Purpose Amid the global transition toward sustainable energy systems, this study examines the determinants of energy intensity across 27 OECD countries from 1990 to 2022, with particular emphasis on technological innovation and demographic dynamics, providing novel insights for energy policy. Design/methodology/approach A dynamic panel framework employing common correlated effects (DCCE) estimators is utilized to account for cross-sectional dependence and slope heterogeneity inherent in interdependent country-level data. Complementary quantile regressions capture heterogeneous effects across the conditional distribution of energy intensity. Findings Empirical results indicate that technological innovation significantly reduces energy intensity through efficiency gains and cleaner technology adoption. Economic openness and GDP per capita are associated with lower energy intensity, whereas higher fertility rates are linked to increased energy intensity, reflecting greater demand for energy-intensive services. Quantile estimates reveal that innovation and credit access exert stronger effects at lower and median quantiles, while fertility’s impact diminishes at higher quantiles. Practical implications The findings underscore the need for integrated policies promoting innovation, financial accessibility and demographic considerations to enhance sustainable energy efficiency. Originality/value The study advances the literature by combining dynamic panel and quantile-specific approaches to elucidate the heterogeneous impacts of technological, economic and demographic factors on energy intensity – nuances often overlooked in prior empirical research.
Agent-based models (ABMs) typically rely on equations to incorporate agents' decision-making, ranging from ad-hoc rules or simple heuristics to complex, theory- or data-driven approaches. Recent advances in large language models (LLMs) make it possible to represent economic decision-making through information-conditioned decisions rather than explicitly specified behavioural equations. However, whether LLM-driven agents can feasibly replace individual components in an ABM and the degree to which they can replicate observed economic dynamics remains insufficiently understood. In particular, it is unclear whether the integration of LLM-driven agents in a macroeconomic ABM can generate historically plausible macroeconomic trajectories and how their performance depends on the information provided to them and on the scale of the underlying model. This paper addresses these questions by introducing an LLM-driven bank agent into a data-driven macroeconomic model of the Greek economy, where it determines consumer credit and lending rates. Results show that the LLM-driven agent's decisions can reproduce historical consumer credit dynamics relatively well, although their performance in replicating lending rates is weaker and more heterogeneous; broader economic information generally improves performance, while larger models do not systematically outperform smaller ones. Overall, the study provides encouraging evidence that LLM-driven agents can be feasibly integrated into macroeconomic ABMs, assimilating economically relevant information and producing outcomes that closely match observed data, without requiring explicit functional forms for behavioural relationships - pointing towards the possibility of enabling economic simulations to shift from equations to prompts.
Το λεύκωμα συνοψίζει το σύνολο του ζωγραφικού και χαρακτικού έργου του Λάμπρου Ορφανού, αποτελώντας την πρώτη ουσιαστική προσπάθεια καταγραφής και ανάδειξής του με συστηματικό τρόπο.
This paper asks whether the digital economy is becoming the new economy: not because traditional economic functions disappear, but because they are increasingly reorganised through digital infrastructure, digital finance and algorithmic capability. We introduce an open-data Digital Architecture framework and build a fully reproducible panel of 38 OECD economies for 2010 to 2024 from World Bank sources. The framework constructs transparent composite indices for digital infrastructure, FinTech, AI/ML capability, the traditional economy, and a clean new economy. Using two-way fixed-effects panels, we provide a descriptive answer: in OECD economies, the digital economy coincides with the new economy mainly through digital infrastructure. First, digital infrastructure is the load-bearing pillar of the digital economy’s association with the new economy: it accounts for about 24 per cent of within-country variation in new-economy outcomes, although its coefficient is imprecise under country-clustered errors. Second, the Digital Architecture Index is useful as a benchmarking construct, but it does not improve the within-country fit because the digital pillars are collinear and infrastructure carries most of the signal. Third, the evidence does not support a robust three-way configuration effect among digital infrastructure, FinTech and AI/ML capability; the association is sensitive to error structure, dynamics, outcome definition and true survey-year estimation. A future-dated placebo loads for the infrastructure block as well, so even the descriptive associations cannot be cleanly separated from slow-moving common trends. The paper therefore contributes a reusable open-data measurement framework and a clear empirical benchmark: in OECD economies, the digital economy aligns with the new economy primarily through digital infrastructure, the pillar that carries the strongest within-country association with productivity and entrepreneurship. The AI/ML capability proxy is externally validated against Eurostat enterprise-AI adoption data, with a cross-country correlation of 0.80.
Banking Union (BU) membership comes with costs, such as foregoing the ability to tailor domestic banking regulation to the country’s needs and preferences; and benefits, such as a more efficient provision of deposit insurance. We use a general equilibrium model that features a rich financial sector, risk shocks, bank capital regulation and government-provided deposit insurance to evaluate the implications of BU membership for welfare, macroeconomic activity and financial stability. A riskier and more consequential –for economic activity– banking sector favors BU membership. However, gains in macroeconomic performance and financial stability alone are not sufficient to make Banking Union membership welfare improving.
