This is the fourth of a 5-piece series we wrote together with Salvi Asefi-Najafabady– Talking Naked: a series of essay-commentaries on climate-economy models, politics in science, environment, ethics, and society. We will be posting them here. This work is a deeper elaboration of a paper we published recently in the journal Globalizations.
In this piece we will examine how current academic approaches to represent Human and Earth Systems are unfit for calculating the real cost of climate change, the social cost of carbon, and for evaluating potential socio-economic policies to address catastrophic and rare climatic conditions. Ultimately, we will examine how integrated models of climate change and economics are a symptom of what has gone wrong with the global political and economic system and why they are also inadequate.
Here is the link to the first installment and the intro to this series: Should we talk about the next generation of climate-economy models?
A link to the second installment: should we talk about the pursuit of amoral economic growth and the enormous pressures it imposes on the Earth and Human system?
And a link to the third part of the series: Should we talk about the fundamentals of Neoclassical Economics (and Equilibrium Models)?
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“Over the last two centuries, the impact of the Human System has grown dramatically, becoming strongly dominant within the Earth System in many different ways. Consumption, inequality, and population have increased extremely fast, especially since about 1950, threatening to overwhelm the many critical functions and ecosystems of the Earth System. Changes in the Earth System, in turn, have important feedback effects on the Human System, with costly and potentially serious consequences. However, current models do not incorporate these critical feedbacks” Motesharrei et al. (2016)
Current economic modeling methods propose an overly optimistic image of the future, predicting the impact of climate change to be only a few points decrease in the world Gross Domestic Product (GDP) per capita by the end of the century—even for high levels of warming. According to these models, even a global temperature increase above +5 degrees Celsius would allegedly cost less than 7% of the world’s future GDP [16, 17]. And even models that favor a carbon tax to slow climate change, show that reaching the UN target of avoiding temperature rise to 1.5 degree Celsius above pre-industrial levels by 2100 would make humanity poorer than doing nothing at all about climate change (Murphy, 2018).
Even the 2018 IPCC Special Report on Global Warming of 1.5oC —which announced with no uncertainty that avoiding cataclysm drastic action and technological improvement by 2050—is built on fictitious wishful thinking (at best) as it does not question economic growth as an underlying assumption in any of the scenarios it created to generate the so-called “socioeconomic pathways” (Ehrenreich, 2021).
That the growth imperative is not even questioned by these models make whatever resulting estimates dangerous and they have led some authors to conclude that humanity has far bigger problems than climate change and that “a century of climate change is about as good/bad for welfare as a year of economic growth” [18]. Moreover, not only are they dangerous, they are also unreliable, misleading and founded on oversimplifying assumptions [19].
In the previous chapter of this series, we introduced the first problem with Integrated Assessment Models (IAMs): their unrealistic assumptions about behavior and therefore their incapability of representing the Human System. In the coming paragraphs we will list other weaknesses of IAM-based economic analysis of climate change policy and explore in detail the fundamental problem of unidirectional coupling in IAMs and scenario analysis.
The second problem of IAMs is more practical. Often, IAMs are formulated in an optimization language such as GAMS (General Algebraic Modeling System) or AMPL (A Mathematical Programing Language). This imposes restrictions in order to find the system’s solution (the idea that an optimal solution, or equilibrium, can be found by solving a system of equations follows from the perfect information assumption discussed above). Technical considerations that limit the type of complexities, nonlinearities, non-convexities, tipping points, and uncertainties a modeler can consider is particularly problematic when studying climate change.
The third drawback of IAMs is that they leave many degrees of freedom for the modeler so that different models can arrive at drastically different conclusions regarding optimal abatement policy and the implied social cost of carbon (SCC) because of the modeler’s choice of functional forms and parameter values. For example, Nordhaus (2008) finds that optimal abatement should initially be very limited, consistent with a SCC of around $20 or less, while Stern (2007) concludes that an immediate and drastic cut in emissions is necessary, consistent with a SCC above $200.
In a recent report, prominent academics warn that economic assessments of future climate change impacts are misleading because the economic models are expressed solely in terms of effects on output (e.g. gross domestic product), or only extrapolate from past experience, or use inappropriate discounting, and therefore do not provide a clear indication of the potential risks to lives and livelihoods (LSE, 2019). In this piece, we argue that IAMs and the scenario analyses that often accompany them suffer from a weakness that is even more profound than an inappropriate framework for analyzing economic risks. We argue that current climate-economy models are incapable of showing the connections between Human and Earth systems. Therefore, whatever conclusions are derived from analyzing these models will fail to recommend that we change the economic system in order to avoid further consequences of damaging the natural environment. Current models will only provide evidence to kick the can a bit further, but cannot be used to derive scientific support to encourage the establishment of a new world system.
Integrated Assessment Models combine different strands of knowledge and can vary in the way they work and the questions they can answer. They tend to be technically rigorous and often impressive in the complexity of their architecture, and researchers across disciplines use IAMs to the best of their potential and use them to develop ingenious and robust tools that address key structural considerations for climate change action. Some of these tools and techniques include dealing with the uncertainties of the IAM models as an issue of deep uncertainty and develop dynamic adaptive pathways [13-15]. Moreover, even simple IAMs are insightful enough to earn one of their creators an Economics Nobel Prize in 2018.
However, it is complicated to accurately represent Earth/Human systems: developers of IAMs require expertise in a wide range of sciences, from Earth sciences to economics to computer science. As a result, IAMs do not model the detailed processes and relationships between Human and Earth systems. Even in complex IAMs, which use additional linked modules representing the global economy, as well as its energy, land, and climate systems to look at the energy technologies, energy use choices, land-use changes, and societal trends behind emissions of greenhouse gases (GHG), fail to represent sound science.
Unfortunately, the lack of consensus or even guidance over what is the “right” approach to representing the future of the Anthropocene, or what are the “best practices” for modeling, make results from IAMs dangerously inaccurate. We say dangerously because oftentimes, the technical rigor and architectural complexity of IAMs give decision makers the impression that they are scientifically sound and trustworthy. As we will show in the paragraphs below, despite the advances and sophistication in modeling, this is not the case—yet.
Integrated but decoupled
The better understood problems with IAMs include how the climate sensitivity parameter (a key parameter in the climate module) is calculated; how the models assume there are infinite potential sinks for carbon; how emission reduction policies have instantaneous effects; and how there is always an optimal rate of fossil fuel extraction. These issues have been discussed extensively elsewhere, so we will just acknowledge them in this commentary (Fiddaman, 2017; Murphy, 2018; [47]). Instead, we will focus on what we have called the fundamental problem of unidirectional coupling in Integrated Assessment Models and scenario analyses.
The most obvious problem of IAMs is their decoupled structure, meaning that the Earth and Human Systems in IAMs do not feedback to each other. In general, that means the tools scientists use to predict the evolution of climate variables in different socio-economic scenarios, do not consider the connection between socio-economic variables and many important climate and environmental variables. Typically, critical Human System variables used in IAMs, such as land use change, demographics, inequality, economic growth, and migration are based on exogenous projections which are demonstrated to be unreliable [4]. This is often referred to as “uni-directional coupling.”
There are many examples of how Earth and Human Systems are coupled, or how output signals in Human (Earth) System are fed back as inputs into the Earth (Human) System. For instance, changes in climate hazards can trigger human migration across different regions of the world, which in turn will have effects on land use, water availability, deforestation, desertification, and so on. Also, it is possible for climate change to eventually make certain areas of the world so dire (and even inhabitable if temperatures rise beyond a certain degree) that economic output will fall, populations will decline, and social inequality and political or even religious tensions will be exacerbated in these regions—as has already happened in places like Syria [21].
The opposite side of the coin would be how declining populations and sluggish economic activity could reduce emissions of GHG, thus mitigating the impact of the Human System on climate change. In this kind of scenario, an economic crisis, which could be aggravated in certain regions by widespread extreme weather events (e.g., along coastlines and in the tropics), could conclude in halting, or at least slowing down, fossil fuel extraction: a scenario that would necessarily be accompanied by a decline of GHG emissions. In fact, recent studies have shown that the 2008 recession caused localized reductions in CO2 emissions from fossil fuel combustion. These declines were experienced mostly in less prosperous regions of the US and the world [22].
A different example of a rather indirect effect of climate change on social tensions are the protests by the Gilets Jaunes in Paris. The protests were triggered by a carbon tax addressing climate change but continue to be fueled by general discontent over increasing inequality and rising living costs in France, which include rising costs of petrol and heating oil. Although the connection between climate change and social tension is much less direct in the case of the Gilets Jaunes than in the case of rural migrants than flee unworkable agricultural areas, the Paris strikes are a legitimate example of political tension that further illustrates the complicated relationship between the two trends that are driving our society towards collapse: environmental degradation and rising inequality.
Current IAMs and complementary scenario analyses (e.g., Representative Concentration Pathways, or RCPs, and Shared Socioeconomic Pathways, or SSPs) do not show these feedbacks, and as a result, current analytical approaches are unable to accurately estimate the economic cost of environmental degradation (including climate change), and scenario analyses are unable to represent realistic human responses to environmental impacts, such as migration, changes in wealth inequality, and changes in regional economic activity (which could include outcomes that are assumed away in the majority of simulated exercises—as negative economic growth rate). Current analytical approaches are unfit for impact evaluation or adaptation and mitigation planning—particularly at the regional or local level (because of scalability issues). This point is made apparent by the finding that population distribution and population density projections from current IAMs are exactly the same for a scenario with sustainable development (SSP1) and a scenario with fossil-fuel intensive development (SSP5) [23].
What is to come?
In the next two chapters of this Talking Naked series, we will identify major significant caveats relating to the way IAMs are designed and the considerations they leave unaddressed. Flaws that even the IPCC, which partly derived its recommendations for the special 2018 report from simulated scenarios generated by IAMs, recognizes (CarbonBrief, 2018).
But we do not want to focus on pointing out the flaws of IAMs or the recommendations that stem from them. Instead, we want to emphasize that the assumptions and the architecture of IAMs are a symptom of an even more profound problem with how social planners, policymakers, and global political and economic powers are dictating the way in which natural and human resources are managed (or mismanaged). Thus, in the final chapter of this series we will argue that current IAMs reflect the values of global social, economic, and political systems that are devised to benefit the few at the expense of the environment, the poor, the vulnerable, and the generations to come. And we will conclude with some thoughts on how the next generation of IAMs can be designed to challenge the very pillars that give rise to an unfair and unsustainable Human system.
REFERENCES:
[4] Motesharrei, S., Rivas, J., Kalnay, E., Asrar, G. R., Busalacchi, A. J., Cahalan, R. F., & Hubacek, K. (2016). Modeling sustainability: population, inequality, consumption, and bidirectional coupling of the Earth and Human Systems. National Science Review, 3(4), 470-494.
[13] McInerney, D., Lempert, R., & Keller, K. (2012). What are robust strategies in the face of uncertain climate threshold responses? Climatic Change, 112(3-4), 547–568. https://doi.org/10.1007/s10584-011-0377-1
[14] Hall, J. W., Lempert, R. J., Keller, K., Hackbarth, A., Mijere, C., & McInerney, D. J. (2012). Robust climate policies under uncertainty: a comparison of robust decision making and info-gap methods. Risk Analysis: An Official Publication of the Society for Risk Analysis, 32(10), 1657–1672. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1539-6924.2012.01802.x/full
[15] Keller, K., Bolker, B. M., & Bradford, D. F. (2004). Uncertain climate thresholds and optimal economic growth. Journal of Environmental Economics and Management, 48(1), 723–741. https://doi.org/10.1016/j.jeem.2003.10.003
[16] Nordhaus, W. (1994). Expert opinion on climatic change. American Scientist, 82(1):4551.
[17] Roson, R. and Van der Mensbrugghe, D. (2012). Climate change and economic growth: impacts and interactions. International Journal of Sustainable Economy, 4(3):270285.
[18] Tol, R. (2018). The Economic Impacts of Climate Change. Review of Environmental Economics and Policy,12(1):425.
[19] Woillez, M.N., Giraud, G., & Godin, A. (2019). Economic impacts of a glacial period: a thought experiment. AFD Research Papers, No. 2019-99, March.
[21] Gleick, P. H. (2014). Water, drought, climate change, and conflict in Syria. Weather, Climate, and Society, 6(3), 331-340.
[22] Asefi‐Najafabady, S., Rayner, P. J., Gurney, K. R., McRobert, A., Song, Y., Coltin, K., & Baugh, K. (2014). A multiyear, global gridded fossil fuel CO2 emission data product: Evaluation and analysis of results. Journal of Geophysical Research: Atmospheres, 119(17), 10-213.
[23] Asefi-Najafabady, S., Vandecar, K. L., Seimon, A., Lawrence, P., & Lawrence, D. (2018). Climate change, population, and poverty: vulnerability and exposure to heat stress in countries bordering the Great Lakes of Africa. Climatic change, 148(4), 561-573.
[47] Ackerman, D. F. (2010). Can we afford the future?: the economics of a warming world. Zed Books Ltd..
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