THE RECENT SLOWDOWN

IN

THE COST OF DELAYING

ACTION TO STEM CLIMATE

CHANGE

 

The Council of Economic Advisers

July 2014
1

Executive Summary

The signs of climate change are all around us. The average temperature in the United States

during the past decade was 0.8° Celsius (1.5° Fahrenheit) warmer than the 1901-1960 average,

and the last decade wasthe warmest on record both in the United States and globally. Global sea

levels are currently rising at approximately 1.25 inches per decade, and the rate of increase

appears to be accelerating. Climate change is having different impacts across regions within the

United States. In the West, heat waves have become more frequent and more intense, while

heavy downpours are increasing throughout the lower 48 States and Alaska, especially in the

Midwest and Northeast.1 The scientific consensus is that these changes, and many others, are

largely consequences of anthropogenic emissions of greenhouse gases.

2

The emission of greenhouse gases such as carbon dioxide (CO2) harms others in a way that is not

reflected in the price of carbon-based energy, that is, CO2 emissions create a negative externality.

Because the price of carbon-based energy does not reflect the full costs, or economic damages,

of CO2 emissions, market forces result in a level of CO2 emissions that is too high. Because of this

market failure, public policies are needed to reduce CO2 emissions and thereby to limit the

damage to economies and the natural world from further climate change.

There is a vigorous public debate over whether to act now to stem climate change or instead to

delay implementing mitigation policies until a future date. This report examines the economic

consequences of delaying implementing such policies and reaches two main conclusions, both of

which point to the benefits of implementing mitigation policies now and to the net costs of

delaying taking such actions.

First, although delaying action can reduce costs in the short run, on net, delaying action to limit

the effects of climate change is costly. 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. Alternatively, if a delayed policy still aims to hit a

given climate target,such aslimiting CO2 concentration to given level, then that delay meansthat

the policy, when implemented, must be more stringent and thus more costly in subsequent years.

In either case, delay is costly.

These costs will take the form of either greater damages from climate change or higher costs

associated with implementing more rapid reductions in greenhouse gas emissions. In practice,

delay could result in both types of costs. These costs can be large:

 

1

For a fuller treatment of the current and projected consequences of climate change for U.S. regions and sectors,

see the Third National Climate Assessment (United States Global Change Research Program (USGCRP) 2014).

2

See for example the Summary for Policymakers in Working Group I contribution to the Intergovernmental Panel

on Climate Change Fifth Assessment Report (IPCC WG I AR5 2013).
2

 Based on a leading aggregate damage estimate in the climate economics literature, a

delay that results in warming of 3° Celsius above preindustrial levels, instead of 2°, could

increase economic damages by approximately 0.9 percent of global output. To put this

percentage in perspective, 0.9 percent of estimated 2014 U.S. 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 onetime, but are rather incurred year after year because of the permanent damage caused

by increased climate change resulting from the delay.

 An analysis of research on the cost of delay for hitting a specified climate target (typically,

a given concentration of greenhouse gases) suggests that net mitigation costs increase,

on average, by approximately 40 percent for each decade of delay. These costs are higher

for more aggressive climate goals: each year of delay means more CO2 emissions, so it

becomes increasingly difficult, or even infeasible, to hit a climate target that is likely to

yield only moderate temperature increases.

Second, climate policy can be thought of as “climate insurance” taken out against the mostsevere

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 monetize like biological diversity.
3

I. Introduction

The changing climate and increasing atmospheric greenhouse gas (GHG) concentrations are

projected to accelerate multiple threats, including more severe storms, droughts, and heat

waves, further sea level rise, more frequent and severe storm surge damage, and acidification of

the oceans (USGCRP 2014). Beyond the sorts of gradual changes we have already experienced,

global warming raises additional threats of large-scale changes, either changes to the global

climate system, such as the disappearance of late-summer Arctic sea ice and the melting of large

glacial ice sheets, or ecosystem impacts of climate change, such as critical endangerment or

extinction of a large number of species.

Emissions of GHGs such as carbon dioxide (CO2) generate a cost that is borne by present and

future generations, that is, by people other than those generating the emissions. These costs, or

economic damages, include costs to health, costs from sea level rise, and damage from

increasingly severe storms, droughts, and wildfires. These costs are not reflected in the price of

those emissions. In economists’ jargon, emitting CO2 generates a negative externality and thus a

market failure. Because the price of CO2 emissions does not reflect its true costs, market forces

alone are not able to solve the problem of climate change. As a result, without policy action,

there will be more emissions and less investment in emissions-reducing technology than there

would be if the price of emissions reflected their true costs.

This report examines the cost of delaying policy actions to stem climate change, and reaches two

main conclusions. First, delaying action is costly. If a policy delay leads to higher ultimate CO2

concentrations, then that delay produces persistent additional economic damages caused by

higher temperatures, more acidic oceans, and other consequences of higher CO2 concentrations.

Moreover, if delay means that the policy, when implemented, must be more stringent to meet a

given target, then it will be more costly.

Second, uncertainty about the most severe, irreversible consequences of climate change adds

urgency to implementing climate policies now that reduce GHG emissions. In fact, climate policy

can be seen as climate insurance taken out against the most damaging potential consequences

of climate change—consequences so severe that these events are sometimes referred to as

climate catastrophes. The possibility of climate catastrophes leads to taking prudent steps now

to sharply reduce the chances that they occur.

The costs of inaction underscore the importance of taking meaningful steps today towards

reducing carbon emissions. An example of such a step is the Environmental Protection Agency’s

(EPA) proposed rule (2014) to regulate carbon pollution from existing power plants. By adopting

economically efficient mechanisms to reduce emissions over the coming years, this proposed

rule would generate large positive net benefits, which EPA estimates to be in the range of $27 -

50 billion annually in 2020 and $49 - 84 billion in 2030. These benefits include benefits to health

from reducing particulate emissions as well as benefits from reducing CO2 emissions.
4

Delaying Climate Policies Increases Costs

Delaying climate policies avoids or reduces expenditures on new pollution control technologies

in the near term. But thisshort-term advantage must be set against the disadvantages, which are

the costs of delay. The costs of delay are driven by fundamental elements of climate science and

economics. Because the lifetime of CO2 in the atmosphere is very long, if a mitigation policy is

delayed, it must take as its starting point a higher atmospheric concentration of CO2. As a result,

delayed mitigation can result in two types of cost, which we would experience in different

proportions depending on subsequent policy choices.

First, if delay means an increase in the ultimate end-point concentration of CO2, then delay will

result in additional warming and additional economic damages resulting from climate change. As

is discussed in Section II, economists who have studied the costs of climate change find that

temperature increases of 2° Celsius above preindustrial levels or less are likely to result in

aggregate economic damages that are a small fraction of GDP. This small net effect masks

important differences in which some regions could benefit somewhat from this warming while

other regions could experience net costs. But global temperatures have already risen nearly 1°

above preindustrial levels, and it will require concerted effort to hold temperature increases to

within the narrow range consistent with small costs.3 For temperature increases of 3° Celsius or

more above preindustrial levels, the aggregate economic damages from climate change are

expected to increase sharply.

Delay that causes a climate target to be missed creates large estimated economic damages. For

example, a calculation in Section II of this report, based on a leading climate model (the DICE

model as reported in Nordhaus 2013), shows that if a delay causes the mean global temperature

increase to stabilize at 3° Celsius above preindustrial levels, instead of 2°, that delay will induce

annual additional damages of approximately 0.9 percent of global output, as shown in Figure 1.

4

To put this percentage in perspective, 0.9 percent of estimated 2014 U.S. GDP is approximately

$150 billion.5 The next degree increase, from 3° to 4°, would incur greater additional annual costs

of approximately 1.2 percent of global output. These costs are not one-time: they are incurred

year after year because of the permanent damage caused by additional climate change resulting

from the delay.

 

3

The Working Group III contribution to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment

Report (IPCC WG III AR5 2014) does not analyze scenarios producing temperatures in 2100 less than 1.5 Celsius

above preindustrial, because this is considered so difficult to achieve.

4 Nordhaus (2013) stresses that these estimates “are subject to large uncertainties...because of the difficulty of

estimating impacts in areas such as the value of lost species and damage to ecosystems.” (pp. 139-140).

5 These percentages apply to gross world output and the application of them to U.S. GDP is illustrative.
5

The second type of cost of delay is the increased cost of reducing emissions more sharply if,

instead, the delayed policy isto achieve the same climate target asthe non-delayed policy. Taking

meaningful steps now sends a signal to the market that reduces long-run costs of meeting the

target. Part of this signal is that new carbon-intensive polluting facilities will be seen as bad

investments; this reduces the amount of locked-in high-carbon infrastructure that is expensive

to replace. Second, taking steps now to reduce CO2 emissionssignalsthe value of developing new

low- and zero-emissions technologies, so additional steps towards a zero-carbon future can be

taken as policy action incentivizes the development of new technologies. For both reasons, the

least-cost mitigation path to achieve a given concentration target typically starts with a relatively

low price of carbon to send these signals to the market, and subsequently increases as new lowcarbon technology becomes available.6

The research discussed in Section II of this report shows that any short run gains from delay tend

to be outweighed by the additional costs arising from the need to adopt a more abrupt and

stringent policy later.7 An analysis of the collective results from that research, described in more

detail in Section II,suggeststhat the cost of hitting a specific climate target increases, on average,

by approximately 40 percent for each decade of delay. These costs are higher for more aggressive

climate goals: the longer the delay, the more difficult it becomes to hit a climate target.

Furthermore, the research also finds that delay substantially decreases the chances that even

concerted efforts in the future will hit the most aggressive climate targets.

 

6 The 2010 National Research Council, Limiting the Magnitude of Future Climate Change, also stressed the

importance of acting now to implement mitigation policies as a way to reduce costs. The NRC emphasized the

importance of technology development in holding down costs, including by providing clear signals to the private

sector through predictable policies that support development of and investment in low-carbon technologies.

7

The IPCC WG III AR5 (2014) includes an extensive discussion of mitigation, including sectoral detail, potential for

technological progress, and the timing of mitigation policies.
6

Although global action is essential to meet climate targets, unilateral steps both encourage

broader action and benefit the United States. Climate change is a global problem, and it will

require strong international leadership to secure cooperation among both developed and

developing countries to solve it. America must help forge a truly global solution to this global

challenge by galvanizing international action to significantly reduce emissions. By taking credible

steps toward mitigation, the United States will also reap the benefits of early action, such as

investing in low-carbon infrastructure now that will reduce the costs of reaching climate targets

in the future.

Climate Policy as Climate Insurance

Individuals and businesses routinely purchase insurance to guard against various forms of risk

such as fire, theft, or other loss. This logic of self-protection also applies to climate change. Much

is known about the basic science of climate change: there is a scientific consensus that, because

of anthropogenic emissions of CO2 and other GHGs, global temperatures are increasing, sea

levels are rising, and the world’s oceans are becoming more acidic. These and other climate

changes are expected to be harmful, on balance, to the world’s natural and economic systems.

Nevertheless, uncertainty remains about the magnitude and timing of these and other aspects

of climate change, even if we assume that future climate policies are known in advance. For

example, the Working Group I contribution to the IPCC’s Fifth Assessment Report (IPCC WG I AR5

2013) provides a likely range of 1.5° to 4.5° Celsius for the equilibrium climate sensitivity, which

is the long-run increase in global mean surface temperature that is caused by a sustained

doubling of atmospheric CO2 concentrations. The upper end of that range would imply severe

climate impacts under current emissions trajectories, and current scientific knowledge indicates

that values in excess of this range are also possible.8

An additional, related source of climate uncertainty is the possibility of irreversible, large-scale

changes that have wide-ranging and severe consequences. These are sometimes called abrupt

changes because they could occur extremely rapidly as measured in geologic time, and are also

sometimes called climate catastrophes. We are already witnessing one of these events—the

rapid trend towards disappearance of late-summer Arctic sea ice. A recent study from the

National Research Council (NRC 2013) found that this strong trend toward decreasing sea-ice

cover could have large effects on a variety of components of the Arctic ecosystem and could

potentially alter large-scale atmospheric circulation and its variability. The NRC also found that

another large-scale change has been occurring, which is the critical endangerment or loss of a

significant percentage of marine and terrestrial species. Other events judged by the NRC to be

likely in the more distant future (after 2100) include, for example, the possible rapid melting of

the Western Antarctic ice and Greenland ice sheets and the potential thawing of Arctic

permafrost and the consequent release of the potent GHG methane, which would accelerate

global warming. These and other potential large-scale changes are irreversible on relevant time

 

8

It is important to note that, as a global average, the equilibrium climate sensitivity masks the expectation that

temperature change will be higher over land than the oceans, and that there will be substantial regional variations

in temperature increases. The equilibrium climate sensitivity describes a long-term effect and is only one

component of determining near term warming due to the buildup of GHGs in the atmosphere.
7

scales—if an ice sheet melts, it cannot be reconstituted—and they could potentially have massive

global consequences and costs. For many of these events, there isthought to be a “tipping point,”

for example a temperature threshold, beyond which the transition to the new state becomes

inevitable, but the values or locations of these tipping points are typically unknown.

Section III of this report examines the implications of these possible climate-related catastrophes

for climate policy. Research on the economic and policy implications of such threats is relatively

recent. As detailed in Section III, a conclusion that clearly emerges from this young but active

literature isthat the threat of a climate catastrophe, potentially triggered by crossing an unknown

tipping point, implies erring on the side of prudence today. Accordingly, in a phrase used by

Weitzman (2009, 2012), Pindyck (2011), and others, climate policy can be thought of as “climate

insurance.” The logic here is that of risk management, in which one acts now to reduce the

chances of worst-case outcomes in the future. Here, too, there is a cost to delay: the longer

emission reductions are postponed, the greater are atmospheric concentrations of GHGs, and

the greater is the risk arising from delay.

Other Costs of Delay and Benefits of Acting Now

An additional benefit of adopting meaningful mitigation policies now is that doing so sends a

strong signal to the market to spur the investmentsthat will reduce mitigation costsin the future.

An argument sometimes made is that mitigation policies should be postponed until new lowcarbon technologies become available. Indeed, ongoing technological progress has dramatically

improved productivity and welfare in the United States because of vast inventions and process

improvements in the private sector (see for example CEA 2014, Chapter 6). The private sector

invests in research and development, and especially in process improvements, because those

technological advances reap private rewards. But low-carbon technologies, and environmental

technologies more generally, face a unique barrier: their benefits – the reduction in global

impacts of climate change – accrue to everyone and not just to the developer or adopter of such

technologies.9 Thus private sector investment in low-carbon technologies requires confidence

that those investments, if successful, will pay off, that is, the private sector needs to have

confidence that there will be a market for low-carbon technologies now and in the future. Public

policies that set out a clear and ongoing mitigation path provide that confidence. Simply waiting

for a technological solution, but not providing any reason for the private sector to create that

solution, is not an effective policy. Although public financing of basic research is warranted

because many of the benefits of basic research cannot be privately appropriated, many of the

productivity improvements and cost reductionsseen in new technologies come from incremental

advances and processimprovementsthat only arise through private-sector experience producing

the product and learning-by-doing. These advances are protected through the patent system and

as trade secrets, but those advances will only transpire if it is clear that they will have current and

 

9 Popp, Newell, and Jaffe (2010) provide a thorough review of the literature regarding technological change and

the environment.
8

future value. In other words, policy action induces technological change. 10 Although a full

treatment of the literature on technological change is beyond the scope of this report, providing

the private sector with the certainty needed to invest in low-carbon technologies and produce

such technological change is a benefit of adopting meaningful mitigation policies now.

Finally, because this report examines the economic costs of delay, it focuses on actions or

consequences that have a market price. But the total costs of climate change include much that

does not trade in the market and to which it is difficult to assign a monetary value, such as the

loss of habitat preservation, decreased value of ecosystem goods and services, and mass

extinctions. Although some studies have attempted to quantify these costs, including all relevant

climate impactsis infeasible. Accordingly, the monetized economic costs of delay analyzed in this

report understate the true total cost of delaying action to mitigate climate change.

 

10 For example, Popp (2003) provides empirical evidence that Title IV of the 1990 Clean Air Act Amendments

(CAAA) led to innovations that reduced the cost of the environmental technologies that reduced SO2 emissions

from coal-fired power plants. Other literature shows evidence linking environmental regulation more broadly to

innovation (e.g., Popp 2006, Jaffe and Palmer 1997, Lanjouw and Mody 1996).
9

II. Costs from Delaying Policy Action

Delaying action on climate change can increase economic costs in two ways. First, if the delayed

policy is no more stringent, it will miss the climate target of the original, non-delayed policy,

resulting in atmospheric GHG concentrations that are permanently higher, thereby increasing

the economic damages from climate change. Second, suppose a delayed policy alternatively

strove to achieve the original climate target; if so, it would require a more stringent path to

achieve that target. But this delayed, more stringent policy typically will result in additional

mitigation costs by requiring more rapid adjustment later. In reality, delay might result in a mix

of these two types of costs. The estimates of the costs of delay in this section draw on large

bodies of research on these two types of costs. We first examine the economic damages from

higher temperatures, then turn to the increased mitigation costs arising from delay.

Our focus here is on targets that limit GHG concentrations, both because this is what most of the

“delay” literature considers and because concentration limits have been the focus of other

assessments. These concentration targets are typically expressed as concentrations of CO2-

equivalent (CO2e) GHGs, so they incorporate not just CO2 concentrations but also methane and

other GHGs. The CO2e targetstranslate roughly into ranges of temperature changes as estimated

by climate models and into the cumulative GHG emissions budgets discussed in some other

climate literature. More stringent concentration targets decrease the odds that global average

temperature exceeds 2°C above preindustrial levels by 2100. According to the IPCC WG III AR5

(2014), meeting a concentration target of 450 parts per million (ppm) CO2e makes it “likely”

(probability between 66 and 100 percent) that the temperature increase will be at most 2°C,

relative to preindustrial levels, whereas stabilizing at a concentration level of 550 ppm CO2e

makes it “more unlikely than likely” (less than a 50 percent probability) that the temperature

increase by 2100 will be limited to 2°C (IPCC WG III AR5 2014).11

Increasing Damages if Delay Means Missing Climate Targets

If delay means that a climate target slips, then the ultimate GHG concentrations, temperatures,

and other changes in global climate would be greater than without the delay.12

A growing body of work examines the costs that climate change imposes on specific aspects of

economic activity. The IPCC WG II AR5 (2014) surveys this growing literature and summarizes the

impacts of projected climate change by sector. Impacts include decreased agricultural

production; coastal flooding, erosion, and submergence; increases in heat-related illness and

other stresses due to extreme weather events; reduction in water availability and quality;

 

11 IPCC WG III AR5 (2014, ch. 6) provides a further refinement of these probabilities, associating a concentration

target of 450 ppm of CO2e with an approximate 70-85 percent probability of maintaining temperature change

below 2°C, and a concentration level of 550 CO2e with an approximate 30-45 percent probability of maintaining

temperature change below 2°C.

12 For information on the impacts of climate change at various levels of warming see Climate Stabilization Targets:

Emissions, Concentrations, and Impacts over Decades to Millennia (NRC 2011).
10

displacement of people and increased risk of violent conflict; and species extinction and

biodiversity loss. Although these impacts vary by region, and some impacts are not wellunderstood, evidence of these impacts has grown in recent years.13

A new class of empirical studies draw similar conclusions. Dell, Jones, and Olken (2013) review

academic research that draws on historical variation in weather patterns to infer the effects of

climate change on productivity, health, crime, political instability, and other social and economic

outcomes. This approach complements physical science research by estimating the economic

impacts of historical weather events that can be used to extrapolate to those expected in the

future climate. The research finds evidence of economically meaningful impacts of climate

change on a variety of outcomes. For example, when the temperature is greater than 100°

Fahrenheit in the United States, labor supply in outdoor industries declines up to one hour per

day relative to temperatures in the 76°-80° Fahrenheit range (Graff Zivin and Neidell 2014). Also

in the United States, each additional day of extreme heat (exceeding 90° Fahrenheit) relative to

a moderate day (50° to 59° Fahrenheit) increases the annual age-adjusted mortality rate by

roughly 0.11 percent (Deschênes and Greenstone 2011).

These studies provide insights into the response of specific sectors or aspects of the economy to

climate change. But because they focus on specific aspects of climate change, use different data

sources, and use a variety of outcome measures, they do not provide direct estimates of the

aggregate, or total, cost of climate change. Because estimating the total cost of climate change

requires specifying future baseline economic and population trajectories, efforts to estimate the

total cost of climate change typically rely on integrated assessment models (IAMs). IAMs are a

class of economic and climate models that incorporate both climate and economic dynamics so

that the climate responds to anthropogenic emissions and economic activity responds to the

climate. In addition to projecting future climate variables and other economic variables, the IAMs

estimate the total economic damages (and, in some cases, benefits) of climate change which

includes impacts on agriculture, health, ecosystems services, productivity, heating and cooling

demand, sea level rise, and adaptation.

Overall costs of climate change are substantial, according to IAMs. Nordhaus (2013) estimates

global costs that increase with the rise in global average temperature, and Tol (2009, 2014)

surveys various estimates. Two themes are common among these damage estimates. First,

damage estimates remain uncertain, especially for large temperature increases. Second, the

costs of climate change increase nonlinearly with the temperature change. Based on Nordhaus’s

(2013, Figure 22) net damage estimates, a 3° Celsius temperature increase above preindustrial

levels, instead of 2°, results in additional damages of 0.9 percent of global output.14 To put this

 

13 The EPA’s Climate Change Impacts and Risk Analysis project collects new research that estimates the potential

damages of inaction and the benefits of GHG mitigation at national and regional scales for many important sectors,

including human health, infrastructure, water resources, electricity demand and supply, ecosystems, agriculture,

and forestry (Waldhoff et al. 2014).

14 Some studies estimate that small temperature increases have a net economic benefit, for instance due to

increased agricultural production in regions with colder climates. However, projected temperature increases even
11

percentage in perspective, 0.9 percent of estimated 2014 U.S. GDP is approximately $150 billion.

The next degree increase, from 3° to 4°, would incur additional costs of 1.2 percent of global

output. Moreover, these costs are not one-time, rather they recur year after year because of the

permanent damage caused by increased climate change resulting from the delay. It should be

stressed that these illustrative estimates are based on a single (albeit leading) model, and there 

is uncertainty associated with the aggregate monetized damage estimates from climate change; 

see for example the discussion in IPCC WG II AR5 (2014).

Increased Mitigation Costs from Delay

The second type of cost of delay arises if policy is delayed but still hits the climate target, for

example stabilizing CO2e concentrations at 550 ppm. Because a delay results in additional nearterm accumulation of GHGs 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.

This section reviews the recent literature on the additional mitigation costs of delay, under the

assumption that both the original and delayed policy achieve a given climate target. We review

16 studies that compare 106 pairs of policy simulations based on integrated climate mitigation

models (the studies are listed and briefly described in the Appendix). 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 modeling assumptions as discussed below. We focus on studies

published in 2007 or later, including recent unpublished manuscripts.

In each case, a model computes the path of cost-effective mitigation policies, mitigation costs,

and climate outcomes over time, constraining the emissions path so that the climate target is hit.

Each path weighs technological progress in mitigation technology and other factors that

encourage starting out slowly 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.

 

under immediate action fall in a range with a strong consensus that the costs of climate change exceed such

benefits. The cost estimates presented here are net of any benefits expected to accrue.
12

The higher carbon prices after a delay typically lead to higher total costs than a policy that would

impose the carbon price today.15

The IPCC WG III AR5 (2014) includes an overview of the literature on the cost of delayed action

on climate change. They cite simulation studies showing that delay is costly, both when all

countries delay action and when there is partial delay, with some countries delaying acting alone

until there is a more coordinated international effort. The present report expands on that

overview by further analyzing the findings of the studies considered by the IPCC report as well as

additional studies. Like the IPCC report, we find broad agreement across the scenario pairs

examined that delayed policy action is more costly compared to immediate action conditional on

a particular climate target. This finding is consistent across a range of climate targets, policy

participants, and modeling assumptions. The vast majority of studies estimate that delayed

action incurs greater mitigation costs compared to immediate action. Furthermore,some models

used in the research predict that the moststringent climate targets are feasible only if immediate

action istaken under full participation. One implication isthat considering only comparisons with

numerical cost estimates may understate the true costs of delay, as failing to reach a climate

target means incurring the costs from the associated climate change.

The costs of delay in these studies depend on a number of factors, including the length of delay,

the climate target, modeling 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.

 

15 Some models explicitly identify the carbon price path that minimizes total social costs. These optimization

models always find equal or greater costs for scenarios with a delay constraint. Other models forecast carbon

prices that result in the climate target but do not demand that the path results in minimal cost. These latter

models can predict that delay reduces costs, and a small number of comparisons we review report negative delay

costs.
13

An important determinant of costs is the role of technological progress and the availability of

mitigation technologies (see the box). The models typically assume technological progress in

mitigation technology, which means that the cost of reducing emissions declines over time as

energy technologiesimprove. As a result, it is cost-effective to start with a relatively lessstringent

policy, then increase stringency over time, and the models typically build in this cost-effective

tradeoff. However, most models still find that immediate initiation of a less stringent policy

followed by increasing stringency incurs lower costs than delaying policy entirely and then

increasing stringency more rapidly.

We begin by characterizing the primary findingsin the literature broadly, discussing the estimates

of delay costs and how the costs vary based on key parameters of the policy scenarios; additional

details can be found in the Appendix. We then turn to a statistical analysis of all the available

THE ROLE OF TECHNOLOGICAL PROGRESS IN COST ESTIMATES

Assumptions about energy technology play an important role in estimating mitigation costs. For

example, many models assume that carbon capture and storage (CCS) will enable point sources

of emission to capture the bulk of carbon emissions and store them with minimal leakage into

the atmosphere over a long period. Some comparisons also assume that CCS will combine with

large-scale bio-energy (“bio-CCS”), effectively generating “negative emissions” since biological

fuels extract atmospheric carbon during growth. Such technology could facilitate reaching a longterm atmospheric concentration target despite relatively modest near-term mitigation efforts.

However, the IPCC warns that “There is only limited evidence on the potential for large-scale

deployment of [bio-CCS], large-scale afforestation, and other [CO2 removal] technologies and

methods” (IPCC WG III AR5 2014). In addition, models must also specify the cost and timing of

availability of such technology, potentially creating further variation in mitigation cost estimates.

The potential importance of technology, especially bio-CCS, is manifested in differences across

models. Clarke et al. (2009) present delay cost estimates for 10 models simulating a 550 ppm CO2

equivalent target by 2100 allowing for overshoot. The three models that assume bio-CCS

availability estimate global present values of the cost of delay ranging from $1.4 trillion to $4.7

trillion. Among the seven models without bio-CCS, four predict higher delay costs, one predicts

that the concentration target was infeasible under a delay, and two predict lower delay costs.

The importance of bio-CCS is even clearer with a more stringent target. For example, two of the

three models with bio-CCS find that a 450 ppm CO2 equivalent target is feasible under a delay

scenario, while none of the seven models without bio-CCS find the stringent target to be feasible.

The Department of Energy sponsors ongoing research on CCS for coal-fired power plants. As part

of its nearly $6 billion commitment to clean coal technology, the Administration, partnered with

industry, has already invested in four commercial-scale and 24 industrial-scale CCS projects that

together will store more than 15 million metric tons of CO2 per year.
14

delay cost estimates that we could gather in a standardized form, that is, we conduct a metaanalysis of the literature on delay cost estimates.

Effect on Costs of Climate Targets, Length of Delay, and International Coordination

Climate Targets

Researchers estimate a range of climate and economic impacts from a given concentration of

GHGs and find that delaying action is much costlier for more stringent targets. Two recent major

modeling simulation projects conducted by the Energy Modeling Forum (Clarke et al. 2009) and

by AMPERE (Riahi et al. 2014) consider the economic costs of delaying policies to reach a range

of CO2e concentration targets from 450 to 650 ppm in 2100. In the Energy Modeling Forum

simulations in Clarke et al. (2009), the median additional cost (global present value) for a 20-year

delay is estimated to be $0.7 trillion for 650 ppm CO2e but a substantially greater $4.7 trillion for

550 ppm CO2e. Many of the models in these studies suggest that delay causes a target of 450

ppm CO2e to be much more costly to achieve, or possibly even infeasible.

Length of Delay

The longer the delay, the greater the cumulative emissions before action begins and the shorter

the available time to meet a given target. Several recent studies examine the cost implications of

delayed climate action and find that even a short delay can add substantial costs to meeting a

stringent concentration target, or even make the target impossible to meet. For example, Luderer

et al. (2012) find that delay from 2010 to 2020 to stabilize CO2 concentration levels at 450 ppm

by 2100 raises mitigation cost by 50 to 700 percent.

16 Furthermore, Luderer et al. find that delay

until 2030 renders the 450 ppm target infeasible. Edmonds et al. (2008) find that additional

mitigation costs of delay by newly developed and developing countries are substantial. In fact,

they find that stabilizing CO2 concentrations at 450 ppm even for a relatively short delay from

2012 to 2020 increases costs by 28 percent over the idealized case, and a delay to 2035 increased

costs by more than 250 percent.

International Coordination

Meeting stringent climate targets with action from only one country or a small group of countries

is difficult or impossible, making international coordination of policies essential. Recent research

shows, however, that even if a delay in international mitigation efforts occurs, unilateral or

fragmented action reduces the costs of delay: although immediate coordinated international

action is the least costly approach, unilateral action is less costly than doing nothing.

17 More

specifically, Jakob et al. (2012) consider a 10-year delay of mitigation efforts to reach a 450 ppm

CO2 target by 2100 and find that global mitigation costs increase by 43 to 700 percent if all

countries begin mitigation efforts in 2020 rather than 2010. However, early action in 2010 by

more developed countries reduces this increase to 29 to 300 percent. In a similar scenario,

 

16 We present a range of cost estimates which comes from the three IAMs – ReMIND-R, WITCH and IMACLIM-R –

used by Luderer et al. (2012). These scenarios also allow temporary overshoot of the target.

17 Waldhoff and Fawcett (2011) find that early mitigation action by industrialized economies significantly reduces

the likelihood of large temperature changes in 2100 while also increasing the likelihood of lower temperature

changes, relative to a no policy scenario.
15

Luderer et al. (2012) find that costs increase by 50 to 700 percent with global delay from 2010 to

2020, however if the industrialized countries begin mitigation efforts unilaterally in 2010 (and

are joined by all countries in 2020), the estimated cost increases range from zero to about 200

percent. Luderer et al. (2013) and Riahi et al. (2014) find that costs of delay are smaller when

fewer countries delay mitigation efforts, or when short-term actions during the delay are more

aggressive.

Jakob et al. (2012) find it is in the best interest of the European Union to begin climate action in

2010 rather than delaying action with all other countries until 2020. They also estimate that the

cost increase to the United Statesfrom delaying climate action with all other countries until 2020

is from 28 to 225 percent, relative to acting early along with other industrialized economies.18

McKibbin, Morris, and Wilcoxen (2014) consider the impact that a delay in imposing a unilateral

price of carbon would have on economic outcomes in the United States including GDP,

investment, consumption and employment. They find that although unilateral mitigation efforts

do incur costs, delay is costlier.

Summary: Quantifying Patterns across the Studies

We now turn to a quantitative summary and assessment, or meta-analysis, of the studies

discussed above.19 The data set for this analysis consists of the results on 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.20 We capture variation acrossstudy and experimental

designs using variables that encode the length of the delay in years; the target 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.21 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 percent increase in costs (alternative methods of handling the outliers are

 

18 Note that the IMACLIM model finds that U.S. mitigation declines to the point in which they are slightly negative

(i.e. net gains compared to business-as-usual).

19 A study of the results of other studies is referred to as a meta-analysis, and there is a rich body of statistical

tools for meta-analysis, see for example Borenstein et al. (2009).

20 For example, if in some paired comparison delay increased mitigation costs from 0.20 percent of GDP to 0.30

percent of GDP, the cost increase would be 50 percent. Comparisons for which the studies provided insufficient

information to calculate the percentage increase in costs (including all comparisons from Riahi et al. 2014) are

excluded. Also excluded are comparisons that report only the market price of carbon emissions at the end of the

simulation, which is not necessarily proportional to total mitigation costs.

21 When measuring delay length for policies with multiple stages of implementation, we count the delay as ending

at the start of any new participation in mitigation by any party after the start of the simulation. We also exclude

scenarios with delays exceeding 30 years. When other climate targets were provided (e.g., CO2 concentration or

global average temperature increase), the corresponding CO2e concentration levels are estimated using

conversions from IPCC WG III AR5 (2014).
16

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).

22 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 2 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.

For example, of the 14 paired simulations with 10 years of delay (these are represented by the

points in Figure 2 with 10 years of delay), the average delay cost is 39 percent. The regression

line shown in Figure 2 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 percent per decade. This analysis ignores possible

confounding factors, such as longer delays being associated with less stringent targets, and the

multiple regression analysis presented 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 3, in which the lowest (most stringent) concentration targets

tend to have the highest cost estimates. In fact, close inspection of Figure 2 reveals a related

pattern: the relationship between delay length and additional costs is steeper for the points

representing CO2e targets of 500 ppm or lessthan for those in the other two ranges. That is, costs

 

22 In the event that a model estimates a cost for a first-best scenario but determines the corresponding delay

scenario to be infeasible, the comparison is coded as having costs exceeding 400 percent. In addition, one

comparison from Clarke et al. (2009) is excluded because a negative baseline cost precludes the calculation of a

percent increase.
17

of delay are particularly high for scenarios with the most stringent target and the longest delay

lengths.

Table 1 presents the results of multiple regression analysis that summarizes how various factors

affect predictions from the included studies, holding constant the other variables included in

the regression. The dependent variable is the cost of delay, measured as the percentage

increase relative to the comparable no-delay scenario, and the length of delay is measured in

decades. Specifications (1) and (2) correspond to Figures 2 and 3, respectively. Each subsequent

specification includes the length of the delay in years, an indicator variable for a partial delay

scenario, and the target CO2e concentration. In addition to the coefficients shown, specification

(4) includes model fixed effects, which control for systematic differences across models, and

each specification other than column (1) includes an intercept.

The results in Table 1 quantify the two main findings mentioned above. The coefficients in

column (3) indicate that, looking across these studies, a one decade increase in delay length is

on average associated with a 41 percent increase in mitigation cost relative to the no-delay

scenario. This regression does not control for possible differences in baseline costs across the

different models, however, so column (4) reports a variant that includes an additional set of

binary variables indicating the model used (“model fixed effects”). Including model fixed effects

increases the delay cost to 56 percent per decade. When the cost of a delay is estimated

separately for different concentration target bins (column (5)), delay is more costly the more

ambitious is the concentration target. But even for the least ambitious target – a CO2e

concentration exceeding 600 ppm – delay is estimated to increase costs by approximately 24

percent per decade. Because of the relatively small number of cases (58 paired comparisons),

which are further reduced when delay is estimated within target bins, the standard errors are

large, especially for the least ambitious scenarios, so for an overall estimate of the delay cost

we do not differentiate between the different targets. While the regression in column (4)

desirably controls for differences across models, other (unreported) specifications that handle
18

the outliers in different ways and include other control variables give per-decade delay

estimates both larger and smaller than the regression in column (3).23 We therefore adopt the

estimate in regression (3) of 41 percent per decade as the overall annual estimate of delay

costs.

One caveat concerning this analysis is that it only considers cases in which model solutions

exist. The omitted, infeasible cases tend to be ones with ambitious targets that cannot be met

when there is long delay, given the model’s technology assumptions. For this reason, omitting

these cases arguably understates the costs of delay reported in Table 1.24 Additionally, we note

that estimates of the effect of a partial delay (when some developed nations act now and other

nations delay action) are imprecisely estimated, perhaps reflecting the heterogeneity of partial

delay scenarios examined in the studies.

 

23 The results in Table 1 are generally robust to using a variety of other specifications and regression methods,

including: using the percent decrease from the delay case, instead of the percent increase from the no-delay case,

as the dependent variable as an alternative way to handle outliers; using median regression, also as an alternative

way to handle outliers; and including the discount factor as additional explanation of variation in the cost of delay,

but this coefficient is never statistically significant. These regressions use linear compounding, not exponential,

because the focus is on the per-decade delay cost not the annual delay cost. An alternative approach is to specify

the dependent variable in logarithms (although this eliminates the negative estimates), and doing so yields

generally similar results after compounding to those in Table 1.

24 An alternative approach to omitting the infeasible-solution observations is to treat their values as censored at

some level. Accordingly, the regressions in Table 1 were re-estimated using tobit regression, for which values

exceeding 400 percent (including the non-solution cases) are treated as censored. As expected, the estimated

costs of delay per year estimated by tobit regression exceed the ordinary least squares estimates. A linear

probability model (not shown) indicates that scenarios with longer delay and more stringent targets are more likely

to have delay cost increases exceeding 400 percent (including non-solution cases). The assumption of bio-CCS

technology has no statistically significant correlation with delay cost increase in a censored regression but is

associated with a significantly lower probability of delay cost increases exceeding 400 percent.
19
20

III. Climate Policy as Climate Insurance

As discussed in the 2013 NRC report, Abrupt Impacts of Climate Change: Anticipating Surprises,

the Earth’s climate history suggests the existence of “tipping points,” that is, thresholds beyond

which major changes occur that may be self-reinforcing and are likely to be irreversible over

relevant time scales. Some of these changes, such as the rapid decline in late-summer Arctic sea

ice, are already under way. Others represent potential events for which a tipping point likely

exists, but cannot at the present be located. For example, there is new evidence that we might

already have crossed a previously unrecognized tipping point concerning the destabilization of

the West Antarctic Ice Sheet (Joughin, Smith, and Medley 2014 and Rignot et. al. 2014). A tipping

point that is unknown, but thought unlikely to be reached in this century, is the release of

methane from thawing Arctic permafrost, which could reinforce the greenhouse effect and spur

additional warming and exacerbate climate change. Tipping points can also be crossed by slower

climate changes that exceed a threshold at which there is a large-scale change in a biological

system, such as the rapid extinction of species. Such impacts could pose such severe

consequences for societies and economies that they are sometimes called potential climate

catastrophes.

This section examines the implications of these potentially severe outcomes for climate policy, a

topic that has been the focus of considerable recent research in the economics literature. The

main conclusion emerging from this growing body of work is that the potential of these events

to have large-scale impacts hasimportant implicationsfor climate policy. Because the probability

of a climate catastrophe increases as GHG emissions rise, missing climate targets because of

postponed policies increases risks. Uncertainty about the likelihood and consequences of

potential climate catastrophes addsfurther urgency to implementing policies now to reduce GHG

emissions.

Tail Risk Uncertainty and Possible Large-Scale Changes

Were some of these large-scale events to occur, they would have severe consequences and

would effectively be irreversible. Because these events are thought to be relatively unlikely, at

least in the near term – that is, they occur in the “tail” of the distribution – but would have severe

consequences, they are sometimes referred to as “tail risk” events. Because these tail risk events

are outside the range of modern human experience, uncertainty surrounds both the science of

their dynamics and the economics of their consequences.

Because many of these events are triggered by warming, their likelihood depends in part on the

equilibrium climate sensitivity. The IPCC WG I AR5 (2013) provides a likely range of 1.5° to 4.5°

Celsius for the equilibrium climate sensitivity. However, considerably larger values cannot be

ruled out and are more likely than lower values(i.e. the probability distribution isskewed towards

higher values). Combinations of high climate sensitivity and high GHG emissions can result in

extremely large end-of-century temperature changes. For example, the IPCC WG III AR5 (2014)

cites a high-end projected warming of 7.8° Celsius by 2100, relative to 1900-1950.
21

A second way to express this risk is to focus on specific large-scale changes in Earth or biological

systems that could be triggered and locked in by GHG concentrations rising beyond a certain

point. At higher climate sensitivities, the larger temperature response to atmospheric GHG

concentrations would make it even more likely that we would cross temperature-related tipping

points in the climate system. The potential for additional releases of methane, a potent GHG,

from thawing permafrost, thus creating a positive feedback to further increase temperatures, is

an example of such a tail risk event. Higher carbon dioxide concentrations in the atmosphere, by

increasing the acidity of the oceans, could also trigger and lock in permanent changes to ocean

ecosystems, such as diminished coral reef-building, which decreases biodiversity supported on

reefs and decreases the breakwater effects that protect shorelines. The probability of significant

negative effects from ocean acidification can be increased by other stressors such as higher

temperatures and overfishing.

The box summarizes some of these potential large-scale events, which are sometimes also

referred to as “abrupt” because they occur in a very brief period of geological time. These events

are sufficiently large-scale they have the potential for severely disrupting ecosystems and human

societies, and thus are sometimes referred to as catastrophic outcomes.
22

ABRUPT IMPACTS OF CLIMATE CHANGE: ANTICIPATING SURPRISES

The National Research Council’s 2013 report, Abrupt Impacts of Climate Change: Anticipating

Surprises, discusses a number of abrupt climate changes with potentially severe consequences.

These events include:

• Late-summer Arctic sea ice disappearance: Strong trends of accelerating late-summersea ice

loss have been observed in the Arctic. The melting of Arctic sea ice comprises a positive

feedback loop, as less ice means more sunlight will be absorbed into the dark ocean, causing

further warming.

• Sea level rise (SLR) from destabilization of West Antarctic ice sheets (WAIS): The WAIS

represents a potential SLR of 3-4 meters as well as coastal inundation and stronger storm

surges. Much remains unknown of the physical processes at the ice-ocean frontier. However,

two recent studies (Joughin, Smith, and Medley 2014, Rignot et. al. 2014) report evidence

that irreversible WAIS destabilization has already started.

• Sea level rise from other ice sheets melting: Losing all other ice sheets, including Greenland,

may cause SLR of up to 60 meters as well as coastal inundation and stronger storm surges.

Melting of the Greenland ice sheet alone may induce SLR of 7m, but it is not expected to

destabilize rapidly within this century.

• Disruption to Atlantic Meridional Overturning Circulation (AMOC): Potential disruptions to

the AMOC may disrupt local marine ecosystems and shift tropical rain belts southward.

Although current models do not indicate that an abrupt shift in the AMOC is likely within the

century, the deep ocean remains understudied with respect to measures necessary for AMOC

calculations.

• Decrease in ocean oxygen: As the solubility of gases decrease with rising temperature, a

warming of the ocean will decrease the oxygen content in the surface ocean and expand

existing Oxygen Minimum Zones. This will pose a threat to aerobic marine life as well as

release nitrous oxide—a potent GHG—as a byproduct of microbial processes. The NRC study

assesses a moderate likelihood of an abrupt increase in oxygen minimum zones in this

century.

• Increasing release of carbon stores in soils and permafrost: Northern permafrost contains

enough carbon to trigger a positive feedback response to warming temperatures. With an

estimated stock of 1700-1800 Gt, the permafrost carbon stock could amplify considerably

human-induced climate change. Small trends in soil carbon releases have been already

observed.

• Increasing release of methane from ocean methane hydrates: This is a particularly potent

long-term risk due to hydrate deposits through changes in ocean water temperature; the

likely timescale for the physical processesinvolved spans centuries, however, and there islow

risk this century.
23

Implications of Tail Risk

An implication of the theory of decision-making under uncertainty is that the risks posed by

irreversible catastrophic events can be substantial enough to influence or even dominate

decisions.

Weitzman’s Dismal Theorem

Over the past few years, economists have examined the implications of decision-making under

uncertainty for climate change policy. In a particularly influential treatment, Weitzman (2009)

proposes his so-called “Dismal Theorem,” which provides a set of assumptions under which the

current generation would be willing to bear very large (in fact, arbitrarily large) costs to avoid a

future event with widespread, large-scale costs. The intuition behind Weitzman’s mathematical

result rests with the basic insight that because individuals are risk-averse, they prefer to buy

health, home, and auto insurance than to take their chances of a major financial loss. Similarly, if

major climate events have the potential to reduce aggregate consumption by a large amount,

society will be better off if it can take out “climate insurance” by paying mitigation costs now that

will reduce the odds of a large-scale—in Weitzman’s (2009) word, catastrophic—drop in

consumption later.25

 

25 This logic has its basis in expected utility theory. Because individuals are risk averse, each additional dollar of

consumption provides less value, or utility, to individuals than the previous dollar. To avoid this major loss, an

individual will buy home insurance. That insurance is provided by the market because an insurance company can

offer home insurance to many homeowners in different regions of the country, and through diversification the

company will on average have many homeowners paying premiums and a few collecting insurance, so

diversification allows the company to run a relatively low-risk business. But risks from severe climate change are

not diversifiable because their enormous costs would impact the global economy. Consequently, as long as there is

a non-negligible probability of a large drop in consumption, and therefore a very large drop in utility, arising from a

large-scale loss in consumption, society today should be willing to pay a substantial amount if doing so would avoid

that loss.

• Rapid state changes in ecosystems, species range shifts, and species boundary changes:

Research shows that climate change is an important component of abrupt ecosystem statechanges, with a prominent example being the Sahel region of Africa. Such state-changes from

forests to savanna, from savanna to grassland, et cetera, will cause extensive habitat loss to

animal species and threaten food and water supplies. The NRC study assesses moderate risk

during this century and high risk afterwards.

• Increases in extinctions of marine and terrestrial species: Abrupt climate impacts include

extensive extinctions of marine and terrestrial species; examples such as the destruction of

coral reef ecosystems are already underway. Numerous land mammal, bird, and amphibian

species are expected to become extinct with a high probability within the next one or two

centuries.
24

Weitzman’s (2009) dismal theorem has spurred a substantial amount of research on the

economics of what this literature often refers to as climate catastrophes. A number of authors

(e.g. Newbold and Daigneault 2009, Ackerman et al. 2010, Pindyck 2011, 2013, Nordhaus 2011,

2012, Litterman 2013, Millner 2013), including Weitzman (2011, 2014), stress that although the

strong version of Weitzman’s (2009) result—that society would be willing to pay an arbitrarily

large amount to avoid future large-scale economic losses—depends on specific mathematical

assumptions, the general principle of taking action to prevent such events does not. The basic

insight is that, just as the sufficiently high threat of a fire justifies purchasing homeowners

insurance, the threat of large-scale losses from climate change justifies purchasing “climate

insurance” in the form of mitigation policies now (Pindyck 2011), and that taking actions today

could help to avoid worst-case outcomes (Hwang, Tol, and Hofkes 2013). According to this line

of thinking, the difficulty of assessing the probabilities of such large-scale losses or the location

of tipping points does not change the basic conclusion that, because their potential costs are so

overwhelming, the threat of very large losses due to climate change warrants implementing

mitigation policies now.

Several recent studies have started down the road of quantifying the implications of the

precautionary motive for climate policy. One approach is to build the effects of large-scale

changes into IAMs, either by modeling the different risks explicitly or by simulation using heavytailed distributions for key parameters such as the equilibrium climate sensitivity or parameters

of the economic damage function. Research along these lines includes Ackerman, Stanton, and

Bueno (2013), Pycroft et al. (2011), Dietz (2011), Ceronsky et al. (2011), and Link and Tol (2011).

Another approach is to focus on valuation of the extreme risks themselves outside an IAM, for

example as examined by Pindyck (2012) and van der Ploeg and de Zeeuw (2013). Kopits, Marten,

and Wolverton (2013) review some of the tail risk literature and literature on large-scale Earth

system changes, and suggest steps forward for incorporating such events in IAMs, identifying

ways in which the modeling could be improved even within current IAM frameworks and where

additional work is needed. One of the challenges in assessing these large-scale events is that

some of the most extreme events could occur in the distant future, and valuing consumption

losses beyond this century raises additional uncertainty about intervening economic growth rates

and questions about how to discount the distant future.26 The literature isrobust in showing that

the potential for such events could have important climate policy implications, however, the

scientific community has yet to derive robust quantitative policy recommendations based on a

detailed analyses of the link between possible large-scale Earth system changes and their

economic consequences.

Implications of Uncertainty about Tipping Points 

Although research that embeds tipping points into climate models is young, one qualitative

conclusion is that the prospect of a potential tipping point with unknown location enhances the

precautionary motive for climate policy (Baranzini, Chesney, and Morisset 2003, Brozovic and

Schlenker 2011, Cai, Judd, and Lontzek 2013, Lemoine and Traeger 2012, Barro 2013, van der

 

26 For various perspectives on the challenges of evaluating long-term climate risks, see Dasgupta (2008), Barro

(2013), Ackerman, Stanton, and Bueno (2013), Roe and Bauman (2013), and Weitzman (2013).
25

Ploeg 2014). To develop the intuition, firstsuppose that the tipping point is a known temperature

increase, say 3° Celsius above preindustrial levels, and that the economic consequences of

crossing the tipping point are severe, and temporarily put aside other reasons for reducing

carbon emissions. Under these assumptions climate policy would allow temperature to rise,

stopping just short of the 3° increase. In contrast, now suppose that the tipping point is unknown

and that its estimated mean is 3°, but that it could be less or more with equal probability. In this

case, the policy that stops just short of 3° warming runs a large risk of crossing the true tipping

point. Because that mistake would be very costly, the uncertainty about the tipping point

generally leads to a policy that is more stringent today than it would be absent uncertainty. To

the extent that delayed implementation means higher long-run CO2 concentrations, then the

risks of hitting a tipping point increase with delay.

As a simplification, the above description assumes away other costs of climate change that

increase smoothly with temperature, as well as the reality that important tipping points in

biological systems could be crossed by small gradual changes in temperatures, so as to focus on

the consequences of uncertainty about large-scale temperature changes. When the two sets of

costs are combined, the presence of potential large-scale changes increases the benefits of

mitigation policies, and the presence of uncertainty about tipping points that would produce

abrupt changes increases those benefits further.27 Cai, Judd, and Lontzek (2013) use a dynamic

stochastic general equilibrium version of DICE model that is modified to include multiple tipping

points with unknown (random) locations. To avoid the Weitzman “infinities” problem, they focus

on tipping events with economic consequences that are large (5 or 10 percent of global GDP) but

fall short of global economic collapses. They conclude that the possibility of future tipping points

increases the optimal carbon price today: in their benchmark case, the optimal pre-tipping

carbon price more than doubles, relative to having no tipping point dynamics. Similarly, Lemoine

and Traeger (2012) embed unknown tipping points in the DICE model and estimate that the

optimal carbon price increases by 45 percent as a result. In complementary work, Barro (2013)

considers a simplified model in which the only benefits of reducing carbon emissions come from

reducing the probability of potential climate catastrophes, and finds that this channel alone can

justify investment in reducing GHG pollution of one percent of GDP or more, beyond what would

normally occur in the market absent climate policy.

 

27 Cai, Judd, and Lontzek (2013) provide a stark example of this dynamic. Their analysis, which is undertaken using

a modified version of Nordhaus’s (2008) DICE-2007 model, includes both the usual reasons for emissions

mitigation (damages that increase smoothly with temperature) and the possibility of a tipping point at an

uncertain future temperature which results in a jump in damages.
26

References 

Ackerman, Frank, Stephen J. DeCanio, Richard B. Howarth, and Kristen Sheeran. 2010. “The

Need for a Fresh Approach to Climate Change Economics.” In Assessing the Benefits of

Avoided Climate Change: Cost-Benefit Analysis and Beyond: 159-181.

Ackerman, Frank, Elizabeth A. Stanton, and Ramón Bueno. 2013. “Epstein-Zin Utility in DICE: Is

Risk Aversion Irrelevant to Climate Policy?” Environmental Resource Economics 56, 1:

73-84.

Barranzani, Andrea, Marc Chesney, and Jacques Morisset. 2003. “The Impact of Possible

Climate Catastrophes on Global Warming Policy.” Energy Policy 31, 8: 691-701.

Barro, Robert J. 2013. “Environmental Protection, Rare Disasters, and Discount Rates.” NBER

Working Paper 19258.

Blanford, Geoffrey J., Richard G. Richels, and Thomas F. Rutherford. 2009. “Feasible Climate

Targets: The Roles of Economic Growth, Coalition Development and Expectations.”

Energy Economics 31, supplement 2: S82-S93.

Borenstein, Michael, Larry V. Hedges, Julian P.T. Higgins, and Hannah Rothstein. 2009.

Introduction to Meta-Analysis. Chichester, U.K.: Wiley.

Bosetti, Valentina, Carlo Carraro, and Massimo Tavoni. 2009. “Climate Change Mitigation

Strategies in Fast-Growing Countries: the Benefits of Early Action.” Energy Economics

31, supplement 2: S14-S151.

Bosetti, Valentina, Carlo Carraro, Alessandra Sgobbi, and Massimo Tavoni. 2009. “Delayed

Action and Uncertain Stabilisation Targets. How Much Will the Delay Cost?” Climatic

Change 96, 3: 299-312.

Brozovic, N. and W. Schlenker. 2011. “Optimal Management of an Ecosystem with an Unknown

Threshold.” Ecological Economics 70, 4: 627-640.

Cai, Yonyang, Kenneth L. Judd, and Thomas S. Lontzek. 2013. “The Social Cost of Stochastic and

Irreversible Climate Change.” NBER Working Paper 18704.

Calvin, Katherine, James Edmonds, Ben Bond-Lamberty, Leon Clarke, Son H. Kim, Page Kyle,

Steven J. Smith, Allison Thomson, and Marshall Wise. 2009a. “Limiting Climate Change

to 450 ppm CO2 Equivalent in the 21st Century.” Energy Economics 31, supplement 2:

S107-S120.

Calvin, Katherine, Pralit Patel, Allen Fawcett, Leon Clarke, Karen Fisher-Vanden, Jae Edmonds,

Son H. Kim, Ron Sands, and Marshall Wise. 2009b. “The Distribution and Magnitude of

Emissions Mitigation Costs in Climate Stabilization under Less Than Perfect International

Cooperation: SGM Results.” Energy Economics 31, supplement 2: S187-S197.

Ceronsky, Megan, David Anthoff, Cameron Hepburn, and Richard S.J. Tol. 2011. “Checking the

Price Tag on Catastrophe: The Social Cost of Carbon under Non-Linear Climate

Response.” ESRI Working Paper 392.

Clarke, Leon, Jae Edmonds, Volker Krey, Richard Richels, Steven Rose, and Massimo Tavoni.

2009. “International Climate Policy Architectures: Overview of the EMF 22 International

Scenarios.” Energy Economics 31, supplement 2: S64-S81.

Council of Economic Advisers. 2014. Economic Report of the President, 2014.

Dasgupta, Partha. 2008. "Discounting Climate Change." Journal of Risk and Uncertainty 37, 2/3:

141-169.
27

Dell, Melissa, Benjamin F. Jones, Benjamin A. Olken. 2013. “What Do We Learn from the

Weather? The New Climate-Economy Literature.” Journal of Economic Literature,

forthcoming.

Deschênes, Olivier and Michael Greenstone. 2011. “Climate Change, Mortality, and Adaptation:

Evidence from Annual Fluctuations in Weather in the US.” American Economic Journal:

Applied Economics 3, 4: 152-185.

Dietz, Simon. 2011. “High Impact, Low Probability? An Empirical Analysis of Risk in the

Economics of Climate Change.” Climatic Change 108, 3: 519-541.

Edmonds, Jae, Leon Clarke, John Lurz, and J. Macgregor Wise. 2008. “Stabilizing CO2

Concentrations with Incomplete International Cooperation.” Climate Policy 8, 4: 355-

376.

Graff Zivin, Joshua and Matthew Neidell. 2014. “Temperature and the Allocation of Time:

Implications for Climate Change.” Journal of Labor Economics 32, 1: 1-26.

Gurney, Andrew, Helal Ahammad, and Melanie Ford. 2009. “The Economics of Greenhouse Gas

Mitigation: Insights from Illustrative Global Abatement Scenarios Modelling.” Energy

Economics 31, supplement 2: S174-S186.

Hwang, In Chang, Richard S.J. Tol, and Marjan W. Hofkes. 2013. “Tail-Effect and the Role of

Greenhouse Gas Emissions Control.” University of Sussex Working Paper Series 6613.

Intergovernmental Panel on Climate Change, Working Group I contribution to the Fifth

Assessment Report (IPCC WG I AR5). 2013. Climate Change 2013: The Physical Science

Basis.

Intergovernmental Panel on Climate Change, Working Group II contribution to the Fifth

Assessment Report (IPCC WG II AR5). 2014. Climate Change 2014: Impacts, Adaptation

and Vulnerability.

Intergovernmental Panel on Climate Change, Working Group III contribution to the Fifth

Assessment Report (IPCC WG III AR5). 2014. Climate Change 2014: Mitigation of Climate

Change.

Jaffe, Adam and Karen Palmer. 1997. “Environmental Regulation and Innovation: A Panel Data

Study.” Review of Economics and Statistics 79, 4: 610-619.

Jakob, Michael, Gunnar Luderer, Jan Steckel, Massimo Tavoni, and Stephanie Monjon. 2012.

“Time to Act Now? Assessing the Costs of Delaying Climate Measures and Benefits of

Early Action.” Climatic Change 114, 1: 79-99.

Joughin, Ian, Benjamin E. Smith, and Brooke Medley. 2014. “Marine Ice Sheet Collapse

Potentially Underway for the Thwaites Glacier Basin, West Antarctica.” Science

344, 6185: 735-738.

Kopits, Elizabeth, Alex Marten, and Ann Wolverton. 2013. “Incorporating ‘Catastrophic’ Climate

Change into Policy Analysis.” Climate Policy ahead-of-print: 1-28.

Krey, Volker and Keywan Riahi. 2009. “Implications of Delayed Participation and Technology

Failure for the Feasibility, Costs, and Likelihood of Staying Below Temperature Targets—

Greenhouse Gas Mitigation Scenarios for the 21st Century.” Energy Economics 31,

supplement 2: S94-S106.

Lanjouw, Jean and Ashoka Mody. 1996. “Innovation and the International Diffusion of

Environmentally Responsive Technology.” Research Policy 25, 4: 549-571.
28

Lemoine, Derek and Christian Traeger. 2012. “Tipping Points and Ambiguity in the Economics of

Climate Change.” NBER Working Paper 18230.

Link, P. Michael and Richard S.J. Tol. 2011. “Estimation of the Economic Impact of Temperature

Changes Induced by a Shutdown of the Thermohaline Circulation: An Application of

FUND.” Climactic Change 104, 2: 287-304.

Litterman, Bob. 2013. “What is the Right Price for Carbon Emissions?” Regulation 36, 2: 38-51.

Loulou, Richard, Maryse Labriet, and Amit Kanudia. 2009. “Deterministic and Stochastic

Analysis of Alternative Climate Targets under Differentiated Cooperation Regimes.”

Energy Economics 31, supplement 2: S131-S143.

Luderer, Gunnar, Valentina Bosetti, Michael Jakob, Marian Leimbach, Jan Steckel, Henri

Waisman, and Ottmar Edenhofer. 2012. “The Economics of Decarbonizing the Energy

System – Results and Insights from the RECIPE Model Intercomparison.” Climatic

Change 114, 1: 9-37.

Luderer, Gunnar, Robert C. Pietzcker, Christoph Bertram, Elmar Kriegler, Malte Meinshausen,

and Ottmar Edenhofer. 2013. “Economic Mitigation Challenges: How Further Delay

Closes the Door for Achieving Climate Targets.” Environmental Research Letters 8, 3.

McKibbin, Warwick J., Adele C. Morris, and Peter J. Wilcoxen. 2014. “The Economic

Consequences of Delay in U.S. Climate Policy.” Brookings: The Climate and Energy

Economics Project.

Millner, Antony. 2013. “On Welfare Frameworks and Catastrophic Climate Risks.” Journal of

Environmental Economics and Management 65, 2: 310-325.

National Research Council. 2010. Limiting the Magnitude of Future Climate Change.

Washington, D.C.: The National Academies Press

National Research Council. 2011. Climate Stabilization Targets: Emissions, Concentrations, and

Impacts over Decades to Millennia. Washington D.C.: The National Academies Press.

National Research Council. 2013. Abrupt Impacts of Climate Change: Anticipating Surprises.

Washington D.C.: The National Academies Press.

Newbold, Stephen and Adam Daigneault. 2009. “Climate Response Uncertainty and the

Benefits of Greenhouse Gas Emissions Reductions.” Environmental and Resource

Economics 44, 3: 351-377.

Nordhaus, William D. 2008. A Question of Balance: Weighing the Options on Global Warming

Policies. New Haven: Yale University Press.

Nordhaus, William D. 2011. “The Economics of Tail Events with an Application to Climate

Change.” Review of Environmental Economics and Policy 5, 2: 240-257.

Nordhaus, William D. 2012. “Economic Policy in the Face of Severe Tail Events.” Journal of

Public Economic Theory 14, 2: 197-219.

Nordhaus, William D. 2013. The Climate Casino: Risk, Uncertainty, and Economics for a

Warming World. New Haven: Yale University Press.

Pindyck, Robert S. 2011. “Fat Tails, Thin Tails, and Climate Change Policy.” Review of

Environmental Economics and Policy 5, 2: 258-274.

Pindyck, Robert S. 2012. “Uncertain Outcomes and Climate Change Policy.” Journal of

Environmental Economics and Management 63, 3: 289-303.

Pindyck, Robert S. 2013. “Climate Change Policy: What do the Models tell us?” Journal of

Economic Literature 51, 3: 860-872.
29

Popp, David. 2003. “Pollution Control Innovations and the Clean Air Act of 1990.” Journal of

Policy Analysis and Management 22, 4: 641-660.

Popp, David. 2006. “International Innovation and Diffusion of Air Pollution Control

Technologies: The effects of NOX and SO2 Regulation in the U.S., Japan, and Germany.”

Journal of Environmental Economics and Management 51, 1: 46-71.

Popp, David, Richard G. Newell, and Adam B. Jaffe. 2010. “Energy, the Environment, and

Technological Change.” In Handbook of the Economics of Innovation 2: 873-937.

Pycroft, Jonathan, Lucia Vergano, Chris Hope, Daniele Paci, and Juano Carlos Ciscar. 2011. “A

Tale of Tails: Uncertainty and the Social Cost of Carbon Dioxide.” Economics 5, 22: 1-29.

Riahi, Keywan, Elmar Kriegler, Nils Johnson, Christoph Bertram, Michel den Elzen, Jiyong Eom,

Michiel Schaeffer, Jae Edmonds, Morna Isaac, Volker Krey, Thomas Longden, Gunnar

Luderer, Aurélie Méjean, David L. McCollum, Silvana Mimai, Hal Turton, Detlef P. van

Vuuren, Kenichi Wada, Valentina Bosetti, Pantelis Caprosm, Patrick Criqui, Meriem

Hamdi-Cherif, Mikiko Kainuma, and Ottmar Edenhofer. 2014. “Locked into Copenhagen

Pledges—Implications of Short-Term Emission Targets for the Cost and Feasibility of

Long-Term Climate Goals.” Technological Forecasting and Social Change. In Press.

Richels, Richard G., Thomas F. Rutherford, Geoffrey J. Blanford, and Leon Clarke. 2007.

“Managing the Transition to Climate Stabilization.” Climatic Policy 7, 5: 409-428.

Rignot, Eric, Jeremie Mouginot, Mathieu Morlighem, Helene Seroussi, and Bernd Scheuchl.

2014. “Widespread, Rapid Grounding Line Retreat of Pine Island, Thwaites, Smith, and

Kohler Glaciers, West Antarctica, from 1992 to 2011.” Geophysical Research Letters 41,

10: 3502-3509.

Roe, Gerard H. and Yoram Bauman. 2013. “Climate Sensitivity: Should the Climate Tail Wag the

Policy Dog?” Climatic Change 117, 4: 647-662.

Russ, Peter and Tom van Ierland. 2009. “Insights on Different Participation Schemes to Meet

Climate Goals.” Energy Economics 31, supplement 2: S163-S173.

Tol, Richard S.J. 2009. “The Feasibility of Low Concentration Targets: An Application of FUND.”

Energy Economics 31, supplement 2: S121-S130.

Tol, Richard S.J. 2014. “Correction and Update: The Economic Effects of Climate Change.”

Journal of Economic Perspectives 28, 2: 221-226.

United States Environmental Protection Agency. 2014. “Carbon Pollution Emission Guidelines

for Existing Stationary Sources: Electric Utility Generating Units.”

https://www.federalregister.gov/articles/2014/06/18/2014-13726/carbon-pollutionemission-guidelines-for-existing-stationary-sources-electric-utility-generating

(USGCRP) U.S. Global Change Research Program. 2014. Climate Change Impacts in the United

States: The Third National Climate Assessment.

van der Ploeg, Frederick. 2014. “Abrupt Positive Feedback and the Social Cost of Carbon.”

European Economic Review 67: 28-41.

van der Ploeg, Frederick and Aart de Zeeuw. 2013. “Climate Policy and Catastrophic Change: Be

Prepared and Avert Risk.” OxCarre Working Papers 118, Oxford Centre for the Analysis

of Resource Rich Economies, University of Oxford.

van Vliet, Jasper, Michael G.J. den Elzen, and Detlef P. van Vuuren. 2009. “Meeting Radiative

Forcing Targets under Delayed Participation.” Energy Economics 31, supplement 2:

S152-S162.
30

Waldhoff, Stephanie A. and Allen A. Fawcett. 2011. “Can Developed Economies Combat

Dangerous Anthropogenic Climate Change Without Near-Term Reductions from

Developing Economies?” Climatic Change 107, 3/4: 635–641.

Waldhoff, Stephanie, Jeremy Martinich, Marcus Sarofim, Ben DeAngelo, James McFarland,

Lesley Jantarasami, Kate Shouse, Allison Crimmins, Sara Ohrel, and Jia Li. 2014.

“Overview of the Special Issue: A multi-model framework to achieve consistent

evaluation of climate change impacts in the United States.” Climatic Change,

forthcoming.

Weitzman, Martin. 2009. “On Modeling and Interpreting the Economics of Catastrophic Climate

Change.” The Review of Economics and Statistics 91, 1: 1-19.

Weitzman, Martin. 2011. “Fat-Tailed Uncertainty in the Economics of Catastrophic Climate

Change.” Review of Environmental Issues and Policy 5, 2: 275-292.

Weitzman, Martin. 2012. “GHG Targets as Insurance against Catastrophic Climate Damages.”

Journal of Public Economic Theory 14, 2: 221-244.

Weitzman, Martin. 2013. “Tail-Hedge Discounting and the Social Cost of Carbon.” Journal of

Economic Literature 51, 3: 873-882.

Weitzman, Martin. 2014. “Fat Tails and the Social Cost of Carbon.” American Economic Review:

Papers & Proceedings 104, 5: 544-546.
31

Appendix: Literature on Delay Costs

This appendix lists the studies reviewed Section II and used in the meta-analysis, and briefly

describes the scenarios they analyzed.

The EMF22 project engaged ten leading integrated assessment models to analyze the climate

and economic consequences of delay scenarios. The EMF22 studies consist of Loulou, Labriet,

and Kanudia (2009), Tol (2009), Gurney, Ahammad, and Ford (2009), van Vliet, den Elzen, and

van Vuuren (2009), Blanford, Richels, and Rutherford (2009), Krey and Riahi (2009), Calvin et al.

(2009a, 2009b), Russ and van Ierland (2009), and Bosetti, Carraro, and Tavoni (2009), with Clarke

et al. (2009) providing an overview of the project. 28 Among other objectives, each study

estimates the mitigation costs associated with five climate targets under both an immediate

action scenario and a harmonized delay scenario. The targets are 450, 550, and 650 ppm CO2e in

2100, and the models consider the first two targets alternatively allowing or prohibiting an

overshoot before 2100.29 In the delay scenario, only more developed countries (minus Russia)

begin mitigation immediately in 2012 in a coordinated fashion (i.e., with the same carbon

pricing), with some countries delaying action until 2030, and remaining countries delay action

until 2050. These scenarios enable calculating the additional mitigation costs associated with

delay for each concentration target.

The AMPERE project engaged nine modeling teams to analyze the climate and economic

consequences of global emissions following the proposed policy stringency of the national

pledges from the Copenhagen Accord and Cancún Agreements to 2030. (The AMPERE scenarios

were not included in the meta-analysis in Section II because Riahi et al. (2014) did not provide

sufficient information to calculate the percent increase in mitigation costs for each delay

scenario.) One of the questions addressed by this project is the economic costs of delaying

policiesto reach CO2e concentration targets of 450 and 550 ppm in 2100 (Riahi et al. 2014). Eight

models simulate pairs of policy scenarios reaching each target. One simulation in each pair

assumes that all countries act immediately in a coordinated fashion (i.e., with the same carbon

pricing), while the other simulation assumes that all countries follow the less stringent emissions

commitments made during the Copenhagen Accord and Cancun Agreements until 2030, when

coordinated international action begins.

The meta-analysis includes the following studies not associated with either AMPERE or EMF22:

Jakob et al. (2012); Luderer et al. (2012, 2013); Edmonds et al. (2008); Richels et al. (2007), and

Bosetti et al. (2009). Jakob et al. (2012) consider a 10-year delay of mitigation efforts to reach a

450 ppm CO2 target by 2100, including variations where more developed countries implement

mitigation immediately. Luderer et al. (2012) consider a similar 10-year delay and the same 450

ppm CO2 target by 2100, with a scenario where Europe and all other industrialized countries

 

28 Russ and van Ierland (2009) did not present estimates of total delay costs, so this paper is not included in the

meta-analysis in Section II.

29 We included three additional scenarios in van Vliet, den Elzen, and van Vuuren (2009) with alternate targets and

models that were not reported in Clarke et al. (2009).
32

begin mitigation efforts in 2010. Luderer et al. (2013) analyze a scenario where countries

implement fragmented policies before coordinating efforts in 2015, 2020, or 2030 to meet a

target of 2°C above preindustrial levels by 2100, allowing for overshooting. Edmonds et al. (2008)

consider targets of 450, 550, and 660 ppm CO2, with newly developed and developing countries

delaying climate action from a start date of 2012 to 2020, 2035 and 2050. Richels et al. (2007)

estimate the additional cost of delay by newly developing countries until 2050 for a 450 and 550

ppm CO2 target. Finally, Bosetti et al. (2009) estimate the additional cost when all countries delay

climate action for 20 years with a goal of reaching a 550 ppm and 650 ppm CO2e target by 2100.