# Scenarios of future annual carbon footprints of astronomical research infrastructures

Jürgen Knödlseder, Mickael Coriat, Philippe Garnier, Annie Hughes

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Jürgen Knödlseder, Mickael Coriat, Philippe Garnier, Annie Hughes. Scenarios of future annual carbon footprints of astronomical research infrastructures. Nature Astronomy, 2024, 10.1038/s41550-024-02346-0. hal-04678247

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Scenarios of future annual carbon footprints of astronomical research infrastructures

Jürgen Knödlseder¹, Mickael Coriat¹, Philippe Garnier¹, Annie Hughes¹

¹Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse, CNRS, CNES, UPS, 9 avenue Colonel Roche, 31028 Toulouse, Cedex 4, France

Research infrastructures have been identified as an important source of greenhouse gas emissions of astronomical research. Based on a comprehensive inventory of 1,211 ground-based observatories and space missions, we assessed the evolution of the number of astronomical facilities and their carbon footprint from 1945 to 2022. We found that space missions dominate greenhouse gas emissions in astronomy, showing an important peak at the end of the 1960ies, followed by a decrease that has turned again into a rise over the last decade. Extrapolating past trends, we predict that greenhouse gas emissions from astronomical facilities will experience no strong decline in the future, and may even rise substantially, unless research practices are changed. We demonstrate that a continuing growth in the number of operating astronomical facilities is not environmentally sustainable. These findings should motivate the astronomical community to reflect about the necessary evolutions that would put astronomical research on a sustainable path.

The unequivocal observation that global warming is caused by human activities leads to the inevitable question on how these activities need to change in the future. Faced with a planet that is continuously heating up, resulting in droughts, wildfires, extreme weather events, freshwater scarcity, famines, poverty, displacement of populations and geopolitical tensions, the consequences of our activities can no longer be ignored. Scientific research is a common good that contributes to our understanding of the world and the anticipation of the future, yet it is increasingly recognised that research activities contribute like all other human activities to the deterioration of the climate.¹

The ethics committee of the National Centre for Scientific Research (CNRS) in France considers
in a recent opinion² that taking into account the environmental impacts of research activities is
an integral part of research ethics, comparable to the respect of humans or experimental animals.
According to the committee, it is the responsibility of scientists to limit the environmental impacts
of their research practises, but also to consider the impacts of choosing particular research topics
and the means to address them.

Astronomers are particularly active in this introspection, and estimates of greenhouse gas (GHG)
emissions related to astronomical research activities are becoming an integral part of the scientific
output of the field.³⁻¹⁰ Primary sources of GHG emissions that were identified include air trav-
elling, purchase of goods and services, and use of astronomical facilities such as ground-based
telescopes or space observatories. It turns out that astronomical facilities are the dominant source
of GHG emissions in the field, with an estimated average contribution of 36.6±14.0 tCO₂ equiv-
alent (CO₂e) per astronomer.¹¹ Based on their analysis, ref. 11 (hereafter referred to as Paper I)
concluded that the pace at which new astronomical facilities are built needs to be reduced to make
the field sustainable.

In this work we present a quantitative estimate of this conclusion by modelling the future carbon
footprint of astronomical facilities for several scenarios. We address in particular the question by
how much the pace needs to be reduced to limit global warming to well below 2°C with respect
to pre-industrial levels, as agreed in the Paris Climate Agreement. Based on assessments by the
Intergovernmental Panel on Climate Change (IPCC), the United Nations Environment Programme,
as well as GHG emission reduction targets of the European Space Agency and the French Ministry
of Higher Education and Research, we translate this ambition into a minimum annual carbon foot-
print reduction of 5% for astronomical facilities, and a goal of 7% that is required to limit global
warming to 1.5°C (see ‘Carbon footprint reduction targets for astronomy’ in Methods).

We based our work on an inventory of all astronomical facilities that were or are built since 1945
to model the evolution of their GHG emissions since the end of World War II (see ‘Inventory of
astronomical facilities’ in Methods). We define a facility as “astronomical” if it is (or was) used
for astronomical research, irrespective of the motivations that led to the realisation of the facil-
ity, which may include geopolitical and economic considerations. Our inventory comprises 586
ground-based facilities and 625 space missions for which we collected start and end dates of con-
struction and operations, as well as size, mass and cost information. Using this information we
computed the GHG emission profile of each facility over time, taking into account emissions from
facility construction and operations (see ‘Computation of annual carbon footprint’ in Methods).
Similar to Paper I, GHG emissions were computed as a product between activity data and emis-
sion factors, but in this work both are now time dependent, and reflect the evolution of the activities
and the emission factors over the years. In contrast to Paper I, where emissions from construction
and operations were integrated over the life cycle of the facilities and released uniformly over the
life time of facility operations, we used in this work a more realistic model that reflects the GHG
emissions as they are released over time. Specifically, construction-related emissions were released
during the construction period and operations-related emissions during the operating period. Fur-
thermore, emission factors evolve with the carbon intensity of gross domestic product (GDP) to
take the gradual decarbonisation of the global economy into account. As activity data, we used like
in Paper I the total cost of construction and the annual cost of operations for ground-based obser-
vatories, and the total payload mass for space missions. Aggregating the GHG emission profiles of
all facilities, we modelled the history of GHG emissions from astronomical facilities over the past
78 years.

We then factorised the GHG emission history into the evolution of the number of active facilities, the evolution of the average activity data per facility, and the evolution of the carbon intensity of astronomical facilities (see ‘Future carbon footprint’ in Methods). We fitted the trends of each factor using analytical functions and predicted on their basis extrapolated trends of future GHG emissions under the hypothesis of a research-as-usual scenario. We then explored alternative scenarios that either freeze or reduce the number of active facilities, or that explore scenarios of enhanced decarbonisation, in order to identify pathways that are compliant with the Paris Climate Agreement.

**Results**

**Evolution of the carbon footprint of astronomical facilities.** Figure 1 shows the evolution of the number of operating astronomical facilities and the associated annual carbon footprint since the year 1945. We qualify as operating all facilities that were in the operations phase in a given
<figure><img src="image_2.png" /><figcaption>**Figure 1:** Evolution of the number of astronomical facilities and their carbon footprint since 1945.</figcaption></figure>

Space missions are represented by blueish colours, distinguishing astrophysics, planetary, plasma physics, and solar missions. Ground-based observatories are represented by greenish colours, split into radio arrays, radio telescopes, and optical-infrared reflectors with diameters between 1-3 m and above. The number of authors on refereed papers in a given year is used as a proxy for the evolution in the number of astronomers in the world (solid black line in panel b). Panel (a) shows the number of operating facilities, panel (b) shows the number of facilities per author, panel (c) shows the annual carbon footprint, and panel (d) shows the annual carbon footprint per author. Note that the y-axes of panels (c) and (d) are split so that the full dynamic range as well as the more recent evolution is readily visible.

year, excluding observatories under construction or space missions that were not yet launched, and facilities for which operations were terminated. The number of operating facilities, shown in panel (a), has steadily increased over the years, raising from 17 in the year 1945 to 473 in the year 2022. Ground-based facilities dominate largely the number of operating facilities for all years, comprising 85% and 79% of the facilities for the years 1980 and 2022, respectively. For the year 2022, among the 374 operating ground-based facilities, 41% were optical-infrared reflectors (OIR) with diameters of 1–3 metres, 9% were OIR telescopes with diameters of at least 3 metres, 33% were radio telescopes, and 17% were radio telescope arrays. Of the 99 operating space missions in 2022, 11% were dedicated to solar physics, 16% to plasma physics, 35% to
planetary exploration, and 37% to astrophysics. Over the last 30 years, the number of operating ground-based facilities has grown by 37%, corresponding to an annual average growth rate of 1.0% per year, while the number of operating space missions has grown by 161%, corresponding to an annual average growth rate of 3.2% per year. As the average lifetime of the facilities has not noticeably increased over the last decades (Supplementary Information), the observed increase in the number of operating facilities is attributable to an increase in the deployment pace of new facilities.

Over the same period, the growth rate of the astronomical community was even larger, estimated to be 4.6% per year (Supplementary Information). This led to a decreasing number of operating facilities that are available per astronomer from the early 1970s on (see panel b of Figure 1). In 2022, there was one facility for every 78 astronomers in operations, while in 1980 a facility was shared by only 20 astronomers. Prior to 1970 the trend was opposite, with a tripling of the number of operating facilities per astronomer between 1950 and 1970 that was mainly driven by the advent of radio astronomy and space flights.

The evolution of the annual carbon footprint that we estimated for the facilities in our inventory is shown in panel (c) of Figure 1. Note that the y-axis of the panel is broken into two ranges to capture the full dynamics of the evolution. The annual carbon footprint peaks in 1967 at a value of 11.8 MtCO₂e yr⁻¹ which is primarily due to construction activities related to the missions of the Apollo program. Excluding these missions, the peak value drops to 3.0 MtCO₂e yr⁻¹, and excluding all lunar exploration missions the peak value drops even further to 1.4 MtCO₂e yr⁻¹.

One may argue that the motivation of the lunar exploration missions was primarily geopolitical and not scientific, and hence that these missions should be excluded from the inventory. Yet, astronomers were profiting from and supporting these missions, hence their co-responsibility in the associated environmental impacts can not be denied. Even if lunar exploration missions were excluded, planetary missions still dominate by large the annual carbon footprint during the space race, which is attributed to missions towards other solar system bodies, including in particular Venus and Mars (Supplementary Information). This situation lasted until the years 1985–1990 and ended with the collapse of the Soviet Union. From then on the annual carbon footprint is fluctuating
between 0.6–1.3 MtCO₂e yr⁻¹, dominated to more than 75% by space missions, and punctuated
by the construction of new large space facilities, such as the Hubble Space Telescope (HST) or
the Compton Gamma-Ray Observatory (CGRO) that were launched in the early 1990s. Over the
last decade the annual carbon footprint of space missions is on the rise again, owing primarily
to planetary exploration missions among which is an increasing number of missions to the Moon
and Mars (Supplementary Information). In 2022, the annual carbon footprint of astronomical
facilities reached a value of 1.3 MtCO₂e yr⁻¹, of which 84% are attributed to space missions and
16% to ground-based observatories. The contribution from space missions originates to 65% from
planetary exploration missions (corresponding to about half of the total annual emissions in 2022),
30% from astrophysics missions, and 3% each from plasma physics missions and solar missions.
The contribution from ground-based observatories originates to 45% from OIR telescopes of at
least 3 metre diameter, 36% from radio arrays, 12% from single radio telescopes, and 8% from
OIR telescopes with diameters between 1–3 metres (totals differ from 100% due to rounding).
Interestingly, the latter category contributes the largest to the number of operating ground-based
facilities, but owing to the small size of the observatories, they provide the smallest contribution to
the carbon footprint.

Dividing the annual carbon footprint by the number of astronomers, panel (d) of Figure 1 shows
that the annual per astronomer footprint decreased from a peak of 4,079 tCO₂e yr⁻¹ during the
space race to a value of about ~100 tCO₂e yr⁻¹ in the 1990s. From there the per astronomer foot-
print decreased further to a level that remained relatively constant over the last decade, comprised
within the band 26–40 tCO₂e yr⁻¹, with the lowest and largest values reached in 2017 and 2019,
respectively.

**Modelling of the future annual carbon footprints.** In order to model the future annual carbon footprints of astronomical facilities, we factorised the latter into the number of operating facilities, the mean annual facility mass or cost, and the carbon intensity, with separate decompositions for space missions and ground-based observatories. For space missions, the mean annual facility mass specifies the total mass of all payloads that are constructed in a given year plus 1/48th of all payload masses that are operating in space, divided by the total number of operating space missions. The
<figure><img src="image_3.png" /><figcaption>**Figure 2:** Modelling of the future annual carbon footprints of astronomical facilities. Filled histograms represent the data for the past 45 years, with ground-based observatories represented in green and space missions in blue. Panel (a) shows the number of operating facilities, panels (b) and (c) the mean annual facility mass or cost, panels (d) and (e) the carbon intensities for space and ground-based facilities, and panel (f) the annual carbon footprints. Past trends were adjusted using analytical functions, with shaded bands obtained by varying the adjustment period from the last 30 to 45 years, and lines corresponding to adjustments over the last 45 years. In panels (a) and (f), solid lines correspond to exponential laws and dashed lines to linear laws used for modelling the number of operating facilities. In these panels, data for space missions are stacked on top of data for ground-based observatories, so that the blue histograms, lines and bands represent total numbers and carbon footprints. In panels (b) and (c), alternative analytical functions that were not used in the analysis are shown as dashed-dotted lines and dotted bands for illustration. In panel (f), the expected trajectory for annual emission reductions of 5–7% is shown as grey band.</figcaption></figure>

factor of 1/48 reflects the proportion of GHG emissions arising from operations with respect to construction for a given payload mass (see ‘Future carbon footprint’ in Methods). For ground-based observatories, the mean annual facility cost corresponds approximately to the average annual spendings on ground-based observatories (Supplementary Information). The carbon intensities specify the GHG emissions as a function of mean annual facility mass or cost. Figure 2 shows the decomposition of the annual carbon footprint over the last 45 years, excluding hence the period of the space race. Past trends were adjusted using either linear or exponential functions to estimate
future trends until the year 2100, with lines representing adjustments over the last 45 years, and
shaded bands representing uncertainties that arise from the choice of the adjustment intervals,
obtained by progressively shortening the interval until it covers only the last 30 years.

Adjusting the growing number of operating facilities using exponential laws over the last 45 years
suggests annual growth rates of 2.5% for space missions and 1.2% for ground-based observatories,
respectively, while linear laws indicate that the number of operating space missions grows by 1.4
per year and the number of operating ground-based observatories by 3.7 per year. We note that
data for space missions are better adjusted by an exponential law, while the data for ground-based
observatories favour a linear law. The mean annual facility mass for space missions decreases
somewhat over the years while showing important peaks that reflect enhanced construction activ-
ities of new facilities (see Supplementary Information for an interpretation of the figure). Fitting
the data with a linear law predicts implausible negative values before the year 2100, while an expo-
nential law provides a more plausible description. Still, the exponential law adjusted over the last
45 years suggests an annual decrease rate of 1.2% with some moderate uncertainty, ending up with
a mean annual facility mass in 2100 that is only half the value of today. Although the mean annual
facility mass does not directly reflect the payload mass (Supplementary Information), an impor-
tant decrease still implies that satellites will become on average lighter over time, a trend that still
needs to be confirmed. In any case, the mean annual facility mass cannot decrease indefinitely, and
will plausibly level off at some time. The mean annual facility cost for ground-based observatories
rises relatively steadily over the last 45 years and is well explained by a linear law with an increase
of 30 k€ yr⁻¹ per facility. An exponential law fits the data less well, and leads to an import raise
of the annual spending per facility that is probably not compatible with current budget constraints.

Also here, a levelling off in the future seems plausible. By construction, the carbon intensities are
well fit by exponentially decaying laws with decay rates of 1.44% (see ‘Computation of annual
carbon footprint’ in Methods).

The evolution of the annual carbon footprint, based on the analytical laws that were adjusted to
the coefficients of the factorisation, is shown in panel (f) of Figure 2. The figure shows as grey
band annual emission reductions at a rate of 5–7% that we assume as targets for astronomy with
the goal to limit global warming to temperatures below 2°C or 1.5°C, respectively (Supplementary Information). Obviously, the predictions depend on the laws that were used to model the number of operating facilities, with linear laws leading to lower annual carbon footprints compared to exponential laws. While the choice of the law impacts the predicted carbon annual carbon footprint for ground-based observatories only moderately, the situation is different for space missions. Using a linear law leads to a very curved annual carbon footprint with an emission maximum in the years 2004–2012 that actually was not observed. Despite some strong fluctuations in the historical annual carbon footprint data, the data are better explained when an exponential law is used, reproducing the gradually increasing trend in the annual carbon footprint. In any case, for both laws the predicted annual carbon footprint is well above the suggested emission reduction trajectory for astronomy.

**Future annual carbon footprints.** In order to investigate possible lever arms for emission reductions we explored five illustrative scenarios (see ‘Future carbon footprint’ in Methods). Figure 3 shows the predicted annual carbon footprint of these scenarios for different modellings of the factorisation coefficients, where panel (a) represents the modelling that reproduces most accurately the historic trends (Supplementary Information). In the ‘Research as usual’ scenario, where no additional measures with respect to current practices are taken, the extrapolation of the historic trends over the last 45 years suggests a relatively constant annual carbon footprint until the end of the century. According to our modelling, the growth in the number of facilities is in this case in balance with the decarbonisation of the industry and the suggested reduction of the mean annual payload mass, stabilising the annual carbon footprint in the long run. Estimating the historic trends over shorter periods suggests however that the annual carbon footprint may also increase over time, reaching a value that is at least twice as large in 2100 compared to current levels. In the ‘Deep decarbonisation’ scenario, where we increased the decarbonisation rate from 1.44% to 3%, corresponding to a doubling of current efforts, the annual carbon footprint in 2050 is reduced by 11–35% with respect to 2022 levels. Alternatively, preventing a rise in the number of operating facilities by keeping the fleet of facilities at the level 2022, as explored in the ‘Freeze’ scenario, bears a reduction potential of 41–47% with respect to 2022 levels. Combining the stabilisation
<figure><img src="image_4.png" /><figcaption>**Figure 3:** Future annual carbon footprints for five illustrative scenarios. The ‘Research as usual’ scenario corresponds to the projections from the carbon footprint factorisation. In the ‘Deep decarbonisation’ scenario, the decarbonisation rate after the year 2022 was increased from 1.44% to 3% per year. In the ‘Freeze’ scenario the number of operating facilities was kept constant at the value of the year 2022, and both measures were combined in the ‘Freeze & deep decarbonisation’ scenario. In the ‘Degrow & deep decarbonisation’ scenario, the number of operating facilities was decreased from value in 2022 at the rate of 3% per year. Each panel corresponds to a different modelling of the factorisation coefficients. For panel (a), the number of operating facilities was modelled using an exponential law for space missions and a linear law for ground-based observatories. In panel (b), exponential laws were used in both cases, while in panel (c) linear laws were employed. For panel (d), in addition to using linear laws, payloads with masses inferior to 20 kg and superior to 10 tons were excluded in the adjustments. Shaded bands were obtained by varying the adjustment period of the analytical laws from the last 30 to 45 years, lines corresponding to adjustments over the last 45 years.</figcaption></figure>
of the number of operating facilities with an enhanced decarbonisation, explored by the 'Freeze & deep decarbonisation' scenario, promises emission reductions of 62-66% with respect to 2022 levels. Still, none of the scenarios meets the annual carbon footprint reduction target, which is situated in 2050 at 76-87% with respect to 2022 levels for the targeted annual decrease rates of 5-7% per year. We therefore went one step further and decreased the number of operating facilities in the 'Degrow & decarbonisation' scenario by a constant rate of 3% per year, combined with a decarbonisation rate of 3% per year, which finally is a scenario that is compliant with a trajectory that keeps global warming to below 2°C. We repeated the exercise for alternative modellings of the factorisation coefficients (panels c-d) with always reaching the same conclusion: the only scenario that fits a trajectory for keeping global warming to below 2°C is the 'Degrow & decarbonisation' scenario (Supplementary Information).

## Discussion

By estimating the carbon footprint of astronomical facilities for the year 2019, Paper I revealed that they were the single largest contributor to the professional GHG emissions of an astronomer. While Paper I was based on a list of 85 facilities from which the global impact of the world-fleet was extrapolated, we assessed in this work the global carbon footprint without any extrapolation. For the year 2019, for which Paper I estimated an annual carbon footprint of 36.6±14.0 tCO₂e yr⁻¹ per astronomer, our analysis suggests an average annual footprint of 39.7 tCO₂e yr⁻¹ per astronomer. Although this is in good agreement with Paper I, we attribute 87% of the emissions in 2019 to space missions, while Paper I concluded that 62% of the emissions originate from ground-based observatories. We explain these differences by an overestimation of GHG emissions from ground-based observatories in Paper I due to a bias towards large telescopes in the extrapolation procedure, combined with an underestimation of GHG emissions from space missions due to the uniform distribution of life cycle emissions over the operating life time (Supplementary Information). As can be seen in panel (b) of Figure 1, the annual carbon footprint fluctuates substantially over time, owing to the construction activities of new facilities, which introduces a large variability in the annual carbon footprint that was smoothed out by the method used in Paper I.

This underlines the importance of studying the full emission history, so that conclusions can be
drawn from long-term trends, and not from particular fluctuations that may occur in a specific year. Studying the long term trends provides also insights into future GHG emissions, and our analysis suggests that they probably will not decline in a research-as-usual scenario, despite the assumption that the current decarbonisation trends will persist. The reason behind the maintenance of GHG emissions at current levels, if not their rise above them, is that the current growth rate of astronomical facilities largely compensates present decarbonisation efforts. The main open question is whether this growth will be linear or exponential, with eventually a modest gradual decline of emissions in case of a linear growth. Yet, even if emissions decline, the rate will be much too slow, and GHG emissions will be well above the required trajectory for limiting global warming to well below 2°C.

So how can GHG emissions from astronomical facilities be reduced? One option is enhancing decarbonisation, by massively investing in renewable energy sources, using bio-sourced and recycled materials, reducing any use of fossil fuels, and limiting the dimensions and weights of future facilities. Probably this will reach limits, in particular for the space sector, since rocket launches are hard to abate. We explored a scenario where current decarbonisation trends are doubled, but this fails largely to meet the required trajectory. Annual decarbonisation of the economy would need to be increased from 1.4% to 7–9% per year for the next decades to allow the continued growth of astronomical facilities while meeting the required trajectory. These levels are only reached in the most ambitious C1 scenario of the IPCC¹² that relies heavily on carbon capture and storage (CCS) and carbon dioxide removal (CDR) technologies that should remove on average 10 GtCO₂ per year (about 25% of the current annual CO₂ emissions¹³) over the next 80 years, which seems to be highly unrealistic.¹⁴⁻¹⁷ A safer more rational solution would be to stop the growth in the number of operating facilities by keeping it at current levels. This would not mean that no new astronomical facilities would be developed, but only existing facilities would be replaced at the end of their life time by facilities of equivalent mass or cost. If more heavy or expensive facilities would be desired (in the sense of the activity data used in this work), the end of life of several outgoing facilities would need to be waited out before new facilities could be put in operations. Freezing the number of astronomical facilities to current levels would reduce the decarbonisation needs to 5–7% per
year in order to meet the required trajectory. This level is comparable to, but slightly larger than, the emission reductions that were reported due to COVID-19 lockdowns in 2020¹⁸, which hence seems to be still very ambitious, but eventually feasible. However, if this level turns out to be un-feasible, the only remaining solution is to decrease the number of operating facilities. In Figure 3 we used the example of an annual decrease of 3% per year, which would relax the decarbonisation requirements to 2–4% per year, a level that is probably achievable with some efforts.

The situation is hence clear: there is no evidence for the possibility of a ‘green growth’ of as-tronomical facilities, which is actually no surprise, as others have evidenced the same for the economy at large.¹⁹⁻²³ We therefore call the astronomical community to reflect on this fact, and to engage into a debate on how astronomical research can be made sustainable. A way forward would be to include sustainability requirements in competitive grant processes²⁴ and to assess en-vironmental impacts of new research facilities prior to taking implementation decisions, pondering carefully over the trade-offs between environmental impacts, community interest, and scientific return. Basing any new facility on a collaboration of the world-wide astronomical community, and hence abandoning the logic of international competition, appears also fundamental, as the latter often leads to the unnecessary multiplication of similar facilities. Furthermore, large volumes of archival data from past and existing facilities remain poorly explored, hence the maintenance and exploration of these treasures should become a priority, and attract more attention and resources. Requesting that each research domain fully exploits any existing archival data before engaging into the implementation of a new facility does not seem to be outrageous. The community may also question whether astronomical research should continue to serve as a pretext for satisfying geopolitical interests and economic expansion, with the rising interest for lunar exploration and exploitation being a typical example.²⁵,²⁶

Astronomy is of course not a singularity, and any scientific field that relies on large facilities will probably come to similar conclusions, once the communities engage in a comprehensive evaluation of their environmental impacts. Astronomy, as many other research fields, creates unquestionably many benefits to society, and it is finally a societal and political choice to decide how much en-vironmental impacts are tolerated for these gains. Astronomy distinguishes itself by a unique
perspective, as it addresses questions about our origins and our place in this world, questions that bear profound cultural implications. Astronomers know best that there is no 'Planet B', and that the only realistic option for humanity is to preserve liveable conditions on Earth. We believe that astronomers have therefore also the ethical responsibility to lead by example, showing how these conditions can be maintained for the generations to come, without compromising human curiosity and excitement about the world in which we are living.

## Methods

**Carbon footprint reduction targets for astronomy.** Although there exist currently no agreed carbon footprint reduction targets for astronomy, the IPCC reminds in the sixth assessment report that limiting warming to 1.5°C or 2°C “involve[s] rapid and deep and in most cases immediate greenhouse gas emission reductions in all sectors”.¹² As has been shown²⁷, astronomy is a sector that contributes to global warming, and consequently it is subject to this imperative. The IPCC reminds that “doing less in one sector needs to be compensated by further reductions in other sectors if warming is to be limited”.¹² Eventually, policy makers and the society at large may set different reduction efforts for different activity sectors, yet until this is done, a reasonable assumption is that astronomy needs to reduce GHG emissions at the same pace as the humanity at large.

According to the 2023 Emission Gap Report of the United Nations Environment Programme (UNEP), global GHG emissions increased by 1.2% from 2021 to 2022 to reach a new record of 57.4 gigatons of CO₂ equivalent (GtCO₂e). Least-cost pathways that are consistent with limiting global warming to 2°C or 1.5°C imply that GHG emissions need to be reduced to 20 GtCO₂e or 8 GtCO₂e in 2050, respectively, corresponding to annual reductions of 3.7% or 6.8% per year. It should be recognised that these are average global reduction targets, and that the targets should be higher for developed countries owing to their larger responsibility in global warming, a principle that was agreed upon in the Paris Climate Agreement.

There do actually exist some carbon footprint reduction targets in fields related to astronomy, which
may also serve as guideline when discussing targets for astronomy. For example, at the ministerial
Council meeting of the European Space Agency (ESA) on 22 November 2022, it was decided to
reduce “the carbon footprint of the Agency by 46% by 2030 as compared to the 2019 baseline”²⁸,
which corresponds to annual reductions of 5.4% per year over the considered period. The Ministry
of Higher Education and Research in France defines an annual carbon footprint reduction goal of
5% for all French research organisations that applies also to astronomical research in that country.²⁹

We therefore required as the strict minimum an annual carbon footprint reduction of 5% for astro-
nomical facilities in our study, with the goal of reaching an annual reduction of 7% that is required
to limit global warming to 1.5°C. We note that the resulting 1.5°C pathway is very similar to the
cross sector net-zero pathway of the Science-Based Targets Initiative³⁰.

**Inventory of astronomical facilities.** Our study is based on an inventory of astronomical ground-
based observatories and space missions that were constructed or active in the period from 1945
to 2022. We initiated our inventory of ground-based observatories from the list of active profes-
sional observatories compiled by Lukac & Miller ³¹, complemented by information gathered from
literature, including in particular topical books³²⁻³⁶, reviews, observatory reports, public websites,
and press articles. For each facility we compiled, among other data, information on years of op-
eration start and end, and, if available, start of construction, as well as construction and annual
operation costs. Cost data were inflation corrected to 2019 economic conditions in their quoted
currencies based on national consumer price indices (CPI), and converted to 2019 Euros using
purchasing power parities (PPP) provided by the Organisation for Economic Co-operation and De-
velopment (OECD). Unfortunately, cost data are only sparsely publicly available, hence we use
cost-diameter scaling relations to infer construction and annual operation costs from the facilities'
diameters in case of lacking cost information (Supplementary Information). For that purpose we
collected the facilities' reflector or antenna diameters, or in case of non-circular geometries, con-
verted the facilities' collection areas A into effective diameters D using $D = 2\sqrt{A/\pi}$. Public
information on construction start dates is also sparse, and in case of lacking information we as-
sumed that construction started five years before operations. Furthermore, we identified in each
category the type of the facility so that we could for example distinguish optical-infrared reflectors (OIR) with monolithic thick mirrors from those with segmented mirrors, and apply dedicated scaling relations based on the technology employed. Finally, we classified ground-based observatories into four categories: large-sized (diameter ≥ 3 metre) and medium-sized (diameter 1 – 3 metre) OIRs, radio telescopes, and radio telescope arrays. Our inventory comprises 586 past, active or planned ground-based facilities, including 43 large-sized OIR telescopes, 202 medium-sized OIR telescopes, 215 radio telescopes, and 126 radio telescope arrays. Although we aimed for completeness from the year 1945 onwards, we cannot exclude that a few facilities, in particular small radio telescopes or medium-sized OIR telescopes, have been missed.

We based our inventory of space missions on the comprehensive list provided on Gunter’s space page³⁷, complemented by information gathered from public websites, including those of space agencies, projects, and research institutes, and press articles. For each mission we compiled, among other data, information on years of mission launch and mission end, if available also start of mission phases A/B (feasibility and preliminary definition) and phases C/D (detailed definition, qualification and production), as well as payload launch (wet) mass, and, if available, construction, operations and full mission costs. We classified space missions according to their scientific purpose, separating missions for solar observations, studies of interplanetary plasma physics, planetary exploration, and astrophysics. Our inventory comprises 625 past, active or planned space missions since the beginning of the space age (1957), including 61 solar missions, 130 plasma physics missions, 279 planetary missions, and 155 astrophysics missions.

**Computation of annual carbon footprint.** Similar to Paper I, we define the carbon footprint of an astronomical facility as the aggregate of all greenhouse gas (GHG) emissions generated by both the construction and the operations of the facility. To enable aggregation of the different gases that cause global warming, GHG emissions are expressed in carbon dioxide equivalents (CO₂e) which take into account the different warming potentials and lifetimes of the various GHGs in the atmosphere. Carbon footprints were computed by multiplying activity data with emission factors. For ground-based observatories we used construction and annual operation costs as activity data, while
for space missions we based our estimates on the payload launch mass. Paper I derived emission factors for astronomical facilities from the literature, finding 240 tCO₂e M€⁻¹ for the construction and 250 tCO₂e M€⁻¹ yr⁻¹ for the operations of ground-based observatories. For space missions, Paper I discussed two options, one using total mission cost and another using payload launch mass as activity data, showing that when GHG emissions are aggregated, both proxies lead to equivalent results. Since payload launch masses are readily available for all space missions and not subject to significant uncertainties, we used them as activity data in our study (an alternative analysis using mission cost as activity data is available in the Supplementary Information). While Paper I could only derive a life-cycle emission factor of 50 tCO₂e kg⁻¹ on the basis of two cases studies³⁸, more detailed information for the two cases studies were provided recently in ref. 39, suggesting emission factors of 48 tCO₂e kg⁻¹ for construction and 1 tCO₂e kg⁻¹ yr⁻¹ for operations. We adopt these factors for our study, so that the results can be directly compared to those of Paper I (Supplementary Information).

Since the publication of Paper I, more estimates for emission factors for astronomical facilities have become available, and they confirm that the adopted emission factors are of the right order of magnitude. Ref. 9 determined the carbon footprint of the LOFAR telescope and found emissions factors of 100 tCO₂e M€⁻¹ for construction and 187 tCO₂e M€⁻¹ yr⁻¹ for operations, emphasising that these were lower limits since some processes were excluded in their analysis. Using a comprehensive Life Cycle Assessment (LCA) of one Mid-Sized Telescope of the Cherenkov Telescope Array, ref. 40 found emission factors of 551 tCO₂e M€⁻¹ for the construction of the telescope structure and 70 tCO₂e M€⁻¹ for the construction of the camera, resulting in 311 tCO₂e M€⁻¹ for one telescope. This suggests that construction of different subsystems have different emission factors, but when combined the resulting global emission factor comes reasonably close to the one adopted in our work. Finally, applying a LCA to the X-IFU instrument on ESA's Athena space mission, ref. 41 determined an emission factor of 115 tCO₂e per kg of instrument mass for instrument development, which is considerably driven by the substantial labour that is needed to develop the instrument.
The emission factors are expected to decrease over the years due to reductions in the carbon intensity of energy production (for example as result of replacing fossil fuels by renewable energies) and due to improvements in the energy efficiency of construction processes and operation activities.⁸ Since quantitative estimates of these evolutions do not exist for astronomical facilities, we assumed that the emission factors follow the general decarbonisation trends that are observed for the economy at large. The latter are readily traced by the carbon intensity of inflation corrected GDP based on PPP, which we used as a proxy for the time dependence of the emission factors. As shown in the Supplementary Information, the observed trends are well described over the period of interest by the piecewise function

$$CI(t) = 2.5 \times \begin{cases} 0.72 - 0.00113 \times (t - 2019) & \text{for } t \le 1973 \\ 0.40 \times \exp(-0.0144 \times (t - 2019)) & \text{else} \end{cases} \quad (1)$$

where t is time given in years. The function applies to economic conditions of 2019 and is normalised to $CI(2019) = 1$ so that it can be directly multiplied with the adopted emission factors that were derived for the same reference year.

To determine the annual carbon footprint $F(t)$ of each facility we assumed that construction emissions occur over a period $t_c \le t < t_o$, where $t_c$ is the year of construction start and $t_o$ is the year of operations start or mission launch. Furthermore, we assumed that emissions from operations occur subsequently over a period $t_o \le t \le t_e$, where $t_e$ is the year when operations end, or 2022 as the end year of the considered period, whichever is smaller. Finally, costs and payload masses used as activity data for construction were annualised by dividing them by $t_o - t_c$, so that they were equally distributed over the construction period. We then computed the annual carbon footprint $F(t)$ in units of tCO₂e yr⁻¹ for each ground-based observatory using

$$F(t) = CI(t) \times \begin{cases} 240 \times \frac{\text{Construction costs (M€)}}{t_o - t_c} & \text{for } t_c \le t < t_o \\ 250 \times \text{Annual operating costs (M€)} & \text{for } t_o \le t \le t_e \\ 0 & \text{else} \end{cases} \quad (2)$$
and for space missions using

$$F(t) = CI(t) \begin{cases} 48 \times \frac{\text{Payload mass (kg)}}{t_o - t_c} & \text{for } t_c \le t < t_o \\ 1 \times \text{Payload mass (kg)} & \text{for } t_o \le t \le t_e \\ 0 & \text{else} \end{cases} \quad (3)$$

over the period $1945 \le t \le 2022$. Illustrations of $F(t)$ for one ground-based observatory and one space mission are given in the Supplementary Information.

**Evolution of the number of astronomers.** To compare the evolution of the world-fleet of astronomical facilities and the associated carbon footprint to the size of the astronomical community, we determined the latter using the publication database of the Astrophysics Data System (Supplementary Information). We used the number of individual authors affiliated to astronomical research institutes that signed publications in refereed journals as a proxy of the size of the astronomical community, and extracted its evolution for the period 1940–2022. We verified using literature data that our estimates match independent assessments of the size of the astronomical community.11,42 From the year 1975 on, where the extracted data do not suffer from incompleteness, the number of astronomers evolves exponentially, with a doubling time of 15 years. The data closely follow the relation

$$\textit{Number of individual authors}(t) = 32018 \times \exp(0.046 \times (t - 2019)) \quad (4)$$

that we adopt for the evolution of the number of astronomers in our study.

**Future carbon footprint.** To estimate the future carbon footprint of astronomical facilities, we factorised the annual footprints of ground-based observatories and space missions using a formula that was inspired by the Kaya identity, which is frequently used in the development of future GHG emission scenarios.43 The Kaya identity is a mathematical identity that expresses GHG emissions as the product of human population, GDP per capita, energy intensity (per unit of GDP), and carbon intensity (emissions per unit of energy consumed). We use a similar approach to model the evolution of the carbon footprint of astronomical facilities by decomposing the footprint into a number of facilities, mean mass (for space missions) or cost (for ground-based observatories) per
facility, and carbon intensity per unit of mass or cost. Specifically we used

$$F(t) = N(t) \times \frac{C(t)}{N(t)} \times \frac{F(t)}{C(t)} \qquad (5)$$

where $N(t)$ is the number of facilities, and $C(t)$ is the activity data that when multiplied with the carbon intensity $F(t)/C(t)$ provides the annual carbon footprint of the facilities. For ground-based observatories, $C_g(t)$ was computed by summing over individual observatories $i$ using

$$C_g(t) = \sum_i \begin{cases} \frac{\text{Construction costs of observatory } i \text{ (M€)}}{t_o^i - t_c^i} & \text{for } t_c^i \le t < t_o^i \\ \frac{250}{240} \times \text{Annual operating costs of observatory } i \text{ (M€)} & \text{for } t_o^i \le t \le t_e^i \\ 0 & \text{else} \end{cases} \qquad (6)$$

and corresponds to the annual spending for observatory construction plus 250/240 times the annual spending for observatory operations. The factor 250/240 takes into account the different emission factors for construction and operations of ground-based facilities, putting both contributions on the same scale so that a single carbon intensity $F_g(t)/C_g(t)$ could be used that corresponds to the emission factor for observatory construction. $C_g(t)$ corresponds roughly to the annual spending on ground-based observatories (Supplementary Information). In analogy, $C_s(t)$ was computed for space missions using

$$C_s(t) = \sum_i \begin{cases} \frac{\text{Payload mass of mission } i \text{ (kg)}}{t_o^i - t_c^i} & \text{for } t_c^i \le t < t_o^i \\ \frac{1}{48} \times \text{Payload mass of mission } i \text{ (kg)} & \text{for } t_o^i \le t \le t_e^i \\ 0 & \text{else} \end{cases} \qquad (7)$$

where the factor 1/48 is the ratio between the emission factors for operations and construction, so that the carbon intensity $F_s(t)/C_s(t)$ for space missions corresponds to the emission factor for satellite construction. $C_s(t)$ corresponds thus to the sum of the payload masses that are constructed in a given year plus 1/48th of all payload masses that are operating in space.

We then modelled each factor of Equation 5 by adjusting either linear or exponential functions to the past evolutions. To avoid the singularly large carbon footprints associated to the initial space race, we limited the adjustments to the years from 1978 onwards. We performed an initial adjustment of the functions over the years 1978–2022, and then successively increased the start
year up to 1993 to assess the uncertainty related to the particular choice of an adjustment period.
In that way, the functional parameters of each term of Equation 5 was estimated from the observed
trends over the past 30–45 years.

We then explored five possible scenarios for the evolution of the future carbon footprint of astro-
nomical facilities. First we explored a ‘Research as usual’ scenario, where N(t) and C(t)/N(t)
evolve according to the adjusted functional laws, and F(t)/C(t) decreases exponentially with a
fixed rate of 1.44% per year, corresponding to the historical decrease of the carbon intensity (see
Eq. 1). Second, we explored a ‘Deep decarbonation’ scenario where the decarbonisation rate was
increased from 1.44% to 3% per year, doubling the current efforts in the reducing the carbon foot-
print of the economy. Third, we explored a scenario dubbed ‘Freeze’, where we fixed the number
of operating facilities to the one in the year 2022. This corresponds to a scenario where the num-
ber of operating facilities would be frozen at the current level, implying that only observatories or
missions that end operations would be replaced by new facilities. We combined the freezing and
the deep decarbonisation in a fourth scenario called ‘Freeze & deep decarbonisation’ to explore
the additional effects of both measures. Finally, we explored a scenario dubbed ‘Degrow & deep
decarbonisation’, where in addition to the decarbonisation rate of 3% per year the number of oper-
ating facilities is decreased by 3% per year with respect to the number in the year 2022. It turned
out that this level of decrease is needed to meet the proposed GHG emission reduction targets for
astronomy.

**Data Availability**

All data used for this work are available for download at https://zenodo.org/records/
12568160⁴⁴.

**Code Availability**

All code used for this work is available for download at https://zenodo.org/records/
12568160⁴⁴.

**Correspondence** Correspondence should be addressed to J.K. (jurgen.knodlseder@irap omp.eu).
**Acknowledgements** This research has made use of NASA's Astrophysics Data System Bibliographic Services (ADS). We thank K. Lockhart for her help with using that service. We further would like to thank D. Barret for useful discussions. This work has also benefited from discussions within the research group and cross-disciplinary collective Labos 1point5, and within the grassroots movement Astronomers for Planet Earth. We thank all members for their engagement and support. This work has made use of the Python 2D plotting library matplotlib<sup>45</sup>.

**Author Contributions Statement** J.K. developed the method, gathered and analysed the data, and drafted the paper. P.G. helped with the collection of some data. All authors contributed to the definition of the analysis method, the elaboration of the discussion section, and the review of the manuscript.

**Competing Interests** The authors declare no competing interests.

**Peer review information**

**Acronyms** Acronyms used throughout this paper represent: Carbon Capture and Storage (CCS), Carbon Dioxide Removal (CDR), Compton Gamma-Ray Observatory (CGRO), Consumer Price Index (CPI), European Space Agency (ESA), Greenhouse Gas (GHG), Gross Domestic Product (GDP), Hubble Space Telescope (HST), Intergovernmental Panel on Climate Change (IPCC), Life Cycle Assessment (LCA), Low Frequency Array (LOFAR), National Centre for Scientific Research (CNRS), Optical Infrared Reflector (OIR), Organisation for Economic Co-operation and Development (OECD), Purchase Power Parity (PPP), United National Environment Programme (UNEP), X-ray Integral Field Unit (X-IFU).
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# Supplementary Information

## Cost scaling relations for ground-based facilities

To cope with lack of publicly available cost data for ground-based facilities we made use of scaling laws between telescope collection diameter $D$ and cost $C$. Such scaling laws have been discussed in the literature, and in their simplest form assume that the cost varies in proportion to some power $n$ of the diameter

$$C = k \times \left( \frac{D}{D_0} \right)^n \qquad (1)$$

with $n$ comprised between 1.7 and 2.8.¹⁻⁶ We fitted Supplementary Equation 1 to our construction cost and diameter data to derive scaling laws for different telescope categories and types. The resulting parameters are summarised in Supplementary Table 1, with quoted uncertainties reflecting the mean absolute deviation of construction costs from the scaling laws. The diameter and construction cost data are shown together with the fitted scaling laws in the left panels of Supplementary Figure 1.

For optical-infrared reflectors (OIR) the large-sized and medium-sized categories were analysed together, with most of the medium-sized telescopes being of the “monolithic thick mirror” type, extending (and hence stabilising) the fit of the scaling relation towards smaller diameters. Further telescopes types used were monolithic thin mirrors with active optics, segmented mirrors that are mostly used for larger telescopes, and liquid mirrors for which a few instances exist in our inventory. A few telescopes were excluded from the fit of the construction cost – diameter relation to avoid biasing by facilities that obviously do not follow the general trend. The Vera Rubin Observatory (VRO) lies on the high side of the general trend of monolithic thin mirrors, plausibly due to the complexity of the system and the camera. The Hobby-Eberly Telescope (HET) and the Southern African Large Telescope (SALT), which is closely based on the former, are well known to have low construction costs, typically 5–6 times less expensive than other telescopes of comparable size.⁷ The ELT construction cost estimate also falls significantly below the trend line for “segmented mirrors”, yet as construction is currently in progress the final cost is actually not yet known. Furthermore, the ELT construction cost estimate is for the year 2023, which is a period with significant inflation, adding a further uncertainty to the estimate. Our analysis suggests that
monolithic thick mirrors are slightly more expensive compared to monolithic thin or segmented
mirrors of the same size, an observation that was made already by ref. 5. Power indices are found
to be within $n \sim 2.1 - 2.4$, which is compatible with literature values.

For radio telescopes we first considered single steerable dishes and dish arrays which are the tele-
scope types for which we have most cost data. For the telescope arrays, we used as *D* the diameter
of individual telescopes in the arrays, and as *C* the total construction cost divided by the number
*N* of telescopes in the array, taking hence the per-telescope cost for our analysis. A few steerable
dish arrays were excluded from the fits because they obviously present outliers with respect to the
trend observed for the other telescopes. Those are the Nobeyama Radioheliograph (NoRH), the
Expanded Owens Valley Solar Array (EOVSA), the Chris cross, the Culgoora Radioheliograph,
and the Giant Metrewave Radio Telescope (GMRT). Our analysis suggests similar power indices
of *n* ~ 2.4 for single steerable dishes and steerable dish arrays, yet with a normalisation that
indicates significantly larger per-telescopes costs for the arrays with respect to single dish observa-
tories. This may indicate that additional costs of combining individual antennae into an array may
become important for steerable dish arrays.

For all other radio telescope types we have less cost data, and eventually the diameters span only
a limited range, making it more difficult to define solid scaling relations. Since our data suggest
no obvious difference in the per-telescope cost for the other radio telescope types, we combined
single radio telescopes with radio telescope arrays for our analysis. Specifically, we distinguish
antenna arrays (which are usually arrays of dipole antennae), cylindrical parabolic antennae, steer-
able millimetre and sub-millimetre dishes, fixed dishes, and telescopes for the observation of the
cosmic microwave background (CMB). For antenna arrays and cylindrical parabolic antennae, the
collection area *A* of the arrays were converted into effective diameters *D* using *D* = $2\sqrt{A/\pi}$. We
consider the full collection area of the antenna arrays in our analysis, as the number *N* of antenna
elements is not always well defined for these facilities. A single facility was excluded from the
determination of the scaling relations, which is the Fred Young Submillimeter Telescope (FYST),
which combines a novel design with special characteristics that likely drive the construction cost
beyond the trend for other sub-millimetre telescopes. Generally, the cost data show a larger scatter
<figure><img src="image_5.png" /></figure>

**Supplementary Figure 1: Construction cost and annual operations cost fraction versus diameter for ground-based facilities.** Data are shown for OIR telescopes (top), steerable radio dishes and dish arrays (centre), and other radio facilities (bottom), with different symbol colours used for different telescope types. Left panels show construction costs, with symbols representing the data and lines showing the best-fitting scaling relations. Right panels show the fraction of annual operations cost over construction cost, with the horizontal lines representing the mean value and the grey band its uncertainty that was determined by successively excluding the five largest outliers from the mean values. Data shown as open circles were excluded from the fit of the scaling relations.
<table><caption>**Supplementary Table 1: Parameters of scaling relations for construction cost.** See Supplementary Equation 1 for the meaning of *k*, *n*, and *D*₀.</caption><thead><tr><th>Type</th><th>k</th><th>n</th><th>Relative uncertainty</th></tr></thead><tbody><tr><td colspan="4">OIR telescopes (D<sub>0</sub>=5 m)</td></tr><tr><td>Monolithic thick mirror</td><td>70.50 M€</td><td>2.238</td><td>70%</td></tr><tr><td>Monolithic thin mirror</td><td>41.21 M€</td><td>2.314</td><td>38%</td></tr><tr><td>Segmented mirror</td><td>40.51 M€</td><td>2.124</td><td>35%</td></tr><tr><td>Liquid mirror</td><td>0.871 M€</td><td>2.415</td><td>69%</td></tr><tr><td colspan="4">Radio dish(es) (D<sub>0</sub>=10 m)</td></tr><tr><td>Single steerable dish</td><td>263.31 k€</td><td>2.411</td><td>81%</td></tr><tr><td>Steerable dish array</td><td>1,016.00 k€</td><td>2.417</td><td>91%</td></tr><tr><td colspan="4">Other radio telescopes and arrays (D<sub>0</sub>=10 m)</td></tr><tr><td>Antenna array</td><td>56.48 k€</td><td>1.908</td><td>366%</td></tr><tr><td>Cylindrical parabolic antennae</td><td>766.97 k€</td><td>0.657</td><td>43%</td></tr><tr><td>Millimetre steerable dishes</td><td>2,975.34 k€</td><td>1.722</td><td>75%</td></tr><tr><td>Sub-millimetre steerable dishes</td><td>13,130.46 k€</td><td>1.953</td><td>16%</td></tr><tr><td>Fixed dishes</td><td>29.05 k€</td><td>2.287</td><td>81%</td></tr><tr><td>CMB array</td><td>12,796.54 k€</td><td>1.134</td><td>125%</td></tr></tbody></table>

for the arrays compared to the single dish data, suggesting that there are further underlying parameters that drive the cost, probably related to combining the different antennas into an array.⁸ For millimetre and sub-millimetre facilities we find power indices of *n* ~ 1.7 and *n* ~ 2.0 that are flatter than those of other steerable dishes, with significantly larger cost normalisations *k* that reflect the increasingly higher demand on surface roughness when moving to smaller operational wavelengths. Fixed dishes show a somewhat steeper power index of *n* ~ 2.3, at a significantly smaller cost with respect to steerable dishes, owing to the lack on a movable support structure. Antenna arrays share the same area in the cost-diameter diagram than the fixed dishes, yet with a large scatter. Cylindric parabolic antennae show a distinctive trend with a much flatter power
<table><caption>Supplementary Table 2: Median annual operations cost fractions.</caption><thead><tr><td>Category</td><td>Fraction</td><td>Uncertainty</td></tr></thead><tbody><tr><td>OIR telescopes</td><td>7.3%</td><td>0.7%</td></tr><tr><td>Radio dish(es)</td><td>6.4%</td><td>1.9%</td></tr><tr><td>Other radio telescopes and arrays</td><td>6.2%</td><td>0.8%</td></tr></tbody></table>

index, which we formally fitted to $n \sim 0.7$, but which is only poorly constrained as only three facilities contribute to the fit. Finally we found a power index of $n \sim 1.1$ for CMB facilities, though with a significant scatter, suggesting that there are further underlying parameters that drive the cost.

Concerning operation costs, they are often considered to be a fraction of the construction costs with typical values of 3–6%, with a possible trend of decreasing fractions with increasing costs.⁹ We show in the right panels of Supplementary Figure 1 the annual operations cost fraction versus the collection diameter for the ground-based facilities in our inventory, which primarily reveals an important scatter of individual data points. Although the largest OIR telescopes show indeed the smallest operations cost fractions, this may also be interpreted as a chance coincidence, as no obvious general trend of decreasing operations cost fractions is visible in our data for any of the facility categories. We therefore adjust the annual operations cost fractions by constants that are shown in Supplementary Figure 1 as solid black lines. By successively excluding the five largest outliers with respect to the constants we assess their uncertainties, which are shown as grey bands in Supplementary Figure 1. The results of this analysis are summarised in Supplementary Table 2. Our analysis suggests annual operations cost fractions of about 6–7%, at the upper side of the values found by ref. 9.

## Evolution of carbon intensity

Emission factors of astronomical facilities are expected to decrease over the years due to reductions in the carbon intensity of energy production (for example as result of replacing fossil fuels by renewable energies) and due to improvements in the energy efficiency of construction processes and operation activities¹⁰. Since quantitative estimates of these evolutions do not exist for astro-
<figure><img src="image_6.png" /><figcaption>**Supplementary Figure 2: Carbon intensity of gross domestic product (GDP).** Black dots show the observed carbon intensity of GDP. The red solid line shows a piecewise function that was adjusted to the data. The blue dotted line shows the carbon intensity evolution for the ‘Deep decarbonisation’ scenario.</figcaption></figure>

nomical facilities, we assume that the emission factors follow the general decarbonisation trends that are observed for the economy at large. The latter are readily traced by the carbon intensity of gross domestic product (GDP) that we show in Supplementary Figure 2 for the period 1935–2100. We derived the carbon intensity data by dividing annual CO₂ emissions through inflation corrected GDP based on purchasing power parity (PPP) so that the results do not depend on economic conditions and relate to the purchase of physical goods. CO₂ emissions were computed by multiplying C emissions from ref. 11 with 3.664. GDP PPP data were taken from ref. 12 which are based on World Bank data from 1990 onwards that were backwards extended based on the growth rates implied by the Maddison Project Database.

We modelled the carbon intensity of GDP by a piecewise function composed of a linear trend that is followed by an exponential decline. We found that an adequate fit is obtained by setting the
break of the function at the year 1973 which resulted in

$$CI_{GDP}(t) = \begin{cases} 0.72 - 0.00113 \times (t - 2019) & \text{for } t \le 1973 \\ 0.40 \times \exp(-0.0144 \times (t - 2019)) & \text{else} \end{cases} \quad (2)$$

where $t$ is time given in years and $CI_{GDP}(t)$ is given in units of kgCO$_2$ per PPP 2019 €. Note that before 1973 the carbon intensity reduced by only about 1 gCO$_2$ per PPP € and year, while after that year an exponential decrease with a rate of 1.44% per year is observed. Multiplying Supplementary Equation 2 by 2.5 gives the relative carbon intensity

$$CI(t) = 2.5 \times CI_{GDP}(t) \quad (3)$$

that satisfies $CI(2019) = 1$ and that we used in our work.

## Illustration of annual carbon footprints

To illustrate the modelling of the annual carbon footprint of astronomical facilities we show in Supplementary Figure 3 the models for a ground-based observatory and a space mission. The left panel shows the model for the 1 metre Schmidt telescope of the Stockholm observatory in Sweden that operated during the period 1963–2001. As we could not determine the year of construction start for this telescope, the default construction duration of five years was adopted, which puts the construction start in the year 1958. The model has an initial peak in the annual carbon footprint that corresponds to the construction-related emissions that were distributed over the construction period. The peak is followed by a tail corresponding to the operations-related emissions, which stop at the moment when the operations cease. For both contributions the annual carbon footprint decreases steadily with time due to the decreasing carbon intensity of the emission factors, with an enhanced decrease from the year 1973 on that corresponds to the transition from a linear to an exponential decrease (see Supplementary Figure 2).

The right panel shows the model for the Hubble Space Telescope (HST), for which construction started in 1978 and that was launched in 1990. The HST is still operating, hence the operations-related emission extend until the end year of 2022 of our study. Note that our model predicts that the construction-related emissions declined by about 15% during the 12 years of construction due to the exponential decrease of the carbon intensity of the emission factors. This decrease continues for the operations-related emission with a reduction by about 60% since the launch of the telescope.
<figure><img src="image_7.png" /><figcaption>**Supplementary Figure 3: Illustrations of annual carbon footprints.** The left panel shows the modelled annual carbon footprint of the 1 metre Schmidt telescope of the Stockholm observatory, the right panel shows the modelled annual carbon footprint of the Hubble Space Telescope (HST). Note that the annual carbon footprint of the 1 m Schmidt telescope is shown in units of tCO₂e yr⁻¹, while that of the HST is shown in units of ktCO₂e yr⁻¹.</figcaption></figure>

## Evolution of number of astronomers

In order to understand how the evolution of the number of active astronomical facilities compares to the evolution of the size of the community, we assessed the latter using the publication database of the Astrophysics Data System (ADS). Expanding on the method developed by ref. 13 we extracted from ADS for each year of the period 1940–2022 the names of all authors that have authored publications in refereed journals assigned by ADS to the “astronomy” collection only. Simplifying the names by retaining the surname and the initials of the first names, we determined the list of individual authors for each year together with their affiliations. From this we created the subset of individual authors that were affiliated to astronomical institutes that we retained as a measure of the size of the astronomical community.

The resulting evolution of the number of authors is shown in Supplementary Figure 4. The number of affiliated authors shows a steady rise over the years, with a break in the growth rate in the year
1975 and a pronounced dip in the year 1996. Both features are linked to the way how ADS links affiliations to authors, illustrated by the fact that the total number of authors shows no break in 1975, but rather a smooth decline for years earlier than 1972, and does not reveal a dip in 1996. Apparently, ADS affiliations of authors lack severe completeness before 1975 and had some issues in the year 1996. Comparing the number of affiliated US authors, shown as dotted brown line, to the number of members of the American Astronomical Society (AAS)¹⁴, shown as orange triangles, reveals furthermore that the drop in the number of authors before 1975 is probably related to the incompleteness of the ADS database, since the number of AAS members grows steadily without any change of slope. We note that the number of AAS members is substantially larger than our assessment of the number of affiliated US authors, which is understandable since AAS members also include physicists, mathematicians, geologists, engineers and others whose research interests lie within the broad spectrum of subjects now comprising contemporary astronomy. To gauge whether the total size of the community is of the right order, we also show two literature values¹³,¹⁵ that were derived independently, and which confirm our numbers.

Since the growth of the astronomical community appears to be steady and exponential over the investigated period, we fitted the number of affiliated authors by an exponential function, adjusted over years 1975–2022 to avoid years of incompleteness. The fit suggests that the number of authors evolves like

$$ \textit{Number of individual authors}(t) = 32018 \times \exp (0.046 \times (t - 2019)) \quad (4) $$

with an exponential growth rate of 4.6% per year. This corresponds to a doubling time of 15 years of the astronomical community, which corresponds to the doubling time that was already suggested in the 1960s for the growth of the scientific community.¹⁶ The fitted function is shown as red line in Supplementary Figure 4, illustrating that the function is a reliable representation of the data.

## Evolution of average facility lifetimes

To investigate the origin of the growth in the number of operating facilities, we determined the average lifetime of all facilities that were operating in a given year and show the result over the period of the study in Supplementary Figure 5. Only facilities were considered for which start and end dates for operations were available in our inventory. Initially, ground-based facilities had
<figure><img src="image_8.png" /><figcaption>**Supplementary Figure 4: Evolution of the number of affiliated authors.** The solid back line shows the evolution of the number of affiliated individual authors, the brown dotted line shows the subset of US authors, and the grey dashed-dotted line shows all individual authors in the ADS database. Some quoted estimates of the community size are shown as symbols. The red line shows an exponential growth model that was adjusted to the number of affiliated individual authors over the period 1975–2022.</figcaption></figure>

a rather long lifetime of over 60 years, yet this number dropped subsequently to settle in a band covering 28–42 years, with a typical lifetime of 38 years. Remarkably, the average lifetime of facilities that were operating around the year 2013 dropped to the lowest average lifetime of 28 years, which we tracked back to a drop in lifetimes of radio telescopes, yet longer facility lifetimes were quickly recovered a few years later. For space missions the dynamic is opposite, with a gradual increase of facility lifetimes from one year (which is the resolution of our analysis) to a typical value of 18 years in about 1990, staying in a narrow band from 14–20 years. From 2010 the average facility lifetime starts to drop, which we tracked back to the contribution of nano satellites that have usually a relatively short lifetime. As neither ground-based facilities nor space missions show an increasing trend in the average facility lifetime over the last decades, we attribute the
<figure><img src="image_9.png" /><figcaption>**Supplementary Figure 5: Evolution of the average facility lifetime.** The green and blue curves show the evolution of the average lifetime of operating ground-based and space-based facilities, respectively. The minimum and maximum variation of the average facility lifetime over the last decades are indicated by grey bands, typical values are shown as solid black lines.</figcaption></figure>

observed increase in the number of operating facilities primarily to an increase in the deployment rate of new facilities.

**Evolution of planetary exploration missions**

Our analysis revealed that planetary exploration missions were the primary driver of the GHG emissions at the beginning of the space age, and even today they still account for about half of the annual carbon footprint of astronomical facilities. As lunar exploration was a primary driver for this footprint in the 1960–1970s, and since one may argue that the motivation of the lunar exploration missions was primarily geopolitical and not scientific, we show in panels (a) and (b) of Supplementary Figure 6 the evolution of the number of astronomical facilities and their carbon footprint without taking into account any lunar missions. While this only removes a small hump
<figure><img src="image_10.png" /><figcaption>**Supplementary Figure 6: Evolution of the number of operating facilities and their carbon footprint excluding lunar exploration missions, and breakdown of planetary missions by explored solar system body.** Panels (a) and (b) show the evolution of the number of operating facilities and their annual carbon footprint when lunar exploration missions are excluded. Panels (c) and (d) show the evolution of the number of operating planetary exploration missions and their annual carbon footprint, split by explored solar system body. Outer bodies include Saturn, Uranus, Neptun and Pluto. Small bodies include missions to comets, asteroids and planetary defence missions. Other missions include test missions or missions with unknown mission objective.</figcaption></figure>

in the number of operating facilities, the peak in the annual carbon footprint of 11.8 MtCO₂e yr⁻¹ at the end of the 1960s is now drastically reduced to 1.4 MtCO₂e yr⁻¹. Yet there is still a peak of GHG emissions in this early period of the space age, and panels (c) and (d) of Supplementary Figure 6 reveal that this peak originates primarily from planetary exploration missions to Venus and Mars. Missions to other planets contribute only at a low level to the annual carbon footprint over the years, although the construction of the Juno and JUICE missions to Jupiter become discernible at the end of the 2000s and around 2020, owing to the relatively large payload masses of 3.6 and 6.1 tons, respectively. Interestingly, while lunar missions have essentially ceased by the mid 1970s, their is renewed interest in the exploration of the Moon that started in the mid 2000s, and that since
<figure><img src="image_11.png" /><figcaption>**Supplementary Figure 7:** Mean annual payload mass compared to the mean mass of operating payloads for the last 45 years. The mean annual payload mass is shown as histograms and the left scale, divided into contributions from construction (red) and operations (blue). For comparison, the mean mass of operating payloads in space is shown as solid line and the right scale. The curve is also shown scaled by a factor of 1/48 as dashed line, now applying to the left scale.</figcaption></figure>

the mid 2010s dominates again the annual carbon footprint of planetary exploration. Given the huge environmental impact of the first space race, there exists a real risk that the ‘NewSpace’ race will be equally harmful.

**Interpretation of the mean annual facility mass or cost**

The mean annual payload mass is used in the carbon footprint factorisation as activity data, and measures the amount of GHG releasing activities in the space sector per facility that is already operating in space. As illustrated in Supplementary Figure 7, most of these activities are related to construction of space missions (red histogram), while operations contribute only little (blue
histogram). Since there is no direct causal link between construction activities on ground, and the number of already operating payloads in space, the mean annual payload mass does not follow the mean mass of payloads that are operating in space (black line). Construction activities on ground will however impact the payload mass in space at later times, as payload mass constructed on ground will add after launch to the payload mass in space. This is clearly seen in the years 1985–1990, where the peak in the mean annual payload mass is partly due to the contemporaneous construction of two heavy satellites, the Hubble Space Telescope (11.1 tons) and the Compton Gamma-Ray Observatory (16.3 tons). Following the launch of both missions in the years 1990 and 1991, respectively, the mean annual payload mass drops while the mean mass of operating payloads rises. In contrast, operation activities are directly linked to the payload mass in space, as illustrated by the perfect match between the blue histogram and the dashed curve in Supplementary Figure 7. Consequently, this contribution will depend directly on the mean mass of operating payloads, which since 1990 is rather stable, with a typical value of 1.5 tons per payload. The mean annual payload mass is thus expected to show a sawtooth pattern on top of a relatively flat profile, with peaks corresponding to periods of intense construction of heavy payloads, followed by decays once these missions are launched into space.

The mean annual facility cost for ground-based observatories is analogous to the mean annual payload mass, and measures the amount of GHG releasing activities per operating observatory. Supplementary Figure 8 shows the mean annual facility cost separated into contributions from construction and operations. Owing to the comparable emission factors for construction and operations of ground-based observatories combined with an average operating life time that is superior to the typical construction period, operations dominate the mean annual facility cost for ground-based observatories. In comparing the mean annual facility cost to the average annual spendings on ground-based observatories a delay between construction spendings and the mean annual facility cost is observed that is equivalent to the situation for space missions. Since the number of operating facilities does not include facilities under construction, the average annual spendings exceed the mean annual facility cost, but drop back to a comparable level once the newly constructed observatories enter operations.
<figure><img src="image_12.png" /><figcaption>**Supplementary Figure 8: Mean annual facility cost compared to the annual spendings per facility for the last 45 years.** The mean annual facility cost is shown as histograms and the left scale, divided into contributions from construction (red) and operations (blue). For comparison, the average annual spendings on ground-based observatories are shown as solid line and the right scale.</figcaption></figure>

## Different modellings of carbon footprint factorisation coefficients

The panels (a–d) of Figure 3 present different modellings of the carbon footprint factorisation coefficients, and we show in Supplementary Figures 9–12 the corresponding carbon footprint factorisations and the adjusted analytical functions. Supplementary Figure 9 presents the reference modelling, where we adjusted the number of operating space missions using an exponential law while for the number of operating ground-based facilities we used a linear law. For the mean annual facility mass of space missions we used an exponential law, while for illustrative purposes we also show a linear law. For the mean annual facility costs of ground-based observatories we did the opposite, using a linear law for the analysis and showing an exponential law for illustrative
<figure><img src="image_13.png" /><figcaption>**Supplementary Figure 9: Reference modelling.** The panels (a–f) show the carbon footprint factorisation when using an exponential law to model the number of operating space missions and a linear law to model the number of operating ground-based facilities. Shaded bands were obtained by varying the adjustment period of the analytical laws from the last 30 to 45 years. Lines corresponding to adjustments over the last 45 years. The grey shaded band in panel (f) corresponds to the annual emission reduction target that is comprised with 5-7%.</figcaption></figure>

purposes. The carbon intensities were adjusted by exponential laws for space and ground-based
facilities. We used the same laws for the mean annual facility mass or cost and the carbon inten-
sities in Supplementary Figures 10–12. The blue curve and band corresponds to the ‘Research as
usual’ scenario, shown as red curve and band red in panel (a) of Figure 3.

In Supplementary Figure 10 we adjusted the number of operating space missions and ground-
based observatories by exponential laws, which leads to an increased prediction of the annual
carbon footprint with respect to Supplementary Figure 9 as a result of an enhanced contribution
from ground-based observatories.

In Supplementary Figure 11 we replaced both exponential laws for the number of operating facilities by linear laws, leading to a much reduced prediction in the number of operating facilities by the end of the century. With these laws the annual carbon footprint is showing a curved shape, owing to the exponential decrease of the mean annual facility mass for space missions and the carbon
<figure><img src="image_14.png" /><figcaption>**Supplementary Figure 10: Exponential laws for number of operating facilities.** Carbon footprint factorisation using exponential laws to model the number of operating space missions. Shaded bands were obtained by varying the adjustment period of the analytical laws from the last 30 to 45 years. Lines corresponding to adjustments over the last 45 years. The grey shaded band in panel (f) corresponds to the annual emission reduction target that is comprised with 5-7%.</figcaption></figure>

intensities that overtakes the rise in the number of operating facilities. Nevertheless, the resulting reduction in the annual carbon footprint is still well above the required suggested carbon footprint reduction trajectory for astronomical facilities.

In Supplementary Figure 12 we show the same modelling of the number of operating facilities by linear laws, but now we excluded all payloads with masses smaller than <20 kg and larger than >10 tons from the adjustments. The lower mass limit is motivated by the rising contribution of nano satellites (NanoSats) to the global inventory of operating space missions, that drive the number of operating facilities upwards and the mean annual facility mass for space missions downwards, while contributing little to the carbon footprint as result of their small mass. In total, 25 NanoSats were excluded by applying the <20 kg mass limit. The upper mass limit is motivated by the large contribution of heavy satellites to the mean annual facility mass for space missions, as seen by the important spikes in the panels (b) of the figures. Applying an upper mass limit of 10 tons excludes the Hubble Space Telescope (HST), the Compton Gamma-Ray Observatory (CGRO) as
<figure><img src="image_15.png" /><figcaption>**Supplementary Figure 11: Linear laws for number of operating facilities.** Carbon footprint factorisation using linear laws to model the number of operating ground-based and space facilities. Shaded bands were obtained by varying the adjustment period of the analytical laws from the last 30 to 45 years. Lines corresponding to adjustments over the last 45 years. The grey shaded band in panel (f) corresponds to the annual emission reduction target that is comprised with 5-7%.</figcaption></figure>

well as the Xuntian space telescope, which is currently under construction in China, from the adjustment. In particular, the HST and CGRO were constructed contemporaneously, hence their construction emissions pile up, leading to an important peak in the mean annual facility mass and the carbon footprint for the years 1985–1990. Applying the mass limits results in fact in a much flatter fitted trend for the mean annual facility mass distribution for space missions that is very similar for exponential and linear laws. This indicates that the exponential decay that is fitted without the mass limits is largely produced by the NanoSats and a few heavy payloads, and that the actual decay when these payloads are excluded is less pronounced. A rather flat trend is in fact expected due to the relatively constant mean mass of operating payloads over the last decades, as revealed in Supplementary Figure 7, although we recall that the mean annual facility mass is primarily determined by construction activities, and it may well be that astrophysical payloads that are currently under construction have on average less mass than those that are already in orbit.
<figure><img src="image_16.png" /><figcaption>**Supplementary Figure 12: Excluding payloads with small and large masses.** Carbon footprint factorisation using linear laws to model the number of operating ground-based and space facilities. Payloads with masses of <20 kg (NanoSats) and >10 tons (HST, GRO & Xuntian) were excluded from the data. Shaded bands were obtained by varying the adjustment period of the analytical laws from the last 30 to 45 years. Lines corresponding to adjustments over the last 45 years. The grey shaded band in panel (f) corresponds to the annual emission reduction target that is comprised with 5-7%.</figcaption></figure>

### Using mission cost instead of payload launch mass as activity data for space missions

In Paper I, two options were discussed to model the carbon footprint of space missions, one using total mission cost and another using payload launch mass as activity data. Since payload launch masses are readily available for all space missions and not subject to significant uncertainties, we used them as activity data in our study. To investigate the impact of this choice, we perform here an alternative modelling where mission cost is used instead of payload launch masses as activity data. Since we have no mission cost estimates for all space missions in our inventory, we estimate missing data from a scaling law that we adjusted to our cost data, with mission cost varying in proportion to some power of the payload launch mass. The results are shown in Supplementary Figure 13, resulting in an almost linear relation

$$ \text{Mission cost (k€)} = 297 \times \text{Payload mass (kg)}^{0.96} \qquad (5) $$
<figure><img src="image_17.png" /><figcaption>Supplementary Figure 13: Mission cost versus payload launch mass for space missions.</figcaption></figure>

though with a considerable scatter (the mean absolute deviation of the mission cost from the scaling law amounts to 84%).

Analogous to the emission factors for payload mass, we derived from ref. 17 emission factors of 135 tCO₂e M€⁻¹ for construction and 2 tCO₂e M€⁻¹ yr⁻¹ for the operations of a space mission, replacing Equation 3 for computing the carbon footprint by

$$F(t) = CI(t) \times \begin{cases} 135 \times \frac{\text{Mission cost (M€)}}{t_o - t_c} & \text{for } t_c \le t < t_o \\ 2 \times \text{Mission cost (M€)} & \text{for } t_o \le t \le t_e \\ 0 & \text{else} \end{cases} \quad (6)$$

and replacing Equation 7 by the annual space mission cost, given by

$$C'_{s}(t) = \sum_{i} \begin{cases} \frac{\text{Cost of mission } i \text{ (M€)}}{t_{o}^{i} - t_{c}^{i}} & \text{for } t_{c}^{i} \le t < t_{o}^{i} \\ \frac{2}{135} \times \text{Cost of mission } i \text{ (M€)} & \text{for } t_{o}^{i} \le t \le t_{e}^{i} \\ 0 & \text{else} \end{cases} \quad (7)$$
<figure><img src="image_18.png" /><figcaption>**Supplementary Figure 14: Modelling of the future annual carbon footprint of astronomical facilities using mission cost instead of payload launch mass as activity data for space missions.** Shaded bands were obtained by varying the adjustment period of the analytical laws from the last 30 to 45 years. Lines corresponding to adjustments over the last 45 years. The grey shaded band in panel (f) corresponds to the annual emission reduction target that is comprised with 5-7%.</figcaption></figure>

The resulting modelling of the future annual carbon footprint of astronomical facilities, that can be directly compared to Figure 2, is shown in Supplementary Figure 14. The predicted trends in the annual carbon footprint are similar to those obtained when using the payload launch mass, though the absolute level of GHG emissions is reduced by about 25%.

## Comparison to estimates of Knödlseder et al. (2022)

We compare in this section the annual carbon footprint results for the year 2019 obtained by Knödlseder et al. (2022)¹³ (Paper I) to this work. Paper I derived the emissions of the world-fleet of astronomical facilities by extrapolation from a list of 85 facilities that were used by astronomers at the Institut de Recherche en Astrophysique et Planétologie (IRAP) in the year 2019. In this work we avoided any extrapolation, and directly computed the annual carbon footprint for a complete census of 1,211 ground-based observatories and space missions. In addition, while in Paper I, emissions from facility construction were released over the entire operating life time of each facility, and counted on top of emissions from operations, we used in this work a scheme where
<table><caption>**Supplementary Table 3: Comparison to estimates of Knödlseder et al. (2022).** Carbon footprints are given in units of ktCO₂e yr⁻¹, columns marked by # indicate number of facilities. The number of facilities of ‘Paper I’ are estimations of the world-fleet of facilities operating in the year 2019. The number of facilities of ‘This work’ give the number of facilities in the inventory of this work that were constructed or operating in the year 2019.</caption><thead><tr><th rowspan="2">Category</th><th colspan="2">Paper I</th><th colspan="6">This work</th></tr><tr><th>Footprint</th><th>#</th><th colspan="2">Total</th><th colspan="2">Construction</th><th colspan="2">Operations</th></tr><tr><th></th><th></th><th></th><th>Footprint</th><th>#</th><th>Footprint</th><th>#</th><th>Footprint</th><th>#</th></tr></thead><tbody><tr><td>Solar</td><td>23±16</td><td>3</td><td>60.8</td><td>19</td><td>52.7</td><td>9</td><td>8.1</td><td>10</td></tr><tr><td>Plasma</td><td>58±20</td><td>13</td><td>40.0</td><td>30</td><td>22.1</td><td>10</td><td>17.9</td><td>20</td></tr><tr><td>Planetary</td><td>226±54</td><td>21</td><td>844.4</td><td>57</td><td>803.8</td><td>36</td><td>40.6</td><td>21</td></tr><tr><td>Astro</td><td>149±43</td><td>18</td><td>163.2</td><td>65</td><td>104.3</td><td>24</td><td>59.0</td><td>41</td></tr><tr><td>Total space</td><td>455±74</td><td>55</td><td>1,108.4</td><td>171</td><td>982.9</td><td>79</td><td>125.6</td><td>92</td></tr><tr><td>OIR (≥3m)</td><td>171±27</td><td>37</td><td>81.6</td><td>43</td><td>32.5</td><td>4</td><td>49.1</td><td>39</td></tr><tr><td>OIR (others)</td><td>147±6</td><td>1,000</td><td>16.7</td><td>174</td><td>1.4</td><td>4</td><td>15.3</td><td>170</td></tr><tr><td>Radio</td><td>127±18</td><td>74</td><td>23.1</td><td>131</td><td>4.3</td><td>5</td><td>18.8</td><td>126</td></tr><tr><td>Radio arrays</td><td>260±115</td><td>27</td><td>42.1</td><td>68</td><td>5.0</td><td>5</td><td>37.1</td><td>63</td></tr><tr><td>Others</td><td>52±53</td><td>4</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Total ground</td><td>757±131</td><td>1,142</td><td>163.5</td><td>416</td><td>43.2</td><td>18</td><td>120.3</td><td>398</td></tr><tr><td>Total</td><td>1,212</td><td>1,197</td><td>1,271.9</td><td>552</td><td>1,026.1</td><td>92</td><td>245.9</td><td>460</td></tr></tbody></table>

construction emissions were released during the construction period and emissions from operations during the operating life time. This reduces the emissions for operating facilities in 2019, as construction emissions were already released before, but construction emissions generated from building facilities in 2019 are now added to the annual carbon footprint of this year.

The estimates of the annual carbon footprint obtained in Paper I are compared in Supplementary Table 3 to the results obtained in this work per facility category (see also Table 4 of Paper I). For space missions, the most notably difference is found for planetary exploration missions, where in
addition to the 21 operating facilities that were estimated in Paper I there are 35 facilities under construction in 2019, which contribute a footprint of 804 ktCO₂e yr⁻¹ that largely explains the observed difference. Planetary exploration missions is in fact the only category where in 2019 more facilities are under construction than operating, which is explained by the recent surge for such missions in the context of the 'NewSpace' race towards the Moon. For all other categories, operating facilities dominate in number. For other space mission categories, we also note that there are more operating space missions in the lists of this work compared to estimations made in Paper I, which is primarily due to small or nano satellites that were not considered in Paper I, and that will only moderately change the estimates of the annual carbon footprint due to their small mass. For ground-based observatories, our annual carbon footprint estimates fall systematically below those found in Paper I. The number of ground-based observatories in the list of Paper I was actually very limited, and it turned out that this introduced a substantial bias towards too large carbon footprints in the extrapolation procedure, which becomes most visible for the 'OIR (others)' category. The bias is related to the fact that telescope costs, which we use as activity data for the carbon footprint estimates, scale with a power *n* of the diameter, with powers of 2.1–2.4 found for example for OIR telescopes (see Supplementary Table 1). The list of telescopes that were considered in the 'OIR (others)' category in Paper I comprised 19 operating telescopes of which 45% had diameters larger than 2 m. Our more extended list of 170 operating telescopes, that represents the complete world-fleet of 1–3 m OIR telescopes, contains only 13% of telescopes with diameters larger than 2 m, revealing that the list considered in Paper I was biased towards telescopes with large diameters. This is a natural consequence of the selection procedure that was used for the list of telescopes in Paper I, since astronomers tend to use large telescopes for their scientific publications due to their better performance. Paper I extrapolated the proportion of 45% telescopes with diameters larger than 2 m to a world-fleet of about 1,000 telescopes, predicting hence a total of 450 such telescopes that were operating in 2019 in the world. Our list suggests, however, that only 22 such telescopes were operational in 2019, which implies an over-estimation by a factor of 20 of the number of telescopes in this category. Similar, yet less extreme, situations exist for the other categories. For example, in the 'OIR (≥ 3 m)' category, Paper I had 67% telescopes with diameters of at least 8 m
in the list, while in our work the proportion is 36%, hence an over-estimation of almost a factor of
two.

Furthermore, from a total of 416 ground-based telescopes, only 18 (4%) contributed emissions
to construction in 2019 while the remaining emissions were attributed to operations, which is
related to the fact that ground-based observatories have a relatively long life time compared to
their construction duration (see Supplementary Figure 5). Construction emissions hence fluctuate
over the years, and for a particular year, they depend on the specific construction activities. To
avoid such fluctuations, construction emissions in Paper I were distributed over the full operat-
ing life times. If we had adopted the same scheme in this work, the total emissions from ‘Radio
arrays’ would, for example, have increased from 42 ktCO₂e yr⁻¹ to 113 ktCO₂e yr⁻¹, and the to-
tal 2019 emissions from ground-based facilities would have increased from 163 ktCO₂e yr⁻¹ to
249 ktCO₂e yr⁻¹. The emissions from space missions would also have been affected, in partic-
ular for planetary exploration mission, as discussed above, which would have dropped from 844
ktCO₂e yr⁻¹ to 258 ktCO₂e yr⁻¹. This would have changed the total emissions for space missions
from 1,109 ktCO₂e yr⁻¹ to 574 ktCO₂e yr⁻¹, and the total GHG emissions of 2019 would have
dropped from 1,272 ktCO₂e yr⁻¹ to 823 ktCO₂e yr⁻¹. This emphasises once more the importance
of the fluctuating nature of the annual carbon footprint of astronomical facilities, driven by the
construction of large and impacting facilities. A snapshot for a given year, as provided in Paper
I, does not capture the full dynamics of the GHG emissions, which only becomes visible once the
temporal evolution of facility construction and operations is taken into account.
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