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Economic complexity
Economic complexity
Historically, physics studied natural phenomena by focusing on their elementary "bricks", using the possibility to deduce the features of the systems from their single elements. This approach, however, cannot be applied to complex systems, in which the interaction of many elements leads to the emergence of new properties. The development of the mathematical apparatus of statistical physics, combined with increasing data availability, allowed the study of complex (and, in particular, economic) systems. In this project, we employ the Economic Complexity (EC) framework, which was formulated precisely to apply tools from statistical mechanics, complex networks, and machine learning to economics, with special attention to comparing theoretical results with empirical evidence. The EC approach provides two concepts that are key for our project. The first is the fitness , a measure of the competitiveness of a country in terms of the complexity of its capabilities. The second one is relatedness , which measures the similarity between two economic sectors and the feasibility of entering into a new economic activity for an economic actor (e.g., a country). Both concepts can be useful to assess the steps to redirect the global system of production toward the sustainability transition, a policy priority for both the European Commission's Green Deal and the Italian Government’s PNRR. The physics of complex systems, and in particular the EC framework, can provide powerful quantitative tools to investigate the Economy and design future strategies of development, as proven by its adoption by both the World Bank and European Commission.
Historically, physics studied natural phenomena by focusing on their elementary "bricks", using the possibility to deduce the features of the systems from their single elements. This approach, however, cannot be applied to complex systems, in which the interaction of many elements leads to the emergence of new properties. The development of the mathematical apparatus of statistical physics, combined with increasing data availability, allowed the study of complex (and, in particular, economic) systems. In this project, we employ the Economic Complexity (EC) framework, which was formulated precisely to apply tools from statistical mechanics, complex networks, and machine learning to economics, with special attention to comparing theoretical results with empirical evidence. The EC approach provides two concepts that are key for our project. The first is the fitness , a measure of the competitiveness of a country in terms of the complexity of its capabilities. The second one is relatedness , which measures the similarity between two economic sectors and the feasibility of entering into a new economic activity for an economic actor (e.g., a country). Both concepts can be useful to assess the steps to redirect the global system of production toward the sustainability transition, a policy priority for both the European Commission's Green Deal and the Italian Government’s PNRR. The physics of complex systems, and in particular the EC framework, can provide powerful quantitative tools to investigate the Economy and design future strategies of development, as proven by its adoption by both the World Bank and European Commission.
The WECARE project
The WECARE project
The WECARE (WEaving Complexity And the gReen Economy) project is financed by the Italian Ministry of University and Research (MUR), Piano Nazionale Di Ripresa e Resilienza (PNRR), funded by the European Union - NextGenerationEU. In this project we will gather, process, and harmonize data about export, patenting activity, and scientific production, with a special focus on green products and technologies. By adopting an open data/open science approach, we plan to publish both the inputs and the outputs of our analyses on a dedicated repository. We will compute the green, the technology, and the scientific fitness of countries/regions, an assessment of the specific ripeness of their economies. We will use these indicators to enhance our GDP forecast model with a multidimensional description. We will compute the relatedness between single economic sectors and technological fields by validating the resulting links against a null model that generates a canonical ensemble of networks. The result will be a map from industrial sectors to green technologies and vice versa. By using machine learning, we will compute the relatedness between green products/technologies and countries/regions to assess their preparedness for the sustainability transition and compare these results with standard economic and environmental indicators. Lastly, we will investigate the theoretical underpinnings of the EC methodology: we believe that a mathematical model from first principles will provide a better understanding of this data-driven framework and pave the way to future improvements.
The WECARE (WEaving Complexity And the gReen Economy) project is financed by the Italian Ministry of University and Research (MUR), Piano Nazionale Di Ripresa e Resilienza (PNRR), funded by the European Union - NextGenerationEU. In this project we will gather, process, and harmonize data about export, patenting activity, and scientific production, with a special focus on green products and technologies. By adopting an open data/open science approach, we plan to publish both the inputs and the outputs of our analyses on a dedicated repository. We will compute the green, the technology, and the scientific fitness of countries/regions, an assessment of the specific ripeness of their economies. We will use these indicators to enhance our GDP forecast model with a multidimensional description. We will compute the relatedness between single economic sectors and technological fields by validating the resulting links against a null model that generates a canonical ensemble of networks. The result will be a map from industrial sectors to green technologies and vice versa. By using machine learning, we will compute the relatedness between green products/technologies and countries/regions to assess their preparedness for the sustainability transition and compare these results with standard economic and environmental indicators. Lastly, we will investigate the theoretical underpinnings of the EC methodology: we believe that a mathematical model from first principles will provide a better understanding of this data-driven framework and pave the way to future improvements.
Team
ISC-CNR: Andrea Zaccaria (Principal Investigator), Francesco Lomonaco, Andrea Napoletano, Alberto Petri, Claudio Castellano
CREF: Dario Mazzilli (Head of Unit), Francesco De Cunzo, Aurelio Patelli, Angelica Sbardella, Pasquale Scaramozzino, Andrea Tacchella
Publications
Albora, G., Diodato, D., Fenoaltea, E., Mazzilli, D., Patelli, A., Sbardella, A., ... & Zaccaria, A. (2025). Challenges and opportunities for the EU labour market from AI development. JRC Policy Brief
Albora, G., Straccamore, M., & Zaccaria, A. (2026). Machine learning-inspired similarity measure to forecast M&A from patent data. PloS one, 21(2), e0341010.
Angelini, O., Gabrielli, A., Tacchella, A., Zaccaria, A., Pietronero, L., & Di Matteo, T. (2024). Forecasting the countries’ gross domestic product growth: the case of Technological Fitness. Chaos, Solitons & Fractals, 184, 115006.
Aufiero, S., De Marzo, G., Sbardella, A., & Zaccaria, A. (2024). Mapping job fitness and skill coherence into wages: an economic complexity analysis. Scientific Reports, 14(1), 11752.
Buffa, L., Mazzilli, D., Piombo, R., Saracco, F., Cimini, G., & Patelli, A. (2025). Maximum entropy modelling of sub-optimal transport. Communications Physics, 9 (39).
Caldarola, B., Mazzilli, D., Napolitano, L., Patelli, A., & Sbardella, A. (2024). Economic complexity and the sustainability transition: A review of data, methods, and literature. Journal of Physics: Complexity, 5(2), 022001.
Cresti, L., Mazzilli, D., Patelli, A., Sbardella, A., & Tacchella, A. (2025). Vulnerabilities and capabilities in the EU Automotive industry: Leveraging Input-Output Analysis and Economic Complexity. arXiv preprint arXiv:2501.01781.
De Cunzo, F., Consoli, D., Perruchas, F., & Sbardella, A. (2025). Mapping critical raw materials in green technologies. Industry and Innovation, 1-34.
De Cunzo, F., Patelli, A., Sbardella, A., & Tacchella, A. (2025). The Functional Role of Critical Raw Materials in Technological Innovation (No. 2516). Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography.
De Stefano, V., Mula, M., Mariani, M. S., & Zaccaria, A. (2025). From macro to micro: Economic complexity indicators for firm growth. arXiv preprint arXiv:2507.21754.
Fenoaltea, E. M., Mazzilli, D., Patelli, A., Sbardella, A., Tacchella, A., Zaccaria, A., ... & Pietronero, L. (2024). Follow the money: a startup-based measure of AI exposure across occupations, industries, and regions. arXiv preprint arXiv:2412.04924.
Fessina, M., Zaccaria, A., Cimini, G., & Squartini, T. (2024). Pattern-detection in the global automotive industry: A manufacturer-supplier-product network analysis. Chaos, Solitons & Fractals, 181, 114630.
Fessina, M., Tacchella, A., & Zaccaria, A. (2025). Product-level value chains from firm data: mapping trophic levels into economic growth. arXiv preprint arXiv:2505.01133.
Fessina, M., Albora, G., Tacchella, A., & Zaccaria, A. (2024). Identifying key products to trigger new exports: an explainable machine learning approach. Journal of Physics: Complexity, 5(2), 025003.
Huang, Y., Xu, S., Lü, L., Zaccaria, A., & Mariani, M. S. (2024). Uncovering key predictors of high-growth firms via explainable machine learning. arXiv preprint arXiv:2408.09149.
Mariani, M. S., Mazzilli, D., Patelli, A., Sels, D., & Morone, F. (2024). Ranking species in complex ecosystems through nestedness maximization. Communications Physics, 7(1), 102.
Napoletano et al. (2026), Green Transition Readiness and Environmental Performance: A Machine-Learning Evaluation of Export Complexity in Renewable Energy Supply Chains. in preparation
Piombo, R., Mazzilli, D., & Patelli, A. (2026). Statistical Mechanics of the Sub-Optimal Transport. arXiv preprint arXiv:2602.04308.
Russo, A., Scaramozzino, P., & Zaccaria, A. (2025). A job-based assessment of economic complexity: from hidden to revealed. arXiv preprint arXiv:2507.05846.
PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) Missione 4 "Istruzione e Ricerca" - Componente C2 Investimento 1.1, PRIN 20223W2JKJ "WECARE", CUP B53D23003880006
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