Abstract
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 CO2 allowances to ensure a steady decrease in CO2 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 CO2 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.
Highlights
Climate change and its consequences are as present and pressing as never before.Carbon emissions are agreed to be an important driver of this phenomenon, and carbon emissions from households constitute a large part of nationwide emissions, e.g., 40% of emissions in the UK are due to personal transport and heating [1]
Meaningful insights about the dynamics and the focal variables in a personal carbon trading (PCT) scheme can be generated by the exploratory use of an agent-based model (ABM)
The initial allocation of allowances has an impact on the number of allowances available later in the model, which, in turn, implies that the initial allocation has a strong impact on price, the number of households buying allowances, and emission reductions
Summary
Climate change and its consequences are as present and pressing as never before.Carbon emissions are agreed to be an important driver of this phenomenon, and carbon emissions from households constitute a large part of nationwide emissions, e.g., 40% of emissions in the UK are due to personal transport and heating [1]. As countries fall behind on their carbon emission reduction targets, it is likely that more ambitious policies will have to be pursued in order to avoid the worst effects of climate change [2]. One such policy is personal carbon trading (PCT), a type of capand-trade scheme where households are allocated allowances for carbon emissions from personal consumption. This paper focuses on PCT and assumes that no other upstream carbon tax or trade schemes are in place It should be noted, that other approaches to limit carbon emissions exist. In order to avoid “double-pricing of certain emissions” ([3], p. 2)—that is, a carbon price upstream as well as downstream—any effective PCT scheme must be carefully embedded into other measures of carbon abatement
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
