Economic Estimates of Damages vary significantly and are beset by difficulties leading to underestimation.
Reflections – Managing Uncertain Climates: Some Guidance for Policy Makers and Researchers. Frank Convery and Gernot Wagner. 2015.
Selected excerpts:
Climate change—and, by extension, climate policy—is beset with unknowns and unknowables.
We believe that what we don’t know only hastens the case for action.
Dealing with uncertainty is hard under the best of circumstances, but the challenge is compounded when examining climate change, an issue that uniquely combines four characteristics—it is global, long-term, irreversible, and uncertain.
Many have pointed to the problem uncertainty poses. Pindyck (2013a), for example, offers a powerful critique of the use of integrated assessment models (IAMs) to assess climate policy, focusing in particular on their treatment of uncertainty: “IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.” Many others, including Stern (2013, 2015), largely agree. Weitzman (2009, 2011, 2012, 2014) and Wagner and Weitzman (2015) highlight the importance of tail risks and grapple with the implications. Heal and Millner (2014b) discuss the implications for decision theory, and Fisher and Le (2014) discuss the implications for policy more broadly.
Meanwhile, the ‘most likely’ value for climate sensitivity has been around 2.5 or 3°C (4.5 or 5.4°F), until the IPCC stopped using any specific number altogether in 2013. Thus, there appears to be greater and more deep-seated uncertainty around this crucial climate parameter than was thought possible only five years earlier. The IPCC’s removal of 3°C (5.4°F) as the ‘most likely’ value may well have been an effort to counter the natural tendency to focus on the average rather than the range. However, that step is still insufficient to capture the full range of uncertainty. As Weitzman (2009, 2011, 2012, 2014), Wagner and Weitzman (2015), and many others demonstrate, the relatively wide ‘likely’ range doesn’t tell all. It is the upper bound (or possible lack thereof) of climate sensitivity that ought to command particular attention because steeply increasing damage functions make even small chances of high temperature increases incredibly costly—‘catastrophic’ to use a more colloquial yet apt description. In the final analysis, climate change is a risk management problem on a planetary scale, with no chance of a do-over. That, in short, is the unprecedented nature of this problem.
All too often, uncertainty has been seen as an excuse for inaction on climate policy. This is clearly the wrong response in the face of uncertainty (Risky Business Project 2014, Wagner and Weitzman 2015). First, the uncertainty about climate sensitivity is only one of many. Just the first step in projecting climatic outcomes—calculating future emissions trajectories—is already beset with enormous uncertainties: The famous ‘IPAT’ equation breaks down impact (here, carbon emissions) into three components: population, affluence, and technology.3 Each of these components is difficult to predict individually. When combined they result in enormous uncertainty around future emissions pathways. Each other step in the climate chain—from emissions at one end to society’s reaction to the final impacts at the other—comes with further compounding uncertainties. … Third, the potentially long and ‘fat’ upper tail of the climate sensitivity distribution may yet wag us.5 This is because although the lower end of the distribution is typically and sensibly cut off at 0°C, consensus science sees no such certain threshold on the upper end. In contrast to the IPCC’s (2013) view that any climate sensitivity realization below 1°C (1.8°F) is “extremely unlikely”—a (perhaps overly precise) probability of 5% and below—it assigns the label “very unlikely”—10% and below—to anything above 6°C (10.8°F). This implies that the climate sensitivity distribution is skewed to the right, which means that higher temperature realizations are more likely than low ones (see Figure 1).
Figure 1—Climate sensitivity calibrated using a log-normal distribution
Few scientists would dispute that global average temperature increases of 2, 3, or even 4°C (3.6, 5.4, or 7.2°F) would entail profound, Earth-as-we-know-it-altering changes. The last time global average temperatures were about 2 to 3.5°C (3.6 to 6.3°F) above preindustrial levels— roughly 1 to 2.5°C (1.8 to 4.5°F) above today’s levels—sea levels were up to 20 meters (66 feet) higher than today, and today’s subtropical fauna roamed the Arctic (IPCC, 2013).6 Eventual global average warming of 5 or even 6°C (9 or 10.8°F) is beyond most scientists’ data and most people’s imagination. But when we combine our climate sensitivity calibration based on the 6 That was a bit over 3 million years ago, when global CO2 concentrations stood at 400 ppm—today’s levels! IPCC’s (2013) consensus statements, conservatively interpreted in Figure 1, with the IEA (2013) 700 ppm scenario, that’s where we end up -- a greater-than-10-percent chance of eventually exceeding average global warming of 6°C (10.8°F). 2015). Average projections are bad enough, but it’s the small-probability, high-impact events that ought to command particular attention. That possibility all but calls for a precautionary approach to climate policy.
Pindyck argues that standard climate-economy models fall victim to two important fallacies in dealing with uncertainty: by necessity, they focus on what is known and can be quantified, thus leaving out what isn’t known and can’t be quantified, and they convey a false sense of precision. Weitzman (2009, 2011, 2012, 2014) makes perhaps the most persuasive case for going beyond standard benefit-cost analysis, arguing that climate change is among a small list of potentially catastrophic low-probability, high-impact events that deserve special attention far beyond what standard treatments can offer (Wagner and Weitzman, 2015).
We would argue that existential risk on a planetary scale deserves quite different attention than, for example, “the construction of levees to avert major flooding,” two of the examples discussed by Martin and Pindyck. One could add asteroids, genetically modified organisms, robots run amok, and many others to that list. It is clear that climate change is not the only potential catastrophe facing the planet. However, climate change may, in fact, be in the unique position of having the biggest gap between the types of investments (both public and private) that science tells us are necessary and current levels of spending on it (Wagner and Weitzman, 2015). Thus, at the very least, persistent uncertainties imply that we need to move beyond benefit-cost analysis as the sole decision criterion. Heal and Millner (2013, 2014b) present a range of alternative decision criteria, with a version of a ‘precautionary principle’ being perhaps the most prominent.
If a society is to implement rational climate policy, one of the most important decisions it must make is how much value to place on future generations (Summers and Zeckhauser, 2008). This raises the crucial issue of which discount rate to use, with all its normative implications
The climate system is beset with tipping points. Witness the irreversible collapse of parts of the West Antarctic ice sheet (Joughin et al., 2014, and Rignot et al., 2014). The (theoretical) possibility and empirical implications of non-linearities and tipping points are beginning to find their way into climate-economy models (e.g., Ceronsky et al. 2011, Keller, Bolker, and Bradford, 2004, Lemoine and Traeger, 2014ab, Lontzek, Cai, and Judd, 2012, van der Ploeg and de Zeeuw, 2014). However, the work is far from done. Some tipping points interact with—and, thus, are as difficult as addressing—irreversibilities, which inevitably invoke the specter of ‘infinity’ with all the difficulties that involves. Other elements of non-linearities ‘simply’ point to the need to explore climate damage functions that don’t follow neat quadratic, exponential, or other simple functional form patterns (e.g., Crost and Traeger 2014, Sterner and Persson 2008). Much empirical work remains to be done to draw definitive conclusions about the importance of different types of damage functions, although one conclusion has already clearly emerged: virtually all non-linearities and possible tipping points point in one direction, that of more steeply rising climate damages. That once again implies a higher social cost of carbon.
Climate-economy models, IAMs, play a crucial role in climate economics and policy. For example, the current U.S. social cost of carbon (around $40 per ton of CO2 emitted in 2015 in current prices) is calculated using inputs from three models: DICE, FUND, and PAGE (U.S. Government Interagency Working Group on Social Cost of Carbon, 2013). All three models share one important characteristic: they each are the brainchild of a single academic—William Nordhaus, Richard Tol, and Chris Hope, respectively. … As of now, IAMs lag years behind the latest climate science.
The economically optimal warming limit of the planet. Falko Ueckerdt et al. 2018.
just including the abstract and a bit of the intro:
Abstract
Both climate-change damages and climate-change mitigation will incur economic costs. While the risk of severe damages increases with the level of global warming (Allen et al., 2018; Dell et al., 2014; IPCC, 2014b; Lenton et al., 2008), mitigating costs increase steeply with more stringent warming limits (Allen et al., 2018; IPCC, 2014a; Rogelj et al., 2015). Here we show that the global warming limit that minimizes this century’s total economic costs of climate change lies between 1.9 and 2°C if temperature changes continue to impact national economic growth rates as observed in the past. The result is robust across a wide range of normative assumptions on the valuation of future welfare and inequality aversion. For our study we estimated climate change impacts on economic growth for 186 countries based on recent empirical insights (Burke et al., 2015a), and mitigation costs using a state-of-the-art energy-economy-climate model with a wide range of highly-resolved mitigation options. Our purely economic assessment, even though it omits non-monetary damages, provides support for the international Paris Agreement on climate change. The political goal of limiting global warming to “well below 2 degrees” is thus also an economically optimal goal.
Introduction
“Holding the increase in the global average temperature to well below 2°C above preindustrial levels and pursuing efforts to limit the temperature increase to 1.5°C” is a central element of the global climate agreement reached in Paris in December 2015 (UNFCCC, 2015). This political goal builds on the scientific insight that a global warming beyond 1.5–2°C poses risks of potentially severe impacts such as insecure food and drinking water supply (Allen et al., 2018; IPCC, 2014b), threatened biodiversity (Dawson et al., 2011; Willis and Bhagwat, 2009), large-scale singular events (Lenton et al., 2008; Schellnhuber et al., 2016), displacement (Hsiang and Sobel, 2016), or human conflict (Hsiang et al., 2013a; Schleussner et al., 2016). Many of these risks and their societal consequences are difficult or even impossible to capture in economic terms. Here we focus on the direct impacts of global warming on economic output. Taking a purely economic perspective that omits non-monetary damages, we derive the optimal warming limit of the planet by minimizing this century’s (2015–2100) costs of climate change. The analysis combines mitigation cost estimates from a detailed energy-economy-climate model with an empirically based damage estimation, which assumes that the observed relation of economic damages and annual temperatures of a country remains valid for the future.
Cost-benefit integrated assessment models (Anthoff and Tol, 2014; Hope, 2013; Nordhaus, 2014, 2010) typically use “damage functions”, which aggregate the economic costs from climate impacts as a function of the global warming. Here we take a different approach. We estimate climate damages from annual gridded temperature data (0.5° x 0.5° resolution) for 186 countries based on the empirical relation between temperature deviations and economic growth rates derived in Burke et al. (Burke et al., 2015a).
In their pioneering work, Burke et al. derive an empirical relation of annual historical temperature deviations and GDP changes based on country-specific data for 50 years (1960-2010) and 166 countries (which we then apply for 186 countries). The regression analysis captures the aggregated climate-related impacts across all economic sectors that contribute to a country's GDP changes. Burke et al. find that growth rates change concave in temperature, i.e. cold-country productivity increases as annual temperature increases, while warm-country productivity decreases and this decline accelerates at higher temperatures (see Fig. A4). Damage aggregates across countries show that losses exceed benefits such that global damage estimates are high (>20% of global GDP in 2100 under RCP8.5, see Fig 1a).
MW: Even this paper, which uses unrealistic assumptions, and which estimates global damages under RCP 8.5 of >20% of global GDP in 2100, when all climate scientists and most scientists in general would argue that RCP 8.5 will lead to billions of lives lost and the collapse of the biosphere and thus civilization, nonetheless argues that it would be economically optimal to keep climate change <2C.
Climate change uncertainty and decision-making. Arun Malik, Jonathan Rothbaum, Stephen Smith. 2010.
Climate Change Uncertainty
The 2007 IPCC report on the physical science basis of climate change includes many models which show the wide range of temperature increase predictions. Figure 1 gives a sense for the uncertainties involved in climate change modeling. Each model attempts to take what we know about the climate system and determine the probability that the climate will stabilize with a global mean temperature increase from 0-10°C. While there is broad agreement across the models that temperature increases will occur, the distributions vary considerably.
The purpose of this section is to show just how pervasive the uncertainties involved in climate change are. These uncertainties and issues can be broken down into broad categories to give a sense of how they might affect different decision-makers.
2.1 Environmental Uncertainties and Issues
2.1.1 Feedback Loops – Ecological and Physical Processes
2.1.4 Extreme Weather Events
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2.2 Economic Uncertainties
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2.3 Model and Parameter Uncertainty
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3.1 Risk vs Ambiguity
3.2 Fat-Tailed Distributions
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3.4 Unknown Unknowns
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5.2 Precautionary Principle
Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus’ Framework Supports Deep Cuts in Carbon Emissions. Nicholas Stern and Simon Dietz. 2015. (selected excerpts)
‘To slow or not to slow’ by Bill Nordhaus (1991) is a landmark in economic research. As the first analysis of the costs and benefits of policies to abate greenhouse gas emissions, it opened the profession to a new field of application – climate change. Its importance is partly illustrated by the number of times that it has been cited – on 1,150 occasions according to Google Scholar; 398 times according to the narrower, journals only measure in ISI Web of Knowledge. The context within which Nordhaus’s paper was written helps us understand its contribution. While the basic science of the greenhouse effect was set out in the nineteenth century by Fourier, Tyndall and Arrhenius, discussions surrounding the possible role of humans in enhancing it – and therefore causing global warming and climate change – began in earnest in the 1970s. For at least a decade, climate change remained largely a scientific/environmentalist’s issue, debated in specialist conferences and networks (Agrawala, 1998). Indeed, it is important to stress that the science of climate change was running years ahead of the economics (something that arguably remains the case today in understanding the impacts of climate change; Stern, 2013).
By the late 1980s, however, climate change was becoming both a policy issue and increasingly political.
… model took ‘existing models and simplified them into a few equations that are easily understood and manipulated’ (p. 920), something that has become a hallmark of Nordhaus’s work in the area.
Once again, the results of the analysis with DICE pointed to modest emissions controls, modestly increasing over time – from 10% initially to 15% in the later twenty-first century. Since these first studies with the DICE model, it has become the pre-eminent integrated assessment model (IAM) in the economics of climate change.
A central purpose of this article is to explore whether a recommendation of modest emissions reductions does indeed follow from using the DICE framework. We ask, can the framework support strong controls on emissions, if restrictive assumptions about growth, damage and climate risk are relaxed? These assumptions arguably lead to gross underestimation of the benefits of emissions reductions in DICE and other IAMs (Stern, 2013). First, we incorporate endogenous drivers of growth and we allow climate change to damage these drivers. This is in stark contrast to the current generation of IAMs… Second, we assume that the damage function linking the increase in global mean temperature with the instantaneous reduction in output is highly convex at some temperature... Third, we allow for explicit and large climate risks. ..
We conduct sensitivity analysis on high values but also specify a probability distribution reflecting the latest scientific knowledge on the climate sensitivity as set out in the recent IPCC report (IPCC, 2013). Its key characteristic is a fat tail of very high temperature outcomes that are assigned low probabilities. By contrast, most IAM studies have ignored this key aspect of climate risk by proceeding with a single, best guess value for the climate sensitivity, typically corresponding to the mode of the IPCC distribution. We note, linking the second and third points here, that the model temperature increase under business as usual a century or so from now of 3.5 or 4°C (IPCC, 2013) could be extremely damaging – this is not just a ‘tail’ issue. …
Science and impact studies tell us that, not only could we cross several key physical tipping points in the climate system by the time the 4°C mark is reached (Lenton et al., 2008), the impacts of such warming on the natural environment, economies and societies could be severe … Given the potential magnitude of transformation illustrated by this example, the assumption that Dt = 0.5 when T = 4 may be no less plausible, to put it cautiously, than assuming, as (2) does with the standard parameterisation, that Dt = 0.04 when T = 4, i.e. only 4% of output is lost as a result of temperatures not seen for 10 million plus years.
[MW: are you f'g kidding me?! that is to say, standard DICE-like IAM economic models are full of shxt]
In standard DICE S = 3°C. However, it has long been known that there is substantial uncertainty about S (Charney, 1979). Moreover investigations in recent years (as collected by Meinshausen et al., 2009) have tended to yield estimates of the pdf of S that have a large positive skew
Figure B5 shows that the optimal mean stock of atmospheric CO2 peaks in our endogenous growth models at no more than about 500 ppm, and as little as 420 ppm, depending on the growth model and damage function. These stock levels are well below those in the standard DICE model.
5. Conclusions
‘To slow or not to slow’ (Nordhaus, 1991) and its subsequent development into the dynamic DICE model have given us what seems to be a coherent and powerful framework for assessing the costs and benefits of climate-change mitigation. But it has in-built assumptions on growth, damage and risk, which together result in gross underassessment of the overall scale of the risks from unmanaged climate change (Stern, 2013). This criticism applies with just as much force to most of the other IAMs that DICE has inspired. …
The study is only a preliminary investigation, whose purpose was to illustrate or sketch the consequences of relaxing assumptions that have limited plausibility and possible large effects on policy conclusions.
Table 4: Optimal Carbon Prices
[MW: So, estimates by Stern of SCC range from $70 to $329, as compared to: ]
“In standard DICE the emissions control rate, that is the percentage reduction in industrial carbon dioxide emissions, is 0.158 in 2015, with an associated carbon price of $44/tC in 2005 prices”
Subject to caveats implicit and explicit from articles above, and subject further to recognition that economic IAMs embed climate models’ ECS, but the economic models lag, so do not reflect the latest climate science, and ECS is now known to be higher than previously assumed; and subject to further recognition that economic models of climate damages are divorced from concepts of ecology and destruction of ecosystem services we require, fwiw, existing studies of economic damages that can be for reference include:
The Economic Consequences of Climate Change. OECD. 2015.
Page 80: In the case of a high climate sensitivity (equal to 4.5 ºC or 6 ºC temperature increase), this annual loss rises to 6% and more than 9%, respectively, by 2100. This insight also holds for climate impacts occurring before 2060: effectively any emission, whether now or in the future, triggers a series of effects and leads to an increase in climate damages for at least a century. Thus, there are damages that are already committed to now due to historical emissions; in the AD-DICE model, these gradually increase to around 0.6% of GDP (for the central ECS estimate), although the model is not fine-grained enough (and not intended to be) to assess current damage levels accurately.
[MW: so, once again, re: the insight noted above.... these types of studies are seriously divorced from reality; the notion that a planet with temperatures 6C higher will suffer only damages of (name whatever f'g arbitrary $$ value you want here) is laughably inane. see Schellnhuber's or Box's or Anderson's scientific assessment of what 4C implies -- the destruction of civilization!]
Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Natl Academies. 2017.
The social cost of carbon (SC-CO2) is an economic metric intended to provide a comprehensive estimate of the net damages—that is, the monetized value of the net impacts, both negative and positive— from the global climate change that results from a small (1 metric ton) increase in carbon dioxide (CO2) emissions. Under Executive Orders regarding regulatory impact analysis and as required by a court ruling, the U.S. government has since 2008 used estimates of the SC-CO2 in federal rulemakings to value the costs and benefits associated with changes in CO2 emissions. In 2010, the Interagency Working Group on the Social Cost of Greenhouse Gases (IWG) developed a methodology for estimating the SC-CO2 across a range of assumptions about future socioeconomic and physical earth systems.
The IWG asked the National Academies of Sciences, Engineering, and Medicine to examine potential approaches, along with their relative merits and challenges, for a comprehensive update to the current methodology. The task was to ensure that the SC-CO2 estimates reflect the best available science, focusing on issues related to the choice of models and damage functions, climate science modeling assumptions, socioeconomic and emissions scenarios, presentation of uncertainty, and discounting.
Integrated assessment models (IAMs) are currently used by the IWG to estimate the economic consequences of CO2 emissions. The IAMs define baseline emission trajectories by projecting future economic growth, population, and technological change. In these IAMs, a 1 metric ton increase in CO2 emissions is added to the baseline emissions trajectory. This emissions increase is translated into an increase in atmospheric CO2 concentrations, which results in an increase in global average temperature. This temperature change, as well as changes in other relevant variables, including CO2 concentrations and income, is translated (either explicitly or implicitly) to physical impacts and monetized damages. These damages include, but are not limited to, market damages, such as changes in net agricultural productivity, energy use, and property damage from increased flood risk, as well as nonmarket damages, such as those to human health and to the services that natural ecosystems provide to society. Because most of the warming caused by an emission of CO2 into the atmosphere persists for well over a millennium, changes in CO2 emissions today may affect economic outcomes for centuries to come. Streams of monetized damages over time are converted into present value terms by discounting. The present value of damages reflects society’s willingness to trade value in the future for value today…. The IWG’s current estimate of the SC-CO2 in the year 2020 for a 3.0 percent discount rate is $42 per metric ton of CO2 emissions in 2007 U.S. dollars.
LIMITATIONS OF SIMPLE EARTH SYSTEM MODELS
In complex climate models, the parameters described in Box 4-1—ECS, TCR, TCRE, and IPT—are resultant behaviors of the climate system, not input parameters. They arise from physical properties of the Earth system, such as the heat capacity of the ocean and the magnitude of different feedbacks that amplify or dampen the temperature change caused by radiative forcing. The strength of these feedbacks depends on the state of the climate; they are not generally constant, and they may vary in response to the magnitude of forcing and spatial pattern of forcing, as well as over time (Knutti and Rugenstein, 2015). By contrast, in simple Earth system models at least some of these metrics are input parameters. For Earth system models. It is therefore important to be aware of three key limitations of this assumption and the use of ECS, TCR, TCRE, and IPT as parameters.
The first limitation is that these metrics are all defined with respect to a reference state, such as the preindustrial state of Earth.
The second limitation is that these parameters are diagnosed using tests that hold certain elements of the climate system constant. This inactivates so carbon cycle feedbacks are also excluded. If these other feedbacks are predominantly positive, then on the timescales on which they are operative, measures such as ECS and TCR will understate the expected warming.
The third limitation is that three important feedbacks are excluded from ECS and TCR: the response to changes in albedo related to land ice, changes in albedo and transpiration related to land cover changes and the dust/aerosol feedbacks that impact biogeochemical cycles. Geological data suggest that these feedbacks may amplify warming by about 50 percent relative to that expected based on ECS alone
…. Currently, the damage component of an SC-IAM translates streams of socioeconomic variables (e.g., income and population and gross domestic product) and physical climatic variables (e.g., changes in temperature and sea level) into streams of monetized damages over time. …. Another attribute of the SC-IAMs that underpin the current IWG estimates is that much of the research on which they are based is dated.
… The committee notes that the Interagency Working Group on the Social Cost of Carbon (2010) identified a number ofpotential shortcomings and critiques of the current damage formulations, which are discussed further below.
These include:
To Slow or Not to Slow: The Economics of the Greenhouse Effect. William Nordhaus. 1992.
The Economics of Climate Change: The Stern Review. Nicholas Stern. 2007.
Climate change uncertainty and decision-making. Arun Malik, Jonathan Rothbaum, Stephen Smith. 2010.
Towards and ecological economics. Peter Victor and Tim Jackson. 2012.
Better Growth, Better Climate: Global Report. New Climate Economy. 2014.
Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus’ Framework Supports Deep Cuts in Carbon Emissions. Nicholas Stern and Simon Dietz. 2015.
Climate Change Risks and Adaptation: Linking Policy and Economics. OECD. 2015.
The Economic Consequences of Climate Change. OECD. 2015.
Reflections – Managing Uncertain Climates: Some Guidance for Policy Makers and Researchers. Frank Convery and Gernot Wagner. 2015.
Technical Update to Environment and Climate Change Canada’s Social Cost of Greenhouse Gas Estimates. Environment and Climate Change Canada. 2016.
Policy tradeoffs under risk of abrupt climate change. Yacov Tsur and Amos Zemel. 2016.
Economics of the Climate. Geoffrey Heal. 2017.
Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Natl Academies. 2017.
Frontiers of Climate Change Economics. Van der Meijden, van der Ploeg, Withagen. 2017.
Pricing Carbon and Adjusting Capital to Fend off Climate Catastrophes. Van der Ploeg, de Zeeuw. 2018.
Climate change and the macro-economy: a critical review. Sandra Batten, Bank of England staff working paper. 2018.
One Step Forward, One Step Back: Assessing the Consequences of Three Decades of Climate Gridlock in the U.S. Joseph Curtin and Max Munchmeyer, IIEA. 2019.
Economics and the Ecosystem. Real World Economics Review. 2019.
Degrowth: a theory of radical abundance. Jason Hickel.
Elements of a political economy of the postgrowth era. Max Koch.
Victim of success: civilisation is at risk. Peter McManners.
Economism and the Econocene: a coevolutionary interpretation. Richard Norgaard.
End game: the economy as eco-catastrophe and what needs to change. William Rees.
An ecosocialist path to limiting global temperature rise to 1.5C. Richard Smith.
Like blending chalk and cheese – the impact of standard economics in IPCC scenarios. Joachim Spagenberg and Lia Polotzek.
Of ecosystems and economies: re-connecting economics with reality. Clive Spash and Tone Smith.
How to achieve the Sustainable Development Goals within planetary boundaries by 2050. Per Espen Stoknes.
See also
Lenton, Timothy. Tipping elements in the Earth’s climate system. 2008.
Rockstrom, Johan. Planetary boundaries: Exploring the safe operating space for humanity. 2009.
Barnosky, Anthony. Approaching a state shift in Earth’s biosphere. 2012.
Steffen, Will. Trajectories of the Earth System in the Anthropocene. 2018.
Ehrlich, Paul and Anne. Can a collapse of global civilization be avoided? 2013.
Selected excerpts:
Climate change—and, by extension, climate policy—is beset with unknowns and unknowables.
We believe that what we don’t know only hastens the case for action.
Dealing with uncertainty is hard under the best of circumstances, but the challenge is compounded when examining climate change, an issue that uniquely combines four characteristics—it is global, long-term, irreversible, and uncertain.
Many have pointed to the problem uncertainty poses. Pindyck (2013a), for example, offers a powerful critique of the use of integrated assessment models (IAMs) to assess climate policy, focusing in particular on their treatment of uncertainty: “IAM-based analyses of climate policy create a perception of knowledge and precision, but that perception is illusory and misleading.” Many others, including Stern (2013, 2015), largely agree. Weitzman (2009, 2011, 2012, 2014) and Wagner and Weitzman (2015) highlight the importance of tail risks and grapple with the implications. Heal and Millner (2014b) discuss the implications for decision theory, and Fisher and Le (2014) discuss the implications for policy more broadly.
Meanwhile, the ‘most likely’ value for climate sensitivity has been around 2.5 or 3°C (4.5 or 5.4°F), until the IPCC stopped using any specific number altogether in 2013. Thus, there appears to be greater and more deep-seated uncertainty around this crucial climate parameter than was thought possible only five years earlier. The IPCC’s removal of 3°C (5.4°F) as the ‘most likely’ value may well have been an effort to counter the natural tendency to focus on the average rather than the range. However, that step is still insufficient to capture the full range of uncertainty. As Weitzman (2009, 2011, 2012, 2014), Wagner and Weitzman (2015), and many others demonstrate, the relatively wide ‘likely’ range doesn’t tell all. It is the upper bound (or possible lack thereof) of climate sensitivity that ought to command particular attention because steeply increasing damage functions make even small chances of high temperature increases incredibly costly—‘catastrophic’ to use a more colloquial yet apt description. In the final analysis, climate change is a risk management problem on a planetary scale, with no chance of a do-over. That, in short, is the unprecedented nature of this problem.
All too often, uncertainty has been seen as an excuse for inaction on climate policy. This is clearly the wrong response in the face of uncertainty (Risky Business Project 2014, Wagner and Weitzman 2015). First, the uncertainty about climate sensitivity is only one of many. Just the first step in projecting climatic outcomes—calculating future emissions trajectories—is already beset with enormous uncertainties: The famous ‘IPAT’ equation breaks down impact (here, carbon emissions) into three components: population, affluence, and technology.3 Each of these components is difficult to predict individually. When combined they result in enormous uncertainty around future emissions pathways. Each other step in the climate chain—from emissions at one end to society’s reaction to the final impacts at the other—comes with further compounding uncertainties. … Third, the potentially long and ‘fat’ upper tail of the climate sensitivity distribution may yet wag us.5 This is because although the lower end of the distribution is typically and sensibly cut off at 0°C, consensus science sees no such certain threshold on the upper end. In contrast to the IPCC’s (2013) view that any climate sensitivity realization below 1°C (1.8°F) is “extremely unlikely”—a (perhaps overly precise) probability of 5% and below—it assigns the label “very unlikely”—10% and below—to anything above 6°C (10.8°F). This implies that the climate sensitivity distribution is skewed to the right, which means that higher temperature realizations are more likely than low ones (see Figure 1).
Figure 1—Climate sensitivity calibrated using a log-normal distribution
Few scientists would dispute that global average temperature increases of 2, 3, or even 4°C (3.6, 5.4, or 7.2°F) would entail profound, Earth-as-we-know-it-altering changes. The last time global average temperatures were about 2 to 3.5°C (3.6 to 6.3°F) above preindustrial levels— roughly 1 to 2.5°C (1.8 to 4.5°F) above today’s levels—sea levels were up to 20 meters (66 feet) higher than today, and today’s subtropical fauna roamed the Arctic (IPCC, 2013).6 Eventual global average warming of 5 or even 6°C (9 or 10.8°F) is beyond most scientists’ data and most people’s imagination. But when we combine our climate sensitivity calibration based on the 6 That was a bit over 3 million years ago, when global CO2 concentrations stood at 400 ppm—today’s levels! IPCC’s (2013) consensus statements, conservatively interpreted in Figure 1, with the IEA (2013) 700 ppm scenario, that’s where we end up -- a greater-than-10-percent chance of eventually exceeding average global warming of 6°C (10.8°F). 2015). Average projections are bad enough, but it’s the small-probability, high-impact events that ought to command particular attention. That possibility all but calls for a precautionary approach to climate policy.
Pindyck argues that standard climate-economy models fall victim to two important fallacies in dealing with uncertainty: by necessity, they focus on what is known and can be quantified, thus leaving out what isn’t known and can’t be quantified, and they convey a false sense of precision. Weitzman (2009, 2011, 2012, 2014) makes perhaps the most persuasive case for going beyond standard benefit-cost analysis, arguing that climate change is among a small list of potentially catastrophic low-probability, high-impact events that deserve special attention far beyond what standard treatments can offer (Wagner and Weitzman, 2015).
We would argue that existential risk on a planetary scale deserves quite different attention than, for example, “the construction of levees to avert major flooding,” two of the examples discussed by Martin and Pindyck. One could add asteroids, genetically modified organisms, robots run amok, and many others to that list. It is clear that climate change is not the only potential catastrophe facing the planet. However, climate change may, in fact, be in the unique position of having the biggest gap between the types of investments (both public and private) that science tells us are necessary and current levels of spending on it (Wagner and Weitzman, 2015). Thus, at the very least, persistent uncertainties imply that we need to move beyond benefit-cost analysis as the sole decision criterion. Heal and Millner (2013, 2014b) present a range of alternative decision criteria, with a version of a ‘precautionary principle’ being perhaps the most prominent.
If a society is to implement rational climate policy, one of the most important decisions it must make is how much value to place on future generations (Summers and Zeckhauser, 2008). This raises the crucial issue of which discount rate to use, with all its normative implications
The climate system is beset with tipping points. Witness the irreversible collapse of parts of the West Antarctic ice sheet (Joughin et al., 2014, and Rignot et al., 2014). The (theoretical) possibility and empirical implications of non-linearities and tipping points are beginning to find their way into climate-economy models (e.g., Ceronsky et al. 2011, Keller, Bolker, and Bradford, 2004, Lemoine and Traeger, 2014ab, Lontzek, Cai, and Judd, 2012, van der Ploeg and de Zeeuw, 2014). However, the work is far from done. Some tipping points interact with—and, thus, are as difficult as addressing—irreversibilities, which inevitably invoke the specter of ‘infinity’ with all the difficulties that involves. Other elements of non-linearities ‘simply’ point to the need to explore climate damage functions that don’t follow neat quadratic, exponential, or other simple functional form patterns (e.g., Crost and Traeger 2014, Sterner and Persson 2008). Much empirical work remains to be done to draw definitive conclusions about the importance of different types of damage functions, although one conclusion has already clearly emerged: virtually all non-linearities and possible tipping points point in one direction, that of more steeply rising climate damages. That once again implies a higher social cost of carbon.
Climate-economy models, IAMs, play a crucial role in climate economics and policy. For example, the current U.S. social cost of carbon (around $40 per ton of CO2 emitted in 2015 in current prices) is calculated using inputs from three models: DICE, FUND, and PAGE (U.S. Government Interagency Working Group on Social Cost of Carbon, 2013). All three models share one important characteristic: they each are the brainchild of a single academic—William Nordhaus, Richard Tol, and Chris Hope, respectively. … As of now, IAMs lag years behind the latest climate science.
The economically optimal warming limit of the planet. Falko Ueckerdt et al. 2018.
just including the abstract and a bit of the intro:
Abstract
Both climate-change damages and climate-change mitigation will incur economic costs. While the risk of severe damages increases with the level of global warming (Allen et al., 2018; Dell et al., 2014; IPCC, 2014b; Lenton et al., 2008), mitigating costs increase steeply with more stringent warming limits (Allen et al., 2018; IPCC, 2014a; Rogelj et al., 2015). Here we show that the global warming limit that minimizes this century’s total economic costs of climate change lies between 1.9 and 2°C if temperature changes continue to impact national economic growth rates as observed in the past. The result is robust across a wide range of normative assumptions on the valuation of future welfare and inequality aversion. For our study we estimated climate change impacts on economic growth for 186 countries based on recent empirical insights (Burke et al., 2015a), and mitigation costs using a state-of-the-art energy-economy-climate model with a wide range of highly-resolved mitigation options. Our purely economic assessment, even though it omits non-monetary damages, provides support for the international Paris Agreement on climate change. The political goal of limiting global warming to “well below 2 degrees” is thus also an economically optimal goal.
Introduction
“Holding the increase in the global average temperature to well below 2°C above preindustrial levels and pursuing efforts to limit the temperature increase to 1.5°C” is a central element of the global climate agreement reached in Paris in December 2015 (UNFCCC, 2015). This political goal builds on the scientific insight that a global warming beyond 1.5–2°C poses risks of potentially severe impacts such as insecure food and drinking water supply (Allen et al., 2018; IPCC, 2014b), threatened biodiversity (Dawson et al., 2011; Willis and Bhagwat, 2009), large-scale singular events (Lenton et al., 2008; Schellnhuber et al., 2016), displacement (Hsiang and Sobel, 2016), or human conflict (Hsiang et al., 2013a; Schleussner et al., 2016). Many of these risks and their societal consequences are difficult or even impossible to capture in economic terms. Here we focus on the direct impacts of global warming on economic output. Taking a purely economic perspective that omits non-monetary damages, we derive the optimal warming limit of the planet by minimizing this century’s (2015–2100) costs of climate change. The analysis combines mitigation cost estimates from a detailed energy-economy-climate model with an empirically based damage estimation, which assumes that the observed relation of economic damages and annual temperatures of a country remains valid for the future.
Cost-benefit integrated assessment models (Anthoff and Tol, 2014; Hope, 2013; Nordhaus, 2014, 2010) typically use “damage functions”, which aggregate the economic costs from climate impacts as a function of the global warming. Here we take a different approach. We estimate climate damages from annual gridded temperature data (0.5° x 0.5° resolution) for 186 countries based on the empirical relation between temperature deviations and economic growth rates derived in Burke et al. (Burke et al., 2015a).
In their pioneering work, Burke et al. derive an empirical relation of annual historical temperature deviations and GDP changes based on country-specific data for 50 years (1960-2010) and 166 countries (which we then apply for 186 countries). The regression analysis captures the aggregated climate-related impacts across all economic sectors that contribute to a country's GDP changes. Burke et al. find that growth rates change concave in temperature, i.e. cold-country productivity increases as annual temperature increases, while warm-country productivity decreases and this decline accelerates at higher temperatures (see Fig. A4). Damage aggregates across countries show that losses exceed benefits such that global damage estimates are high (>20% of global GDP in 2100 under RCP8.5, see Fig 1a).
MW: Even this paper, which uses unrealistic assumptions, and which estimates global damages under RCP 8.5 of >20% of global GDP in 2100, when all climate scientists and most scientists in general would argue that RCP 8.5 will lead to billions of lives lost and the collapse of the biosphere and thus civilization, nonetheless argues that it would be economically optimal to keep climate change <2C.
Climate change uncertainty and decision-making. Arun Malik, Jonathan Rothbaum, Stephen Smith. 2010.
Climate Change Uncertainty
The 2007 IPCC report on the physical science basis of climate change includes many models which show the wide range of temperature increase predictions. Figure 1 gives a sense for the uncertainties involved in climate change modeling. Each model attempts to take what we know about the climate system and determine the probability that the climate will stabilize with a global mean temperature increase from 0-10°C. While there is broad agreement across the models that temperature increases will occur, the distributions vary considerably.
The purpose of this section is to show just how pervasive the uncertainties involved in climate change are. These uncertainties and issues can be broken down into broad categories to give a sense of how they might affect different decision-makers.
2.1 Environmental Uncertainties and Issues
2.1.1 Feedback Loops – Ecological and Physical Processes
- Carbon Cycle
- Atlantic Ocean Meridional Overturning Circulation (MOC)
- Clouds
- Methane and Permafrost Melting
- Sea Level Rise
- MOC
- Vegetation Cover
- CO2 Persistence in the Atmosphere
2.1.4 Extreme Weather Events
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2.2 Economic Uncertainties
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2.3 Model and Parameter Uncertainty
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3.1 Risk vs Ambiguity
3.2 Fat-Tailed Distributions
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3.4 Unknown Unknowns
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5.2 Precautionary Principle
Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus’ Framework Supports Deep Cuts in Carbon Emissions. Nicholas Stern and Simon Dietz. 2015. (selected excerpts)
‘To slow or not to slow’ by Bill Nordhaus (1991) is a landmark in economic research. As the first analysis of the costs and benefits of policies to abate greenhouse gas emissions, it opened the profession to a new field of application – climate change. Its importance is partly illustrated by the number of times that it has been cited – on 1,150 occasions according to Google Scholar; 398 times according to the narrower, journals only measure in ISI Web of Knowledge. The context within which Nordhaus’s paper was written helps us understand its contribution. While the basic science of the greenhouse effect was set out in the nineteenth century by Fourier, Tyndall and Arrhenius, discussions surrounding the possible role of humans in enhancing it – and therefore causing global warming and climate change – began in earnest in the 1970s. For at least a decade, climate change remained largely a scientific/environmentalist’s issue, debated in specialist conferences and networks (Agrawala, 1998). Indeed, it is important to stress that the science of climate change was running years ahead of the economics (something that arguably remains the case today in understanding the impacts of climate change; Stern, 2013).
By the late 1980s, however, climate change was becoming both a policy issue and increasingly political.
… model took ‘existing models and simplified them into a few equations that are easily understood and manipulated’ (p. 920), something that has become a hallmark of Nordhaus’s work in the area.
Once again, the results of the analysis with DICE pointed to modest emissions controls, modestly increasing over time – from 10% initially to 15% in the later twenty-first century. Since these first studies with the DICE model, it has become the pre-eminent integrated assessment model (IAM) in the economics of climate change.
A central purpose of this article is to explore whether a recommendation of modest emissions reductions does indeed follow from using the DICE framework. We ask, can the framework support strong controls on emissions, if restrictive assumptions about growth, damage and climate risk are relaxed? These assumptions arguably lead to gross underestimation of the benefits of emissions reductions in DICE and other IAMs (Stern, 2013). First, we incorporate endogenous drivers of growth and we allow climate change to damage these drivers. This is in stark contrast to the current generation of IAMs… Second, we assume that the damage function linking the increase in global mean temperature with the instantaneous reduction in output is highly convex at some temperature... Third, we allow for explicit and large climate risks. ..
We conduct sensitivity analysis on high values but also specify a probability distribution reflecting the latest scientific knowledge on the climate sensitivity as set out in the recent IPCC report (IPCC, 2013). Its key characteristic is a fat tail of very high temperature outcomes that are assigned low probabilities. By contrast, most IAM studies have ignored this key aspect of climate risk by proceeding with a single, best guess value for the climate sensitivity, typically corresponding to the mode of the IPCC distribution. We note, linking the second and third points here, that the model temperature increase under business as usual a century or so from now of 3.5 or 4°C (IPCC, 2013) could be extremely damaging – this is not just a ‘tail’ issue. …
Science and impact studies tell us that, not only could we cross several key physical tipping points in the climate system by the time the 4°C mark is reached (Lenton et al., 2008), the impacts of such warming on the natural environment, economies and societies could be severe … Given the potential magnitude of transformation illustrated by this example, the assumption that Dt = 0.5 when T = 4 may be no less plausible, to put it cautiously, than assuming, as (2) does with the standard parameterisation, that Dt = 0.04 when T = 4, i.e. only 4% of output is lost as a result of temperatures not seen for 10 million plus years.
[MW: are you f'g kidding me?! that is to say, standard DICE-like IAM economic models are full of shxt]
In standard DICE S = 3°C. However, it has long been known that there is substantial uncertainty about S (Charney, 1979). Moreover investigations in recent years (as collected by Meinshausen et al., 2009) have tended to yield estimates of the pdf of S that have a large positive skew
Figure B5 shows that the optimal mean stock of atmospheric CO2 peaks in our endogenous growth models at no more than about 500 ppm, and as little as 420 ppm, depending on the growth model and damage function. These stock levels are well below those in the standard DICE model.
5. Conclusions
‘To slow or not to slow’ (Nordhaus, 1991) and its subsequent development into the dynamic DICE model have given us what seems to be a coherent and powerful framework for assessing the costs and benefits of climate-change mitigation. But it has in-built assumptions on growth, damage and risk, which together result in gross underassessment of the overall scale of the risks from unmanaged climate change (Stern, 2013). This criticism applies with just as much force to most of the other IAMs that DICE has inspired. …
The study is only a preliminary investigation, whose purpose was to illustrate or sketch the consequences of relaxing assumptions that have limited plausibility and possible large effects on policy conclusions.
Table 4: Optimal Carbon Prices
[MW: So, estimates by Stern of SCC range from $70 to $329, as compared to: ]
“In standard DICE the emissions control rate, that is the percentage reduction in industrial carbon dioxide emissions, is 0.158 in 2015, with an associated carbon price of $44/tC in 2005 prices”
Subject to caveats implicit and explicit from articles above, and subject further to recognition that economic IAMs embed climate models’ ECS, but the economic models lag, so do not reflect the latest climate science, and ECS is now known to be higher than previously assumed; and subject to further recognition that economic models of climate damages are divorced from concepts of ecology and destruction of ecosystem services we require, fwiw, existing studies of economic damages that can be for reference include:
The Economic Consequences of Climate Change. OECD. 2015.
[MW: so, once again, re: the insight noted above.... these types of studies are seriously divorced from reality; the notion that a planet with temperatures 6C higher will suffer only damages of (name whatever f'g arbitrary $$ value you want here) is laughably inane. see Schellnhuber's or Box's or Anderson's scientific assessment of what 4C implies -- the destruction of civilization!]
Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Natl Academies. 2017.
The social cost of carbon (SC-CO2) is an economic metric intended to provide a comprehensive estimate of the net damages—that is, the monetized value of the net impacts, both negative and positive— from the global climate change that results from a small (1 metric ton) increase in carbon dioxide (CO2) emissions. Under Executive Orders regarding regulatory impact analysis and as required by a court ruling, the U.S. government has since 2008 used estimates of the SC-CO2 in federal rulemakings to value the costs and benefits associated with changes in CO2 emissions. In 2010, the Interagency Working Group on the Social Cost of Greenhouse Gases (IWG) developed a methodology for estimating the SC-CO2 across a range of assumptions about future socioeconomic and physical earth systems.
The IWG asked the National Academies of Sciences, Engineering, and Medicine to examine potential approaches, along with their relative merits and challenges, for a comprehensive update to the current methodology. The task was to ensure that the SC-CO2 estimates reflect the best available science, focusing on issues related to the choice of models and damage functions, climate science modeling assumptions, socioeconomic and emissions scenarios, presentation of uncertainty, and discounting.
Integrated assessment models (IAMs) are currently used by the IWG to estimate the economic consequences of CO2 emissions. The IAMs define baseline emission trajectories by projecting future economic growth, population, and technological change. In these IAMs, a 1 metric ton increase in CO2 emissions is added to the baseline emissions trajectory. This emissions increase is translated into an increase in atmospheric CO2 concentrations, which results in an increase in global average temperature. This temperature change, as well as changes in other relevant variables, including CO2 concentrations and income, is translated (either explicitly or implicitly) to physical impacts and monetized damages. These damages include, but are not limited to, market damages, such as changes in net agricultural productivity, energy use, and property damage from increased flood risk, as well as nonmarket damages, such as those to human health and to the services that natural ecosystems provide to society. Because most of the warming caused by an emission of CO2 into the atmosphere persists for well over a millennium, changes in CO2 emissions today may affect economic outcomes for centuries to come. Streams of monetized damages over time are converted into present value terms by discounting. The present value of damages reflects society’s willingness to trade value in the future for value today…. The IWG’s current estimate of the SC-CO2 in the year 2020 for a 3.0 percent discount rate is $42 per metric ton of CO2 emissions in 2007 U.S. dollars.
LIMITATIONS OF SIMPLE EARTH SYSTEM MODELS
In complex climate models, the parameters described in Box 4-1—ECS, TCR, TCRE, and IPT—are resultant behaviors of the climate system, not input parameters. They arise from physical properties of the Earth system, such as the heat capacity of the ocean and the magnitude of different feedbacks that amplify or dampen the temperature change caused by radiative forcing. The strength of these feedbacks depends on the state of the climate; they are not generally constant, and they may vary in response to the magnitude of forcing and spatial pattern of forcing, as well as over time (Knutti and Rugenstein, 2015). By contrast, in simple Earth system models at least some of these metrics are input parameters. For Earth system models. It is therefore important to be aware of three key limitations of this assumption and the use of ECS, TCR, TCRE, and IPT as parameters.
The first limitation is that these metrics are all defined with respect to a reference state, such as the preindustrial state of Earth.
The second limitation is that these parameters are diagnosed using tests that hold certain elements of the climate system constant. This inactivates so carbon cycle feedbacks are also excluded. If these other feedbacks are predominantly positive, then on the timescales on which they are operative, measures such as ECS and TCR will understate the expected warming.
The third limitation is that three important feedbacks are excluded from ECS and TCR: the response to changes in albedo related to land ice, changes in albedo and transpiration related to land cover changes and the dust/aerosol feedbacks that impact biogeochemical cycles. Geological data suggest that these feedbacks may amplify warming by about 50 percent relative to that expected based on ECS alone
…. Currently, the damage component of an SC-IAM translates streams of socioeconomic variables (e.g., income and population and gross domestic product) and physical climatic variables (e.g., changes in temperature and sea level) into streams of monetized damages over time. …. Another attribute of the SC-IAMs that underpin the current IWG estimates is that much of the research on which they are based is dated.
… The committee notes that the Interagency Working Group on the Social Cost of Carbon (2010) identified a number of
These include:
- incomplete treatment of noncatastrophic damages;
- incomplete treatment of potential catastrophic damages;
- uncertainty in extrapolation of damages to high temperatures;
- incomplete treatment of adaptation and technological change;
- omission of risk aversion with respect to high-impact damages;
- failure to incorporate intersectoral and interregional interactions; and
- imperfect substitutability of consumption for environmental amenities.
Other research on Valuing Climate Damages / Social Cost of Carbon / Integrated Assessment Models:
To Slow or Not to Slow: The Economics of the Greenhouse Effect. William Nordhaus. 1992.
The Economics of Climate Change: The Stern Review. Nicholas Stern. 2007.
Climate change uncertainty and decision-making. Arun Malik, Jonathan Rothbaum, Stephen Smith. 2010.
Towards and ecological economics. Peter Victor and Tim Jackson. 2012.
Better Growth, Better Climate: Global Report. New Climate Economy. 2014.
Endogenous Growth, Convexity of Damage and Climate Risk: How Nordhaus’ Framework Supports Deep Cuts in Carbon Emissions. Nicholas Stern and Simon Dietz. 2015.
Climate Change Risks and Adaptation: Linking Policy and Economics. OECD. 2015.
The Economic Consequences of Climate Change. OECD. 2015.
Reflections – Managing Uncertain Climates: Some Guidance for Policy Makers and Researchers. Frank Convery and Gernot Wagner. 2015.
Technical Update to Environment and Climate Change Canada’s Social Cost of Greenhouse Gas Estimates. Environment and Climate Change Canada. 2016.
Policy tradeoffs under risk of abrupt climate change. Yacov Tsur and Amos Zemel. 2016.
Economics of the Climate. Geoffrey Heal. 2017.
Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon Dioxide. Natl Academies. 2017.
Frontiers of Climate Change Economics. Van der Meijden, van der Ploeg, Withagen. 2017.
Pricing Carbon and Adjusting Capital to Fend off Climate Catastrophes. Van der Ploeg, de Zeeuw. 2018.
Climate change and the macro-economy: a critical review. Sandra Batten, Bank of England staff working paper. 2018.
One Step Forward, One Step Back: Assessing the Consequences of Three Decades of Climate Gridlock in the U.S. Joseph Curtin and Max Munchmeyer, IIEA. 2019.
Ecological Economics
Economics and the Ecosystem. Real World Economics Review. 2019.
Degrowth: a theory of radical abundance. Jason Hickel.
Elements of a political economy of the postgrowth era. Max Koch.
Victim of success: civilisation is at risk. Peter McManners.
Economism and the Econocene: a coevolutionary interpretation. Richard Norgaard.
End game: the economy as eco-catastrophe and what needs to change. William Rees.
An ecosocialist path to limiting global temperature rise to 1.5C. Richard Smith.
Like blending chalk and cheese – the impact of standard economics in IPCC scenarios. Joachim Spagenberg and Lia Polotzek.
Of ecosystems and economies: re-connecting economics with reality. Clive Spash and Tone Smith.
How to achieve the Sustainable Development Goals within planetary boundaries by 2050. Per Espen Stoknes.
See also
Lenton, Timothy. Tipping elements in the Earth’s climate system. 2008.
Rockstrom, Johan. Planetary boundaries: Exploring the safe operating space for humanity. 2009.
Barnosky, Anthony. Approaching a state shift in Earth’s biosphere. 2012.
Steffen, Will. Trajectories of the Earth System in the Anthropocene. 2018.
Ehrlich, Paul and Anne. Can a collapse of global civilization be avoided? 2013.
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