Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence

A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a...

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Autores principales:	Anna-Katharina Kothe, Alexander Kuptel, Roman Seidl
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	personal carbon trading agent-based model computational approach price adaptive reduction rate (target) Technology T
Acceso en línea:	https://doaj.org/article/d564ddacf24349c292582ebd302b4dcb
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spelling	oai:doaj.org-article:d564ddacf24349c292582ebd302b4dcb2021-11-25T17:26:04ZSimulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence10.3390/en142274971996-1073https://doaj.org/article/d564ddacf24349c292582ebd302b4dcb2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7497https://doaj.org/toc/1996-1073A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO<sub>2</sub> allowances to ensure a steady decrease in CO<sub>2</sub> emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO<sub>2</sub> abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM.Anna-Katharina KotheAlexander KuptelRoman SeidlMDPI AGarticlepersonal carbon tradingagent-based modelcomputational approachpriceadaptive reduction rate (target)TechnologyTENEnergies, Vol 14, Iss 7497, p 7497 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	personal carbon trading agent-based model computational approach price adaptive reduction rate (target) Technology T
spellingShingle	personal carbon trading agent-based model computational approach price adaptive reduction rate (target) Technology T Anna-Katharina Kothe Alexander Kuptel Roman Seidl Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
description	A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO<sub>2</sub> allowances to ensure a steady decrease in CO<sub>2</sub> emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO<sub>2</sub> abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM.
format	article
author	Anna-Katharina Kothe Alexander Kuptel Roman Seidl
author_facet	Anna-Katharina Kothe Alexander Kuptel Roman Seidl
author_sort	Anna-Katharina Kothe
title	Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
title_short	Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
title_full	Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
title_fullStr	Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
title_full_unstemmed	Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence
title_sort	simulating personal carbon trading (pct) with an agent-based model (abm): investigating adaptive reduction rates and path dependence
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d564ddacf24349c292582ebd302b4dcb
work_keys_str_mv	AT annakatharinakothe simulatingpersonalcarbontradingpctwithanagentbasedmodelabminvestigatingadaptivereductionratesandpathdependence AT alexanderkuptel simulatingpersonalcarbontradingpctwithanagentbasedmodelabminvestigatingadaptivereductionratesandpathdependence AT romanseidl simulatingpersonalcarbontradingpctwithanagentbasedmodelabminvestigatingadaptivereductionratesandpathdependence
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Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence

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