This introductory chapter sets out the rationale for this publication’s in-depth look at measuring the contribution of science and innovation to sustainable growth. It outlines the main mechanisms through which science and innovation may contribute to sustainable transitions and provides the basis for the overall structure of the publication. It makes the case that measuring science and innovation’s contribution to sustainable growth plays a vital role in informing policy and the broader public debate. The chapter also lays out the main challenges for tracing the impact of innovation on sustainable growth. Finally, it describes the approach followed in the remainder of this publication to develop indicators and address gaps.
Measuring Science and Innovation for Sustainable Growth
Abstract
The world today faces several headwinds against preserving and expanding economic opportunities for current and future generations. These include natural resource and environmental pressures that, if left unaddressed, undermine not only society’s ability to seize new opportunities but also risk compromising well-being standards. Governments have taken steps to implement energy, climate and environmental policies, working to achieve a mutually agreed goals to improve natural resource and environmental sustainability. However, rising geopolitical tensions, supply chain disruptions, inflationary pressures, and a growing emphasis on national security have contributing towards reshaping policy priorities. In this context of complex interdependencies across policy objectives, governments face multiple imperatives and difficult choices in pursuing sustainable economic growth (Box 1.1).
Paraphrasing the definition of sustainable development originally introduced by the Brundtland Commission (WCED, 1987[1]), sustainable economic growth refers to economic growth that meets the needs of the present without compromising the ability of future generations to meet their own needs. This concept of sustainability highlights the key role of natural assets in shaping the planet’s potential to sustain economic growth, i.e. the increase in living standards as measured by the value of goods and services produced in an economy.
Similarly, the Green Growth Strategy and framework adopted by the OECD recognised natural capital as a key factor of production and its central role in preserving and enhancing the well-being of current and future generations (OECD, 2011[2]).
In this challenging context, leveraging the full potential of science and innovation becomes critical, and so does the evidence base upon which such choices are based. Indeed, scientific and technological developments have in the past helped humankind address seemingly intractable resource constraints that imposed subsistence-level livelihoods.
The example provided by the demonstrable success of the Montreal Protocol in addressing ozone layer depletion illustrates the multiple channels through which science and innovation can underpin solutions to complex and global environmental problems. These provide a guide to measurement approach in this document. Scientific understanding of the thinning of the ozone layer, its causes and impact on human health helped not only raise awareness of the issues but also galvanised public opinion and helped forge an international consensus on the case for action. However, science-based awareness was a necessary but not sufficient condition for transformation. Action was also enabled by the rapid technology-based innovation in developing and implementing alternatives to ozone-depleting substances, which allowed for the timely substitution of the most damaging pollutants in the marketplace (Whitesides, 2020[3]; Gonzalez, Taddonio and Sherman, 2015[4]).
This experience, while not necessarily representative of how other pressing natural resource and environmental challenges can be addressed, highlights the combined potential role of science, technology and innovation (or “science and innovation” or STI hereafter for brevity) in enabling sustainable growth, spelling out the roles of scientific knowledge, technology development, market adoption and diffusion and society readiness and policies. These functions effectively articulate the structure of this publication.
Reflecting this potential and the systemic features of science and innovation, the OECD Agenda for Transformative Science, Technology and Innovation Policies was endorsed in April 2024 by science and innovation ministers from OECD and several other countries. This document argues that sustainable economic growth calls for substantive and multifaceted transformations that call upon the comprehensive mobilisation of the science and innovation system (OECD, 2024[5]).
The road ahead towards achieving sustainable growth objectives remains long as the adoption of the currently best available technologies and practices, while necessary, is far from sufficient. For example, the International Energy Agency (IEA) estimated that in order to limit the global temperature rise to 1.5 °C with at least a 50% probability without sacrificing economic growth, 35% of the required greenhouse gas reductions in 2050 would need to come from technologies that are not yet on the market (IEA, 2021[6]). Technologies that have yet to prove their potential and reach the market are particularly critical for certain sectors where pollution is currently difficult to abate, such as steel, cement, or maritime transport (OECD, 2025[7]).
There are justifiable concerns that the uptake of “net zero” and other sustainability-related commitments and the implementation of increasingly stringent policies to achieve them may, without complementary policies, introduce excessive costs on businesses (Stock, 2022[8]) and lead to a loss of economic activity in those jurisdictions that take a lead, for instance through the so-called “carbon leakage” in the context of climate change regulation (Dechezleprêtre et al., 2022[9]; Fowlie and Reguant, 2018[10]). Technological advances are therefore key to mitigating the risk of negative economic and social consequences that arise from policies, such as carbon pricing mechanisms, that impose a direct cost on certain sectors and communities with limited capability to adjust or be compensated. For instance, strategic investment in research and development (R&D) can reduce the carbon price that is needed to achieve a given level of emission reductions, thus sheltering industries and their induced employment from possible detrimental consequences of high carbon prices (Acemoglu et al., 2012[11]). By the same token, innovations that make low-carbon technologies more affordable can help mitigate passed-through price impacts of environmental policies. Without innovation, “net zero” targets can only be achieved at a much higher cost (Creutzig et al., 2023[12]).
Research breakthroughs underpin much of the progress towards technological discoveries and their eventual adoption, ultimately enabling the expansion of economic opportunities for production and consumption while reducing environmental harm. This encompasses not only applied research, which as defined in the OECD Frascati Manual is “original investigation undertaken in order to acquire new knowledge, directed primarily towards specific practical aims or objectives”, such as better environmental performance and cost-effectiveness; but also, basic research, which is “experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundation of phenomena and observable facts” (OECD, 2015[13]). Curiosity-driven research is a major factor of serendipity in scientific breakthroughs that open pathways to solutions previously deemed unworkable.
As illustrated in the previous example about the ozone layer, science is also critical for generating reliable knowledge of the natural environment with its complex array of feedback loops and tipping points. It is an essential prerequisite for understanding the rate and scale of environmental change, including the factors driving it. Last but not least, science across all domains can play an additional key role in informing public policy and private behaviour by enabling an enhanced and trustworthy understanding of change and performance of environmental, technological and socio-economic systems.1
Measuring science and innovation for sustainable growth matters
It is important to develop an accurate and realistic appreciation of uncertainty about the potential and limitations of science and innovation to affect desired changes.2 The outcomes of different research and innovation activities result in a wide range of possible short- and longer-term environmental and economic outcomes. Exploring questions such as the role of policy and its impacts requires careful study of how innovation systems work. If science and innovation policies are going to have the intended effect of enabling desired energy and environmental transitions, they need to be supported by effective measurement and analysis of a qualitative and quantitative nature. Timely, reliable and comparable data and information on the role of STI systems in this area, and its policies, inform steps to support desired transitions and shed light on progress and outcomes, including impacts on people and communities.
There is a strong global measurement culture underpinning environmental monitoring and policy. The OECD provides foundational data and metrics to assess country progress towards several environmental goals and distil the main implications for economic collaboration and economic development policies (OECD, 2012[14]; OECD, forthcoming[15]). Likewise, the IEA monitors the global energy system, providing a comprehensive view of its past evolution and future outlook (IEA, 2024[16]). The OECD Council Recommendation on Environmental Information and Reporting (OECD, 2022[17]) recommends that “adherents take a comprehensive approach to improving environmental information and reporting, as well as information systems and measurement frameworks.” It calls for improvements in “scientific knowledge, information, statistics, accounts and indicators on the environment and sustainable development, in order to contribute to the evaluation of: a) the state of the environment; b) activities that affect or are likely to affect the environment; c) policies, plans, actions and programmes that affect or are likely to affect the environment; d) environment policies themselves.” This mandate not only explicitly calls out the role of scientific knowledge and statistics but also provides the basis for considering, from a measurement perspective, science and innovation as human activities that affect the environment.
The challenge of measuring science and innovation for sustainable growth
Evidence gaps and uncertainties about the ability of economic and social systems to support sustainable growth call for specific measurement and scientific research efforts in this area. Within the objective of improving understanding of activities that affect or are likely to affect natural resources and the environment, understanding the role of science and innovation systems is potentially one of the most challenging, but by the same token, one for which there is significant potential for improvement and positive impact for decision makers. Innovation and environmental performance can be particularly challenging and “evasive” objects of measurement.
Science and innovation possess unique features as a domain for policy analysis owing to the intangible nature of knowledge and how it is created and diffused, which leaves few unambiguous and reliably measurable traces (OECD, 2018[18]). The outcomes of innovation efforts can be highly uncertain and valuable to those concerned. This makes it difficult not only to anticipate what comes out of them but can also create incentives to conceal or exaggerate claims, depending on what is at stake. Knowledge about the wide range of different STI activities can be a source of strategic comparative advantage for individuals and organisations that compete for resources, market share and hegemony.
Furthermore, the empirical study of innovation and innovation policy faces the challenge of seeking to measure how activities that are themselves difficult to measure affect other outcomes that are also difficult to measure. This applies when attempting to identify and demonstrate the link between STI activities and potential or actual environmental sustainability outcomes that can also be uncertain or far in the future.
Characterising environmental sustainability is an equally challenging empirical question when it comes to economic activities in general and science and innovation in particular. As environmental sustainability is broadly perceived as positive, and several policies promote it actively, there are incentives for overstating “green” claims, i.e. what is commonly known as “greenwashing” although in recent years there is increasing evidence of the opposite behaviour, also referred to as “greenhushing”. This can distort measurement of the relevance and impact of all economic activities towards environmental sustainability objectives (ESOs), including science and innovation (Box 1.2).
Goodhart’s Law states that “when a measure becomes a target, it ceases to be a good measure”. This maxim captures the predicament that once a metric is considered an indicator of success or effective compliance, there is an incentive to manipulate it. When formulated with respect to environmental credentials of products and services, this phenomenon is also often known as “greenwashing” (Gatti, Seele and Rademacher, 2019[19]; OECD, 2025[20]). More recently evidence of “greenhushing” - a practice which refers to organisations deliberatively under-communicating or remaining silent about their environmental efforts - has also started to emerge (Ginder, Kwon and Byun, 2019[21]).
Greenwashing and greenhushing can affect surveys as respondents may seek to show they are behaving in a manner they interpret as desirable. While confidentiality and other survey protocol adjustments can be implemented counter bias of all types (Bloom and Van Reenen, 2007[22]; Yong et al., 2024[23]), these may not be fully effective in all contexts. Behaviours such as “satisficing”, where the respondents select the first acceptable answer or drop out of surveys to reduce further burdens, may be further sources of measurement error, though not necessarily only affecting sustainability-relevant science and innovation indicators. Greenwashing, greenhushing and other types of bias might also distort indicators based on data other than surveys, such as indicators based on corporate disclosures and descriptions of R&D projects and firms or data reported for administrative purposes. Information is revealed in those instances to meet compliance or strategic aims and may not necessarily be accurate.
Building awareness and developing new techniques to attenuate possible biases in suitable datasets (e.g. text-rich opportunity data) offers a potential avenue for developing a new generation of more robust indicators. Policymakers need to work with indicator experts to put in place robust, hard-to-cheat systems, approaching the challenge strategically. As the stakes (economic, reputational, environmental) keep growing, the larger the risk that biased information will displace quality data and analysis. A perspective of “policies for good data” should try to ensure, when monitoring conditions are imperfect, that reporting and measurement are linked to small-stakes outcomes that induce truthful reporting; otherwise, they are likely to be the object of major distortions.
Source: Authors’ own elaboration.
Figure 1.1 sums up the measurement challenge by spelling out the intrinsic challenges of innovation measurement, the uncertain and multidimensional nature of environmental sustainability indicators and the specific challenges of inferring relevance and impacts from limited available data sources. Broadly speaking, there are two main approaches for establishing connections in the data between STI and environmental sustainability.
A direct approach is to rely on what data providers themselves report to be the case against specific questions and reporting requirements on green outcomes, which might map on to ESO or related “green” taxonomies. The data providers may be the actors themselves or third parties accrediting some particular aspect, such as a patent examiner confirming the domain of a patent application and its fulfilment of patentability requirements.
A derived approach is to draw inferences from the reported information, e.g. when the party responsible for measurement deploys judgements and tools aimed at characterising the STI activity on which it has information. This might be the case of an analyst using artificial intelligence (AI) tools to infer patterns about the relevance of an R&D project or a public procurement contract description to some environmental outcome. It may also apply when a statistician connects data on the environmental performance of the actor against what it reports in terms of STI activity.
Source: Authors’ own elaboration.
Measurement of relevance, e.g. through application and goal-oriented perspectives, can be biased against upstream knowledge activities for which the link may not be explicit or deterministic. Such a goal-oriented approach may, for example, contribute to understating the role of basic science and its contribution to sustainable growth since the information and statistical categories that describe activities upstream of STI are formulated in terms that are distant from application goals. A comprehensive study on science and innovation for sustainable growth needs to account for these indirect but foundational contributions.
Conversely, many indicators on STI, especially those relating to policy levers, tend to focus on available indicators on support for R&D and, as a result, miss out on support provided for demonstration and deployment activities closer to the market and final users, so there can also be biases against capturing several downstream activities.
Against this backdrop, the evidence required to inform policy for sustainable growth cannot rely solely on descriptive measurement. In order to empirically assess impacts, it also has to incorporate modelling and counterfactual analysis, which requires an effective data cycle in which ex ante and ex post evaluation build on each other and draw upon reliable measurements.
In the green transition challenge, policymakers’ main motivation for evidence is to be able to assess options lying ahead to drive consensus on the course of action and have the relevant means to reassess policies. To achieve this, effective governance also requires measurement for accountability to attribute merit and rewards. The options that policymakers need to consider entail complex trade-offs and a multiplicity of actors, so tools are required to bring disparate data sources together.
One key challenge for building evidence in this area is the limited capacity to bring together data on innovation inputs with data about material flows that matter for energy and sustainability transitions. While progressively advancing at a general level, the statistical data-linking agenda has not been moving as fast as required by the severity of the policy challenge and needs to go beyond the domain of economic statistics. This requires more effective policy co-ordination and regulation to ensure secure spaces in which data that may be deemed confidential can be safely processed and analysed to its full potential.
Use and connect existing definitions and taxonomies
Working at the measurement interface between science and innovation and sustainable growth requires consistent, clear and unambiguous definitions and classifications. This helps build a reference framework and build towards developing and maintaining consistent and internationally comparable measurements over time to inform decision making. In this “Measuring” publication initiative, concepts and taxonomies relating to resource and environmental impact, as well as those relating to science and innovation, must be brought together.
A structure based on the depiction of the science and innovation system
This publication depicts a variety of indicators under a comprehensive narrative of potential impact and relevance pathways for science and innovation, as outlined in this introduction in its depiction of the science and innovation system. This covers impact pathways for knowledge generation and activities concerning its practical application, such as the adoption of innovation. It also covers specific indicators on the roles played by government policies, markets and society. These different elements provide the structure for the publication.
A broad-based view of resource and environmental sustainability and its taxonomies
This publication adopts a flexible view and gathers detailed information about the definitions used. It classifies relevant data and indicators according to the definitions and taxonomies3 they rely on, both along the research and innovation dimension and along the environmental dimension. It does not limit the scope of the enquiry only to specific environmental issues (e.g. climate change only).
There are relatively few instances of environmental taxonomies embedded into STI measurement. For example, in the area of measuring business innovation, international standardisation efforts that began in the early 1990s efforts have not yet converged into an internationally agreed definition and classification of energy or environment-related innovation. Some measurement experiences, such as those initially promoted by the European Community Innovation Survey, provide an initial basis for international comparisons.
Critical use of taxonomies for classification purposes
Existing standard classifications used to report the broad socio-economic objectives of government support for R&D help to identify funding for environmental and energy objectives. However, they do not provide a means to assess whether, for example, energy-focused support for R&D or block grant support for R&D in universities and major research institutes contribute to environmental sustainability.
This publication makes extensive use of methods developed through parallel, experimental work to monitor the relevance of science and R&D efforts towards the United Nations Sustainable Development Goals (SDGs) across some of its new experimental measurement work, an initiative first put in motion following the OECD Blue Sky Conference (OECD, 2018[18]). The use of classification frameworks for societal goals such as SDGs or the NABS (Nomenclature for the analysis and comparison of scientific programmes and budgets) in classifying socio-economic objectives highlights that relevance to an objective is not mutually exclusive of others. However, its operationalisation for quantification purposes that allow adding up to meaningful totals requires the use of apportioning or prioritisation procedures for the units of analysis classified. This work reveals the limitations and trade-offs of classification frameworks and their practical application to different purposes.
Data sources and methods for assessing relevance
Build on existing “designed” and “opportunity” data
A significant part of this work is based on a diverse body of existing indicators, rarely presented under a common structure. The publication assesses their strengths and limitations in terms of providing robust and unbiased measurement of a particular element of the STI system with respect to the green transition. Adjustments to these well-established indicators have been made wherever possible to maximise their relevance.
Measurement of science and innovation can be based on a diverse set of sources and draw upon multiple methods (Figure 1.2). Statistical surveys, especially those designed with the explicit purpose of providing representative and comparable statistics, are a primary source of information at the national level and a keystone of international benchmarking efforts. Surveys can allow respondents to self-declare green attributes of their STI activities and outputs within the space and other limitations of the survey vehicle and the respondent’s knowledge and incentives.
While surveys are well suited to answering certain types of questions and can elicit valuable information from a wide range of important stakeholders, including firms and households, their use to inform analysis of “STI” for sustainable growth is still limited and faces significant challenges that limit the ability to introduce new, more targeted, questions. In a complementary fashion, this publication also exploits opportunities provided by additional information sources generated for purposes other than producing statistics, e.g. for administrative or commercial purposes. Some of those are relatively well tested and understood, such as patent data, while others are still in the early stages of development, such as databases on R&D project awards, company financial reports and others that are being increasingly exploited to deliver policy-relevant insights. The publication thus dedicates considerable effort to laying out what available administrative sources may be best suited for and where the main limitations lie.
Source: Authors’ own elaboration.
Experiment in assessing environmental sustainability in relevance in STI data
Many internationally comparable indicators and measures already exist and reveal policy-relevant information regarding the inputs or outputs at various points of the science and innovation chain, as well as regarding eventual outcomes. The aim of the publication is, however, also to go beyond these existing indicators and develop new ones that are thus far not available in OECD or other broad intergovernmental fora. To that end, the publication exploits relatively new data sources and methods that may offer promising avenues for gathering additional insights to fill known measurement gaps.
Examples of measurement experiments based on the application of advanced methods include the use of:
Natural Language Programming (NLP) AI methods for identification and classification of text descriptions of STI activities
combined use of survey and administrative data
data linking of disparate data sources to connect STI inputs and outputs.
Showcase experiences demonstrating potential
Where appropriate, when international indicators are not readily available and not possible to derive from opportunity data, the publication will rely on single-country deep dive analyses and case studies, both quantitative and, where appropriate, qualitative. Showcasing novel approaches is vital to enriching understanding of the multitude of ways in which STI underpin the green transition.
Identify gaps and recommend possible future steps
This publication aims, above all, to promote action towards improving understanding how science and innovation contribute to sustainable growth. Available indicators provide only a partial picture that barely scratches the complexity of issues and uncertainties at stake. This publication’s final and concluding chapter sets out some main takeaways and proposals for a measurement agenda in this vital area.
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← 1. Examples include the influential Stern review on the economics of climate change (Stern, 2008[25]), which provided economic policy-oriented insights on the implications of alternative courses of action in response to the climate challenge. This study highlighted not only the important of incorporating natural science into economic analysis for effective measurement of costs of environmental change, but also social science and humanistic debates embedded in the economic analysis of factors such as risk, uncertainty and discounting. Science provides tools for understanding the relationships of mutual interdependence across actors in the economy and society and how these can shape incremental change and transformations.
← 2. The historically proven transformational potential of science and innovation has been taken by the most “techno-optimists” as conclusive evidence that scientific and technical progress can be entirely relied upon to offset the loss of natural capital that results from economic development and growth. Those on the more pessimistic end argue that, despite the emergence of short-term solutions, sustained growth is ultimately an unattainable goal, pointing to the fact that the economy, where all factors of production are made of materials and use energy, is a subsystem of the larger finite and nongrowing ecosystem that is the Earth and the Solar System. Indeed, these processes are governed by the laws of thermodynamics, which ensure that all resources are turned back into wastes, in a more “entropic”, or disordered (and therefore often polluting), state (OECD, 2020[26]). “Techno-pessimists” also point to the experience of technologies resulting in unintended environmental effects of some dire consequences. This report does not attempt to enter into such a debate, to which there is probably no satisfactory consensus answer at present, but provides a key driver for current and future scientific and policy enquiry. Regardless of the view one takes on the possibilities of economic growth, the fact that such debate is ongoing is actual proof of the importance for societies to understand science and innovation and its impact, actual and potential, on the natural environment.
← 3. Definitions and taxonomies, such as the EU green taxonomy of sustainable activities (European Union, 2020[24]), are not necessary solely for measurement but also help guide the decisions of investors, consumers, enforcement agencies and others. It is necessary to pay attention to the diverse set of relevant natural resource and ESOs and taxonomies, that are used by several other practitioners. As science and innovation are economic activities, they fall within the scope of application of ESO taxonomies. Companies, universities, research institutes and other stakeholders in the STI space are looking into this and what it implies for them, especially when it comes to action and disclosure to investors, funders, users, consumers, regulators or those authorities who provide the funding. It is therefore relevant for STI measurement to take heed of this opportunity.