Question:
You are given the full text of a scientific research paper.

    Summarize the **research methods** used in the study in a way that is informative for policy audiences. Your response should:
    - Describe the overall study design, data sources, tools, and models used
    - Avoid unnecessary technical details or equations
    - Use accessible but precise language

    Write a short paragraph suitable for the “Scientific Research Study Methods” section of a policy brief.

    Scientific Research Paper:
    Analysis
https://doi.org/10.1038/s41558-021-01217-0
1Department of Environmental and Occupational Health and Justice, Rutgers School of Public Health, Center for Population-Level Bioethics, and
Department of Philosophy, Rutgers University, New Brunswick, NJ, USA. 2Division of Social Sciences, Yale-NUS College, Singapore, Singapore. 3Energy
and Resources Group, University of California, Berkeley, CA, USA. 4School of Public and International Affairs, Princeton University, Princeton, NJ,
USA. 5Mercator Research Institute on Global Commons and Climate Change (MCC), Berlin, Germany. 6Department of Economics of Climate Change,
Technische Universität Berlin, Berlin, Germany. 7Department of Global Health and Population, Harvard TH Chan School of Public Health, Harvard
University, Boston, MA, USA. 8Paris School of Economics, Paris, France. 9Joint Research Centre, European Commission, Seville, Spain. 10Potsdam
Institute for Climate Impact Research, Potsdam, Germany. 11Department of Economics, University of Oklahoma, Norman, OK, USA. 12CNRS, CIRED,
Nogent-sur-Marne, France. 13School of International Affairs & Department of Civil and Environmental Engineering, Pennsylvania State University, University
Park, PA, USA. 14Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA. 15Department
of Economics, University of Texas, Austin, TX, USA. 16International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria. 17Paris School of
Economics (CNRS), Centre d’Economie de la Sorbonne, Paris, France. ✉e-mail: mark.budolfson@rutgers.edu
A
familiar theme from research on climate policy and eco-
nomic development is that there is an important trade-off
between climate action and near-term poverty reduction;
this literature is based in part on results from existing cost–benefit
climate policy models1–5, which assume that the burden of a nation’s
climate mitigation must fall to some extent on the poor. If this
assumption were correct, some trade-off between climate action
and poverty alleviation would be inevitable. The key question would
then be to what extent benefitting the future poor through avoiding
future climate damages can justify (from a development or equity
perspective) reduced near-term development for the current poor6,7.
However, these models ignore the possibility that the revenues
from a carbon tax could be used in a progressive way that gener-
ates immediate net benefits for the current poor. A large literature
has now investigated the implications of these ‘revenue recycling’
opportunities and identified an equal per capita refund of the rev-
enues as a salient option8–18. The evidence indicates that an equal
per capita refund typically makes immediate net beneficiaries out
of most citizens and is often more progressive and potentially more
feasible than other salient options for using revenues19–21.
Findings from studies of revenue recycling have not been incor-
porated into optimal policy analyses at the global level, including to
model possible synergies with other development goals, for exam-
ple sustainable development goals (SDGs)22–24. This is an important
oversight, as many of the arguments that there are trade-offs between
climate action and poverty alleviation or other SDGs depend on the
premise that climate action must harm the current poor25,26. Indeed,
because the possibility of progressive revenue recycling is not taken
into account in existing optimal climate policy calculations, these
models have a built-in bias against mitigation, since they imply that
mitigation must entail costs for the poorest citizens within regions
in the coming decades and, more generally, imply an intergenera-
tional trade-off in well-being27,28.
Modelling progressive revenue recycling
In the climate economics literature, the ‘initial burden’ of a carbon
tax—the distribution of tax payments and mitigation costs before
any possible redistribution of revenues—is generally found to sub-
tract from all income groups (and thus would increase poverty
in the absence of redistribution) but in a way that is progressive
in poorer countries and regressive in richer countries; in poorer
countries fossil fuels are disproportionately consumed (relative to
income) by richer citizens, whereas in rich countries fossil fuels are
disproportionately consumed by poorer citizens18,29,30. Therefore, as
poorer countries get richer and consumption patterns change, this
regionally differentiated driver of the initial burden of carbon taxes
will probably evolve.
To confirm this relationship and quantify these dynamics, we
conducted a review of the literature on the initial burden of a car-
bon or gasoline tax (Supplementary Section 1). We included studies
from around the world to capture estimates for regions with differ-
ent levels of wealth. Figure 1 displays the results, reporting the rela-
tionship between gross domestic product (GDP) per capita and the
distribution of the initial burden before redistribution of revenues
Climate action with revenue recycling has benefits
for poverty, inequality and well-being
Mark Budolfson   1 ✉, Francis Dennig   2, Frank Errickson   3,4, Simon Feindt   5,6, Maddalena Ferranna7,
Marc Fleurbaey8, David Klenert   9, Ulrike Kornek5,10, Kevin Kuruc   11, Aurélie Méjean   12,
Wei Peng   13, Noah Scovronick   14, Dean Spears   15, Fabian Wagner16 and Stéphane Zuber   17
Existing estimates of optimal climate policy ignore the possibility that carbon tax revenues could be used in a progressive
way; model results therefore typically imply that near-term climate action comes at some cost to the poor. Using the Nested
Inequalities Climate Economy (NICE) model, we show that an equal per capita refund of carbon tax revenues implies that achiev-
ing a 2 °C target can pay large and immediate dividends for improving well-being, reducing inequality and alleviating poverty. In
an optimal policy calculation that weighs the benefits against the costs of mitigation, the recommended policy is characterized
by aggressive near-term climate action followed by a slower climb towards full decarbonization; this pattern—which is driven
by a carbon revenue Laffer curve—prevents runaway warming while also preserving tax revenues for redistribution. Accounting
for these dynamics corrects a long-standing bias against strong immediate climate action in the optimal policy literature.
Nature Climate Change | VOL 11 | December 2021 | 1111–1116 | www.nature.com/natureclimatechange
1111

Analysis
Nature Climate Change
(the consumption elasticity of the initial burden, where an elastic-
ity of ϵ means that if a person’s consumption increases by 1%, that
person’s initial burden increases by ϵ%). An elasticity <1 means the
initial burden of the carbon tax falls disproportionately on the poor
(the tax is regressive before redistribution of revenues), whereas a
value >1 indicates that the tax burden falls disproportionately on
the rich (the tax is progressive before redistribution of revenues).
We use this relationship as an estimate of the distributional impli-
cations of carbon taxation, assuming that the initial burden is dis-
tributed within a region on the basis of the consumption elasticity
estimated by the best-fit line in Fig. 1. As a region grows richer over
time, the elasticity used to estimate the distribution of its initial bur-
den declines.
To investigate the impact of an equal per capita refund of tax rev-
enues on well-being, poverty and inequality, we modify the Nested
Inequalities Climate Economy (NICE), a 12-region global climate
policy model that represents inequality within regions by grouping
the population into five equally populous quintiles, ranked from
poorest to richest. We modify NICE to implement two distinct
policy scenarios. In the first scenario, the ‘no recycling’ scenario,
mitigation costs affect consumption but tax payments do not. This
is the standard assumption in this type of model and is implemented
by returning tax revenues in proportion to the initial burden. In the
second scenario, the ‘recycling’ scenario, the tax revenue in each
region is redistributed on an equal per capita basis. As a result, some
quintiles are net beneficiaries in the recycling scenario if the refund
is greater than the initial burden; this is in contrast to the no recy-
cling scenario where all quintiles bear a net cost from the climate
policy. (See Methods and in particular equation (4), for a detailed
description of the two scenarios.)
2 °C benefits for poverty, inequality and well-being
As a first demonstration of the potential impact of revenue recycling,
we model the difference in consumption of the poorest quintile in
all NICE regions under a 2 °C scenario relative to business-as-usual
(BAU), both with equal per capita revenue recycling (the recycling
scenario) and without it (the no recycling scenario) (Fig. 2a,b).
There is a similar pattern in all regions: without progressive revenue
recycling, climate action does indeed involve a substantial trade-off
where the poorest lose from climate policy in the short-to-medium
term as they shoulder their share of mitigation costs without com-
pensation. In contrast, with the equal per capita dividend, climate
action involves a synergy with poverty alleviation. Yet even in the
recycling scenario, consumption falls below BAU for several regions
later in the century. This occurs because it is after the point where
there are substantial revenues to be distributed (see section on the
carbon Laffer curve) but before the point where the benefits of cli-
mate action are large. Nevertheless, consumption in the recycling
scenario is always above the no recycling scenario in the early peri-
ods due to the benefits of redistribution. After the year 2100, both
cases produce increasing benefits from avoided climate damage.
Note that once carbon revenues disappear in the future, people will
also be much wealthier than their counterparts today.
Focusing on inequality—measured by the Gini index (Fig.
2b,d)—also demonstrates the benefits of progressive redistribu-
tion. Equal per capita recycling generates a reduction in inequal-
ity in all regions while revenues are available for redistribution.
Once full decarbonization occurs and revenues disappear, miti-
gation has a regressive impact compared with BAU due to the
relationship reported in Fig. 1 combined with the continued cost
of decarbonization even after there are zero net emissions. The
impacts on inequality without recycling, which are determined by
the elasticity estimated in Fig. 1, are small overall and switch from
progressive to regressive once a region’s GDP per capita surpasses
~US$21,500 (Fig. 1).
Examining the impact of the equal per capita refund on all quin-
tiles in the United States, China and India—chosen to represent
countries at different levels of wealth—reveals that in all three coun-
tries, more than half the population (namely, those in the lower part
of the distribution) benefits in the near term, particularly those in
the bottom quintile (Fig. 3). In India, the poorest 40% never experi-
ence a loss relative to BAU over the full time horizon. This redistri-
bution towards the lower quintiles has a positive effect on poverty
alleviation by reducing the percentage of the population below the
poverty line (Supplementary Tables 2–4).
Furthermore, the progressive equal per capita dividend increases
aggregate well-being in every region relative to the BAU over the
next decades and in the far future (Supplementary Fig. 3). The inter-
generational trade-off between costs of reducing emissions now
and benefits in the future is weakened over the entire time horizon:
aggregate well-being over time is higher with the equal per capita
dividend than without it in all regions and both are better overall
than BAU.
All results presented above assume that revenues raised in a
given region are distributed only within that region. However, there
are well-being- and justice-based arguments for redistributing total
global revenues on an equal per capita basis globally21,31,32. Under
this redistribution framework, more dramatic improvements occur
for inequality and consumption in the poorest regions of the world
(Supplementary Fig. 4).
The carbon Laffer curve
The stringent 2 °C constraint means that the world will rapidly
decarbonize and so there will be less and less revenue from car-
bon taxation to recycle. This highlights an important caveat to our
storyline: the positive effect of the carbon tax through progressive
redistribution is initially strong but diminishes once the economy
decarbonizes enough for revenues to decline. In short, there is a ‘car-
bon Laffer curve’. Conceptually, the Laffer curve is the widely recog-
nized fact that tax revenue does not monotonically increase with the
tax rate—in the case of sufficiently large taxes, market transactions
2.0
1.5
1.0
Elasticity
0.5
United States
European Union
Middle East
Eurasia
Latin America
India
China
Japan
Other high income
Other Asia
Africa
0
1,000
5,000
10,000
GDP per capita (2005 US$)
25,000
50,000
Fig. 1 | Estimates from the literature on the distribution of the initial
burden of a carbon or gasoline tax and the resulting relationship
with per capita GDP. This relationship (black line) is used to estimate
the consumption elasticity of the initial burden before any possible
redistribution as a function of regional per capita GDP in each NICE model
region at each point in time. Section 1 of the Supplementary Information
describes the methods of the literature review; Supplementary Table 1
cites all included studies, and Supplementary Figs. 1 and 2 detail multiple
sensitivity tests.
Nature Climate Change | VOL 11 | December 2021 | 1111–1116 | www.nature.com/natureclimatechange
1112

Analysis
Nature Climate Change
(for example, fossil fuel use) reduce to the point where there is little
taxable activity to generate revenue33. As a quantitative illustra-
tion of the carbon Laffer curve in NICE, Fig. 4 shows this nonlin-
ear relationship between global near-term (2025) decarbonization
and tax revenue. Total revenue is highest in the 55–75% decarbon-
ization range and decreases thereafter until full decarbonization
ultimately implies that no revenue is generated (under full decar-
bonization there are no industrial emission to be taxed).
9
a
b
c
d
6
3
Change in consumption (% of BAU)
Change in Gini index (relative to BAU)
Change in consumption (% of BAU)
Change in Gini index (relative to BAU)
0
–3
0
United States (40)
European Union (27)
Japan (30)
Russia (28)
Eurasia (40)
China (31)
India (35)
Middle East (44)
Africa (52)
Latin America (43)
Other high income (31)
Other Asia (43)
–0.25
–0.50
–0.75
–1.00
–1.25
2020
2040
2060
Gini index: no recycling
Gini index: recycling
Bottom quintile consumption: no recycling
Bottom quintile consumption: recycling
2080
2100
2020
2040
2060
2080
2100
0
–0.25
–0.50
–0.75
–1.00
–1.25
2020
2040
2060
2080
2100
9
6
3
0
–3
2020
2040
2060
Year
Year
Year
Year
2080
2100
Fig. 2 | Trade-offs between climate action, poverty alleviation and inequality turn into synergies with an equal per capita carbon dividend. a,b, For a
2 °C mitigation pathway, the change in per capita consumption of the bottom quintile in each region is shown, without (a) and with (b) equal per capita
recycling, compared with the BAU case with no climate policy. c,d, The change in the Gini index, without (c) and with (d) equal per capita recycling, where
a higher value indicates more inequality. The numbers in the legend are the initial Gini values. Diamond symbols identify the year of maximum carbon tax
revenue as a percentage of regional consumption. (Results assume that each region’s aggregate climate damages are distributed to quintiles in proportion
to consumption, an assumption that makes the welfare impact of damages essentially equivalent to what they would be in more aggregated cost–benefit
models including DICE, RICE, PAGE and FUND51,52. Further below and in Supplementary Fig. 14 we discuss results that modify this important assumption,
showing that our main findings hold with other damage specifications and distributions).
6
Q1 (poorest)
Q2
Q3
Q4
Q5 (richest)
2020
2040
2060
Year
Year
Year
United States
a
b
c
China
India
2080
2100
Year of
maximum revenue
4
2
Change in consumption (% of BAU)
Change in consumption (% of BAU)
Change in consumption (% of BAU)
0
–2
6
2020
2040
2060
2080
2100
4
2
0
–2
6
2020
2040
2060
2080
2100
4
2
0
–2
Fig. 3 | Change in consumption of all quintiles in the 2 °C mitigation pathway with the equal per capita recycling compared with the BAU case with no
climate policy. a–c, Change in consumption as a percentage of BAU over time for the United States (a), China (b) and India (c). The vertical dotted line in
each panel identifies the year of maximum carbon tax revenue as a percentage of regional consumption. Q, income quintile of population.
Nature Climate Change | VOL 11 | December 2021 | 1111–1116 | www.nature.com/natureclimatechange
1113

Analysis
Nature Climate Change
This relationship implies that an optimal climate policy with an
equal per capita carbon dividend must balance the value to society
of (1) lower CO2 emissions—and thus reduced climate change—that
will result from high carbon taxes and (2) some level of continuing
emissions, which enables the progressive redistribution that tax rev-
enues can fund. Note that unlike income tax where going beyond
the peak of the Laffer curve is inefficient, in the case of climate we
ought to go beyond that point to curb climate change.
Strong action now and steady action later
To investigate the trade-off between the benefits of lowering emis-
sions and the benefits of continued carbon tax revenue, we perform
an optimal policy calculation; optimal policy refers to the policy
that maximizes (discounted) net benefits through time and does
not feature a temperature constraint as in the results above. With
revenue recycling, the model recommends high decarbonization
initially—there are dual benefits of redistributable revenue and
lower future temperatures—but postpones full decarbonization
for many decades as redistribution continues (Fig. 5). Without the
equal per capita revenue recycling, the model at first recommends
more moderate ambition, to protect the current poor from high
mitigation costs, followed by a rapid increase in decarbonization
to avoid extreme warming. Despite this different temporal pattern
of mitigation, the maximum temperature rise is similar in both
scenarios, although it peaks later with revenue recycling, a poten-
tially valuable delay if it reduces the rate of temperature change and
enables more time for adaptation34. The carbon tax and carbon divi-
dend trajectories corresponding to the decarbonization paths are
reported in Supplementary Figs. 7 and 8. (Unless otherwise stated,
results assume standard discounting parameters from the Regional
Integrated Climate Economy (RICE) model: pure time prefer-
ence = 1.5% per year; consumption elasticity of marginal utility = 1.5
(representing the diminishing marginal utility of consumption) and
distribution of climate damages proportional to consumption.)
The optimal decarbonization pathway is not exclusively driven
by the motive to redistribute. To demonstrate this, the ‘no dam-
ages’ scenario depicts the optimal carbon tax with revenue recycling
but where climate damages are artificially set to zero regardless of
warming (Fig. 5, black line). In this case, the only benefit of a car-
bon tax is the redistribution it allows. Global decarbonization that
is optimal purely from this motive is substantial and ranges between
~50 and 60%, as this ensures maximum redistribution to the poor.
Still, this is much lower than the case where climate benefits exist
alongside redistributional benefits, demonstrating that substantial
incentive to decarbonize further remains even at such high levels of
decarbonization.
An equal per capita global redistribution leads to similar decar-
bonization trajectories to those reported in Fig. 5 (which assume
within-region redistribution only), a result driven largely by the
carbon Laffer curve (Supplementary Fig. 5). However, it would
lead to far greater improvements in global well-being, particularly
for Africa, India and Other Asia (Supplementary Fig. 6 and associ-
ated text).
Discussion and sensitivity analyses
We have shown that an equal per capita refund of carbon tax rev-
enues improves the well-being of individuals toward the bottom of
the income distribution and reduces poverty and inequality. The
implication is that adopting strong climate policy need not entail
a trade-off where the people of today (and the poor in particular)
must sacrifice for the benefit of future generations.
This finding contributes to the debate over whether there should
be a gradual ramp up to aggressive policy (for example, as advocated
by Nordhaus27) or a large-scale push toward immediate maximum
feasible reductions (for example, as advocated by Stern28). Even with
the relatively high discounting parameters preferred by Nordhaus,
progressive revenue recycling leads to high levels of decarbonization
immediately—comparable in the initial decades to strict climate
target pathways (for example, 1.5 or 2 ˚C)—followed by less decar-
bonization in later periods (Fig. 5). With lower carbon emitted in
the atmosphere early on, and anticipating the carbon Laffer curve,
the initial period of high decarbonization is followed by a gradual
long-term increase toward full decarbonization to keep peak warm-
ing at a moderate level and preserve revenue for redistribution.
The temporal difference in optimal decarbonization pathways
between scenarios with and without revenue recycling (the crossing
pattern seen in Fig. 5a) appears robust to several key uncertainties.
While our main results assume background inequality remains con-
stant in all regions, Supplementary Fig. 9 shows that the crossing
pattern persists in several scenarios involving narrowing or widen-
ing background inequality. The crossing is repeated in all scenarios
but is less extreme with more reductions in background inequal-
ity. When background inequality is lower, initial decarbonization is
again much higher with the progressive recycling but, unlike in the
other scenarios, it then remains relatively high through time. This
occurs because a greater decrease in background inequality reduces
the incentive to delay decarbonization to preserve tax revenues for
redistribution, thus bringing forward the optimal date of full decar-
bonization to avoid more climate harms.
Our qualitative results are also robust to choices about key dis-
counting parameters, namely the rate of pure time preference and
the consumption elasticity of marginal utility. As explained in the
Methods, normative and descriptive disagreements exist about
the appropriate value of these parameters27. Under a range of dis-
counting parameter combinations typically considered in the lit-
erature, revenue recycling always induces stronger short-term
emission reductions and a slower transition to full decarbonization
(Supplementary Fig. 10).
Our findings raise important questions about feasibility. One is
whether it is technically feasible to decarbonize as quickly in the
early periods as the model recommends. This question is beyond
the scope of this paper; however, we note that the initial decades
of the optimal trajectory reported here are comparable to many
IPCC 1.5–2 ˚C pathways35. Another question relates to negative
emissions, both whether they are needed and how they would be
funded if all carbon dividends are redistributed. Consistent with
some IPCC scenarios, our trajectories do not require negative emis-
sions (Supplementary Fig. 11). Nevertheless, even if a substantial
fraction of revenues was diverted to subsidize negative emission
1.5
1.0
0.5
Total revenue (trillions 2005 US$)
0
0
25
50
Global decarbonization (%)
75
100
Fig. 4 | The carbon Laffer curve. The curve is illustrated by plotting
near-term decarbonization versus global revenue generated (here for
2025). Global decarbonization is the percentage reduction in carbon
emissions compared with a BAU scenario with no climate policy.
Nature Climate Change | VOL 11 | December 2021 | 1111–1116 | www.nature.com/natureclimatechange
1114

Analysis
Nature Climate Change
technologies, the benefits of redistributing the remaining dividends
remains strong (Supplementary Fig. 12). However, we acknowledge
that negative emission technologies would have unprecedented and
currently poorly understood implications.
A second dimension of feasibility concerns public opinion and
political will. An emerging literature indicates that communicat-
ing the co-benefits of climate action may increase policy support,
in particular for co-benefits that lead to economic development
and more compassionate communities36,37. Similarly, bundling
climate policy with social and economic programmes, a feature
of widely discussed strategies across the political spectrum from
the Climate Leadership Council to the Green New Deal, may also
increase support for action38. Overall, the literature suggests that
progressive redistribution may have relatively broad appeal, at least
given effective communication of the benefits, although this may
be tempered by evidence from Pigouvian taxation studies which
indicates that people may be resistant to policies that start with
high tax rates19,21,39–42.
A third feasibility concern is whether governments would actu-
ally have the capacity to perform progressive transfers, even if there
was political will to do so. In Supplementary Fig. 13, we report
optimal policy results under imperfect recycling programmes,
including that the bottom quintile does not receive any transfers or
if a large proportion of the revenue was lost in policy implementa-
tion cost; in both cases the pattern of high initial decarbonization
followed by the gradual progression to full decarbonization remains
intact, although is somewhat muted. Supplementary Fig. 13 also
reports results for a scenario that is more progressive than an equal
per capita redistribution.
One potential limitation of our study is that NICE does not
include the full suite of policy levers available to alleviate distribu-
tional concerns. Within countries, for example, one might consider
changes to income taxation. We model synergies between carbon
taxation and inequality reduction under the assumption that, apart
from the distribution of mitigation costs and the distribution of
the tax revenue, inequality is not otherwise affected by economic
incentive effects of the policy (Supplementary Information Section
9 gives details about the relation with optimal taxation theory).
Complementary work could use subregional agent-based or micro-
simulation models to estimate how such incentive effects and other
interaction effects may influence inequality levels and optimal pol-
icy with revenue recycling.
In addition, some NICE regions consist of multiple countries.
Therefore, our main results implicitly assume some level of inter-
national transfers between countries within these multicountry
regions. This could be important for multicountry regions with
heterogeneous levels of development and differing capacities.
Nevertheless, NICE represents several key countries as individual
regions (United States, China, India, Russia and Japan) and avoids
transfers across regions in the main results.
Further studies could investigate the role of the distribution of
carbon tax revenues when regions apply different carbon taxes. In
the absence of international transfers such as those modelled in
Supplementary Fig. 6, the assumption of a global carbon price is
certainly a constraint to the alleviation of distributional concerns,
since it requires a high policy burden from poor countries. In mod-
els allowing for differential carbon prices by region, all high emitters
are required to mitigate at least as much as under the global carbon
price assumption43–45. Our results here with a global carbon price
could thus be seen as a price floor for high emitters, as recently pro-
posed by the International Monetary Fund45.
We also do not consider the question of horizontal inequality—
that is the heterogeneous effects of a carbon tax on households with
the same income level but different consumption patterns—which
recent evidence suggests may be important46–48. Including horizon-
tal inequality would be a worthwhile extension of our work.
Recent research also indicates that the damage functions used in
cost–benefit models, such as NICE, may underestimate future cli-
mate impacts49. We test this possibility in two ways. First, we keep
the total damages the same but assume that they disproportionately
harm the poor (thus having a greater well-being impact). Second,
we double the total size of the damages. Both cases display the
characteristic crossing pattern of Fig. 5, although full decarboniza-
tion occurs earlier (Supplementary Fig. 14). The crossing pattern
is also evident when we replace the NICE climate module with the
FAIR climate model (Supplementary Fig. 15), as recommended in a
recent report by the National Academies50.
Conclusions
Estimates of optimal climate policy have ignored the possibility that
revenues from a carbon tax could be used in a progressive way that
generates immediate net benefits for the current poor. As a con-
sequence, they mistakenly imply that climate action must come at
some cost to overall well-being and especially to the poor. We have
shown that this storyline of the climate, development and inequal-
ity nexus reverses when progressive revenue recycling is taken into
account. Our approach corrects a long-standing bias against strong
100
a
b
Global decarbonization
Global temperature
80
60
40
Global decarbonization (%)
Increase in temperature (°C)
20
Recycling
No recycling
No damages
0
4
3
2
1
2050
2100
Year
Year
2150
2200
2050
2100
2150
2200
Fig. 5 | Optimal mitigation with and without equal per capita carbon
dividend. a,b, Optimal decarbonization (a) and temperature (b) with and
without revenue recycling, and a comparison case that assumes no climate
damages; the latter shows how much mitigation is driven by progressive
redistribution alone, as opposed to being driven by avoided climate
damages. Global decarbonization in a is the percentage reduction in carbon
emissions compared with a BAU scenario with no climate policy.
Nature Climate Change | VOL 11 | December 2021 | 1111–1116 | www.nature.com/natureclimatechange
1115

Analysis
Nature Climate Change
immediate climate action. We find that with progressive revenue
recycling, aggressive climate action can pay large dividends for
improving well-being, reducing inequality and alleviating poverty.
In an optimal policy calculation, the recommended policy is charac-
terized by aggressive near-term climate action followed by a slower
climb towards full decarbonization; this pattern prevents runaway
warming while also preserving tax revenues for redistribution. The
benefits from progressive use of carbon revenues are most pro-
nounced in the early decades, when the revenues are largest and the
needs of the poor are most urgent.
Online content
Any methods, additional references, Nature Research report-
ing summaries, source data, extended data, supplementary infor-
mation, acknowledgements, peer review information; details of
author contributions and competing interests; and statements of
data and code availability are available at https://doi.org/10.1038/
s41558-021-01217-0.
Received: 23 August 2020; Accepted: 9 October 2021;
Published online: 29 November 2021
References
	1.	 Anthoff, R. & Tol, R. The Climate Framework for Uncertainty, Negotiation and
Distribution (FUND) Technical Description, Version 3.9 (Univ. of Notre
Dame, 2014); https://www3.nd.edu/~nmark/Climate/Fund-3-
9-Scientific-Documentation.pdf
	2.	 Stern, N. Stern Review: The Economics of Climate Change (Her Majesty’s
Treasury, 2006).
	3.	 Nordhaus, W. D. & Boyer, J. Warming the World: Economic Models of Global
Warming (MIT Press, 2000).
	4.	 Nordhaus, W. D. Revisiting the social cost of carbon. Proc. Natl Acad. Sci.
USA 114, 1518–1523 (2017).
	5.	 Tol, R. The damage costs of climate change toward more comprehensive
calculations. Environ. Resour. Econ. 5, 353–374 (1995).
	6.	 Anthoff, D. & Tol, R. S. in Climate Change and Common Sense: Essays in
Honour of Tom Schelling (eds Hahn, R. W. & Ulph, A.) 260–274 (Oxford
Univ. Press, 2012).
	7.	 Hahn, R. W. & Ulph, A. in Climate Change and Common Sense: Essays in
Honour of Tom Schelling (eds Hahn, R. W. & Ulph, A.) Ch. 1 (Oxford Univ.
Press, 2012).
	8.	 Cullenward, D., Wilkerson, J. T., Wara, M. & Weyant, J. P. Dynamically
estimating the distributional impacts of US climate policy with NEMS:
a case study of the Climate Protection Act of 2013. Energy Econ. 55,
303–318 (2016).
	9.	 Fawcett, A., McFarland, J., Morris, A. C. & Weyant, J. (eds) Special issue on
EMF 32 study on US carbon tax scenarios Climate Change Econ. 9, https://
www.worldscientific.com/toc/cce/09/01 (2018).
	10.	Goulder, L. H. & Hafstead, M. A. Tax reform and environmental policy:
options for recycling revenue from a tax on carbon dioxide. Resources for the
Future https://doi.org/10.2139/ssrn.2338210 (2013).
	11.	Goulder, L. H., Hafstead, M. A., Kim, G. & Long, X. Impacts of a Carbon Tax
across US Household Income Groups: What Are the Equity-Efficiency
Trade-Offs? (National Bureau of Economic Research, 2018).
	12.	Horowitz, J., Cronin, J.-A., Hawkins, H., Konda, L. & Yuskavage, A. Methodology
for Analyzing a Carbon Tax (US Department of the Treasure, 2017).
	13.	Jorgenson, D. & Goettle, R. Double Dividend: Environmental Taxes and Fiscal
Reform in the United States (MIT Press, 2013).
	14.	Mathur, A. & Morris, A. Distributional effects of a carbon tax in broader US
fiscal reform. Energy Policy 66, 326–334 (2014).
	15.	Metcalf, G. E. Paying for Pollution: Why a Carbon Tax is Good for America
(Oxford Univ. Press, 2018).
	16.	Murray, B. C., Bistline, J., Creason, J., Wright, E. & Kanudia, A. The EMF 32
Study on Technology and Climate Policy Strategies for Greenhouse Gas
Reductions in the US Electric Power Sector: An Overview (Elsevier, 2018).
	17.	Rausch, S., Metcalf, G. E. & Reilly, J. M. Distributional impacts of carbon
pricing: a general equilibrium approach with micro-data for households.
Energy Econ. 33, S20–S33 (2011).
	18.	Sterner, T. Fuel Taxes and the Poor: The Distributional Effects of Gasoline
Taxation and their Implications for Climate Policy (RFF Press, 2012).
	19.	Klenert, D. et al. Making carbon pricing work for citizens. Nat. Clim. Change
8, 669–677 (2018).
	20.	Klenert, D., Schwerhoff, G., Edenhofer, O. & Mattauch, L. Carbon taxation,
inequality and Engel’s law: the double dividend of redistribution. Environ.
Resour. Econ. 71, 605–624 (2018).
	21.	Carattini, S., Kallbekken, S. & Orlov, A. How to win public support for a
global carbon tax. Nature 565, 289–291 (2019).
	22.	McCollum, D., Gomez Echeverri, L., Riahi, K. & Parkinson, S. SDG7: Ensure
Access to Affordable, Reliable, Sustainable and Modern Energy for All (IIASA,
2017); http://pure.iiasa.ac.at/id/eprint/14621/1/SDGs-interactions-
7-clean-energy.pdf
	23.	Nerini, F. F. et al. Mapping synergies and trade-offs between energy and the
Sustainable Development Goals. Nat. Energy 3, 10–15 (2018).
	24.	Sustainable Development Goals (United Nations Department of Economic
and Social Affairs, accessed 6 May 2020); https://sustainabledevelopment.un.
org/sdgs
	25.	Davies, J. B., Shi, X. & Whalley, J. The possibilities for global inequality and
poverty reduction using revenues from global carbon pricing. J. Econ.
Inequal. 12, 363–391 (2014).
	26.	Franks, M., Lessmann, K., Jakob, M., Steckel, J. C. & Edenhofer, O.
Mobilizing domestic resources for the Agenda 2030 via carbon pricing. Nat.
Sustain. 1, 350–357 (2018).
	27.	Nordhaus, W. D. A review of the ‘Stern review on the economics of climate
change’. J. Econ. Lit. 45, 686–702 (2007).
	28.	Stern, N. The economics of climate change. Am. Econ. Rev. 98, 1–37 (2008).
	29.	Dorband, I. I., Jakob, M., Kalkuhl, M. & Steckel, J. C. Poverty and
distributional effects of carbon pricing in low- and middle-income
countries—a global comparative analysis. World Dev. 115, 246–257 (2019).
	30.	Sterner, T. Distributional effects of taxing transport fuel. Energy Policy 41,
75–83 (2012).
	31.	Marron, D. B. & Maag, E. How to Design Carbon Dividends (SSRN, 2018).
	32.	Gardiner, S., Caney, S., Jamieson, D. & Shue, H. Climate Ethics: Essential
Readings (Oxford Univ. Press, 2010).
	33.	Laffer, A. B. The Laffer curve: past, present, and future. Backgrounder 1765,
1–16 (2004).
	34.	Burke, M., Davis, W. M. & Diffenbaugh, N. S. Large potential reduction in
economic damages under UN mitigation targets. Nature 557, 549–553 (2018).
	35.	IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V.
et al.) (WMO, 2018).
	36.	Bain, P. G., Hornsey, M. J., Bongiorno, R. & Jeffries, C. Promoting
pro-environmental action in climate change deniers. Nat. Clim. Change 2,
600–603 (2012).
	37.	Bain, P. G. et al. Co-benefits of addressing climate change can motivate action
around the world. Nat. Clim. Change 6, 154–157 (2016).
	38.	Bergquist, P., Mildenberger, M. & Stokes, L. C. Combining climate, economic,
and social policy builds public support for climate action in the US. Environ.
Res. Lett. 15, 054019 (2020).
	39.	Carattini, S., Carvalho, M. & Fankhauser, S. Overcoming public resistance to
carbon taxes. WIREs Clim. Change 9, e531 (2018).
	40.	Marshall, G. et al. Guide to Communicating Carbon Pricing (World
Bank, 2018).
	41.	Pahle, M. et al. Sequencing to ratchet up climate policy stringency. Nat. Clim.
Change 8, 861–867 (2018).
	42.	Carattini, S., Baranzini, A., Thalmann, P., Varone, F. & Vöhringer, F. Green
taxes in a post-Paris world: are millions of nays inevitable? Environ. Resour.
Econ. 68, 97–128 (2017).
	43.	Budolfson, M. & Dennig, F. in Handbook on the Economics of Climate Change
(eds Chichilnisky, G. et al.) 224–238 (Edward Elgar Press, 2020).
	44.	Budolfson, M. et al. Utilitarian benchmarks for emissions and pledges
promote equity, climate, and development. Nat. Clim. Change 11,
827–833 (2021).
	45.	Parry, I., Black, S. & Roaf, J. Proposal for an International Carbon Price Floor
Among Large Emitters (International Monetary Fund, 2021).
	46.	Cronin, J. A., Fullerton, D. & Sexton, S. Vertical and horizontal
redistributions from a carbon tax and rebate. J. Assoc. Environ. Resour. Econ.
6, S169–S208 (2019).
	47.	Douenne, T. The vertical and horizontal distributive effects of energy taxes: a
case study of a French policy. Energy J. 41, 231–253 (2020).
	48.	Fischer, C. & Pizer, W. A. Horizontal equity effects in energy regulation. J.
Assoc. Environ. Resour. Econ. 6, S209–S237 (2019).
	49.	Carleton, T. et al. Valuing the Global Mortality Consequences of Climate
Change Accounting for Adaptation Costs and Benefits (Univ. of Chicago, 2018).
	50.	Valuing Climate Damages: Updating Estimation of the Social Cost of Carbon
Dioxide (National Academies Press, 2017).
	51.	Budolfson, M., Dennig, F., Fleurbaey, M., Siebert, A. & Socolow, R. H. The
comparative importance for optimal climate policy of discounting,
inequalities and catastrophes. Clim. Change 145, 481–494 (2017).
	52.	Dennig, F., Budolfson, M. B., Fleurbaey, M., Siebert, A. & Socolow, R. H.
Inequality, climate impacts on the future poor, and carbon prices. Proc. Natl
Acad. Sci. USA 112, 15827–15832 (2015).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature Limited 2021
Nature Climate Change | VOL 11 | December 2021 | 1111–1116 | www.nature.com/natureclimatechange
1116

Analysis
Nature Climate Change
Methods
All model code and data used to generate results for this article are archived53
and a running version is available at https://github.com/Environment-Research/
revenue_recycling.
The NICE model52 used here is a modification of the RICE model3,54, which
was developed by W. Nordhaus. RICE is the regional counterpart to the global
dynamic integrated climate–economy (DICE) model, which is one of three
leading cost–benefit models used by researchers and governments for regulatory
analysis, including to estimate the social cost of carbon55. RICE3,54 and NICE51,52
have been described in great detail elsewhere. Since their basic architecture is
the same, we first describe this RICE architecture and then explain the model
developments that make RICE into NICE, noting from the outset that all models of
this class are reduced-form representations of reality with associated strengths and
limitations56,57.
RICE is a regionally disaggregated optimization model that includes an
economic component and a geophysical (climate) component that are linked.
RICE divides the world into 12 regions, some of which are single countries while
others are groups of countries. Each region has a distinct endowment of economic
inputs including capital, labour and technology, which together produce that
region’s gross output via a Cobb–Douglas production function. Carbon emissions
are a function of gross output and an exogenously determined, region-specific,
carbon intensity pathway. These carbon emissions can be abated (mitigating
climate change) at a cost to gross output via regional control policies that are
selected so that in every period the marginal cost of abatement—or carbon
price—is the same for all regions. The climate module determines how unabated
carbon emissions affect global temperature and, ultimately, the future economy
through climate-related damages. Region-specific damage functions capture this
relationship between increased temperature and economic damage, with poorer
regions generally more vulnerable as a proportion of income.
The original RICE model is solved by choosing decarbonization and savings
rates in all regions and periods to maximize an objective function which sums, over
periods and regions, a concave utility function of regional per capita consumption
with a discount factor applied to future values. To simplify the optimization, the
solution concept implemented in this study takes the savings rates as given—rather
than solving for their optimal values—and maximizes only over the control rates
(decarbonization). In the default implementation of RICE, Negishi weights are
added to the objective function to ensure that the marginal cost of reducing
emissions by a ton (the carbon price) is the same for all regions, period by period.
NICE achieves equality of carbon prices without using Negishi weights52.
The NICE model extends RICE by disaggregating regional consumption
into five socioeconomic groups with consumption levels reflecting the current
distribution of consumption within the regions58. So as not to affect any of the
aggregate economic variables (investment, capital, output and so on), this is
done by splitting average regional consumption into five units (or quintiles)
after aggregate savings have been determined. The background consumption
distribution and the distributions of damage and mitigation cost are determined in
the way described below.
We denote regions by index i, quintiles by j and periods by t. Quantities
without a j index are regional aggregates and are identical to the quantities in the
more aggregated RICE model. Net output Yit is given by
Yit = 1−λit
1+Dit Qit
(1)
where Qit denotes gross output, λit mitigation cost (opportunity costs of reducing
CO2 emissions as a share of GDP) and Dit climate damages. The basic trade-off
of the RICE model—mitigation costs in the present for the reduction of climate
damages in the future—is embodied in this equation. As mentioned above, in each
period the regional mitigation costs are chosen so that they are consistent with a
globally uniform carbon price, which is implemented as a local tax, taxt, in each
region.
Defining the aggregate savings rate, sit, and population, Lit, the average regional
consumption is
¯cit = 1−sit
Lit Yit
(2)
while the average gross consumption (predamage and premitigation cost) is
¯cpre
it
= 1−sit
Lit Qit = 1+Dit
1−λit ¯cit
(3)
We assume that gross consumption is distributed across population quintiles
according to a baseline distribution, yielding gross consumptions for each quintile.
Under the no recycling scenario, final consumption of each quintile is computed
by subtracting climate damages and mitigation costs from gross consumption
according to distributions that reflect different exposures and vulnerabilities of
consumption groups to these impacts. Under the recycling scenario, carbon taxes
are raised according to the same distribution as mitigation costs and redistributed
as equal per capita payments within regions.
The baseline distribution is given by quintile weights, qijt, that denote the ratio
between quintile consumption and average consumption. If for quintile j in region i
and period t, qijt > 1, its consumption is greater than average regional consumption
in that period; if qijt < 1, its consumption is less than the average. Since the five
quintiles comprise equal proportions of the population, ∑
j qijt = 5 in all regions
and periods. In the base implementation these quintile weights are fixed across
time and estimated to the current distribution of consumption in the region by
aggregating country level distributional data from the World Income Inequality
Database58 to regional distributions. The aggregation is described in detail in
Section 6 of the Supplementary Information.
The initial burden of the carbon tax is the sum of the mitigation costs and
tax payments. Within a region, the initial burden is distributed across quintiles
according to the weights, τijt. The substantive assumption of our analysis is that the
two components of the initial burden—the mitigation cost and the tax payment—
are distributed according to the same weights, τijt, which are calculated on the basis
of Fig. 1, as described in more detail below.
We denote by dijt the weights of the distribution of damage to consumption in
region i and period t, which we also describe in more detail below.
With this notation the consumption of quintile j in region i and period t is
given by
cijt =
¯cpre
it qijt

Gross consumption
−¯citDitdijt

Damage cost
−
Initial burden






¯cpre
it λitτijt

Mitigation cost
+ Eit
Lit
taxtτijt

Tax payments




+ Eit
Lit
taxtδijt

Refund
(4)
The value of the parameter δijt in the expression for the refund distinguishes
our two policy scenarios: no recycling and recycling. In the no recycling scenario,
carbon tax revenues are refunded within each region according to the distribution
of the initial burden, so that δijt = τijt. From equation (4) we can see that this implies
that tax payments and the refund cancel each other out. Hence the carbon tax
components disappear, leaving the mitigation cost as the only impact of the climate
policy, as is standard in cost–benefit Integrated Assessment Models (IAMs). That
is the reason we call this the no recycling scenario. Under this implementation, all
quintiles bear some cost from climate policy.
In the recycling scenario, carbon tax revenues are refunded equally per capita
within each region, so that δijt = 1. As a hypothetical example to illustrate the
distributional impact of this scenario, if τijt = 1 for all quintiles, the tax would be
raised equally per capita and cancelled out with the equal per capita dividend,
resulting in the same situation as in the no recycling scenario. But in all of our
model runs τijt >1 for the top quintile and <1 for the bottom quintile, so that the
recycling scenario always yields a more equal distribution than the no recycling
scenario when the same (positive) tax is applied.
The essential ingredients for the process of downscaling regional consumption
to subregional consumption quintiles are the distributional weights qijt, dijt and τijt .
As described in the Supplementary Information, the qijt for the first model period
are estimated from current regional consumption distributions. Under our baseline
assumption these remain constant over time and in Supplementary Fig. 9 we
consider alternative projections.
For the distributional weights of damage and of the initial burden (dijt and τijt)
we assume a constant elasticity relationship to the consumption distribution:
dijt = 5
qξ
ijt
∑
k qξ
ikt
and
τijt = 5
qωit
ijt
∑
k qωit
ikt
In the main results of the paper we take the damage elasticity of consumption,
ξ, to be equal to 1 in all periods and in all regions. In Supplementary Fig. 14 we
consider alternative values of this parameter. Previous applications of the NICE
model study the importance of this parameter to optimal carbon prices51,52.
Because the distributional weights, τijt, of the initial burden are central to our
policy analysis and because there is substantial evidence that the consumption
elasticity of the initial burden, ωit, decreases with a region’s per capita GDP,
we estimate a relationship between this elasticity and GDP per capita with a
simple ordinary least squares regression of the estimates from the literature on
the distributional impact of carbon and fuel taxes, summarized in Fig. 1. For
each study, k, we estimated the elasticity, ωk, as the slope of the regression of
log initial burden reported in the study with respect to log consumption level
of the population quintile. In Fig. 1 (and Supplementary Fig. 1) these estimated
elasticities are plotted against the (log) GDP per capita of the country-year on
which the study is based, yk.
The result is an estimated relationship between the consumption elasticity of
the initial burden, ωk, and the log of GDP per capita, log yk : ωk = ˆα + ˆβ log yk.
The analysis is described in more detail in Section 1 of the Supplementary
Information.
Nature Climate Change | www.nature.com/natureclimatechange

Analysis
Nature Climate Change
To project elasticities, ωit, for each region and period in the model, we compute
the predicted elasticities ˆωit = ˆα + ˆβyit according to the regression above for the
model GDP per capita, yit, of region j in period t.
Data availability
All data used in our version of the model are archived53 and freely available at
https://github.com/Environment-Research/revenue_recycling.
Code availability
All model code used to generate results and create figures for this article is
archived53 and freely available at https://github.com/Environment-Research/
revenue_recycling.
References
	53.	Budolfson, M. et al. Climate action with revenue recycling has benefits for
poverty, inequality, and wellbeing. Zenodo https://doi.org/10.5281/
zenodo.5552749 (2021).
	54.	Nordhaus, W. D. Economic aspects of global warming in a post-Copenhagen
environment. Proc. Natl Acad. Sci. USA 107, 11721–11726 (2010).
	55.	Technical Update on the Social Cost of Carbon for Regulatory Impact
Analysis—Under Executive Order 12866 (Interagency Working Group on
Social Cost of Carbon, United States Government, 2013).
	56.	Pindyck, R. S. Climate change policy: what do the models tell us? J. Econ. Lit.
51, 860–872 (2013).
	57.	Weyant, J. Some contributions of integrated assessment models of global
climate change. Rev. Environ. Econ. Policy 11, 115–137 (2017).
	58.	World Income Inequality Database (UNU-WIDER, accessed 6 May 2020);
https://www.wider.unu.edu/project/wiid-world-income-inequality-database
Acknowledgements
This article has received funding from the NAVIGATE project of the European Union’s
Horizon 2020 research and innovation programme under grant no. 821124 (S.F., M.
Fleurbaey, U.K., A.M., F.W. and S.Z.) and the NIEHS-funded HERCULES Center
P30ES019776 (N.S.). We thank C. Burnham and the Climate Futures Initiative at
Princeton University for support.
Author contributions
M.B., F.D., F.E., U.K., K.K. and N.S. are co-lead authors and contributed equally to
the study. M.B., F.D., D.K., F.E., M. Ferranna, U.K., K.K., A.M., N.S. and S.Z. designed
the research. M.B., F.D., S.F., M. Ferranna, D.K., U.K., K.K., A.M. and S.Z. conducted
the literature review on the distributional impact of a carbon tax. F.D., F.E. and K.K.
conducted the modelling. F.D., K.K., S.F., M. Ferranna, M. Fleurbaey, D.K., U.K., A.M.
and S.Z. led the social welfare and tax analysis. M.B. and N.S. wrote the first draft of the
manuscript with contributions from U.K. and K.K. All authors interpreted the results and
edited the manuscript.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41558-021-01217-0.
Correspondence and requests for materials should be addressed to Mark Budolfson.
Peer review information Nature Climate Change thanks Allen Fawcett and the other,
anonymous, reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at www.nature.com/reprints.
Nature Climate Change | www.nature.com/natureclimatechange

    Scientific Research Study Methods:

Answer: Our results build on an extensive economics literature that focuses on single nations or regions. To bring this literature together into a global analysis, we begin with a global cost–benefit climate policy model known as NICE (the nested inequalities climate–economy model), which divides the world into 12 regions and further divides each region into 5 income groups. We then add a new component to the model — calibrated to the literature — that quantifies how both the costs of a carbon tax and the benefits from an equal per capita refund of the revenues impact different income groups in different nations. We do this for a 2 °C scenario, as well as a scenario without a temperature constraint in which the model maximizes wellbeing through time via a uniform global carbon price. We evaluate the benefits of the revenue redistribution in terms of improvements in wellbeing, changes to inequality and reductions in poverty.