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

    Your task is to generate a structured **policy brief** that includes the following components:

    - A concise, descriptive **title** that captures the central theme or policy issue addressed in the paper (do not label it with “Title:”).
    - **Policy Problem** - What societal or strategic challenge is the paper addressing?
    - **Scientific Research Findings** - What are the key results relevant to this issue?
    - **Scientific Research Study Methods** - What methodology or data supports these findings?
    - **Policy Implications** - How might the findings guide or influence policy?

    Write each section in a clear, accessible style suitable for policymakers. Avoid overly technical language, but use essential terms when necessary. Structure the output as a labeled five-part policy brief document.

    Scientific Research Paper:
    Nature Climate Change | Volume 14 | January 2024 | 48–54
48
nature climate change
Article
https://doi.org/10.1038/s41558-023-01870-7
Inequality repercussions of financing
negative emissions
Pietro Andreoni
  1,2
, Johannes Emmerling
  1 & Massimo Tavoni1,2
Negative emissions technologies are attracting the interest of investors
in the race to make them effective and profitable. When deployed at scale,
they will be financed through public funds, reducing the fiscal space for
a socially inclusive climate transition. Moreover, if the private sector
owns negative emissions technologies, potentially large profits would
disproportionally benefit investors and equity holders. Here we quantify
the inequality repercussions of direct air capture of CO2 in a 1.5 °C scenario,
using a regional integrated assessment model that features within-country
income heterogeneity. We find that, under a single carbon market, financing
negative emissions technologies could double the increase in income
inequality of climate policy. The effects are highest around the time of net
zero and in scenarios with carbon budget overshoot. We identify the drivers
of the inequality increase and discuss policy provisions to mitigate the
equity concerns of CO2 removal strategies.
In 2022 alone, foundations linked to private donors such as Google
and Facebook pledged almost US$1 billion to advance the research and
development of negative emissions technologies (NETs)1. In a world
of insufficient climate mitigation efforts, these technologies can help
the decline of CO2 emissions, offset hard-to-abate emissions and aid
recovery from temperature overshoot2.
Although they are criticized for being currently immature or
speculative3,4, as well as for the possibility of crowding out emission
reductions5–7, there is consensus that NETs will be needed at scale to
achieve the Paris Agreement targets. Therefore, research and devel-
opment on these technologies is necessary to reduce the uncertainty
about their cost, potential and scalability, and to build an effective and
efficient portfolio of options to reach net-zero or net-negative emis-
sions. However, leaving this effort exclusively to the private sector
could pose additional challenges.
Once they mature, the cost-minimizing solution to incentivize NETs
is deemed to be their integration into a single carbon market alongside
traditional mitigation strategies such as renewables8. In this paper, we
point out that this ‘first-best’ setting could entail major drawbacks: if the
carbon price is a tax from the perspective of carbon emitters, it would
be seen as a subsidy for carbon removal companies. The carbon prices
necessary to reach 1.5 °C or well below 2 °C are likely to exceed several
hundred dollars per ton of CO2 (ref. 9). Meanwhile, carbon removal
costs might become as low as one to two hundred dollars per ton of
CO2 avoided3. Therefore, unless NETs are assumed unbounded and
thus behave like a perfect backstop technology (setting the price for
the carbon market), the profit margin for the companies owning NETs
could become excessively large in such an institutional set-up.
The public would pay for these windfall profits: up to net-zero
emissions, NET revenues would be paid for by the carbon tax rev-
enues, reducing other recycling options. After that, unless intertem-
poral mechanisms such as carbon funds have been put into place10,
governments will finance NETs by increasing taxes, issuing debt or
cutting spending11.
Financing these potentially large profits with public funds suggests
that NETs can substantially influence the income distribution via two
channels. First, remunerating negative emissions reduces the amount
of carbon tax revenues potentially available to tackle inequality and
alleviate poverty12 (revenue drying effect). Second, if these technolo-
gies are privately owned and are allowed to reap windfall profits due to
increasing carbon prices, their financing will disproportionately benefit
the high end of the income distribution that holds stocks and capital
(ownership effect). Since the carbon tax is expected to be mostly regres-
sive12–14 and equity ownership is concentrated at the top of the income
distribution, both mechanisms can increase inequality and erode the
support for the ambitious climate goals of the Paris Agreement.
Received: 4 August 2022
Accepted: 18 October 2023
Published online: 30 November 2023
 Check for updates
1RFF-CMCC European Institute on Economics and the Environment, Fondazione Centro Euromediterraneo sui Cambiamenti Climatici, Milan, Italy.
2Politecnico di Milano, Milan, Italy.
 e-mail: pietro.andreoni@eiee.org

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redistributed through neutral transfers to households (pink area).
When NETs payments exceed carbon tax revenues, the government
must raise income taxes (dark blue area) to balance the budget.
Since we do not consider the cost of public funds nor potential
aggregate growth effects due to variations in the income distribution,
these fluxes have no aggregate impact on the economy or emissions.
However, they still influence welfare distribution because each flux
impacts different income deciles differently (Fig. 1b). Emission reduc-
tion costs and the carbon tax are assumed to be regressive for rich
countries and progressive for developing countries, following empiri-
cal evidence12. We do not consider dynamics such as employment
effects and stranded asset ownership21 that could have distributional
implications22 both within and across societies. Costs and revenues of
NETs are assumed to fall mainly on the wealthiest two deciles of each
country, which own most stocks and capital. For better data availability,
we use wealth distribution as a proxy for equity ownership. In our main
scenarios, we assume that all capital is owned exclusively by each coun-
try’s citizens. Finally, the redistribution of carbon tax revenues offsets
the regressivity of the carbon tax. The income taxes raised after net
zero are progressive to reflect the structure of most taxation systems
(see the section ‘Calibration of elasticities’ in Methods and Extended
Data Fig. 1 for details).
Scenario design
To quantify the inequality implications of NET ownership, we run sce-
narios consistent with 1.5 °C with 33% likelihood, imposing a carbon
budget of 700 GtCO2 from 2019 to 21002 achieved through a global
uniform carbon tax (see Supplementary Annex G for alternative speci-
fications of the carbon tax and different carbon budgets). To assess the
implications of scenario design, we consider a case without overshoot
of the carbon budget where emissions remain at net zero in exchange
Several studies have found that an equitable allocation of CO2
removal can shift part of the mitigation burden to countries with higher
historical responsibility15–17. However, no existing literature considers
the potential repercussions of financing negative emissions at scale
in shaping future inequality within societies. To explore and quan-
tify these dynamics, we use an open-source integrated assessment
model18. This model has high regional detail, featuring 57 interacting
countries/regions, and allows for a granular representation of income
dynamics between countries. We extend it by differentiating income
groups within countries19 and introducing direct air capture (DAC) as
a representative NET (see Methods for details). DAC is an example of a
NET which has catalysed notable financial interest; it is considered to
have considerable deployment potential and lower environmental and
social trade-offs than biological NETs such as bioenergy with carbon
capture and storage (BECCS)3,20.
We quantify the inequality implications of financing negative
emissions in 1.5 °C scenarios under the assumptions of global carbon
taxation, a single carbon market for emission reductions and removal,
and private ownership of carbon removal companies. We quantify the
within-country and global inequality implications under a broad set of
scenarios and assumptions.
Modelling NETs and their distributional
implications
We distinguish the economic implications of climate policies with six
components (Fig. 1a). Total mitigation costs (black dotted line) are
composed of the sum of emission reduction costs (orange area) and
NET costs, which include investment and operational costs (green area).
The remaining components sum to zero for each timestep and
country. The government receives the carbon tax revenues (brown
area), which are used to subsidize NETs at the carbon price or are
NET costs (%)
−5.0
−2.5
0
2.5
a
b
2020
2040
2060
2080
2100
Costs and gains,
 global aggregate (% GDP)
0
10
20
30
40
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
Decile
Year
Percentage of flow falling on decile D,
 country median
Carbon tax, emission reduction costs and redistribution
Income taxes
NETs profits
Source
Carbon tax payments
Cost of NETs
Emission reduction costs
Income taxes
NET revenues
Redistribution
Fig. 1 | Characterization of aggregate and distributional effects of financial
flows. a, Global (aggregated for all countries and deciles) financial flows and
climate costs in percentage of GDP, in a scenario with a 700 GtCO2 carbon budget
with budget overshoot. The black dotted line represents global net costs—that
is, the sum of emission reduction costs (orange area) and NET costs (green
area). The other fluxes sum to zero at each point in time. Income taxes (dark blue
area) can only be raised to finance negative emissions after budget overshoot.
Carbon tax revenues (brown area) may be used to pay for NET revenues (light
blue area), and the excess is recycled back into the economy (pink area). b, Share
of resources (median across countries) accruing to each income decile per type
of flow according to the calibration of carbon tax, equity ownership and income
taxes’ regressivity, in the year 2075. The black dashed line represents the median
across countries of income distribution across deciles. If the flow is a cost from
the perspective of the households (carbon taxes, emission reduction costs,
income taxes), it is regressive if it falls above the quantile line for low deciles and
below for high deciles. If the flow is a gain from the perspective of the households
(revenue recycling, NET revenues), it is regressive if it falls above the quantile
line for high decile and below for low deciles. The net regressivity of a flow (for
example, financing NET revenues with carbon taxes or income taxes) can be
visualized as proportional to the distance between the two lines (light and dark
blue or light blue and brown, respectively).

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for faster short-term reductions (Fig. 2). This leads to higher carbon
prices and policy costs before net zero and lower thereafter, confirming
recent evidence23,24. It also leads to a smaller temperature overshoot,
which has been shown to reduce climate risks23. In this paper, we limit
the scope to mitigation and do not consider the climate damages: while
highly uncertain, they will only exacerbate our main findings given
their adverse distributional consequences19.
Negative emissions increase within-country
inequality
We identify a clear connection between carbon removals and inequality
(Fig. 3). Regions that deploy more NETs experience a higher increase in
inequality (Fig. 3a as measured by the Gini index and see Supplemen-
tary Annex L for alternative inequality metrics). The financing of NETs
primarily drives this effect. Using a Shapley–Owen decomposition, we
can isolate the contribution of each economic flux to the total inequality
variation (bars in Fig. 3a): the percentage of inequality increase due to
NET financing increases with brackets. It accounts for more than 70%
of the total for the countries highly relying on carbon removal. Most
of the total NET contribution to inequality is due to the ownership
effect (filled bar in Fig. 3a). Partly, this is because we do not consider
progressive schemes for carbon tax recycling that would exacerbate
the revenue drying effect. We distinguish three phases in the evolution
of inequality variation (Fig. 3b). In the beginning, inequality decreases
since the costs of NET technologies are initially higher than the carbon
price, and firms do not make profits (inset chart in Fig. 3b). Afterwards,
as companies start making profits, inequality increases and peaks by the
time emissions reach net zero. Finally, in the latter part of the century,
the inequality increase is reduced: technology reaches maturity, its
costs do not fall anymore and the carbon prices level off after climate
neutrality (Fig. 2). Furthermore, after net zero, negative emissions are
financed by raising progressive income taxes instead of the carbon
tax. In principle, it would be possible to redistribute tax revenues and
raise income taxes to finance negative emissions even before net zero,
which would reduce adverse distributional effects but could hinder
economic growth. It is worth noting that if government subsidies were
to cover the early losses of NET companies, which is observed in the
current policy environment, then the early—albeit small—reduction in
inequality would not happen, thus exacerbating the inequality reper-
cussion of NET financing.
These results depend on the interaction between the amount of
carbon removed, the geographical distribution of CO2 removal, the
carbon price, NET costs and assumptions about wealth concentration.
We developed a simplified analytical model to identify the underly-
ing drivers (see the section ‘A simple analytical model for NETs’ in
Methods, Supplementary Annex A for validation and derivation and
Annexe B for a sensitivity analysis on these drivers). We find that three
factors drive the distributional implications of NETs resulting from the
ownership effect:
	(a)	The regressivity of financing carbon removal, expressed in the
model by the elasticities ηwealth and ηtax. The more concentrated
the capital (high ηwealth) and the more regressive the taxes used
to finance NETs (low ηtax), the more negative emissions will cause
an increase in inequality. In the standard set-up, the elasticities
of wealth and taxes are constant, while the carbon tax elastic-
ity varies with income per capita. In the real world, capital con-
centration and taxation schemes’ progressivity vary across
countries. However, wealth tends to be more concentrated than
income and carbon taxes (see the section ‘Calibration of elas-
ticities’ in Methods and Supplementary Annex C for a sensitivity
analysis on wealth concentration and income taxes).
	(b)	The share of NET revenues over GDP in each country. The larger
this term, the higher the inequality due to NETs. This factor de-
pends mainly on negative emissions potential. If this potential
is concentrated in small economies, NET revenues could ac-
count for several percentage points of their GDP, exacerbating
the distributional effects. Therefore, these countries might be
unwilling or unable to perform large amounts of removal with-
out international financial transfers. If properly designed, such
international transfers could increase feasibility of the carbon
removal effort and reduce between-country inequality (see Sup-
plementary Annex E for a scenario including international trans-
fers related to CO2 removal effort). However, these transfers
would still benefit the higher end of the income distribution in
the receiving countries, exacerbating internal inequality.
	(c)	The profit margin for a ton of carbon removed, expressed as the
ratio between NET revenues and costs. The higher this term,
the higher the inequality increase due to NETs. In our model,
the profit margin peaks at 80–90% at the time of net zero, when
carbon prices are the highest and technology has attained ma-
US$111 tCO2
–1
US$952 tCO2
–1
US$192 tCO2
–1
US$1,031 tCO2
–1
With overshoot
Without overshoot
2020
2040
2060
Year
Year
2080
2100
2020
2040
2060
2080
2100
−10
0
10
20
30
Emissions and removal (GtCO2 yr–1)
Carbon removal
Industrial emissions
Land use change
Fig. 2 | Emissions and carbon prices under a 1.5 °C scenario with and without
overshoot. The solid black lines represent net emissions (sources − sinks) and
dotted lines the average of carbon prices across regions, weighted by GDP.
Carbon prices in 2030 and at the peak are highlighted. Cumulative CO2 removal
in the century equals 332 GtCO2 in the overshoot scenario and 127 GtCO2 in the
peak budget scenario. In the overshoot scenario, net-negative emissions from
NETs account for 256 GtCO2 through the century (compatible with the [0,680]
net-negative emission range of C1–C3 scenarios of the IPCC Sixth Assessment
Report).

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turity. We explore scenarios with more pessimistic technologi-
cal change in the last section of the results, the consequences of
policy provisions to reduce the profit margin by considering a
profit cap for NETs in Supplementary Annex F and the role of dif-
ferent carbon pricing schemes in Supplementary Annex G.
The role of overshoot for inequality
We found that large-scale negative emissions increase within-country
inequality. We now ask whether this result varies across scenarios with
and without overshoot. In the ‘no overshoot’ scenario, each region is
not allowed to have net-negative emissions. This results in a global
cumulative carbon removal of 126 GtCO2, less than half of the amount
sequestered in the scenario with overshoot. On the other hand, the no
overshoot scenario has higher emission reductions in the first part of
the century.
We find that the no overshoot scenario has higher inequality
before net zero as emission reductions, which are regressive, are
frontloaded (Fig. 4). This increase is more accentuated for regions
with high removal since more negative emissions are deployed in the
2030s and 2040s24 and these are financed with a higher carbon price
(Fig. 2). However, negative emissions are lower in the second half of
the century if the budget is not overshot. Consequently, the inequality
implications of negative emissions are reduced, especially for highly
impacted countries (fourth bracket).
Thus, promoting net-negative emissions by overshooting emis-
sions and temperature leads to an intertemporal inequality trade-off
between regressive mitigation in the short term and regressive NET
ownership in the long term. For the countries that are more impacted by
the inequality repercussions of negative emissions, Fig. 4 indicates that
a smaller overshoot is preferable. Furthermore, while not estimated in
this study, climate damages avoided with lower temperature overshoot
would be likely to have progressive effects and support the rationale
for reducing overshoot.
The intertemporal trade-off we identified partly rests on the
assumption that emission reduction costs are, for most countries,
regressive, in line with the literature19. However, for regions with high
NET deployment, most of the increase in inequality between the over-
shoot and no overshoot scenarios in the first part of the century is
22%
50%
63%
72%
Revenue drying
Ownership
Abatement
0
0.5
1.0
0
20
40
60
Carbon removed
(% abated + removed)
Year
Year
Inequality increase, net-zero year
 (∆ Gini points)
Carbon sequestered (GtCO2)
10
20
30
40
50
a
Breakeven year
0
0.25
0.50
0.75
1.00
2020
2040
2060
2080
2100
Change in Gini index (points)
Carbon removed
(% abated + removed)
Low
Medium
High
Very high
−100
−50
0
50
100
2040 2070 2100
Profit margin (%)
b
Fig. 3 | Within-country inequality variation due to climate policy in a 1.5 °C
scenario with overshoot. a, Inequality increase expressed as difference in
Gini index relative to the baseline in 2070 against cumulative carbon removed
as share of total mitigation effort (carbon removed plus abated). Each point
represents a country/region. Bubble size indicates the amount of carbon
removed until 2100. Countries/regions are clustered into four brackets using an
equal frequency method according to their share of removal over total mitigation
effort: Low = (0%, 3.5%); Medium = (3.5%, 6%); High = (8%, 18%); Very high = (18%,
65%). The map shows which countries belong to each bracket. Bars show, for
each bracket, the median contribution of emission reduction costs (unfilled
bar), the ownership effect (filled bar) and the revenue drying effect (striped bar).
The percentage of the total increase due to NETs is highlighted (that is, sum of
ownership and revenue drying effects). The dashed grey tendency line shows
the positive correlation among the plotted variables. b, Time trend of inequality
increases per countries/regions, clustered as in a. The small time chart represents
the median profit margin for NET companies over time starting from 2040.
Breakeven year is when the median profit margin becomes positive. Shaded areas
represent confidence intervals (33rd and 66th percentiles).
−0.50
−0.25
0
0.25
2020
2040
2060
Year
2080
2100
Inequality increase (∆ Gini points)
Carbon removed
(% abated + removed)
Low
Medium
High
Very high
Fig. 4 | Inequality difference (variation in the Gini index) in the scenario
without budget overshoot relative to a scenario with budget overshoot.
Countries/regions are clustered by their share of cumulative removal over
total mitigation effort in the overshoot scenario (that is, the brackets and
the countries belonging to each bracket are the same as in Fig. 3). As in
Fig. 3, bars represent the median inequality increase per bracket in selected
years (2050, 2075, 2100) and filled bars represent the median contribution
to the inequality increase due to NETs (sum of ownership and revenue
drying effects). Shaded areas represent confidence intervals (33rd and
66th percentiles).

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due to NETs financing. Therefore, the results are less dependent on
assumptions about the emission reduction costs.
Global inequality repercussions
Global inequality is affected by negative emissions not only through
the within-country effect we have analysed so far but also through
between-country inequality, since the geographical distribution of
negative emissions varies across regions. If rich countries take up most
of the carbon removal effort and bear the associated costs, this could
compensate for the inequality impacts seen so far, with ramifications
for global equity.
We quantify the between-country effects of different interna-
tional distributions of removal effort and compare them to the within-
country effect. In our model, the costs of NETs and the international
distribution of the removal effort drive between-country inequality.
Since both dimensions are highly uncertain, we consider two cost
profiles for the NET (low costs and high costs) and two international
distributions of removal potential (Global North and Global South)
to compare the within- and between-country effects of NETs (Fig. 5,
opaque and transparent areas). We group the inequality contributions
into NET costs (green areas) and NET financing (blue areas), which
include the sum of the carbon tax, income taxes, NET revenues and
transfers. This last factor affects within- but not between-country
inequality because we assume it does not change the aggregate GDP.
Across all combinations of NET costs and potential (Fig. 5 and
Supplementary Annex L for alternative inequality metrics), the
within-country effects (sum of opaque blue and opaque green areas)
are dominant compared to between-country ones (transparent green
area). In our central specification of DAC potential and costs (Global
North, low costs), the between-country effect of CO2 removal is an order
of magnitude lower than the within-country one around the time of
net zero. At the end of the century, the effect is more relevant because
the total cost of removal peaks, but it is insufficient to compensate
for within-country regressivity. An allocation of removal potential
towards the Global South consistently exacerbates global inequal-
ity, unless trade of carbon removal is considered. In this case, poorer
countries could benefit from selling negative emissions permits to
wealthy countries (see Supplementary Annex E for a simulation of
international transfers). On the contrary, relaxing the assumption that
capital is owned exclusively by each country’s citizens would increase
between-country inequality because most financial centres are located
in the Global North (see Supplementary Annex D for a scenario includ-
ing international ownership of capital).
Assuming higher costs of negative emissions amplifies their
between-country distributional effects. At the same time, it reduces
the within-country regressivity because it decreases the profit margin
for NET companies. Towards the end of the century, under a progres-
sive allocation of removal potential (Global North, high costs) the two
effects roughly counterbalance (bottom-right panel of Fig. 5). These
results indicate that a progressive international distribution of removal
effort, or a properly designed set of international transfers (Supple-
mentary Annex E), can indeed contribute to reducing global inequality.
However, especially under the assumption of cheap NETs and around
net zero, our results suggest that the within-country inequality increase
is more relevant in shaping the global income distribution.
Discussion on policy instruments and key
assumptions
The results described so far rest upon two main policy assumptions:
NETs are privately owned and a single carbon market exists for emission
reduction and removal. While these assumptions align with the current
policy landscape, alternative policy choices can mitigate the inequal-
ity implications of NETs. First, the regressive nature of financing NETs
could be alleviated by altering either the ownership structure of NETs
companies or the taxation system. Taxes levied to finance NET revenues
could be tailored to target the same income brackets as those who own
the negative emissions industry. Alternatively, the public sector could
participate in or own the NET companies (see Supplementary Annex C
for sensitivity on NET ownership and taxation systems). Second, the
market structure could be redesigned to lower the profit margin. For
instance, separating markets for emission reduction and CO2 removal
could reduce the carbon price applicable to NET companies and thus
their profit margin. Alternatively, the maximum profit allowed for
these companies could be capped even within a single carbon market
(see Supplementary Annex F for scenarios with profit caps for NETs).
All these policy design options face hurdles and limitations. Pro-
gressive taxes would alleviate the inequality increase after net zero,
but with limited effect until NET revenues are paid for with carbon
tax revenues. Separated targets for emission reduction and removal
might lead to under- or overprovision of negative emissions. Similarly,
Total NETs efect
2075
2100
Low costs
High costs
Global North
Global South
Global North
Global South
−0.2
0
0.2
0.4
0.6
0.8
−0.2
0
0.2
0.4
0.6
0.8
Inequality driver
NETs costs
NETs transfers
Inequality contribution
Between
Within
Fig. 5 | Global inequality variation due to NET deployment (excluding
emission reduction effects on inequality) as the difference in the mean log
deviation index relative to the baseline, in selected years and scenarios. The
inequality contribution of NETs is divided into four factors: between-countries
effect of NET costs (green area, transparent), within-countries effect of NET
costs (green area, opaque), within-countries effect of NET financing (blue area,
transparent) and between-countries effect of NET financing (blue area, opaque).
The last factor is always null because NET financing does not affect GDP but only
its distribution. NET financing (blue areas) groups the algebraic sum of carbon
tax payments, redistribution of residual carbon tax revenues, NET revenues and
income taxes. Dots indicate the net inequality contribution of CO2 removal. The
contribution of the total inequality increase due to each group is decomposed
via a Shapley–Owen decomposition of the mean log deviation index. For each
group, the Theil index is calculated in its within- (transparent areas) and between-
(opaque areas) country contributions. The Theil index is an entropy measure
often used to measure inequality. Unlike the Gini index, it allows to disaggregate
the within- and between-country contributions of total inequality variation. The
Shapley–Owen decomposition allows us to determine how much each income
flow contributes to the total variation of inequality. In the ‘low costs’ scenario,
our representative technology reaches an average cost of around US$150 per
tCO2 avoided in 2100, while in the ‘high costs’ scenario the cost is around US$250
per tCO2 avoided in 2100. In the ‘Global North’ scenario, the upper bound
constraint on negative emissions is distributed regionally following our central
specification case—that is, geological storage capacity. In the ‘Global South’
scenario, the constraint is allocated with an equal per capita rule—that is, more
populous countries perform more CO2 removal.

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profit or price caps would distort the market, incentivize strategic
behaviour from the companies, might undermine long-term incentives
for cost reduction and would reduce the attractivity of investments in
NETs. Nonetheless, especially around and after net-zero emissions, the
severity of the distributional dynamics we highlighted suggests that
the drawbacks of a fully integrated carbon market justify preventive
intervention. At the same time, alternative policy provisions should
provide adequate attractiveness and profitability for investments in
NETs (see Supplementary Annex F for a quantification of this trade-off)
and should strive to maintain overall policy efficiency. Therefore, the
economic and political economy implications of the different policy
options sketched here should be carefully explored.
Regarding the broad mitigation policy context, relaxing the cli-
mate target reduces the distributional concerns of financing NETs
because of the contextual reduction of negative emissions and carbon
prices (see Supplementary Annex G for a 1,300 GtCO2 carbon budget).
However, higher temperature increases climate change impacts and
their adverse distributional implications, which are not considered
in this setting.
The inequality consequences of financing NETs described in
this paper depend on policy design but also on the characteristics of
the technology. Here we focus on DAC, but other negative emission
strategies exist. For example, considering BECCS would affect some
of our implicit assumptions about the business model, value chain
and regional potential (Box 1). Similarly, other technologies such as
enhanced rock weathering or ocean alkalinization come with their own
specificities. To fully understand how different technologies shape
the distributional implications of a diversified CO2 removal portfolio,
a more detailed approach is necessary. Nonetheless, the dynamics
described so far would at least partly hold true for other technologies
so long as privately owned NET companies make large profits from
the carbon market.
Conclusions
The framework we have developed—incorporating technological and
social dynamics into a climate-economy model—allows us to highlight
the adverse distributional implications of financing negative emissions
in the race to meet the Paris Agreement targets. Although robust to
different specifications, our results rest upon assumptions that, while
common in the climate mitigation literature (for example, a single
carbon market) or reasonable given the current policy environment
(for example, private ownership of NET companies), depend on policy
and ethical choices: who should finance, within and across societies,
the provision of a global public good such as CO2 removal, who should
own the means to provide this public good, and how to design policies
which avoid unintended and detrimental consequences? Our findings
suggest that, when confronted with these questions, policymakers
designing institutions for effective and fair CO2 removal should focus
on their distributional implications: policy provisions that affect the
ownership structure and the market for NETs can mitigate the inequal-
ity concerns described here but require political capital and a sound
understanding of the repercussion of these crucial climate strategies.
Methodologically, the paper shows that, when evaluating decarboni-
zation strategies, there is a need for models that are able to capture
the interactions of technology and finance with societal goals such as
inequality control.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-023-01870-7.
References
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Box 1
Other NETs could entail
different distributional
implications
Unlike DAC, the supply chain of BECCS involves biomass, the
sale of which could transfer part of the revenues to farmers with
positive distributional effects. This could reduce the regressivity of
financing carbon removal as explained in the ‘Negative emissions
increase within-country inequality’ section. Furthermore, the
average net cost of removal with BECCS is generally assessed to
be lower than with DAC3, therefore increasing the profit margin at
the same carbon price and exacerbating inequality. Finally, one of
the main constraints for BECCS deployment is biomass availability,
which would move the international distribution of removal effort
to the Global South. Depending on whether international transfers
are considered, this could have positive or detrimental global
inequality implications. However, we showed in Fig. 5 that different
international distributions of removal effort tend to have a smaller
impact on global inequality relative to ownership effect, especially
for technologies with a lower average cost, as is the case for BECCS.

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Publisher’s note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Springer Nature or its licensor (e.g. a society or other partner) holds
exclusive rights to this article under a publishing agreement with
the author(s) or other rightsholder(s); author self-archiving of the
accepted manuscript version of this article is solely governed by the
terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Springer Nature Limited
2023

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Methods
The baseline model
RICE50+ is an open-source highly regionalized integrated assessment
model18. It represents 57 different countries and regions, each char-
acterized by a marginal abatement cost curve function calibrated on
detailed-process models and by projections about socioeconomic
drivers and baseline emission intensity of GDP. Abatement costs vary
by region, such that fully abating emissions can cost hundreds to thou-
sands of dollars per ton of CO2, and are subject to exogenous technolog-
ical learning. The model solves a problem of discounted consumption
maximization with a time horizon until 2300 and five-year timestep
periods. The model allows solutions with multiple possible coalitions,
including the singleton coalitions (Nash solution) and the grand coali-
tion (cooperative). Each planner solves an intertemporal maximization
problem of discounted consumption and the utility function explicitly
includes inequality aversion other than intertemporal25. Different
baseline projections of socioeconomic determinants are represented
with the shared socioeconomic pathways (SSPs)26. The model is open
source and available at https://github.com/witch-team/RICE50xmodel
and a description and presentation of the model is available at https://
doi.org/10.18174/sesmo.18038 (ref. 27).
Implementation of the income distribution
To model the distributional effects of NETs, we introduce a decile-based
representation of household heterogeneity, conceptually following
Dennig et al.19.
As in the standard DICE model, in our model final income Y is cal-
culated for each timestep t and region n by subtracting from the gross
income YGROSS that emerges from a Cobb–Douglas production function
(calibrated to match exogenous GDP projections) the cost of emission
reductions and carbon removal:
YGROSS (t, n) = tfp (t, n) × K (t)
α × pop (t, n)
1−α
(1)
where tfp is the total factor productivity, calibrated to match exog-
enous GDP projections from different socioeconomic projections
(SSPs) assuming an optimal level of savings rate (S). Population (pop)
is exogenous and also follows SSP projections, while capital (K) at each
timestep is given by (∂k is the depreciation rate of capital)
K (t + 1, n) = K (t, n) × (1 −∂k) + S (t, n) × Y(t, n)
(2)
The final output Y is derived by subtracting emission reduction
costs (COSTER) and NET costs (COSTNET):
Y (t, n) = YGROSS (t, n) −COSTER (t, n) −COSTNET (t, n)
(3)
To assign these costs to each decile d, we implement weights wη
that are driven by an elasticity η:
wη (t, n, d) =
q0(t, n, d)
η
∑dq0(t, n, d)
η
(4)
where q0(t,n,d) are the baseline quantiles, obtained from a disaggrega-
tion of Gini index projections to 2100 from Rao et al.28, as described in
ref. 29. The definition of wη gives that ∑
10
d=1 wη (t, n, d) = 1.
The elasticities η defined as such imply that if η = 0 the costs
are distributed equally per capita and therefore will benefit the
richest deciles, and if η = 1 the cost is assigned distribution neutrally
across the deciles. Therefore, the higher the elasticity, the more
progressive the distribution of the cost. If the flow is positive (that
is, it describes gains, such as the recycling of the carbon tax or NET
revenues) the opposite holds, and a lower elasticity indicates more
progressive flows.
The final income for each decile YDIST is described by
YDIST (t, n, d) = YGROSS (t, n) × q0 (t, n, d)
−(COSTABATE (t, n) + CTX (t, n))
×wctax(t, n, d) + (REVNET (t, n) −COSTNET (t, n))
×wnet (t, n, d) + TRANSFER (t, n) × wredist
(t, n, d) −GENTAX(t, n) × wtax (t, n, d)
(5)
with each term defined as follows. CPRICE is the carbon price
in the region, EIND the emissions from the industry and energy sector,
ENEG the carbon removed with NETs (nature-based solutions are not paid
for since land use change is exogenous in the model) and
CTX (t, n) = CPRICE (t, n) × EIND(t, n)
REVNET (t, n) = CPRICE (t, n) × ENEG(t, n)
GENTAX (t, n) = max (0, REVNET (t, n) −CTX (t, n))
TRANSFER (t, n) = max (0, CTX (t, n) −REVNET (t, n))
Since we do not consider the marginal cost of raising public funds
nor intertemporal and international transfers, the algebraic sum across
deciles of carbon tax revenues, subsidies to NETs, income taxes and
transfers must for each period and region equal zero:
GENTAX (t, n) + CTX (t, n) −REVNET (t, n) −TRANSFER (t, n) = 0
This is equivalent to modelling an implicit government tasked with
balancing the difference in its budget due to climate policy. Because
∑
10
d=1 wη (t, n, d) = 1, it holds also that
∑dREVNET (t, n) × wnet (t, n, d) + TRANSFER
(t, n) × wredist (t, n, d) −GENTAX (t, n)
×wtax (t, n, d) −CTX (t, n) × wctax (t, n, d) = 0
from which it follows that Y (t, n) = ∑dYDIST (t, n, d), providing the
consistency between the aggregate income equation (equation (3))
and the income equation with resolved inequality (equation (5)).
A simple analytical model for NETs
We derive an analytical reduced-form model that only considers
the direct contribution of NETs to within-country inequality, to
analyse the determinants of the behaviour of the numerical model.
This model excludes the revenue drying effect—that is, the fact
that carbon tax revenues used for financing NETs could have been
otherwise redistributed progressively. From the income equation
(dependency from time and region is dropped for readability, sub-
script d indicates the variable or parameter that depends on the
decile), considering only the terms linked to NET revenues and
neglecting the contributions of abatement costs and the carbon
tax redistribution, we get
Yd = Y0,d + REVNET × (wnet
d
−wtax
d ) −COSTNET × wnet
d
(6)
Normalizing for total income Y0 and rearranging variables (see
Supplementary Annex A for the analytical proof and validation)
qd × gdploss = qd,0 + revgdp × (profmargin × wnet
d
−wtax
d )
(7)

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with the newly defined parameters defined as follows:
•
profmargin =
ENEG×CPRICE−COSTNET
ENEG×CPRICE
 is the profit margin of NETs
•
revgdp =
ENEG×CPRICE
Y0
 is the revenue of NETs relative to baseline GDP
•
wnet
d  is the equity share of quantile d, from wnet
d
=
qd,0
ηnet
∑iqd,0
ηnet
•
wtax
d  is the share of tax paid by quantile d, from wtax
d
=
qd,0
ηtax
∑iqd,0
ηtax
•
qd,0 is the baseline share of income accruing to quantile d
•
qd is the final share of income accruing to quantile d
•
gdploss =
Y0−COSTNET
Y0
 is the aggregate GDP loss due to NET costs
To exemplify the behaviour of this model, let us assume a repre-
sentation by just quantiles of the economy, where the bottom 50%
hold 20% of national income and the top 50% hold the remaining 80%,
that is, q1,0 = 0.2 and q2,0 = 0.8. Let us assume a carbon price of US$500
per tCO2 and an average cost for NETs of US$100 per tCO2 of carbon
avoided, that is, a profit margin of 0.8. Furthermore, let us assume
ηnet = 1.8 and ηtax = 0.8 (the median elasticity of the carbon tax in our
calibration), and that NET revenues cover 5% of GDP of the nation.
Under this assumption, the following will hold:
q1 = 0.2 + 0.05 ((1 −1
5 ) × 0.076 −0.248) = 0.8
q2 = 0.8 + 0.05 ((1 −1
5 ) × 0.924 −0.752) = 0.19
Now with q1 + q2 = 0.99 because the cost of NETs is lost from the
economy, gdploss = 0.01. Renormalizing, we get q1 = 0.81, q2 = 0.19, and
calculating the difference in the Gini index before and after the NET
effect gives
ΔGini = Gini (0.81, 0.19) −Gini (0.2, 0.8) = 0.01
The net effect of NET deployment under these assumptions causes
an inequality increase of 1 point of the Gini index.
Calibration of elasticities
Abatement cost and carbon tax elasticities are calibrated as in Budolf-
son et al.12, in which they perform a meta-study on the distributional
effect of carbon taxation and regress the result against GDP, finding a
weak negative correlation. To complete our model, two more calibrated
parameters are needed: the income-to-capital-ownership elasticity and
the income-to-taxation elasticity.
To estimate the first one, since data on capital ownership are avail-
able for only a small subset of countries and years, we use the wealth
income distribution as a proxy of capital ownership. We use a panel
of data of 37 countries from 2000 to 2020 at the decile level from the
publicly available WID database (https://wid.world/). These are mostly
OECD countries for which both pre- and post-tax income distribution
is available. We filter the years before 2000 to get a large but relatively
homogeneous sample of the characteristics of the global economy. We
consider post-tax income and wealth distributions. We estimate the
elasticity by fitting the following model for each country and year with
nonlinear least square methods (nls function in stats package R), where
q°,d represents the quantiles on wealth and income (pre-tax income is
specified with the ‘pre’ subscript):
qwealth,d =
qincome,d
ηwealth
∑i qincome,d
ηwealth d {1 ∶10}
(8)
We assume perfect coincidence between the deciles of income
and the deciles of wealth; that is, a household in the nth decile for
the income distribution will belong to the same decile of the wealth
distribution.
The elasticity of the taxation system is extrapolated in a similar
manner. First, we build the percentage of total government revenues
from taxes per percentile, calculated as
td =
qincome−pre,d −qincome,d
∑i qincome−pre,d −qincome,d
d {1 ∶10}
(9)
This quantity is not a common metric of taxation progressivity
but represents, for a dollar of taxes, how much on average is taken from
each percentile of the income distribution. We then relate this quantity
as before, estimating the following model for each country and year:
td =
qincome,d
ηtax
∑i qincome,d
ηtax d {1 ∶10}
(10)
The resulting estimated ηtax and ηwealth are shown in Extended Data
Fig. 1. The median of both values is relatively stable and we choose a
value of 1.8 for ηwealth and of 1.4 for ηtax.
Scenarios
We implement the global constraint on cumulative CO2 emissions
via a global uniform carbon tax, which increases exponentially at a
growth rate of 5% per year. In the first periods, the trajectory of the tax
is smoothed out from the exponential path to avoid abrupt changes in
the pricing profile. To mimic the existence of hard-to-abate sectors and
emissions, the maximum rate of abatement via conventional mitigation
MIUmax is capped at 0.975 for all countries, corresponding to 97.5% of
baseline emissions and around 2 GtCO2 in 2100 globally. Non-CO2 gases
are not priced and do not contribute to the global budget.
To decrease the computational complexity of the problem and
to avoid implicit transfers across regions via mitigation effort shift-
ing, each region solves as an independent optimization and conver-
gence is achieved by solving iteratively all regions. Nonetheless, the
superimposed shape of the global carbon tax produces a de facto
cooperative scenario even though each regional optimization prob-
lem is solved independently, in the sense that implicit cooperation
is assumed in negotiating (and abiding to) the carbon tax level and
growth across countries.
Abatement is determined at each iteration by the carbon tax tra-
jectory and saving rates are fixed to the long-term convergence value
of 25%. Therefore, the only free variable in the model is investment in
negative emissions. At each iteration, the starting level of the carbon
tax is shifted via a bisection algorithm until the budget is reached and
all free variables are stable across iterations.
Within the optimization problem at each iteration, the carbon
tax is fixed and constrains the emission control rate at the equivalent
point in the marginal abatement cost curve MACt,n (°), such that, for
each region n and period t
MACt,n (MIU(t, n)) = CPRICE (t, n)
(11)
with the carbon price, CPRICE, defined as
CPRICE (t, n) = min (ctax(t), CPRICEmax (t, n))
where CPRICEmax(t,n) = MACt,n(MIUmax(t,n)) and ctax is the exogenous
carbon tax fitted iteratively. This correction is to avoid, in the late part
of the century, that the carbon price stays below the maximum sensible
marginal cost in each region.
The carbon price so defined also regulates the amount of NETs
in the system thanks to a subtractive term that modifies the income
equation (equation (3)) into:
Y (t, n) = YGROSS (t, n) −COSTER (t, n) −COSTNET (t, n) −CPRICE (t, n)
× (E (t, n) −Eit−1 (t, n))
(12)

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where E represents total emissions and Eit−1 are the emissions in the
previous model iteration. Total emissions E are written as
E (t, n) = EIND(t, n) −Eneg (t, n) −Eland (t, n)
(13)
with EIND(t,n) = Ebaseline(t,n) × (1 − MIU(t,n)), and baseline emissions
Ebaseline calibrated from SSP projections and exogenous land use emis-
sions Eland.
Since MIU is fixed at each model iteration by the carbon tax, the
only free decision variable in the model is the investment in the repre-
sentative NETs technology that regulates the amount of gross negative
emissions.
Equation (12) therefore ensures that, given the carbon price at
each iteration of the model, negative emissions will be deployed if
the marginal cost of deployment (including storage) is lower than
the carbon price and no other constraint is reached. At convergence,
the difference between emissions across the past two iteration is
below a certain threshold of tolerance E(t,n) − Eit−1(t,n) < eps and the
solution stabilizes, while the term −CPRICE(t,n) × (E(t,n) − Eit−1(t,n))
in equation (12) goes to approximately zero in the income equation
(equation (3)), therefore producing no effect in the final solution
of the model.
DAC as a representative technology for NETs
In the standard release of RICE50+, negative emissions are allowed,
as in the original DICE model, because the decision variable MIU(t,n)
that controls the percentage of baseline emissions abated can reach
higher values than 100% (specifically 120%, meaning that each region
can remove at most 20% of its baseline emissions in each given year).
Since CPRICE = MACt,n(MIU(t,n)), this removal happens at the top of
the marginal abatement cost curve, for carbon prices that can exceed
US$1,000 per tCO2. However, most proposed NETs4 are projected to
be competitive at much lower costs, ranging from US$100 per tCO2
of BECCS to US$200 per tCO2 of DAC. While there is large uncertainty
surrounding these costs, this means that such technologies would
become competitive at lower levels of the marginal abatement costs
curve, de facto modifying its shape. Moreover, embedding negative
emissions into a single MAC does not allow the explicit representation
of offsetting that is necessary in our set-up to model the distributional
implications of financing NETs even before net-zero emissions.
Therefore, we explicitly model a representative technology for
NETs. We characterize it as an investable technology in the model, such
that each region can choose to reduce emissions with either standard
abatement or via carbon removal. Since we explicitly model NETs, MIU
is capped below 100%, meaning the investable technology is the only
means to reach net-negative emissions.
We choose to model our representative NET technology as DAC.
DAC is modelled as in Realmonte et al.30 as an investable technology
with depreciating capital, and emissions captured must be stored
geologically. Total costs are divided into investment and variable
costs (which implicitly include fuel and operation and maintenance
costs), as well as storage costs. Total costs shrink with time due to learn-
ing by doing. The storage cost depends on the storage type (aquifer,
exhausted oil and gas field, for instance) and each storage type has a
regional cumulative capacity limit. No leakage is considered from the
geological sites. See Supplementary Annex I for the implications of
this representation on the shape of the marginal cost curve for abate-
ment and removal.
The DAC module in our model is characterized by the following
equations and parameters:
Capital ENEG[GtCO2 per year] (since we assume full utilization, capac-
ity corresponds to the carbon removed each year) in the next period
t + 1 depends on the residual stock of capital net of depreciation ∂ times
the new capacity in the period expressed as the investments
INET[T US$ per year] over the unitary investment cost:
ENEG (t + 1, n) = ENEG (t, n) × (1 −∂tstep)
+
INET(t,n)
totcost(t)×lifetime×fraccapex × tstep
(14)
The total cost per ton of carbon avoided, totcost, is time depend-
ent, because the technology is characterized by learning by doing and
costs decrease with the global cumulative capacity installed:
totcost (t) = max (totcostfloor, totcost0 ×
∑n,t∗={t0∶t}ENEG(t∗, n)
∑nENEG(t0, n)
−learn
)
(15)
where totcost0 = 453 US$ per tCO2 is the initial investment total unitary
cost of DAC for carbon removed, totcostfloor = 100 US$ per tCO2 is the
floor cost, and the learning rate, learn, is set at 13.6% in the central
specification case and at 6% for the high cost DAC scenario. Therefore,
the average cost of the technology in the low cost scenarios starts from
450 US$ per tCO2 in 2020 to 160, 110 and 100 US$ per tCO2 in 2050, 2075
and 2100, respectively. In the high cost scenarios, it starts from
450 US$ per tCO2 in 2020 to 300, 250 and 235 US$ per tCO2 in 2050, 2075
and 2100, respectively.
To obtain the unitary investment cost to install a plant with removal
capacity of 1 GtCO2 per year, the total cost, totcost, is multiplied by the
lifetime of the plant (assumed to be 20 years) and by the fraction of
total cost accruing to capital costs, which we assume to be 40% of
total costs31. Under this cost structure, the investment costs are paid
upfront and therefore tend to increase the average cost profile (relative
to the numbers shown above) when the technology is ramping up and
decrease it to the variable costs (for DAC, mainly energy expenditures)
when the technology is scaling down and no new investments are made.
The total costs for NETs COSTNET[
T$
year ] that enter the income equa-
tion (equation (3)) are as follows:
COSTNET = INET + ENEG (t, n) × (1 −fraccapex)
×totcost(t) + ∑storstorcost(stor) × ESTOR(stor, t, n)
(16)
Here, the second term identifies operating expenditures for NETs
and the last term represents storage costs. ESTOR is the yearly carbon
dioxide stored underground in each type of storage type, stor (saline
aquifers, onshore and offshore exhausted oil and gas fields), each of
which possesses a characteristic storage cost, storcost.
The yearly carbon dioxide stored is subject to the following condi-
tions, which ensure that total yearly carbon store matches removals and
that the carbon stored in each storage type does not exceed the storage
capacity STORCAP, which depends on regional capacity:
ENEG (t, n) = ∑
stor
ESTOR(stor, t, n)
∑
t∗={t0∶t}
ESTOR (stor, t∗, n) < STORCAP (stor, n)
Two additional constraints are considered. First, to avoid exces-
sive amount of removal in a model that does not include constraints,
maximum yearly global capacity of DAC is capped to 30 GtCO2 per
year of removal. The way this constraint is distributed across regions
varies with the scenario analysed: in our central specification case,
each regional constraint on yearly removal is set to mimic the storage
capacity, such that
ENEG (t, n) <
∑storSTORCAP (stor, n)
∑stor,nSTORCAP (stor, n) × maxneg
The Global South scenario distributes the constraint according
to 2020 population levels:

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ENEG (t, n) <
pop(2020, n)
∑npop(2020, n) × maxneg
Since our model does not explicitly consider the cost of energy and
the increase in investments in clean energy technologies necessary to
cover the demand of large-scale DAC systems, we add an additional con-
straint on the global maximum yearly removal, maxneg, that depends
on the carbon budget (that is, the temperature target in 2100) with
a logistic function, such that the total amount of carbon captured is
consistent with projections from the ENGAGE scenarios of the WITCH
model23, an energy-detailed integrated assessment model. The maxi-
mum amount of global yearly NETs is therefore given by:
maxneg = 21.3 × (1 + e0.00631×(carbonbudget−1069))
Finally, NET deployment is subject to a market growth constraint
to mimic the characteristic time of scale-up of the technology. This
constraint is written as
INET (t + 1, n)
totcost (t + 1) × lifetime × fraccapex
≤
INET (t, n)
totcost (t) × lifetime × fraccapex
×(1 + mktgwt)
tstep + tstep × mktgwt,0
(17)
where mktgwt = 6% per year is the maximum allowed market growth and
mktgwt,0 is the initial NET capital in 2015.
Data availability
All scenario runs and code used to produce the figures and data
analysis are available in open source at https://doi.org/10.5281/
zenodo.8397488.
Code availability
The model used to produce the scenarios is available in open source
at https://github.com/witch-team/RICE50xmodel. To run the model,
an active GAMS licence with CONOPT3 is required.
References
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27.	 Gazzotti, P. RICE50+: DICE model at country and regional level.
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28.	 Rao, N. D., Sauer, P., Gidden, M. & Riahi, K. Income inequality
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29.	 Emmerling, J. et al. Global inequality consequences of climate
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Acknowledgements
The authors have received no external funding for this work.
Author contributions
All the authors designed and conceptualized the research questions
and scenarios. P.A. and J.E. performed the model advancements.
P.A. ran the scenarios, analysed the data and produced the figures.
P.A. and M.T. wrote the first draft. All the authors extensively
contributed to the final version of the paper.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41558-023-01870-7.
Supplementary information The online version
contains supplementary material available at
https://doi.org/10.1038/s41558-023-01870-7.
Correspondence and requests for materials should be addressed to
Pietro Andreoni.
Peer review information Nature Climate Change thanks Carl-Friedrich
Schleussner 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
Article
https://doi.org/10.1038/s41558-023-01870-7
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Extended Data Fig. 1 | Calibration of income taxes and wealth elasticities. Income taxes elasticities (red) and wealth-to-income elasticity (blue) for each year
between 2000 and 2020. Boxplots highlight median, 25th and 75th percentiles, points represent outliers. The number of observations and the median of the values is
highlighted in text for each year.

    Policy Brief:

Answer:
**Financing negative emissions leads to windfall profits and inequality at net zero**

**Policy Problem**Policymakers face the question of which policy instruments are suitable for financing negative emissions. For example, the European Union is discussing the opportunity of introducing negative emission technologies (NETs) into its emission trading scheme, the largest carbon market in the world. In the long run, the inclusion of carbon removal in a carbon pricing framework alongside other emission reduction methods, such as renewable energy, holds appeal: this approach theoretically allows for the achievement of emission reduction targets at the lowest possible cost. In a carbon market, however, the carbon price serves as a revenue for NET companies. Owing to technical progress, the average cost of carbon removal might fall well below the anticipated carbon price as emissions near zero. Consequently, owners of NETs might reap windfall profits, leading to a substantial increase in economic inequality if these technologies are privately owned.

**Scientific Research Findings**We find that privately owned NETs integrated into a carbon market designed for emission reductions increases economic inequality. Within each country, three factors drive the severity of the inequality increase: (1) the profit margin of NET companies; (2) the concentration of ownership of NET companies towards the top of the income distribution; and (3) the amount of negative emissions in the market. Therefore, small economies with high carbon-removal potential, concentrated equity ownership and expensive mitigation options are particularly susceptible to the inequality risk. In our analysis, we model a single NET (direct air capture) and global climate policy in line with the Paris Agreement. However, our results generalize to other negative emission options and to other climate policies (for example, net-zero pledges) whenever the above considerations apply and the technology value chain mostly benefits the rich.

**Scientific Research Study Methods**We carried out our analysis with an open-source integrated assessment model at high geographical resolution; we improved the numerical model with an explicit representation of income distribution, which allows the dynamics of both within-country and global inequality to be captured. We projected key variables over time, such as gross domestic product (GDP), emissions, baseline inequality, emission reduction costs, concentration of capital, and distributional incidence of climate policy and income taxes, assuming continuation of current trends. Our central scenario assumes a global uniform carbon tax consistent with the well-below 2 °C target embedded in the Paris Agreement. To identify robust insights, we carried out a large number of numerical simulations, varying international ownership of capital, international trade of negative emission permits, double markets for negative emissions and different interpretations of climate policies.

**Policy Implications**• In a net-zero emissions world, NETs could become a trillion-dollar business globally.
• If financed through an unregulated carbon market, the owners of these companies would enjoy windfall profits, potentially leading to a large increase in economic inequality.
• Market regulation, such as profit caps, could reduce the inequality increase, but at the risk of stimulating too much or too little carbon removal — policymakers should be aware of this trade-off.
• Concentrating removal efforts in the Global North or transferring resources to the Global South could, to some extent, offset the increase in inequality at the global level.
• These dynamics mostly apply to a net-zero and post-net-zero world. The current priority of policymakers should remain to provide adequate resources to scale up NETs towards technology maturity.