Attitude Towards Risk and Production Decision: an Empirical Analysis on French Private Forest Owners

This paper deals with the forest owner’s attitude towards risk and the harvesting decision in several ways. First, we propose to characterize and quantify the forest owner’s attitude towards risk. Second, we analyze the determinants of the forest owner’s risk attitude. Finally, we determine the impact of the forest owner’s risk attitude on the harvesting decision. The French forest owner’s risk attitude is tackled by implementing a questionnaire, including a context-free measure borrowed from experimental economics. The determinants of the forest owner’s risk attitude and harvesting decision are estimated through a recursive bivariate ordered probit model. We show that French forest owners are characterized by a relative risk aversion coefficient close to 1 with a DARA assumption. In addition, we find that the forest owner’s risk aversion is influenced positively and significantly by the level of risk exposure, the geographical location of the forest and the fact to be a forester, and negatively by the income. Finally, we obtain that the forest owner’s risk aversion has a positive and significant impact on the harvesting decision.     

Spatial Distribution and Temporal Change of Carbon Storage in Timber Biomass of Two Different Forest Management Units

Forests make up large ecosystems and can play an important role in mitigating the emissions of CO₂, the most important greenhouse gas. However, they are sources of atmospheric carbon when they are disturbed by human or natural causes. Storage of carbon through expansion and adaptive management of forest ecosystems can assist in reducing carbon concentrations in atmosphere. This study proposes a methodology to produce spatially explicit estimates of the carbon storages (aboveground plus belowground) depending on land use/cover changes in two different forest ecosystems during various periods. Carbon storages for each forest ecosystem were projected according to inventory data, and carbon storages were mapped in a geographic information system (GIS). Results showed that total carbon stored in above and belowground of both forest ecosystems increased from one period to other because of especially increase of productive forest areas and decline of degraded forest areas as well as protection of spruce forests subject to insect attacks.     

Fire risk evaluation using multicriteria analysis—a case study

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.     

Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands—Southeast Asia

The present study outlines an approach to classify forest density and to estimate canopy closure of the forest of the Andaman and Nicobar archipelago. The vector layers generated for the study area using satellite data was validated with the field knowledge of the surveyed ground control points. The methodology adopted in this present analysis is three-tiered. First, the density stratification into five zones using visual interpretation for the complete archipelago. In the second step, we identified two island groups from the Andaman to investigate and compare the forest strata density. The third and final step involved more of a localised phytosociological module that focused on the North Andaman Islands. The results based on the analysis of the high-resolution satellite data show that more than 75% of the mangroves are under high- to very high-density canopy class. The framework developed would serve as a significant measure to forest health and evaluate management concerns whilst addressing issues such as gap identification, conservation prioritisation and disaster management—principally to the post-tsunami assessment and analysis.     

Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.     

基于多元回归理论的太湖湖泛预警模型研究

在太湖宜兴段藻源性湖泛高发区设立4个监测点,以湖泛发生的物质基础"藻类生物量"为研究对象,运用数据分析软件SPSS对监测点的藻类生物量、水质、气温等数据进行相关分析,建立了以藻密度为因变量的多元逐步回归模型。结合往年太湖藻源性湖泛发生时的气象条件等历史资料以及相关藻密度阈值的报道,构建了太湖宜兴段藻源性湖泛高发区监测预警模型系统,该模型能够基于监测点的实时水质数据和气象预报数据,对监控区域湖水在未来某时间段内发生湖泛风险的可能性进行分级预警。     

Linking stream and landscape trajectories in the southern Appalachians

A proactive sampling strategy was designed and implemented in 2000 to document changes in streams whose catchment land uses were predicted to change over the next two decades due to increased building density. Diatoms, macroinvertebrates, fishes, suspended sediment, dissolved solids, and bed composition were measured at two reference sites and six sites where a socioeconomic model suggested new building construction would influence stream ecosystems in the future; we label these "hazard sites." The six hazard sites were located in catchments with forested and agricultural land use histories. Diatoms were species-poor at reference sites, where riparian forest cover was significantly higher than all other sites. Cluster analysis, Wishart's distance function, non-metric multidimensional scaling, indicator species analysis, and t-tests show that macroinvertebrate assemblages, fish assemblages, in situ physical measures, and catchment land use and land cover were different between streams whose catchments were mostly forested, relative to those with agricultural land use histories and varying levels of current and predicted development. Comparing initial results with other regional studies, we predict homogenization of fauna with increased nutrient inputs and sediment associated with agricultural sites where more intense building activities are occurring. Based on statistical separability of sampled sites, catchment classes were identified and mapped throughout an 8,600 km(2) region in western North Carolina's Blue Ridge physiographic province. The classification is a generalized representation of two ongoing trajectories of land use change that we suggest will support streams with diverging biota and physical conditions over the next two decades.     

An inventory-based carbon budget for forest and woodland ecosystems of Turkey

Environmental monitoring of national-level comparisons of CO(2) emissions is needed to quantify sources and sinks of carbon (C) in national ecosystems. In this study, a national forest inventory database was used to estimate the past and current pools and fluxes of C in deciduous and coniferous forest and woodland ecosystems (20.7 x 10(6) ha) of Turkey. Growing C stock was 12.63 t C ha(-1) in 1960 and 16.55 t C ha(-1) in 1995. Total C store in the whole live woody biomass was estimated at 22.77 t C ha(-1) in 1996. The total flux of C from the atmosphere into the forest and woodland ecosystems driven by primary productivity was about 1.46 t C ha(-1)(or 30.2 Mt C) in 1996. The estimated net release of C from the forest and woodland ecosystems of Turkey to the atmosphere was about 1.34 t C ha(-1)(or 21.5 Mt C) in 1996. When C released was taken into account, net ecosystem sequestration (NES) resulted in 0.12 t C ha(-1) per year. Such analytical tools as national forest C budgets are needed to improve our preventive and mitigative strategies for dealing with global climate change.     

Exploratory Analysis and a Stochastic Model for Humusdisintegration

Ulrich (1981) supposes in the hypothesis of humusdisintegrationthat the balance between polymerisation and breakdown of organicmaterial may be disturbed in chemically well buffered Europeanforest soils. This new aspect of aluminium toxicity may causenitrogen exceedance in forest ecosystems and subsequent seasonalnitrate output (Eichhorn and Hütterman, 1999).In a research program the substances in the seepage water aremonitored in a small woodland in central Germany. We explorethese multivariate data for examining possible influences on theprocess of humusdisintegration and its temporal evolution. As aresult, a regression model for carbon is developed, whichincludes covariables, i.e., other substances, as well as spatialand temporal terms describing systematic variability. Especiallyiron and aluminium turn out to be very influential in the model.So far our work is a basic step for monitoring the seepage waterdata by means of stochastic modelling.     

Uncertainties of forest area estimates caused by the minimum crown cover criterion

Defining “forest land” is a complex issue and has been discussed for decades. Today, a confusing multitude of definitions of forest land are in use making comparison of forest area figures difficult. But currently, comparability is receiving much attention when it comes to install market mechanisms for ecosystem services. Minimum crown cover is among the most frequently employed criteria of forest definitions. However, the size of the reference area on which the crown cover percent is to be measured is usually not defined. But how does a change of the size of the reference area affect the derived forest cover? In this study, we analyze the interactions of the crown cover threshold and the size of the reference area. We start with analyzing the interactions using a simple geometric model of the forest edge. Then, we extend the analysis by simulating artificial landscapes where we study how the interaction is affected by different degrees of forest fragmentation, crown cover proportion, and spatial resolution of the data source used. The simulation showed that large differences in forest cover (>50 %) may result for a fixed crown cover threshold value, just by changing the size of the reference area, where the magnitude of this effect is a function of the chosen threshold value and the spatial configuration of the crowns. As a consequence of the findings, we see an urgent need to complete forest definitions by defining a reference area in order to reduce uncertainties of forest cover estimates.     

A habitat-based microscale forest classification system for zoning wood production areas to conserve a rare species threatened by logging operations in south-eastern Australia

The production of timber from native forests is presently one of the most controversial land management issues in Australia. Part of this controversy results from the potential impacts of forestry practices on forest-dependent fauna, particularly those that are rare and endangered, such as Leadbeater's Possum Gymnobelideus leadbeateri McCoy, in the forests of central Victoria, south-eastern Australia. A significant proportion of the highly limited distribution of this species overlaps with some of the most valuable wood production forests in Australia within which extensive clearfelling operations are employed to produce timber and pulpwood. These operations can destroy the habitat of G. leadbeateri. The Victoria government agency that is responsible for forest and wildlife management has devised a forest zoning system as part of the management strategies to conserve G. leadbeateri within timber production areas. This is designed to partition the forest into three types of areas: (1) where the conservation of G. leadbeateri is a priority, (2) where wood production is a priority, and, (3) where both land uses are a joint priority. The classification of areas of forest where the conservation of G. leadbeateri is the primary land use is based on an understanding of the habitat requirements of the species. The results of recent field studies, where statistical models of the habitat requirements of G. leadbeateri have been developed and their performance subsequently tested using a new dataset, highlights the need for a new basis to guide the classification of areas for the conservation of the species within wood production forests. We describe a method for devising a forest management zoning system that is based on a statistical model of the habitat requirements of G. leadbeateri and which will better integrate wood production and the conservation of the species. This procedure accounts for the uncertainty in the statistical model and, in turn, reduces the risk that areas where G. leadbeateri occurs are logged, whilst ensuring that other areas are not unnecessarily excluded from timber harvesting.     

A multi-scale metrics approach to forest fragmentation for Strategic Environmental Impact Assessment

Forests are becoming severely fragmented as a result of land development. South Korea has responded to changing community concerns about environmental issues. The nation has developed and is extending a broad range of tools for use in environmental management. Although legally mandated environmental compliance requirements in South Korea have been implemented to predict and evaluate the impacts of land-development projects, these legal instruments are often insufficient to assess the subsequent impact of development on the surrounding forests. It is especially difficult to examine impacts on multiple (e.g., regional and local) scales in detail. Forest configuration and size, including forest fragmentation by land development, are considered on a regional scale. Moreover, forest structure and composition, including biodiversity, are considered on a local scale in the Environmental Impact Assessment process. Recently, the government amended the Environmental Impact Assessment Act, including the SEA, EIA, and small-scale EIA, to require an integrated approach. Therefore, the purpose of this study was to establish an impact assessment system that minimizes the impacts of land development using an approach that is integrated across multiple scales.This study focused on forest fragmentation due to residential development and road construction sites in selected Congestion Restraint Zones (CRZs) in the Greater Seoul Area of South Korea. Based on a review of multiple-scale impacts, this paper integrates models that assess the impacts of land development on forest ecosystems. The applicability of the integrated model for assessing impacts on forest ecosystems through the SEIA process is considered.On a regional scale, it is possible to evaluate the location and size of a land-development project by considering aspects of forest fragmentation, such as the stability of the forest structure and the degree of fragmentation. On a local scale, land-development projects should consider the distances at which impacts occur in the vicinity of the forest ecosystem, and these considerations should include the impacts on forest vegetation and bird species. Impacts can be mitigated by considering the distances at which these influences occur. In particular, this paper presents an integrated environmental impact assessment system to be applied in the SEIA process. The integrated assessment system permits the assessment of the cumulative impacts of land development on multiple scales.     

Mercury Contamination of Biota from Acadia National Park, Maine: A Review

We reviewed literature reporting both total and methylmercury from biota from Acadia National Park, Maine, USA. Our review of existing data indicates that 1) mercury contamination is widespread throughout the Park’s various aquatic ecosystems; 2) mercury pollution likely represents a moderate to high risk to biota inhabiting the Park; and 3) biota at all trophic levels possess elevated concentrations of both total and methylmercury. Watershed fire history and the resulting post-fire forest succession patterns are an important landscape attribute governing mercury cycling at Acadia National Park. Therefore, park service personnel should consider these factors when planning and implementing Hg biomonitoring efforts. Additional baseline funding from the National Park Service for Hg research and biomonitoring will likely be required in order to further evaluate the spatial and temporal patterns of mercury contamination in the park’s biota. An erratum to this article can be found at     

Decision-Tree-based data mining and rule induction for predicting and mapping soil bacterial diversity

Soilmicrobial ecology plays a significant role in global ecosystems. Nevertheless, methods of model prediction and mapping have yet to be established for soil microbial ecology. The present study was undertaken to develop an artificial-intelligence- and geographical information system (GIS)-integrated framework for predicting and mapping soil bacterial diversity using pre-existing environmental geospatial database information, and to further evaluate the applicability of soil bacterial diversity mapping for planning construction of eco-friendly roads. Using a stratified random sampling, soil bacterial diversity was measured in 196 soil samples in a forest area where construction of an eco-friendly road was planned. Model accuracy, coherence analyses, and tree analysis were systematically performed, and four-class discretized decision tree (DT) with ordinary pair-wise partitioning (OPP) was selected as the optimal model among tested five DT model variants. GIS-based simulations of the optimal DT model with varying weights assigned to soil ecological quality showed that the inclusion of soil ecology in environmental components, which are considered in environmental impact assessment, significantly affects the spatial distributions of overall environmental quality values as well as the determination of an environmentally optimized road route. This work suggests a guideline to use systematic accuracy, coherence, and tree analyses in selecting an optimal DT model from multiple candidate model variants, and demonstrates the applicability of the OPP-improved DT integrated with GIS in rule induction for mapping bacterial diversity. These findings also provide implication on the significance of soil microbial ecology in environmental impact assessment and eco-friendly construction planning.     

Global Warming, Uncertainty and Endogenous Technical Change

What impact does ecological uncertainty have on agents' decisions concerning domestic emissions abatement, physical investments, and R&;D expenditures? How sensitive are the answers to these questions when we move from exogenous to endogenous technical change? To investigate these issues we modify the ETC-RICE model described in Buonanno et al. (2001) by embedding in it a hazard rate function as in Bosello and Moretto (1999). With this model at hand we run numerical optimisations focusing our attention on the control variables of the representative agents, i.e., domestic abatement rate, investments in physical capital, and R&;D spending, as well as on the endogenous patterns of GDP level and CO₂ emissions. Our results show that uncertainty strongly influences agents behaviour; in particular, agents slow down their emissions in order to maintain a more sustainable growth path. In addition, R&;D expenditures trigger the “engine of growth” exclusively when environmental technical change is formalized in an endogenous fashion. However, even if environmental uncertainty may stimulate technical change, long-run growth it turns out to be negatively affected by the former, as also predicted by Clarke and Reed (1994) Tsur and Zemel (1996) and Bosello and Moretto (1999).     

The environmental behaviour of polychlorinated phenols and its relevance to cork forest ecosystems: a review

Pentachlorophenol (PCP) has been used as a herbicide, biocide and preservative worldwide since the 1930s and as a result, extensive and prolonged contamination exists. The environmental impact increases when its many degradation products are taken into consideration. A number of chloroanisols and their related chlorophenols have been found in cork slabs collected from Portuguese oak tree forests before stopper manufacturing, and contamination by PCP and polychlorinated anisole (PCA) has been detected in Canadian forests. It is suggested that the use of polychlorinated phenols, in particular PCP, is thought to be a cause of the cork taint problem in wine, a major socio-economic impact not only for industry but on sensitive and highly biodiverse ecosystems. It also highlights particular issues relating to the regional regulation of potentially toxic chemicals and global economics world wide. To fully understand the impact of contamination sources, the mechanisms responsible for the fate and transport of PCP and its degradation products and assessment of their environmental behaviour is required. This review looks at the current state of knowledge of soil sorption, fate and bioavailability and identifies the challenges of degradation product identification and the contradictory evidence from field and laboratory observations. The need for a systematic evaluation of PCP contamination in relation to cork forest ecosystems and transfer of PCP between trophic levels is emphasised by discrepancies in bioaccumulation and toxicity. This is essential to enable long term management of not only transboundary contaminants, but also the sustainable management of socially and economically important forest ecosystems.     

Investments in Power Generation Under Uncertainty—a MIP Specification and Large-Scale Application for EU

Investments in power generation constitute a typical budget allocation problem in the context of multiple objectives, while all factors influencing investor’s decisions for power plants are subject to considerable uncertainties. The paper introduces a multi-objective stochastic model designed to optimize budget allocation decisions for power generation in the context of risk aversion taking into account several sources of uncertainty, especially with regard to volatility of fossil fuel and electricity prices, technological costs, and climate policy variability. Probability distributions for uncertain factors influencing investment decisions are directly derived from the stochastic global energy model PROMETHEUS and thus they take into account complex interactions between variables in the systemic context. In order to fully incorporate stochastic characteristics of the problem, the model is specified as an optimization problem in which the probability that an objective exceeds a given threshold is maximized (risk aversion) subject to a set of deterministic and probabilistic constraints. The model is formulated as a mixed integer program providing complete flexibility on the joint distributions of rates of return of technologies competing for investments, as it can handle non-symmetric distributions and take automatically into account complex covariance patterns as emerging from comprehensive PROMETHEUS stochastic results. The analysis shows that risk is a crucial factor for power generation investments with investors not opting for technologies subject to uncertainty related to climate policies and fossil fuel prices. On the other hand, combination of options with negative covariance tends to benefit in the context of risk-hedging behavior.     

Accurate Ozone Prognostic Patterns for Madrid Area by Using a High Spatial and Temporal Eulerian Photochemical Model

The ANA Air Quality Model (ANA stands for Atmospheric Mesoscale Numerical Pollution Model for Regional and Urban Areas) has been applied over Madrid during a five day period in June, 1995. The domain is 80 × 100 km2 and the spatial resolution is 2000 m. The ANA system is driven by a meteorological model REMEST and it includes a detailed emission model for anthropogenic and biogenic sources with 250 m spatial resolution and 60 minutes temporal resolution. Different deposition processes are used such as the Wesely (1989) and Erisman et al. (1994) resistance approaches and the simple aerodynamic resistance. The photochemical processes and the general chemistry is based on the CBM-IV mechanism for the organic compounds and solved by the SMVGEAR method (CHEMA module).The model uses 14 different landuse types which are obtained by using the REMO module which uses the information provided by the LANDSAT-5 satellite image over the domain. The emission module EMIMA takes into account the point, line and area emissions over the domain. Special importance is given to the biogenic emissions which are obtained by using the satellite landuse classification for caducous, perenneal and mixed terrain. The emission module considers the EPA and CORINAIR emission factors. The results show an accurate prediction of the ozone maxima for the five days and also the general pattern of the ozone observed data. The five day simulation is characterized by a local low pressure over the Madrid Area and high pressures over Spain and West of Europe. The ozone surface patterns show the diurnal cycle and the maxima concentrations up to 140-160 ppb for suburban areas during afternoon hours. The general performance of the model is considered quite good. The computer power requirements continue to be very high for standard workstations. Future progress on parallel platforms should improve considerably the computer time requirements.     

An ecological framework for resource management in British Columbia

British Columbia's landmass encompasses a complex diversity of ecosystems as a result of its diverse physiography, geology and climate. Resource planners and managers, depending upon their management objectives, use ecological information at different scales, from the very broad regional level to the local or site-specific level. The Ecoregion Classification and the Biogeoclimatic Ecosystem Classification systems provide the means for resource managers and others in British Columbia concerned with the environment to understand, manage, and communicate about the diverse ecosystems of the province.This paper outlines this multi-level regional ecological classification and describes how it is being applied by resource managers from various resource agencies and organizations responsible for forest, wildlife and habitat management in British Columbia.     

Natural forest regrowth as a proxy variable for agricultural land abandonment in the Swiss mountains: a spatial statistical model based on geophysical and socio-economic variables

In many European mountain regions, natural forest regrowth on abandoned agricultural land and the related consequences for the environment are issues of increasing concern. We developed a spatial statistical model based on multiple geophysical and socio-economic variables to investigate the pattern of natural forest regrowth in the Swiss mountain area between the 1980s and 1990s. Results show that forest regrowth occurred primarily in areas with low temperature sum, intermediate steepness and soil stoniness as well as close to forest edges and relatively close to roads. Model results suggest that regions with weak labor markets are favored in terms of land abandonment and forest regrowth. We could not find an effect of population change on land abandonment and forest regrowth. Therefore, we conclude that decision makers should consider non-linearities in the pattern of forest regrowth and the fact that labor markets have an effect on land abandonment and forest regrowth when designing measures to prevent agricultural land abandonment and natural forest regrowth in the Swiss mountains.