin Administration (1991-2000) was asymmetrical; largely owing to the varying amount of structural autonomy distributed among the federation s 89 constituent units. While providing an improved understanding as to which political and socio-economic structures contributed to federal asymmetry, it is felt that associated large N-studies have underemphasised the role played by actor agency in re-shaping Russian federal institutions. It is the main task of this thesis to reintroduce /re-emphasise the importance of actor agency as a major contributing element of institutional change in the Russian federal system. By focusing on the strategic agency of regional elites simultaneously within regional and federal contexts, the thesis adopts the position that political, ethnic and socio-economic structural factors alone cannot fully determine the extent to which regional leaders were successful in their pursuit of economic and political pay-offs from the institutionally weakened federal centre. Furthermore, this work hypothesises that under conditions of federal institutional uncertainty, it is the ability of regional leaders to simultaneously interpret various mutable structural conditions then translate them into plausible strategies which accounts for the regions ability to extract variable amounts of economic and political pay-offs from the Russian federal system. The thesis finds that while the hypothesis is accurate in its theoretical assumptions, several key conclusions provide paths for further inquiry posed by the initial research question. First, without reliable information or stable institutions to guide their actions, both regional and federal elites were forced into ad-hoc decision-making in order to maintain their core strategic focus: political survival. Second, instead of attributing asymmetry to either actor agency or structural factors exclusively, the empirical data shows that both agency and structures interact symbiotically in the strategic formulation process, thus accounting for the sub-optimal nature of several of the actions taken in the adopted cases. Third, as actor agency and structural factors mutate over time, so, too do the perceived payoffs from elite competition. In the case of the Russian federal system, the stronger the federal centre became, the less likely it was that regional leaders could extract the high degree of economic and political pay-offs that they clamoured for earlier in the Yeltsin period. Finally, traditional approaches to the study of federal systems which focus on institutions as measures of federalism are not fully applicable in the Russian case precisely because the institutions themselves were a secondary point of contention between competing elites. Institutional equilibriums between the regions and Moscow were struck only when highly personalised elite preferences were satisfied. Therefore the Russian federal system is the product of short-term, institutional solutions suited to elite survival strategies developed under conditions of economic, political and social uncertainty.

Accelerator Mass Spectrometry and Bayesian Data Analysis

Accelerator mass spectrometry (AMS) is an ultrasensitive technique for measuring the concentration of a single isotope. The electric and magnetic fields of an electrostatic accelerator system are used to filter out other isotopes from the ion beam. The high velocity means that molecules can be destroyed and removed from the measurement background. As a result, concentrations down to one atom in 10^16 atoms are measurable. This thesis describes the construction of the new AMS system in the Accelerator Laboratory of the University of Helsinki. The system is described in detail along with the relevant ion optics. System performance and some of the 14C measurements done with the system are described. In a second part of the thesis, a novel statistical model for the analysis of AMS data is presented. Bayesian methods are used in order to make the best use of the available information. In the new model, instrumental drift is modelled with a continuous first-order autoregressive process. This enables rigorous normalization to standards measured at different times. The Poisson statistical nature of a 14C measurement is also taken into account properly, so that uncertainty estimates are much more stable. It is shown that, overall, the new model improves both the accuracy and the precision of AMS measurements. In particular, the results can be improved for samples with very low 14C concentrations or measured only a few times.

Angel Investing in Finland: An Analysis Based on Agency Theory and the Incomplete Contracting Theory

Wealthy individuals - business angels who invest a share of their net worth in entrepreneurial ventures - form an essential part of an informal venture capital market that can secure funding for entrepreneurial ventures. In Finland, business angels represent an untapped pool of capital that can contribute to fostering entrepreneurial development. In addition, business angels can bridge knowledge gaps in new business ventures by means of making their human capital available. This study has two objectives. The first is to gain an understanding of the characteristics and investment behaviour of Finnish business angels. The strongest focus here is on the due diligence procedures and their involvement post investment. The second objective is to assess whether agency theory and the incomplete contacting theory are useful theoretical lenses in the arena of business angels. To achieve the second objective, this study investigates i) how risk is mitigated in the investment process, ii) how uncertainty influences the comprehensiveness of due diligence as well as iii) how control is allocated post investment. Research hypotheses are derived from assumptions underlying agency theory and the incomplete contacting theory. The data for this study comprise interviews with 53 business angels. In terms of sample size this is the largest on Finnish business angels. The research hypotheses in this study are tested using regression analysis. This study suggests that the Finnish informal venture capital market appears to be comprised of a limited number of business angels whose style of investing much resembles their formal counterparts’. Much focus is placed on managing risks prior to making the investment by strong selectiveness and by a relatively comprehensive due diligence. The involvement is rarely on a day-to-day basis and many business angels seem to see board membership as a more suitable alternative than involvement in the operations of an entrepreneurial venture. The uncertainty involved does not seem to drive an increase in due diligence. On the contrary, it would appear that due diligence is more rigorous in safer later stage investments and when the business angels have considerable previous experience as investors. Finnish business angels’ involvement post investment is best explained by their degree of ownership in the entrepreneurial venture. It seems that when investors feel they are sufficiently rewarded, in terms of an adequate equity stake, they are willing to involve themselves actively in their investments. The lack of support for a relationship between increased uncertainty and the comprehensiveness of due diligence may partly be explained by an increasing trend towards portfolio diversification. This is triggered by a taxation system that favours investments through investment companies rather than direct investments. Many business angels appear to have substituted a specialization strategy that builds on reducing uncertainty for a diversification strategy that builds on reducing firm specific (idiosyncratic) risk by holding shares in ventures whose returns are not expected to exhibit a strong positive correlation.

Flexible Budgeting under Uncertainty: A Real Options Perspective

This paper examines the relationships between uncertainty and the perceived usefulness of traditional annual budgets versus flexible budgets in 95 Swedish companies. We form hypotheses that the perceived usefulness of the annual budgets as well as the attitudes to more flexible budget alternatives are influenced by the uncertainty that the companies face. Our study distinguishes between two separate kinds of uncertainty: exogenous stochastic uncertainty (deriving from the firm’s environment) and endogenous deterministic uncertainty (caused by the strategic choices made by the firm itself). Based on a structural equations modelling analysis of data from a mail survey we found that the more accentuated exogenous uncertainty a company faces, the more accentuated is the expected trend towards flexibility in the budget system, and vice versa; the more endogenous uncertainty they face, the more negative are their attitudes towards budget flexibility. We also found that these relationships were not present with regard to the attitudes towards the usefulness of the annual budget. Noteworthy is, however, that there was a significant negative relationship between the perceived usefulness of the annual budget and budget flexibility. Thus, our results seem to indicate that the degree of flexibility in the budget system is influenced by both general attitudes towards the usefulness of traditional budgets and by the actual degree of exogenous uncertainty a company faces and by the strategy that it executes.

Real Estate Investment and Uncertainty: Econometric Modelling using Finnish Data

The purpose of this paper is to test for the effect of uncertainty in a model of real estate investment in Finland during the hihhly cyclical period of 1975 to 1998. We use two alternative measures of uncertainty. The first measure is the volatility of stock market returns and the second measure is the heterogeneity in the answers of the quarterly business survey of the Confederation of Finnish Industry and Employers. The econometric analysis is based on the autoregressive distributed lag (ADL) model and the paper applies a 'general-to-specific' modelling approach. We find that the measure of heterogeneity is significant in the model, but the volatility of stock market returns is not. The empirical results give some evidence of an uncertainty-induced threshold slowing down real estate investment in Finland.