Technology adoption and carbon emissions with dynamic trading among heterogeneous agents

Huayi Chen and Tieju Ma

Energy Economics, 2021, vol. 99, issue C

Abstract: The existing systematic technology adoption models with interacting agents commonly assume that agents function as cooperative entities in a planned economy, with the aim of finding the Pareto optimal solutions of all agents. However, what if the agents are somewhat selfish and act more as entities in a market economy? Little work has been done to explore this question with systematic technology adoption models. This study explores this question by developing a systematic technology adoption model in which each agent intends to minimize its own total cost with the dynamic trading of technology and goods with others, and a Walrasian auction is performed at every time unit to achieve the dynamic trading equilibrium. By using this model, this paper investigates how the dynamic trading among heterogeneous agents affects the adoption of a new advanced technology as well as the corresponding total cost and carbon emissions of the entire system. The previous studies in this modeling stream rarely used applications with real social and economic backgrounds. This study makes a first step by using the model to analyze how the dynamic trading among different regions influences technology adoption, total system costs, and emissions worldwide. The main finding of the study is that dynamic trading among heterogeneous agents is a cost-effective way to accelerate the adoption of a new advanced technology, and to reduce total carbon emissions. Our study implies that global trading is not only related to the economy itself, but can also contribute to technology development and carbon reductions worldwide.

Keywords: OR in societal problem analysis; Technology adoption; Heterogeneous interacting agents; Equilibrium prices and trading volumes (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:99:y:2021:i:c:s0140988321001687

DOI: 10.1016/j.eneco.2021.105263

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