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The sustainability transition and the digital transformation: two challenges for agent-based macroeconomic models

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Abstract

Digital transformation and sustainability transition are complex phenomena characterized by fundamental uncertainty. The potential consequences deriving from these processes are the subject of open debates among economists and policy-makers. In this respect, adopting a modeling and simulation approach represents one of the best solutions in order to study potential effects linked to these complex phenomena. Agent-based modeling represents an appropriate paradigm to address complexity. This research aims at showing the potential of the large-scale macroeconomic agent-based model Eurace in order to investigate challenges like sustainability transition and digital transformation. In particular, two different simulation studies, i.e., the digital transformation and the sustainability transition are presented, in order to show the potential of the Eurace model in addressing such kinds of complex phenomena. As regards the digital transformation, the Eurace model is able to capture interesting business dynamics characterizing the so-called increasing returns world and, in case of high rates of digital technological progress, it shows significant technological unemployment. As regards the sustainability transition, it displays a rebound effect on energy savings that compromises efforts to reduce greenhouse gas emissions via electricity efficiency improvements. Furthermore, it shows that a carbon tax could be not sufficient to decouple the economy from carbon consumption and that a feed-in tariff policy fostering renewable energy production growth may be more effective.

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Notes

  1. The task-based approach proposed by Acemoglu and Restrepo (2018d) is based on the pioneering contribution of Zeira (1998).

  2. In this respect, the main purpose of equilibrium models is to determine the vector of prices which equals agent’s demand and supply, whereas agent-based modeling investigates how agent’s actions, strategies, and expectations vary out-of-equilibrium and the aggregate dynamics of the system emerging from agents interactions at the micro-level (see Arthur (2006, 2010)).

  3. The set of “digital technologies” skills can be as large as the number of DADs present in the economic system.

  4. Economic benefits emerge indirectly from the interaction between different groups (see Farrell and Klemperer 2007; Belleflamme and Peitz 2018).

  5. HHs are characterized by five different education level. DADs hire workers with a high degree of education, i.e., from the third upward. Although CGPs hire workers disregarding their education level, they prioritized highly educated workers during the labor market sessions

  6. The fifteen countries considered are the following: Italy (IT), Germany (DE), Netherlands (NL), United Kingdom (UK), United States (USA), France (FR), Sweden (SW), Spain (ES), Denmark (DK), Portugal (PT), Austria (AT), Finland (FI), Ireland (IE), Greece (GR) and Luxembourg (LU). The analysis is focused on a time period of twenty-two years, namely, from 1995 to 2016. Moreover, the time span is also divided in a pre and post crisis time period, i.e., from 1995 to 2007 and from 2008 to 2016.

  7. ICT capital investments are composed by tangible investments in ICT equipment and intangible investments in software and database. The combination between these two typologies of investments results to be crucial by virtue of the intrinsic complementarity characterizing hardware and software: the hardware is useless without software and vice versa. Through the combination of these investments we are capable to evaluate the effective importance that digital technologies have on our productive system.

  8. The TFP shape has been modeled as exponential in order to represent a significant influence of digital technological progress on our economy. Moreover, OECD data on TFP growth rates suggest a long-term exponential trend of TFP, albeit with a declining rate (https://stats.oecd.org).

  9. An organizational unit represents a group of workers organized according to a specific criterion. According to Mintzberg (1979), we can distinguish between two different criteria in order to group employees, i.e., the functional criterion and the divisional one. In the first case, workers are grouped by knowledge, skills, work process or function, while in the second case human resources are grouped to produce (or provide) a specific product (or service) or to serve a specific area or client.

  10. As mentioned previously, each unit of hard capital is combined with a software license.

  11. In case of an increase in sales, each DAD raises its price trying to exploit a potential expansionary phase, otherwise it opts for a price reduction in order to increase its market share. In fact, a lower price could determine an increase in the number of users (see Bertani et al. (2021a) for further details).

  12. The optimum quantities of both capital and labor in order to meet the planned production are determined through the mathematical optimization methods of Lagrange multipliers (see Bertani et al. (2021b) for further details).

  13. The values used in order to obtain Fig. 9 are as follows: α = 0.3, β = 0.7, w = 1, r = 0.03, cK = 2

  14. As mentioned previously, η does not represent the rate of technological progress within the economy. However, it influences the innovation rate significantly. Indeed, the higher the value of η, the higher the endogenous rate of technological progress.

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  1. University of Genoa, Genoa, Italy

    Marcello Nieddu & Filippo Bertani

  2. LIUC - Cattaneo University, Castellanza, VA, Italy

    Linda Ponta

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  1. Marcello Nieddu
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Nieddu, M., Bertani, F. & Ponta, L. The sustainability transition and the digital transformation: two challenges for agent-based macroeconomic models. Rev Evol Polit Econ 3, 193–226 (2022). https://doi.org/10.1007/s43253-021-00060-5

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