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

    Identify and summarize the **policy problem** that this research addresses. Your response should:
    - Clearly state the societal, environmental, or strategic issue motivating the study
    - Be written in accessible, policy-brief style sentences
    - Focus on the core challenge the research aims to solve
    - Avoid excessive detail or technical jargon unless necessary for clarity

    Write a short paragraph suitable for inclusion under the “Policy Problem” section of a policy brief.

    Scientific Research Paper:
    Nature Energy | Volume 9 | July 2024 | 883–893
883
nature energy
https://doi.org/10.1038/s41560-024-01530-w
Analysis
The effects of corporate investment and
public grants on climate and energy startup
outcomes
Kathleen M. Kennedy
  1
, Morgan R. Edwards2,3, Claudia Doblinger
  4,
Zachary H. Thomas3, Maria A. Borrero1, Ellen D. Williams5,6,
Nathan E. Hultman
  1 & Kavita Surana
  1,7,8
Climate and energy (climate-tech) startups can accelerate the
commercialization of innovative technologies but face low investment and
high failure rates. Here we analyse the effects of recent growth in corporate
investments, combined with public grants and other private investments,
on startup outcomes. We apply the Cox Proportional Hazards model to
a dataset of 2,910 US climate-tech startups founded 2005–2020. We find
that corporate and other private investments are significantly associated
with both exits (initial public offerings, mergers/acquisitions) and failures
(bankruptcy, going out of business). While public grants are not significantly
associated with these outcomes, they fill important funding gaps in high-risk
sectors. Publicly funded startups also exit at a higher rate with the addition
of corporate investment (155% increase) compared with other private
investment (78% increase). These findings highlight the roles of different
investors in scaling startup technologies to meet climate goals and are robust
across sectors, timelines and types of public funding (national, subnational).
Startups aiming to rapidly commercialize new climate and energy
technologies, that is, climate tech, are a vital part of climate change
mitigation, with over one-third of emissions reductions needed to reach
net-zero relying on technologies currently in pre-commercial stages1.
Yet despite their importance, climate-tech startups face substantial
barriers. They often involve hardware that requires long development
timelines and extensive capital—and thus carry high investment risks
with large potential returns to society (but not necessarily to inves-
tors)2,3. Consequently, these startups have historically suffered from
underinvestment and often failed to overcome the ‘valley of death.’ To
overcome these challenges and deploy at scale, climate-tech startups
need direct involvement from the public and private sectors. Research
and development can be de-risked through early-stage funding (for
example, public grants from regional or federal agencies and equity
investment from venture capital, corporations or impact investors)4.
Demonstration phases, previously underfunded by private equity, can
advance with increased public co-investment enabled by government
agencies such as the US Department of Energy Office of Clean Energy
Demonstrations. Early deployment and growth require public policies
(for example, regulations and market incentives for demand pull) and
private sector uptake (for example, adoption by corporate ‘first mov-
ers’)5,6. With the rapidly growing diversity of public and private actors
and public–private partnerships7,8, corporations stand out in terms of
the number and scale of recent investments in climate-tech startups9.
Received: 2 May 2023
Accepted: 15 April 2024
Published online: 15 May 2024
 Check for updates
1Center for Global Sustainability, School of Public Policy, University of Maryland College Park, College Park, MD, USA. 2La Follette School of Public
Affairs, University of Wisconsin-Madison, Madison, WI, USA. 3Nelson Institute Center for Sustainability and the Global Environment, University of
Wisconsin-Madison, Madison, WI, USA. 4Campus Straubing for Biotechnology and Sustainability and School of Management, Technical University of
Munich, Straubing, Germany. 5Earth System Science Interdisciplinary Center, University of Maryland College Park, College Park, MD, USA. 6Department
of Physics, University of Maryland College Park, College Park, MD, USA. 7Institute for Data, Energy and Sustainability, Vienna University of Economics and
Business, Vienna, Austria. 8Complexity Science Hub, Vienna, Austria.
 e-mail: kmkenne5@umd.edu; kavita.surana@wu.ac.at

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Analysis
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Distinct investor roles for startup sectors
and stages
Startups receive financing progressively over time as they mature.
In the early stages, startups acquire grants that do not require them
to give up shares or repay the investment. While their dollar value is
typically smaller, grants can have an outsized impact, particularly for
high-risk technologies or underfunded communities and regions. As
startups grow, they acquire funding through equity investment rounds
(seed, series A, series B and growth equity) where they give up shares
in exchange for investment. Eventually, more mature startups access
commercial debt where they do not give up shares but must repay the
investment2. Across equity investment rounds, the growth rounds
typically represent the largest dollar amounts as startups prove them-
selves to investors and seek funding to expand operations and widely
deploy their technologies. We focus on grants and equity investment
rather than loans because they reflect the funding required to bring
pre-commercial technologies to market.
Corporate investors are differentiated from public grants and
other private investors in a few key ways. First, they consistently
increased in number of investments and amount invested since 2005,
even as public grants and other investors saw uneven spates of growth
and decline, followed by continuous growth since 2016 (Fig. 1a,b).
Second, corporate investment deals peak later in the startup life
cycle and skew towards high-value growth equity and late-stage
rounds rather than cheaper seed rounds (42.0% of corporate invest-
ment deals are for growth equity, compared with 28.6% for other
private investment; Fig. 1c). Finally, corporations tend to invest in
partnership with other forms of investment (86.9% of startups funded
by corporations also received a public grant or other private invest-
ment), whereas both public and other private investors fund a greater
number of startups alone (44.5% of startups funded by other private
investment and 53.1% of startups that received public grants also
received funding from a different investor type; Fig. 1d). Combined,
these features suggest that corporations could help startups grow
through difficult periods such as the infamous ‘valley of death’ by
providing funding in the later stages of the ten-year venture capital
cycle, when VC funding is less available and may disproportionately
provide critical funds for startups that have previously been successful
in receiving grants or other private investment (Fig. 1 and Supplemen-
tary Fig. 1)2.
Climate-tech startups are active in multiple sectors, and the pat-
terns of how different groups of investors engage with these sectors
demonstrate their varying priorities (Figs. 2 and 3). Public grants are
most dominant in sectors with very few startups, with the six sec-
tors with the highest percentage of public funding all including fewer
than 60 startups (compared with an average of 145). In each of these
sectors, public grants account for at least 24% of investments (com-
pared with 11% of investments overall). This dominance is potentially
due to less profitable markets or technologies that are more difficult to
commercialize (for example, nuclear) and suggests that public grants
fill a key role in the climate-tech ecosystem by funding sectors that are
less popular with traditional equity and corporate investors11,23. Other
private investors make up the bulk of investments in most sectors, with
a particular dominance in certain sectors such as recycling and waste,
where both corporate investment and public grants are low. This phe-
nomenon is prominent in more recent years, as public grants receded
in certain sectors during Cleantech 2.0 (2012–2020), and corporate
and other private investors provided a larger share of investments
(Supplementary Fig. 2).
In contrast to public grants, corporate investment does not show a
clear trend based on sector size but instead depends on other strategic
choices that may connect to core business areas or enable expan-
sion into new areas9. Figure 3 shows investments by different catego-
ries of corporate investors in each startup sector, revealing heavy
investment in related sectors. For instance, 42% of investments from
Understanding the role of different investors and their inter-
actions is essential to effectively incentivize innovation. However,
analyses on climate-tech startups tend to focus on public agencies10,11
or financially motivated venture capital (VC)2,5 in isolation, without
considering the combined effects of different investors. For example,
public funding agencies can de-risk technologies when securing tra-
ditional investment is difficult12 or support technologies relevant for
strategic national goals13. Traditional VC investors aim for high financial
returns from startups exiting through an initial public offering (IPO)
or acquisition. Climate-impact investors have emerged as a subset of
profit-focused investors, with a focus on supporting climate tech while
still pursuing financial returns. Corporations, in contrast, are strategic
investors that may seek to increase profits but also to achieve goals
related to long-term business plans and competitive standing9,14–19. They
can help startups build commercial capacity and credibility20, increase
innovation output21 and provide learning benefits22, but they can also
leave startups at risk for predatory acquisitions that undercut compe-
tition and misappropriate intellectual property19,22. The motivations
of these different investors collectively determine how investments
flow to technology areas and risk levels12. These investments, in turn,
can have distinct effects on startup outcomes. However, policymakers
currently have limited empirical evidence on how different investors
shape startup outcomes, which could enable more effective strategies
to address the threat of climate change.
The higher involvement of corporate investors in climate tech is
relatively recent compared with traditional VC investors. The first wave
of investment, known as Cleantech 1.0 (2005–2011; Supplementary
Note 1), resulted in many failures that scholars attribute in large part
to the poor fit with traditional VC models that expect exits within ten
years2,23,24. In response to these failures, traditional venture capital
initially shifted to less risky software-based climate-tech investments2.
However, after 2015, new investment models emerged, and corpora-
tions in particular became increasingly involved as investors9. By 2020,
an estimated 34% of climate-tech startups received investment from
corporations (representing 24% of investment dollars), compared
with 57% from venture capital representing 31% of investment dollars9.
This indicates that corporations make fewer investments, but these
investments are larger and thus potentially more influential for shaping
innovation9. Whereas other investors such as traditional VC firms have
been increasingly analysed in literature2,5, scholars and policymakers
have limited quantitative evidence on the effects of growing corporate
funding in climate tech and the interactions and synergies between
corporate investment and public and other private funding sources.
In this paper, we examine whether corporate investment, cou-
pled with public grants and other private investment, improves
climate-tech startup outcomes. We focus on key outcomes linked
to the deployment of technologies (that is, exits and failures). An
exit occurs when a startup has an IPO or merges with/is acquired
by another entity, indicating that their product has developed suf-
ficiently to draw interest from acquirers or on the stock market. A
failure occurs when a startup goes bankrupt or out of business and
therefore is probably unable to commercialize their product. Our
empirical approach builds on a comprehensive dataset of climate-tech
startups from the Cleantech Group i3 database25. The original dataset
contains 38,089 startups and 17,737 investors worldwide. After a com-
prehensive cleaning and verification process (Methods), we generate
a dataset of 2,910 United States-based startups founded between 2005
and 2020 (with outcomes considered through 2021) that received at
least one grant or equity investment. These startups were supported
by 3,979 unique investors participating in 15,108 investment deals.
We expand this dataset with additional data on public funding and
patents obtained from publicly available government records. We
use this dataset and the Cox Proportional Hazards (CPH) model to
perform time-to-event analysis to quantify the associations between
different funding sources and startup outcomes.

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transportation corporations go to transportation startups, followed
by advanced materials (14%) and energy storage (14%), which are both
necessary inputs for electric vehicles. Similarly, agriculture corpora-
tions invest most heavily in agriculture startups (59%) whereas clean
energy investors favour solar (25%) and energy storage (24%) startups.
This alignment of investment with sector expertise can be beneficial
for startups, as deep knowledge of the sector allows corporations to
provide specialized assistance to new companies26. This advantage may
be particularly strong for startups that are trying to scale, especially
given that corporations invest proportionally more in growth equity
compared with other private investors (Supplementary Table 2). Thus,
a key role of corporations in the climate-tech ecosystem lies in identi-
fying promising startups within their area of expertise and providing
essential funding to grow and scale.
1,750
a
80
Public grant
Other growth equity
Other series B
Other series A
Other seed
Other grant
Corporate growth equity
Corporate series B
Corporate series A
Corporate seed
Corporate grant
60
40
20
0
2,500
2,000
1,500
1,000
500
0
1,500
1,250
1,000
750
500
Number of investments
Investments amount (billion US$)
Number of investments
250
0
2005
0
5
10
Startup age
15
2010
2015
Year
Year
2020
2005
Corporate
investment
Other
investment
Public grant
368
203
189
25
133
669
1,323
2010
2015
2020
b
c
d
Fig. 1 | Interplay between public, corporate and other private investors in
climate-tech startups. a, Number of investments over time. b, Dollar value of
investments over time. c, Number of investments by startup age. d, Number
of startups receiving investment from different combinations of investors.
Other investment includes all forms of non-corporate private investment
(Supplementary Table 1). Data for a–c are provided in Supplementary Tables 2–5.
Supplementary Fig. 1 provides alternate presentation of these data.
100
400
Corporate investors
Other investors
Public grants
Number of startups
350
300
250
200
150
100
50
0
80
60
40
Investment deals (%)
Number of startups
20
Climate-tech sector
0
Geothermal
Nuclear
Hydro and marine power
Biomass generation
Fuel cells and hydrogen
Wind
Uncategorized
Air
Conventional fuels
Smart grid
Recycling and waste
Water and wastewater
Biofuels and biochemicals
Energy storage
Agriculture and food
Solar
Other clean tech
Advanced materials
Transportation
Energy eficiency
Fig. 2 | Investment breakdown by climate-tech sector and investor type.
Sectors are ordered by the number of startups in that sector (lowest to
highest). Public grants typically account for a larger portion of investment
deals for smaller sectors, whereas corporate investment varies across sector
sizes. Other investors include all sources of non-corporate private investment
(Supplementary Table 1). Data include startups founded through the full period
of analysis (2005–2020).

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Analysis
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Different forms of investment have synergies and
trade-offs
Using time-to-event regression analysis, we investigate the interplay
between publicly funded startups and different types of private inves-
tors, distinguishing between corporations and others, and discuss
how these actors jointly affect outcomes. Time-to-event models are
commonly used with entrepreneurial data because many of the quanti-
ties of interest for startups are highly time dependent27. Specifically,
we use the Cox Proportional Hazards model to analyse exit and failure
outcomes for each of the populations summarized in Table 1. In the CPH
model, time-dependent covariates such as investment are accounted
for by separating variables and outcome events into intervals, thus
providing a way to regress an outcome at a given point in time based
solely on past covariate values, without knowledge of future values28.
Clustering connects the identity of an individual startup between mul-
tiple time intervals. We stratify by the year the startup was founded
and the sector of the startup, thereby generating a separate baseline
hazard function for each instance of these variables that accounts for
effects caused by these variables29.
Across our full dataset of climate-tech startups (Model 1), corpo-
rate investment and other private investment are both highly signifi-
cant and positively correlated with exits and failures, whereas public
Table 1 | Summary of model populations used in time-to-event analysis
Model 1
Model 2
Model 3
Model 4
Model 5
Description
Full dataset
Startups that received
a public grant
Startups that did NOT
receive a public grant
Startups that received a
corporate investment
Startups that did NOT receive
a corporate investment
Number of startups
2,910
783
2,127
1,016
1,894
Number of exits
440 (15%)
98 (13%)*
p = 0.020
342 (16%)*
p = 0.020
208 (20%)***
p < 0.001
232 (12%)***
p < 0.001
  Number of IPOs
76 (2.6%)
24 (3.0%)
p = 0.424
52 (2.4%)
p = 0.424
52 (5.1%)***
p < 0.001
24 (1.3%)***
p < 0.001
  Number of MAs
372 (13%)
78 (10%)**
p = 0.007
2,934 (14%)**
p = 0.007
162 (16%)***
p < 0.001
210 (11%)***
p < 0.001
Number of failures
591 (20%)
154 (20%)
p = 0.639
437 (21%)
p = 0.639
243 (24%)***
p < 0.001
348 (18%)***
p < 0.001
Number of private startups after
ten years
700 (24%)
282 (36%)***
p < 0.001
418 (20%)***
p < 0.001
187 (18%)***
p < 0.001
513 (27%)***
p < 0.001
Average age of startups (years)
9.16
10.2***
p < 0.001
8.78***
p < 0.001
9.32
p = 0.120
9.08
p = 0.120
Average number of patents
5.63
6.01
p = 0.489
5.48
p = 0.489
9.97***
p < 0.001
3.29***
p < 0.001
Note that subset Models 2–5 may overlap in coverage of startups. Percentages in parentheses indicate the percentage of startups within a model that experienced the indicated outcome.
Significance levels are shown for two-sided proportion tests for differences between binary variables in models (that is, Model 2 vs 3 and Model 4 vs 5), where asterisks denote *p < 0.05,
**p < 0.01, ***p < 0.001. Significance levels are shown for two-sided t tests for difference between continuous variables in models. Full descriptive statistics are provided in Supplementary
Tables 6 and 7. MA = merger/acquisition.
Advanced materials
Agriculture and food
Air
Biofuels and biochemicals
Biomass generation
Conventional fuels
Energy eficiency
Energy storage
Fuel cells and hydrogen
Geothermal
Hydro and marine power
Nuclear
Other clean tech
Recycling and waste
Smart grid
Solar
Transportation
Uncategorized
Water and wastewater
Wind
Startup sector
Transportation
Manufacturing, hardware,
semiconductors and chemicals
Fossil fuels and utilities
Food, beverages
and agriculture
Digital and
financial services
Clean energy
Corporate sector
Percentage of investments
0
10
20
30
40
50
60
Fig. 3 | Corporate climate-tech investment by corporate and startup sectors.
Corporate sectors are shown on the y axis and startup sectors are shown on the
x axis. The size and colour of each data point corresponds to the percentage
of investments from each corporate sector to each startup sector. Corporate
sectors were categorized by the authors; startup sectors were determined by
Cleantech Group.

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grants are not (Table 2). Public grants also do not show any consist-
ently significant effect when grantors were separated into national,
subnational or international sources (Supplementary Tables 8 and 9).
Models 2 and 3 consist of startups that did or did not receive a public
grant, respectively, which allows us to analyse how the presence of a
public grant changes the effect of other investors and key variables.
Similarly, Models 4 and 5 consist of startups that did or did not receive
a corporate investment, respectively. Table 2 summarizes the regres-
sion results for each model, with corporate investment, other private
investment, public grants, patent count and location in a climate-tech
‘hotspot’ (that is, a location with a density of two climate-tech startups
per 100,000 population or greater) as predictor variables (Methods).
Our results indicate that corporate investment and other private
investment are highly significant for startup outcomes, in terms of
both success and failure, whereas public grants are not. Startups with at
least one corporate investment exited at a rate 110% higher than those
without corporate investment (hazard ratio = 2.10). Similarly, startups
with at least one investment from other private investors exited at a
rate 150% higher than those without such investments (hazard ratio =
2.50). Each additional patent held by a startup was associated with a 2%
increase in rate of exits, whereas being headquartered in a climate-tech
‘hotspot’ location was associated with a 23% increase in exit rate. For
the same population of startups, corporate investment was associated
with a 62% increase in rate of failure, and other private investment
was associated with a 265% increase in rate of failure. While startups
are high-risk investments where most will fail, and thus correlation
with failure is not necessarily surprising, the lower hazard of failure
for corporate investment compared with other private investment
sources may indicate a role for corporations in providing more patient
capital. Public grants, patents and startup location were not found to
be significantly associated with failure.
However, climate-tech sectors are not homogeneous, and startups
that receive a public grant are more likely to be in high-risk sectors
(Fig. 2). Startups that received public grants remained privately held
at the highest rate after ten years (Table 1), which could reflect longer
timescales associated with high-risk technologies. Our results suggest
particularly favourable outcomes associated with corporate invest-
ment for this group compared with other private investment for both
exits (Model 2, 155% higher rate of exit with corporate investment
compared with 78% with other private investment) and failure (77%
higher rate of failure with corporate investment compared with 134%
with other private investment). For startups that do not receive public
grants, corporate investment continues to be significant (Model 3, 99%
higher rate of exit and 60% higher rate of failure), but other private
investment is associated more strongly with both positive and nega-
tive outcomes (139% higher rate of exit and 388% higher rate of failure).
Other factors such as location (28% higher rate of exit for startups in
hotspots) and patents (2% higher rate of exit associated with each
additional patent) also play a significant role.
Startups that received corporate investment (Model 4) had the
highest rates of both IPO (5.1%) and merger/acquisition (16%) exits
across all models (Table 1), yet none of the modelled variables were
found to be significantly associated with these outcomes (Table 2).
This could be due to only 25 startups in the dataset receiving corporate
investment without also receiving other private investment (Fig. 1d),
limiting the strength of this finding (likelihood ratio test in Table 2).
Table 2 | Hazard ratios from CPH regression for startup exits, defined as an IPO or merger/acquisition, and failures, defined
as bankruptcy or going out of business
Model 1
Model 2
Model 3
Model 4
Model 5
Description
Full dataset
Startups that received
a public grant
Startups that did NOT
receive a public grant
Startups that received a
corporate investment
Startups that did NOT receive a
corporate investment
Startup exit
Corporate investment
2.10*** (1.74, 2.53)
p = <0.001
2.55*** (1.57, 4.12)
p = <0.001
1.99*** (1.62, 2.46)
p = <0.001
–
–
Other private investment
2.50*** (1.91, 3.26)
p = <0.001
1.78* (1.07, 2.96)
p = 0.028
2.39*** (1.71, 3.34)
p = <0.001
1.40 (0.967, 2.02)
p = 0.075
5.11*** (3.42, 7.63)
p = <0.001
Public grant
1.01 (0.808, 1.26)
p = 0.927
–
–
1.29 (0.949, 1.76)
p = 0.104
1.23 (0.881, 1.71)
p = 0.228
Patent count
1.02** (1.00, 1.03)
p = 0.009
1.06 (0.979, 1.14)
p = 0.156
1.02* (1.00, 1.03)
p = 0.020
1.03 (0.993, 1.07)
p = 0.117
1.02 (0.999, 1.04)
p = 0.064
Location
1.23* (1.03, 1.47)
p = 0.024
1.04 (0.699, 1.56)
p = 0.830
1.28* (1.04, 1.56)
p = 0.019
1.20 (0.913, 1.58)
p = 0.191
1.37* (1.07, 1.76)
p = 0.014
Likelihood ratio test
119***
p = <0.001
28.2***
p = <0.001
65.9***
p = <0.001
9.04
p = 0.06
79.1***
p = <0.001
Startup failure
Corporate investment
1.62*** (1.40, 1.88)
p = <0.001
1.77*** (1.29, 2.41)
p = <0.001
1.60*** (1.34, 1.91)
p = <0.001
–
–
Other private investment
3.65*** (2.84, 4.69)
p = <0.001
2.34*** (1.63, 3.73)
p = <0.001
4.88*** (3.23, 7.37)
p = <0.001
4.05*** (2.57, 6.36)
p = <0.001
3.60*** (2.63, 4.92)
p = <0.001
Public grant
1.07 (0.903, 1.28)
p = 0.461
–
–
0.946 (0.736, 1.22)
p = 0.665
1.09 (0.838, 1.43)
p = 0.510
Patent count
0.996 (0.976, 1.02)
p = 0.741
1.01 (0.954, 1.08)
p = 0.640
0.997 (0.976, 1.02)
p = 0.799
0.979 (0.951, 1.01)
p = 0.163
1.01* (1.00, 1.03)
p = 0.14
Location
1.08 (0.935, 1.25)
p = 0.307
1.07 (0.822, 1.39)
p = 0.623
1.11 (0.933, 1.32)
p = 0.241
1.10 (0.876, 1.38)
p = 0.415
0.994 (0.822, 1.20)
p = 0.949
Likelihood ratio test
156.6***
p = <0.001
37.91***
p = <0.001
90.78***
p = <0.001
44.96***
p = <0.001
70.27***
p = <0.001
95% confidence intervals are shown in parentheses. Asterisks denote *p < 0.05, **p < 0.01, ***p < 0.001. Startup sector and founding year are accounted for as separate strata. Data included in
each of the models are described in Table 1. Sensitivity analysis demonstrates that results are robust across multiple representations of investment variables (Supplementary Tables 10, 11, 18
and 19).

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Failure for startups with corporate investment was only significantly
associated with other private investment (305% higher rate of failure
than startups without other private investment). Startups that did not
receive corporate investment (Model 5) saw the lowest rate of IPOs
(1.3%) across all models. Exit outcomes were significantly associated
with other private investment (411% higher rate of exit) and location
in a climate-tech hotspot (37% higher rate of exit). Failure for start-
ups without corporate investment was significantly associated with
other private investment (260% higher rate of failure), as seen in other
models. However, this model was the only one that found a significant
association between patents and failure (1% higher rate of failure with
each additional patent).
Effects are robust across sectors and time periods
Differences in the characteristics of specific technologies in various
climate-tech sectors could lead to different interplays between funding
sources at the sector level2,5. However, our results are largely consistent
across individual climate-tech sectors. Figure 4 shows the relationships
between public grants, corporate investments and exit and failure out-
comes across the six largest sectors by number of startups. We generate
survival curves, which represent the proportion of the population that
has not experienced an outcome by time t (in units of years since the
startup was founded). Corporate investment is consistently associated
with higher rates of exits (Fig. 4b), and this association is robust across
founding years (Supplementary Fig. 3 and Supplementary Table 12)
Energy eficiency
Transportation
Advanced materials
Other clean tech
Solar
Agriculture and food
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
0.25
0.50
0.75
1.00
Time since founding (yr)
Survival probability
No public grant
Public grant
a
Relationship between public grants and startup exits
Energy eficiency
Transportation
Advanced materials
Other clean tech
Solar
Agriculture and food
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
0.25
0.50
0.75
1.00
Time since founding (yr)
Survival probability
No corporate investment
Corporate investment
b  Relationship between corporate investment and startup exits
Energy eficiency
Transportation
Advanced materials
Other clean tech
Solar
Agriculture and food
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
0.25
0.50
0.75
1.00
Time since founding (yr)
Survival probability
No public grant
Public grant
c
 Relationship between public grants and startup failure
Energy eficiency
Transportation
Advanced materials
Other clean tech
Solar
Agriculture and food
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
4
8
12
16
0
0.25
0.50
0.75
1.00
Time since founding (yr)
Survival probability
No corporate investment
Corporate investment
d
Relationship between corporate investment and startup failure
Fig. 4 | Survival curves for exit and failure outcomes for the six largest sectors
with and without public grants or corporate investment. Survival curves use
the Kaplan–Meier estimator to show the proportion of the population that has
not experienced the outcome of interest by a given time t, and shading represents
a 95% confidence interval. All curves begin with a survival probability of 1, which
indicates no startups have experienced an exit or failure. a–d, As startups age,
survival probability decreases based on how many startups exit (a,b) or fail
(c,d); for instance, a survival probability of 0.8 would indicate 20% of startups
have experienced an exit (a,b) or failure (c,d). The regressions represent the
effect solely due to public grants and corporate investment, respectively; other
covariates are not included.

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and other sectors (Supplementary Fig. 4). Public grants have mixed
results across sectors, with grants significantly related to lower rates of
exit and failure in the energy efficiency sector, but with no significant
trends in other large sectors. There are also sectoral differences in the
overall rate of exit and failure, such as particularly high rates of failure in
the solar sector, and relatively high rates of exits in the transportation
sector. These differences validate our choice to assign each sector their
own baseline hazard function to account for variation in sector-level
characteristics.
We examine the sensitivity of our results with respect to time
and patenting activity. Climate-tech investment has matured from
Cleantech 1.0 to Cleantech 2.0, potentially leading to different invest-
ment effects across time periods (Supplementary Note 1)2,30. To assess
potential differences, we replicate the analysis in Table 2 for both time
periods. Whereas there is little difference in trends for exits, corpora-
tions are significantly associated with failure only in the Cleantech
1.0 period (Supplementary Tables 13 and 14). Further investigation is
needed to determine the causes of this change (Discussion); however,
it may indicate a lower hazard of startup failure from future corporate
investments. All other trends remain the same, indicating the analysis
presented here is robust to temporal differences. We also examine if
our results vary for a subset of high-patenting startups, which may
indicate ‘unicorn’ startups that potentially skew our results (Supple-
mentary Note 2). We find that no investment source is a significant
predictor of exits or failures for high-patenting startups, but location
in a climate-tech hotspot is highly significant and correlates with an
increase in likelihood of exit (Supplementary Table 15). Corporate
investment shows a unique relationship for these high-patenting
startups with a negative but insignificant correlation with failure,
contrasted with other private investment, which still has a positive
and significant correlation with failure as in the overall dataset (Sup-
plementary Table 16).
Discussion and conclusions
With recent US legislation such as the Inflation Reduction Act (IRA)
making historic investments in climate change mitigation31, including
in climate-tech innovation and deployment32, this analysis provides
an enhanced understanding of how corporate investment and public
grants fill private investment gaps and play complementary roles that
support climate-tech startups.
We find that public grants are not associated with a significant like-
lihood of either exits or failures (Models 1, 4 and 5). However, startups
funded by public grants represent technologies of interest to national
policy, and as they grow and bring in different investors, corporate
investment correlates with a higher likelihood of exits and a lower
likelihood of failure compared with other forms of private investment
(Model 2). This correlation is unlikely to arise purely from selection bias
because both corporate and other investment are also associated with
a higher likelihood of startup failure overall. The lack of significance
of public grants in this analysis similarly does not necessarily indicate
a lack of effect on startups. Possible explanations include preferen-
tial selection of higher-risk startups, or differing impacts in different
sectors or startups choosing to pursue additional grants rather than
an exit outcome (Supplementary Note 3)11,33.
Corporations are significantly more likely to fund climate-tech
startups that achieve a successful exit (Model 1, Fig. 4) across sectors,
reflecting a breadth of core business areas and priorities (including
the need to decarbonize). This correlation is also seen in startups
that received a public grant, a subpopulation of particular interest
to policymakers (Model 2). Corporations consistently show a lower
correlation with failure than other investors (Models 1–3), indicating
that corporations either selectively fund startups that are less likely to
fail or can partially mitigate failure risks through their unique invest-
ment strategies. Additionally, corporate investment is not significantly
associated with failure in Cleantech 2.0—a clear deviation from other
results (Models 1–3, Supplementary Table 14). This difference could
be because corporations learned from the losses in Cleantech 1.0 and
adjusted their investment strategies or because outside pressures have
influenced a change in their behaviour. There are also some sectoral
differences in correlation with failure (for example, strong significance
in energy efficiency and weak significance in advanced materials;
Fig. 4d), which could be due in part to the proportion of hardware and
software startups found in each sector, which have different likelihoods
of exit and failure2,34.
Corporate investors can be particularly important to the success of
difficult-to-scale, research-intensive technologies by providing access
to testing or supply chains that other investors cannot. This potentially
means that corporations can support startup success when investees
are located near their own operations21,35, which may not be as tied to
traditional venture capital hotspots such as Silicon Valley. Our analysis
shows evidence of this in the insignificance of location for startups with
corporate investment (Model 4). The absence of corporate investors
could be detrimental for research-intensive startups, as startups that
did not receive corporate investment showed a significant relationship
between failure and patenting activity, a metric for innovation and a
potential indicator of a hardware-focused company (Model 5). Con-
versely, corporations may be particularly beneficial for high-patenting
startups, which showed no correlation between corporate investment
and failure (Supplementary Table 16). Ultimately, corporations may
mitigate risks for technologies by providing more patient capital than
other investors and thereby offsetting the timelines for fast returns
expected by traditional venture capital23,24.
Our results suggest three key recommendations for innovation-
focused policy, which may be particularly important in light of the
surge in public funding established in the IRA to leverage private capital
and the shift in attitudes towards globalization36. First, whereas public
grants are not significant drivers of outcomes on their own, they prob-
ably act as catalysts for startups that work on difficult sectors before
they advance and acquire capital through corporate or other private
investors (Fig. 2). Publicly funded startups exit at a higher rate with the
addition of corporate investment (155% increase) compared with other
private investment (78% increase) (Model 2). This suggests that increas-
ing public grant funding at both the national (for example, Advanced
Research Projects Agency-Energy (ARPA-E)) and regional (for example,
New York State Energy Research and Development Authority) level is
critical to de-risk technologies and create opportunities for beneficial
public–private partnership. Second, corporate investors’ association
with positive outcomes for grant recipients and high-patenting start-
ups (compared with other private investors) highlights the importance
of incentivizing these investors to mobilize capital to invest in startups.
Policymakers can build on successes from networks enabled through
key grant agencies (for example, the ARPA-E annual summit) to create
opportunities for high-risk startups. In addition, public–private part-
nerships (for example, the First Movers Coalition focused on aviation,
steel and other key technologies) could create similar opportunities
in other regions or technologies. Third, the association between both
corporate and other private investment and increased rates of failure
means that policymakers should act to minimize harmful practices
such as intellectual property misappropriation (for example, misap-
propriation of the startups’ knowledge by the larger corporate partner,
also known as ‘corporate sharks’)22,37.
This analysis presents several avenues for future research that
could further inform evidence-based climate innovation policy. Access
to more comprehensive data, particularly from startups that are typi-
cally reticent to share details, would provide a deeper understanding of
sectoral variation and technology maturity (for example, technology
readiness levels) for different types of investors and their impact. Such
data can potentially create avenues to improve models that inform
climate policy. It could also be used to explore different types of public
investors besides grant agencies that aim to generate financial returns

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(for example, green banks or public agencies that invest equity in
startups) or private investors (for example, financially motivated
climate-impact investors) to provide a more complete picture of the
public and private funding landscape. With more data over time, future
work could potentially establish causal relationships between variables
and assess the potential impact of systematic factors such as startup
maturity on investment outcomes. Mixed-method analyses that com-
bine data on individual grant programmes such as ARPA-E with insights
on specific types of investor (for example, corporate investors with
various core businesses, climate-impact investors, other private inves-
tors) could further inform public policies on how to most effectively
support grant recipients through all phases of startup development.
Finally, this pre-IRA analysis can be used for comparison with future
post-IRA analysis to identify changes in investment patterns or other
effects resulting from the IRA.
Methods
Description of raw dataset
Data from the Cleantech Group i3 database on startups, investors,
initial public offerings (IPOs), mergers and acquisitions, and corporate
relationships was accessed on 19 January 2022. These proprietary data
are available to subscribers at https://i3connect.com. The Cleantech
Group data are used in other major reports such as the International
Energy Agency’s (IEA) Tracking Clean Energy Innovation Report and the
Silicon Valley Bank’s The Future of Climate Tech Report8,38,39. In addi-
tion, it has been previously used in multiple peer-reviewed research
papers3,9,10. Given the extensive use of the Cleantech Group’s i3 database
in prominent reports and peer-reviewed papers and the fact that infor-
mation in the database is regularly updated by the database provider’s
research teams or by the firms themselves (with the last updated year
reported), to our knowledge, it represents the most thorough dataset
available for climate tech.
The raw data had 38,089 startups and 17,737 investors worldwide.
Each startup has information on sector, location, founding year, cur-
rent status (for example, public, private or bankrupt), a description
of the startup activity and keyword terms associated with the startup.
Data cleaning—startups
The raw dataset was filtered to obtain a dataset that only includes
startups of interest to this analysis using the following steps and build-
ing on the approach in Surana et al.9. First, startups that did not have
a clear connection to climate were identified and excluded from the
dataset. This was done using keyword terms in the description, such
as startups focused on ride sharing for which the climate benefit is
unclear. Supplementary Table 17 provides all filtering terms. Competi-
tors of these excluded ventures, as identified by i3, were also excluded.
In addition, startups that i3 categorized in the ‘other clean tech’ sec-
tor were excluded unless they were described by a relevant keyword
indicating a clear climate mitigation link (Supplementary Table 17).
Manual verification revealed that many of these ventures were tech-
nology firms with minimal or unclear climate relevance (for example,
Airbnb). Second, startups were excluded due to lack of information if
no sector or industry group was specified, the description or tags did
not include any relevant keywords and no founding year was provided.
Third, startups were excluded from the temporal or geographical
scope of this analysis. Startups located outside of the United States
were excluded. Startups founded before 2005, the year the first major
global climate agreement (the Kyoto Protocol) entered into force, were
excluded. When the founding year was not included in the i3 data, it was
identified by the authors using previously cleaned data provided by
Kurowski and Doblinger and publicly available information from data-
bases such as Pitchbook, Crunchbase and OpenCorporates. Fourth,
entities in the dataset were also excluded if they were determined to
be a large group or corporation (for example, EON Group), a public
agency (for example, the US Department of Energy) or a university or
research institute rather than a startup. Finally, startups were excluded
from the analysis if they had not received a single investment or grant
during the 2005–2020 period.
In the resulting filtered dataset, companies were identified that
lacked data for their year of failure (defined as going out of business or
filing for bankruptcy) and location of company headquarters. Updates
to this missing information were first taken from previously cleaned
data by Kurowski and Doblinger. The remaining missing information
was obtained from other databases such as Pitchbook, Crunchbase,
OpenCorporates and publicly searchable state-level business records.
Finally, the status of startups with no recorded activity for
2–5 years before 2021 were manually verified by the authors. Statuses
were updated based on public records, newspaper articles, archival
website information and comparison with the startup’s status in other
databases such as Crunchbase or Pitchbook.
The resulting dataset has 2,910 startups that were used for this
analysis.
Data cleaning—investments and investors
For each startup, the Cleantech Group data provide information on
the investment round, the amount and the investors that participated.
The investments in the Cleantech Group data were filtered to only
include those most relevant to startups, which were taken to be those
classified as grants, seed, series A, series B and growth equity. This
approach is consistent with methodologies used by PwC, the IEA and
other reports9,39–44. The i3 investor categories were then aggregated
into the categories of Public, Corporate and Other Investors as shown
in Supplementary Table 1. Here we note that other types of financing
such as loans, loan guarantees or project finance were not included
as these may involve far more technically and commercially mature
technologies where comprehensive data might not be available. This
is consistent with the approach used in multiple analyses that focus
on startups9–11.
Corporate investors were further manually categorized into
six sectors: ‘clean energy’, ‘fossil fuels and utilities’, ‘transportation’,
‘manufacturing, hardware and semiconductors’, ‘food, beverages and
agriculture’ or ‘digital and financial services’, building on the approach
in Surana et al.9. These categories were defined using the highest level
of aggregation of the International Standard Industrial Classification
of All Economic Activities (ISIC) Rev.4 and were chosen considering
the contribution of each sector to climate change and the information
available in the i3 and SBTi databases. Additionally, some categories
were aggregated or divided based on the classification used by the
SBTi, such as ‘food, beverages and agriculture’ and ‘transportation’.
ISIC Rev.4 categories ‘mining, oil and gas’, ‘electricity, gas, steam
and air conditioning supply’ and ‘water supply; sewerage, waste man-
agement and remediation activities’ were mixed and transformed
into two categories ‘fossil fuels and utilities’ and ‘clean energy’. This
classification aggregates fossil fuel companies and utilities that are
not supplying renewable electricity in one group and establishes a
unique category for companies that produce renewable energy or
provide renewable equipment such as solar panels or wind turbines.
The category ‘manufacturing, hardware and semiconductors’ includes
all activities considered in the ISIC Rev.4 category ‘manufacturing’,
except the production of motor vehicles that are contained in ‘trans-
portation’. Finally, the category ‘digital and financial services’ includes
all companies in the database that provide digital services or goods,
financial services such as banks, investment and insurance firms and
other services as corporate consultancy or real estate agencies.
The public grants information in the Cleantech Group data was
supplemented by downloading grant data from https://www.usaspend-
ing.gov/ for ARPA-E and Small Business Innovation Research (SBIR)
(accessed 21 January 2022 and 19 January 2022, respectively). These
grants were matched with startups in the i3 database to verify and
update existing grant information or to add missing grant data.

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Data cleaning—patent activity
Patent data were obtained from the US Patent and Trademark Office
(USPTO) using the PatentsView application programming interface and
the R package patentsview45. Patent data were accessed on 4 May 2022.
We matched patent assignee names with startup names after multiple
and extensive cleaning steps (for example, removing stems such as
‘Inc.’ and ‘Ltd.’), consolidating any duplicates and manual verification.
We used these data to identify the number of patents applied in each
year by each startup.
Regression analysis—exits and failure
The regression method used in this work for exit and failure outcomes
is the Cox Proportional Hazards (CPH) model, which has the form:
h (t, x1, … , xn) = ho (t) e∑
n
1 β1x1
(1)
The term ho (t) is the baseline hazard function, t is time and β1 … βn
are the coefficients associated with the covariates x1 … xn. When a
covariate is time varying, such as with investments, this is represented
as x(t)1 … x(t)n. This allows for recurrent event analysis of instances
where the same startup received multiple investments within
the studied period. The hazard ratio is defined as the exponentiated
coefficient eβ. The model was implemented using the survival package
and visualized using the survminer package in R46,47.
The applicability of the CPH model was verified by testing for
proportionality of hazards, influential observations and nonlinearity.
Schoenfeld residuals were used to verify the proportionality of hazards
both for individual variables and with a global test for the entire model,
where an insignificant result means the proportional hazards assump-
tion is valid. To check for influential observations, dfbeta values were
visualized and determined to be small compared with the regression
coefficients. Nonlinearity is not considered to be an issue for the binary
and categorical variables used in the regressions.
The selection and representation of variables for the analysis
was based on prior literature on survival analysis and CPH models27,48,
relevant variables for startups in general11 and specifically in climate
and energy3,10 and the availability of data in the Cleantech Group i3
dataset. Investment data represent a startup’s funding strategy across
the range of possible investors, and data on funding rounds are used in
startup literature both as a predictor of future outcomes and as metric
for growth3,11,48. Here investment was categorized as corporate invest-
ment, other private investment or public grant (Main text provides
reasons). Investment data were represented as time-varying binary
covariates in the model as in refs. 27,48. Patent count is an indicator of
innovation and research intensity for startups3,11. It was represented as a
time-dependent continuous variable indicating the number of patents
a given startup received each year48. Location is important for startups
as it often determines access to key networks that can provide human
and financial capital49 and sometimes highly specialized access to tech-
nical knowledge and support through public, academic or corporate
partnerships3,21,50. Location was represented as a constant binary vari-
able indicating whether the startup was founded in a ‘hotspot’ state for
climate tech. The location in a ‘hotspot’ was operationalized as having
a density of greater than two climate-tech startups per 100,000 popu-
lation (Supplementary Fig. 5), based on similar approaches that seek
to account for variation in funding availability and other key factors
based on geographic location3,27. California, Colorado, Massachusetts
and Washington, DC, were identified as climate-tech ‘hotspots’ under
this definition, which is consistent with findings in other literature8.
Additionally, several different ways of representing these variables
in the model were tested to verify robustness. Each of the following
was tried separately and in combination when the structure of the
model allowed it (clustering and frailty terms are incompatible, so
these could not be tried together). Investments by each investor type
were represented as continuous count variables rather than binary
variables and in US dollar amounts (Supplementary Tables 10 and 11).
Public grant funding was separated to identify international, national
and subnational funding sources (Supplementary Tables 8 and 9). The
log of the number of patents was used instead of the count of patents.
Startup sector was represented as a factor variable or accounted for
through clustering rather than stratification. Frailty terms were used to
account for sector or startup specific effects. Each of these variations
produced similar results with coefficients of the same sign. Finally,
interaction terms were introduced for combinations of funding types
(Supplementary Tables 18 and 19).
Sensitivity analysis—time period and patenting activity
We further tested the sensitivity of our results by performing sepa-
rate analyses of Cleantech 1.0 (startups founded 2005–2011) and 2.0
(startups founded 2012–2020) investment periods and by examining
the difference between startups that exhibit high-patenting activity vs
low-patenting activity. High patenting was defined as being in the top
decile of startups in terms of number of patents (greater than 12 patents).
The results of these analyses are provided in Supplementary Tables 13–16.
These robustness and sensitivity analyses attempted to account
for as many factors as possible. However, other potentially relevant
variables were not included in this analysis due to lack of data availabil-
ity, which is a common problem due to a widespread focus on secrecy
in competitive startup sectors, or difficulty accessing granular data
for the large numbers of entities included in this analysis. If data were
available, future research could include considerations such as relation-
ships with founder networks, public–private partnerships3, proximity
to local branches of a corporate investor21, degree of digitalization34,
startup size and generalizability to other sectoral and country contexts.
Data availability
The main startup and investment dataset is proprietary and available
for purchase from the Cleantech Group at https://www.cleantech.com/.
Additional data on patents were obtained from the publicly available
PatentsView API, and data on public grants were obtained from the
publicly available datasets at https://www.usaspending.gov/. With
the available code and access to the i3 database and spreadsheets, our
analysis could be fully replicated.
Code availability
All data-processing steps are outlined here, and the associated
code is publicly available at https://github.com/Climate-tech-Team/
startup-outcomes. Code is also provided to generate all figures in the
main text and Supplementary Information and to obtain patent data
through the PatentsView API. With the available code and access to the
i3 database and spreadsheets, our analysis could be fully replicated.
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Acknowledgements
Funding for this research was provided by the Energy and Environment
Program at the Alfred P. Sloan Foundation under grant number
G-2021-14177 (K.M.K., K.S., M.R.E., M.A.B., Z.H.T., N.E.H., E.D.W.). K.S.
acknowledges support from the BMK (Austrian Federal Ministry
for Climate Action, Environment, Energy, Mobility, Innovation and
Technology) under the BMK endowed professorship for data-driven
knowledge generation: climate action. We thank R. Fedorchak and R.
Lucas for their assistance with data cleaning.
Author contributions
Conceptualization: K.S., M.R.E., K.M.K. Data curation: K.M.K., K.S.,
Z.H.T., M.A.B. Formal analysis: K.M.K. Funding acquisition: K.S., M.R.E.,
N.E.H. Methodology: K.M.K., K.S., C.D. Project administration: K.S.,
M.R.E., K.M.K., N.E.H. Software: K.M.K., K.S., Z.H.T. Supervision: K.S.,
K.M.K. Visualization: K.M.K. Writing–original draft: K.M.K. Writing–
review and editing: K.M.K., K.S., C.D., M.R.E., E.D.W., N.E.H.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41560-024-01530-w.
Correspondence and requests for materials should be addressed to
Kathleen M. Kennedy or Kavita Surana.
Peer review information Nature Energy thanks Hans Lööf and the
other, anonymous, reviewer(s) for their contribution to the peer review
of this work.
Reprints and permissions information is available at
www.nature.com/reprints.
Publisher’s note Springer Nature remains neutral with regard to
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Springer Nature or its licensor (e.g. a society or other partner) holds
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© The Author(s), under exclusive licence to Springer Nature Limited
2024, corrected publication 2024

    Policy Problem:

Answer: Supporting climate‑tech startups that can rapidly bring innovations to market is a policy priority for meeting net‑zero goals. However, many climate‑tech startups face high risks and fail (for example, bankruptcy, going out of business). Policymakers can support these startups by increasing public funding and mobilizing private investment to increase the likelihood of success or exits (for example, IPOs, mergers/acquisitions). But current policies treat all private investors the same, neglecting differences in their effects and potential synergies with public funding. Alongside various financial‑ or impact‑focused venture capital investors, corporations are strategic investors that seek to increase profits while achieving goals related to long‑term business plans, competitive standing or decarbonization. Corporations make fewer investments than other private investors, but these investments are often substantially larger and come with sectoral experience, supply chains and networks. Corporations are thus potentially more influential at shaping innovation outcomes than other investors, yet remain poorly understood in innovation policy discussions.