Conference Agenda

The objective of this study is to empirically examine the impact of the joint adoption of eco-innovation and digital technologies—so-called “twin” transition—on firms’ sales performance in the Korean manufacturing sector. The analysis, which uses data from the Korea Innovation Survey 2022 and employs propensity score matching techniques, shows significant positive effects on sales for firms that adopt both eco-innovation and digital technology compared to nonadopters. Specifically, the average treatment effect on the treated firms was estimated to range between 38% and 46%. We also found that significant sales increase for firms that combined eco-innovation with big data, cloud computing, or 5G telecommunications. These findings highlight the strategic importance of firms that integrate eco-innovation with such technologies that can enhance their business performance. This research underscores the need for policies and incentives that support sustainable growth and competitive advantage by promoting the joint adoption of eco-innovations and emerging digital technologies.

This paper evaluates the macroeconomic and financial risks of the energy transition using an extended MATRIX model, a multi-agent, multi-sector integrated assessment framework for the Euro Area. The model features endogenous, directed technical change in the energy sector and a decentralized electricity market based on merit-order rule. Energy firms switch technologies based on relative profitability, capturing feedback loops between R&D, productivity gains, and competitiveness, which may lead to either brown lock-in or green energy transition. We compare conventional policies -- brown tax (BT), unconditional green subsidy (GS), and conditional green subsidy (CGS) linked to R&D -- with alternative policy mixes, such as coordinated monetary policy, green finance and green industrial policy. Results show that while conventional policies modestly increase transition likelihood, they entail GDP losses due to production and financial constraints. These can be mitigated with green industrial policy and green finance, which alleviate sectoral bottlenecks and foster a more effective transition.

The relationship between natural capital and economic growth has gained considerable attention in recent years, especially within the framework of sustainable development and European regional cohesion policies. This paper highlights the critical role of natural capital in driving regional economic growth across European NUTS 2 regions, providing new evidence on both its direct effects and spatial spillovers. A 10% increase in natural capital leads to a 0.7% rise in gross value added, with 0.4% attributed to direct effects and 0.3% to benefits from neighbouring regions. These findings indicate that natural capital not only supports the economic performance of individual regions but also fosters regional cohesion. The study underscores the importance of integrating natural capital into regional development policies and provides evidence to guide future investments in biodiversity protection and ecosystem restoration, aligning with the EU’s green transition and cohesion goals.

This paper investigates the impact of two significant ongoing transitions on labor market dynamics: the green transition and automation. Our analysis consists of three parts. First, we use a descriptive approach to map transition-related patterns and trends. Second, we identify individual and occupational drivers of transition-related job switches. Finally, we use an event-study approach to study the consequences for worker-level labor market outcomes of these job switches. Our analysis draws on European Labor Force Survey data and on Dutch administrative data spanning from 2003 to 2023. On an individual level, green and low-automation-risk jobs attract higher-educated, younger, male workers, while, at an occupational level, skill gaps and geographic concentration hinder transition-related mobility. Controlling for individual characteristics, we find that transitions to green jobs yield stronger and more persistent wage gains (4% vs. 3%) than to low-automation-risk jobs, while wage losses after leaving brown jobs (9%) are larger and longer-lasting than those after exiting high-automation-risk jobs (6%)