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Policy Analysis 2

Analysis of costs and timeframe for reducing CO emissions by 70% in the U.S. auto and energy sectors by 2050 Sarang D. Supekar, and Steven J. Skerlos Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01295 • Publication Date (Web): 14 Sep 2017 Downloaded from http://pubs.acs.org on September 17, 2017

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Environmental Science & Technology

Analysis of costs and timeframe for reducing CO2 emissions by 70% in the U.S. auto and energy sectors by 2050 Sarang D. Supekar†, Steven J. Skerlos*,†,‡ †

Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, United

States ‡

Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI

48109, United States

*

Corresponding author. Address: 3001F EECS, 1301 Beal Avenue, Ann Arbor, MI 48109-

2122, USA. Phone: (734) 615-5253. Fax: (734) 647-3170. Email: [email protected]

ACS Paragon Plus Environment

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ABSTRACT

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Using a least-cost optimization framework, it is shown that unless emissions reductions beyond

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those already in place begin at the latest by 2025 (± 2 years) for the U.S. automotive sector, and

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by 2026 (– 3 years) for the U.S. electric sector, 2050 targets to achieve necessary within-sector

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preventative CO2 emissions reductions of 70% or more relative to 2010 will be infeasible. The

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analysis finds no evidence to justify delaying climate action in the name of reducing

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technological costs. Even without considering social and environmental damage costs, delaying

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aggressive climate action does not reduce CO2 abatement costs even under the most optimistic

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trajectories for improvements in fuel efficiencies, demand, and technology costs in the U.S. auto

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and electric sectors. In fact, the abatement cost for both sectors is found to increase sharply with

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every year of delay beyond 2020. When further considering reasonable limits to technology

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turnover, retirements, and new capacity additions, these costs would be higher, and the feasible

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timeframe for initiating successful climate action on the 70% by 2050 target would be shorter –

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perhaps having passed already. The analysis also reveals that optimistic business-as-usual

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scenarios in the U.S. will, conservatively, release 79 – 108 billion metric tons of CO2. This

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could represent up to 13% of humanity’s remaining carbon budget through 2050.


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INTRODUCTION

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The global carbon budget to limit the average Earth temperature anomaly to 2ºC over

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preindustrial times is estimated to be 1356 – 1500 Gt CO2 eq. between the years 2000 and 2050.1

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Corresponding to this global carbon budget, the Intergovernmental Panel on Climate Change

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(IPCC) has assessed that greenhouse gas (GHG) emissions must globally be reduced by 40 –

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70%2 by 2050 (relative to 2010) to be consistent with the RCP 2.6 scenario for achieving the 2ºC

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target by the end of the century. The 2016 Paris Agreement ratified by 151 countries is a

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significant step towards achieving this goal. However, reductions in emissions beyond what

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participating countries in the Paris climate accord have pledged will likely be necessary to

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achieve the 2 ºC target,3,4 particularly considering that over 546 Gt CO2 eq. of the global carbon

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budget has already been consumed by 2015.5

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The 70% reduction target

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As an industrialized nation and the world’s largest cumulative emitter of GHGs,6 the United

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States’ 2050 emission reduction goals are much higher, in the range of 70 – 80%.7–9 The U.S.

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electric and light duty vehicle (auto) sectors currently account for nearly half of U.S. annual

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GHG emissions10 (see Fig. S1 in the Supporting Information (SI)). Since CO2 from fossil fuel

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combustion accounts for more than 95% of GHG emissions from these two sectors,10 our focus

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in this study is on direct CO2 emissions from the two sectors. As a result, this paper focuses on

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the high-level technology strategies that must be deployed in the U.S. electric and light duty

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vehicle (auto) sectors to achieve more than 70% reduction in GHG emissions by 2050 at least

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cost to society.


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Is there an optimal time for initiating climate action?

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Most studies in the climate policy literature unequivocally point to an increase in the social

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cost of unabated GHG emissions with time, including irreversible damage to ecosystems, loss in

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economic output, displacement of populations and communities, and adaptation to a changed

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climate.11–21 However, the private costs of low-carbon technologies such as photovoltaic solar

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power and batteries for electric vehicles22,23 have been steadily declining due to advancing

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technologies, maturing supply chains, and gradually changing markets.

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maximize social welfare by reducing GHG emissions today could compete with the goal to

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minimize the private costs of reducing those emissions using presumably cheaper and more

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advanced technologies in future.

Thus, the goal to

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To evaluate this type of trade-off, studies on energy and transportation systems in the literature

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typically use general or partial equilibrium models that work from a set of technology and

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climate policy options to pick an optimal outcome that maximizes welfare while pursuing a set

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emissions goal. Welfare in turn is calculated using a discounted utility framework in which

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environmental and social benefits are monetized and costs are represented as losses in GDP that

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result from changes in the consumption of goods and services. These macroeconomic models

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are often combined with models of the climate system to create Integrated Assessment Models

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(IAMs).

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The widespread use of IAMs for climate policy evaluation follows, in part, from their ability to

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collectively model complex interactions between the economy and the environment using

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technology and policy instruments.24,25 IAMs have been extensively used to inform energy

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policies,26–39 and we direct interested readers to that literature for an in-depth understanding of

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IAMs. Most relevant to the subject of this paper, some IAM-based studies have examined or


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commented on questions surrounding the optimal timing of climate action.32,40–44 These studies

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collectively point to a list of factors that introduce uncertainties in the optimal timing of climate

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action, including rate of change in marginal cost curves, discount rate, uncertainties in future

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social benefits or harm, uncertainties in existing technologies, and the efficacy of R&D

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programs.

66

When the emphasis of climate change mitigation pathways and policies is technological, the

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use of “least-cost” models for analysis has been recommended.45,46 The goal of least-cost

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models is usually to minimize the total cost to achieve a certain emission target using the

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technologies modeled within a given timeframe. Least-cost engineering or technology-centric

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models such as MARKAL,26 TIMES,27,28 and MESSAGE,29 often referred to as bottom-up

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models, have been commonly used either as standalone models or in combination with other

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models in several studies30,47–53 of energy and transportation technology trajectories to achieve

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emissions objectives. However, to the best of our knowledge, the question of optimal climate

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action timing for meeting CO2 emission targets with a specific focus on the United States at a

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sectoral scale has not yet been evaluated using a least-cost approach.

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In this paper, a bottom-up model called the Least-cost Energy and Transportation Sectoral

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Analysis for Climate Targets (LETSACT) model is created to focus on the U.S. light duty

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vehicle (auto) and utility-scale electricity generation (electric) sectors. We use the LETSACT

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model to answer two questions: (1) do the private technological costs of climate action to meet

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CO2 targets decrease with time given optimistic improvements in low-carbon technologies in

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these sectors; and (2) till when can we delay climate action before we prematurely exhaust our

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carbon budget from now through 2050? In addressing these questions, we aim to provide some

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insights into the policy implications of the results. We also discuss the limitations of the


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LETSACT framework, concluding that the real-world factors not included in the LETSACT

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framework will likely reinforce and strengthen the main conclusions of this paper.

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METHODS

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We begin with the mathematical formulation of the LETSACT model for achieving a given

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sector-specific carbon budget.

From this structural discussion, we then discuss how the

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formulation is informed by a stock-and-flow model of the U.S. electric generation and light duty

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vehicle fleets. Key input data and assumptions are discussed along the way.

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Optimization framework

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At the core of the LETSACT model is the optimization problem in Eq. (1) that minimizes the

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net present value of the sum of all capital, operating and maintenance (O&M), fuel, and

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retirement costs for technologies deployable in the U.S. auto and utility-scale electric sectors

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between 2015 – 2050. Note that these costs exclude social costs and/or benefits such as those

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associated with pollution caused or avoided by these technologies. The technologies are applied

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either to a unit passenger vehicle (in the light duty vehicle sector) or to a unit of 1 MWh of

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electricity generation (in the electric sector). The number of new, old, and retired units of each

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technology and age serve as the decision variables for each of the 36 years in the analysis.

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These decision variables collectively represent the stocks and flows of vehicles and electricity

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generating units over the analysis time horizon.

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In Eq. (1), N is the set of all sector-specific technologies considered in the model indexed by

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the variable i , T is the set of typical service lives of all technologies in N and is indexed by the

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variable j , and Y is the set of years in the analysis time horizon over which model runs and is

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indexed by the variable k . The decision variables new , old , and ret respectively represent

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new, existing, and retired MWh of electricity generation or passenger vehicles. NPV is the net


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present value objective function with r as the discount rate, cnew representing the unit capital,

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O&M, and fuel cost of a new unit, cold representing the unit O&M and fuel cost of an old unit,

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and cret representing the unit early retirement cost of an old unit. D is a vector annual demand

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projections set by various scenarios, and E is a scalar value representing the total CO2 emissions

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budget for the analysis time horizon also obtained exogenously. The precise value of E is

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chosen assuming a 71% emissions reduction, which is the mean of the 70 – 72% range specified

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as the upper bound of emissions reduction targets in the IPCC AR5 report.2 The terms enew and

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eold respectively represent the unit emission factors for new and old units. A detailed description

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of the sets, inputs, variables, objective function, and constraint functions is provided in the SI.

Y

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Minimize: NPV ( new,old,ret ) = ∑

     ∑ new ( i, k ) ⋅ cnew ( i, k )   i∈N  T     +  ∑ ∑ ( old ( i, j, k ) ⋅ cold ( i, j, k )) + ( ret ( i, j, k ) ⋅ cret ( i, j, k ))     i∈N j=1

k=1

(1+ r )k−1

new ( i, k ) ≥ 0  ∀i ∈N, j ∈T , k ∈Y  old ( i, j, k ) ≥ 0  ret ( i, j, k ) ≥ 0  T

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Subject to:

∑ new ( i, k ) + ∑ ∑ old ( i, j, k ) ≥ D ( k ), ∀k ∈Y i∈N

(1)

i∈N j=1

T   new i, k ⋅ e i, k + ( ) ( ) ∑  ∑ ∑ ∑ old ( i, j, k ) ⋅ eold ( i, j, k ) ≤ E new k=1 i∈N i∈N j=1 Y

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The optimization problem in Eq. (1) is subject to the constraint that the sum of the stocks and

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flows of units of all ages and technologies in any given year be equal to or greater than a

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projected demand for total annual vehicle stocks and electric generation. The total demand in

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each sector is not known but is assigned according to several scenarios following from previous


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literature.

Eq. (1) is also constrained by the total sector-specific CO2 emissions budget

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associated with a 70% reduction relative to 2010 in the sector’s annual emissions by 2050.

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When this constraint is activated, we call the scenario “climate action”. When this constraint is

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not activated, we call the scenario “Business-as-Usual” (BAU). We can study delays in climate

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action by awaiting until a given year for the emissions constraint to be activated. This is called

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“delayed climate action”. Given the analysis time horizon is 2015 – 2050, we factor in the

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actual emissions from 2010 – 2014 that happened already as an initial condition.

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To avoid potential multiple local optima and to reduce the computational cost associated with

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non-linear optimization, we implement the optimization problem described in Eq. (1) as a linear

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program. This linear programming approach to a least-cost decision model in the context of

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energy systems has been adopted in other established least-cost models such as MARKAL and

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TIMES, as well as in recent energy policy-focused operations research literature.54,55

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effects of inherent nonlinear factors such as consumer choice, technology diffusion, and demand

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elasticities on the technology selection and costs in the linear optimization model can be

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incorporated by imposing linear constraints on parameters such as the total demand, market

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shares of technologies, and annual production capacity growth limits that are affected by these

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factors. In this study, we incorporate uncertainties in total demand and additionally perform a

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comprehensive uncertainty analysis using a wide range of values for other key parameters

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influencing costs and emissions to assure that the conclusions of the study are supported under a

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full range of reasonable market and policy assumptions. This modeling approach allows the

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LETSACT model to provide many of the same functionalities as partial or general equilibrium

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models, but with fewer data requirements, reduced complexity, lower computational costs, and a

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more flexible model structure.


The

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The presence of electric vehicles in the market introduces a key nonlinearity between the

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electric and auto sectors, whereby technology selection in the auto sector depends on the electric

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grid mix and its carbon intensity, which in turn depends on the total electric charging demand.

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This nonlinearity was managed by iteratively, and separately, running the electric and auto sector

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models until the annual average grid emissions factor and the total annual electric vehicle

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charging demand values over the analysis time horizon converged.

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Stock-and-flow model of vehicles and power plants

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Stocks and flows of passenger vehicles and electric generation units are updated on a yearly

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basis in the model.

Four automotive technologies are considered: conventional internal

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combustion engine-based vehicles (ICEV), hybrid electric vehicles (HEV), all-electric battery

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vehicles (BEV), and plug-in hybrid electric vehicles (PHEV) (see ref. 56 for additional

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information on each of the alternative technologies). The maximum service life of all vehicle

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technologies is assumed to be 25 years.57 For tractability, all vehicle segments such as compact,

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mid-size sedan, SUVs, and light trucks within each technology are treated as one representative

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vehicle that has a sales-weighted average fuel economy and cost based on 2015 data from the

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U.S. Energy Information Administration (EIA)58 (see Table S2 for specific values and data

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sources). The fuel economy and cost values for the representative vehicle change with time

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based on a range of forecasts from various sources in the literature.59–65 The aggregation of

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vehicle segments utilized in this work has been adopted by other studies in the literature based on

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the MARKAL and TIMES least-cost models.66–68 While the LETSACT framework permits

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disaggregated representation of vehicle segments, as well as additional vehicle and electric

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generation technologies, we contend in the discussion below that their inclusion will not have a


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material impact on the conclusions of this paper, despite possibly changing the technology

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trajectories used as evidence to support the conclusions.

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Eq. (1) requires an initial stock condition based on which future stocks and flows are modeled.

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For the auto sector, the initial condition is given by an age-wise composition of the 2014 stock of

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existing vehicles, which is obtained by applying a logistic vehicle discard probability function57

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(that varies with vehicle age) to the total automotive sales data69 for each technology during the

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25 year period between 1989 and 2014. This discard probability function, which is assumed to

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be identical for all four automotive technologies considered, is also applied to future flows of

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vehicles. The coefficients of the discard probability function are calculated so as to minimize the

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RMS error relative to reported total values of registered number of vehicles70 and auto sector

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CO2 emissions71 in 2014. Values of these coefficients and the detailed calculations behind them

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are provided in the SI.

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In addition to the capital cost of passenger vehicles reflected in their price, we consider

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operation and maintenance (O&M) and fuel costs. Fuel costs are derived based on fuel economy,

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as well as estimates of fuel and electricity prices obtained from forecasts by the U.S. EIA.58

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Additionally, the model allows a policy mechanism whereby vehicles are retired and removed

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from service early, i.e., before their assumed maximum lifespan of 25 years. This retirement is

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different from and in addition to the natural attrition modeled by the discard probability. The

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retirement cost of a vehicle of a given technology and age is treated as being equal to its value,

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which decreases with increasing age. Values for input parameters such as costs, annual vehicle

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miles traveled, deterioration in fuel economy with vehicle age, and ratio of electric-only to gas-

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only driving for PHEVs as well as key assumptions are listed in the SI along with data sources in

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Table S3. While we do not advocate early retirement as a feasible policy mechanism, its


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inclusion in the analysis is enlightening and necessary to extend the climate action timeframes

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for the sectors into the mid-2020s.

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LETSACT model by other researchers, who can access the model input files, result files, and the

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computer codes via links to publicly available files provided in the SI.

All parameters discussed here can be changed in the

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In the electric utility sector, eleven technologies are considered: pulverized coal (PC), gas

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turbine (NGGT), natural gas combined cycle (NGCC), petroleum (P), biomass (B), nuclear (N),

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hydroelectric (H), on-shore wind (W), solar photovoltaic (SPV), solar thermal (STH), and

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geothermal (G) (see ref. 72 for additional information on each of these technologies). The

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typical maximum service life is assumed to be 60 years for PC, NGCC, N, and H plants, 40 years

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for GT, P, and G plants, and 30 years for all other technologies. Age-wise compositions and heat

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rates of the initial condition (stock of power plants in 2014) are modeled using U.S.

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Environmental Protection Agency’s (EPA) NEEDS v5.14 data which includes electricity from

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combined heat and power plants.73

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factor with ageing of power plants, and so the discard probability of a unit of electricity

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generation is zero until it reaches its typical service life, at which point the discard probability

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becomes 1.

We assume no attrition of generation capacity or capacity

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Data on new power plants including ranges of projected overnight capital costs, O&M costs,

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heat rates, and capacity factors for all technologies considered are based on estimates developed

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by NREL,74 U.S. EIA,58,75 and Lazard.76 CO2 emission rates for all power plants are derived

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from their respective heat rates and carbon intensities of their fuels. Heat rates are assumed to

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increase slightly with ageing of fossil fuel plants.77 Like the automotive sector, we also allow the

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early retirement of MWh of generation before its typical service life. The retirement cost is

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calculated as the sum of the value of the unpaid capital liability, if any, assuming a 20-year


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financing period at a 20% capital charge rate (3% risk, 5% tax, and 12% return on

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equity/investment) and the value of lost revenue from sale of the electricity that would have been

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generated till the end of a plant’s service life had it not been retired early. Capital costs of new

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power plants exclude transmission and distribution costs.

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assumptions, and sources for the electric sector are provided in Table S4 in the SI along with

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links to input data files.

Specific values for inputs,

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For both sectors, we assume that all costs are incurred at the beginning of each year. It is also

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assumed that all technology types are available for production and sale, and that all technologies

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that are built are sold (supply equals demand). This analysis does not consider any delays or

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costs associated with deploying vehicles or power plants such as those that may arise from

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production ramp-up, construction activities, or policy enforcement. CO2 emissions from vehicles

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and power plants during only their use phase are considered since CO2 pollution during the use

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phase is most germane to the sectoral scope of our study.

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For the mathematical representation of the stock-and-flow model, we define P ( i, j ) as the

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cumulative probability that a vehicle or MWh of generation of type i and age j will survive to

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the age of j + 1. P ( i, j ) is therefore the discard probability d ( i, j ) subtracted from 1. We then

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define initstock as the initial stock of vehicles or MWh characterized by technology and age, and

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new , old , ret , and tot as the number of new, old, early retired, and total vehicles or MWh of a

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given technology in a given year and of a given age. For a given technology i ∈N , the stock of

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vehicles or MWh (units) of age j ∈T in a given year k ∈Y can be expressed as shown in Eq.

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(2). The number of old units of age j in year k can in turn be expressed in terms of old units

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j − 1 in the previous year k − 1 that survived to year k , and retired units of age j in year k as

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shown in Eq. (3). In fact, old ( i, j, k ) can be expressed in terms of initstock ( i, j − k ) if j ≥ k or


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new ( i, k − j ) if j < k , and retired units between year 1 of the analysis time horizon and year k ,

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with the weights for the different terms in the combination comprised of products of appropriate

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survival probability terms as described in Supekar et al.78 Thus, the stock of vehicles and electric

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generation from all technologies in any given year can be expressed as a linear combination of

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the initial stock and/or the flows of vehicles and electric generation up to that year. Similarly,

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using unit emission factors e (tons CO2/unit/year) and cost factors c (USD/unit/year), the

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emissions and costs associated with the stocks and flows of vehicles and electric generation can

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also be expressed as linear combinations of the initial stock and/or the flows of vehicles and

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electric generation up to that year.

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tot ( i, j, k ) = new ( i, k ) + old ( i, j, k )

(2)

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old ( i, j, k ) = old ( i, j − 1, k − 1) ⋅ P ( i, j − 1) − ret ( i, j, k )

(3)

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Business-as-usual and uncertainty analysis

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Emission reductions and abatement costs are calculated using the BAU case as a reference.

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While many BAU assumptions could be selected, we assumed for the sake of consistency that

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BAU also follows a least-cost trajectory to meet the demand constraint, with only the emissions

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constraint in Eq. (1) absent. Additionally, BAU in the automotive sector assumes fleet-wide

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compliance of new vehicles each year with U.S. EPA’s corporate average fuel economy (CAFE)

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standards. BAU in the electric sector assumes 20% generation from renewables (W, SPV, STH,

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and G) by 2025, which is the emission-weighted value obtained based on 2015 and 2020 targets

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set by various states set prior to the 2016 U.S. presidential election. As of the submission of this

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paper, the U.S. EPA’s Clean Power Plan (CPP) was under a court-ordered stay and therefore has

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been excluded from the BAU scenario. Further, new nuclear, hydro, and biomass capacity is


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limited to no more than 1000 MW per year based on the history of nuclear power in the U.S. and

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availability considerations for hydropower and bioenergy sources. Details about data sources and

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how these constraints are implemented in the model are provided in the SI.

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Table 1. List of parameters within each sector that are evaluated in the uncertainty analysis and grouped according to the metric they affect the most. Metric

Automotive Sector

Electric Sector

Costs

Vehicle capital, O&M, and retirement costs; fuel and electricity prices; ratio of new sales of PHEVs to BEVs

Power plant capital, O&M, and retirement costs; fuel and electricity prices; ratio of capacity additions of wind to other renewables

Emissions

Fuel economy; decrease in fuel economy with ageing; electric charging emissions

Plant thermal efficiency; decrease in efficiency with ageing

Demand

Projected vehicle population; miles traveled per year; ratio of electric to nonelectric driving for PHEVs

Projected electric generation; vehicle charging demand; capacity factors

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A scenario analysis is performed to test conclusions generated from the analysis. We identify

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costs, CO2 emissions, vehicle miles traveled (auto demand), and installed electric generation

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capacity (electricity demand) as the most important scenario analysis parameters. As shown in

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Table 1, model parameters are classified as cost, emission, or demand parameters depending on

267

which metric they predominantly affect. All parameters within the cost, emission, and demand

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categories are simultaneously evaluated at three levels (low, nominal, and high) within the

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scenario analysis. The values for each level are obtained from EIA’s 2015 Annual Energy

270

Outlook report and various other estimates in the literature (see SI for details on values and their

271

sources), with an emphasis on the most optimistic technology development assumptions. With

272

the scenario analysis encompassing three parameter sets evaluated at three levels, there are a

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total of 33 or 27 scenarios against which the climate action and BAU cases are evaluated (see

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Table S1 for individual scenario definitions). Note that the BAU case changes along with each

275

scenario, since the BAU and climate action scenarios share parameters. For analyses in which


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climate action is delayed until a certain year, the stocks, flows, emissions, and costs are assumed

277

to follow the respective BAU case until that year.

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RESULTS AND DISCUSSION

279 280 281 282 283

Fig. 1. Emission and abatement cost trajectories under 27 different scenarios defined by various parameters describing the evolution of technology costs, technology efficiencies and carbon intensities, and energy services demand in the (A) auto sector and (B) electric utilities sector. Note that the left ordinate axis representing total abatement cost has a logarithmic scale.


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Our analysis shows that for both sectors, the private technological cost to society to reduce

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CO2 emissions by 70% of their 2010 levels by 2050 does not decrease with time. Even the most

286

optimistic assumptions are found to be inadequate, both in quantity and at the necessary rate, to

287

compensate for the emissions that are expected from existing vehicle and power plants fleets.

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This phenomenon is referred to as “emission lock-in” elsewhere.4,15,16,79

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Each scenario is depicted using a 3 × 3 grid representation in Fig. 1. The rows represent the

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parameter groups (cost, emission, demand; see Table 1) and the columns represent the level of

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the parameter values within each group (low, nominal, high). A dark marker indicates the level

292

of a given parameter set within a scenario. Thus, for example in a grid where the first, second,

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and third columns in the first, second, and third rows respectively are darkened, represents the

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scenario in which parameters such as battery costs and fuel prices take their low value,

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parameters such as fuel consumption and heat rate take their nominal value, and parameters such

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as annual vehicle miles driven and total electricity demand take their high value from the range

297

of values considered in this study.

298

Inertia of legacy technologies and their remediation cost

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The emissions benefit of new low-carbon technologies cannot be fully realized unless they

300

replace older technologies. However, the private societal cost of taking a vehicle or power plant

301

out of commission before its typical service life is quite significant since vehicles and power

302

plants stay in service on a decadal time scale. This technological inertia associated with older

303

technologies and infrastructure together is an important reason as to why the total climate action

304

cost of meeting each sector’s CO2 budget through 2050 does not decrease with time. In fact, we

305

find that for both sectors and all 27 scenarios, the private technological cost of climate increases

306

dramatically with every year of delay in initiating climate action after about 2020. The “hockey


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307

stick” shape of the green (dotted) abatement costs curves in Fig. 1 shows this trend. For

308

instance, the median total abatement cost through 2050 considering all 27 scenarios if climate

309

action is initiated in 2018 would be 835 billion USD for the auto sector and 417 billion USD for

310

the electric sector. By comparison, delaying climate action by four more years to 2022 (into the

311

next U.S. presidential administration) will push the median total abatement cost to 1229 billion

312

USD for the auto sector and 638 billion USD for the electric sector, even though technology

313

costs and emission intensities are ascribed optimistic improvements during this delay.

314

The nearly 50% increase in costs in just a few years is due to the significantly higher use of

315

more expensive EVs in the auto sector (180 million vehicles through 2050), and the large-scale

316

early retirement of an additional 85 GWs of PC, NGGT, and NGCC capacity through 2050

317

(median values for all 27 scenarios) that would be necessary to achieve the much faster rate of

318

emission reduction needed to compensate for the excess emissions in previous years of inaction.

319

Early retirement of ICEVs would become necessary if climate action delays beyond 2023 in the

320

nominal scenario, and by as early as 2019 in some high-emission factor scenarios.

321

retirement costs compound with the high capital costs of new vehicles and power plants that

322

would supplant the retired technologies to meet the necessary demand. Savings in operating

323

costs from lower fuel consumption are not nearly enough to offset the high capital and retirement

324

costs of delayed climate action. Faced with such high costs of early retirement caused by

325

delayed climate action, it would seem much more likely in the real world that the 2050 target

326

would be extended in time prior to paying these large costs and destroying large quantities of

327

recent capital investments. Fig. 2 (which is also best viewed in color) shows the cost breakdown

328

and annual emission reduction rates for both sectors as a function of the year in which climate

329

action is initiated.


High

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330 331 332 333 334

Fig. 2 Breakdown of total abatement costs through 2050 in the (A) auto sector and (B) electric sector, and (C) annual emission reduction rates for both sectors, all as a function of year in which climate action is initiated. Median values shown for 27 technology scenarios examined, and bands indicate their respective bounds. Negative values for costs imply savings relative to BAU.

335

The driving impact of legacy technology-induced inertia observed by these results is not an

336

original conclusion. Most notably, den Elzen et al.80 suggest that technological inertia can

337

increase the average annual emission reduction rates from 2.8% to about 5% under a delayed

338

climate action scenario compared to an immediate climate action scenario. They also note that

339

delays until 2030 would preclude a 40% reduction in global CO2 emissions (relative to 1990).

340

These results are like what is observed here, though a different methodology was utilized. It is

341

important to note as well that the technological inertia discussed here is separate from, and in

342

addition to, the inertia associated with uptake of CO2 by land, biomass, and water bodies that has

343

been identified as a key factor contributing to cost uncertainties and the feasibility of preventive

344

climate change mitigation in global IAM-based studies.19,80–83

345

Continued delay in initiating climate action would require early retirement of larger numbers

346

of relatively efficient ICEVs (< 10 years old) as well as PC and NGCC capacity to be replaced

347

with EVs and renewables to achieve the 2050 CO2 reduction target. We find that the large costs

348

associated with this retirement and corresponding new vehicles/capacity additions cause the net


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349

present value function described in Eq. (1) to rapidly blow up. At a certain point in time between

350

2023 and 2027 for the auto sector and between 2023 and 2026 for the electric sector, as indicated

351

by circles at the end of the green (dotted) abatement cost curves in Fig. 1, additional delays in

352

initiating climate action lead to infeasibility in the primal-dual interior-point formulation of the

353

linear programming model used in this study. That is, beyond this point, achieving more than

354

70% reduction in sectoral emissions by 2050 is mathematically infeasible and these sectors

355

would be locked into higher levels of emissions than the target.

356

The practical point of infeasibility will be realized much sooner because it would require

357

producing tens of millions more vehicles than today’s maximum annual production capacity and

358

building hundreds of GWs of additional electric capacity within one or two years. Treating 5%

359

as the maximum feasible year-on-year emission reduction rate corresponding to this practical

360

infeasibility timeframe based on Friedlingstein et al.,84 the practical point of infeasibility for both

361

sectors would likely be reached by about 2020 as seen in Fig. 2C. Recent work by Figueres et

362

al.85 predicts similar timeframes at a global scale using a different methodology. It should be

363

noted that abatement costs increase with delays even before 2020 for both sectors, albeit at a

364

relatively lower rate compared to the sharp increases after 2020. Practical results of missing the

365

target could include: a delay/abandonment of the targets for these sectors, deployment of

366

atmospheric carbon removal technologies such as direct air capture86,87 or other forms of

367

geoengineering,88 shifting the emissions reduction efforts to other sectors and/or countries to

368

offset missed targets in these sectors,3 and/or hope for unforeseen breakthroughs in disruptive

369

technologies such as nuclear fusion89 or an economy-wide shift from personal car ownership

370

towards shared autonomous vehicles.90


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371

The 27 different scenarios for cost, efficiency, and demand parameters considered in this study

372

govern the range of time beyond which achieving emission targets would be infeasible. For the

373

both sectors, the scenarios with certain combinations of low demand and/or low emission

374

intensities (see Tables S1, S3, and S4 for parameter values) afford the longest delays in initiating

375

climate action at the lowest cost penalties for short delays. Similarly, scenarios with high

376

demand and/or high emission intensities allow the shortest delays at high cost penalties for any

377

delay. Most strikingly, the range of years within which climate action will be feasible remains

378

quite narrow even though the values for cost, efficiency, and demand parameters that define the

379

scenarios considered in this study are spread over a wide range. Thus, while the emissions

380

trajectories and their underlying least-cost technology trajectories vary somewhat with cost,

381

efficiency, and demand parameters (Fig. S6), the timeframe for feasibility of climate action is

382

found to not be particularly sensitive to these parameters over their wide ranges examined in this

383

study.

384

For this analysis, we did not include several real-world factors such as market shares,

385

technology diffusion, separate vehicle segments, electric dispatch considerations, production

386

capacity ramp up and ramp down times, construction times, and political and regulatory

387

considerations. An analysis similar to the one in this study could be performed with active

388

constraints incorporated from these real-world factors in the optimization formulation. However,

389

such additional constraints would only impede the high rates of technology turnover and

390

corresponding annual emission reductions that allow the compensation of excess (BAU)

391

emissions from several years of delay in initiating climate action. This would further shorten the

392

feasible climate action timeframe shown in Fig. 1. In this sense, the presented results can be

393

seen as best case timeframes and costs for meeting the 70% target in the auto and electric sectors


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394

by 2050. Given the absence of real-world factors noted above from this analysis and the

395

unlikelihood of the U.S. government bearing large early-retirement costs (see Fig. 2) for vehicles

396

and power plants, it is possible that the practical timeframe has already passed.

397

For the nominal values of cost, emission, and demand parameters used in this study (Scenario

398

14), the idealized least-cost technology trajectory for both sectors if climate action were to be

399

initiated in 2018 would involve new vehicle sales moving from predominantly ICEVs to HEVs

400

by 2024, and from HEVs to PHEVs and BEVs by the year 2034 leading to a complete phase out

401

of ICEVs by 2040. These results compare well with estimates of Yeh et al.,47 who forecast

402

exclusively non-ICEV vehicle stocks comprised of HEVs and PHEVs beyond 2035, and by

403

McCollum et al.,91 who project 2050 vehicle stocks to be about 60% electrified. In the electric

404

sector, we would see a high adoption of wind and solar electricity generation in the U.S. during

405

the 2030 – 2050 period leading to over 50% renewable generation by 2035 with coal and

406

petroleum generation nearly phased out by 2040. These results compare well with the projection

407

of about 60% renewable capacity by 2030 developed by MacDonald et al.,92 who use a weather

408

data-informed model.

409

compiled by Zwaan et al.93 wherein they show MESSAGE and REMIND models projecting 65%

410

of new capacity additions through 2030 and about half of the total installed capacity by 2050

411

comprised of renewables, with coal being completely or nearly phased out by 2050. Fig. S4 and

412

Fig. S5 respectively show the idealized least-cost technology trajectories for both sectors under

413

immediate climate action and their respective BAU cases.

Our results also compare well with results from IAM-based studies

414

We note that pilot stage or commercially nascent technologies excluded from our analysis such

415

as fuel cell vehicles (FCVs) with renewable hydrogen and carbon capture and sequestration

416

(CCS) would not change our conclusions about sharply increasing abatement costs with delays


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417

and short climate action timeframes for meeting 2050 emission targets. Even if FCVs begin

418

competing with the optimistic technology development assumed for BEVs and PHEVs in this

419

study, their emission factors and costs would be generally comparable to EVs.94,95 Such new

420

automotive technologies would thus simply supplement or substitute the technologies considered

421

in this study without affecting abatement costs or climate action timeframes. We initially

422

included CCS with PC and NGCC plants in our analyses. However, CCS did not feature in the

423

least-cost technology trajectories in these analyses, and was thus excluded from future model

424

runs. This outcome is unsurprising for a 2050 time horizon analysis since renewables with

425

increasingly better capacity factors and lower capital costs96 have been shown to be more cost

426

effective at reducing CO2 emissions.92,97 Longer-term analyses through 2100 should however

427

include CCS.

428

To contextualize the least-cost trajectories under immediate climate action in terms of a carbon

429

price, we ran the LETSACT model for nominal parameter values (Scenario 14) with a carbon

430

price imposed on CO2 emissions from all vehicles and power plants through 2050 instead of

431

imposing an explicit CO2 emission constraint in the model. One benefit of considering carbon

432

price is that, unlike CO2 abatement costs, carbon prices in our framework are independent of

433

BAU technology and emission trajectories. Starting from zero, the carbon price, whose value

434

was conservatively assumed to follow a 1.5%98 year-on-year rise, was increased in increments of

435

1 USD/tonne of CO2 until the total mass of CO2 reduced through 2050 matched the 70% target

436

within 1% error. The carbon price estimated for the auto sector using this approach is 363

437

USD/tonne of CO2, which corresponds to an increase of about 3.23 USD/gal in the price of

438

gasoline. This result matches well with the results by Morrow et al.99 who used a general

439

equilibrium model-based approach to find that a gasoline tax of about 3.6 USD/gal would result


22

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440

in a 65% reduction in CO2 emissions from the U.S. LDV sector by 2030 relative to 2005. The

441

carbon price for the electric sector is estimated to be 73 USD/tonne of CO2. This corresponds to

442

an increase of about 3.87 USD/mmBtu in the price of natural gas. The price of natural gas today

443

would thus need to be at its projected 2040 value following EIA’s estimates.58

444

Given the growing interplay between the electrified vehicles and electric utilities, one

445

approach to reduce abatement cost and carbon prices discussed by Chen et al.55 is to “jointly

446

regulate” the transportation and electricity sectors. They find that the total abatement cost for

447

both sectors can be reduced by as much as 50% by allowing the sectors to interact under a

448

combined emission constraint to choose least cost technology trajectories in both sectors. We

449

ran the LETSACT model under their analysis time frame (2011 – 2050) with a combined

450

emissions constraint using model inputs from Supekar100 and found a 58% reduction in total

451

abatement costs for the two sectors, thus concurring with Chen et al.’s55 assessment.

452

Additionally, a low-carbon electricity-powered fleet of EVs emerging from such cooperation

453

between the two sectors would also help reduce the sectors’ combined primary energy

454

consumption,101,102 reduce radiative forcing from reduced black carbon emissions,103 and

455

improve air quality.104,105

456

Examining the BAU emission trajectories for the 27 scenarios considered in this study, we find

457

that missing the feasible timeframe for climate action would “lock in” emissions of at least 26.8–

458

36.2 Gt CO2 in the auto sector and 52.4 – 71.5 Gt CO2 in the electric sector. Further, the least-

459

cost assumption employed for BAU cases in this study leads to BAU emission trajectories that

460

are more optimistic than corresponding projections in EIA’s Annual Energy Outlook (see Fig.

461

S6), particularly till 2030. These locked in emissions could likely be higher when accounting for

462

the real-world factors discussed earlier. This would further shorten the feasible timeframe for


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463

climate action since the climate action scenarios would have to offset greater amounts of CO2

464

emitted on the BAU trajectory up until the climate action year. Using the optimistic BAU

465

emission estimates, historic emission data,6 and the estimated global budget through 2050,1,2

466

BAU emissions in the U.S. auto and electric sectors alone would thus consume at least 9.7 – 13.3

467

% of the remaining global GHG emissions budget through 2050.

468

Overall, the results show that it will not be possible to achieve within-sector CO2 emissions

469

reductions of 70% or more relative to 2010 by the year 2050 in the U.S. unless efforts beyond

470

those already in place begin with the 2050 target in mind at the latest by 2025 (±2 years) for the

471

automotive sector, and by 2026 (–3 years) for the electric sector. Delaying aggressive climate

472

action does not reduce private technological CO2 abatement costs even under the most optimistic

473

trajectories for improvements in fuel efficiencies, demand, and technology costs. In fact, the

474

abatement cost increases sharply with every year of delay beyond 2020. Real-world factors

475

discussed in this paper but not included in the quantitative model results will reduce the climate

476

action timeframe further, and are likely to further increase costs relative to what is reported in

477

this analysis.

478

ACKNOWLEDGEMENTS

479

The authors are grateful to Dr. Mark Daskin for his pivotal guidance on the model structure

480

and its mathematical representation, to Dr. Karsten Kieckhäfer for the data on battery costs and

481

vehicle value depreciation, and to Tae-Hwan Lim and Jesse Streicher for their help with model

482

testing and validation. This material is based upon work supported by the University of Michigan

483

Energy Institute (BCN Seed Grant # U052191) and the National Science Foundation (Grant #

484

CBET 1235688).


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485


SUPPORTING INFORMATION

486

Mathematical descriptions of the LETSACT model; detailed assumptions, input data, and data

487

sources; publicly accessible URLs for model input files, code, and result files; and least-cost

488

technology trajectories for nominal scenario are provided.

489

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Emission and abatement cost trajectories under 27 different scenarios defined by various parameters describing the evolution of technology costs, technology efficiencies and carbon intensities, and energy services demand in the (A) auto sector and (B) electric utilities sector. Note that the left ordinate axis representing total abatement cost has a logarithmic scale. 160x321mm (300 x 300 DPI)


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Breakdown of total abatement costs through 2050 in the (A) auto sector and (B) electric sector, and (C) annual emission reduction rates for both sectors, all as a function of year in which climate action is initiated. Median values shown for 27 technology scenarios examined, and bands indicate their respective bounds. Negative values for costs imply savings relative to BAU. 55x19mm (300 x 300 DPI)



46x40mm (300 x 300 DPI)


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Analysis of Costs and Time Frame for Reducing ... - ACS Publications

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