Abstract

As many societal needs compete with sustainability for a finite pool of public resources, understanding the cost of mitigating greenhouse gas emissions has become a critical sustainability question. The Marginal Abatement Cost Curve (MACC) is a widely used approach to characterize mitigation costs. It is constructed by ordering technological and other interventions to mitigating an emission from lowest to highest cost, indicating thecumulative mitigation possible for each intervention. It is clearly a simplification of a complex techno-economic system and has been criticized for neglecting factors such as dynamics, consumer behavior, non-cost barriers to adoption, and interactions between technologies. However, as long as the limitations of the information provided by a MACC are understood, it does provide valuable and easily comparable information on costs and mitigation potential.

In this thesis I try to address some of the issues with the abatement cost curve, namely three. 1 - Heterogeneity: The traditional MACC assumes all users to have an average behavior and hence have an average (same) gain from the adoption of a technology. However, gains from adoption vary widely between users and merits inspection. 2 – Learning: MAC curves neglect technology progress, the reduction in price to later adopters due to capacity addition brought about my early adaptors. 3 – Interaction: Technologies that interact with each other would impact the abatement cost of each other depending on where they are placed in the adoption order. For example, renewable technologies would reduce emission intensity of the grid. If they are succeeded by electric vehicles (EVs), the EVs would displace more CO 2 since they are now operating off a cleaner grid. I analyze three sectors and several technologies: Electricity generation (Utility-scale wind, utility-scale photovoltaic), Single Family Residential Buildings (Furnace, central AC, Wall insulation, Attic insulation, Air sealing) and Transportation (Battery electric vehicle (BEV), Plugin hybrids vehicles (PHEV) and Hybrid vehicles (HEV)) I use an iterative cost minimization methodology which chooses the lowest abatement cost technology for each round based on the impact of the previous technology on the carbon intensity of the grid. Heterogeneity mixes technologies in the MACC for electric vehicles (EVs) and home efficiency measures such that particulars of users is far more relevent in the ordering of the MACC that specific technologies Accounting for interactions has mixed impacts. Adding technologies that increase electricity demand (e.g. BEVs) increases the carbon benefits (reduces abatement cost) of subsequent efficiency technologies like residential efficiency technologies, while reducing the carbon benefits of their subsequent counterparts. The opposite is true with technologies that reduce energy demand, reducing the carbon benefits of subsequent efficiency technologies while increasing the benefits from electric vehicles. Learning reduces cost of abatement. The net impact towards emission reduction potential in the integrated model with learning is positive, increasing abatement potential from 1.07 billion tonnes to 1.24 billion tonnes when compared with a traditional MACC. Also, by accounting for heterogeneities, although the potential for “free carbon” does not change much, the potential for savings can go up by as much as 50%. Moreover, total cost of meeting various mitigation targets in the integrated model is significantly lower than the traditional MACC.

Accounting for heterogeneity and interaction significantly alters the priority for technology disbursement from an emission reduction perspective. It shows the importance of targeted marketing as a more effective mechanism as opposed to technology-based subsidy. It also shows that the initial adopters make the technology cheaper for the latter adopters. This work significantly improves the accuracy of the MACC in both emission and cost saving potential, making it more applicable in policy decision making.

Publication Date

4-8-2020

Document Type

Dissertation

Student Type

Graduate

Degree Name

Sustainability (Ph.D.)

Department, Program, or Center

Sustainability (GIS)

Advisor

Thomas Trabold

Advisor/Committee Member

Jeffrey Wagner

Advisor/Committee Member

Eric Williams

Campus

RIT – Main Campus

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