disposition caption captionlabelcaption

#  Knowledge for a warmer world:
A patent analysis of climate change adaptation technologies

Kerstin Hötte1,2,3111 Corresponding author:
<EMAIL_ADDRESS>
ORCiD: KH: 0000-0002-8633-4225; SJ: 0000-0001-9582-8289 Su Jung Jee2,4
1 Oxford Martin Programme on Technological and Economic Change, University of
Oxford, UK
2 Institute for New Economic Thinking at the Oxford Martin School, University
of Oxford, UK
3 Faculty of Business Administration and Economics, University of Bielefeld,
DE
4 School of Management, Faculty of Management, Law & Social Sciences,
University of Bradford, UK

###### Abstract

Technologies can help strengthen the resilience of our economy against
existential climate-risks. We investigate climate change adaptation
technologies (CCATs) in US patents to understand (1) historical patterns and
drivers of innovation; (2) scientific and technological requirements to
develop and use CCATs; and (3) CCATs’ potential technological synergies with
mitigation. First, in contrast to mitigation, innovation in CCATs only slowly
takes off, indicating a relatively low awareness of investors for solutions to
cope with climate risks. Historical trends in environmental regulation, energy
prices, and public support can be associated with patenting in CCATs. Second,
CCATs form two main clusters: science-intensive ones in agriculture, health,
and monitoring technologies; and engineering-intensive ones in coastal, water,
and infrastructure technologies. Analyses of technology-specific scientific
and technological knowledge bases inform directions for how to facilitate
advancement, transfer and use of CCATs. Lastly, CCATs show strong
technological complementarities with mitigation as more than 25% of CCATs bear
mitigation benefits. While not judging about the complementarity of mitigation
and adaptation in general, our results suggest how policymakers can harness
these technological synergies to achieve both goals simultaneously.

JEL: O33; O38; Q54; Q55; Q58
Keywords: Climate Change; Adaptation; Innovation; Patent Data; Technology-
Science Interactions; R&D Policy

## Highlights

  * •

Patenting in adaptation stagnates indicating a low awareness of investors for
climate risks.

  * •

Adaptation technologies rely more strongly on public support than others.

  * •

Analysis of science base informs how to leverage regional absorptive capacity
to transfer adaptation technologies.

  * •

Analysis of technological base informs how to stimulate the advancement of
adaptation technologies.

  * •

Complementarities between adaptation and mitigation show scope to maximize
synergies.

###### Contents

  1. 1 Introduction
  2. 2 Background
    1. 2.1 Adaptation and the role of technology
    2. 2.2 Adaptation and mitigation: Substitutes or complements?
  3. 3 Methods
    1. 3.1 Data sources
    2. 3.2 The scientific base
    3. 3.3 Adaptation-mitigation complementarities
  4. 4 Results
    1. 4.1 A slow start for adaptation
      1. 4.1.1 Drivers of innovation in adaptation and mitigation
      2. 4.1.2 Reliance on public support
    2. 4.2 The knowledge base of adaptation
      1. 4.2.1 Reliance on science: Two clusters
      2. 4.2.2 Composition of the scientific base
      3. 4.2.3 Composition of the technological base
    3. 4.3 Complementarities with mitigation
      1. 4.3.1 Adaptation technologies with mitigation co-benefit
      2. 4.3.2 Potential knowledge spillovers between mitigation and adaptation
  5. 5 Discussion
  6. 6 Conclusion
  7. 7 Data availability
  8. 8 Acknowledgments
  9. 9 Author contributions
  10. 10 Competing interests statement
  11. A.1 Y02A classes and definitions
  12. A.2 Additional results
    1. A.2.1 Summary statistics of adaptation and mitigation patents
    2. A.2.2 The scientificness of adaptation and mitigation over time
  13. SI.1 Relative frequencies of adaptation and mitigation patents
    1. SI.1.1 Additional information on the science base of adaptation
    2. SI.1.2 Co-classification of adaptation technologies
    3. SI.1.3 Dual purpose technologies for mitigation and adaptation
      1. SI.1.3.1 Technological and scientific similarities
      2. SI.1.3.2 Science base of dual purpose technologies
      3. SI.1.3.3 Co-classification of dual purpose patents

##  1 Introduction

Climate change is an existential threat to human livelihoods [12, 70]. Recent
extreme weather events have shown that significant adaptation is needed to
help communities, cities, and economic activity adjust to new climatic
conditions [42]. Technological innovation plays an important role in
addressing this challenge [27, 24]: climate-smart agriculture helps to adapt
to droughts, floods, and increasing threats of pest infestation [51, 3]; new
types of hazard defense and weather prediction tools help protect
infrastructure and human lives from storms, floods, and heatwaves [94]; water
conservation and catchment technologies help address water scarcity [21];
vaccines, new drugs, and preventive public health inventions strengthen
people’s resistance against infectious diseases and heatwave-induced risks
that become more prevalent under climate change [34, 16]. Alongside nature-
based solutions and behavioral changes, adaptation technologies are needed to
cope with current and future climate risks [94, 93].

Governments under the Paris Agreement committed to strengthening their
adaptation capacities, including technological solutions, but progress to
achieve this goal has been rarely evaluated at a comprehensive level [13, 56].
Measuring progress is important as it helps identify adaptation gaps, enables
impact assessments of adaptation strategies, and mutual learning when decision
makers share their experience with undertaken adaptation efforts. Here, we
systematically take stock of existing technologies for adaptation using patent
data addressing three questions: _(1) To what extent have these technologies
been developed and which were the drivers of innovation? (2) How can
governments support the development and adoption of these technologies? (3)
How do technologies for adaptation interact with climate change mitigation?_

Existing studies on climate change adaptation technologies (CCATs) often
focused on specific regions, technologies, or climate risks. To date,
systematic analyses of innovation in CCATs have been limited [74, 24], not
least because, until 2018, there was no classification of CCATs in patent
databases. The most closely related study was made by [24] who investigated
the diffusion of CCATs using patent data.

Leveraging the recent Cooperative Patent Classification (CPC) of ‘climate
change adaptation technologies’ [8], we investigated how innovation in various
adaptation technologies has changed over time. We analyzed the composition of
the scientific and technological knowledge bases of CCATs to show which
scientific and technological capabilities are needed to advance, adopt, and
utilize CCATs. We further identified technological complementarities between
adaptation and mitigation showing in which areas both targets can be achieved
at the same time. To the best of our knowledge, this is the first systematic
analysis of the current state of technological knowledge for adaptation. From
our analysis of CCATs, we have documented five key insights:

  1. 1.

Despite increased awareness of climate change, patenting in most adaptation
technologies has not experienced a surge in the past two decades, much unlike
patenting in climate change mitigation which has been increasing
significantly.

  2. 2.

Adaptation technologies form two clusters: those that are science-intensive
(health-related adaptation, agriculture, and indirectly enabling technologies
for weather forecasting and natural resource assessment) and those that are
engineering-based (adaptation in coastal, infrastructure, and water supply).
The qualitative details of the knowledge base reveal scientific and
technological requirements needed to develop, adopt, and utilize these
technologies and inform policy makers how to facilitate advancement and
transfer of adaptation technologies.

  3. 3.

Invention in various CCATs greatly differ by magnitude: Adaptation related to
human health has the highest number of patented inventions ($>$16k patents),
followed by agriculture (8k). Coastal adaptation has the lowest number of
patented inventions ($<$0.9k).

  4. 4.

Since mid-2000s, more than 40% of adaptation patents have been reliant on
government support, which is about 10% higher than average [29]. For most
mitigation technologies, the reliance on government support is much lower
during the same period, except for nascent mitigation technologies such as
carbon capture and storage (CCS).222The technology class CCS also includes the
capture and storage of non-carbon greenhouse gases such as $SO_{2}$ or
$SF_{6}$. We use the term CCS to simplify the notation.

  5. 5.

26% of all adaptation technologies simultaneously help with mitigation. The
highest overlap exists in infrastructure, where 70% of adaptation patents also
help reduce emissions. Many of these inventions came as byproducts of
innovations developed to cope with environmental regulation and high energy
prices.

The observation that 26% of adaptation technologies simultaneously contribute
to mitigation is of high theoretical and practical importance. In many
theoretical discourses, climate change adaptation and mitigation were treated
as substitutes [11, 78]. Our results question this perspective. We argue that
well-designed policy can encourage innovations that meet the twin goals of
adaptation and mitigation simultaneously.

We documented a substantial scope for technological complementarity between
certain adaptation and mitigation options. While not promising a universal
solution for all adaptation and mitigation options, we illustrate examples of
how emission-increasing _maladaptation_ can be avoided [10].333Note that the
definition of maladaptation goes beyond emission-increasing adaptation but
includes any adaptation action with adverse side effects. In this article, we
refer to subset of emission-increasing adaptation when using the term
maladaptation.  For example, thermal insulation in buildings achieves both:
adaptation to heatwaves and emission-reduction through energy savings, while
air-conditioning would be an example of maladaptation. Energy-intensive
desalination to cope with water scarcity, another example of maladaptation,
can be complemented with the integrated use of solar PV. Our analysis
identifies additional cases, for example in agriculture, infrastructure, and
clean production where public R&D support can encourage inventions that meet
adaptation and mitigation goals at the same time.

As technological development is path-dependent [9, 79], subsequent
technological development cumulatively builds on pre-existing technology and
knowledge. Technology choices in the early phase of development are essential
to prevent lock-in effects in adaptation options that undermine mitigation
efforts or in mitigation strategies that increase vulnerability against
climate change.

The remainder of this paper is structured as follows: In the next section 2,
we offer an introduction to the economics of adaptation, mitigation, and
technology. In section 3 we describe the methodology and data. Section 4
outlines the results, first documenting innovation trends in adaptation
(Section 4.1), continuing with an analysis of the technological and scientific
base (Section 4.2), and ending with an analysis of adaptation-mitigation
complementarity (Section 4.3). Section 5 concludes.

##  2 Background

###  2.1 Adaptation and the role of technology

Governments typically employ a portfolio of different actions to adapt to
climate change. For instance, these portfolios can comprise of behavioral and
nature-based solutions, technological adaptation of physical infrastructure,
and insurance-like mechanisms that facilitate the economic recovery after the
occurrence of an extreme event [13].

Behavioral solutions can comprise of awareness and information campaigns that
strengthen the risk-preparedness in the face of wildfires, storms, and floods;
or teach the population about appropriate behavior during heatwaves [95].
Nature-based solutions for adaptation either strengthen the resilience of
ecological systems, such as through biodiversity protection, or leverage the
provision of ecosystem services for water supply or green zones to alleviate
heatwaves in urban areas [81, 82]. Technologies for adaptation comprise both
high-tech and low-tech solutions, and even non-patented technological
solutions [24, 43, 94]. Next to these, financial instruments and social safety
nets play a crucial role, as financial and economic capabilities are essential
to enable recovery after extreme weather events. These instruments consist of,
for example, weather insurances in agriculture or real-estate, but also public
recovery schemes. Furthermore, poverty reduction is an effective adaptation
strategy, which is most prevalent in low-income countries [57].

However, these adaptation options interact and mutually enhance their
effectiveness. Behavioral risk-preparedness is easier to achieve if
technologies provide reliable weather forecasts [95], and the costs of
financial insurance schemes can be significantly reduced if technological
adaptation strengthens the resilience of physical assets against extreme
weather [66].

In this study, we focus on patented technological solutions for adaptation.
Technological solutions are appealing when other adaptation options are
prohibitively expensive or infeasible, and they bear the potential to overcome
some of the limits to adaptation. For example, two-thirds of the world’s
cities are located on coastlines, which are vulnerable to sea level rise.
Relocating assets and citizens is often infeasible or prohibitively expensive
due to financial and social constraints [26]. Many nature-based solutions like
the restoration of mangroves for flood protection, agroforestry dealing with
water scarcity, or green zones in cities to alleviate heatwaves, only work
provided that extreme weather events are sufficiently moderate [91]. In a
world with ongoing climate change as currently projected [86, 42], societies
need to prepare for weather events that exceed these limits. In these
situations, technological solutions can play an important role [92, 43].

###  2.2 Adaptation and mitigation: Substitutes or complements?

Although it has been said that —given our current knowledge— mitigation
remains the cheapest and best adaptation, climate change is already happening
today at a worrying pace and societies need to adapt to these unavoidable
changes.

In the literature, the relationship between climate change mitigation and
adaptation is an interesting controversy. Theoretical studies suggest that
mitigation and adaptation efforts can be considered as strategic substitutes,
as increased mitigation efforts reduce the need for future adaptation, while
future adaptation may compensate for the lack of mitigation today [11, 78, 15,
98]. Game theoretical considerations suggest that policymakers’ ambitions to
mitigate climate change may be undermined by the prospect of future
technological solutions that neutralize the negative impact of climate change
[11, 15].

This line of argument was believed to undermine the progress of international
climate negotiations about mitigation and underpinned ethical concerns about
research on climate engineering [89] and adaptation [78]. The controversy
about adaptation-mitigation trade-offs is of very practical relevance today,
acknowledging that adaptation is a necessity of both today and the future [43,
10]. Research has shown that short-term mitigation policies may undermine the
future adaptation. For example, the production of carbon-neutral biofuels or
rapid deforestation to sequester carbon may come with the cost of biodiversity
losses, which may be essential to assist ecological systems in adapting to
changing climatic conditions [46, 18]. Other examples of maladaptation are
emission-increasing solutions for adaptation, such as energy-intensive
desalination techniques to improve water supply or air-conditioning in
response to heatwaves [10].

However, theoretical models upon which the trade-off considerations build are
difficult to calibrate for three major reasons:

(1) The models explore a trade-off between current costs of mitigation
compared to future costs of adaptation. This valuation is highly sensitive to
the appropriate choice of the discount rate which is empirically controversial
[32]. Moreover, those making decisions about adaptation and mitigation may be
disparate as adaptation benefits are mostly locally specific, and often
private, while climate change mitigation contributes to a global (uncertain)
public good [1].

(2) The economic impact of climate change is subject to uncertainty: Once
tipping thresholds in the climate system are crossed, it may become
unpredictable and an existential threat to human livelihood, which may be
beyond the scope of any available and expected technological solutions [54].

(3) Existing models suggest that investments made in mitigation cannot be
spent on adaptation and vice versa. However, empirically mitigation and
adaptation are not necessarily mutually exclusive and examples exist where
adaptation efforts contribute to mitigation and vice versa [82, 85, 14].

We provide evidence that the trade-off consideration may need to be
reconsidered as we identify adaptation-mitigation complementarities in R&D and
show in which areas these co-benefits can be harnessed. In addition, we state
that some examples of emission-increasing maladaptation can be a matter of
technology choice, for example in desalination or air-conditioning [10, see].

##  3 Methods

###  3.1 Data sources

We used US patent data from the US Patent and Trademark Office (USPTO) and
GooglePats compiled for an earlier project [40, 39]. We used USPTO data since
most high-value inventions are filed in the US, and US patents can be regarded
as a good proxy for the global technological frontier [5]. To ensure the
uniqueness of inventions, we used the patent DOCDB family ID of patents
downloaded from PATSTAT (Spring 2021 version) as the unit of analysis [49,
69].444Throughout this document, we use simple DOCDB patent families as the
unit of analysis, but we use ‘patent’ for ‘patent family’ as shorthand. We
supplemented the patents with CPC classifications obtained from the master
classification file (April 2021 version) provided by
USPTO.555https://bulkdata.uspto.gov/data/patent/classification/cpc/ To
identify adaptation and mitigation technologies, we used the CPC Y02-tags [8,
87].

We obtained 37,341 unique patents that are tagged as _technologies for
adaptation to climate change_ as indicated by the CPC tag Y02A. We categorize
them as patents for coastal adaptation (Y02A1), water supply and conservation
(Y02A2), infrastructure resilience (Y02A3), agriculture (Y02A4), human health
protection (Y02A5), and indirectly enabling technologies such as weather
forecasting, monitoring, and water-resource assessment (Y02A9) (see A.1 for a
detailed overview). We also sourced mitigation-related patents distinguishing
technologies at the 4-digit level (buildings (Y02B), CCS (Y02C), energy-saving
ICT (Y02D), clean energy (Y02E), clean production (Y02P), clean transportation
(Y02T), and clean waste (Y02W)[96, 8, see]. The tagging scheme for climate
change mitigation and adaptation technologies is based on the search
algorithms that identify mitigation- and adaptation-related CPC symbols, IPC
symbols, and keywords [96, 8, see]. Our analysis relies on the CPC version
from April 2021.

The USPTO regularly re-classifies patents whenever a new version of the CPC
system becomes available. Hence, old and new patents are assigned to
technology classes according to uniform principles [52]. This enables the
identification of the technological ancestors of today’s inventions. For
example, early patented inventions in windmills are the technological
ancestors of today’s high-tech wind turbines [40, cf.]. An adaptation-related
example is health-related patents for improvements in medical compounds
developed in the late nineteenth century to fight cholera. These inventions
build the foundations of today’s technology to fight infectious diseases.
Similarly, many inventions in agriculture (e.g. ecological buffer zones or
organic fertilizers), water conservation, and insulation in buildings have
their origins in the nineteenth century.

Some of the patents in our data serve multiple adaptation purposes. We double-
counted these patents, arguing that knowledge is non-rival and patents that
serve multiple adaptation purposes contribute equally to the knowledge base of
these CPC 6-digit categories. This argument is further supported by the high
variation in the value of patents: patents with a higher number of co-
classifications tend to represent more valuable inventions [55, 88, 63]. In
our data, 295 out of 37,341 unique patent families are multi-purpose
adaptation technologies, i.e. are assigned to two or more 6-digit
Y02A-classes.666Note that we applied the same double-counting rule for
mitigation patents that are classified into multiple 4-digit subclasses of
Y02.

We further supplemented the data with information on the reliance of
individual patents on governmental support [29, 28]. Patents are defined as
being reliant on governmental support if at least one of the following five
conditions hold: (1) The patent is directly owned by a governmental
institution. (2) Governmental support is explicitly acknowledged in the patent
document. (3) The patent cites a patent owned by a governmental institution or
that acknowledged governmental support. (4) The patent cites research
published by a governmental institution. (5) The patent cites research where
governmental support is mentioned in the acknowledgments. The first two
conditions are related to the patents created by direct financial support of
the government and the last three conditions are related to the patents being
reliant on prior knowledge created by financial support of the government.
Although there can be other routes of governmental support such as human- or
facility-based ones, we focus on the direct financial support and prior
knowledge base support, main areas of governmental support that can be
captured comprehensively at the patent-level.

To analyze the technological and scientific knowledge base, we used data on
(1) citations from patents to patents from [72] and (2) citations from patents
to science provided by [60].

###  3.2 The scientific base

To describe the scientific base of adaptation, we used data on citations from
patents to science. Citations in a patent can be made in the text body or at
the front page of a patent, and can be added by the applicant or patent
examiner. We included all types of citation into our analyses. [61] extracted
the citation links using a sequential procedure based on text recognition and
matched the data with the scientific database Microsoft Academic Graph (MAG)
[83]. The matching procedure is probabilistic. [61] tagged citation links with
a so-called confidence score, which indicates the precision and recall rate of
the matching [58, 59, more detail available in]. We only included patents with
a confidence score $>4$ which is associated with a precision rate of more than
99%. The citation links are complemented with meta-information on the
scientific paper that is cited, e.g. title, DOI (if available), outlet,
publication year, and scientific field of research.

We analyzed the scientific base in two ways: (1) We computed time series of
the share of the number of citations to papers to the number of total
citations to patents and papers. The data are aggregated into 5-year bins
gathering all patents classified as certain adaptation technology that were
granted during the considered period. (2) We provided a qualitative
description of the science base. Every paper is tagged by the Web-of-Science
(WoS) category into which the article is classified. The assignment of WoS
categories to papers is made on the paper level [40, further explanations are
provided in]. We used this information to show, for each type of adaptation
technology, the six most often cited WoS fields as a share in all scientific
citations during the different time periods.

###  3.3 Adaptation-mitigation complementarities

Analyzing the technological base of patents relies on the hierarchical CPC
system, which classifies patents into broad sections (A-H, Y) which are
sequentially sub-divided into classes, sub-classes, groups, and sub-groups.
The section ‘Y’ is a special, cross-sectional tagging scheme that is used to
identify climate-related technologies. We removed ‘Y10’-tags from our analysis
because these tags are assigned to patents for technical reasons (for example
to ensure compatibility with other classification systems).

Patents can be classified into multiple CPC classes. Co-classification
indicates interdependence among different technologies. We used co-
classification data to identify patents that are tagged as adaptation and
mitigation technologies. We call these patents ‘dual purpose’ technologies.

To better understand overlaps in the knowledge base of adaptation and
mitigation technologies, we used backward citations. The cosine similarity of
two technology types is computed as the normalized dot product of the vectors
of backward citation shares made to 4-digit CPC classes for the technological
similarity and to WoS-fields for scientific similarity. We rely on the
methodology used in [40] to create similarity networks. We illustrated the
pairs of 6-digit mitigation and adaptation complements that show strong
overlaps in their technological and scientific knowledge base.

##  4 Results

###  4.1 A slow start for adaptation

We analyzed the technological frontier in climate change adaptation by looking
at patents granted by the USPTO that are tagged as _technologies for
adaptation to climate change_. This leads to a population of 37,341 unique
patent families that are explicitly recognized as technologies that help in
climate change adaptation. We also collected 408,348 unique patent families
related to climate change mitigation to explore the technological relationship
between mitigation and adaptation.

Currently, there are six main categories of climate change adaptation patents:
(1) coastal adaptation, (2) water supply and conservation, (3) infrastructure
resilience, (4) agriculture, (5) human health protection and (6) indirect
adaptation i.e. measurement technologies such as weather forecasting,
monitoring invasive species, and water-resource assessments (see [94] and A.1
for details).

Figure 1: Patented inventions in adaptation technologies over time

(a) Mitigation & adaptation counts

(b) Adaptation patents counts

(c) Mitigation & adaptation shares

(d) Adaptation technology shares

(e) Reliance on public support

(f) Adaptation’s reliance on public support

Fig. 1(a) (1(b)) shows the number of annually granted US patents (unique by
DOCDB patent family) in 4-digit mitigation and adaptation (6-digit adaptation)
technologies since 1836. Fig. 1(c) (1(d)) shows the number of 4-digit
mitigation and adaptation (6-digit adaptation) patents as a share of all US
patents. Fig. 1(e) (1(f)) shows the share of these patents that relies on
governmental support in 1935-2017 (see Sec. 3.1 for a definition). Note that
the axes may differ in scale due to differences in the data by time coverage
and scale.

In Fig. 1(f), we show on the left-hand side the evolution of patents in
mitigation and adaptation technologies as identified by 4-digit CPC codes. At
the right-hand side, we show analogous figures for different adaptation
technologies at the more disaggregate 6-digit level. The upper two figures
1(a) and 1(b) show the number of annually granted patents since the mid-
nineteenth century. In the mid row, we show these time series measured as a
share in all annually granted USPTO patents.

Until the second half of the twentieth century, patenting in mitigation and
adaptation ranged at very low levels, both in absolute patent counts and
measured as a share. The only exception is renewable energy with a share of up
to 4% already in the late nineteenth century. This share corresponds to the
level of clean energy inventions today. The historically high share is in line
with previous research and historical accounts showing that patenting for
energy technologies like windmills and water wheels was already very prevalent
in the nineteenth century [40].

Since the early twentieth century, we observe the number of annually granted
patents in mitigation and adaptation to grow slowly. However, when showing
these inventions as a share in all US inventions, we find the growth of green
inventions to be non-monotonous.

Patenting in climate change mitigation began growing after the 1950s. After
then, patenting in mitigation technologies experienced several periods of
acceleration, such as during the Oil Crisis of the 1970s [30, 33]. Since the
2000s, patenting in climate change mitigation (especially in energy and
transportation) increasingly took off.

For adaptation, we find that inventions have not increased substantially over
time except for the category of health-related technologies (Fig. 1(d)).
Adaptation has seen only modest increases in response to the oil price crisis
and in the subsequent decades. In 2020, about 0.5% of all US patents were
classified as being helpful for adaptation, while green energy and transport
patents account for more than 3% and 2.5% and exibited a steep increase since
the 2000s (Fig. 1(c), 1(d)).

Among the adaptation technologies, health-related adaptation dominates by the
number of patents. With more than 16,300 unique patents over the full time
horizon, health accounts for more than twice as many patents as agriculture
(8,089 patents), which is the second largest category. Coastal adaptation is
the smallest category with only 857 unique patents (Table A.1). These
differences may not only reflect high levels of innovative activity in health-
related adaptation but also the fact that these technologies can be more
effectively protected through patents compared to the other technologies [20].
Moreover, as coastal adaptation has public goods characteristics which may
explain that private incentives for innovation —and patenting— can be
relatively dampened. Patenting in adaptation related to water and
infrastructure peaked in the 1980s but subsequently tapered off.

####  4.1.1 Drivers of innovation in adaptation and mitigation

Previous research has shown that innovation and patenting in green
technologies respond to price signals and the size of the market for the
technology [73, 2]. The market size of a technology can grow through an
increased demand, for example induced by environmental disasters [65] or
regulatory pressure [7, 50]. These drivers may explain patterns in the time
series data.

Health- and water-related adaptation rose in the aftermath of the first
regulatory initiatives by the US government to reduce the pollution of the air
and water resources by industrial processes (e.g. the Clean Air Act in 1963
and Clean Water Act in 1972). The majority of health-related CCATs during the
1960-1980s are air-pollution control technologies, and water-related
adaptation technologies comprise many inventions for water treatment and
pollution control (see A.1). Innovation in pollution-control technologies is
one response to regulatory pressure [7, 50] with an enhanced technological
capacity for health- and water-related adaptation as a byproduct.

The rise in infrastructure adaptation can be associated with the Oil Crisis.
Increased energy costs in response to that crisis were an important driver of
energy-saving innovations [73, 37]. Patents for infrastructure adaptation
comprise many energy-saving insulation technologies, for example preserving
thermal comfort in buildings or making power lines for energy transmission
more robust (see A.1).

Innovation can be also triggered by an _entrepreneurial state_ [62] that
actively engages in basic and applied research and creates markets for novel
technological solutions. Many of the early inventions in low-carbon energy
technologies during the 1950s and 1960s can be attributed to upcoming
government-led initiatives in nuclear energy [22], but also in renewable
energy technologies emerging from early US government initiatives from the
Department of Energy and the US space program [62].

####  4.1.2 Reliance on public support

Patenting in adaptation strongly relies on governmental support with over 40%
of patents since mid-2000s being linked directly or indirectly to government
support (Fig. 1(e)). This is about 10% higher than the value for average
patents in the US [29, cf.]. Indirect adaptation and health-related adaptation
show the highest levels of reliance on public support (Fig. 1(f)). For many
climate change mitigation technologies, by contrast, we observe a lower
reliance on public support today compared to earlier periods. Especially clean
energy and green ICT were heavily supported in the past, but have seen a
significant private-sector take off. Mitigation technologies with insufficient
market demand (e.g. CCS) show comparably high levels of public support as
adaptation technologies.

The reliance on public support serves as an indicator of the stage of market
development: if sufficient market demand for a technology exists, innovators
have a commercial interest to develop these technologies and the reliance on
public support is low. In contrast, if markets are underdeveloped (as in the
case for adaptation and CCS), the public sector can play a critical role to
stimulate innovation [62, cf.].

Reliance on public support also includes the reliance on research that is
funded by the government. As we shall see below in 4.2 and A.2.2, adaptation
technologies are more science-reliant than many other technologies. This
contributes to the relatively higher reliance on public support of adaptation
technologies (but also of science-reliant CCS), but the time trends suggest
that this is not sufficient to explain this pattern. For example, for both
clean energy and green ICT, the science reliance increased over time, but we
observe a decreasing reliance on public support. Moreover, among the different
categories of adaptation technologies, we also find that technologies like
coastal, water-related, and infrastructure adaptation exhibit relatively
higher shares of reliance on public support despite low levels of
scientificness.

###  4.2 The knowledge base of adaptation

To study the knowledge base of adaptation technologies, we combine data on
patent citations, co-classifications [39], and science citations [61].
Citations from patents to science indicate the scientific origins of patented
inventions [64, 4]. Similarly, citations from patents to other patents
describe technological base of patented inventions [44, 97].

####  4.2.1 Reliance on science: Two clusters

We find that adaptation technologies, as reflected by patents, can be grouped
into two clusters: (i) science-intensive technologies (agriculture, health,
and indirect adaptation); and (ii) engineering-based technologies (coastal,
water, and infrastructure).

Figure 2: Science intensity of adaptation technologies

Notes: Science-intensity of different adaptation patents measured by the share
of citations to science in the number of total citations (sum of citations to
other patents and scientific articles).

We measure the scientificness of adaptation technologies by the share of
patent citations to science over the sum of citations to other patents plus
citations to science. This ratio indicates to which extent a patent relies on
science rather than applied technological development as encoded in patent
citations [40, see]. The evolution of the CCATs’ scientificness over time
since 1976 is shown in Fig. 2. Coastal, water, and infrastructure adaptation
technologies exhibit low shares of citations to science (0-5%) while health,
agriculture, and indirect adaptation are highly science-intensive (50-80%).
This reflects the idiosyncratic nature of different technologies. To be
specific, science-intensive adaptation technologies include, for example,
crops that are climate resilient, treatments for diseases that will become
more prevalent in hotter temperatures, and complex early warning and
monitoring systems. By contrast, engineering-based adaptation, which relies
significantly less on science, includes technologies such as fixed
construction to provide flood defense, cliff stabilization, water
purification, and methods to strengthen the resilience of infrastructure.

The rise in share of citations to science for agriculture and health in the
1970s-1980s coincides with the rise of the US biotechnology. This period was
characterized by many spin-offs from universities and public research
laboratories that undertook innovation in basic necessities [76, 77]. Even
within the engineering-intensive adaptation technologies such as water and
infrastructure adaptation, we observe that these technologies became more
science-intensive. We find that this phenomenon is related to the increased
scientificness of chemistry-reliant water-conservation technologies (such as
desalination, reverse osmosis), advances in material sciences for
infrastructure adaptation, and increased interactions between developments in
science-reliant solar photovoltaics with water and infrastructure adaptation,
for example to supply energy for water treatment or heating and cooling in
buildings.

Coastal adaptation, the smallest category in our sample, did not show an
increase in its reliance on science. This is exceptional as an increasing
reliance on science is a general trend in innovation during the second half of
the twentieth century [40]. This indicates that other knowledge sources rather
than science are important for patented technologies in coastal adaptation,
although interpretations must be made with caution due to the low number of
patents.

####  4.2.2 Composition of the scientific base

We studied the knowledge base of adaptation showing which fields of science
are cited by adaptation technologies over time (Fig. 3). This gives an idea of
the scientific disciplines policymakers can support to strengthen innovation
in adaptation technologies. Complex technologies require a so-called
_absorptive capacity_ to be effectively used and further developed [19, 17,
23]. In many environmental technologies, off-the-shelf solutions available on
global markets require adaptive innovation to become useful under locally
specific conditions [75]. Hence, having expertise in scientific fields that
are relevant for adaptation can spur the adoption, adaptation, and indigenous
development of CCATs and ensure their maintenance. This can facilitate the
efficient transfer of CCATs to regions where being exposed to climate risk,
which is particularly urgent in many developing countries [41, 3, 53]. These
regions can stimulate adoption of adaptation technologies by investing in
laboratories of local universities or public research institutions having
relevant scientific understanding and thereby stimulate the transfer of
adaptation skills to the local community.

Figure 3: Composition of scientific knowledge base by scientific fields

Notes: These figures show the 8 most often cited scientific fields (by Web-of-
Science categories). The numbers on top of each bar indicate the number of
papers cited by patents granted in the respective time period. The size of the
colored fields in each bar plot indicates the share of citations that goes to
the respective WoS field. Black color is used for the residuum of fields that
are cited less often than the 8th most often cited field.

Distinguishing between applied and basic research following [71], we find that
science-intensive CCATs (agriculture, health, and indirect adaptation) rely
mostly on basic research, while adaptation technologies with a low science-
intensity (coastal, water, and infrastructure) build to a higher extent on
applied research.

Among the science-intensive CCATs, both health and agriculture largely build
on biochemistry and molecular biology. Health adaptation further relies on
immunology, oncology, and virology, while agricultural adaptation further
relies on plant sciences. Indirect adaptation technologies which cover
monitoring, assessment, and forecasting technologies rely on physics-related
areas such as electrical engineering and optics, which form foundations of
sensor and measurement technologies. Further, they build on biology-related
areas such as biochemistry and immunology. Manual inspections of patents
reveal that indirect adaptation technologies cover not only weather
forecasting and monitoring technologies but also bioinformatics technologies
for medicine and chemical assessment. Therefore, university or public research
laboratories in the field of biochemistry or molecular biology would be a good
starting point for transferring many of the science-intensive adaptation
technologies to the regions in need of such skills and knowledge [3].

In engineering-based CCATs, applied sciences dominate. Coastal adaptation
relies on several different types of engineering (civil, electric,
environmental, and geological), but it also has weak linkages to some basic
research of meteorology, maths, geosciences and environmental science. Water-
related adaptation also relies on engineering but also basic research in
chemistry, which is relevant for water conservation, filtration, recovery, and
desalination that make use of chemical processes. The scientific knowledge
base of infrastructure adaptation consists of material science,
thermodynamics, construction, and electrical engineering, among other fields.
To sum up, to transfer the engineering-based CCATs to the regions in need, the
role of laboratories in the engineering department of local universities will
be particularly important, though basic science is also necessary in some
fields. For example, in regions at high risk of sea level rise, civil
engineers, and geologists in local universities may work together to
efficiently adopt and advance technologies for coastal adaptation, and to
adapt them to locally-specific conditions. Similarly, in regions where water
adaptation is urgent, chemical engineers in the local universities may play a
pivotal role in facilitating the adoption and further development of water
adaptation technologies.

####  4.2.3 Composition of the technological base

A single patent can belong to multiple technology classes, reflecting a
combinatory nature of knowledge creation [67]. Investigating co-classification
patterns of adaptation patents can reveal technological capabilities other
than Y02A that are needed to develop each type of CCAT. In addition, the co-
classification patterns can be also interpreted as reflecting the promising
fields of technological convergence with adaptation technologies [45, e.g.].

Therefore, organizations equipped with capabilities in fields frequently co-
classified with Y02A can be understood as being in a competitive position in
developing and exploiting adaptation technologies. Motivating these
organizations, particularly in the private sector, to engage in the
development of adaptation technologies can be a reasonable direction to spur
innovation in climate change adaptation. In addition to encouraging the supply
side, governments can also stimulate targeted foreign direct investments (FDI)
or foreign licensing and connect these organizations with potential regions
where demand exists, the regions being exposed to a higher risk of a certain
type of climate change [27, 75, 80]. Targeted technology-transfer policy may
not only stimulate the diffusion of environmentally related technologies, but
also spur technological learning and indigenous innovation by local firms.

Fig. 4 shows the overall patterns of co-classification for each type of CCAT.
For example, we can see that the vast majority of coastal adaptation patents
are co-classified as fixed constructions technology.777Coastal adaptation
significantly relies on solutions that are difficult to patent as well, such
as mangrove reforestation and nature-based solutions. We should note that Fig.
4 includes a bias towards coastal adaptation solutions that are patentable,
rather than the hard to be patented solutions. The results imply that firms
with fixed construction engineering skills are in a good position to develop
and utilize coastal adaptation technologies. Targeted government support on
these firms to motivate their investment in coastal adaptation technologies
and to match them with regions with high risk of sea level rise would play an
important role in stimulating innovation in coastal adaptation.

Many indirect adaptation patents are co-classified as physics (see Fig. 4).
In-depth analysis with further technological details (see SI.2) shows that
this is due to technological interdependencies between indirect adaptation and
applied physics including measurement, detection, and prediction technologies.
Therefore, to stimulate innovation in indirect adaptation, governments can
incentivize firms with advanced skills in measurement, detection, and
prediction to invest in indirect adaptation technologies, as well as connect
these firms to regions where precise, timely sensoring and forecasting of
climate disaster are critical.

Fig. 4 also shows the extent to which different categories of adaptation
patents are labeled as mitigation patents, indicated by purple color. The next
section explores this duality in more detail.

Figure 4: Co-classification of adaptation technologies

Notes: These figures show the co-classification of adaptation technologies at
the CPC section level (1-digit). The numbers on top of each bar indicate the
number of patents granted in each sub-period. Note that the bar plots rely on
the number of co-classifications. Patents that serve multiple adaptation
purposes, i.e. are classified into multiple adaptation technology types, are
double-counted. The size of the colored fields in each bar plot indicates the
share of co-classifications for different subgroups by adaptation technology
type.

###  4.3 Complementarities with mitigation

We next focus on complementarities between adaptation and mitigation
technologies to inform technology-choices that help achieve climate change
mitigation and adaptation at the same time. We use two different approaches:
(1) analyzing patents that are co-classified as adaptation and mitigation
technologies to identify ‘dual purpose’ technologies, and (2) examining the
extent to which adaptation and mitigation technologies rely on similar
technological and scientific knowledge (i.e., cite the same patents and
papers). The knowledge base similarity of adaptation and mitigation
technologies helps understand how mutual knowledge spillovers between
adaptation and mitigation can be stimulated. For example, public support may
be directed towards the fields in which both adaptation and mitigation rely
on.

####  4.3.1 Adaptation technologies with mitigation co-benefit

Table 1: Overview statistics of dual purpose patents

|  Total patents |  Share dual purpose |  Citing/total patents |  Share gov. supported
---|---|---|---|---
_Dual purpose adaptation technologies_
Coastal |  71 |  0.08 |  0.11 |  0.30
Water |  926 |  0.20 |  0.25 |  0.32
Infrastructure |  3276 |  0.70 |  0.10 |  0.17
Agriculture |  1768 |  0.22 |  0.21 |  0.23
Health |  3668 |  0.22 |  0.19 |  0.20
Indirect |  39 |  0.01 |  0.54 |  0.69
Gov. support (No) |  6440 |  0.27 |  0.10 |
Gov. support (Yes) |  1657 |  0.17 |  0.41 |
All |  9645 |  0.26 |  0.23 |  0.20
_Dual purpose mitigation technologies_
Buildings |  3033 |  0.07 |  0.09 |  0.16
CCS |  429 |  0.08 |  0.44 |  0.46
Green ICT |  15 |  0.00 |  0.67 |  0.36
Energy |  920 |  0.01 |  0.24 |  0.31
Production |  1356 |  0.02 |  0.25 |  0.23
Transport |  2821 |  0.03 |  0.12 |  0.14
Waste |  1071 |  0.05 |  0.24 |  0.27
Gov. support (No) |  6440 |  0.02 |  0.10 |
Gov. support (Yes) |  1657 |  0.02 |  0.41 |
All |  9645 |  0.02 |  0.29 |  0.20

Notes: This table summarizes the subsets of dual purpose patents, i.e. patents
that are simultaneously classified as adaptation and mitigation technology.
The upper part of the table shows these patents from the angle of adaptation,
the lower part from the angle of mitigation technologies. Column Share dual
purpose shows the share of dual purpose patents in all patents. Column
Citing/total patents shows the ratio of patents that cite at least one
scientific paper over the number of all patents. Column Share gov. supported
shows the share of patents that benefited from governmental support. The rows
Gov. support: Yes and No show statistics for the subset of patents that do and
do not rely on public support, respectively. Sum of the number patents in each
sector is not exactly same with the number of all patents because a patent can
be classified into multiple sectors at the same time.

Starting off with co-classifications, we find that many adaptation patents
except for those in indirect adaptation include a significant proportion of
dual purpose patents helping in not only adaptation but also mitigation
(purple bars in Fig. 4). In total, 26% of adaptation patents are co-classified
as mitigation patents, showing that more than a quarter of adaptation
technologies have the potential to be used in both adaptation and mitigation
areas (Table 1). The highest overlap is in infrastructure adaptation where 70%
of the patents are co-classified as mitigation technologies. For example,
thermal insulation in buildings achieves both adaptation and mitigation
purposes: it preserves thermal comfort during extreme temperature events, but
it may also help reduce energy consumption and associated emissions. This is
an example of how maladaptation relying on the intensified use of air-
conditioning to cope with heatwaves can be avoided [10]. Other illustrative
examples are extreme weather resistant electricity grids that rely on
insulation technologies that help reduce energy losses during the transmission
through the grid, or integration of production and use of renewable energy
into buildings for heating and cooling purposes.

For health-, agriculture-, and water-related adaptation, roughly 20% of
patents simultaneously serve mitigation purposes (Table 1). Co-benefits in
health adaptation arise for example from clean transportation that reduce
emissions. This represents a preventive intervention improving public health
as air-pollution control helps prevent respiratory and cardivascular diseases.
Research has shown that these diseases increase the vulnerability to heatwaves
and some infectious diseases [36], including Covid-19 [25].

In agriculture, we find technologies that improve the climate resilience of
plants can simultaneously sequester carbon. Some technologies that contribute
to an improved handling of bio-related waste or energy efficiency of
greenhouses can simultaneously be used in cooling systems for food storage.

In addition, some adaptation technologies used for water treatment,
purification, and desalination also help reduce emissions in wastewater and
solid waste treatments. [10] mentioned energy-intensive desalination as an
example of emission-increasing maladaption. Our analysis shows that
mitigation-friendly alternatives exist, combining renewable energy with
desalination.

By contrast, the occurrence of dual purpose technologies is relatively weak in
coastal (8%) and indirect adaptation technologies (1%).

When examining the degree to which mitigation patents can be co-classified as
adaptation patents, we find that CCS, clean buildings, and waste management
related mitigation technologies include 8By the number of patents, clean
transportation, efficient production, and low-carbon energy patents have
significant co-classification with adaptation. However, due to the large
number of patents in these categories the share of co-classification is low,
ranging between 1-3%.

The fact that some adaptation technologies bear mitigation co-benefits does
not tell us much about the climate impact of the remainders beyond examples
mentioned in the literature on maladaptation. We cannot say —based on our
analysis— whether mitigation technologies that are not co-classified as
adaptation have a negative or positive impact on the economy’s climate
resilience. While not judging whether adaptation and mitigation are
complements in general, we show that some adaptation-mitigation options are
complementary. Complementarity may be a matter of technology choice and our
analysis identifies areas that are promising to achieve adaptation-mitigation
co-benefits.

Although our analysis shows the technological potential to achieve both
adaptation and mitigation goals, the absence of co-benefits does not
necessarily imply an inferior technology choice. Other factors such as
competing policy objectives, economic constraints, different time horizons,
and locality of events may constrain the set of available technology options.
For example, health-related adaptation technologies to cope with risks from
vector borne diseases are urgent in some developing countries although
disconnected from any mitigation technology. For nuclear energy, in some
countries, political objectives to achieve short-term mitigation may weigh
higher than the long-term resistance to climate change. At least in the short
term, adaptation co-benefits of nuclear energy are absent and there is good
reason to believe that these technologies rather undermine than strengthen the
vulnerability against extreme climate shocks [35, 47]. Nevertheless, short
term mitigation benefits of this technology are strong and —assuming a
positive mitigation impact— it also contributes to adaptation in the long run
if it helps reduce the impact of climate change.

####  4.3.2 Potential knowledge spillovers between mitigation and adaptation

Figure 5: Technological and scientific similarity among CCATs and CCMTs

(a) Technological similarity

(b) Scientific similarity

Notes: These figures illustrate technological similarities of different types
of adaptation and mitigation technologies that are complements, i.e.
simultaneously classified as adaptation and mitigation technologies. The
figures are based on shares of (a) citations to CPC 4-digit technology classes
and (b) citations to scientific fields (WoS). The upper two figures show
similarity networks. A link between a pair of technologies indicates the
cosine similarity of their references to scientific fields and technology
classes, respectively. For clarity only the most significant links are shown.
The widths of connecting edges are proportional to the degree of similarity
and the node sizes are proportional to the number of patents. The node colors
indicate the 4-digit technology class (i.e., red for adaptation, gray for
buildings, black for CCS, blue for green ICT, green for energy, orange for
production, yellow for transport, and brown for waste). The lower two figures
illustrate the numerical values of the cosine similarity of adaptation
(columns) and mitigation (rows) technologies at the 6-digit level. The letters
in the beginning indicate the type of mitigation technology (i.e., B for
buildings, C for GHG disposal, D for green ICT, E for energy, P for
production, T for transport, and W for waste).

We investigate the extent to which different mitigation and adaptation
technologies build on a common knowledge base. To identify domains of shared
knowledge, we analyze similarities of the technological and scientific
knowledge base for pairs of different adaptation and mitigation technologies.
Previous research has shown that similarities enable knowledge spillovers
across technologies at the organizational, regional, and national level, and
they are an indicator of absorptive capacity as it is easier for firms,
industries, and countries to adopt a new technology if the adopter has pre-
existing relevant knowledge [19, 17, 23]. This also matters for policy: if two
technologies build on the same knowledge sources, R&D policy may focus on
these areas to support the development of both technologies at the same time.

In Fig. 5, we illustrate knowledge similarities through network plots and
correlation charts. Similarities are measured via backward citation patterns:
two technologies are more similar if they rely more on common sources of
knowledge. This is measured by the cosine similarity based on shares of
citations to CPC 4-digit technology classes (Fig. 5(a)) and citations to
scientific Web of Science (WoS) fields (Fig. 5(b)).

The upper two figures show similarity networks. A link between a pair of
technologies indicates the cosine similarity of their references to scientific
fields and technology classes, respectively. For clarity, only the most
significant links are shown.888We use the median weight of connecting links as
significance threshold and show only those links whose weight is larger than
that. The widths of connecting edges are proportional to the degree of
similarity and the node sizes are proportional to the number of patents. The
node colors indicate the 4-digit technology class (i.e., red for adaptation,
gray for buildings, black for CCS, blue for green ICT, green for energy,
orange for production, yellow for transport, and brown for waste).

The lower two figures illustrate the numerical values of the cosine similarity
of adaptation (columns) and mitigation (rows) technologies at the 6-digit
level. The letters in the beginning indicate the type of mitigation technology
(B for buildings, C for GHG disposal, D for green ICT, E for energy, P for
production, T for transport, and W for waste). Our similarity analysis shows:

(1) Citing similar patents, mitigation technologies for energy efficiency in
buildings and green ICT have a similar technological knowledge base to
infrastructure-related adaptation technologies. Technologies that reduce
transmission losses and improve the energy efficiency of ICTs rely on similar
technological knowledge as technologies that strengthen the resilience of
physical infrastructure to extreme weather events. The same holds for
insulation, efficient heating, and renewable energy in buildings.

(2) Clean energy, especially clean combustion and bio-fuels, exhibits strong
scientific similarities with science-intensive adaptation technologies such as
agriculture, health, and indirect adaptation. This is particularly due to
their common reliance on chemistry (see Section 4.2 and for more detail Fig.
SI.3-SI.5, SI.8 in the Supplementary Material).

(3) Water-related adaptation technologies exhibit a high degree of scientific
similarity with clean industrial processing technologies for metal and oil,
with waste treatment, and CCS. This can be explained by their joint reliance
on chemistry. We also observe a high potential for scientific and
technological knowledge spillovers between water adaptation and clean energy
that mostly arise from interactions with non-fossil fuels and renewables. Our
data shows that examples of energy intensive water treatment technologies like
desalination explicitly make use of photovoltaics, which explains their
reliance on the same science (see A.1 and for more detail Fig. SI.4-SI.5 in
the Supplementary Material).

(4) We observed the rise in mitigation technologies for clean production that
have adaptation co-benefits (see orange node in Fig. 5 and SI.4-SI.8). Not
surprisingly, the spillover potential is highest in between adaptation and
mitigation in agricultural production. In addition, we observe a large
potential for technological knowledge spillovers between enabling technologies
in production and various fields of adaptation. This suggests that there is a
high potential to harness knowledge spillovers and to realign efforts to
mitigate emissions in production processes with adaptation goals.

This analysis suggests that many adaptation and mitigation technologies in
various domains share a common knowledge base. The reliance on similar
technological and scientific knowledge suggests that R&D investments in one
area have positive side effects on another. Economically, the existence of
positive knowledge spillovers is a justification for higher levels of public
support, as the social returns of these investments exceed those from
investments in technologies that show a lower spillover potential [6].

##  5 Discussion

Despite the urgency of climate change and the substantial long-term economic
benefits of adaptation [90], the study of innovation in adaptation has
attracted relatively little scholarly attention [74, 24] and markets for
adaptation technologies seem underdeveloped given their benefits [24].
However, this is likely to change: the requirement of countries to disclose
their adaptation plans under the Paris Agreement [56, 13], increasing
awareness of firms’ climate risks and efforts by regulators to make risk
disclosures mandatory will incentivize the public and private sector to take
action towards adaptation [31, 84]. This study offers the first systematic
analysis of adaptation technologies and their knowledge base addressing three
questions.

First, we asked: _To what extent have these technologies been developed, and
which were the drivers of innovation?_ We find that patenting in most
adaptation technologies did not increase substantially over the past decades,
with the exception of health-related and indirect adaptation. Historically, we
observed several phases of increased inventive activity, especially since the
late 1960s and during the Oil Crisis in the 1970s. The 1960s were the starting
date of many environmental initiatives including regulatory measures such as
the Clean Air Act from 1963 and Clean Water Act from 1972. As discussed above,
many adaptation technologies, especially in water and health, interacting with
pollution control bear a positive externality improving the environmental
quality. Despite not providing causal evidence, the rise in certain water and
health adaptation technologies we observe might be a byproduct of
environmental regulatory policy.

Similarly, our analysis revealed that many solutions for adaptation,
especially in infrastructure and agriculture, have the potential to
simultaneously improve energy efficiency. Many other technologies integrate
off-grid renewable energy into their processes, for example for desalination,
food processing and conservation, or cooling and heating in buildings. High
prices for fossil fuel energy during the Oil Crisis stimulated investments in
energy efficiency and the demand for alternative energy solutions, which
offers one explanation for the rise adaptation, again as a byproduct of the
seemingly unrelated energy price.

Second, we addressed the question: _How can governments support the
development and adoption of these technologies?_ Our analysis has further
revealed that public R&D support may be supportive for early-stage
technological development. This is particularly important for science-
intensive technologies, as private sector incentives to engage in basic
research with uncertain returns are limited. Agriculture, health, and indirect
adaptation technologies are highly science-intensive, while adaptation for
coastal defense, infrastructure, and water is rather engineering-based.

Analyzing the scientific base of adaptation, we have further discussed that
science-intensive CCATs rely more heavily on basic rather than applied
sciences. This gives insights into policies for transferring adaptation
technologies to regions in need. Local universities and public research
institutions equipped with relevant scientific knowledge base (e.g.,
biochemistry and molecular biology for science-intensive CCATs) can be key
actors in facilitating technology transfer, as they contribute to the regional
absorptive capacity for science-intensive technologies. An analysis of co-
classification patterns of adaptation patents helps identify organizations
with complementary technological capabilities, which can be used to develop
and exploit different adaptation technologies, for example firms with
construction skills for coastal adaptation. Above, we discussed directions in
which the government should provide targeted support for both supply and
demand of adaptation solutions to stimulate innovation in climate change
adaptation.

Finally, we wanted to find out: _How do technologies for adaptation interact
with climate change mitigation?_ From a technological perspective, climate
change mitigation and adaptation are complements: on average, 26% of
adaptation technologies also help in mitigation. In some sub-fields such as
infrastructure-adaptation, the complementarities are particularly large, with
70% of adaptation patents simultaneously contributing to climate change
mitigation. Well-designed policy may exploit and strengthen these
complementarities to ensure that climate change technologies serve the twin
goals of adaptation and mitigation.

Adaptation-mitigation co-benefits have been recognized in many adaptation case
studies [48, 82, 14]. Our analysis shows that this can be also seen
systematically in aggregate data. This enables a systematic understanding of
the drivers of innovation behind adaptation and shows many examples of how
adaptation and mitigation efforts can be aligned. We have also seen that
adaptation technology development often came as a byproduct of other economic
trends. Identifying complementarity with other larger technological
developments, for example in artificial intelligence and biotechnology, may
help to make R&D for adaptation more effective. Furthermore, systematic
analyses of technological overlaps of adaptation with response strategies to
major shocks such as Covid-19, the Ukraine war, or financial crises can also
mobilize additional financial resources to create a resilient economy.

##  6 Conclusion

In this paper, we have taken stock of the current technological frontier of
adaptation technologies. We have shown that —compared to mitigation—
innovation in the field of adaptation has not yet taken off. In the analysis,
we have identified and discussed major drivers of innovation in adaptation
such as responses to regulation and shocks in the market, but we also
highlighted a prominent role of the government stimulating the development of
these technologies.

Our analysis has further shown how governments can effectively stimulate the
development and adoption of technologies through targeted investments in
scientific and technological capacities, and we discussed how this can help
enable technology transfer to countries where adaptation needs are high.

Finally, we addressed the nexus between climate change mitigation and
adaptation and have shown that —from a technological perspective— adaptation
and mitigation efforts may be complementary. However, this may be a matter of
technology choice and our analysis may provide guidance on how these choices
can be made to achieve mitigation and adaptation objectives at the same time.

This study is limited to the technological frontier of adaptation as reflected
in patent data. Although the granted patents capture inventions that have high
(perceived) market value, patent data as a measure of innovation has well-
documented limitations [68] being biased towards the technological frontier
solutions and being silent about other aspects such as nature-based or
behavioral solutions. A promising avenue for future research is to develop
measures for these other solutions of adaptation that can be systematically
compared to the technologies analyzed in this paper. This would help
understand the multiple trade-offs and synergies among different solutions for
adaptation and their interaction with mitigation, which is highly relevant to
address the climate challenge in an efficient way.

##  7 Data availability

The research data including R-scripts used for the data compilation and
empirical analysis are publicly available under a CC-BY-4.0 license to ensure
the full reproducibility of the results and re-use [38]. The data can be
downloaded here: https://doi.org/10.4119/unibi/2958327.

##  8 Acknowledgments

The authors want to thank Sugandha Srivastav who significantly contributed to
an earlier version of this article, in both intellectual and practical ways.
Further gratitude is owed to Anton Pichler and François Lafond whose work on
an earlier project contributed significantly to the methodological basis of
this work. The authors also want to thank Matthias Endres, Peter Persoon,
Vilhelm Verendel and their colleagues from the Institute for New Economic
Thinking (INET), the Oxford Martin Pogramme on Technological and Economic
Change (OMPTEC), and Future of Work for helpful feedback. Gratitude is owed to
Elizabeth Champion for her proofreading assistance. K.H. acknowledges support
from OMPTEC and Citi. S.J. acknowledges support from Basic Science Research
Program through the National Research Foundation of Korea (NRF) funded by the
Ministry of Education (2020R1A6A3A03037237).

##  9 Author contributions

K.H. developed the research idea, study design, visualized the results, and
wrote the initial draft. K.H. and S.J. compiled and analyzed the data. S.J.
validated the results. All authors contextualized the results, reviewed, and
edited the manuscript.

##  10 Competing interests statement

The authors do not have any competing interests to declare.

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##  Appendix A.1 Y02A classes and definitions

This list is downloaded from:
https://worldwide.espacenet.com/classification?locale=en_EP#!/CPC=Y02A [April
2021].

See pages 1 of inputs_natCC/cpc-scheme-Y02A.pdf

See pages 2 of inputs_natCC/cpc-scheme-Y02A.pdf

##  Appendix A.2 Additional results

###  A.2.1 Summary statistics of adaptation and mitigation patents

In Table A.1, we summarize the adaptation patents (at the DOCDB family level)
granted over the full time horizon covered by our analysis. We have a
relatively low number of patents in coastal adaptation (857) and the largest
number of patents in health adaptation (16,363). We also report the number of
patents that make at least one citation to science, the share of patents that
cite to science, the number of scientific citations, average number of
citations made by patents, average number of citations made by citing patent
and the share of patents that are reliant on governmental support. Overall, we
find that patents relying on public support tend to be more science intensive
(i.e., exhibit a higher share of science reliant patents and make more
citations to science than others).

Table A.1: Overview of adaptation adaptation technologies

Technology |  Total patents |  Citing patents |  Citing/total patents |  Scientific citations |  Citations per patents (all) |  Citations per patents (citing) |  Share Gov. support
---|---|---|---|---|---|---|---
Coastal |  857 |  62 |  0.07 |  328 |  0.38 |  5.29 |  0.18
Water |  4678 |  720 |  0.15 |  14173 |  3.03 |  19.68 |  0.22
Infrastructure |  4671 |  421 |  0.09 |  3828 |  0.82 |  9.09 |  0.16
Agriculture |  8089 |  2482 |  0.31 |  149341 |  18.46 |  60.17 |  0.23
Health |  16363 |  9331 |  0.57 |  490310 |  29.96 |  52.55 |  0.39
Indirect |  2978 |  1749 |  0.59 |  72759 |  24.43 |  41.60 |  0.58
All |  37341 |  14681 |  0.30 |  730739 |  12.85 |  31.40 |  0.31
Gov. support (No) |  21706 |  5717 |  0.26 |  158897 |  7.32 |  27.79 |
Gov. support (Yes) |  9853 |  6911 |  0.70 |  480611 |  48.78 |  69.54 |

Notes: This table summarizes the characteristics of patents classified as
climate change adaptation technologies. The categories are distinguished at
the 6-digit CPC level. The column Citing patents shows the number of patents
that rely on science, i.e. make at least one citation to the scientific
literature. The row entry All corresponds to total numbers for columns total
patents, citing patents and scientific citations and to averages for the other
columns. We double-count patents that fall into multiple adaptation technology
categories, i.e. totals in row _All_ are smaller than the sum of totals by
technology type. The rows Gov. support: Yes and No show statistics for the
subset of patents that do and do not rely on public support, respectively. The
data on government support is only available for the period 1928-2017, i.e.
the patent counts do not sum up.

Table A.2 summarizes all Y02-tagged technologies in our data differentiating
between different types of mitigation and adaptation technologies. Fig. SI.1
shows a pie-chart illustrating the relative frequencies of different types of
mitigation and adaptation technologies at the aggregate and disaggregate
level.

Table A.2: Overview of adaptation and mitigation technologies

Technology |  Total patents |  Citing patents |  Citing/total patents |  Scientific citations |  Citations per patents (all) |  Citations per patents (citing) |  Share Gov. support
---|---|---|---|---|---|---|---
Adaptation |  37341 |  14681 |  0.39 |  672420 |  18.01 |  45.80 |  0.31
Buildings |  43371 |  6243 |  0.14 |  39477 |  0.91 |  6.32 |  0.16
CCS |  5111 |  2074 |  0.41 |  22261 |  4.36 |  10.73 |  0.34
Green ICT |  32735 |  8281 |  0.25 |  55787 |  1.70 |  6.74 |  0.11
Energy |  166061 |  40075 |  0.24 |  417128 |  2.51 |  10.41 |  0.32
Production |  83967 |  27315 |  0.33 |  310779 |  3.70 |  11.38 |  0.21
Transport |  109997 |  10457 |  0.10 |  70373 |  0.64 |  6.73 |  0.19
Waste |  22368 |  4361 |  0.19 |  33671 |  1.51 |  7.72 |  0.19
All |  445689 |  98084 |  0.26 |  1621896 |  4.17 |  13.23 |  0.23
Gov. support (No) |  272606 |  46150 |  0.17 |  378647 |  1.39 |  8.20 |
Gov. support (Yes) |  81245 |  36919 |  0.45 |  1005000 |  12.37 |  27.22 |

Notes: This table summarizes the characteristics of adaptation and mitigation
technologies at the aggregate 4-digit CPC level. The column Citing patents
shows the number of patents that rely on science, i.e. make at least one
citation to the scientific literature. The row entry All corresponds to total
numbers for columns total patents, citing patents and scientific citations and
to averages for the other columns. We double-count patents that fall into
multiple technology categories, i.e. totals in row _All_ are smaller than the
sum of totals by technology type. The rows Gov. support: Yes and No show
statistics for the subset of patents that do and do not rely on public
support, respectively.

###  A.2.2 The scientificness of adaptation and mitigation over time

Fig. A.1 (Fig. A.2) shows time series plots of counts of 6-digit adaptation
(4-digit adaptation and mitigation) patents (blue line) and counts of patents
that cite at least one scientific paper (orange line) at a logarithmic scale.

Among the adaptation technologies, adaptation in agriculture and health are
the oldest technologies with first patents being granted in the mid 19th
century. Indirect adaptation technologies emerged in the 1960s and exhibit a
strong reliance on science. While agriculture, health and indirect exhibit
exponential growth, the other three technologies rather stagnated for a long
time. Post-2005, the number of annually granted patents was increasing.

Clean energy technologies show historically a relatively high number of
patents, and also for adaptation, mitigation related to buildings, production
and transport, patenting began already during the nineteenth century. Green
ICT and CCS are the by far youngest technologies starting off in early to mid-
twentieth century. For all technologies, we observe an increasing reliance on
science starting off from the 1950s. The reliance on science has been
increasing for all technologies, though we observe a strong heterogeneity
across technology groups with adaptation, clean production, and CCS having the
highest share of patents that make at least once citation to a scientific
article (see also Table A.2).

Figure A.1: Adaptation patents and their science reliance

Notes: Total number of patents by technology type and number of these patents
that cite to science over time at a logarithmic scale.

Figure A.2: Adaptation and mitigation patents and their science reliance

Notes: Total number of patents by technology type and number of these patents
that cite to science over time at a logarithmic scale.

In Fig. A.3 we show an alternative measure of the scientificness of patents
given by the ratio of citations to science over the sum of citations to
patents and science. This figure confirms the pattern observed before with
adaptation showing the highest reliance on scientific rather than applied
knowledge, but also CCS, clean production, energy, waste and green ICT to
become increasingly scientific. However, as seen in Section 4.2, the there is
a high heterogeneity across subfields as technology. For example, the high
science intensity of adaptation is mainly driven by health technologies and
previous research has show that solar PV and biofuels are key drivers of the
scientificness of clean energy technologies [40].

Figure A.3: Science intensity of adaptation and mitigation

Notes: Science-intensity of different 4-digit adaptation and mitigation
patents measured by the share of citations to science in the number of total
citations (sum of citations to other patents and scientific articles).

Online Supplementary Material

_Knowledge for a warmer world: A patent analysis of climate change adaptation
technologies_

Kerstin Hötte, Su Jung Jee, Sugandha Srivastav

##  Online Supplementary Material SI.1 Relative frequencies of adaptation and
mitigation patents

Figure SI.1: Relative frequencies of different technologies

Notes: These pie charts illustrate the relative frequencies of different types
of mitigation and adaptation technologies. The figures at the left show pie
charts for all Y02-tagged technologies (with adaptation technologies (Y02A)
indicated in red color). The figures at the right show these numbers for the
subset of adaptation technologies.

###  SI.1.1 Additional information on the science base of adaptation

Table SI.1 gives an overview of the characteristics of the data on citation
links from patents to science in our data showing the number of patents that
cite science, the number of different papers being cited, the number of
citation links, the number of citation links with the highest reliability
score, the average confidence score, the percentage share of citations made by
the applicant and by the examiner, and the percentage of citations made on the
front page of the patent and in the text body. Interestingly, science
citations in science-intensive technologies (agriculture, health, indirect)
are most often made by the applicant (63.6-83.5%) while citation links in
technologies with low science reliance (coastal, water, infrastructure) are
mostly added by the patent examiner (24.4-35.7% of citations made by the
applicant).

Table SI.1: Overview statistics of the subset of science-reliant adaptation
patents and their citation links

Technology |  # patents |  # papers |  # citations |  # CS = 10 |  Avg CS |  % app |  % exm |  % text |  % front
---|---|---|---|---|---|---|---|---|---
Coastal |  62 |  225 |  328 |  284 |  9.69 |  35.67 |  64.33 |  15.55 |  81.10
Gov. support (No) |  31 |  70 |  115 |  88 |  9.37 |  25.22 |  74.78 |  17.39 |  81.74
Gov. support (Yes) |  28 |  154 |  204 |  187 |  9.85 |  40.69 |  59.31 |  12.75 |  82.35
Water |  720 |  3967 |  14173 |  13609 |  9.91 |  32.46 |  67.54 |  10.10 |  77.50
Gov. support (No) |  299 |  858 |  2346 |  1967 |  9.61 |  36.91 |  63.09 |  19.82 |  68.12
Gov. support (Yes) |  287 |  2405 |  8493 |  8344 |  9.97 |  29.59 |  70.41 |  9.22 |  85.46
Infrastructure |  421 |  1651 |  3828 |  3648 |  9.90 |  24.40 |  75.60 |  11.65 |  83.10
Gov. support (No) |  171 |  405 |  1108 |  1004 |  9.82 |  29.78 |  70.22 |  18.23 |  74.64
Gov. support (Yes) |  175 |  836 |  2014 |  1967 |  9.94 |  20.56 |  79.44 |  7.75 |  88.53
Agriculture |  2482 |  40962 |  149341 |  149039 |  9.99 |  83.46 |  16.39 |  66.52 |  23.30
Gov. support (No) |  1114 |  15873 |  51788 |  51596 |  9.99 |  91.21 |  8.79 |  80.91 |  13.16
Gov. support (Yes) |  991 |  27196 |  77459 |  77377 |  10.00 |  80.00 |  19.71 |  57.29 |  29.28
Health |  9331 |  238282 |  490310 |  489397 |  10.00 |  67.59 |  32.36 |  36.20 |  46.39
Gov. support (No) |  3638 |  62045 |  94135 |  93488 |  9.99 |  78.22 |  21.78 |  55.45 |  30.14
Gov. support (Yes) |  4495 |  170407 |  336138 |  335912 |  10.00 |  65.33 |  34.59 |  31.38 |  50.31
Indirect |  1749 |  44865 |  72759 |  72596 |  10.00 |  63.58 |  36.42 |  30.75 |  55.82
Gov. support (No) |  495 |  7666 |  9405 |  9333 |  9.98 |  82.74 |  17.26 |  64.01 |  26.87
Gov. support (Yes) |  974 |  34299 |  56303 |  56240 |  10.00 |  63.29 |  36.71 |  26.25 |  59.70
All |  14681 |  313244 |  730739 |  728573 |  9.99 |  69.51 |  30.43 |  41.21 |  43.42
Gov. support (No) |  5717 |  84017 |  158897 |  157476 |  9.98 |  81.73 |  18.26 |  63.44 |  25.32
Gov. support (Yes) |  6911 |  223825 |  480611 |  480027 |  10.00 |  66.63 |  33.28 |  34.46 |  48.82

Notes: Columns show the number of (1) unique patents, (2) unique papers, (3)
citation links, (4) citations with highest confidence score (CS = 10), (5) the
average confidence score, (6) the share of applicant and (7) examiner added
citations (remaining share is of unknown type), (8) share of citations made
exclusively in the text body or (9) front page of the patent document
(remaining shares account for citations made in both).

Table SI.2 summarizes the age characteristics of science-reliant patents and
papers being cited. The oldest science reliant patents were granted between
1935 (agriculture) and 1965 (coastal). The oldest scientific papers cited are
from the nineteenth century.

Table SI.2: Age characteristics of science-reliant adaptation patents and
cited papers

Technology |  Oldest patent |  Youngest patent |  Avg year patent |  Oldest paper |  Youngest paper |  Avg year paper |  Avg lag
---|---|---|---|---|---|---|---
Coastal |  1965 |  2020 |  2006.04 |  1856 |  2015 |  1993.93 |  12.11
Water |  1963 |  2020 |  2012.39 |  1867 |  2018 |  1998.38 |  14.01
Infrastructure |  1939 |  2020 |  2011.29 |  1931 |  2018 |  1999.21 |  12.07
Agriculture |  1935 |  2020 |  2009.84 |  1855 |  2018 |  1993.06 |  16.77
Health |  1948 |  2020 |  2010.02 |  1831 |  2019 |  1995.48 |  14.54
Indirect |  1949 |  2020 |  2010.57 |  1879 |  2019 |  1996.91 |  13.66
All |  1935 |  2020 |  2010.09 |  1831 |  2019 |  1995.20 |  14.88

Notes: This table summarizes information on the age of science-reliant patents
and cited papers. Columns show the (1) grant year of the oldest patent, (2)
grant year of the most recent patent, (3) average grant year of patents, (4)
publication year of the oldest cited paper, (5) publication year of the most
recent cited paper, (6) average publication year of papers and (7) average
citation lag.

Table SI.3 the most frequently cited scientific fields by adaptation
technology. Biochemistry and molecular biology turn out to be the by far most
often cited field of research.

Table SI.3: Most frequently cited WoS fields

Technology |  WoS field |  # citations (by tech) |  # citations (total)
---|---|---|---
Coastal |  Environmental Sciences |  39 |  3,660
Water |  Eng., Chem. |  2,777 |  4,368
Infrastructure |  Energy & Fuels |  474 |  3,000
Agriculture |  Biochem. & Molec. Biology |  68,260 |  231,750
Health |  Biochem. & Molec. Biology |  144,666 |  231,750
Indirect |  Biochem. & Molec. Biology |  18,447 |  231,750
All |  Biochem. & Molec. Biology |  231,750 |  231,750

Notes: This table shows the number of citations from patents to different
scientific fields. The third and fourth columns show the number of citations
that the WoS field has received by patents in each adaptation technology field
and by patent from all adaptation technology fields, respectively.

Table SI.4 shows the most important journals cited by adaptation patents.
Table SI.6 shows the most science-reliant patent, i.e. the patent making most
scientific citations and table SI.5 shows the papers that have been most
important for different types of adaptation technologies.

Table SI.4: Most frequently cited journals

Technology |  Journal |  # citations (by tech) |  # citations (total)
---|---|---|---
Coastal |  Electrochimica Acta |  10 |  10
Water |  Desalination |  1,087 |  1,087
Infrastructure |  Science |  113 |  113
Agriculture |  PNAS |  10,483 |  10,483
Health |  PNAS |  22,732 |  22,732
Indirect |  PNAS |  2,967 |  2,967
All |  PNAS |  36,267 |  36,267

Notes: This table shows the journals that are cited most frequently. The third
and fourth columns show the number of citations that the journal has received
by patents in each adaptation technology field and in all adaptation
technology fields, respectively. PNAS is the abbreviation of Proceedings of
the National Academy of Sciences of the United States of America.

Table SI.5: Most frequently cited papers

Technology |  Paper title |  Year |  WoS field |  # cit. (techn) |  # cit. (total)
---|---|---|---|---|---
Coastal |  Characterization Of Dredged River Sediments In 10 Upland Disposal Sites Of Alabama |  1995 |  Construction & Building Tech. |  5 |  5
Water |  The Philips Stirling Engine |  1991 |  Thermodynamics |  66 |  66
Infrastructure |  Conventional Wallboard With Latent Heat Storage For Passive Solar Applications |  1990 |  Thermodynamics |  23 |  23
Agriculture |  Identification Of Dna Sequences Required For Activity Of The Cauliflower Mosaic Virus 35s Promoter |  1985 |  Plant Sciences |  484 |  505
Health |  Antibodies A Laboratory Manual |  1988 |  Computer Sci., Artif. Intell. |  547 |  813
Indirect |  Antibodies A Laboratory Manual |  1988 |  Computer Sci., Artif. Intell. |  61 |  813
All |  Basic Local Alignment Search Tool |  1990 |  Biochem. & Molec. Biology |  908 |  908

Notes: This table shows the papers that are most frequently cited by
adaptation patents. Year indicates the publication year of the paper and WoS
field indicates the Web-of-Science field of research by which the paper is
categorized. The fifth and sixth columns show the number of citations that the
paper has received by patents in each adaptation technology field and in all
adaptation technology fields, respectively.

Table SI.6: Patents with highest number of citations to papers

Technology |  Number |  Title |  CPC code |  Year |  # cit.
---|---|---|---|---|---
Coastal |  27361969 |  Method For Dewatering Flocculated Materials |  Y02A10/00 |  1999 |  35
Water |  39942683 |  Water Vapor Distillation Apparatus, Method And System |  Y02A20/00 |  2011 |  2,486
Infrastructure |  35432262 |  Insulation Containing Inorganic Fiber And Spherical Additives |  Y02A30/00 |  2012 |  286
Agriculture |  40456023 |  Tomato Line Fir 128-1018 |  Y02A40/10 |  2010 |  740
Health |  10642047 |  Plasmodium Falciparum Thrombospondin-Related Anonymous Proteins (Trap), Fragments And Functional Derivatives |  Y02A50/30 |  1998 |  1,614
Indirect |  23131391 |  Methods Of Determining Polypeptide Structure And Function |  Y02A90/10 |  2007 |  3,930
All |  44196417 |  Genetic Markers Associated With Drought Tolerance In Maize |  Y02A40/132 |  2014 |  3,930

Notes: Columns show (1) patent family number (DOCDB), (2) patent title, (3)
full CPC code(s), (4) grant year of the patent and (5) the number of citations
made by the patent.

###  SI.1.2 Co-classification of adaptation technologies

Fig. SI.2 shows the relative frequencies that adaptation technologies are co-
classified with other CPC classes at the 4-digit level.

Figure SI.2: Co-classification of adaptation technologies

Notes: These figures show which technology subgroups are most often co-
classified with adaptation technologies. Technology subgroups are identified
by the 4-digit CPC code. The numbers on top of each bar indicate the number of
patents granted in each sub-period. Note that the bar plots rely on the number
of co-classifications where patents that belong to multiple classes are
double-counted. The size of the colored fields in each bar plot indicates the
share of co-classifications for different subgroups by adaptation technology
type. Co-classifications made for technical reasons (e.g. to ensure the
compatibility with other classification systems) are excluded (see 3.3). Black
color is used for the residuum of groups that do not belong to the 8 most
often co-classified technology groups. The letters in the beginning of the
verbal description of each technology group indicate the broad CPC section
with A for Human Necessities; B for Performing Operations; C for Chemistry; D
for Textiles; E for Fixed Constructions; F for Metallic Engineering; G for
Physics and Y as general tagging scheme for cross-sectional technologies
(including Y02-tagged green technologies).

###  SI.1.3 Dual purpose technologies for mitigation and adaptation

####  SI.1.3.1 Technological and scientific similarities

Technological and scientific knowledge spillovers are likely to occur if
knowledge bases of two technologies are sufficiently similar such that the
knowledge is mutually useful. Fig. SI.3 and SI.4 show the scientific and
technological similarities among adaptation technologies and among adaptation
and mitigation technologies at the 6-digit CPC level for the subperiods
1976-2005 and 2006-2020. The rows with mitigation technologies are ordered by
their cumulative cosine similarity with adaptation technologies, i.e. by the
row sum showing those technologies with the highest overlap with adaptation
first. An extract with the fifteen highest scoring technologies is shown in
Sec. 4.3 in the main text.

Figure SI.3: Scientific & technological similarities of adaptation &
mitigation in 1976-2005

(a) By patent citations (CPC 4-digit)

(b) By science citations (WoS fields)

Notes: These figures illustrate technological and scientific similarities
among adaptation technologies and among adaptation and mitigation technologies
at the 6-digit CPC level. We use a cosine similarity-based methodology as
explained in [40]. Mitigation technologies are ranked by the row-sum in a
decreasing order, i.e. technologies with a higher the spillover potential
across all types of adaptation technologies rank higher. Column-wise reading
illustrates for which types of adaptation technology the highest spillover
potential is found. The letters preceding the name of the technology type
indicate the type of 6-digit mitigation or adaptation technology (i.e., A for
adaptation, B for buildings, C for capture and storage of greenhouse gases, D
for green ICT, E for energy, P for production, T for transport, and W for
waste).

Figure SI.4: Scientific & technological similarities of adaptation &
mitigation in 2006-2020

(a) By patent citations (CPC 4-digit)

(b) By science citations (WoS fields)

Notes: These figures illustrate technological and scientific similarities
among adaptation technologies and among adaptation and mitigation technologies
at the 6-digit CPC level. We use a cosine similarity-based methodology as
explained in [40]. Mitigation technologies are ranked by the row-sum in a
decreasing order, i.e. technologies with a higher the spillover potential
across all types of adaptation technologies rank higher. Column-wise reading
illustrates for which types of adaptation technology the highest spillover
potential is found. The letters preceding the name of the technology type
indicate the type of 6-digit mitigation or adaptation technology (i.e., A for
adaptation, B for buildings, C for capture and storage of greenhouse gases, D
for green ICT, E for energy, P for production, T for transport and W for
waste).

Technological similarity by CPC4 citations:

Scientific similarity by WoS field citations:

Figure SI.5: Cosine similarity networks of mitigation-adaptation complements

Notes: These networks illustrate technological and scientific similarities of
different types of adaptation and mitigation technologies. The networks are
based on the shares of (a) citations to CPC 4-digit technology classes and (b)
citations to scientific fields (WoS). A link between a pair of technologies
indicates the cosine similarity of their references to scientific fields
(scientific similarity) and technology classes (technological similarity). For
clarity only the most significant links are shown. The widths of connecting
edges are proportional to the degree of similarity and the node sizes are
proportional to the number of patents. The node colors indicate the 4-digit
technology class (red for adaptation, gray for buildings, black for CCS, blue
for green ICT, green for energy, orange for production, yellow for transport,
and brown for waste).

Fig. SI.5 illustrates scientific and technological similarities between
mitigation and adaptation technologies at the disaggregate level (6-digit CPC)
using the subset of adaptation patents with mitigation co-benefit. Fig. SI.6
illustrates technological and scientific similarities of mitigation and
adaptation technologies using the full sample of mitigation and adaptation
technologies.

Technological similarity by CPC4 citations:

Scientific similarity by WoS field citations:

Figure SI.6: Cosine similarity networks of mitigation and adaptation
technologies

Notes: The networks illustrate the technological and scientific similarity of
different adaptation and mitigation technologies computed on the basis of (a)
citations to CPC 4-digit technology classes and (b) citations to scientific
fields (WoS). A link between a pair of technologies indicates the cosine
similarity of their references to scientific fields (scientific similarity)
and technology classes (technological similarity). To simplify the
representation, only the most significant links are shown. The widths of
connecting edges are proportional to the level of similarity and the node
sizes are proportional to the number of patents. The node colors indicate the
4-digit technology class (red for adaptation, gray for buildings, black for
CCS, blue for green ICT, green for energy, orange for production, yellow for
transport and brown for waste).

####  SI.1.3.2 Science base of dual purpose technologies

Figure SI.7: Scientific base of adaptation technologies with mitigation co-
benefit

Notes: This figure shows the 8 scientific fields (Web-of-Science categories)
that are most frequently cited by patents for adaptation that have a co-
benefit for climate change mitigation.

Figure SI.8: Scientific base of mitigation technologies with adaptation co-
benefit

Notes: This figure shows the 8 scientific fields (Web-of-Science categories)
that are most frequently cited by patents for mitigation that have a co-
benefit for climate change adaptation.

####  SI.1.3.3 Co-classification of dual purpose patents

Figure SI.9: Co-classification of adaptation technologies with mitigation co-
benefit

Notes: This figure shows the 8 most frequent co-classifications (3-digit CPC
classes) of adaptation technologies that are simultaneously classified as
mitigation technology.

Figure SI.10: Co-classification of dual-purpose adaptation and mitigation
technologies

Notes: This figure shows the 8 most frequent co-classifications (3-digit CPC
classes) of dual-purpose adaptation and mitigation patents, i.e. patents that
are simultaneously classified as adaptation and mitigation technology.