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World Scientists’ Warning of a Climate Emergency
William J. Ripple1*, Christopher Wolf1*, Thomas M. Newsome2, Phoebe Barnard3,4, William R.
Moomaw5, xxxxx scientist signatories from xxx countries (list in supplemental file S1)
1

Department of Forest Ecosystems and Society, Oregon State University, Corvallis, OR 97331, USA

2

School of Life and Environmental Sciences, The University of Sydney, Sydney, NSW 2006, Australia

3

Conservation Biology Institute, 136 SW Washington Avenue, Suite 202, Corvallis, OR 97333, USA

4

African Climate and Development Initiative, University of Cape Town, Cape Town, 7700, South Africa.

5

The Fletcher School and Global Development and Environment Institute, Tufts University, Medford, MA, USA

*These authors contributed equally to the work.

Scientists have a moral obligation to clearly warn humanity of any catastrophic threat and ‘tell
it like it is.’ Based on this obligation and the data presented below, we herein proclaim, with more than
10,000 scientist signatories from around the world, a clear and unequivocal declaration that a climate
emergency exists on planet Earth.
Exactly 40 years ago, scientists from 50 nations met at the First World Climate Conference
(Geneva, 1979) and agreed that alarming trends for climate change made it ―urgently necessary‖ to act.
Since then, similar alarms have been made through the 1992 Rio Summit, the 1997 Kyoto Protocol, the
2015 Paris Agreement, as well as scores of other global assemblies and scientists‘ explicit warnings of
insufficient progress (Ripple et al. 2017). Yet greenhouse gas (GHG) emissions are still rising, with
increasingly damaging effects on the Earth‘s climate. An immense change of scale in endeavors to
conserve our biosphere is needed to avoid untold suffering due to the climate crisis (IPCC 2018).
Most public discussions on climate change are based on global surface temperature only, an
inadequate measure to capture the breadth of human activities and real dangers stemming from a
warming planet (Briggs et al. 2015). Policymakers and the public now urgently need access to a set of
indicators that convey the effects of human activities on GHG emissions and the consequent impacts on
climate, our environment, and society. Building on prior work (see supplemental file S2), we present a
suite of graphical vital signs of climate change over the last 40 years for human activities that can affect
GHG emissions/climate change (Figure 1), and actual climatic impacts (Figure 2). We use only relevant
datasets that are clear, understandable, systematically collected for at least the last five years, and
updated at least annually.
The climate crisis is closely linked to excessive consumption of the wealthy lifestyle. The most
affluent countries are mainly responsible for the historical GHG emissions, and generally have the
greatest per capita emissions (Table S1). Here we show general patterns, mostly at the global scale, as
there are many climate efforts that involve individual regions and countries. Our vital signs are designed
to be useful to the public, policymakers, the business community, and those working on implementation
of the Paris climate agreement, the UN‘s Sustainable Development Goals, and the Aichi Biodiversity
Targets.
Profoundly troubling signs from human activities include sustained increases in both human and
ruminant livestock populations, per capita meat production, world gross domestic product, global tree
cover loss, fossil fuel consumption, number of air passengers carried, carbon dioxide (CO2) emissions,

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and per capita CO2 emissions since 2000 (Figure 1, supplemental file S2). Encouraging signs include
decreases in global fertility (birth) rates (Figure 1b), decelerated forest loss in the Brazilian Amazon
(Figure 1g), increases in the consumption of solar and wind power (Figure 1h), institutional fossil fuel
divestment of more than seven trillion U.S. dollars (Figure 1j), and the proportion of GHG emissions
covered by carbon pricing (Figure 1m). However, the decline in human fertility rates has substantially
slowed during the last 20 years (Figure 1b), and the pace of forest loss in Brazil‘s Amazon has now
started to increase again (Figure 1g). Consumption of solar and wind energy has increased 373% per
decade, yet in 2018 it was still 28 times smaller than fossil fuel consumption (combined gas, coal, oil)
(Figure 1h). As of 2018, approximately 14.0% of global GHG emissions were covered by carbon pricing
(Figure 1m), but the global emissions-weighted average price per tonne of carbon dioxide was only
~$15.25 U.S. (Figure 1n). A much higher carbon fee price is needed (IPCC 2018, Section 2.5.2.1).
Annual fossil fuel subsidies to energy companies have been fluctuating, and due to a recent spike they
were greater than 400 billion U.S. dollars in 2018 (Figure 1o).
Especially disturbing are concurrent trends in the vital signs of climatic impacts (Figure 2,
supplemental file S2) Three abundant atmospheric GHGs (CO2, methane, and nitrous oxide) continue to
increase (see Figure S1 for ominous 2019 spike in CO2), as does global surface temperature (Figure 2ad). Globally, ice has been rapidly disappearing, evidenced by declining trends in minimum summer
Arctic sea ice, Greenland and Antarctic ice sheets, and glacier thickness worldwide (Figure 2e-h). Ocean
heat content, ocean acidity, sea level, area burned in the United States, and extreme weather and
associated damage costs have all been trending upward (Figure 2i-n). Climate change is predicted to
greatly impact marine, freshwater, and terrestrial life, from plankton and corals to fishes and forests
(IPCC 2018, 2019). These issues highlight the urgent need for action.
Despite 40 years of global climate negotiations, with few exceptions, we have generally
conducted business as usual and have largely failed to address this predicament (Figure 1). The climate
crisis has arrived and is accelerating faster than most scientists expected (Figure 2, IPCC 2018). It is
more severe than anticipated, threatening natural ecosystems and the fate of humanity (IPCC 2019).
Especially worrisome are potential climate tipping points and nature‘s reinforcing feedbacks
(atmospheric, marine, and terrestrial) that could lead to a catastrophic ―Hothouse Earth,‖ well beyond
the control of humans (Steffen et al. 2018). These climate chain-reactions could cause significant
disruptions to ecosystems, society, and economies, potentially making large areas of Earth
uninhabitable.
To secure a sustainable future, we must change how we live, in ways that improve the vital signs
summarized by our graphs. Economic and population growth are among the most important drivers of
increases in CO2 emissions from fossil fuel combustion (Pachauri et al. 2014, Bongaarts and O‘Neill
2018); thus, we need bold and drastic transformations regarding economic and population policies. We
suggest six critical and interrelated steps (in no particular order) that governments, businesses and the
rest of humanity can take to lessen the worst effects of climate change. These are important steps, but
are not the only actions needed or possible (Pachauri et al. 2014; IPCC 2018, 2019).
1) Energy. The world must quickly implement massive energy efficiency and conservation
practices, replace fossil fuels with low carbon renewables (Figure 1h) and other cleaner sources
of energy if safe for people and the environment (Figure S2). We should leave remaining stocks
of fossil fuels in the ground [see timelines in IPCC (2018)], and carefully pursue effective
negative emissions using technology such as carbon extraction from the source and capture from
the air, and by enhancing natural systems (Step 3). Wealthier countries need to support poorer
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nations in transitioning away from fossil fuels. We must swiftly eliminate subsidies to fossil fuel
corporations (Figure 1o) and use effective and fair schemes for steadily escalating carbon prices
to restrain the use of fossil fuels.
2) Short-lived pollutants. We need to promptly reduce emissions of short-lived climate pollutants,
including methane (Figure 2b), black carbon (soot), and hydrofluorocarbons (HFCs). Doing this
could slow climate feedbacks and potentially reduce the short-term warming trend by >50% over
the next few decades while saving millions of lives and increasing crop yields due to reduced air
pollution (Shindell et al. 2017). The 2016 Kigali amendment to phase down HFCs is welcomed.
3) Nature. We must protect and restore Earth‘s ecosystems. Phytoplankton, coral reefs, forests,
savannas, grasslands, wetlands, peatlands, soils, mangroves, and sea grasses contribute greatly to
sequestration of atmospheric CO2. Marine and terrestrial plants, animals, and microorganisms
play significant roles in carbon and nutrient cycling and storage. We need to quickly curtail
forest and biodiversity loss (Figure 1f-1g), protecting the remaining primary and intact forests,
especially those with high carbon stores and younger forests with the capacity to rapidly
sequester carbon (proforestation), while accomplishing reforestation and afforestation where
appropriate at enormous scales. Although available land may be limiting in places, up to a third
of emissions reductions needed by 2030 for the Paris agreement (< 2˚C) could be obtained with
these natural climate solutions (Griscom et al. 2017).
4) Food. Eating mostly plant-based foods while reducing the global consumption of animal
products (Figure 1c-1d), especially ruminant livestock (Ripple et al. 2014), can improve human
health and significantly lower GHG emissions (including methane in step 2). Moreover, this will
free up croplands for growing much needed human plant food instead of livestock feed, while
releasing some grazing land to support natural climate solutions (step 3). Cropping practices such
as minimum tillage that increase soil carbon are vitally important. We need to drastically reduce
the enormous amount of food waste around the world.
5) Economy. Excessive extraction of materials and overexploitation of ecosystems, driven by
economic growth, must be quickly curtailed to maintain long-term sustainability of the
biosphere. We need a carbon-free economy that explicitly addresses human dependence on the
biosphere and policies that guide economic decisions accordingly. Goals need to shift from GDP
growth and the pursuit of affluence toward supporting ecosystem and human wellbeing by
prioritizing basic needs and reducing inequality.
6) Population. Still increasing by roughly 80 million people per year or >200,000 per day (Figure

1a-1b), we must stabilize and ideally gradually reduce the world population within a framework
that ensures social integrity. There are proven and effective policies that strengthen human
rights, while lowering fertility rates and lessening the impacts of population growth on GHG
emissions and biodiversity loss. These policies involve making family planning services
available to all people (and removing barriers to their access) and achieving full gender equity,
including primary and secondary education as a global norm for all, especially girls and young
women (Bongaarts and O‘Neill 2018).
Mitigating and adapting to climate change while honoring the diversity of humans entails major
transformations in the ways our global society functions and interacts with natural ecosystems. We are
encouraged by a recent surge of concern. Governmental bodies are making climate emergency
declarations. Schoolchildren are striking. Ecocide lawsuits are proceeding in the courts. Grassroots
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citizen movements are demanding change, and many countries, states and provinces, cities, and
businesses are responding.
As an Alliance of World Scientists, we stand ready to assist decision makers in a just transition to a
sustainable and equitable future. We urge widespread use of vital signs, which will better allow
policymakers, the private sector, and the public to understand the magnitude of this crisis, track
progress, and realign priorities for alleviating climate change. The good news is that such transformative
change, with social and economic justice for all, promises far greater human wellbeing in the long run
than does business as usual. We believe that prospects will be greatest if decision makers and all of
humanity promptly respond to this warning and declaration of a climate emergency, and act to sustain
life on planet Earth, our only home.
Contributing reviewers
Franz Baumann, Ferdinando Boero, Doug Boucher, Stephen Briggs, Peter Carter, Rick Cavicchioli,
Milton Cole, Eileen Crist, Dominick A. DellaSala, Paul Ehrlich, Iñaki Garcia-De-Cortazar, Daniel
Gilfillan, Alison Green, Tom Green, Jillian Gregg, Paul Grogan, John Guillebaud, John Harte, Nick
Houtman, Charles Kennel, Christopher Martius, Frederico Mestre, Jennie Miller, David Pengelley, Chris
Rapley, Klaus Rohde, Phil Sollins, Sabrina Speich, David Victor, Henrik Wahren, and Roger
Worthington
Funding
The Worthy Garden Club furnished partial funding for this project.
Project Website
To view the Alliance of World Scientists website or sign this paper, go to
https://scientistswarning.forestry.oregonstate.edu/
Supplemental material
Supplementary data are available at BIOSCI online including supplemental file 1 (full list of all
xxxxx signatories) and supplemental file 2.
References
Briggs S, Kennel CF, Victor DG. 2015. Planetary vital signs. Nature Climate Change 5:969.
Bongaarts J, O‘Neill BC. 2018. Global warming policy: Is population left out in the cold? Science
361:650–652.
Griscom BW et al. 2017. Natural climate solutions. Proceedings of the National Academy of Sciences 114:11645–11650.
IPCC. 2018. Global Warming of 1.5° C: An IPCC Special Report. Intergovernmental Panel on Climate Change.
IPCC. 2019. Climate Change and Land. Intergovernmental Panel on Climate Change.
Pachauri RK et al. 2014. Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth
assessment report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change.
Ripple WJ, Smith P, Haberl H, Montzka SA, McAlpine C, Boucher DH. 2014. Ruminants, climate change and climate
policy. Nature Climate Change 4:2–5.
Ripple WJ, Wolf C, Newsome TM, Galetti M, Alamgir M, Crist E, Mahmoud MI, Laurance WF. 2017. World Scientists‘
Warning to Humanity: A Second Notice. BioScience.
Shindell D, Borgford-Parnell N, Brauer M, Haines A, Kuylenstierna J, Leonard S, Ramanathan V, Ravishankara A, Amann
M, Srivastava L. 2017. A climate policy pathway for near-and long-term benefits. Science 356:493–494.
Steffen W et al. 2018. Trajectories of the Earth System in the Anthropocene. Proceedings of the National Academy of
Sciences 115:8252–8259.

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Figure 1. Change in global human activities from 1979 to the present. These indicators are linked at
least in part to climate change. In panel (f), annual tree cover loss may be for any reason (e.g. wildfire,
harvest within tree plantations, or conversion of forests to agricultural land). Forest gain is not involved
in the calculation of tree cover loss. In panel (h), ―Gt oe/yr‖ is short for gigatonnes of oil equivalent per
year; hydroelectricity and nuclear energy are shown in Figure S2. Rates shown in panels are the
percentage changes per decade across the entire range of the time series. Annual data are shown using
gray points. Black lines are local regression smooth trend lines. Sources and additional details about
each variable are provided in supplemental file S2, including Table S2.

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Figure 2. Climatic response time series from 1979 to the present. Rates shown in panels are the decadal
change rates for the entire ranges of the time series. These rates are in percentage terms, except for the
interval variables (d, f, g, h, i, m), where additive changes are reported instead. For ocean acidity (pH),
the percentage rate is based on the change in hydrogen ion activity,
(where lower pH values
represent greater acidity). Annual data are shown using gray points. Black lines are local regression
smooth trend lines. Sources and additional details about each variable are provided in supplemental file
S2, including Table S3.

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Supplemental File S2: World Scientists‘ Warning of a Climate Emergency
by William J. Ripple, Christopher Wolf, Thomas M. Newsome, Phoebe Barnard, William R. Moomaw, xxxxx scientist
signatories from xxx countries (list in supplemental file S1)

Table of Contents
Figure S1. Monthly mean CO2 at Mauna Loa, Hawaii ......................................................................... 8
Figure S2. Hydroelectricity and nuclear energy consumption rates ...................................................... 9
Table S1. Regional summaries for 24 countries and The European Union ......................................... 10
Table S2. Summary of human activity indicators ................................................................................ 11
Table S3. Summary of climatic response indicators ............................................................................ 12
Other graphical indicators ................................................................................................................. 13
Methods ................................................................................................................................................ 13
Indicators of human activities ............................................................................................................ 14
Indicators of actual climatic impacts ................................................................................................ 17
Supplemental references..................................................................................................................... 19

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Figure S1. ―Monthly mean carbon dioxide measured at Mauna Loa Observatory, Hawaii. The carbon
dioxide data ([black] curve), measured as the mole fraction in dry air, on Mauna Loa constitute the
longest record of direct measurements of CO2 in the atmosphere. […] The [black line represents] the
monthly mean values, centered on the middle of each month. The [red line represents] the same, after
correction for the average seasonal cycle. The latter is determined as a moving average of SEVEN
adjacent seasonal cycles centered on the month to be corrected, except for the first and last THREE and
one-half years of the record, where the seasonal cycle has been averaged over the first and last SEVEN
years, respectively.‖ Source https://www.esrl.noaa.gov/gmd/ccgg/trends/

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Figure S2. Annual consumption rates for nuclear energy and hydroelectricity (British Petroleum
Company 2019). Non-fossil fuel energy supply pathways in the future may include hydro and nuclear
power in addition to wind and solar power (IPCC 2018). Rates shown in the legend are decadal change
rates for the entire ranges of the time series (in percentage terms). See British Petroleum Company
(2019) for other minor energy sources not shown in this figure. Figure 1h in the main text shows
consumption of fossil fuels as well as solar/wind energy.

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Supplemental Tables
Table S1. Regional summaries for 24 countries and The European Union. Variables shown are ―CO2‖
(total CO2 emissions associated with fossil fuel consumption in mega tonnes CO2), ―Population‖ (human
population size in millions), ―CO2/capita‖ (CO2 emissions per capita in tonnes per person), ―Share‖
(percentage of all CO2 emissions associated with fossil fuel consumption compared to the global total),
and ―GDP/capita‖ (per capita gross domestic product in US dollars per person). All data are for the year
2018, except GDP for Iran, which is from 2017 (2018 estimate was not yet available). Additional details
on the variables are provided in the supplementary information below.
CO2 Population CO2/capita

Share GDP/capita

China

9429

1447

6.5

28.4%

$9,400

United States

5145

327

15.7

15.5%

$62,736

The European Union

3470

510

6.8

10.4%

$36,806

India

2479

1354

1.8

7.5%

$2,016

Russia

1551

144

10.8

4.7%

$11,531

Japan

1148

127

9.0

3.5%

$39,077

South Korea

698

51

13.6

2.1%

$31,663

Iran

656

82

8.0

2.0%

$5,536

Saudi Arabia

571

34

17.0

1.7%

$23,305

Canada

550

37

14.9

1.7%

$46,274

Indonesia

543

267

2.0

1.6%

$3,898

Mexico

463

131

3.5

1.4%

$9,330

Brazil

442

211

2.1

1.3%

$8,868

South Africa

421

57

7.3

1.3%

$6,376

Australia

417

25

16.8

1.3%

$57,726

Turkey

390

82

4.8

1.2%

$9,363

Thailand

302

69

4.4

0.9%

$7,299

United Arab Emirates

277

10

29.0

0.8%

$43,389

Malaysia

250

32

7.8

0.8%

$11,048

Kazakhstan

248

18

13.5

0.7%

$9,292

Singapore

230

6

39.7

0.7%

$62,846

Vietnam

225

96

2.3

0.7%

$2,539

Egypt

224

99

2.3

0.7%

$2,526

Pakistan

196

201

1.0

0.6%

$1,559

Ukraine

187

44

4.2

0.6%

$2,977

Top 25

30511

5460

5.6

91.8%

$13,960

World

33243

7550

4.4 100.0%

$11,363

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Table S2. Summary of human activity indicators. Table columns show the variable name, the most
recent year with data, the value of the variable in that year, the rank for that year (rank #1 is the highest
possible value), and the total number of years with data (since 1979). For example, human population
was most recently estimated in 2018 to have a value of 7.63 billion individuals, which ranked as the
greatest value among the 40 years of data available since 1979.
Variable

Year Value Rank Total years

Human population (billion individuals)

2018

7.63

1

40

Total fertility rate (births per woman)

2017

2.43

39

39

Ruminant livestock (billion individuals)

2017

3.93

1

39

Per capita meat production (kg/yr)

2017

44.3

1

39

World GDP (trillion current US $/yr)

2018

85.8

1

40

Global tree cover loss (million hectares/yr)

2018

24.8

3

18

Brazilian Amazon forest loss (million hectares/yr)

2018

0.79

22

31

Coal consumption (gigatonnes oil equivalent/yr)

2018

3.77

5

40

Oil consumption (gigatonnes oil equivalent/yr)

2018

4.66

1

40

Natural gas consumption (gigatonnes oil equivalent/yr)

2018

3.31

1

40

Solar/wind (gigatonnes oil equivalent/yr)

2018

0.42

1

40

Air transport (billion passengers carried/yr)

2017

3.98

1

39

Total assets divested (trillion USD)

2018

6.17

1

6

CO2 emissions (gigatonnes CO2 equivalent/yr)

2018

33.9

1

40

Per capita CO2 emissions (tonnes CO2 equivalent/yr)

2018

4.44

9

40

GHG emissions covered by carbon pricing (%)

2018

14

1

29

Carbon price ($ per tonne CO2 emissions)

2018

15.2

28

29

Fossil fuel subsidies (billion USD/yr)

2018

427

6

9

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Table S3. Summary of climatic response indicators. Table columns show the variable name, the most
recent year with data, the value of the variable in that year, the rank for that year (rank #1 is the highest
possible value), and the total number of years with data (since 1979). For example, atmospheric carbon
dioxide concentration was most recently estimated in 2018 to have a value of 407 parts per million,
which ranked as the greatest value among the 39 years of data available since 1979.
Variable

Year Value Rank Total years

Carbon dioxide (CO2 parts per million)

2018

407

1

39

Methane (CH4 parts per billion)

2018

1860

1

35

Nitrous oxide (N2O parts per billion)

2018

331

1

40

Surface temperature change (°C)

2018

0.85

4

40

Minimum Arctic sea ice (million km2)

2018

4.6

35

40

Greenland ice mass change (gigatonnes)

2016

-3660

14

14

Antarctica ice mass change (gigatonnes)

2016

-1640

13

14

Glacier thickness change (m of water equivalent)

2018

-21.1

40

40

Ocean heat content change (1022 joules)

2016

21.9

1

38

Ocean acidity (pH)

2017

8.06

29

29

Sea level change (cm)

2018

42.8

1

26

Area burned in the United States (million hectares/yr)

2018

3.55

6

36

Extreme weather/climate/hydro events (#/yr)

2018

798

1

39

Annual losses due to weather/climate/hydro events (Bn. $)

2018

166

4

39

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Other graphical indicators
Global Climate Observing System (GCOS)- uses seven climate indicators including surface temperature,
ocean heat, atmospheric CO2, ocean acidification, sea level, glaciers, and arctic and Antarctic sea ice
extent. https://gcos.wmo.int/en/home
NASA vital signs of the planet- uses five climate indicators including global temperature, arctic ice
minimum, ice sheets, sea level, and CO2. https://climate.nasa.gov/
2 Degrees Institute- uses six climate indicators including global temperature record, CO2 levels, methane
(CH4) levels, nitrous oxide (N2O) levels, oxygen (O2) levels, and global sea levels.
https://www.2degreesinstitute.org/
IPCC 1.5C Report- uses the global warming index. https://report.ipcc.ch/sr15/pdf/sr15_spm_final.pdf

Methods
We compiled a set of global time series related to human actions that affect the environment (e.g. fossil
fuel consumption) and environmental and climatic responses (e.g. temperature change). Descriptions
and sources for each variable are given in the next section. Although the data used are from sources
believed to be reliable, no formal accuracy assessment for these datasets has been made by us and users
should proceed with caution. We only considered indicator variables that are updated at least every year.
We converted each variable to annual format by averaging together observations within each calendar
year if necessary, excluding data from the first and last years when incomplete (first year incomplete:
ocean acidity, Greenland and Antarctica ice mass; last year incomplete: nitrous oxide, Greenland and
Antarctica ice mass). For each variable, we removed years prior to 1979. We then computed smooth
trend lines using locally estimated scatterplot smoothing. We fit the trend lines in R using the ‗loess‘
function with default settings (degree 2, span 0.75) (R Core Team 2018).
We used the trend lines to calculate the rate of change of each variable. For ratio variables (i.e. those
with a ‗true‘ zero, like atmospheric CO2 concentration), we computed percentage change, and for
interval variables (which can be shifted up or down arbitrarily, like sea level) we computed additive
change. For ratio variables, we used the following formula for 10-year percentage change:
[(

)

]

Where
and
are the start and end values of the trend line and
and
are the start and
end years. This is the 10-year percentage change with a decadal compounding interval. For example, a
variable that increased at a rate of 15% per decade over its entire time span would have a value of 15%
according to this formula. For ocean acidity (pH), we calculated percentage change in terms of hydrogen
ion activity (
) (lower pH values represent greater acidity). For interval variables, we used the
formula

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Indicators of human activities that can affect GHG emissions or climate
change (Figure 1)
Below, we list sources and provide brief descriptions of indicators in our analysis. Full methods for each
indicator are available at the provided sources.
Human population (Figure 1a)
We used the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) as our
source of human population data (FAOSTAT 2019). For human population estimates, the source data
used by FAOSTAT are from national population censuses.
Total fertility rate (Figure 1b)
We obtained this variable from the World Bank (The World Bank 2019a). The full variable name is
―Fertility rate, total (births per woman)‖ and the World Bank variables ID is SP.DYN.TFRT.IN. This
variable was derived using data from multiple sources, including the United Nations Population
Division. The full list of original sources is available at The World Bank (2019a). Total fertility rate is
defined as ―the number of children that would be born to a woman if she were to live to the end of her
childbearing years and bear children in accordance with age-specific fertility rates of the specified year‖
(The World Bank 2019a).
Ruminant livestock population (Figure 1c)
We used the Food and Agriculture Organization Corporate Statistical Database (FAOSTAT) as our
source of ruminant livestock population data (FAOSTAT 2019). We considered ruminants to be
members of the following groups: cattle, buffaloes, sheep, and goats. For livestock estimates, the
primary data sources are national statistics obtained using questionnaires or collected from countries‘
websites or reports. When national livestock statistics were unavailable, they were estimated by
FAOSTAT using imputation (FAOSTAT 2019).
Per capita meat production (Figure 1d)
We used total meat production data from FAOSTAT along with FAOSTAT human population size
estimates (Figure 1a) to estimate per capita meat production (FAOSTAT 2019). These data ―are given in
terms of dressed carcass weight, excluding offal and slaughter fats‖ (FAOSTAT 2019).
Gross domestic product (Figure 1e)
We obtained this variable from the World Bank (The World Bank 2019b). The full variable name is
―GDP (current US$)‖ and the World Bank variable ID is NY.GDP.MKTP.CD. This variable was
derived from multiple sources, including World Bank national accounts. The full list of sources is
available at The World Bank (2019b). Gross domestic product is ―the sum of gross value added by all
resident producers in the economy plus any product taxes and minus any subsidies not included in the
value of the products‖ (2019b).
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Global tree cover loss (Figure 1f)
We obtained data on annual global tree cover loss from Global Forest Watch (Hansen et al. 2013). These
data express loss globally in million hectares (Mha) and were derived from remotely-sensed forest
change maps. It should be noted that loss is general and not linked to a specific type of deforestation. So,
it includes wildlife, conversion to agriculture, disease, etc. Additionally, tree cover loss does not take
tree cover gain into account. Thus, net forest loss may be lower than the reported numbers.
Brazilian Amazon forest loss (Figure 1g)
We obtained annual Brazilian Amazon forest loss estimates from Butler (2017). Brazil contains about
60% of the Amazon rainforest. The sources used by Butler (2017) were the Brazilian National Institute
of Space Research (INPE) and the United Nations Food and Agriculture Organization (FAO). Although
the INPE has not provided a deforestation estimate for 2019, their wildfire activity data show a major
spike associated with widespread deforestation (Amigo 2019).
Energy consumption (Figure 1h)
We used the British Petroleum Company‘s 2019 Statistical Review of World Energy as our source of
data on energy consumption (British Petroleum Company 2019). For energy consumption, we used the
following time series: coal, oil, natural gas, solar, and wind. We grouped solar and wind together into a
single category. Coal consumption data are only for commercial solid fuels. In each case, the units of
energy consumption are gigatonnes oil equivalent (Gt oe). Other sources of low carbon energy such as
hydropower and nuclear power are shown in Figure S2. Although not used in this report, global energy
consumption data are also available from the International Energy Agency (IEA 2018).
Air transport (Figure 1i)
We obtained this variable from the World Bank (The World Bank 2019c). The full variable name is ―Air
transport, passengers carried.‖ The corresponding World Bank variable ID is IS.AIR.PSGR. This
variable was derived from multiple sources, including the International Civil Aviation Organization. The
full lists of sources is available at The World Bank (2019c). Air transport includes both domestic and
international travelers.
Divestment (Figure 1j)
Divestment data were obtained from 350.org (350.org 2019; Fossil Free 2019). They cover institutional
divestment by 1,117 organizations. The most commonly represented institutions were faith-based
organizations, philanthropic foundations, educational institutions, governments, and pension funds
(Fossil Free 2019). Using 350.org‘s divestment database, we calculated cumulative total institutional
divestment by year (since 2013) based on the ―date of record‖ variable, which ―generally represents the
organization‘s divestment commitment announcement date‖ (350.org 2019).

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CO2 emissions (Figure 1k)
We used the British Petroleum Company‘s 2019 Statistical Review of World Energy as our source of
data on CO2 emissions (British Petroleum Company 2019). These CO2 emissions data ―reflect only […]
consumption of oil, gas and coal for combustion related activities‖ (British Petroleum Company 2019).
They do not account for carbon sequestration, other CO2 emissions, or other greenhouse gas emissions.
Per capita CO2 emissions (Figure 1l)
We converted total CO2 emissions (Figure 1k) to per capita CO2 emissions using FAOSTAT human
population size estimates (Figure 1a).
Greenhouse gas emissions covered by carbon pricing (Figure 1m)
The data on percentage of greenhouse gas emissions covered by carbon pricing schemes are taken
directly from World Bank Group (2019). When multiple schemes covered the same emissions, the
emissions were associated with the earliest of the schemes. The data were accessed using the Carbon
Pricing Dashboard. They were last updated on April 1, 2019.
Carbon price and share of greenhouse gas emissions covered by carbon pricing (Figure 1n)
These data were derived from World Bank Group (2019). To estimate the global carbon price, we used
the average of the individual scheme prices weighted by the percentage of greenhouse gas emissions
covered by each scheme. When multiple schemes covered the same emissions, the emissions were
associated with the earliest of the schemes. The data were accessed using the Carbon Pricing Dashboard.
They were last updated on April 1, 2019.
Fossil fuel subsidies (Figure 1o)
We obtained data on fossil fuel subsidies from the International Energy Agency (2019a).
Fossil fuel consumption subsidies are global totals in 2018 billion US dollars. They cover oil, electricity,
natural gas, and coal. Subsidy values are estimated using the price-gap approach, which involves
comparing ―average end-user prices paid by consumers with reference prices that correspond to the full
cost of supply‖ (International Energy Agency 2019b). The subsidy amount is equal to the product of this
price gap and the amount consumed (International Energy Agency 2019b).

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Indicators of actual climatic impacts (Figure 2)
Atmospheric CO2 (Figure 2a)
We obtained globally averaged estimates of atmospheric CO2 concentration from NOAA‘s Global
Greenhouse Gas Reference Network (NOAA 2019a). Specifically, we used the variable ―Globally
averaged marine surface annual mean data.‖ It is based on data collected by The Global Monitoring
Division of NOAA/Earth System Research Laboratory using a global network of sampling sites. Global
means were estimated by first smoothing observations from each site across time and then estimating the
relationship between atmospheric CO2 and latitude.
Atmospheric methane (Figure 2b)
We obtained globally-averaged annual estimates of atmospheric methane (CH4) concentration from
NOAA (Ed Dlugokencky, NOAA/ESRL 2019). We used the ―Globally averaged marine surface annual
mean data‖ dataset. These data are derived from measurements made at a global network of sampling
sites that were smoothed across time and plotted versus latitude (Dlugokencky et al. 1994; Masarie &
Tans 1995). The data are reported as a ―dry air mole fraction‖ (Ed Dlugokencky, NOAA/ESRL 2019).
Atmospheric nitrous oxide (Figure 2c)
We obtained data on nitrous oxide (N2O) concentration from the NOAA/ESRL Global Monitoring
Division (―Combined Nitrous Oxide data from the NOAA/ESRL Global Monitoring Division‖)
(NOAA/ESRL Global Monitoring Division 2019). We used the global monthly mean estimates
(measured in parts per billion). As noted in their description, the dataset is a weighted average of
estimates from NOAA/ESRL/GMD measurement programs.
Surface temperature change (Figure 2d)
We obtained global mean surface temperature anomaly data from NASA/GISS (2019). We used the
unsmoothed annual Land-Ocean Temperature Index variable. The temperature anomaly/change
estimates combine land and ocean surface temperatures. The baseline period used for setting zero is the
1951-1980 mean.
Minimum Arctic sea ice (Figures 2e)
We obtained minimum Arctic sea ice estimates from NASA (2019). They are derived from satellite
observations. For each year, the data show the average Arctic sea ice extent for the month of September,
which is when the annual minimum occurs. According to NASA (2019), ―Arctic sea ice reaches its
minimum each September. September Arctic sea ice is now declining at a rate of 12.8 percent per
decade, relative to the 1981 to 2010 average. The graph above shows the average monthly Arctic sea ice
extent each September since 1979, derived from satellite observations. The 2012 extent is the lowest in
the satellite record.‖

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Greenland ice mass (Figure 2f)
We obtained total land ice mass change measurements for Greenland from NASA (2019). These data
show the changes in ice sheet mass (in Gt) since April 2002. They come from NASA‘s GRACE
satellites. According to NASA (2019), the Greenland ice sheet has ―seen an acceleration of ice mass loss
since 2009.‖
Antarctica ice mass (Figure 2g)
We obtained total land ice mass change measurements for Antarctica from NASA (2019). These data
show the changes in ice sheet mass (in Gt) since April 2002. They come from NASA‘s GRACE
satellites. According to NASA (2019), the Antarctica ice sheet has ―seen an acceleration of ice mass loss
since 2009.‖
Cumulative glacier thickness change (Figure 2h)
We obtained cumulative glacier mass balance data from the World Glacier Monitoring Service (WGMS
2019). These data were derived from a database with information about changes in mass, volume, etc. of
individual glaciers over time. They are based on averaging over a global set of reference glaciers and are
measured relative to 1970.
The units of these data are meters of water equivalent. According to the World Glacier Monitoring
Service, ―A value of -1.0 [meter of water equivalent] per year is representing a mass loss of 1,000 kg per
square meter of ice cover or an annual glacier-wide ice thickness loss of about 1.1 m per year, as the
density of ice is only 0.9 times the density of water‖ (WGMS 2019).
Ocean heat content (Figure 2i)
We obtained pentadal ocean heat content time series data from NOAA‘s National Centers for
Environmental Information (NCEI) (NOAA 2019b). These data are in units of 1022 joules and cover the
depth range 0-2000 m. The reference period is 1955-2006 (Levitus et al. 2012).
Ocean acidity (Figure 2j)
As a proxy for global ocean acidity, we used a time series of seawater pH from the Hawaii Ocean Timeseries surface CO2 system data product (HOT 2019). This data product was adapted from Dore et al.
(2009). The data were collected at Station ALOHA (22°45'N, 158°00'W). We used the variable
―pHmeas_insitu,‖ which is described as the ―mean measured seawater pH, adjusted to in situ
temperature, on the total scale‖ (HOT 2019). To report percentage change for this variable, we first
converted pH to hydrogen ion activity (
) using the formula
=10-Ph.
Extreme weather events (number) (Figure 2k)
These data come from Munich Re‘s NatCatSERVICE (Munich Re 2019). Extreme weather events are
meteorological, hydrological, or climatological events that ―have caused at least one fatality and/or
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produced normalized losses ≥ US$ 100k, 300k, 1m, or 3m (depending on the assigned World Bank
income group of the affected country).‖ The entire database contained 18,169 events, but we excluded
geophysical events, leaving a total of 16,585 events. These span three categories: meteorological events
(tropical cyclones, extratropical storms, etc.), hydrological events (floods, mass movements), and
climatological events (droughts, forest fires, etc.).
Extreme weather events (economic losses) (Figure 2l)
These data come from Munich Re‘s NatCatSERVICE (Munich Re 2019) as described above. Economic
losses (in 2018 USD) were ―Inflation adjusted via country-specific consumer price index and
consideration of exchange rate fluctuations between local currency and US$‖ (Munich Re 2019).
Sea level change (Figure 2m)
We obtained data on global mean sea level from GSFC (2017) [linked to from NASA (2019)]. As of
September 6, 2019, the data are accessible from NASA (2019) at https://podaactools.jpl.nasa.gov/drive/files/allData/merged_alt/L2/TP_J1_OSTM/global_mean_sea_level/GMSL_TPJ
AOS_4.2_199209_201906.txt. As noted in the dataset description, the graph available at
http://climate.nasa.gov is based on plotting heights ―with respect to the first cycle (January) of 1993.‖
The variable we used was ―GMSL (Global Isostatic Adjustment (GIA) not applied) variation (mm) with
respect to 20-year TOPEX/Jason collinear mean reference.‖ According to the dataset description, the
―TOPEX/Jason 20 year collinear mean reference is derived from cycles 121 to 858, years 1996-2016.‖ It
should be noted that temperature increase and the warming of the entire ocean is a major contributor to
sea-level rise (WCRP Global Sea Level Budget Group 2018).
Total area burned by wildfires in the United States (Figure 2n)
These data come from the National Interagency Coordination Center at The National Interagency Fire
Center (National Interagency Coordination Center 2018) and include Alaska and Hawaii. They are
derived from information published in Situation Reports. Because sources of the figures are unknown
prior to 1983, we omitted data before 1983. The total for 2004 does not include state lands within North
Carolina.
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