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on Innovation |
| By: | Riccardo Priore (Patlib Centre - Area Science Park, Trieste (Italy)); Marco Compagnoni (University of Milano-Bicocca, Milan (Italy)); Marinella Favot (Area Science Park, Trieste (Italy)) |
| Abstract: | The rare earth elements (REE) are currently essential enablers of the digital and decarbonization transitions. Nonetheless, their supply chain is highly concentrated and their extraction has high environmental impacts. Circular economy solutions could provide a twofold benefit, reducing the supply risk for import-dependent countries and mitigating REE mining impacts. This article focuses on REE recycling, providing a comprehensive, global overview of innovation dynamics in that sector by means of patent data. We propose a two-steps patent search methodology for the identification of REE recycling patents, based on OECD ENV-TECH classification for green technologies and keywords occurrence. Hence, we develop a series of quantitative and qualitative metrics to explore innovation dynamics at the country, applicant and technology type level. China clearly emerges as the most attractive market for REE recycling patents and Chinese universities as the most active applicants globally. Conversely, patent applications in all other countries registered stagnating trends over the last decade. In Europe, in particular, a lower number of patents are both filed and developed with respect to the US and Japan. However, patent quality indicators present a quite different picture, with US and Japanese applicants that seem to be at the technological forefront, receiving more citations and being more oriented to protect their inventions internationally. Therefore, our analysis underlines the importance of considering both quantitative and qualitative patent metrics when exploring innovation trends in REE recycling. We discuss the determinants of these observed phenomena and provide policy implications, particularly for countries dependent on REE imports. |
| Keywords: | innovation, patents, critical raw materials, rare earths, recycling, circular economy |
| Date: | 2024–04 |
| URL: | https://d.repec.org/n?u=RePEc:srt:wpaper:0424 |
| By: | Edurne Magro Montero (Orkestra - Basque Institute of Competitiveness); James R. Wilson (Orkestra - Basque Institute of Competitiveness); Mari Jose Aranguren (Orkestra - Basque Institute of Competitiveness) |
| Abstract: | The concept of smart specialisation strategies (S3) has dominated the regional policy panorama in the last decade, which implied a shift from neutral and horizontal regional innovation policies towards priority setting in research and innovation. Despite the focus of S3 on research and innovation, we can find some similarities between these strategies and the literature around new industrial policy. The socioeconomic crisis caused by the COVID-19 pandemic highlights the need to adopt a broader view of innovation and industrial policy in which the intertwined green and digital transitions should play a core role. However, this is not an easy task as it implies changes in policy rationales, new instruments, a more entrepreneurial role for government, and a broader, multi-domain and longer-term consideration of intertwined industrial and innovation strategies, among other issues. The aim of this paper is to reflect on the nexus of industrial policies and S3, and the potential that their combination offers for sustainable transitions in the context of experiences in the Basque Country. |
| Date: | 2022–11–16 |
| URL: | https://d.repec.org/n?u=RePEc:ivc:wpaper:2022r02 |
| By: | Vincenzo Lanzetta; Cristina Ponsiglione |
| Abstract: | The European Union Regional Innovation Scoreboard (EURIS) is currently and broadly used for the definition of regional innovation policies by European policymakers; it is a regional innovation measuring tool for the analysis of each specific innovation indicator, from which it is possible to analyze the overtime evolution of each regional innovation indicator; according to the importance of the European Union Regional Innovation Scoreboard for innovation policy purposes, we state that European regional policymakers need integrative and synergistic methodological tools, with respect to the EURIS one, for innovation policy purposes. Thus, we highlight the need to integrate the current methodology of the European Regional Innovation Scoreboard with a Factorial K-means (FKM) tool for clustering purposes, and with a neural network (NN) tool for performing what-if policy analyses. We claim that our proposed FKM-NN tool could be used, by regional innovation policymakers, as a very effective synergistic instrument of the European Union Regional Innovation Scoreboard. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.13316 |
| By: | OECD |
| Abstract: | The global productivity slowdown, characterised by a widespread deceleration in aggregate productivity growth rates, is a prevailing concern for policy makers and academics. In this context, this report summarises evidence on productivity growth and business dynamics, highlighting long-term trends and their drivers, as well as insights specific to the COVID-19 period, with relevant implications for future productivity and innovation. It underscores the role of productivity for employment and wages, and discusses challenges related to the digitalisation of the economy and the green transition. Additionally, it considers how the resurgence of industrial policies necessitates additional analysis to measure and coordinate government action. |
| Keywords: | Artificial intelligence, Business dynamism, COVID-19, Diffusion, Employment, Industrial policy, Innovation, Labour share, Productivity, Technological change |
| JEL: | J30 L10 O25 O30 O33 L52 |
| Date: | 2024–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:oec:stiaaa:2024/7-en |
| By: | Corentin Lobet; Patrick Llerena; André Lorentz |
| Abstract: | We propose a disaggregated representation of production using an agent-based fund-flow model that emphasizes inefficiencies, such as factor idleness and production instability, and allows us to explore their emergence through simulations. The model incorporates productivity dynamics (learning and depreciation) and is extended with time-saving process innovations. Specifically, we assume workers possess inherent creativity that flourishes during idle periods. The firm, rather than laying off idle workers, is assumed to harness this potential by involving them in the innovation process. Results show that a firm's organizational and managerial decisions, the temporal structure of the production system, the degree of workers' learning and forgetting, and the pace of innovation are critical factors influencing production efficiency in both the short and long term. The co-evolution of production and innovation processes emerges in our model through the two-sided effects of idleness: the loss of skills through forgetting and the deflection of time from the production of goods to the production of ideas giving birth to idleness-driven innovations. In doing so, it allows us to question the status of labour as an adjustment variable in a productive organisation. The paper concludes by discussing potential solutions to this issue and suggesting avenues for future research. |
| Keywords: | Production Theory; Firm Theory; Agent-based model; Idleness; Innovation; Fund-flow |
| Date: | 2024–10–09 |
| URL: | https://d.repec.org/n?u=RePEc:ssa:lemwps:2024/27 |
| By: | W. Benedikt Schmal |
| Abstract: | I explore the concept of growth being rooted in the recombination of existing technology as an explanation for the remarkable growth witnessed during the Industrial Revolution as it was recently proposed by Koppl et al.(2023). I adapt their combinatorial growth theory to assess its applicability in generating academic knowledge within universities and research institutions, particularly in the field of economics. The central question is whether significant combinatorial growth can also be anticipated in academia. The current career structures discourage the recombination of ideas, theories, or methods, making it more advantageous for early career researchers to stick to the status quo. I employ machine-learning-based natural language analysis of the top 5 journals in economics. The analysis reveals limited correlations between topics over the past three decades, suggesting the presence of isolated topic islands rather than productive recombination. This confirms the theoretical considerations beforehand. Overall, the institutional order of academia makes combinatorial growth at the research frontier unlikely. |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2409.20282 |
| By: | Bachmann, Ronald (RWI, Heinrich-Heine-Universität Düsseldorf, IZA); Janser, Markus (Institute for Employment Research (IAB), Nuremberg, Germany); Lehmer, Florian (Institute for Employment Research (IAB), Nuremberg, Germany); Vonnahme, Christina (RWI) |
| Abstract: | "Using a text-mining approach applied to task descriptions of occupations together with worker-level administrative data, we explore the growth in the greenness of employment in Germany between 2012 and 2022. We first demonstrate that the greening of the labour market occurs both through an increase of green tasks and a decrease of brown tasks. Furthermore, the greening of occupations over time (“within-effect”) is at least as important for the overall greening of employment as shifting occupational employment shares (“between-effect”). Second, we show which occupations and which task types (brown or green) contribute most to the within-effect, and which worker flows are mainly responsible for the between-effect. Third, we investigate individual-level consequences of the greening of employment. We find that the employment prospects of foreign and of low-skilled workers are most at risk from the green transition, which may therefore increase existing labour-market inequalities." (Author's abstract, IAB-Doku) ((en)) |
| Keywords: | IAB-Beschäftigtenhistorik |
| JEL: | J23 J24 O33 Q55 R23 |
| Date: | 2024–09–17 |
| URL: | https://d.repec.org/n?u=RePEc:iab:iabdpa:202412 |
| By: | Michele Andrea Tagliavini (Dept. of Management, Venice School of Management, Università Ca' Foscari Venice) |
| Abstract: | This study explores the topic of entrepreneurial ecosystems for coastal regeneration by combining an analysis of relevant literature with the experiences lived within the European project Bauhaus of the Seas Sails, in particular relating to the development of a key framework called "Territorial Identity Card”, a tool designed to capture the essence of each coastal territory. By identifying ten key parameters common to the seven cities involved, this tool serves to guide the development of future strategies. Although the overall goal of the research is to define a sound strategy to promote sustainable entrepreneurial ecosystems in coastal regions, this paper focuses primarily on the preliminary phase of the research, laying the groundwork for subsequent in-depth analysis. This working paper serves as a compass, charting the course for future research efforts to translate key insights into actionable strategies. |
| Keywords: | Urban and territorial regeneration, entrepreneurial ecosystems, innovation, sustainability, inclusion, coastal areas, replication |
| Date: | 2024–01 |
| URL: | https://d.repec.org/n?u=RePEc:vnm:wpdman:210 |
| By: | Kim, Moogeon; Ryu, Min Ho |
| Keywords: | Internet Platform, Social Innovation Activities, SDT, Participatory Platform |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:itsb24:302524 |
| By: | Känzig, Diego R.; Williamson, Charles |
| Abstract: | We explore the increasing divergence between economic growth and energy consumption through energy-saving technical progress. Proposing a new measure of energy-saving technology, we study the underlying drivers in a semi-structural model of the U.S. economy. Our analysis shows that energy price shocks reduce consumption and stimulate energy-saving innovation, but also cause economic downturns and crowd out other innovations. Only energy-saving technology shocks can explain the negative co-movement between output and energy use. These sudden efficiency gains emerge as the primary driver of energy-saving technical change. Our findings highlight the importance of fostering energy-saving innovations in transitioning to a low-carbon economy. JEL Classification: E0, O30, Q32, Q43, Q55 |
| Keywords: | directed technical change, energy-saving, energy prices, innovation |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:ecb:ecbwps:20242984 |
| By: | Vardanyan, David (Warwick University) |
| Abstract: | This paper explores the effects of automation and the creation of new tasks on labour market outcomes by incorporating the task-based production of Acemoglu and Restrepo (2018a) into a modified Diamond-Mortensen-Pissarides (DMP) search and matching framework. While the effect on wages aligns with existing literature, the introduction of search frictions offers new insights regarding effects on unemployment. Automation is found to have a dual impact: it displaces workers from routine tasks but simultaneously generates productivity gains which can offset its negative effects. The net impact on unemployment and wages depends on the relative magnitude of these displacement and productivity effects which are analytically derived in the research. In contrast, the creation of new tasks has a more uniformly positive impact, as it both enhances the productivity and reinstates displaced workers, leading to lower unemployment and higher wages. The findings suggest that policies should ensure not to promote excessive automation, where it negatively affect the labour market. In contrast, fostering innovation and task creation can be effective ways to benefiting from technological advancements. |
| Keywords: | Automation ; labour market frictions ; productivity ; technology ; unemployment JEL classifications: E22 ; E24 ; J23 ; J24 ; O33 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:wrkesp:79 |
| By: | Pablo Guerron-Quintana (Boston College); Tomoaki Mikami (Boston College); Jaromir Nosal (Boston College) |
| Abstract: | We study the potential impact of the generative artificial intelligence (Gen-AI) revolution on the US economy through the lens of a multi-sector model in which we explicitly model the role of Gen-AI services in customer base management. In our model with carefully calibrated input-output linkages and the size of the Gen-AI sector, we find large spillovers of the Gen-AI productivity gains into the overall economy. A 10% increase in productivity in the Gen-AI sector over a 10 year horizon implies a 6% increase in aggregate GDP, despite the AI sector representing only 14% of the overall economy. That shock also implies a significant reallocation of labor away from the AI sector and into non-AI sectors. We decompose these effects into parts coming from the input-output structure and customer base management and find that they each contribute equally to the rise in GDP. In the absence of either channels, real GDP essentially does not respond to the increase in productivity in the AI sector. |
| Keywords: | artificial intelligence, AI, productivity |
| Date: | 2024–10–16 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1080 |