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Innovations incorporating environmental and social considerations can address many sustainability challenges. Such sustainable innovations emerge in networks often comprising actors from business, academia, civil society, and government. The crucial interactions here are the (co-)creation and transfer of knowledge, mutual learning, and experimentation in different environments. To better understand these knowledge processes and hence the eventual outcome of sustainable innovations, we analyze the actors’ relationships with the help of proximity and its five dimensions, namely geographical, cognitive, institutional, organizational, and social proximity. Building upon findings from sustainability science and innovation system theory, we present a refined proximity framework, introducing a differentiation of institutional proximity into micro- and macro-institutional proximity and a differentiation of cognitive proximity into systems-cognitive, normative-cognitive, and transformative-cognitive proximity. Analyzing examples from the literature by applying this framework, we see that all proximity dimensions and their interdependencies help to better understand knowledge processes and innovations in sustainability-oriented innovation networks. We find that such networks often depict low levels of micro-institutional and systems-cognitive proximity, which coheres with the prevalence of inter- and transdisciplinary approaches and the wide inclusion of relevant stakeholders for addressing sustainability issues. Our framework further reveals that successful networks show high levels in other proximity dimensions, with normative-cognitive proximity appearing to play a crucial role, highlighting the importance of shared goal orientations. Our results provide valuable input for the formation of sustainability-oriented innovation networks by pointing out the necessary combination of distances that allow for creativity and learning, combined with appropriate proximities for exchange and mutual understanding.

This study investigates how structural change that leads to increasing output variety was gradually perceived by economists and eventually incorporated into models of economic growth. We trace the evolution of growth models from exclusively macroeconomic models to those that include micro and meso levels of aggregation, and further, to those that permit explicit and endogenous representation of innovation and technological change. We consider the structure of an economic system as constituted by (i) a variable number of industrial sectors producing highly diversified goods and services, (ii) an increasing range of other activities, such as education or healthcare—which, while not being strictly economic, interact heavily with industrial sectors, and (iii) a series of interactions between these sectors and activities, the intensity of which can vary in the course of economic development. As a consequence, structural change consists of (i) the emergence of new sectors and activities and the reduction or extinction of older ones, (ii) increase in quality and differentiation of sectoral output, and (iii) the changing interactions between industrial sectors and other activities. In this paper, structural change is not considered an epiphenomenon of economic development but one of its fundamental mechanisms, since the emergence of new sectors and activities and their internal diversification contribute to overcoming the development bottleneck. This type of structural change gives rise to a growing diversification of the system through the co-evolution of industrial sectors, other activities, technologies, and institutions. This growing diversification of economic systems has recently been confirmed by an abundance of research, both theoretical and empirical: those studies are examined and analyzed in detail here.

We study the projected impact of automation on employment in the forthcoming decade, both at the macro-level and in actual (types of) sectors. Hereto, we unite an evolutionary economic model of multisectoral structural change with labor economic theory. We thus get a comprehensive framework of how displacement of labor in sectors of application is compensated by intra- and intersectoral countervailing effects and notably mopped up by newly created, labor-intensive sectors. We use several reputable datasets with expert projections on employment in occupations affected by automation (and notably by the introduction of robotics and AI) to pinpoint which and how sectors and occupations face employment shifts. This reveals how potential job loss due to automation in “applying” sectors is counterbalanced by job creation in “making” sectors as well in complementary and quaternary, spillover sectors. Finally, we study several macro-level scenarios on employment and find that mankind is facing “the usual structural change” rather than the “end of work”. We provide recommendations on policy instruments that enhance the dynamic efficiency of structural change.

Since Klaus Schwab and the World Economic Forum declared the arrival of the Fourth Industrial Revolution, there has been much discussion about it. However, there is no commonly agreed-upon definition of the Fourth Industrial Revolution. Therefore, we attempted to answer the following four research questions. “What is the definition of the Fourth Industrial Revolution?”, “How can we respond to the Fourth Industrial Revolution in terms of institutions?”, “How can we respond to the Fourth Industrial Revolution in terms of technology?”, “How can we respond to the Fourth Industrial Revolution in terms of firm innovation and start-up strategy?” Brainstorming was conducted by 11 scholars from several countries to answer these four research questions. Therefore, this research is not the end product of four research questions, but a kind of advanced template to answer the four research questions for continuing research.

The circular economy (CE) and bioeconomy (BE) are recognized as potential solutions for achieving sustainable development, yet little research has examined their potential contribution to the United Nations’ Sustainable Development Goals (SDGs). In this study, we conducted a bibliometric analysis of 649 articles published between 2007 and 2022, as well as a systematic literature review of 81 articles, to assess the extent to which the CE and BE communities have addressed the SDGs. Our analysis identified 10 research gaps including the limited number of empirical quantitative papers, particularly in the context of BE, and the underrepresentation of developing regions such as Latin America and Africa in the literature. Our main finding reveals that the CE community primarily focuses on SDG 12, Responsible Consumption and Production, followed by SDG 9, Industry, Innovation, and Infrastructure; SDG 7, Affordable and Clean Energy; and SDG 6, Clean Water and Sanitation. The BE community, on the other hand, focuses primarily on SDG 7, followed by SDG 9 and SDG 12. However, both communities lack attention to social SDGs such as quality education, poverty, and gender equality. We propose that a combination of CE and BE, known as circular bioeconomy, could help countries achieve all SDGs. Further research is needed to develop and implement circular bioeconomy policies that address these gaps and promote sustainable development. In this sense, our study identified an important research gap that needs more attention in the future.

Western economies nowadays are confronted with a predicted productivity slowdown resulting in diminishing rates of economic growth. While some scholars see these developments as an indication of the approaching end of growth due to fully exploited technological opportunities, this article contends that the possibilities for radical, paradigm changing innovations are far from being exploited. Building on contributions from Schumpeter and Georgescu-Roegen, we argue that the human capacity to expand technological and intellectual frontiers must not be underestimated. In a selective retrospect, our narrative identifies and describes four historical incidents reflecting different perceptions of the power of the human mind. It synthesizes the mentioned economists' viewpoints with the effects of these incidents to reproduce the intellectual roots of the recently developed concept of Dedicated Innovation Systems (DIS). We conclude that traditional macro-level indicators are not suitable to capture transformation processes, which is why we propose to interpret growth indicators and the alleged productivity slowdown quite differently. We argue that human ingenuity and transformation processes dedicated to sustainability will open up new opportunity spaces, thereby combining an increase in economic welfare and social justice with a reduction of negative environmental impact

Ever since the emergence of economics as a distinct scientific discipline, policy makers have turned to economic models to guide policy interventions. If policy makers seek to enhance growth of an open capitalist economy, they have to take into account, firstly, the uncertainties, inefficiencies, and market failures faced by the agents in the economy, and, secondly, the activities, network structure, and interactions in the innovation and production system. The authors discuss ins-and-outs of developing and using (encompassing and empirically calibrated) agent-based models for (i) abductive theorizing about causes for empirical realities, and (ii) evaluating effects of policy interventions. To ensure that derived policies are suitable to intervene in the real world and not just the stylization of it, they discuss validity and operationalization of agent-based models as well as interpretation of simulation results.

For a successful transformation towards a sustainable bioeconomy, cooperative knowledge creation leading to innovations through research at the company and academic level are important. Urban regions are the centre of economic and research activities. The example of the region of Stuttgart, which aims to complement its mature industrial structure with new opportunities related to the knowledge-based bioeconomy, is an interesting case for the application of social network analysis to shed light on the dynamics of innovation networks to support the transformation of urban regions. As with smaller spatial levels of observation connectivity in network decreases, we find a scale-free network structure for the supra-regional network and a star-like network structure for the regional network, with two universities and one transfer-oriented research institutes at the core. While research collaborations beyond regional borders and across different industries foster knowledge co-creation, the central actors can be recognized as gatekeepers who dominantly influence knowledge flows. To potentially strengthen the resilience of the network, policy and industry associations serving as network facilitators can foster collaboration between periphery actors. The case of the Stuttgart region impressively illustrates the opportunities of the knowledge-based bioeconomy for urban regions and the complementary role traditional manufacturing sectors can take in the transformation towards higher degrees of sustainability.

Innovation policy and business strategy often expect that investing in private and public research and development will immediately produce a flow of products and processes with high commercial and social returns. Policymakers and managers implicitly follow the logic underlying most linear innovation models assuming a well-defined and uni-directional relationship between R&D spending as input and innovation rents as output of the innovation process. Modern innovation economics dismisses the simplified approximation of knowledge by R&D investment and, instead, considers complex knowledge generation and diffusion processes in innovation networks. From this angle, the disappointing performance of traditional approaches is traced back to strong limits of conventional steering, control, and policy instruments. In this paper, we show that the new view of knowledge generation and diffusion in innovation networks allows for an alternative and has led to systemic approaches in innovation analyses. Combined with computational approaches like agent-based modeling, this new view enables today innovative tools in policy consulting. Using the example of regional innovation policy, we introduce a policy laboratory in which innovation processes can be analyzed in depth to see the impact of different innovation policy instruments in-silico. This ex-ante evaluation helps considerably to improve the understanding of innovation processes and with it the performance of innovation policy.