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Climate change and green transitions in an agent-based integrated assessment model

F. Lamperti, Giovanni Dosi (), Mauro Napoletano (), Andrea Roventini and A. Sapio

Technological Forecasting and Social Change, 2020, vol. 153, issue C

Abstract: In this paper we employ an agent-based integrated assessment model to study the likelihood of transition to green, sustainable growth in presence of climate damages. The model comprises heterogeneous fossil-fuel and renewable plants, capital- and consumption-good firms and a climate box linking greenhouse gasses emission to temperature dynamics and microeconomic climate shocks affecting labour productivity and energy demand of firms. Simulation results show that the economy possesses two statistical equilibria: a carbon-intensive lock-in and a sustainable growth path characterized by better macroeconomic performances. Once climate damages are accounted for, the likelihood of a green transition depends on the damage function employed. While energy efficiency shocks (which raise the demand of energy) exert little effects on the macroeconomic performance compared to labour productivity impacts, they disproportionally harm the chances of an energy transition by exacerbating path-dependence in the process of technical change in favour of fossil-fuel technologies. Finally, we run a series of policy experiments on carbon (fossil fuel) taxes and green subsidies. We find that the effectiveness of such market-based instruments is limited, though it also depends upon the different channels climate change affects the economy through. Complementary policies might be required to avoid carbon-intensive lock-ins.

Keywords: Climate change; Agent based models; Transitions; Energy policy; Growth (search for similar items in EconPapers)
JEL-codes: C63 Q40 Q50 Q54 (search for similar items in EconPapers)
Date: 2020
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Working Paper: Climate change and green transitions in an agent-based integrated assessment model (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:153:y:2020:i:c:s0040162518312460

DOI: 10.1016/j.techfore.2019.119806

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