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on Technology and Industrial Dynamics |
| By: | Nelli, Linnea; Virgillito, Maria Enrica; Vivarelli, Marco |
| Abstract: | The aim of this paper is to understand whether what has been labelled as “twin transition”, at first as a policy flagship, endogenously emerges as a new technological trajectory stemming by the convergence of the green and digital technologies. Embracing an evolutionary approach to technology, we first identify the set of relevant technologies defined as “green”, analyse their evolution in terms of dominant blocks within the green technologies and concurrences with digital technologies, drawing on 560, 720 granted patents by the US Patent Office from 1976 to 2024. Three dominant blocks emerge as relevant in defining the direction of innovative efforts, namely energy, transport and production processes. We assess the technological concentration and underlying complexity of the dominant blocks and construct counterfactual scenarios. We hardly find evidence of patterns of actual endogenous convergence of green and digital technologies in the period under analysis. On the whole, for the time being, the “twin transition” appears to be just a policy flagship, rather than an actual endogenous technological trajectory driving structural change. |
| JEL: | O33 O38 Q55 Q58 |
| Date: | 2025–03–17 |
| URL: | https://d.repec.org/n?u=RePEc:unm:unumer:2025008 |
| By: | Yijia Chen; Kangmin Wu; |
| Abstract: | Artificial intelligence (AI) is a key driver of the Fourth Industrial Revolution. Despite growing interest in the geography of AI, our understanding of how AI integrates into regional contexts remains limited. In response, we examine the integration of AI into regional technological domains in China and the United States using patent data. Theoretically, we develop a framework by introducing the concepts of intra- and extra-regional AI relatedness. Our findings reveal that the integration of AI into regional technological domains is positively associated with both intra-regional and extra-regional AI relatedness. Additionally, extra-regional AI relatedness can moderate the lack of intra-regional AI relatedness. |
| Keywords: | integration of artificial intelligence, intra-regional AI relatedness, extra-regional AI relatedness, regional technological domains, China, the United States |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:egu:wpaper:2507 |
| By: | Jesus Fernandez-Villaverde (University of Pennsylvania); Yang Yu (Shanghai Jiao Tong University); Francesco Zanetti (University of Oxford) |
| Abstract: | Defensive hiring of researchers by incumbent firms with monopsony power reduces creative destruction. This mechanism helps explain the simultaneous rise in R&D spending and decline in TFP growth in the US economy over recent decades. We develop a simple model highlighting the critical role of the inelastic supply of research labor in enabling this effect. Empirical evidence confirms that the research labor supply in the US is indeed inelastic and supports other model predictions: incumbent R&D spending is negatively correlated with creative destruction and sectoral TFP growth while extending incumbents’ lifespan. All these effects are amplified when ideas are harder to find. An extended version of the model quantifies these mechanisms’ implications for productivity, innovation, and policy. |
| Keywords: | Productivity growth, innovation, R&D, patents, creative destruction |
| JEL: | E22 L11 O31 O33 |
| Date: | 2025–03–11 |
| URL: | https://d.repec.org/n?u=RePEc:pen:papers:25-007 |
| By: | A.C. Pinate; M. Dal Molin; M.G. Brandano |
| Abstract: | This paper analyses the relationship between institutional quality and green innovation in Italian regions (NUTS2). We examine how varying levels of institutional quality influence the regional capacity to generate green innovation, disentangling the effects related to economic institutions (corruption, government effectiveness, and regulatory quality) from the impacts associated with political institutions (rule of law and voice and accountability). Using a panel of data for 2004–2018 on green patents, we use an instrumental variable IV approach to control for endogeneity and several robustness checks. Our results show that the most important drivers of green innovation are related to the quality of political institutions. These findings remain robust, even when checking for economic and environmental controls, demonstrating that green innovation is more related to political decisions and social capital than innovation in general is. |
| Keywords: | regional green innovation;green patents;Institutional Quality;italy |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:cns:cnscwp:202508 |
| By: | Wu, Qiang; Zhou, Peng (Cardiff Business School, Cardiff University) |
| Abstract: | Artificial intelligence (AI) is crucial in achieving the carbon peak and neutrality goals and mitigating climate change. Although previous studies have explored cross-sectional differences in corporate carbon emissions, temporal heterogeneities in firm lifecycles have been overlooked. Therefore, this study investigates the effect of AI adoption on carbon emission intensity over firm lifecycles and the micro-level mechanisms of this effect. This study examines panel data from Chinese listed companies (2010–2021) using a two-way fixed-effects model and the difference-in-differences method. The empirical results demonstrate that AI significantly reduces enterprises’ carbon emission intensity. However, this effect is mainly observed in growth-stage enterprises and not in decline-stage enterprises. The mechanism analysis reveals that AI primarily reduces enterprises’ carbon emission intensity by improving productivity and promoting innovation. The effect on productivity is particularly evident in growth-stage enterprises, whereas the effect on innovation is dominant in decline-stage enterprises. Heterogeneity tests indicate that the effect on state-owned enterprises, medium-sized enterprises, the manufacturing sector, heavily polluting industries, non-high-tech industries, and capital-intensive industries is more pronounced than that on other enterprises. These findings suggest that enterprises should actively adopt AI, and differentiated AI adoption strategies should be formulated based on the needs of enterprises at different lifecycle stages. |
| Keywords: | artificial intelligence; carbon emission intensity; firm lifecycle; productivity |
| JEL: | O31 O32 O33 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2025/9 |
| By: | Keuschnigg, Christian; Stalenis, Giedrius Kazimieras |
| Abstract: | We study a small open economy that must implement an emissions reduction plan and eventually phase out fossil fuel. R&D leads to the design of energy saving new machines. Endogenous scrapping eliminates old inefficient machines. We identify two distortions that delay the adoption and diffusion of energy saving technology: scrapping of old equipment and investment in new machines are both too low. The optimal policy to manage the energy transition thus combines a carbon tax with a profit tax to speed up exit, and an investment subsidy to speed up investment in new equipment. The optimal policy increases capital turnover, the diffusion of energy saving technology, and thereby mitigates the costs of the energy transition. Compared to a policy that exclusively relies on carbon taxes, the optimal policy could reduce the GDP loss of moving to net zero from 7.8 to 6.1% of GDP. |
| Keywords: | Energy saving innovation, vintage capital, emissions reduction |
| JEL: | D21 D62 H23 O33 Q41 Q43 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:usg:econwp:2025:03 |
| By: | VILLANI Davide (European Commission - JRC); GONZALEZ VAZQUEZ Ignacio (European Commission - JRC); FERNANDEZ MACIAS Enrique (European Commission - JRC) |
| Abstract: | The green transition is set to transform labour markets, yet its impact remains difficult to measure. This paper critically examines the occupational approach—based on task-based measures—which is the most widely used framework among researchers and institutions for estimating green employment. First, we identify theoretical shortcomings in this approach, emphasizing that its reliance on occupational titles leads to false positives by misclassifying non-green jobs as green, while also producing false negatives by excluding key contributors to the green transition. Second, we highlight methodological issues, such as inconsistent categorizations, arbitrary task definitions, outdated classifications, and the flawed assumption that occupational content remains stable across time and countries. Third, we apply the occupational approach using the O*NET framework to quantify green employment in 24 European countries from 2011 to 2022. Our analysis reveals that, according to this method, there has been virtually no net creation of green jobs in Europe. Moreover, we find no meaningful correlation between the presence of green jobs and various aggregate and sectoral environmental indicators. These findings underscore the fundamental limitations of the occupational approach, suggesting that it is an inadequate tool for assessing the labour market effects of the green transition. We discuss how this measure is suitable for policy benchmarking in the context of the European green transition. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:ipt:laedte:202502 |
| By: | Seda Koymen Ozer (Baskent University, Ankara, Turkey); Alessia Lo Turco (Department of Economics and Social Sciences, Universita' Politecnica delle Marche (UNIVPM)); Daniela Maggioni (Department of Economics, Catholic University of the Sacred) |
| Abstract: | We investigate how the import of automation impacts upgrading within firm production networks. We use comprehensive data on product mix, foreign trade, balance sheets, employment, and firm-to-firm transactions for Turkish manufacturing firms from 2009 to 2020. By employing Propensity Score Matching (PSM) alongside event study analyses and an instrumental variable (IV) approach, our research provides robust evidence that firms importing automation enhance the quality and lower quality-adjusted prices of their products. Importantly, the benefits of automation extend downstream throughout the supply chain to firms sourcing inputs from suppliers that have adopted automation. No significant effects propagate, instead, to upstream firms supplying automation adopters. |
| Keywords: | buyer-supplier links, product upgrading, manufacturing, Turkiye |
| JEL: | O14 F61 F63 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:anc:wpaper:495 |
| By: | Sandström, Christian (Linnaeus University); Stenkula, Mikael (Research Institute of Industrial Economics (IFN)) |
| Abstract: | This paper critically evaluates the European Union’s shift towards large-scale green industrial policies. It highlights the risks of government-directed resource allocation, such as inefficiencies, misaligned incentives, rent-seeking, and lobbying. Politicians and bureaucrats at the EU level lack the ability to identify the future industries, products, and technologies for this policy to work effectively. The EU is not designed to operate large top-down interventions successfully. There is a substantial risk that large amounts of resources will be spent on initiatives that ultimately fail. Instead, this paper emphasizes competition- and technological-neutral frameworks, emissions trading systems, and general policy incentives. The paper concludes that a decentralized, market-driven approach is more sustainable for fostering innovation. |
| Keywords: | New industrial policy; Green investments; Innovation policy; Mission-oriented policies |
| JEL: | H50 L52 O38 P16 |
| Date: | 2025–02–25 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:iuiwop:1523 |
| By: | Guido Ascari; Andrea Colciago; Timo Haber; Stefan Wöhrmüller |
| Abstract: | The EU aims for 42.5% green energy consumption by 2030. What are the effects of the European green transition on inequality? We answer this question using a heterogeneous-agent model with non-homothetic preferences for energy and non-energy goods, calibrated to European data. We study the impact of an increase in carbon taxes designed to meet the EU target under different revenue-recycling strategies. Redistributing tax revenues via uniform transfers reduces consumption inequality, shifts the welfare burden to high-income households, but leads to significant output losses. Subsidizing green energy producers boosts energy production, reduces output losses, and requires a smaller carbon tax to meet the EU target. However, it increases consumption and income inequality, with the highest welfare costs borne by low-income and asset-poor households. Our findings highlight key trade-offs between equity and efficiency in green transition policies. |
| Keywords: | Green Transition; Inequality; Carbon Pricing |
| JEL: | Q43 Q52 E6 |
| Date: | 2025–03 |
| URL: | https://d.repec.org/n?u=RePEc:dnb:dnbwpp:830 |