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Building an ecosystem services value at risk conceptual framework for sustainability, efficiency and… Infante, Maria Cristina 2014

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  BUILDING AN  ECOSYSTEM SERVICES VALUE AT RISK  CONCEPTUAL FRAMEWORK FOR  SUSTAINABILITY, EFFICIENCY AND FAIRNESS  IN RESOURCE MANAGEMENT:   STARTING VALUES FROM MARINE ECOSYSTEMS  by  Maria Cristina Infante  M.B.A., The University of Pennsylvania, 1991  M.A., The University of Pennsylvania, 1991  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF   DOCTOR OF PHILOSOPHY  in  The Faculty of Graduate and Postdoctoral Studies  (Resource Management and Environmental Studies)    THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)   April 2014   © Maria Cristina Infante 2014    ii  Abstract The primary research problem addressed in this thesis is the development of a conceptual framework for a novel ecological economics risk measure called “ecosystem services value at risk” to guide sustainability, efficiency and fairness in the human use of natural ecosystems.  Ecosystem services value at risk integrates ecosystem services valuation with financial value at risk to provide an estimate of a “worst likely loss” in ecosystem services under alternative policies.  This new approach estimates the uncertainty around ecosystem services values from human activities.   By framing the risk to ecosystem services in terms of loss, this risk measure conveys a more powerful message about the need to protect nature.  The core elements of an ecosystem services value at risk framework are: ecosystem services valuation; total economic value; stochastic ecological resource use models; financial value at risk; intergenerational discounting; society’s time frame for evaluation; and decision rules.  This research is multidisciplinary, drawing insights from ecological economics, finance, fisheries economics, ecological modeling and decision analysis.    Ecosystem services value at risk is illustrated with marine examples, which are timely and critical.   Marine ecosystems, which provide valuable and essential benefits to humankind, are being severely altered all over the world from overfishing, climate change, marine pollution and habitat destruction.  The risk measure is shown to be meaningful by applying a stochastic Schaefer surplus production model to a well-studied marine example, Namibian hake.   A case study of the collapsed Georges Bank yellowtail flounder further demonstrates its usefulness to marine policy evaluation.  A simple risk measure based on the market values of catch provisioning services leads to selecting conservative harvest policies, ruling out high levels of fishing intensity.   The non-market values of a second type of ecosystem service, the regulating services from conserved biomass, are next considered.  Both values provide a distribution for a lower bound estimate of the ecosystem services value at risk for the marine ecosystem.  Including regulating services allows even more precautionary strategies that favor conservation.  The ecosystem services value at risk framework thus supports sustainability and ecosystem resilience, promising to help protect the flow of benefits from nature for current and future generations.    iii  Preface This dissertation is original, unpublished research that is the independent work of the author.  My research committee members Dr. Peter N. Nemetz, chair, Dr. Rashid Sumaila, and Dr. Adlai Fisher provided assistance in identifying the important literature to review in the main bodies of knowledge of ecological economics, fisheries economics, and finance.    Dr. Peter Nemetz, Dr. Rashid Sumaila, and Dr. Adlai Fisher provided assistance with the research design and with editing the manuscript.   Dr. Adlai Fisher provided assistance in identifying a meaningful method for the computation of the ecosystem services value at risk measure.    I am responsible for the research design, envisioning the conceptual framework of an ecosystem services value at risk, conducting the literature review, identifying the stochastic fisheries model and marine examples, collecting data and material for the case study, writing the MATLAB® simulation programs for the Monte Carlo experiments, performing parameter estimation, running the simulations and writing the results.                   iv  Table of contents Abstract ................................................................................................................................................ii Preface ............................................................................................................................................... iii Table of contents ................................................................................................................................ iv List of tables ....................................................................................................................................... vi List of figures .....................................................................................................................................vii Acknowledgments .............................................................................................................................. ix Chapter 1 Introduction:  Ecosystem services value at risk conceptual framework……………………..1 1.1 Motivation ..................................................................................................................................... 1 1.2 Research problem .......................................................................................................................... 3 1.3 Ecosystem services value at risk conceptual framework ............................................................. 23 1.4 Importance of this research .......................................................................................................... 29 1.5 Research questions and methods ................................................................................................. 43 1.6 Research design ........................................................................................................................... 43 Chapter 2 Ecosystem services value at risk:  from finance to sustainable resource management…….46 2.1 Introduction ................................................................................................................................. 46 2.2 Literature review .......................................................................................................................... 48 2.3 Ecosystem services value at risk framework ............................................................................... 59 2.4 Financial value at risk .................................................................................................................. 65 2.5 Properties and limitations of VaR ................................................................................................ 70 2.6 Discussion .................................................................................................................................... 72 Chapter 3 Foundations of an ecosystem services value at risk measure for marine ecosystems………75 3.1 Introduction ................................................................................................................................. 75 3.2 Importance of a marine example ................................................................................................. 75 3.3 Fish stock assessment and fisheries management ........................................................................ 86 3.4 Fisheries models under uncertainty ........................................................................................... 103 3.5 The Schaefer surplus production model .................................................................................... 105 3.6 Discussion .................................................................................................................................. 115 Chapter 4 Ecosystem services value at risk: a simple marine example………………………………119 4.1 Introduction ............................................................................................................................... 119 4.2 Materials and methods ............................................................................................................... 121 4.3 Results ....................................................................................................................................... 144 4.4 Decision rules with ecosystem services value at risk ................................................................ 153 4.5 Discussion .................................................................................................................................. 159  v  Chapter 5 Ecosystem services value at risk: a case study of the Georges Bank yellowtail flounder…164 5.1. Introduction .............................................................................................................................. 164 5.2 Georges Bank marine ecosystem ............................................................................................... 168 5.3. A realistic example based on the collapsed Georges Bank yellowtail flounder ....................... 179 5.4. Materials and methods .............................................................................................................. 191 5.5. Results ...................................................................................................................................... 197 5.6 Decision rule with ecosystem services value at risk .................................................................. 213 5.7 Discussion .................................................................................................................................. 218 Chapter 6 What values? What risk? Conclusion and future research directions……………………..232 6.1 Novel risk measure for sustainability ........................................................................................ 232 6.2 Considerations of risk, uncertainty and values within the framework ....................................... 234 6.3 Conclusion and future research directions ................................................................................. 240 References ....................................................................................................................................... 247 Appendices ...................................................................................................................................... 276 Appendix A – Glossary ................................................................................................................... 276 Appendix B - Common fisheries reference points for the Schaefer surplus production model ...... 281 Appendix C - Parameter estimation using maximum likelihood ..................................................... 283 Appendix D - Intergenerational discounting ................................................................................... 287 Appendix E - Namibian hake simulation results: tables and figures ............................................... 289 Appendix F - Georges Bank yellowtail flounder data ..................................................................... 306 Appendix G – Georges Bank yellowtail flounder simulation results: tables and figures ................ 308     vi  List of tables Table 2.1 Comparison of the value at risk application to resource management in this research and Webby et al. (2007) ................................................................................................................................................. 60 Table 2.2 Analogy between financial and ecological values as a basis for an ecosystem services value at risk measure ................................................................................................................................................ 64 Table 3.1 Marine and coastal ecosystem services: values and impacts from global problems ................... 77 Table 4.1 Constant harvest rate strategies evaluated in this research ....................................................... 132 Table 4.2 Threshold harvesting strategies evaluated in this research ....................................................... 134 Table 4.3 Basis for the threshold strategies evaluated in this research ..................................................... 135 Table 4.4 Notation for a parameter estimation model for a Schaefer stochastic surplus production model with process-error only ............................................................................................................................. 145 Table 5.1 Parameter estimates for the Georges Bank yellowtail flounder stochastic surplus production model ........................................................................................................................................................ 199 Table 5.2 Management parameters for the Georges Bank yellowtail flounder from fisheries studies compared with estimates from this research ............................................................................................. 201 Table 5.3 Policy matrix for the Georges Bank yellowtail flounder, collapsed case ................................. 215 Table 5.4 Accuracy of ecosystem services value at risk estimates for the collapsed case Georges Bank yellowtail flounder for comparative strategies .......................................................................................... 217    vii  List of figures Fig. 1.1 Value at risk at the 99% cl and given time horizon ......................................................................... 8 Fig. 1.2 Relation of the proposed ecosystem service value at risk framework and a global valuation database ....................................................................................................................................................... 26 Fig. 1.3 Ecosystem services value at risk framework for a marine ecosystem ........................................... 32 Fig. 2.1 Value at risk as a quantile value of a distribution of values .......................................................... 65 Fig. 3.1 Generic surplus production model compared with a stochastic Schaefer surplus production model assuming process uncertainty.................................................................................................................... 107 Fig. 4.1 Namibian hake catch history (1965-2011)................................................................................... 122 Fig. 4.2 Example of a biomass-based threshold harvest control rule used for analysis in this research ... 136 Fig. 4.3 Ecosystem services value at risk Namibian hake depleted case under an intense exploitation rate .................................................................................................................................................................. 147 Fig. 4.4 Ecosystem services value at risk for catch value for under the “best” constant harvest rate strategy uref and an intense fishing strategy ucoll ..................................................................................................... 148 Fig. 4.5 Biomass sample paths and kernel density of sample paths for Namibian hake base case under the “best” constant harvest rate strategy ......................................................................................................... 150 Fig. 4.6 Biomass sample paths and kernel density of sample paths for Namibian hake depleted case under the “best” constant harvest rate strategy ................................................................................................... 150 Fig. 4.7 Sample paths of fish biomass for Namibian hake for base and depleted case under a constant harvest rate policy ..................................................................................................................................... 151 Fig. 4.8 Sample paths of fish biomass and predicted catch under the best threshold harvest strategy ..... 151 Fig. 4.9 Ecosystem services value at risk as a distribution of asset values of catch values, for Namibian hake depleted case for two comparative strategies ................................................................................... 153 Fig. 4.10 Cumulative distribution function (cdf) for the ecosystem services value at risk for two comparative harvest strategies when only catch value is considered........................................................ 158 Fig. 4.11 Cumulative distribution function (cdf) for the ecosystem services value at risk for two comparative harvest strategies when only catch value is considered........................................................ 159 Fig. 5.1Georges Bank yellowtail flounder landings 1935-2012 ............................................................... 166 Fig. 5.2 Map of the Georges Bank and Gulf of Maine marine ecosystem ................................................ 169 Fig. 5.3 The Georges Bank Marine Ecosystem submarine plateau on the Northeast US Continental Shelf .................................................................................................................................................................. 170 Fig. 5.4 Map of Northeast US multispecies groundfish ports ................................................................... 172 Fig. 5.5 Northeast US Continental Shelf Large Marine Ecosystem landings in real 2000 $ .................... 174 Fig. 5.6 Georges Bank marine ecosystem closed areas ............................................................................ 176  viii  Fig. 5.7 Yellowtail flounder (Limanda ferruginea) .................................................................................. 180 Fig. 5.8 Georges Bank marine ecosystem and NEFSC statistical areas for management of the Georges Bank yellowtail flounder and Closed Areas ............................................................................................. 181 Fig. 5.9 Georges Bank marine ecosystem and NAFO statistical areas for Canadian management of the Georges Bank yellowtail flounder ............................................................................................................ 182 Fig. 5.10 Georges Bank yellowtail flounder historical commercial landings ........................................... 183 Fig. 5.11 Georges Bank yellowtail flounder estimated catch biomass (age 1+) and estimated fishing mortality (ages 4+) .................................................................................................................................... 184 Fig. 5.12 Stock status determination for three Georges Bank groundfish stocks under the Northeast Multispecies Fishery Management Plan (FMP) ........................................................................................ 186 Fig. 5.13 Georges Bank yellowtail flounder landings, discards, and quotas for US and Canadian fisheries .................................................................................................................................................................. 189 Fig. 5.14 Georges Bank yellowtail flounder retrospective pattern in biomass and fishing mortality estimates for 2007 (left figure) and 2008 (right figure) ............................................................................ 190 Fig. 5.15 Catch history and US fall biomass survey index 1963-2011 ..................................................... 192 Fig. 5.16 Georges Bank yellowtail flounder starting biomass estimates from a Schaefer surplus production model with process error only model from this research 1963-2003 ........................................................ 200 Fig. 5.17 Sample paths of Georges Bank yellowtail flounder for the base case assuming the stock starts out as unfished .......................................................................................................................................... 203 Fig. 5.18 Ecosystem services value at risk of total catch value for the Georges Bank yellowtail flounder base case ................................................................................................................................................... 204 Fig. 5.19 Sample paths of biomass and kernel density of sample paths of Georges Bank yellowtail flounder for the collapsed case.................................................................................................................. 206 Fig. 5.20 Ecosystem services value at risk for Georges Bank yellowtail flounder collapsed case for catch value under the “best” constant harvest strategy and a threshold harvest strategy ................................... 207 Fig. 5.21 Annual exploitation rates for sample biomass-based threshold harvest strategies applied to the Georges Bank yellowtail flounder ............................................................................................................ 208 Fig. 5.22 Georges Bank yellowtail flounder collapsed case, cumulative distribution function of ecosystem value at risk catch and intergenerational discounted catch values with regulating services ..................... 210 Fig. 5.23 Georges Bank yellowtail flounder collapsed case, cumulative distribution function of the ecosystem value at risk of catch and regulating services values compared with catch values only ......... 211 Fig. 5.24 Ecosystem services value at risk of annual catch based on the Georges Bank yellowtail flounder stochastic surplus production model from this research ........................................................................... 213  ix  Acknowledgments I gratefully acknowledge the kind encouragement and intellectual support of my research supervisor, Peter Nemetz, and my research committee members, Adlai Fisher and Rashid Sumaila, who believed in the importance of this research on a novel risk measure that highlights the uncertainty around the benefits that people receive from natural ecosystems.  I especially thank Peter Nemetz for sharing a broad vision and knowledge of sustainable resource management,  for repeatedly questioning my assumptions and providing steady encouragement that allowed this work to flourish over many years.  I also express my gratitude to Adlai Fisher for his invaluable assistance in the extension of financial value at risk to a stochastic fisheries model.  I am also deeply grateful to Rashid Sumaila for sharing his vast knowledge in fisheries economics, providing guidance on the application of intergenerational discounting and for cheering me on over many years.         I thank Carl Walters of UBC Fisheries Centre who greatly assisted in my understanding of fisheries stock assessment.  Steve Cadrin of the University of Massachusetts, Dartmouth kindly assisted my understanding of stochastic biomass dynamics modeling.  Chris Legault and Heath Stone of the Transboundary Resource Assessment Committee were of great assistance with questions on the stock assessment of the collapsed Georges Bank yellowtail flounder.  Perry de Valpine of the University of Berkeley, California provided helpful clarification on parameter estimation methods.  I appreciate the assistance of Rachel Kuske and Edward Perkins, UBC Mathematics, in mathematical modeling and stochastic systems; and from David Tait, UBC Forestry, in statistics and simulation modeling.   Thanks also to Peter Carbonetto, UBC Computer Science, for helpful advice on Monte Carlo simulation, and to my colleague Divya Varkey for invaluable assistance with parameter estimation.  I am very grateful to the UBC professors who gave feedback and encouragement on the initial research proposal that began this work, including Gordon Munro, Michael Healey, Les Lavkulich, Maurice Levi and Tan Wang.   Special thanks are owed to the kind assistance of the faculty and staff at IRES, especially to Terre Satterfield, Kai Chan, Marcy Caouette, and Lisa Johannessen; also to Jennifer Fletcher and all the staff at Graduate and Postdoctoral Studies who kindly assisted with preparation for doctoral defence and with formatting of the dissertation.   I am also thankful to my colleagues at RMES, Jane Lister, an awesome mentor, Marivic Pajaro, Jonathan Anticamara, and Divya Varkey, who provided friendship and encouragement.    I am immensely grateful to my husband, Gordon Lucyk, whose love and support made this research possible.   x  Dedication To my beloved husband Gord  and  in loving memory of my mother and my father     1  Chapter 1 Introduction:  Ecosystem services value at risk conceptual framework 1.1 Motivation        All over the world, we find a sense of urgency about worsening environmental degradation and losses in benefits from natural ecosystems, of reduced rainforest cover, dwindling species richness and abundance, vanishing areas of wildlife habitat, melting glaciers.  Rachel Carson (1962) had evocatively written in “Silent Spring” of “a strange stillness” in their farm community from the disappearance of songbirds – eventually linked to toxic pesticides that also accounted for many unexplained deaths among adults and children.  It has been five decades since “Silent Spring” chronicled man’s capability of rendering spaces mute, but humanity’s capacity to wreak havoc on nature appears to be little changed.   Recent media coverage – the  images of polar bears – now  officially a threatened species – trapped on drifting ice floes in a melting sea of ice; the impending collapse of global fisheries in fifty years; the mystery of disappearing bees imperiling the future of orchards, farms and gardens – all are alarming.   The damage being done to natural ecosystems appears to be widespread and relentless, and there is the possibility that changes now being wrought will be irreversible.  These warnings about the prospect of extreme loss is underlined in recent scientific studies showing the increasing risk of biodiversity loss and extinction risk from anthropogenic impacts.  Thomas et al. (2004) predicted that human-caused climate change (under a mid-range scenario) could lead to a loss of 15-37% of species in the study sample by 2050.  In a study of the world’s large marine ecosystems from 1950 to 2003, Worm et al. (2006) found that 30% of all fish species being fished today have collapsed, warning that if the rate of overfishing continues unabated, all major species currently being fished will collapse by 2048. Marine fish stocks that have been depleted over long periods of time are often unable to recover to previous levels of abundance even after many decades (Hutchings 2000; Hutchings and Reynolds 2004).  Overfishing has well-known ecosystem effects (Pauly et al., 1998; Walters and Kitchell 2001; Worm et al., 2006), leading to possibly irreversible changes in the marine ecosystem when a “regime shift” occurs (Folke et al., 2004; Scheffer et al., 2005).   When critical thresholds are exceeded by the unsustainable use of ocean resources, significant losses in the provision of valuable ecosystem services and a loss of ecosystem resilience can result (Worm et al., 2006).  Extreme weather-related events have increased in frequency and severity all over the planet; the world is waking up to the realization that climate change is occurring now – rainfall usually seen over a month coming down in a matter of days, hurricanes at higher wind speeds and lasting for longer durations than ever observed or recorded, meters high waves damaging coastal neighborhoods, severe droughts.  The brave, new world of global environmental change that analysts have been predicting for years is today’s new reality (McKibben, 2011).  The changes already wrought may mean there is very  2  little humankind can do except to brace for the worst and plan on how best to leave a permanent record of science and knowledge for future generations (Lovelock, 2006). The Millennium Ecosystem Assessment (MEA, 2005) offered a comprehensive report on the persistent and escalating damage to nature from human activity with chilling conclusions:  “Over the past 50 years, humans have changed ecosystems more rapidly and extensively than in any comparable time in human history…” (MEA, 2005, p.1).   The changes already wrought include an increase of CO2 in the atmosphere by over 30% mainly from human activity; significant conversion of land to agriculture, with 2/3 of the world’s major land biomes converted to cropland; the loss of 20% of the world’s coral reefs that are vital as nurseries for fish; and a rate of extinctions of species that is unparalleled in human history (MEA, 2005).  Four decades after the watershed UN Conference on Human Environment (1972), our planet is more degraded and threatened than before.  The persistence of four gaps – environmental, policy, vulnerability to risk and lifestyle – has made it difficult to attain the goals of sustainable development (MEA, 2005).  Sustainable development as a way of protecting the earth’s life support systems came to the forefront with the publication of “Our Common Future” (WCED, 1987), also known as the Brundtland Report.  Our Common Future had called for development that looked beyond the narrow confines of individuals and nations to address three interconnected dimensions of economic, social and ecological concerns (WCED, 1987, Nemetz 2004).    Nemetz (2004) tells us that sustainable development is “arguably the greatest challenge mankind has ever faced” (p.83).  Sustainable development addresses the three interconnected dimensions of economic, social and ecological concerns.  The Brundtland Report (1987) made the bold claim that sustainable development was possible:       Humanity has the ability to make development sustainable – to ensure that it meets the needs of the present without comprising the ability of future generations to meet their own needs (WCED, 1987, p. 8).   The concept of sustainable development is marked by a strong sense of equity, both intergenerational and intragenerational: “a concern for social equity between generations, a concern that must logically be extended to equity within each generation” (WCED, 1987, p. 43).  Ecological systems must be conserved because they play an essential role in providing for human welfare:  At a minimum, sustainable development must not endanger the natural systems that support life on Earth:  the atmosphere, the waters, the soils, and the living beings (WCED, 1987, p. 45).    While development will always entail some impact on nature, humanity’s use of nature should not drive environmental systems beyond their capacity to regenerate; or deprive present and future people of their essential needs (WCED, 1987).  Care should also be taken out of a sense of fairness, since  3  environmental degradation does not harm people equally.  Unequal access to resources leads results in some people having to “suffer more than their fair share of the health, property, and ecological damage costs” (WCED, 1987, p. 48), usually, the poor who are more dependent on natural ecosystems for their basic needs and thus more vulnerable when nature is degraded (WCED, 1987; MEA, 2005; Gowdy et al. 2010).   As a system approaches ecological limits, inequalities sharpen.  Thus when a watershed deteriorates, poor farmers suffer more because they cannot afford the same anti-erosion measures as richer farmers.  When urban air quality deteriorates, the poor, in their more vulnerable areas, suffer more health damage than the rich, who usually live in more pristine neighborhoods.  Globally, wealthier nations have more resources, both financially and technologically, to cope with the effects of possible climatic change (WCED, 1987, p. 49). MEA (2005) added a new dimension to the task of sustainable development with its singular focus on the critical link between ecosystem services and human well-being.  Ecosystems provide essential goods and services to humankind such as food, water, air, habitat, recreation, aesthetic enjoyment and even spiritual contemplation (Daily, 1997, Costanza et al., 1997, de Groot et al., 2002; MEA, 2005, de Groot et al., 2012).   While human changes to ecosystems have provided materially to human well-being, in many cases, human use of ecosystems have caused widespread damage and increased risk of nonlinear changes that can potentially worsen poverty for the world’s poor (MEA, 2005), a concern raised in Our Common Future (1987).  There is an increased risk of nonlinear changes to ecosystems from global climate change (IPCC 2007).  Some would ask, in light of recent climate change reports, whether it is too late to do anything to prevent more widespread loss and damage to ecosystems and the ensuing harm to human well-being (Lovelock 2005, McKibben 2011).  Clearly there is a need to find ways to address the damage already brought upon the earth’s fragile ecosystems and protect nature for the sake of people today and in the future.  To counter the destructive trends in the human use of nature, MEA (2005) set specific goals of eradicating poverty and achieving environmental sustainability for the billion people without adequate food, water, a place to live or access to health services.  The valuation of ecosystem services was seen as an important research agenda for the protection of valuable ecosystems (MEA, 2005).    1.2 Research problem An innovative risk framework for ecosystem services valuation This thesis presents an innovative approach to clarifying the values of the benefits from nature to support sustainability efforts.   I wrestled with the question of whether it would be possible to develop a risk assessment framework that could signal when a part of nature was approaching a threshold in its capacity to regenerate, a threshold that if exceeded would cause a sudden change in natural ecosystems, possibly a collapse of critical functions, a decline in quality or loss of species, that might potentially be  4  irreversible.  Familiar with the way financial risk managers look at risk, I asked if a leading financial risk measure used to regulate banks might be applied as a way of guiding decisions about the use of earth’s valuable ecosystems. This led me to propose a new way of thinking about environmental risks, to evaluate the uncertainty around the benefits humans receive from natural ecosystems by linking a financial risk measure, known as “value at risk,” to a valuation of the goods and services, or benefits, that people receive from natural ecosystems.  I call this conceptual framework “ecosystem services value at risk” – an approach that integrates a value at risk measure from finance with a valuation of ecosystem services approach used in Costanza et al. (1997).   The ecosystem services value at risk measure would present an estimate of a “worst likely loss” which is just the potential for a large magnitude loss in the left tail of a distribution of losses in ecosystem services.     Emerging research on the catastrophic risks involved with global climate change (Weitzman, 2007; Watson, 2008; Pindyck, 2007; Pindyck, 2011) lends urgency to the need for incorporating uncertainty in the valuation of ecosystem services.    The Stern Review had justifiably brought wide attention to the alarming scenarios of global climate change involving melting Antarctica ice sheets, unknown feedback effects of global warming on the atmosphere, sea-level effects, severe weather patterns, and loss of ecosystems and mass species extinctions (Weitzman, 2007).  Weitzman found that the essential problem of the economics of climate change is the distribution of large-scale, low probability events that are found in the extreme tails of the distribution, which are marked by “uncertainty about uncertainties” (Weitzman, 2007, p. 718).  The possibility of a rare but disastrous outcome falls in the far right tail of the distribution of high temperatures, while economic growth prospects corresponding to such a catastrophic event falls in the far left tail of the distribution (Weitzman, 2007).  For Weitzman (2007) it is the significance of uncertainty, the potential irreversibility of the damage and the presence of thick-tailed probability distributions that justifies the immediate and urgent investment needed to address the possible catastrophic damage from climate change.  The emphasis must be on catastrophe insurance rather than consumption smoothing.   Pindyck (2007) likewise called for a focus on the likelihood of severe or catastrophic outcomes when evaluating environmental policy.  When the uncertainties involve irreversible, nonlinear, and catastrophic impacts over long time periods, then what matters is the tail of the distribution, and early, costly action to prevent damage could be justified (Pindyck, 2007).  Watson (2008) provided evidence from climate science of disastrous climate events having a skewed distribution with long tails lying in the area of high climate sensitivities, implying significant economic costs should they occur.  Since the costs of climate change are expected to increase nonlinearly with increasing temperatures, then paying attention to the tail in the probability distributions (Watson, 2008).  The uncertainty surrounding climate change and biodiversity loss sets the stage for a new agenda in ensuring  5  that our human use of natural ecosystems is sustainable, efficient and fair:  the need to focus at the possibility of large magnitude losses in the tail of the distribution.  As far as the author is aware, this is a novel approach that integrates financial value at risk with an ecosystem services valuation and natural capital framework from Costanza et al. (1997).  While Webby et al. (2007) apply a financial value at risk measure to the valuation of losses in catch and damages for a riverine fishery, the authors fail to identify the need for including Total Economic Value (TEV) of the ecosystem services from a given natural ecosystem.  Their application of Value at Risk to resource management is limited to the provisioning services of the catch from a riverine fishery, or as an estimate of the cost of international aid.  This research presents a framework for an ecosystem services value at risk measure that takes into account a comprehensive valuation of all the benefits that humankind can receive from natural ecosystems.  This new approach requires a stochastic ecological resource use model to represent the uncertainty of the ecological-economic interactions rather than an empirical model.    Costanza et al.’s (1997) study is the most notable effort to value nature, estimating the flow of benefits from the world’s ecosystems at an average of $33 trillion every year.  Costanza et al. (1997) sought to estimate the total economic value of vast range of benefits from the world’s marine and terrestrial ecosystems broken down into 16 biomes and 17 categories of ecosystem services to highlight that these services have economic value even if not explicitly valued by markets.  Ecosystem services are the wide range of benefits that people receive from natural ecosystems that are important for human well-being (Daily et al., 1997; Costanza et al., 1997; de Groot et al., 2002; MEA, 2005; de Groot et al., 2010; de Groot et al., 2012).  Costanza et al. utilized a wide range of environmental valuation studies from around the world that had used many environmental valuation methodologies, largely based estimates of “willingness to pay” (WTP), the estimates of individual preferences to pay for an increase in an environmental good or to avoid the loss of an environmental good.  See also the following section on the Ecosystem services value at risk conceptual framework for a discussion of valuation methods.    The average prices for these services from nature based on the WTP estimates derived from these studies were used as proxies for the non-market values or “shadow prices” of these ecosystem services.  Values for each ecosystem service were summed across each biome or ecosystem type and then aggregated for all the biomes to obtain a valuation for all of the world’s ecosystem services.  This method of assigning monetary values to ecosystem services is termed a “value of ecosystem services” (VES) approach (Costanza et al., 1997, Howarth and Farber 2002).  These goods and services from nature have both market and non-market values, but the non-market values can be significant (Costanza et al., 1997; Adamowicz, 2004; MEA, 2005).  Valuation of ecosystem services (VES) is thought to provide valuable information to decision makers and society so that decisions can be made that favor sustainability (Costanza et al., 1997).  Recent research supports the usefulness of monetary valuation of ecosystem  6  services; The Economics of Ecosystems and Biodiversity (The Economics of Ecosystems and Biodiversity [TEEB], 2010), a global study of the impacts of environmental degradation and biodiversity loss intended to provide policy makers with information about the significant market and non-market values of from environmental systems and to stimulate debate as well as policy action (de Groot et al., 2010, 2012).  An overview of the Ecosystem Services Value Database (ESVD), a global database of the values of 22 ecosystem services and 10 biomes with 1,350 unique values, is provided in de Groot et al. (2012).   The failure of conventional markets to recognize the significant non-market values from ecosystem services is thought to be a primary reason for the continuing degradation of ecosystems from human activities with losses in valuable benefits for humankind (Costanza et al., 1997, MEA 2005).   Within conventional economics, these uncompensated losses in well-being to society from the unsustainable use of natural ecosystems are regarded as negative externalities that need to be addressed by environmental policy.  Failed markets and flawed institutional arrangements are thought of as the primary causes for the degradation of natural ecosystems that result in significant losses in human welfare (Arrow et al., 1995a; Chichilnisky, 1996; Heal, 2000).  Monetary valuation of ecosystem services can address these diseconomies by providing information about the “relative scarcity of the natural environment” (Howarth and Farber 2002).  As noted in Brauer (2003), there is an intuition about the usefulness of monetary measures; environmental valuation may also be necessary in the political domain of environmental decisionmaking (Heal, 2000).   There are problematical aspects, however, to the underlying assumption that there are “correct prices” to be discovered to be used in cost-benefit analysis (CBA) that can address the environmental degradation from market failures, even when the notion of economic efficiency is adjusted to take into account non-market goods and services (Gowdy and Erickson, 2005).  A majority of economists are steeped in conventional cost-benefit analysis, emphasizing policies that correct for market failures (Gowdy, 2004).  The emphasis on economic efficiency and the assumption that “objectivity” in economics precludes prescriptions on distributional problems is problematic since this unnecessarily restricts the assessments of human well-being (Gowdy, 2004; Gowdy and Erickson, 2005).    There is evidence from behavioral economics and psychological studies that people have motivations other than self-interest, such as altruism and a sense of responsibility; and that they make decisions taking into account community norms, including fairness (Howarth, 1995; Gowdy, 2004).  Psychological studies have demonstrated that imbuing tasks with a monetary context led to reduced helpfulness (Vohs et al., 2006); while monetary incentives tended to “crowd out” intrinsic motivation to perform a task, unless there were also opportunities for active participation (Frey, 1997).  There is empirical evidence that people don’t make decisions according to expected utility since they require to be paid more for losses  7  than gains (Knetsch, 2005; 2007).  The implication from these studies is that modifications must be made to properly account for people’s welfare, calling for a shift in the criteria commonly used in environmental decisionmaking, from the valuation of environmental assets or environmental damages, or in the evaluation of environmental policy (Knetch, 2007). More importantly, when the results from environmental valuation are incorporated into decision making, an approach geared to economic efficiency alone is insufficient to provide for equity, unless there is commitment by each generation to provide for future generations (Howarth, 1992).  Howarth and Norgaard (1990) note that it is possible to achieve economic efficiency that is unjust; economi