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Stochastic Integrated Assessment of Ecosystem Tipping Risk

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One of the major potential consequences of climate change is damage to earth’s ecosystems, damage which could manifest itself in the form of tipping risks. We establish an economic growth model of ecosystem tipping risks, set in the context of possible forest dieback. We consider different specifications of impacts arising from the forest dieback tipping point, specifications such as changes in the system dynamics of the forests, changes in the forest mass, and impacts on economic output. We also consider endogenous and exogenous tipping point probabilities. For each specification we compute the optimal policies for forest management and emission control. Our results show qualitative differences in patterns of post-tipping event, optimal forest harvest, and either precautionary or aggressive pre-tipping event harvest patterns, a feature consistent with the findings of the existing literature. Optimal control of deforestation and carbon dioxide emission reduction also exhibits varied patterns of post- and pre-tipping levels depending on the nature of the tipping risk. Still, today’s optimal policy is one of more stringent emissions control in presence of a potential forest dieback tipping point.

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  1. The words “tipping points” and “regime shifts” are often used in similar contexts without clarifying whether the one necessarily accompanies the other or not. A definition distinguishing the two concepts is that by Biggs et al. (2011): they term “regime shifts” “large, abrupt persistent changes in the structure and function of ecosystems” entailing “the shift of a system from one basin of attractor to another when a critical threshold or tipping point is exceeded.” By this definition, a regime shift could occur only in the presence of a tipping point, but the reverse is not necessarily the case. In the following, we mainly use the term “tipping points” even in the cases also involving a regime shift, as that term is the one frequently used in the literature of the economics of climate change, such as in Lemoine and Traeger (2014), Cai et al. (2015b), or Lontzek et al. (2015). In discussing our model, however, we make clear which of our examined cases could be also seen as “regime shifts” by Biggs et al.’s definition and which are not.

  2. In some economies, the forestry sector provides a significant proportion of economic output. For example, 22 countries have over 3 % of GDP coming from the forestry sector in 2011 (FAO 2014).

  3. Note that emissions can be proportional to output even if Y is a function of the renewable resource that originates from the natural system and does not involve fossil fuel combustion itself. For example, the burning of wood does not produce net emissions itself (it is carbon-neutral) but the use of timber accompanies emissions as a form of either logging and transportation of products or of enhanced activities in housing construction, etc.

  4. For comparison, we have also computed a business-as-usual scenario (not reported in the graphs) where \(q_{t}\) remains at its level of today \((q_{t}=0.0152)\) and there is no mitigation control \((m_{t}=0)\). In such a case temperature would rise much higher with 2.94 in 2100 and 4.46 in 2200. Thus, we see that optimally controlling emissions (on average about 30 % over the next two centuries) and optimally reducing the harvesting of the forest resource (an average reduction of 50 %) will have a strong effect on reducing global warming.


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Correspondence to Thomas S. Lontzek.

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Lontzek, T.S., Narita, D. & Wilms, O. Stochastic Integrated Assessment of Ecosystem Tipping Risk. Environ Resource Econ 65, 573–598 (2016).

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