11. Innovation and New Technology for Enabling Economic and Social Progress
The evolving dynamics of innovation in digital, bioeconomy, and clean energy are discussed, focusing on the systemic dimension of change. Systems-based policy approaches that could help policymakers influence these dynamics to achieve societal and environmental goals are presented. Conceptual models of innovation systems have been developed that describe the positive outcome of innovation efforts within a multidimensional, interacting space involving knowledge, actors and institutions, and resource mobilisation, as well as innovation outcomes. These interacting dimensions are complementary and need to be addressed simultaneously by policy. Formal systems modelling can also assist innovation policies to tackle deep innovation uncertainty. Models drawing on portfolio theory provide a quantitative framework of the economic value of risk diversification. In these models, different degrees of risk aversion (to innovation failure) become an input variable specified by policymakers. “Optimal” diversification portfolios given pre-specified innovation uncertainties and policy-specified risk aversion can be determined mathematically.
Innovation is a force for social and economic progress. Its benefits are huge: productivity growth, new jobs, new technologies, and new solutions to human needs, raising average incomes, increasing health outcomes, and improving social welfare. But a focus on the economic costs and benefits associated with innovation has blinded us to the environmental and social costs and benefits of new technologies. It has also blinded us to the societal impacts of new technologies, notably the exclusion they can create between those with access to capital, skills, and now data, and those without. The current wave of innovation and new technology has both commonalities and differences with previous waves. Digitalisation and automation are potentially disrupting entire sectors and industries and changing the demand for skills. The cheaper costs of automated manufacturing also suggests that developing countries that traditionally enjoyed a cost advantage which enabled them to channel surplus labour from agriculture, must find a new development path. This should be facilitated by the tremendous opportunities offered by digital technologies, (e.g. mobile communication that allows dense connectivity even in rural areas) or e-commerce (which makes global markets more easily accessible to small producers).
There is now a need to steer innovation and new technology towards responding better to societal and environmental needs: developing greener energy and chemistry, transportation systems, smart cities, sustainable agriculture, food systems, etc. For that to happen, market and non-market forces must be aligned around a number of goals reflecting these concerns.
Innovation and new technology have become a pervasive force, penetrating all aspects of social and personal life and influencing their development, involving all actors in society. At the same time, the dynamics of innovation itself has become more complex, diverse, and unpredictable, hence more difficult for any specific actor, such as government, to anticipate, plan, and guide.
Advanced systems-based analytical approaches such as system-level modelling, technology foresight and scenarios, anticipatory governance - which involves , inter alia, the intensive use of data and advanced modelling in prediction and decision-making combined with greater participation by citizens - can help policymakers understand more fully how technology and innovation can be better harnessed to meet the goals of sustainable development. While innovation policies and theories have long integrated systemic thinking (for example by recognising the importance of the quality of industry-science relations in the commercialisation of technologies, the role of different actors involved and the need for co-ordination, or the critical interdependencies and knowledge feedbacks between the supply and demand for innovations) they have only recently begun integrating it explicitly in managing the transition of socio-technical systems towards sustainability (e.g. energy, transport). By bringing into focus the social, economic, and environmental impacts, trade-offs, and interdependencies generated by the introduction of new technologies, as well as the motivations of actors and their interactions, system approaches can help identify strategies that maximise synergies and minimise trade-offs between innovation and development objectives and any resulting barriers or leverage points for technology diffusion and uptake. The involvement of consumers, industry, and civil society in managing these transition processes is a crucial element of systems thinking, as well as the initiative of all levels of government: local, national, and transnational.
Innovation studies have been among the first fields in social sciences to implement a systems-based approach. The reasons are that many students of innovation have a hard sciences background that endows them with the appropriate technical skills, but also that innovation is particularly affected by systems-type mechanisms: it is an emergent phenomenon; it is non-linear; it is complex.
This note reviews the evolving dynamics of innovation in various fields (digital, bioeconomy, clean energy) with a focus on the systemic dimension of change. It presents new, systems-based policy approaches that could help policy makers influence these dynamics to achieve societal and environmental goals.
Digitalisation as catalyst for operationalising systems-based strategies (digital innovation: acceleration, data as inputs, collaboration)
The rapid and long-term advance of the power of computers, the expansion of the internet as a repository of all data and as a general connector, and the increasing sophistication of software have all contributed to making digitalisation the transformative force of 21st century economies and societies. The transformation brought about by digitalisation is systemic in nature. It concerns all aspects of economies (manufacturing, daily life, administration, entertainment); and it affects all actors in society and their interconnections. Digitalisation changes the frontiers that structure social and economic life - between industries, between activities, between actors, between spaces. Digitalisation also affects all the interconnections between actors: the circulation of information as well as the allocation of power. The phenomenon of “fake news” illustrates one negative aspect of the disintermediation of information management, although there are many positive aspects as well. Hierarchical and filtered relations have been replaced by more horizontal, unfiltered connections with a network shape. The functioning of a system is highly dependent on the allocation of information across the actors, and digitalisation is transforming that as well. Digitalisation for society needs also to be conducted in close coordination with other transformations: smart cities require a lot of digital tools, but also skills, physical infrastructures, regulation, and evolving social relations and behaviours.
Bioeconomy/circular economy
Concepts like a “bioeconomy” or “circular economy” are often proposed as a solution to addressing environmental challenges. Systems-level modelling however reveals that simple input substitution efforts are likely to be counterproductive without a radical transformation in the entire resource provision and consumption system in the direction of vastly improved materials and energy efficiency and conservation, where the technical potentials are vast but the associated innovation and behavioural and lifestyle changes constitute formidable barriers. The development of a bioeconomy is also a complex field that includes a variety of sectors and stakeholders involved in far-reaching changes in production systems and consumption patterns. The demand-side of this transformation remains particularly under-researched and the effect of policy signals remains uncertain both with respect to effectiveness as well as in terms of political and social acceptability. The transformation would require policy signals from a broad range of domains, notably agriculture, energy, water, land, environment, trade, and research. It would also require changes in government regulations, ranging from regulations on the generation and use (and re-use) of waste, to limits on emissions, land zoning, etc. In addition, most importantly, it will require organisational and changes in individual/consumer behaviours. Using a systems approach can reveal the trade-offs and synergies that are likely to occur in the transition to a bioeconomy (OECD, 2018). However, synergies and trade-offs will have to be managed, which will require stakeholder engagement (with business, policymakers, civil society, scientists, financing) and coherence across policy domains. Many countries inside and outside the OECD are attempting to develop coherent and integrated bioeconomy strategies. The real value of a systems approach is to cast doubt on simplistic notions of “bioeconomy” (even circular economy, or all renewable energy systems). The critical interplay between demand and supply for resources needs to be a central concern. Without step changes in efficiency (that needs technological as well as behavioural and lifestyle innovations in the direction of “less is more”) and changing consumption patterns, any significant transition towards a bioeconomy risks creating more environmental impacts than it aims to resolve. Systems-based policy tools, such as agent-based modelling, could help to explore such strategies, regulations, and policies to ensure that novel concepts are tested before they are implemented.
Innovation and clean energy systems
System innovation argues that policies aimed at transitioning sociotechnical systems to more environmentally-sustainable configurations differ significantly from those aimed at increasing the economic performance of existing systems with unchanged, even growing resource demands. The transition from a fossil fuel based energy system to one based on renewable and low-carbon energy sources is a living case study that many countries are grappling with. Among the challenges facing policymakers in the energy transition is the need to develop a vision of what future energy systems will look like, including which technologies – and combinations of technologies - are likely to play important roles in the future system, and which energy infrastructures will be needed, as well as how business models (e.g. shared urban mobility) regulations, and patterns of consumer behaviour will need to change (e.g. promoting energy efficiency). Such visions have to be developed using both bottom-up approaches and top-down visioning. Top-down, addressing such complexity requires not only lengthening financial planning and investment horizons, but also co-ordination across government ministries and different levels of government. Bottom-up, it means linking local and community-based initiatives to national goals and international commitments (e.g. SDGs, the Paris Climate Agreement). Systemic analytical approaches to such portfolio diversification models can help to craft appropriate diversification strategies in the face of persistent innovation uncertainty and often-unknowable ultimate environmental and social impacts of the technological options considered. Integrated Assessment Models (IAMs) are increasingly becoming available to assess the impacts of alternative transformation strategies across a wide range of SDGs.
The policy response to systemic changes needs to be systemic itself. For that to happen, it needs both the vision and the appropriate instruments. Over the past years, older instruments have been modernised and new instruments have appeared which endow policy makers with a rich toolkit.
Strategic policy intelligence. Strategic policy intelligence can be defined as “the set of activities to search, process, diffuse, and protect information in order to make it available to the right persons at the right time, so that they can make the right decisions”. In the Strategic Territorial Intelligence (STI) policy space, these include such policy support instruments such as foresight and technology assessment, monitoring, benchmarking, regional innovation auditing, technology road mapping, horizon scanning, specialisation indices, and strategic evaluation (Acheson, 2008). Many governments use foresight exercises, a form of “strategic policy intelligence”, as part of their priority-setting procedures to stimulate dialogue. Horizon scanning is a distinct futures methodology that researches and draws out key trends on the margins of current thinking that will affect people’s lives in the future. Most horizon scanning exercises aim to provide advance notice of significant new and emerging risks and opportunities, to exchange information, and to evaluate potential impacts. This involves the review of a broad spectrum of information beyond the usual timescales and sources and the participation of various sectors of society. Smaller economies have perhaps been the most active with regard to using foresight and other future-oriented studies to inform priority setting because of the need to focus and get returns from relatively small investments. Strategic policy intelligence, whether foresight or other tools, depends on timely quantitative and qualitative data of high quality. Many OECD countries still struggle with gaps in their data especially as regards understanding and measuring the socioeconomic impact of public R&D in science and technology. The nonlinearity of research impacts is not adapted to the input/output models of R&D budgeting and evaluation. For instance, mathematical research can advance science and innovation in areas as varied as artificial intelligence (AI), advanced manufacturing, or synthetic biology, but current systems for measuring the impact of funding priorities will be unable to ascertain such effects. Improving data analysis on both the input and output side of innovation will necessarily require work to develop up-to-date definitions and taxonomies. A renewed effort to perform a range of empirical studies across technologies, countries, and the economic, social, and environmental returns of past innovation projects is also long overdue.
Digital science and innovation policy (DSIP). Several OECD countries and partner economies have started exploring the potential of exponentially-increasing data volumes and advances in computational power for science and innovation policy by launching DSIP initiatives. DSIP initiatives refer to the adoption or implementation by public administrations of new or re-used procedures and infrastructures relying on an intensive use of digital technologies and data resources, to support the formulation and delivery of science and innovation policy. DSIP initiatives are becoming increasingly instrumental in steering national science and innovation policy in a highly uncertain environment. The Japanese digital system SPIAS uses big data and semantic technologies to process data on R&D activities to guide decisions of government agencies on investments in science and innovation. The system was used to map the impacts of regenerative medicine in Japan and formulate new policy measures to promote its further development. Another example is a Welsh system, Arloesiadur, designed to provide policymakers with intelligence on industrial and research strengths of the region, domestic and international networks, and opportunities for economic growth. Arloesiadur uses natural language processing and machine learning to analyse data from administrative sources, research repositories, and the web to inform the decisions of policymakers. While being mainly used for supporting a current mode of operations of STI policies, DSIP initiatives can potentially be used to facilitate the transition of socio-technical systems as well. For instance, by providing analyses with high granularity and scope that it is not possible to achieve using conventional methods and approaches, DSIP initiatives can effectively guide policymakers in improving STI policy frameworks by making them more responsive to inclusiveness and other societal challenges.
Participatory approaches in research funding /priority setting. Governments are increasingly involving industry and society upstream in the policy debate through participatory approaches to setting priorities, e.g. Argentina, Chile, Denmark, Greece, Netherlands, and Turkey (OECD, 2016). The involvement of participatory approaches in the evaluation of research and innovation policies is rarer.
Mission innovation. One way governments are trying to mobilise STI for grand challenges is through mission-oriented R&D and innovation programmes. Mission-oriented programmes align policies, public R&D programmes, and public-private collaboration to define priorities and set targets to overcome a concrete problem. This in turn helps to address a broader societal challenge or “wicked problem” - one that is complex, systemic, interconnected, and urgent - such as climate change, environmental degradation, and public health challenges. Mission-oriented programmes often involve all stakeholders in their design, and mobilise various actors in their implementation (ministries, agencies, businesses, scientific and technological disciplines). At the core of the mission-oriented approach is the understanding that governments must not only correct market failures, but also actively drive and direct innovation by co-creating and co-shaping markets (Foray, Mowery and Nelson, 2012; Mazzucato, 2015).
Smart regulation. From an innovation perspective, “smart regulation” approaches can facilitate the diffusion of new technologies if they achieve consumer and environmental protection at minimum cost and maximum simplification. The challenge for governments is to design and apply regulations that do not stifle competition between innovations (and associated actors) and existing technology (and incumbent actors): regulating too much or too soon can stifle the challenger to existing incumbents, especially when innovations have applications in other product markets with different regulatory traditions (e.g. 3D printing in automobile and health applications).
Systems modelling for innovation policy. The first important role for systems modelling is to apply a systemic approach to identify opportunities as well as potential trade-offs for policy interventions in complex coupled socio-economic and natural systems. A prominent example is the Sustainable Development Goals (SDGs) that suggest policy priorities along a broad range of societal objectives from economic to social development, as well as environmental preservation. Systems thinking and resulting modelling can help to identify which policies offer potential for synergies among various SDGs, and which policies could lead to important trade-offs. These trade-offs do not arise between the various SDGs (policy objectives) as such, but rather from particular policies proposed to address any single SDG in an isolated manner. For instance a climate policy objective translated into an input substitution policy, e.g. biofuels for fossil fuels in transport, almost inevitably leads to important trade-offs for competing uses of land, water, and other resources between energy production and food and fibre provision, as well as ecosystems services.
Conversely a demand-side strategy, e.g. promoting comprehensive shared mobility schemes, particularly in urban settings (see e.g. the modelling work of OECD ITF, 2016 and 2018) can lower resource use, environmental impacts, and mobility costs at the same time, illustrating SDG synergies that can be harnessed by integrated policy approaches that above all first consider the most important systems interdependencies: i.e. between supply and demand. Recent advances in Integrated Assessment Modelling tools such as those in use at IIASA help to shed light on these potential synergies and trade-offs among various policy options (Nilsson et al., 2018). The potential for policy integration and holistic strategies for addressing the SDGs have been recently described in the transformation scenarios of “The World in 2050” Initiative hosted at IIASA (TWI2050, 2018) underpinned by systems modelling in the food-water-energy nexus (Parkinson et al., 2018) as well as climate policy with a focus on demand-side solutions (Grubler et al, 2018).
A second important area of application of systems thinking is innovation policy. Important new conceptual models of innovation systems have been developed that describe the positive outcome of innovation efforts within a multidimensional, interacting space involving knowledge, actors, and institutions, resource mobilisation, and innovation outcomes (Gallagher et al., 2012). These interacting dimensions of innovations systems are not substitutive, but rather complementary and need to be addressed simultaneously by policy. As a simple example, consider an enhanced R&D programme for large-scale carbon capture and sequestration. In the absence of corresponding policies that put a price on the carbon externality, these innovation efforts will be stymied by a lack of market deployment incentives. In other words, the R&D efforts remain in the proverbial innovation “valley of death” (viable prototype technologies cannot be brought to market). Currently data limitations preclude a formal model representation of entire innovation systems, but the approach has been fertile in explaining relative success or failure of innovation initiatives across different technology fields and across countries (see the case studies assessed in Grubler and Wilson, 2014) and has enabled systemic biases in innovation policies for climate protection to be identified across all OECD countries, that unduly focus on supply-side options, marginalising end-use innovations (Wilson et al, 2012).
Formal systems modelling can also assist innovation policies to tackle the perennial problem of deep innovation uncertainty. The biblical quote of “many are called, but few are chosen” describes the inherent uncertainty of innovation outcomes, despite well-funded innovation efforts and aligned market incentives. Drawing on portfolio theory, new models have become available that can assist innovation policy via a quantitative framework of the economic value of risk diversification via a portfolio approach (Grubler and Fuss, 2012; for methods see Krey and Riahi, 2013). A novel feature of these models is that different degrees of risk aversion (to innovation failure) become an input variable specified by policy makers. “Optimal” diversification portfolios given pre-specified innovation uncertainties and policy-specified risk aversion can be determined mathematically, although computational limitation currently restricts the application of these approaches to portfolios of less than two dozen innovation projects. A robust finding from the modelling studies is that expanding innovation portfolios is a direct function of innovation risks. The higher the risks, the more diversified the portfolio should be. In many cases, such diversification might not be possible within the limited resources available for national innovation strategies. International co-operation and joint risk hedging can thus be proven to be an economically rational and optimal innovation strategy.
There is a pressing need to steer innovation and new technology towards societal and environmental needs. A systems approach to innovation policy argues that policies aimed at transitioning socio-technical systems to more environmentally-sustainable configurations differ significantly from those aimed at increasing the economic performance of existing systems with unchanged, even growing, resource demands.
A traditional focus on the economic benefits of innovation has blinded policymakers to the environmental and social costs and benefits of innovation and new technologies.
Many OECD countries still struggle to understand and measure the socioeconomic impact of public R&D. The nonlinearity of research impacts is not adapted to the input/output models of traditional R&D budgeting and evaluation.
Systems thinking and systemic approaches in the domain of innovation (such as formal system modelling, strategic policy intelligence, participatory approaches in research funding and agenda setting, digital science and innovation policies, and mission innovation) can help reduce uncertainty in R&D and innovation, and identify strategies that maximise synergies and minimise trade-offs between the different goals of innovation policy interventions.
References
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