By Jason Furman, Chairman, Council of Economic Advisers; Ron Shadbegian, Jim Stock, Economist in the Benefits Assessment and Methods Development Division, National Center for Environmental Economics; and Professor of Political Economy in the Department of Economics, Harvard. Originally published at VoxEU
The cost of delaying climate action has been studied extensively. This column discusses new findings based on a meta-analysis of published model runs. A one-decade delay in addressing climate change would lead to about a 40% increase in the net present value cost of addressing climate change. If anything, the methodology used in this analysis could understate the cost of delay. Uncertainty and the possibility of tipping points provide a motivation for more action as a form of insurance against worse outcomes.
Climate scientists and economists have shown widespread agreement that anthropogenic climate change has the potential to cause substantial economic damage, and that taking action to mitigate carbon emissions is essential for reducing the risk of catastrophic effects. Of course, there is scientific uncertainty about the magnitudes of these effects and an active debate about the most appropriate course of action. Some have argued that the uncertainty in the climate science is an argument to delay any response until we know enough to identify the best course of action. However, a recent meta-analysis by the Council of Economic Advisers shows that reaching a climate target is costlier – or even impossible – if policies designed to reach that target begin at a later date. Because a delay results in additional near-term accumulation of greenhouse gases in the atmosphere, delay means that the policy, when implemented, must be more stringent to achieve the given long-term climate target. This additional stringency increases mitigation costs, relative to those that would be incurred under the least-cost path starting today.
The Cost of Delayed Action Based on a Meta-Analysis of 16 Studies
We reviewed 16 studies that compare 106 pairs of policy simulations based on integrated climate mitigation models.1 The simulations comprising each pair implement similar policies that lead to the same climate target (typically a concentration target but in some cases a temperature target) but differ in the timing of the policy implementation, nuanced in some cases by variation in when different countries adopt the policy. Because the climate target is the same for each scenario in the pair, the environmental and economic damages from climate change are approximately the same for each scenario. The additional cost of delaying implementation thus equals the difference in the mitigation costs in the two scenarios in each paired comparison. The studies reflect a broad array of climate targets, delayed timing scenarios, and modelling assumptions as discussed below. We focus on studies published in 2007 or later.
In each case, a model computes the path of cost-effective mitigation policies and mitigation costs, constraining the emissions path so that the climate target is hit. Each path weighs technological progress in mitigation technology and other factors that in the beginning generally allow for a less stringent, less costly policy against the costs that arise if mitigation, delayed too long, must be undertaken rapidly. Because the models typically compute the policy in terms of a carbon price, the carbon price path computed by the model starts out relatively low and increases over the course of the policy. Thus a policy started today typically has a steadily increasing carbon price, whereas a delayed policy typically has a carbon price of zero until the start date, at which point it jumps to a higher initial level then increases more rapidly than the optimal immediate policy. The higher carbon prices after a delay typically lead to higher total costs than a policy that would impose the carbon price today.
The costs of delay in these studies depend on a number of factors, including the length of delay, the climate target, modelling assumptions, future baseline emissions, future mitigation technology, delay scenarios, the participants implementing the policy, and geographic location. More aggressive targets are more costly to achieve, and meeting them is predicted to be particularly costly, if not infeasible, if action is delayed. Similarly, international coordination in policy action reduces mitigation costs, and the cost of delay depends on which countries participate in the policy, as well as the length of delay.
We construct a data set describing each delay cost estimate and information about the simulation. The data include all available numerical estimates of the average or total cost of delayed action from our literature search. Each estimate is a paired comparison of a delay scenario and its companion scenario without delay. To make results comparable across studies, we convert the delay cost estimates (presented in the original studies variously as present values of dollars, percent of consumption, or percent of GDP) to percent change in costs as a result of delay.3 We capture variation across studies and experimental designs using variables that encode the length of the delay in years; the target carbon dioxide equivalent (CO2e) concentration; whether only the relatively more-developed countries act immediately (partial delay); the discount rate used to calculate costs; and the model used for the simulation.4 All comparisons consider policies and outcomes measured approximately through the end of the century. To reduce the effect of outliers, the primary regression analysis only uses results with less than a 400% increase in costs (alternative methods of handling the outliers are discussed below as sensitivity checks), and only includes paired comparisons for which both the primary and delayed policies are feasible (i.e. the model was able to solve for both cases).5 The dataset contains a total of 106 observations (paired comparisons), with 58 included in the primary analysis. All observations in the data set are weighted equally.
Analysis of these data suggests two main conclusions, both consistent with findings from specific papers in the underlying literature. The first is that, looking across studies, costs increase with the length of the delay. Figure 1 shows the delay costs as a function of the delay time. Although there is considerable variability in costs for a given delay length because of variations across models and experiments, there is an overall pattern of costs increasing with delay.
Figure 1. Additional mitigation costs of delay by length
Source: Council of Economic Advisers calculations.
Notes: Data points are percentage increase in mitigation costs from delay and the associated length of delay for a given paired simulation. The scatterplot presents a total of 58 paired delay simulations. The solid line is the regression fit to these data, restricted to pass through the origin.
For example, of the 14 paired simulations with ten years of delay, the average delay cost is 39%. The regression line shown in Figure 1 estimates an average cost of delay per year using all 58 paired experiments under the assumption of a constant increasing delay cost per year (and, by definition, no cost if there is no delay), and this estimate is 37% per decade. This analysis ignores possible confounding factors, such as longer delays being associated with less stringent targets, and the multiple regression analysis presented in the Annex below controls for such confounding factors.
The second conclusion is that the more ambitious the climate target, the greater are the costs of delay. This can be seen in Figure 2, in which the lowest (most stringent) concentration targets tend to have the highest cost estimates. In fact, close inspection of Figure 1 reveals a related pattern – the relationship between delay length and additional costs is steeper for the points representing CO2e targets of 500 ppm or less than for those in the other two ranges. That is, costs of delay are particularly high for scenarios with the most stringent target and the longest delay lengths.
Figure 2. Additional mitigation costs by CO2 concentration target
Notes: Data points are percentage increase in mitigation costs from delay and the associated CO2 concentration target for a given paired simulation. The scatterplot presents a total of 58 paired delay simulations. The solid line is the regression line fit to these data.
This analysis concerned the cost of trying to hit the same climate target with a later start date for the policies. It showed that the cost of achieving a given target would rise by about 40%. This, however, is only one factor to consider in assessing the timing and magnitude of action on climate change. Because CO2 accumulates in the atmosphere, delaying action increases CO2 concentrations. Thus, if a policy delay leads to higher ultimate CO2 concentrations, that delay produces persistent economic damages that arise from higher temperatures and higher CO2 concentrations. Based on the DICE model as reported by William Nordhaus (2013), a delay that results in warming of 3° Celsius above preindustrial levels, instead of 2°, could increase economic damages by approximately 0.9% of global output.6 To put this percentage in perspective, 0.9% of estimated 2014 US Gross Domestic Product (GDP) is approximately $150 billion. The incremental cost of an additional degree of warming beyond 3° Celsius would be even greater. Moreover, these costs are not one-time, but are rather incurred year after year because of the permanent damage caused by increased climate change resulting from the delay.
Moreover, there is considerable uncertainty around all of these estimates – including the possibility of damages significantly greater than assumed in these calculations, potentially as a result of ‘tipping points’ above which climate change becomes a self-amplifying cycle. Climate policy can be thought of as ‘climate insurance’ taken out against the most severe and irreversible potential consequences of climate change. Events such as the rapid melting of ice sheets and the consequent increase of global sea levels, or temperature increases on the higher end of the range of scientific uncertainty, could pose such severe economic consequences as reasonably to be thought of as climate catastrophes. Confronting the possibility of climate catastrophes means taking prudent steps now to reduce the future chances of the most severe consequences of climate change. The longer that action is postponed, the greater will be the concentration of CO2 in the atmosphere and the greater is the risk. Just as businesses and individuals guard against severe financial risks by purchasing various forms of insurance, policymakers can take actions now that reduce the chances of triggering the most severe climate events. And, unlike conventional insurance policies, climate policy that serves as climate insurance is an investment that also leads to cleaner air, energy security, and benefits that are difficult to monetise like biological diversity.
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