Abstract

This research builds on the regional innovation literature, and aims to better understand the potential for, and development of, low-carbon technologies in the European Union. Exploiting the OECD’s REGPAT for regionalised patent data, we estimate the potential advantage of European NUTS2 regions have in 14 green technologies. We use network proximity between technologies and between regions to understand technological/regional clusters of revealed technological advantage and build the regressors for estimating regional potential advantage in specific technologies via zero-inflated beta regressions. Based on this, we explore the region-technology networks, finding two gravity centres for green innovation in France’s and Germany’s industrial and high-tech hubs (Île de France, Stuttgart, and Oberbayern). We also construct a dataset of lagged potentials and labour market, economic and demographic variables, and perform an elastic net regularisation to understand the association with current revealed advantages. Our approach indicates an association between technological advantage in green technologies and the (lags of) participation rates in labour markets, sectoral employment in science and technology, general higher education, duration of employment, percentage of GDP spent on R&D (public and private) and other expenditure on R&D. If confirmed by causality tests, the established associations could help in designing horizontal economic policies to enable specific regions to realise their specialisation potential in specific green technologies.

Highlights

  • Keeping the global temperature increase below 2 ◦ C above pre-industrial levels will require the almost complete decarbonisation of our energy system early in the second half of this century

  • To enable domestic companies to flourish in these new sectors, policymakers seek to complement the creation of early markets for decarbonisation technologies with some form of industrial policy

  • While some works have focused on specific country-sectors as candidates for industrial policy exercises (Huberty and Zachmann, 2011) [1], other contributions exploited the product space (Hidalgo et al, 2007) [2] methodology to infer information from the most promising sectors and products from complexity-grounded statistics

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Summary

Introduction

Keeping the global temperature increase below 2 ◦ C above pre-industrial levels will require the almost complete decarbonisation of our energy system early in the second half of this century. This will require a growing and diverse deployment of low-carbon technologies (including vehicles, power plants, appliances, and batteries), which will replace the existing stock of high-carbon technologies. While some works have focused on specific country-sectors as candidates for industrial policy exercises (Huberty and Zachmann, 2011) [1], other contributions exploited the product space (Hidalgo et al, 2007) [2] methodology to infer information from the most promising sectors and products from complexity-grounded statistics. Compared to other works exploiting these methodologies, we focus on technological specialisation rather than export specialisation, using patent data for this analysis

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