Averting CO2 is more effective than cash-transfers
Table of Contents
Word count: 924
Reading time: 4.5 mins
Keywords: Climate change, climate policy, global development, global health, cause prioritization, prioritization research, comparing diverse benefits
Acknowledgments: Thanks to John Halstead and Danny Bressler for helpful comments. Any errors are mine.
“What value should we use for the social cost of carbon to adequately reflect the greater marginal utility of consumption for low-income people?”
Here, we tried to answer this question. To our surprise, the results suggested that climate change interventions are generally more effective than global development interventions.
We base this on the following three claims:
This new modelling appears to be the first that uses climate model projections, empirical climate-driven economic damage estimations, and also socio-economic projections which take into account greater marginal utility of consumption for every country individually.
In other words, this takes into account “your dollar does (>)100x or more good if you give to the poorest rather than people in high-income countries”). More on income weighting in Appendix 1.
Other more canonical IAMs DICE only have one value for the whole world, and, while RICE has 12 regions, this understates the heterogeneous geography of climate damage. This research first estimated the social cost of carbon for every country in the world. Then, the authors summed up all the country-level costs of carbon to arrive at the global cost of carbon: $417 per tonne of CO2 (66% CI: $177–805, data explorer). This allows us to directly compare climate change to cash-transfers to the poorest (and other global development interventions). In other words, even though this global estimate also includes damages to advanced economies (e.g. US/EU), people in the future who are richer, and countries that are not as affected by climate change, these do not weigh as heavily in these calculations.
The estimate is roughly in line with expert surveys (also see Appendix 2), but 10x higher than the $42/tCO2 EPA estimate based on IAMS. This because it contentiously assumes impacts on GDP growth permanently alter a country’s GDP.
However, even if we downward adjust the estimate by 10x to $41.7 per tonne of CO2 (66% CI: $17.7–80.5), our central claim here still works. The key point here is that the new model properly accounts for geographical heterogeneity and diminishing returns to consumption and we now need no longer worry that the social cost of carbon obscures that climate change will be much worse for the poorest people who we could send unconditional cash transfers.
If a climate change intervention has a cost-effectiveness of $417 / X per tonne of CO2 averted, then it is X times as effective as cash-transfers. For instance, a climate change intervention with an effectiveness of $10 per tonne of CO2 averted would be ~41.7x more effective than cash-transfers.
A frequently cited advantage of cash-transfers is that they can absorb more funding with consistently high cost-effectiveness than any other intervention.
So even if many other high-risk, high-reward projects have a higher benefit-cost ratio than cash-transfers in expectation, they usually have smaller funding gaps and one needs to do more research to find them.
But this advantage is not unique to cash-transfers: climate change is also able to absorb very large amounts of funding at scale with consistently high cost-effectiveness and only slowly diminishing returns.
Examples of interventions with cost-effective scalable interventions:
Multiplying the number of tonnes avertable by the (average) cost per tonne averted equals the funding gap, which for some of these interventions is in the trillions. To get a rough estimate of the overall funding gap of climate change, we can multiply global emissions—roughly 37 billion tonnes per year—and assume an average cost-effectiveness of $50 per tonne. This suggests a funding gap of $1.85 trillion/year.
Implications for cause prioritization: If none of these three claims above can be falsified, then it follows that, as a rule, we should consider prioritizing climate change over global development interventions. In other words, averting carbon could replace unconditional cash-transfers to the poorest as the new ‘benchmark’ and climate change could replace global development as the new ‘mainstream EA flagship cause’.
Of course, one could object that climate modelling is more uncertain than global development interventions (which can be studied with RCTs) and the effects of climate change are in the future (see Appendix 2).
However, the social cost of carbon modelling uses sensitivity analyses to account for uncertainty and uses discounting and so the estimates are at least somewhat robust to this objection. And even lower bound of the paper’s income $177 or even EPA’s $42/tCO2, which is roughly in line with expert consensus, makes many climate change interventions more effective than cash transfers. Nevertheless, altruists with high risk / uncertainty aversion and/or high discount rates might want to continue prioritize global development interventions.
A recent paper estimates the country-level social cost of carbon, using not only climate, but also socio-economic projections. For the marginal utility substitution, they use a μ-value of 1.5 as a central value.
What concretely does this mean?
All else being equal, money going to poorer countries or people is better than money going to richer countries or people. Weyl suggests that assuming logarithmic utility giving 1 dollar to an extremely poor person is like giving 66 dollars to an American. (“That is, if marginal utility is declining in levels of income, say utility is the natural log of consumption, then the marginal utility is 1/consumption. This implies a dollar’s worth of consumption in utility terms of a person at the global poverty line is worth 64 times as much as a dollar to person in the highest decile of consumption in the USA (63.6=(1/(1.9*365))/(1/44,152) so transferring income from a rich person in the USA to a globally poor person produces, in and of itself, massively higher total global utility (even if not Pareto improving).”).
Weyl suggests that logarithmic utility is canonical in economics and supported by a wide range of data, “including recent happiness studies (Stevenson and Wolfers, 2008) and labour supply decisions (Chetty, 2006)”. This is also in line with work that finds a correlation between log income and happiness :
The law of logarithmic utility can be found in other areas such as research funding as well .
The general form of modelling utility consumption relationships using isoelastic utility function is: :
Ord  explains this function as follows:
“This equation has one free parameter, known as η (‘eta’, which sounds ‘e’ for ‘elasticity’), which represents how steeply returns to consumption diminish. η must be between 0 and ∞, and can be estimated empirically.
The equation, for utility (u) at a given consumption level (c), and elasticity (η) is:
From this it follows that for η = 0 utility is linear in consumption, for η = ½ utility is the square root of consumption, and for η = 1 utility is logarithmic in consumption. Values of η above 1 correspond to utility having a finite upper bound, which is approached hyperbolically as consumption increases.
However, the main use of the equation is to just compare the slope of the curve at one consumption level to the slope at another consumption level. For example the ratio of the slope at $1,000 per annum to the slope at $10,000 per annum shows us the relative value of giving an extra dollar to someone with annual consumption $1,000 versus to someone with $10,000. When performing this calculation, the equation is very simple:
Giving a dollar to someone with k times as much consumption is worth only:
times as much.
There have been many attempts to measure η, and it is typically found to be between about 1 and 2. If η equals 1, then we have logarithmic utility of consumption and we have the very simple rule that a dollar is worth 1/k times as much if you are k times richer (and that doubling someone’s income is worth the same amount no matter where they start). If η equals 2, then we have to raise this to the power of 2, so being 10 times richer would mean a dollar is worth just 1/100th as much (and doubling your income is worth much less the higher your starting income). The truth is probably in between these limits.”
Integrated assessment models have been heavily criticised. Consider the following quote by MIT Economics Professor Robert S. Pindyck from his paper “The Use and Misuse of Models for Climate Policy”:
"In a recent article, I argued that integrated assessment models (IAMs) “have crucial flaws that make them close to useless as tools for policy analysis.” In fact, I would argue that calling these models “close to useless” is generous: IAM-based analyses of climate policy create a perception of knowledge and precision that is illusory, and can fool policy-makers into thinking that the forecasts the models generate have some kind of scientific legitimacy. IAMs can be misleading – and are inappropriate – as guides for policy, and yet they have been used by the government to estimate the social cost of carbon (SCC) and evaluate tax and abatement policies. What are the crucial flaws that make IAMs so unsuitable for policy analysis? They are discussed in detail in Pindyck (2013b), but the most important ones can be briefly summarized as follows: 1. Certain inputs – functional forms and parameter values – are arbitrary, but have huge effects on the results the models produce. An example is the discount rate. There is no consensus among economists as to the “correct” discount rate, but different rates will yield wildly different estimates of the SCC and the optimal amount of abatement that any IAM generates. For example, these differences in inputs largely explain why the IAMbased analyses of Nordhaus (2008) and Stern (2007) come to such strikingly different conclusions regarding optimal abatement. Because the modeler has so much freedom in choosing functional forms, parameter values, and other inputs, the model can be used to obtain almost any result one desires, and thereby legitimize what is essentially a subjective opinion about climate policy. 2. We know very little about climate sensitivity, i.e., the temperature increase that would eventually result from a doubling of the atmospheric CO2 concentration, but this is a key input to any IAM. The problem is that the physical mechanisms that determine climate sensitivity involve crucial feedback loops, and the parameter values that determine the strength (and even the sign) of those feedback loops are largely unknown, and are likely to remain unknown for the foreseeable future. 3. One of the most important parts of an IAM is the damage function, i.e., the relationship between an increase in temperature and GDP (or the growth rate of GDP). When assessing climate sensitivity, we can at least draw on the underlying physical science and argue coherently about the relevant probability distributions. But when it comes to the damage function, we know virtually nothing – there is no theory and no data that we can draw from. 4. IAMs can tell us nothing about the likelihood or possible impact of a catastrophic climate outcome, e.g., a temperature increase above 5°C that has a very large impact on GDP. And yet it is the possibility of a climate catastrophe that is (or should be) the main driving force behind a stringent abatement policy."
Yet, in a later paper Pindyck estimates the social costs of carbon through expert surveys to be in the hundreds of dollars range. This is in line with IAMs and leads me to believe that
“An estimate of the social cost of carbon (SCC) is crucial to climate policy. But how should we estimate the SCC? A common approach uses an integrated assessment model (IAM) to simulate time paths for the atmospheric CO2 concentration, its impact on temperature, and resulting reductions in GDP. I have argued that IAMs have deficiencies that make them poorly suited for this job, but what is the alternative? I present an approach to estimating an average SCC, which I argue can be a useful guide for policy. I rely on a survey of experts to elicit opinions regarding (1) probabilities of alternative economic outcomes of climate change, but not the causes of those outcomes; and (2) the reduction in emissions required to avert an extreme outcome, i.e., a large climate-induced reduction in GDP. The average SCC is the ratio of the present value of lost GDP from an extreme outcome to the total emission reduction needed to avert that outcome. I discuss the survey instrument, explain how experts were identified, and present results. I obtain SCC estimates of $200/mt or higher, but the variation across experts is large. Trimming outliers and focusing on experts who expressed a high degree of confidence in their answers yields lower SCCs, $80 to $100/mt, but still well above the IAM-based estimates used by the U.S. government.”
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