The leakage effect may undermine the circular economy efforts.

In this paper, we examine the impact of the circular economy on global resource extraction. To this end, we make an input-output analysis dynamic by combining it with an agent-based model of the capital sector. This approach allows us to study the evolution of the circular economy due to the endogenous decisions of firms on whether to invest in the capital expansion of primary or secondary sectors. Previous studies have examined the macroeconomic effects of the circular economy using scenarios that exogenously impose higher recycling rates, improved resource efficiency, or lowered demand on the economy. Such studies typically assume static consumer budgets, no price adjustments, capital investments in recycling infrastructure, or technological innovation. We relax these assumptions in a novel agent-based input-output model (ABM-IO). We show that the circular economy can significantly reduce the extraction of iron, aluminum, and nonferrous metals if implemented globally. However, the leakage effect may also cause some metal-intensive industries to relocate outside the EU, offsetting the circular economy efforts. The risk of the leakage effect is especially high for copper.

Compounding COVID-19 and climate risks: The interplay of banks' lending and government's policy in the shock recovery.

We assess the individual and compounding impacts of COVID-19 and climate physical risks in the economy and finance, using the EIRIN Stock-Flow Consistent model. We study the interplay between banks' lending decisions and government's policy effectiveness in the economic recovery process. We calibrate EIRIN on Mexico, being a country highly exposed to COVID-19 and hurricanes risks. By embedding financial actors and the credit market, and by endogenising investors' expectations, EIRIN analyses the finance-economy feedbacks, providing an accurate assessment of risks and policy co-benefits. We quantify the impacts of compounding COVID-19 and hurricanes on GDP through time using a compound risk indicator. We find that procyclical lending and credit market constraints amplify the initial shocks by limiting firms' recovery investments, thus mining the effectiveness of higher government spending. When COVID-19 and hurricanes compound, non-linear dynamics that amplify losses emerge, negatively affecting the economic recovery, banks' financial stability and public debt sustainability.

Why do we need agent-based macroeconomics?

We are entering the third decade of the twenty-first century with profound uncertainties and crucial challenges for the world economy. Phenomena like climate change, digital transformation, migration, demographic changes, and the ongoing COVID pandemic need to be understood and promptly addressed. We argue that the agent-based approach in economics is well suited to tackle these topics, because of its capacity to integrate the "micro" and "macro" dimensions by modelling the network of interactions among heterogeneous economic agents and their aggregate outcomes. This paper explains why the agent-based methodology is needed to overcome the limitations of the neoclassical approach in economics, which has not been able to properly address those challenges. To do so, the paper retraces the main stages of the scientific evolution in a general historical and epistemological perspective, showing how the paradigm of reductionism, which led to extraordinary advances after the scientific revolution of the seventeenth century, is less effective when addressing the main challenges ahead. On the other hand, the sciences of chaos theory and complex systems can provide the economic discipline with more suitable instruments to face those challenges. Finally, the paper briefly presents the contributions of the special issue, which use applications of agent-based models to study the main problems of our times.

Semi-Markov graph dynamics.

In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first ingredient is a Markov chain on the space of possible graphs. The second ingredient is a semi-Markov counting process of renewal type. The model consists in subordinating the Markov chain to the semi-Markov counting process. In simple words, this means that the chain transitions occur at random time instants called epochs. The model is quite rich and its possible connections with algebraic geometry are briefly discussed. Moreover, for the sake of simplicity, we focus on the space of undirected graphs with a fixed number of nodes. However, in an example, we present an interbank market model where it is meaningful to use directed graphs or even weighted graphs.