Demographics shape public preferences for carbon dioxide removal and solar geoengineering interventions across 30 countries

Abstract

Climate intervention technologies such as carbon dioxide removal and solar geoengineering are becoming more actively considered as solutions to global warming. The demographic aspects of the public serve as a core determinant of social vulnerability and the ability for people to cope with, or fail to cope with, exposure to heat waves, air pollution, or disruptions in access to modern energy services. This study examines public preferences for 10 different climate interventions utilizing an original, large-scale, cross-country set of nationally representative surveys in 30 countries. It focuses intently on the demographic dimensions of gender, youth and age, poverty, and income as well as intersections and interactions between these categories. We find that support for the more engineered forms of carbon removal decreases with age. Gender has little effect overall. Those in poverty and the Global South are nearly universally more supportive of climate interventions of various types.

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communications earth & environment Article
https://doi.org/10.1038/s43247-024-01800-1
Demographics shape public preferences
for carbon dioxide removal and solar
geoengineering interventions across
30 countries
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Benjamin K. Sovacool 1,2,3 , Darrick Evensen4,5,ChadM.Baum 1, Livia Fritz 1&SeanLow 1,6
Climate intervention technologies such as carbon dioxide removal and solar geoengineering are
becoming more actively considered as solutions to global warming. The demographic aspects of the
public serve as a core determinant of social vulnera bility and the ability for people to cope with, or fail to
cope with, exposure to heat waves, air pollution, or disruptions in access to modern energy services.
This study examines public preferences for 10 differe nt climate interventions utilizing an original, large-
scale, cross-country set of nationally representative surveys in 30 countries. It focuses intently on the
demographic dimensions of gender, youth and age, poverty, and income as well as intersections and
interactions between these categories. We find that support for the more engineered forms of carbon
removal decreases with age. Gender has little effect overall. Those in poverty and the Global South are
nearly universally more supportive of climate interventions of various types.
Radical and frequently contested climate intervention technologies such as
carbon removal and solar geoengineering are attracting increasing attention
from researchers, investors, and policymakers as the adverse impacts of
climate change are increasingly evident1–3.
Carbon removal technologies including soil carbon sequestration,
afforestation and reforestation, direct air capture, and bioenergy with car-
bon capture and storage may be employed to remove greenhouse gases from
the Earth’s atmosphere. These options are assigned, to a varying extent, an
expandingly critical role within the range of strategies and trajectories that
aim to reduce global temperature change or meet the longer-term targets
embedded in the Paris Agreement4. Solar geoengineering technologies such
as stratospheric aerosol injection, aimed at reflecting a portion of incoming
sunlight back into space before it reaches the Earth’s surface, could serve as a
measure to slow the risks of global warming, or create a stop-gap period of
adjustment that gives countries time to adapt to the impacts of climate
change5,6. Other options, such as marine cloud brightening or cirrus cloud
thinning, are being assessed for their potential to remediate the risk of
pending “tipping points”in the climatic system, and to diversify the port-
folio of options we must use to arrest increases in temperature7.
Some commentators promote these options collectively to meet the
goal of keeping climate change impacts well below 2 degrees Celsius, an
ambition deemed still possible (although difficult)8. Others argue that
carbon removal options are necessary to reach net-zero emissions targets,
tackle the problem of residual emissions, or account for gaps in imple-
mentation inherent within the United Nations Framework Convention on
Climate Change process9. Still others point out that insufficient climate
action during the previous decade means that transformational develop-
ment pathways are now required to reduce greenhouse-gas emissions at a
scale of four times the work (greater emissions reductions) but one-third the
time (stabilizing the climate by 2030, if not sooner)10.
And yet, the public remains substantially unfamiliar with these tech-
nologies, restricting its ability to participate in ongoing discussions about
science, policy, and deployment11–15. Research on demographic attributes
such as gender, age, and socio-economic status remains particularly
important, but also rarely examined. For instance, demographic aspects
such as gender or income can be strongly differentiating variables that
contribute to social vulnerability and that can help explain how the
experiences of men, women, poor, and wealthy people differ during and
after times of climate crisis, given that demographics shape cultural prac-
tices, social norms, work functions, and even access to security and resources
of protection and safety. Women frequently confront conditions of vul-
nerability in multiple spheres (e.g., monetary poverty, hunger, unemploy-
ment, under-education) and are also more vulnerable to extreme weather
events, to their impacts, therefore triggering situations of violence16. Current
1Department of Business Development and Technology, Aarhus University, Aarhus, Denmark. 2Science Policy Research Unit (SPRU), University of Sussex
Business School, Brighton, UK. 3Department of Earth and Environment, Boston University, Boston, USA. 4Institute for Global Sustainability, Boston University,
Boston, USA. 5University of Edinburgh, Edinburgh, UK. 6Wageningen University and Research, Wageningen, Netherlands. e-mail: benjaminso@hih.au.dk
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research practices and technological designs concerning carbon removal
and solar geoengineering tend to endorse masculine values of control and
tend to produce gendered impacts which are only beginning to be
understood17–20. Mahajan and colleagues “hypothesize that women will
express greater concerns about solar geoengineering’s unpredictability and
that this concern will decrease support for its use and research when we
control for other confounding variables”21.
Youth are more vulnerable to the duration and severity of impacts of
climate change than adults, given that they will generally live longer
(incurring more exposure) but also presently have physiological factors
(such as smaller lungs and less developed immune systems) that make
particular impacts such as air pollution or heat stress more extreme. Ten-
tative conclusions from the few studies that have examined youth per-
spectives on geoengineering have noted that younger people tend to
prioritize climate action more strongly, but also to more strongly emphasize
the need for international cooperation and governance22,23.Itisalsoyouth
that are more likely to be on social media, a platform they can use to reach
millions of other individuals when they discuss climate policy or
technology24.
Finally, concerns have been raised in the literature that carbon removal
and solar geoengineering could constitute technological imperialism and
colonialism25–28, and could also have net positive (or negative) impacts on
rates of global poverty29,30. Schneider even goes so far as to write that both
forms of climate intervention are “bound to exacerbate concomitant socio-
ecological and socio-economic global crises, deepen societal dependence on
technocratic elites and large-scale technological systems and create new
spaces for profit and power for new and old economic elites”31. Buck adds
that “a critical reading views geoengineering as a class project that is
designed to keep the climate system stable enough for existing production
systems to continue operating”32.
Many of the foregoing hypotheses and projections are theoretical
expectations that have not yet been supported with robust empirical data.
Drawing on a large-scale, cross-country set of nationally representative
surveys (n = 30,284 participants, with at least 1000 in each country) in 30
countries and 19 languages, this article more rigorously and systematically
examines public preferences for 10 climate-intervention technologies in
relation to the demographic dimensions of gender, youth and age, and
poverty and income. Because most of these technologies are novel, and
several exist only at a conceptual level, the public have at present little
understanding of them. Consequently, we needed to provide our survey
respondents with factual descriptions of the technologies (see Supplemen-
tary Information). The responses to the technologies are built off of the
foundation of these descriptions. These 10 technologies are:
•Stratospheric Aerosol Injection: this aims to limit the effects of climate
change by using planes or balloons to spray small particles (aerosols)
into the upper atmosphere;
•Marine Cloud Brightening: this aims to limit the effects of climate
change by spraying small particles, such as sea salt, into the air over the
oceans, to make clouds brighter;
•Space-based Geoengineering: this aims to limit the effects of climate
change by putting a giant mirror or other reflective material in outer
space between the Earth and the sun;
•Afforestation and Reforestation: both aim to limit the effects of climate
change by planting trees;
•Soil Carbon Sequestration: this aims to limit the effects of climate
change by changing agricultural techniques to store more carbon
dioxide in soils;
•Marine Biomass and Blue Carbon: both aim to limit the effects of
climate change by improving how much carbon dioxide is stored in the
oceans;
•DirectAir Capture with Carbon Storage: this aims to limit the effects of
climate change by using very large fans to remove carbon dioxide from
the air;
•Bioenergy with Carbon Capture and Storage: this aims to limit the
effects of climate change by growing and harvesting plants as a source
of energy and then storing the emissions permanently in rocks or
underground reservoirs;
•Enhanced Rock Weathering: this aims to limit the effects of climate
change by increasing the ability of rocks to absorb carbon dioxide from
the atmosphere;
•Biochar: this aims to limit the effects of climate change by heating
organic material, such as tree branches and cornstalks, inside a con-
tainer with no oxygen.
Although much previous work has tended to look at each of these
technologies by itself, or in comparison with only 2-3 other interventions33,
weexaminealltentogetherasanintegratedportfoliobecausethisishow
they may be synergistically deployed together as part of a future climate
policy package, and because both suites of carbon removal and solar
geoengineering technologies are shaping climate governance and mirrors
the policymaking dilemma of choosing options with limited resources and
uncertainty34.
Our primary contribution rests on our contention that demographic
attributes such as gender, age, or income could strongly relate to the per-
ceived risks of climate impacts or preferences for energy or climate policy. It
is demographic aspects of people (including preexisting conditions or pat-
terns of deprivation) that serve as the key determinants of social vulner-
ability and the ability for people to cope with, or fail to cope with, exposure to
heat waves, air pollution, or disruptions in access to modern energy
services35. Moreover, the impacts of climate change are becoming increas-
ingly appreciated by researchers and policymakers alike for being a deeply
social problem, one that therefore needs further inquiry revealing the social
factors that may accelerate, or block, engagement on this critical issue.
Lastly, a fundamental reason for identifying demographic predictors is
because those are proxies for what information different groups tend to
think of (and how they think about it) when they evaluate emerging
technologies36.
Social and behavioral science research from multiple disciplines,
including philosophy, psychology, communication studies, political science,
and sociology, has yielded precious insight into the ways that gender, age or
income can fundamentally shape public engagement with climate change
and can interact with partisan and other sociocultural factors (e.g., indivi-
dualistic and hierarchical worldviews) to influence how people perceive
climate risks37. The present study highlights the critical utility of additional
research examining how public perceptions of diversity and economic
inequality both between and within nations color collective perceptions
about climate change and radical climate interventions. Understanding
points of support, or opposition, across different individual perceptions can
more broadly reveal patterns of incipient social acceptance or social license
to operate38, or patterns of anticipated opposition39, both of which have high
relevance for decisionmakers. Demographic groups for whom the issue of
climatechangemaybelesspoliticallycharged,or those more willing to make
sacrifices or act on climate change, represent critical audiences for bridging
partisan disagreements and building consensus on policy.
Definitions, terms, and positionality
Given the sensitivity of the topic, some definitions and reflections on gender,
youth, and poverty are warranted.
Most simply, by gender, we refer to whether one identifies as male,
female,orother,butwedorecognizethat gender includes a statement about
biological aspects (one’s sex) but also social and cultural aspects (one’ssocial
identity)40.
We adopt the convention of the United Nations Department of
Economic and Social Affairs to define youth as consisting of those
between the ages of 15 and 24 years of age, although in our particular
survey instrument, we include respondents between the ages of 18 and24
years old (given that our ethics approval was not granted for minors
below the age of 18). Implicit in this definition of youth is that it
represents not only a range of ages but also a developmental stage
demarcated by growing capacity and a broadening of perspectives as well
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as growth in personality and maturity associated with moving into
adulthood; this definition also appreciates the diversity of beliefs, values,
worldviews, and expectations held by youth41.
Finally, by poverty, we refer to those whose income falls below a
minimum threshold of resources, e.g. a poverty line. But we do so with an
appreciation that such a monetary definition does not adequately capture
other forms of poverty including those focused on capabilities (deprivation
of one’s abilities to achieve a life they have reason to value) or social inclusion
(the exclusion of particular groups from participating wholly and mean-
ingfully in the society in which they live)42. That said, a monetary focus is
well attuned to capturing many of the channels by which households escape
or fall into poverty, including those related to income, prices, assets, pro-
ductivity, and opportunity43.
A corpus of scientific and media literature—some of which the study
will present below—depicts the disproportionate and severe impacts that
climate change and energy infrastructure development have on women,
youths, or those in poverty. This framing can be implicit and complicit in
presenting such groups as lacking agency and competence or depicting
them in need of help or rescuing from others, including depictions of
women as weak (as always vulnerable victims to climate change) or virtuous
(as holding superior values and norms about the environment)44,45.This
study subscribes to neither narrative, and instead represents the complex
viewpoints of people identifying as women, youth, or in poverty in their own
frames of reference. In sum: we aim to incorporate the complexity and
variety of views and perspectives concerning climate change and nature,
which is also intended to better reveal heterogeneity in values and
preferences.
Gender, youth and poverty in climate vulnerability and
protection
This section summarizes insights from three different bodies of evidence,
drawing from the broader work on climate protection, climate intervention,
and climate change mitigation, including climate preferences and behavior.
The extant literature tends to identify disparities in climate change impacts,
and disparities in access or burdens related to low-carbon technology
adoption, by gender, age, and poverty (inclusive of income and class), all of
which are relevant for geoengineering and its climate interventions.
Importance of gender
Climate change has gendered dimensions across themes as diverse as the
impacts of climate change, disparities in concern over climate action, dif-
fering values and norms, and disparities in the adoption of, or impacts to,
low-carbon technologies, policies and practices.
Firstly, women are much more likely to suffer death or injury from
severe climate change events, and they are far more vulnerable to mal-
nourishment and poverty when climate change threatens food and water
security46,47. During droughts, it is women that are the most likely to starve—
intentionally or unintentionally—when food insecurity becomes severe48.
Women are known to have poorer resistance to changing disease vectors
and disease outbreaks compared to men, especially when combined with
poorer access to medical care and health services49.Womenarealsomore
prone to the impacts of extreme heat, given that they differ from men in their
physiological compensation to elevated temperatures, and that women
dissipate less heat by sweating, have higher working metabolic rates, and
have other biological vulnerabilities to heat50. These vulnerabilities to heat
become even more pronounced when women are pregnant, and prolonged
exposure to high temperatures are even associated with a greater risk of
menarche, still birth, congenital birth defects, and preterm delivery—
regardless of maternal ethnicity or age, with younger mothers having an
even greater risk of negative outcomes50–52. Furthermore, women experience
greater deposition of inhaled particles in their lungs from air pollution, are
more sensitive to toxicological exposure, suffer from higher rates of anemia,
are at greater risk of violence (including sexual violence) and suffer dis-
proportionate mortality and decreased life expectancy during and after
disasters53,54.
More generally, women are disproportionately affected by water
scarcity, and tend to be more gravely impacted by water mismanagement,
yet they face greater barriers than men in participating in water governance
bodies55. An investigation into recovery efforts following the 2010 Pakistani
floods revealed that not only were a majority of the victims women, but also
that women systematically “were either overlooked in the distribution of
relief or were unable to reach places of relief distribution due to social norms
that restricted their mobility”18. Climate change impacts have a greater
negative impact on the mental health of women, too, given that it is generally
women who have caregiving responsibilities and disproportionately carry
the burden of cleaning, cooking, and ensuring family wellbeing during
disasters or floods, leading to significant mental trauma and stress54.Evi-
dence has even revealed that climate disasters have led to increased cases of
depression and suicides among women in the Maldives due to climate
change related displacement and destitution48.
Secondly, and relatedly, differences in concern for climate change exist
between men and women. In an extensive and authoritative review of the
literature, Pearson and colleagues evidenced a consistent gender gap in
environmental concern in that women typically express greater levels of
concern than men and demonstrate heightened perceptions of risks across a
broad range of environmental hazards37. That same review noted that
women have a greater likelihood of believing that climate change is real and
caused by humans, perceive a greater numberofclimatechangerisks,
express more knowledge about it, are less likely than men to endorse
denialist claims, and are less likely than men to express skepticism about
climate change on social media. This holds true across various cultures as
diverse as Australia, Canada, Italy, the United Kingdom, and United States37.
A recent survey in the UK focusing specifically on the threat of climate
tipping points also established greater levels of concern among women56.
Another study from the solar geoengineering literature identifieswomen (in
the UK) as more likely to place trust in climate science57.
Thirdly, another body of research emphasizes gendered values or
norms—suggesting that women hold more pro-environmental or pro-
sustainability values that they can transmit or pass onto others, especially
their children58–61.“Gender Socialization Theory”suggests that “females
tend to be socialized toward a feminine identity stressing attachment,
empathy, and care, and males tend to be socialized toward a masculine
identity stressing detachment, control, and mastery in many countries
around the world”62. According to this theory, women also have a greater
proclivity to express compassion, to show an “ethics of care,”to be more
nurturing, and to be more concerned about the needs of others as well as the
needs of the environment or biosphere37. Relatedly, women are also found to
be more averse to tampering with nature63, a factor which has proven
influential for predicting support for climate-intervention technologies64,65.
Lastly, evidence reveals gendered disparities in technology adop tion, or
preferences for low-carbon practices or policies. Due in part to more
restrictive gender roles and also in part to being more prone to poverty,
multiple studies have shown that womenintheGlobalSoutharelesslikely
to adopt low-carbon agricultural practices or efficiency improvements on
farms, including precision agriculture, ploughing, drought resistant seeds, or
advanced management techniques48,66–68. A comparative lack of literacy to
men and lack of ownership of land preclude women from pursuing a
multitude of climate adaptation practices in the Global South69,where
women are generally integrated poorly into new technology sectors70.
Women are often excluded from household energy decision making, but
more immediately and severely suffer the impacts of energy insecurity;
womenalso tend to lack the skills needed to maintainand repair innovations
such as new cookstoves or solar home systems71.
The patriarchal nature of gender relations in many cultures demand
that women subsume responsibility for the private sphere and the house-
hold in nurturing and caring roles, thereby limiting women’sfreedomto
assume positions of power or participation in the labor market, and rein-
forcing gender inequality in patterns of mobility72–74.Itisalsomenwho
report greater usage rates for electric vehicles, greater chances for EV
ownership, and greater distances traveled by cars75. Women are also
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disproportionately affected by the burdens of electronic waste that arise
from many low-carbon innovations such as solar panels or electric vehicle
batteries, especially those that affect fertility and morbidity76.
Violence is another category of harm differentiated by gender. Women
are more at risk to technology abuse and even domestic violence pertaining
to the adoption of smart homes, smart meters, household energy control
systems and digitalization of energy practices77,78. Natural gas extraction and
shale gas production, considered a bridge to low-emissions economies by
some, also perpetuates increased rates of prostitution, sexually transmitted
diseases and stillbirths, and erode food security, all which disproportionately
affectwomen79. This is because it is women who are more at the risk of being
coerced into sexual trafficking, who bear the burden of pregnancy, and who
usually are responsible for food preparation in the home. The implication is
that women could be more vulnerable to the violent impacts of any natural
disasters or technology deployment caused by geoengineering.
Governance and lack of procedural governance is a final gendered
dimension. Energy and climate policymaking around the world has also
been critiqued for not adequately engendering the participation of
women80–82. As Pearse summarized in their review, “in climate mitigation
and adaptation projects in the Global South, women have comparatively few
opportunities to participate in and influence decision-making”40.
Importance of youth and age
The literature on youth and age is not as extensive as that on gender, but the
extant evidence does tend to focus on two areas: disparities in climate change
impacts, and in technology adoption and preferences, especially a will-
ingness to protest and undertake direct action.
Youth are more susceptible to the impacts of climate change than
adults across a range of physical and mental dimensions of health. The
WorldHealthOrganizationsuggeststhatchildrenwillsuffermorethan80%
of the injuries, illnesses, and deaths attributable to climate change83.The
greater vulnerability of youth to climate change impacts—notably fatalities
and injuries during disasters, heat stress, exposure to environmental toxins,
and increased exposure to diseases in warmer temperatures—can be
explained in part by physiology. This includes their less mature physiolo-
gical defense systems, the fact that they interact with their environment
more directly, that they depend on adults or others more for care, and that
they accumulate risks and threats over a longer period of time (since their
lifetimes are largely to unfold in the future)84. In Africa, it is youth who
constitute the largest demographic group, and the largest labor force
dependent on the land, but this only exposes such youth to the impacts that
climate change is having on water quality and availability85. Psychological
and mental health impacts abound as well for youth, including posttrau-
matic stress disorders, depression, anxiety, learning problems, sleep pro-
blems, and difficulties in learning86. Youth already struggling with
depression and anxiety are at an elevated risk of worsening symptoms in the
face of climate impacts, and young people are extremely vulnerable to
depression when faced with climate-induced parental injury87. Troublingly,
increased levels of domestic violence against youth and children have been
reported following climate-change related events such as hurricanes, and
education is jeopardized whenever extreme weather events destroy schools,
or limit the ability for families to send their children to school84.Resource
depletion and degradation of the environment have even been linked to
violent conflict between youth groups over the scarce use of resources in
places such as sub-Saharan Africa88.
Although youth are historically underrepresented in decision-making
processes, especially those below the voting age, they still possess differ-
entiated preferences for technology adoption and disparate trends in pre-
ferences for climate action. Youth in many parts of the globe are more
connected digitally, and more likely to independently assess climate change
science and other information about the environment via the internet89.In
India, youth are far more likely than adults to state that climate change is
occurring, and to express awareness of major international organizations
working on climate change90.Aperson’sagecaninfluence low-carbon
mobility patterns and preferences as well. Multiple studies have found that
the relationship between age and transport emissions takes on an inverse
u-shape with multiple turning points: both the young and old travel less than
households in the middle with children91,92. Electric vehicle interest is higher
among youth and younger adults, and that cohort also expresses the most
familiarity with electric mobility as well as the greatest importance attached
to the environmental impacts of automobiles93. Youth express greater
knowledge and awareness than adults on things like willingness to use
renewable energy94, or literacy over electric mobility brands, performance,
range, and price95,96. Youth are more likely to view ecosystem services as
important and more likely to view nature tourism as a deeper healing
experience97. Youth are also leading campaigns and “green carnivals”to
promote energy efficiency or community based climate science efforts98,99 as
well as expressing greater trust in science in general57. It is low-income youth
who are driving the adoption of solar energy in Tanzania100,andyouth
groups associated with Indigenous peoples that are aspiring for a future with
renewable energy rather than fossil fuels across India and the United
States101.Finally,numerousstudieshaveargued that youth are far, far more
likely to take direct climate action, to protest or strike, and to join social
movements committed to addressing climate change, envisioning such
activism as dutiful and disruptive41,85,102–104.
Importance of poverty and socioeconomic status
Our last demographic dimension is that of poverty and socioeconomic
status. According to the most recent data from the World Bank, approxi-
mately 9.2% of the world, or 719 million people, live in extreme poverty, or
what the World Bank calculates as less than $2.15 a day, which makes them
unable to meet basic needs105. Using a different estimation technique, 1.2
billion people in 111 developing or Global South countries live in multi-
dimensional poverty, accounting for 19% of the world’spopulation,
including 593 million children. However, poverty is a Global North problem
as well, with the same data suggesting that more than 37 million people were
living in poverty in the United States, of which 11.1 million were children105.
Poverty is intimately connected with class (people’seconomicorsocial
status) and income (people’s money, property, or financial resources). Such
stark levels of poverty intersect with climate change in four meaningful
ways. It creates disparities in carbon emissions, impacts beliefs on climate
change, generates differential climate impacts, and reflects disparities in
technology adoption.
Poverty, income, and employment can have strong effects on carbon
emissions or knowledge about climate change risks. Multivariate studies
that include income and employment status tend to note that unemploy-
ment or lower income is negatively associated with carbon emissions
regardless of location106, especially for home energy services such as
heating107. Full-time employment and rising income tends to increase
consumption levels which can increase both primary emissions and sec-
ondary impacts such as trafficcongestion
91,108, contributing to dis-
proportionately high emissions of people with high socioeconomic status109.
Other studies have noted that when demographic variables such as race,
education, or politics are accounted for, income still has a unique positive
effect on whether people believe that climate change is occurring, and are
knowledgeable about climate actions37.
Moreover, attempts at theorizing why income and class shape
decision-making or behavior have hypothesized that differential vulner-
ability and sensitivity to effects of climate change exist among individuals of
lower socioeconomic status compared to individuals of higher socio-
economic status. That is, wealthier individuals may have lower risk per-
ceptions related to climate change because theyhave the economic means to
address threats posed by climate change, whereas poorer people might feel a
heightened sense of vulnerability to negative impacts of climate change
because they lack the financial means to address such threats37.Socio-
economic status—including both income and educational attainment—
also predicts stronger partisan divides on climate change beliefs and risk
perceptions37. Ballew and colleagues found in very large sample of U.S.
adults (N = 20,024) that across all beliefs, higher education and higher
income are very strong determinants of the degree to which individuals
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support climate policy or view climate change as a risk rather than an
opportunity110. Bellamy also identified significantly greater concern about
climate tipping points among higher “social grade”respondents in the UK56.
Other research reports on how poverty and low socioeconomic status
are key factors that increase the propensity for individuals and house holdsto
be physically harmed by climate change impacts, acting as a threat multi-
plier. As Leichenko and Silva write, “While climate change is never seen as a
sole cause of poverty, research has identified numerous direct and indirect
channels through which climatic variability and change may exacerbate
poverty, particularly in less developed countries and regions”42.Thereasons
behind this heightened vulnerability are manifold, and include: lower
income households have fewer assets to help them recover from climate
shocks; depend more on climate sensitive sectors such as agriculture, for-
estry, fishing, or pastoralism for their livelihood; are more likely to live in
areas of higher exposure to climate extremes; are less likely to have insur-
ance; and are less likely to have the skills and capabilities to handle stress
including higher levels of illness, mental stress, and stigmatization. As a case
in point, poverty and income distribution are one of the most significant
factors in determining one’s vulnerability to food insecurity caused by cli-
mate change111.
In addition, climate change can have longer, structural impacts on
“poverty traps,”the creation of self-reinforcing mechanisms such as market
failures, inadequate legal protections, or even social norms that make it
difficult for households to escape poverty42. Other studies have noted that
“an increase in climate change vulnerability is positively associated with
rising income inequality”112 and that “poorpeople may be heavily affected by
climate change even when impacts on the rest of the population remain
limited”113. In Nigeria, the poorest 20% of the population are 50% more
likely to be affected by a flood, 130% more likely to be affected by a drought,
and 80% more likely to be affected by a heat wave than an average
Nigerian113. The relationship between poverty and climate change can swing
the other way as well. In India, a household affected by droughts in the past
was 15 times more likely to fall into poverty114. Strong, consistent findings in
the literature suggest that poor people are more exposed to environmental
shocks and stressors and are more vulnerable to the impacts of natural
disasters or hazards, losing generally a greater share of their assets than other
socioeconomic groups. As Fig. 1indicates based on a qualitative and
descriptive study, whereas many studies have explored the exposure of poor
and non-poor households to climate hazards, all but one case found that the
poor were more vulnerable than non-poor115.
Additionally, poverty and socioeconomic status have been found in the
literature to predict cooperation and pro-social behavior, and a willingness
to adopt low-carbon solutions. For instance, a series of experiments con-
cluded that compared to people from higher-social-class backgrounds,
those from lower-social-class backgrounds—measured both in terms of
resources and perceived class rank—were more charitable toward others116.
Other work has noted that people’s perceptions of their relative position in a
social hierarchy, as well as subjective perceptions of resource scarcity and
diminished rank, predict psychological motives, behaviors, and important
life outcomes117,118. Still other work reveals vulnerabilities for low-income
households who are unable to adopt new technologies, locking them into
high-carbon and thus more vulnerable lifestyles. This encompasses those
who are excluded from household solar energy schemes or electric vehicle
charging due to lack of financial resources119, or that risks from active
transport—such as pedestrian and bicycle crashes and fatal cyclist crashes—
tend to occur more often in low-income communities120. Research has also
shown how more progressive or costly energy and climate policy, including
carbon taxes, tends to disproportionately burden low-income homes121.
Research design
To investigate the prospective importance of gender, age and income on
perceptions of climate interventions, this paper presents findings from a
large-scale, cross-country set of surveys involving n = 30,284 participants in
30 countries (see Fig. 2). The surveys were nationally representative in terms
of age, gender, and geographic region within those countries, and our
approach also had quotas set for income and education. The survey
instrument examined all ten climate intervention technologies, broken
down into three technology groups: SRM (stratospheric aerosol injection,
marine cloud brightening, space-based geoengineering); ecosystem-based
CDR (afforestation and reforestation, soil carbon sequestration, marine
biomass and blue carbon); engineered CDR (direct air capture with carbon
storage (DACCS), bioenergy with carbon capture and storage (BECCS),
enhanced weathering, biochar).
Design of the survey instrument
Our survey instrument was conducted online across multiple platforms
including those for mobile or handheld devices, as well as those using laptop
or desktop computers. We ran the survey in a total of 30 countries with 19
languages (see Supplementary Table 1). Criteria for selecting countries
included region, type of economy, population size, political organization,
and carbon storage or solar geoengineering innovation potential, among
others. Each survey had at least N= 1000 respondents for each country, and
was nationally representative in terms of age, gender, and subnational
geographic regions along with broadquotasforeducationandincome.
The survey was designed to investigate public perceptions of climate-
intervention technologies by means of different thematic dimensions such
as perceived risks and benefits, support or lack of support for each climate
intervention, support for various policy incentives as well as support for
various policy restrictions, along with questions on sociodemographic
characteristics, beliefs about climate change and environment, and trust in
institutions and actors,and credibility of sources of information (Supple-
mentary Table 2).
Statistical analysis
Data were analyzed using SPSS v28.0. Descriptive statistical analysis
included frequency distributions and comparison of group means. Sig-
nificance testing employed Mann–Whitney Utests(forgender,povertyor
not, and Global North vs Global South). In all analyses, the dependent
variables were support for climate interventions. One-way analyses of var-
iance were used for assessing the relationship with age (these results were
verified for robustness with non-parametric Kruskal Wallis H tests). Three-
way (factorial) analyses of variance (ANOVAs) were used to test for
Fig. 1 | Assessing the impact of climate hazards by groups self-identifying
themselves as in poverty. Source: Hallegatte, Stephane. Shock waves: managing the
impacts of climate change on poverty. World Bank Publications, 2016. Note: Ban-
gladesh 1 and 2 refer to separate studies done on the same country.
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interaction effects when including multiple independent variables in the
same model. We ran ten three-way (factorial) ANOVAs with age, gender,
and income (all binary) as the factor variables. We then ran ten additional
three-way ANOVAs withage, gender,and GlobalNorth vs GlobalSouth (all
binary) as the factor variables. Eta-squared (for ANOVAs) and r (for
Mann–Whitney Utests) effect sizes are reported throughout the analyses.
Given that the three support measures were strongly correlated (i.e., the
lowest Spearman’s rho correlation was 0.956 between small-scale field trials
and broader deployment), we constructed a composite measure for support
by taking the average of them. From a principal component analysis (var-
imax rotation), a one-factor solution was obtained for each of the ten
technologies. Reliability was more than sufficient, with values of Cronbach’s
αfor all technologies > 0.90.
Ethical review statement
All components of the research were granted ethical approval by relevant
authorities at Aarhus University. Full and informed consent was given by
all participants before the beginning of the study, along with all parti-
cipants being notified about the fact that their data would be handled in a
fully anonymous manner and in complete accordance with the General
Data Protection Regulation and any other pertinent data-security reg-
ulations, that any data would be analyzed in an aggregate fashion and
would not be personally identifiable in any way, and that they had the
right to withdraw their participation at any time. In addition, any
questions about particular data being sensitive, including those that
emerged in the course of the survey(s), were handled by erring on the side
of caution and not asking a question in a given market. For instance,
from the outset we decided not to ask about “political views”in China
and the question on whether one self-identified as a “member of an
ethnic minority or indigenous group”was removed in Estonia following
feedback from participants.
Contributions of the approach
By conducting surveys with such scale and scope, this exercise helps to
provide a global baseline of SRM and CDR perceptions, in response to the
information we provided about these approaches to SRM and CDR (see
Supplementary Information). Given the newness and lack of public famil-
iarity with the technologies, the determination was made to avoid ‘priming’
participants by valenced descriptions that overly focused on risks versus
benefits, or vice versa, and as much as possible to talk about how technol-
ogies would work rather than what might go wrong, especially where sig-
nificant uncertainty still prevailed122.
The distinction between ecosystem-based CDR and engineered or che-
mical CDR might be imperfect, but we defend it on the basis that the
categories entail different kinds of resource and energy demands as well as
regarding how land is used, such that these differences may be significant
across geographies and polities. We group carbon removal technologies
based on the classifications and typologies in the literature offered by
Morrow et al.123,Lowetal.
124, and Sovacool et al.125 These all distinguish
nature-based solutions (afforestation, soil management, blue carbon) from
engineered solutions (biochar, enhanced weathering, DAC and BECCS).
While there are obvious connections between the ecosystem-based and
engineered carbon removal options, distinctions are made based on the
degree of technical sophistication and maturity, capital intensity, and supply
chains for carbon storage. Ecosystem-based approaches are those that fea-
ture a more prominent role of biological, ecosystem-based sinks with a
relative focus on applications in terrestrial and marine environments.
Engineered approaches differ by being more technological or chemical in
nature, with a relatively stronger reliance on antecedent systems of resource
extraction or mining, carbon capture and storage as well as transportation
infrastructures. Biochar and enhanced weathering represent more hybrid
approaches that blur these distinctions, but we classified them as more
engineered than nature-based, at least in a comparative aspect. While we use
Fig. 2 | Overview of 30 Countries Surveyed on Climate Intervention Technologies. Note: Gray shaded areas indicate those where we conducted nationally representative
surveys of the public, white color indicates no data. Diagram has been modified from Baum et al.11.
https://doi.org/10.1038/s43247-024-01800-1 Article
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these categories for the presentation of our findings, we strictly avoided
introducing any of the approaches to the survey participants as more or less
natural, as engineered or ecosystem-based, thus attending to a potential
framing effect or biasing related to “naturalness”.
As participants were asked to evaluate multiple technologies within a
technology category, this approach enables us to gain insights into relative
preferences between technologies. Also, by asking members of the public for
the first time in a survey on climate-intervention technologies about their
support for different technological approaches, we can draw a distinction
between the level and nature of support among the two types of CDR and
SRM, as well as examine how such support varies across age, gender, and
income. We must note that although each respondent only answered
questions about either three or four technologies (i.e., one of the three
technology categories), all respondents were randomly assigned to one
technology category and approximately one-third of the respondents from
each country were assigned to each category. Van den Brakel126.identifies
randomized message treatments within probability samples as a means of
simultaneously establishing strong internal and external validity (respec-
tively via the random assignment and random selection). Furthermore, in ‘a
self-weighted sample design where sampling units are allocated pro-
portionally to the treatments’, Analysis of Variance tests (ANOVAs) can be
used to examine differences across groups126.
Our main dependent variable for all of our analyses is level of support
for the ten climate interventions; for each intervention, we provide a com-
posite value for support, generated from three measured support items
(related to support for research, small-scale trial activities, and broad
deployment, respectively). For extensive details about our research design,
see Supplementary Information. Results of the full dataset, arising from the
survey across the 30 countries, have been reported elsewhere (Baum et al.11);
however, that prior analysis analyzed data only aggregated at the country
level, whereas the focus in this article is heavily on the relationship between
multiple individual-level demographic variables and support for the climate
intervention technologies.
In terms of limitations, the survey instrument and its distribution across
the 30 national samples offers extensive “horizontal”coverage without similar
depth in terms of “vertical”coverage, either across time or having nested levels
of geographical representation within nations. Although desirable in many
contexts, a longitudinal design simply would not be possible with varying
attrition rates across 30 countries and the necessary differences in how data
collection needs to occur in different nations. In some contexts, it is not
possible to reliably conduct repeat sampling, to say nothing of the expense. As
we are seeking to identify intersectionally marginalized populations, includ-
ing Global South nations is more important in sample selection than follow-
up data collection with those members of the Global North about whom we
already know the most from extant published research33. Another limitation
in data analysis is that each respondent only answered questions about one of
the three technology groups. This complicates in some ways comparisons
across the three groups, and introduces potential of methods effects affecting
responses. Nevertheless, random assignment and ensuring that relatively
equal number of respondents from each country were assigned to each
technology group helps to mitigate such concerns.
Results and discussion
For all our analyses, we considered ten dependent variables: these are
composite measures of support for each of the climate intervention tech-
nologies. (See Supplementary Information for additional details on con-
struction of these composite measures.) We provide data for the results for
all dependent variables in the main text or the supplementary materials; only
indicative results are present in figuresinthemaintextduetothenumberof
analyses run. We discuss all results in the main text. The means, standard
deviations, and variance for the ten dependent variables are presented in
Table 1. Mean support for three of the climate interventions was over 4.0 on
the five-point scale, indicating individuals were somewhat supportive on
average. For the other seven climate inte rventions, the mean lay between 3.0
and 4.0, indicating a level between ‘neither reject nor support’and ‘some-
what support’.
Again, note that for these relatively unknown technologies, respondent
perceptions will be based heavily on the understanding they gained from the
information provided in the survey (see Supplementary Information), and
each respondent only answered questionsinrelationtoonetechnology
category—to keep the information provision and questioning to a reason-
able length. Respondents were randomly assigned, in equal numbers from
each country, to each technology category. Therefore, the respondents
providing their views on SRM are different people from the respondents
providing their views on the first set of CDR approaches, and again are
different from the respondents assessing the second set of CDR approaches.
The respondents rated each technology on a scale of 1-5 (strictly reject to
strongly support); they were not asked to rank order their preferences for
technologies. Whilst it remains possible that information from one tech-
nology could have influenced responses to the other technologies, or that
questions about one technology led to an ‘anchoring and adjustment
heuristic’, we believe the rating scale approach used always for reasonable
comparison across all ten technological approaches.
We also tested for significant differences among the groups of
respondents assigned to the respective technology categories (i.e., in terms of
gender, age, education, income, living in an urban (versus suburban or rural)
area, religiosity, political views, or self-identification as belonging to an
ethnic minority or indigenous group). Having found no evidence for such
significant differences, we thus identify no reason for any such extraneous
biases on how the groups respond to the information provided.
Table 1 | Descriptive statistics from our survey for support for ten climate intervention technologies
Climate intervention technology Mean Confidence interval, 95% Standard deviation Variance
Stratospheric Aerosol Injection (n=9943) 3.33 3.30–3.35 1.15 1.32
Marine Cloud Brightening (n=9953) 3.50 3.48–3.52 1.12 1.24
Space-based Geoengineering (n=9945) 3.40 3.38–3.43 1.20 1.44
Afforestation and Reforestation (n=10002) 4.43 4.41–4.44 0.77 0.60
Soil Carbon Sequestration (n=9973) 4.16 4.15–4.18 0.86 0.74
Marine Biomass and Blue Carbon (n=9954) 4.13 4.11–4.14 0.87 0.76
Direct Air Capture with Carbon Storage (n=9920) 3.73 3.71–3.75 1.04 1.07
Bioenergy with Carbon Capture and Storage (n=9926) 3.73 3.71–3.75 0.99 0.98
Enhanced Rock Weathering (n=9918) 3.48 3.46–3.50 1.11 1.24
Biochar (n=9922) 3.85 3.83–3.87 0.97 0.94
Note: Support for the technologies was measured on a scale of 1-5: 1 =strictly reject, 2 =somewhat reject, 3 =neither reject nor support, 4 =somewhat support, 5 =fully support.
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Bivariate analyses: gender, age, and income
Our initial analyses examine the individual effects of age, gender, and
income on support for the ten climate interventions. For parsimony of
display, in all tables and figures, our ten climate interventions will be
abbreviated as follows:
•Solar radiation management interventions: SAI (Stratospheric Aerosol
Injection), MCB (Marine Cloud Brightening), Space (Space-based
Geoengineering).
•Carbon dioxide removal group 1: Afforest (Afforestation and Refor-
estation), Soil Carbon (Soil Carbon Sequestration), Blue Carbon
(Marine Biomass and Blue Carbon).
•Carbon dioxide removal group 2: DACCS (Direct Air Capture with
Carbon Storage), BECCS (Bioenergy with Carbon Capture and Sto-
rage), ERW (Enhanced Rock Weathering), Biochar (Biochar Added
to Soil).
Gender. We ran ten Mann–Whitney Utests to examine variation in
support for climate interventions between males and females (due to the
quite small number of participants selecting “Other”or “Prefer not to
say”, these were excluded from the analysis). Due to our very large survey
sample size, differences in support varied significantly (at p< 0.05, after
Bonferroni corrections) between males and females for the following:
Space, Afforest, Blue Carbon, DACCS, BECCS, and ERW. Nevertheless,
as Fig. 3indicates, in no instance was the mean difference in support
larger than 0.14 (on a scale of 1–5) between the genders; effect sizes were
uniformly small, ranging from an rof 0.00 (for SAI) to 0.08 (for DACCS).
In all six instances where the genders differed significantly on the level of
support, males supported the intervention more than females.
There is often a presumption that men are more likely to prefer
technical climate intervention than women, emerging from some earlier
studies using smaller sample sizes in more limited national contexts. An
older survey in the UK found that men tended to be more supportive of
climate geoengineering127, whereas a follow-up study by some of the same
authors found no such difference128. Another survey in Switzerland in 2018
found that men were more likely to support DACCS and SAI129—though
there was no effect for eight other technologies, including all ecosystem-
based CDR options. In a more recent survey in the UK, men appraised
engineered (DACCS, BECCS) and ecosystem-based CDR approaches
(afforestation, wood in construction) more highly130. Men were also more
likely to support a proposed DACCS project in a survey in the Pacific
Northwest of North America—though only after receiving a tutorial on the
need for carbon removal131. Meanwhile, a US survey conducted in 2019
identified women as most likely to support the use of soil carbon
sequestration with biochar—with no differences for the engineered CDR
options (DACCS, BECCS)64.
However, our findings contradict a nationally representative survey on
solar geoengineering in the Fall of 2016 distributed to the United States
electorate which found that support is higher among women than men21.
Having inquired into specific attributes of the technology, Mahajan et al.
posited that “women place more importance on the high speed and low cost
of solar geoengineering than men do, whereas the importance of the risk of
moral hazard and unpredictability does not differ across genders.”Women
in a 2013 German survey were similarly more supportive of solar geoen-
gineering—though not afforestation or carbon capture and storage132.One
of the very few prior cross-country surveys to include a Global South
country (China), alongside five Western ones (UK, US, Canada, Germany,
Switzerland) also found no variation in support by gender, in any country133.
The same was also true for two enhanced weathering-specific surveys, one in
the UK, US, and Australia, and one in only the UK134,135.Inoursample,even
when looking only at the United States sub-sample, males were still more
supportive of every climate intervention, with the relationship statistically
significant in two cases and the magnitude of difference higher in most cases
compared to for the full international sample.
Age. All ten climate interventions show a clear effect of age in analysis of
variance tests. Non-parametric Kruskal–Wallis Htests revealed sub-
stantially similar results, based on significance, mean ranks, and eta2
effect sizes. All three interventions in carbon dioxide removal group 1
(afforestation and reforestation, soil carbon, and blue carbon) reveal a
negative effect of age on support, with youth (18-24 years) having sig-
nificantly lower support than every other age category, after including
Bonferroni corrections for multiple comparisons (Fig. 4). The opposite
effect was true for the other seven interventions. For none of these seven
interventions did any age group have significantly higher support com-
pared to youth. For BECCS, ERW, and Biochar, youth support was higher
than 55-74 year olds. For DACCS, Space, and MCB, youth support was
higher than 45–74-year old. For SAI, youth support was higher than
35–74-year old. The effect of age on support was strongest for MCB and
SAI (largest effect sizes, though all effects are “small”in nature; see Fig. 4).
In general, our findings support previous literature that shows a gen-
erally negative relationship between age and support for climate
interventions128–130. However, the comprehensiveness of the current study,
by including ten different climate interventions, adds nuance. We reveal a
clear preference for more ecosystems-based interventions amongst older
groups. Indeed, the actual level of support for the interventions shows that
all age groups prefer the three ecosystem-based interventions the most (i.e.,
Fig. 3 | Support for climate interventions by gen-
der identified in our survey. Note: Sample sizes for
Fig. 3: SAI (female = 4981, male = 4924), MCB
(female = 4990, male = 4925), Space (female = 4984,
4923), Afforest (female = 4954, male = 5012), Soil
Carbon (female = 4937, male = 4999), Blue Carbon
(female = 4931, male = 4986), DACCS (female =
4918, male = 4967), BECCS (female = 4920, male =
4971), ERW (female = 4917, male = 4966), Biochar
(female = 4920, male = 4967). Note: Support for the
technologies was measured on a scale of 1-5:
1=strictly reject, 2=somewhat reject, 3=neither
reject nor support, 4=somewhat support, 5=fully
support.
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they have the highest mean in all six age categories). However, because
support declines with age for the seven interventions and increases with age
for the other three, this means the difference in levels of support according to
age across the climate interventions grows increasingly large as people
become older. This partially affirms earlier studies. In their own survey of the
UK, Corner and colleagues found that older people tended to have less
support for carbon removal and solar geoengineering128. They speculated
that this could be because older participants were more unfamiliar with the
options, or that they may have more experience with the hype cycles that
often surround new technologies that don’t end up being adopted. They also
hypothesized that older participants are more likely to be skeptical of
technological fixes.
At the same time, Carlisle and colleagues identified a similar pattern in
a cross-country survey of Western countries (US, UK, Australia, New
Zealand) of decreasing support by age for six engineered CDR and solar
geoengineering approaches136. Similarly, a survey in the US by Sweet et al.
found younger groups more generally supportive of CDR (except for
afforestation)64. Adding to this, Spence and colleagues revealed in their
cross-country survey that younger groups were more likely to support
enhanced weathering in the US—there was no effect though in the UK or
Australia134. Interestingly, in their survey on DACCS focusing on a potential
project in the Pacific Northwest of North America, Satterfield and colleagues
revealed that older age had a negative effect on support, but only before
receiving a tutorial on the need for carbon removal –afterthistutorial,the
effect disappeared131.
We do note that not all surveys are consistent (particularly for
CDR); for instance, a survey in the UK found that older individuals tend
to appraise engineered and ecosystem-based CDR options more
highly130. However, looking narrowly at differences between age groups—
since this is the only other study besides the present one (to our
knowledge) to, e.g., distinguish those 18–24—the youngest group is never
the one significantly appraising any of the options more negatively, but
rather those slightly older (i.e., 25–34 or 35–44). In any case, Dunlop and
Rushton found in their own work with young adults from Albania,
Belgium, Czech Republic, the Netherlands, Poland, Portugal, and the
United Kingdom that youth were more likely to have anxiety over cli-
mate change impacts, and to promote solutions to address it23.Intheir
study, young adults talked about the importance of using geoengineering
to “empower first, shame later”and that using geoengineering was like
helping treat a terminal illness facing the planet. They lastly documented
youth being frustrated with adults (the “older generation”) for failing to
take proper action on climate change.
Income. Ten Mann–Whitney Utests examined variation in support for
climate interventions between respondents in poverty and those not in
poverty. We used a threshold of $6.85/day, defined as the threshold for
poverty in higher income countries by the World Bank (Table 2). Of the
30,284 respondents to our survey, 999 (or 3.3%) met this criterion. We
acknowledge that what poverty means across the thirty different coun-
tries varies widely, and that there is likely notable additional variability
within individual countries. We selected a relatively high poverty defi-
nition to capture as many relevant respondents as possible in this defi-
nition. Noting that all of our respondents identified as being in poverty
come from only eight of the thirty countries, the country of residence
clearly has an effect on whether someone meets our definition of being in
poverty or not (see notes for Table 2). This poverty analysis is admittedly
imperfect, but is a first attempt at offering empirical evidence that begins
to shed light on the relationship between poverty and reactions to climate
interventions—to help inform future research directions.
Differences in support varied significantly (at p< 0.05, after including
Bonferroni corrections for multiple comparisons) between impoverished
versus not for the following: SAI, MCB, Space, and DACCS (Fig. 5). These
represent all three SRM methods and the most engineered CDR approach.
Effect sizes were quite small for all four of the significant differences (ranging
from r=0.03to r= 0.06). In all instances where those in poverty differed
from those not in poverty, the respondents in poverty were more supportive
of the climate interventions.
Surprisingly little research on perceptions of climate-intervention
technologies has considered income, let alone poverty. Of the few studies
that do, income tends to be included as a covariate, without the findings
reported137.Theabovefindings are thus something of a first in the literature,
since the only research which to our knowledge considers class and income
is restricted to the UK context. Of note, Bellamy found that appraisal of CDR
optionsishigheramongthoseofhighersocialgrades,whoalsotendto
report being more aware of climate tipping points56,130. Class, particularly in
the rather unique setting of the UK, captures something quite different than
income or poverty.
Global South vs Global North. Income, and whether someone falls
below a poverty threshold, are individual indicators. Another more
widely used societal-level indicator of economic wellbeing is whether
someone is from a Global North or Global South nation. We split our
sample into Global North (N= 19,201) and Global South (N= 11,083):
•Global North countries (N =19): Australia, Austria, Canada, Den-
mark, Estonia, France, Germany, Greece, Italy, Japan, Netherlands,
Fig. 4 | Support for climate interventions by age
category identified in our survey. Note: Eta2values
for ANOVAs on each intervention: SAI (.027), MCB
(.032), Space (.022), Afforest (.011), Soil Carbon
(.005), Blue Carbon (.005), DACCS (.021), BECCS
(.013), ERW (.014), Biochar (.012). F statistics for all
ten ANOVAs are significant at p< 0.001. Sample
sizes for Fig. 4: 18–24 years = 4583, 25–34 years =
6569, 35–44 years = 6133, 45–54 years = 5627, 55–64
years = 4481, and 65–74 years = 2891. Each of the ten
carbon removal technologies had between 9918 and
10,002 respondents. Note: Support for the technol-
ogies was measured on a scale of 1–5: 1 = strictly
reject, 2 = somewhat reject, 3 = neither reject nor
support, 4 = somewhat support, 5 = fully support.
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Norway, Poland, Spain, Sweden, Switzerland, Turkey, United King-
dom, United States.
•Global South countries: (N= 11): Brazil, Chile, China, Dominican
Republic, India, Indonesia, Kenya, Nigeria, Saudi Arabia, Singapore,
South Africa.
The results of the Global North vs Global South comparison, using
independent samples t-tests, mirrored the findings from the income/pov-
erty analysis, only the effect was much stronger for the societal-level tests
(see Fig. 6). For Global North vs Global South, all of the differences in
support for each of the ten climate interventions were significant (at
p< 0.001, after Bonferroni corrections). The effect sizes were generally large,
with Cohen’s d values of MCB (0.57), Space (0.53), SAI (0.52), DACCS
(0.47), BECCS (0.42), ERW (0.40), Biochar (0.34), Soil Carbon (0.24), Blue
Carbon (0.19), and Afforest (0.06).
Multivariate analyses
To explore further the intersectionality amongst the core demographic
variables in our analysis, we ran a series of three-way (factorial) ANOVA
tests, with the climate interventions entered as dependent variables and
three binary variables entered as factor variables: age (binary—youth or
not), gender (male or female), and poverty status (yes or no). In each
ANOVA, we examined whether the demographics, and interactions
between each of the demographics, remained significant influences on
support for the climate interventions, when accounting for the multiple joint
influences.
Poverty was still a significant predictor of support for five interventions:
SAI, MCB, Space, DACCS, and BECCS (see Figs. 7–9, and Supplementary
Information). Youth was still significant for SAI, MCB, Afforest, Soil Car-
bon, and Blue Carbon. Gender was significant only for DACCS and BECCS.
Across the ten climate-intervention options, three general patterns emerged.
Table 2 | Poverty rates (% below $6.85/day) in study countries and in our sample identified by our surveya
Country % in country below $6.85/day % in sample below $6.85/day % in country below thenational poverty lineb
1 Nigeria (n=1008) 91 14 40
2 Kenya (n=1006) 86 34 36
3 India (n=1018) 84 0 22
4 South Africa (n=1016) 62 9 56
5 Indonesia (n=1002) 60 16 10
6 Brazil (n=1007) 28 0 No data
7 China (n=1008) 25 0 0
8 Dominican Republic (n=1002) 23 15 21
9 Turkey (n=1024) 13 4 15
10 Chile (n=1010) 8 6 11
11 Greece (n=1005) 4 0 20
12 Spain (n=1005) 3 0 22
13 Italy (n=1002) 2 0 20
14 Australia (n=1019) 1 0 No data
15 Austria (n=1005) 1 0 15
16 Canada (n=1005) 1 0 No data
17 Estonia (n=1006) 1 0 21
18 Japan (n=1011) 1 0 No data
19 Norway (n=1002) 1 0 13
20 Poland (n=1006) 1 2 15
21 Sweden (n=1024) 1 0 16
22 United Kingdom (n=1028) 1 0 19
23 United States (n=1000) 1 0 No data
24 Denmark (n=1010) 0 0 12
25 France (n=1003) 0 0 14
26 Germany (n=1025) 0 0 16
27 Netherlands (n=1018) 0 0 14
28 Switzerland (n=1003) 0 0 16
29 Saudi Arabia (n=1002) No data 0 No data
30 Singapore (n=1004) No data 0 No data
aWe calculated percentage of the sample below the $6.85/day threshold by using our survey data on respondents’monthly household income in localcurrency, converted to USD. This is a conservative
estimate, because we only have data on household income, whereasthe $6.85/day poverty metric relates to personal income. Additionally, for some countries (e.g., Brazil, China, and India), we report 0% in
poverty because the lowest response category on our income variable has an upper limit too high to determine whether the respondent meets the poverty threshold or not. For example, the lowest income
category for China is monthly income of less than 4000 yuan; however, 4000 yuan would equate to an income of 17.83 USD per day, which is substantially higher than the $6.85/day threshold. Consequently,
we cannot determine whether any of our Chinese respondents fall below the poverty line which we designate. Therefore, only if the lowest category falls entirely below the threshold of $6.85/day, can we
include respondents in that category as meeting the definition for poverty (e.g., Kenya’s lowest income category in our survey is monthly income below 15,000 Kenyan shillings, which equates to a daily
income of less than $3.71—placing the entirely income category below the $6.85/day threshold). However, if the upper limit of the lowest category extends higher than $6.85/day, we cannot reliably identify
any survey respondents as impoverished.
bData from World Bank (2023). Poverty headcount ratio at national poverty lines (% of population). https://data.worldbank.org/indicator/SI.POV.NAHC.
https://doi.org/10.1038/s43247-024-01800-1 Article
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Fig. 5 | Support for climate interventions by
income identified by our survey. Note: Support for
the technologies was measured on a scale of 1-5:
1=strictly reject, 2=somewhat reject, 3=neither
reject nor support, 4=somewhat support, 5=fully
support.
Fig. 6 | Support for climate interventions by Glo-
bal South vs Global North identified by our sur-
vey. Note: Support for the technologies was
measured on a scale of 1-5: 1=strictly reject,
2=somewhat reject, 3=neither reject nor support,
4=somewhat support, 5=fully support.
Fig. 7 | Support for marine cloud brightening by
age, gender, and poverty identified in our survey. *
In a three-way ANOVA, poverty (partial eta2= 0.02)
and youth (partial eta2= 0.00) were significant (at
p< 0.05), but gender, all three two-way interaction
effects, and the three-way interaction were non-
significant. F statistic for the ANOVA was sig-
nificant at p< 0.001. Sample sizes: Youth not poverty
female = 729, Youth not poverty male = 603, Youth
poverty female = 68, Youth poverty male = 71, Older
not poverty female = 4096, Older not poverty male =
4158, Older poverty female = 97, Older poverty male
= 93. Note: Support for the technologies was mea-
sured on a scale of 1-5: 1=strictly reject, 2=somewhat
reject, 3=neither reject nor support, 4=somewhat
support, 5=fully support.
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First, there were instances in which poverty and youth both shaped support
for climate intervention, but no interaction effects were present. This is seen
in SAI and MCB (Fig. 7). Higher support comes from youth and those in
poverty.
Second, there were instances in which gender and poverty both shaped
support for climate intervention, but no interaction effects were present.
This is seen in DACCS (Fig. 8) and BECCS. Higher support comes from
men and those in poverty.
Third, there were instances in which interactions occurred, notably for
the nature-based CDR category –afforestation, soil carbon, and blue carbon.
For afforestation, support increases more for younger males than younger
females (interaction between age and gender, Supplemental Materials). For
soil carbon and blue carbon, the influence of being youth increases support
more for those in poverty than not (interaction between poverty and age),
see Fig. 9.
Beyond the individual-level effects of gender, youth, and poverty, we
sawnotableinteractioneffectswhenlookingatGlobalSouthvsGlobal
North. For SAI, support drops substantially as age increases in the Global
North, but there is little difference across youth versus older cohorts in the
Global South (see Fig. 10). A similar interaction effect is manifest for MCB,
Space, DACCS, BECCS, ERW, and Biochar, with some of these even
showing increases in support in the Global South increasing as age increases,
whilst the opposite is revealed in the Global North (see Supplementary
Information). A reverse interaction effect is also revealed for Soil Carbon
and Blue Carbon –support in the Global North remains relatively stable
across youth and older cohorts, but in the Global South, support for both of
these interventions increases with age.
Conclusions
Carbon removal and solar geoengineering options could become pertinent
strategies for curtailing and even stabilizing greenhouse gas emissions, or
lowering global temperatures by midcentury. We presented results from an
original, first of its kind cross-country set of 30 nationally representative
surveys (n = 30,284 participants, with at least 1000 in each country), with
embedded random-assignment information conditions, to examine public
knowledge and perceptions of these emerging climate intervention tech-
nologies. In doing so, we reveal complicated social dynamics behind how
potential adopters and other members of the public hold views and pre-
ferences for nature-based climate interventions, engineered carbon removal
options, and solar radiation management techniques. Our empirical results
can inform ongoing discussions about energy and climate policy, the drivers
of environmental change, and deliberations over future sustainability
transitions.
Demographic attributes such as gender, age, and income feature cru-
cially in explaining public preferences of climate-intervention technologies.
On age alone, the standout observation is that for seven of the climate
interventions support declines with age, and for three (the nature-based
CDR options) support increases with age. Even in the three-way ANOVAs,
used to explore interactions between the factors, the main effect of age is
maintained in all three instances of older age relating to higher support.
These three cases are clearly different from the others. The age differences
matter for targeting communication about the approaches to CDR, and
consideration of which policy options might be worth presenting to dif-
ferent audiences. It also suggests that over time, the seven CDR options
where younger respondents supported the approach most heavily may
come to see higher levels of support than they do today. These reactions, of
course, depend on the information provided within the survey for these
climate interventions. The information focused on functional descriptions
of how the interventions work, concluding with a possible limitation on
their potential effectiveness –in this way, we avoided making more valenced
assessments of their merits and drawbacks. Nevertheless, as is necessarily the
case whenproviding information, had different information been provided,
respondent evaluations of theinterventions could have differed. We haveno
reason, however, a priori or from the data itself, to suggest why the infor-
mation we provided on the technologies would lead to any of these age-
related effects.
We must note that different people answered the questions about the
nature-based CDR options from the questions about the other climate-
intervention technologies –due to each respondent only receiving infor-
mation on three or four of the technologies. Nevertheless, due to random
assignment to one of the three sets of technologies, and observing similar
patterns amongst all seven other technologies even though they were in two
separate groups, we believe the differences are robust that we see in how age
influences support across the technologies.
Results from the Global North vs Global South three-way ANOVAs
also importantly reveal that these age relationships for the seven engi-
neered CDR and SRM interventions are stronger in the Global North,
whilst the relationship for Blue Carbon and Soil Carbon is stronger in the
Global South. The findings involving age are clearly relevant for deci-
sionmakers developing communication strategies about climate change
in general as well as those considering climate interventions. When
interacting with youth in the Global North, a range of climate inter-
ventions can be targeted, but for older audiences in the Global North,
there is a decidedly clear preference for nature-based solutions. In the
Global South, support is higher overall and varies less across the age
groups.
Fig. 8 | Support for Direct Air Capture by age,
gender, and poverty identified in our survey. *In a
three-way ANOVA, gender (partial eta2= 0.01) and
poverty (partial eta2= 0.01) were significant (at
p< 0.05), but youth, all three two-way interaction
effects, and the three-way interaction were non-
significant. F statistic for the ANOVA was sig-
nificant at p< 0.001. Sample sizes: Youth not poverty
female = 733, Youth not poverty male = 619, Youth
poverty female = 75, Youth poverty male = 61, Older
not poverty female = 4030, Older not poverty male =
4195, Older poverty female = 80, Older poverty male
= 92. Note: Support for the technologies was mea-
sured on a scale of 1–5: 1 = strictly reject, 2 =
somewhat reject, 3 = neither reject nor support, 4 =
somewhat support, 5 = fully support.
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For gender, perhaps the most intriguing finding is how little effect this
demographic characteristic had overall. All the effects from gender, when
examined on its own, were small, and in the three-way ANOVAs the main
effect of gender was relevant for only two of the ten climate interventions.
Furthermore, a two-way interaction involving gender was only relevant for
one climate intervention (afforestation). This finding is perhaps against
expectations, given presumptions based on early survey research that men
were more likely to support climate interventions, but it shows both the
growing inconsistency of findings related to gender in the literature and the
importance of robust empirical data to verify whether theoretical expecta-
tionsareactuallymetornot.Thelackofvariationacrossgenderislikelya
beneficial finding when it comes to policy and communication, suggesting
that these CDR approaches can benefit from support from both men and
women, and that individual technologies are not seen as particularly pro-
blematic by either gender.
For income levels and poverty, the key findingisthatonaggregate
those in poverty were more supportive of climate interventions, compared
to people not in poverty (observed for four of the ten interventions, with the
other six interventions showing no relationship in either direction). This
should be seen as a preliminary indication of a possible emergent rela-
tionship, due to the very coarse-grained manner in which we needed to
define ‘poverty’in our data set. Whether the respondent lived in the Global
North or Global South shows the same relationship, only stronger. The
Global South respondents supported each of the ten climate interventions
more than the Global North respondents did. This seems to go against
predictions based on literature about the effects on people in the Global
South (e.g., reviewed earlier in this article). Especially for the three solar
radiation management interventions, the effect of poverty, and of being
from the Global South, on support are notable. Further research into the
reasons behind the strongly positive effect of poverty and living in the Global
South on support for geoengineering would be valuable, especially given the
fundamental lack of such research to date. Subsequent research examining
the effect of poverty on support for climate interventions should be speci-
fically designed to over-sample from low-income populations and should
Fig. 9 | Support for blue carbon and marine bio-
mass interventions by age, gender, and poverty
identified in our survey. *In a three-way ANOVA,
youth (partial eta2= 0.03) and the interaction
between poverty and youth (partial eta2= 0.00) were
significant (at p< 0.05), but gender, poverty, the
remaining two two-way interaction effects, and the
three-way interaction were non-significant. F sta-
tistic for the ANOVA was significant at p< 0.001.
Sample sizes: Youth not poverty female = 706, Youth
not poverty male = 661, Youth poverty female = 74,
Youth poverty male = 77, Older not poverty female =
4049, Older not poverty male = 4152, Older poverty
female = 102, Older poverty male = 96. Note: Sup-
port for the technologies was measured on a scale of
1–5: 1 = strictly reject, 2 = somewhat reject, 3 =
neither reject nor support, 4 = somewhat support, 5
= fully support.
Fig. 10 | Support for stratospheric aerosol injec-
tion by age, gender, and Global South vs North
identified in our survey. *In a three-way ANOVA,
Global South (partial eta2= 0.021), youth (partial
eta2= 0.003), and the interaction between Global
South and youth (partial eta2= 0.002) were sig-
nificant (at p< 0.05), but gender, the other two two-
way interaction effects, and the three-way interac-
tion were non-significant. F statistic for the ANOVA
was significant at p< 0.001. Sample sizes: Youth
Global North female = 426, Youth Global South
female = 370, Youth Global North male = 329, Youth
Global South male = 345, Older Global North female
= 2770, Older Global South female = 1415, Older
Global North male = 2728, Older Global South male
= 1522. Note: Support for the technologies was
measured on a scale of 1–5: 1 = strictly reject, 2 =
somewhat reject, 3 = neither reject nor support, 4 =
somewhat support, 5 = fully support.
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Communications Earth & Environment | (2024) 5:642 13
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employ income thresholds in their demographic data gathering that align
directly with individual national poverty levels, to allow for more precise
comparisons between those in poverty and those not.
Nevertheless, even this initial, tentative finding has substantial policy
relevance. Governments and government-industry partnerships seeking to
deliver CDR projects in Global South and high-poverty areas can have more
confidence than has been suggested by prior research that there is at least
potential for public support for the CDR approaches. Of course, this does
not in any way negate the necessity of fully considering and addressing
concerns of procedural, distributive, and recognition justice, but it does
dispel the a priori concern that CDR projects are simply more highly
opposed in the Global South.
In relation to the more nuanced insights revealed in this study, it is
notable that interaction effects (within the analyses using age, gender, and
poverty) come only from the carbon removal interventions in group 2 (the
nature-based options). The main take-home message here is that, whilst it is
important to check for and understand intersectionality, it is the main effects
of age, gender, and income that seem to be more important for support for
climate interventions. For the nature-based interventions, we see some
indication that the strong effects of age (here meaning that support is higher
among the older cohort) are more pronounced in males and those in
poverty. For the interactions effects in the analyses using age, gender, and
Global South vs North, our repeated finding that age has different effects is
valuable. It shows the importance of truly cross-national studies, and the
inability to transfer simple lessons about demographic influences on climate
change attitudes or beliefs between countries. It also points to the need for
further research on why age-based dynamics operate quite differently across
these macroscopic global regions.
Reporting summary
Further information on research design is available in the Nature Portfolio
Reporting Summary linked to this article.
Data availability
The data that support the findings of this study are available at https://doi.
org/10.5281/zenodo.13942571.
Received: 28 September 2023; Accepted: 17 October 2024;
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