Analysis of Costs and Time Frame for Reducing CO2 Emissions by 70

Subscriber access provided by Caltech Library

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

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 36

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

1


Page 2 of 36

1

ABSTRACT

2

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

3

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

4

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

5

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

6

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

7

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

8

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

9

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

10

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

11

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

12

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

13

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

14

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

15

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

16

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


2

Page 3 of 36

17


INTRODUCTION

18

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

19

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

20

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

21

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

22

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

23

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

24

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

25

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

26

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

27

budget has already been consumed by 2015.5

28

The 70% reduction target

29

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

30

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

31

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

32

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

33

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

34

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

35

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

36

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

37

cost to society.


3


38

Page 4 of 36

Is there an optimal time for initiating climate action?

39

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

40

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

41

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

42

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

43

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

44

technologies, maturing supply chains, and gradually changing markets.

45

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

46

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

47

advanced technologies in future.

Thus, the goal to

48

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

49

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

50

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

51

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

52

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

53

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

54

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

55

(IAMs).

56

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

57

collectively model complex interactions between the economy and the environment using

58

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

59

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

60

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


4

Page 5 of 36


61

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

62

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

63

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

64

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

65

programs.

66

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

67

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

68

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

69

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

70

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

71

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

72

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

73

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

74

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

75

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

76

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

77

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

78

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

79

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

80

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

81

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

82

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

83

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


5


Page 6 of 36

84

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

85

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

86

METHODS

87

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

88

sector-specific carbon budget.

From this structural discussion, we then discuss how the

89

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

90

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

91

Optimization framework

92

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

93

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

94

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

95

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

96

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

97

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

98

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

99

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

100

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

101

generating units over the analysis time horizon.

102

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

103

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

104

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

105

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

106

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


6

Page 7 of 36


107

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

108

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

109

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

110

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

111

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

112

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

113

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

114

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

115

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

Y

116

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

117

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

118

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

119

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

120

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

121

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


7


Page 8 of 36

122

literature.

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

123

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

124

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

125

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

126

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

127

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

128

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

129

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

130

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

131

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

132

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

133

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

134

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

135

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

136

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

137

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

138

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

139

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

140

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

141

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

142

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

143

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

144

more flexible model structure.


The

8

Page 9 of 36


145

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

146

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

147

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

148

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

149

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

150

charging demand values over the analysis time horizon converged.

151

Stock-and-flow model of vehicles and power plants

152

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

153

basis in the model.

Four automotive technologies are considered: conventional internal

154

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

155

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

156

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

157

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

158

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

159

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

160

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

161

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

162

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

163

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

164

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

165

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

166

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


9


Page 10 of 36

167

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

168

trajectories used as evidence to support the conclusions.

169

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

170

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

171

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

172

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

173

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

174

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

175

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

176

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

177

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

178

are provided in the SI.

179

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

180

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

181

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

182

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

183

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

184

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

185

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

186

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

187

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

188

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

189

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


10

Page 11 of 36


190

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

191

for the sectors into the mid-2020s.

192

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

193

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

All parameters discussed here can be changed in the

194

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

195

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

196

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

197

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

198

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

199

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

200

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

201

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

202

combined heat and power plants.73

203

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

204

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

205

becomes 1.

We assume no attrition of generation capacity or capacity

206

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

207

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

208

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

209

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

210

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

211

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

212

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


11


Page 12 of 36

213

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

214

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

215

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

216

power plants exclude transmission and distribution costs.

217

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

218

links to input data files.

Specific values for inputs,

219

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

220

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

221

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

222

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

223

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

224

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

225

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

226

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

227

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

228

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

229

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

230

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

231

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

232

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

233

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

234

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

235

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


12

Page 13 of 36


236

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

237

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

238

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

239

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

240

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

241

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

242

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

243

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

244

electric generation up to that year.

245

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

(2)

246

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

(3)

247

Business-as-usual and uncertainty analysis

248

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

249

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

250

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

251

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

252

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

253

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

254

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

255

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

256

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

257

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


13


Page 14 of 36

258

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

259

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

260

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

261 262

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

263

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

264

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

265

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

266

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

268

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

269

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

273

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

274

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


14

Page 15 of 36


276

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.

278

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.


15


Page 16 of 36

284

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

285

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.

288

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

289

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

290

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

291

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,

293

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

294

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

295

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

296

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

299

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


16

Page 17 of 36


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

17


Page 18 of 36

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


18

Page 19 of 36


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


19


Page 20 of 36

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


20

Page 21 of 36


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


21


Page 22 of 36

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

Page 23 of 36


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


23


Page 24 of 36

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).


24

Page 25 of 36

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

REFERENCES

490 491 492

(1)

Meinshausen, M.; Meinshausen, N.; Hare, W.; Raper, S. C. B.; Frieler, K.; Knutti, R.; Frame, D. J.; Allen, M. R. Greenhouse-gas emission targets for limiting global warming to 2 degrees C. Nature 2009, 458 (7242), 1158–1162; DOI 10.1038/nature08017.

493 494

(2)

IPCC. Climate Change 2014: Synthesis Report; Core Writing Team; Pachauri, R. K.; Meyer, L., Eds.; IPCC: Geneva, Switzerland, 2014.

495 496 497 498

(3)

Rogelj, J.; den Elzen, M.; Höhne, N.; Fransen, T.; Fekete, H.; Winkler, H.; Schaeffer, R.; Sha, F.; Riahi, K.; Meinshausen, M. Paris Agreement climate proposals need a boost to keep warming well below 2 °C. Nature 2016, 534 (7609), 631–639; DOI 10.1038/nature18307.

499 500 501 502

(4)

Peters, G. P.; Andrew, R. M.; Canadell, J. G.; Fuss, S.; Jackson, R. B.; Korsbakken, J. I.; Le Quéré, C.; Nakicenovic, N. Key indicators to track current progress and future ambition of the Paris Agreement. Nat. Clim. Chang. 2017, 7 (2), 118–122; DOI 10.1038/nclimate3202.

503 504 505

(5)

Le Quéré, C.; Andrew, R. M.; Canadell, J. G.; Sitch, S.; Korsbakken, J. I.; Peters, G. P.; Manning, A. C.; Boden, T. A.; Tans, P. P.; Houghton, R. A.; et al. Global Carbon Budget 2016. Earth Syst. Sci. Data 2016, 8 (2), 605–649; DOI 10.5194/essd-8-605-2016.

506 507 508

(6)

Climate Analysis Indicators Tool: WRI’s Climate Data Explorer; World Resources Institute.; Washington, DC, 2014; http://www.wri.org/resources/data-sets/cait-countrygreenhouse-gas-emissions-data#.

509 510 511

(7)

den Elzen, M.; Höhne, N. Reductions of greenhouse gas emissions in Annex I and nonAnnex I countries for meeting concentration stabilisation targets. Clim. Change 2008, 91 (3–4), 249–274; DOI 10.1007/s10584-008-9484-z.

512 513 514 515

(8)

How to Avoid Dangerous Climate Change: A Target for U.S. Emissions Reductions; Luers, A. L.; Mastrandrea, M. D.; Hayhoe, K.; Frumhoff, P. C.; Cambride, MA, 2007; http://www.ucsusa.org/sites/default/files/legacy/assets/documents/global_warming/emissi ons-target-report.pdf.

516 517 518

(9)

U.S. Reports its 2025 Emissions Target to the UNFCCC; The White House Office of the Press Secretary.; Washington, DC, 2015; https://obamawhitehouse.archives.gov/the-pressoffice/2015/03/31/fact-sheet-us-reports-its-2025-emissions-target-unfccc.


25


Page 26 of 36

519 520 521 522

(10)

Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2011; U.S. Environmental Protection Agency.; U.S. Environmental Protection Agency: Washington, DC, 2013; http://www.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory-2013Main-Text.pdf.

523 524

(11)

Stern, N. The Economics of Climate Change. Am. Econ. Rev. 2008, 98 (2), 1–37; DOI 10.1257/aer.98.2.1.

525 526 527

(12)

Rogelj, J.; McCollum, D. L.; Reisinger, A.; Meinshausen, M.; Riahi, K. Probabilistic cost estimates for climate change mitigation. Nature 2013, 493 (7430), 79–83; DOI 10.1038/nature11787.

528 529 530

(13)

Butler, M. P.; Reed, P. M.; Fisher-Vanden, K.; Keller, K.; Wagener, T. Inaction and climate stabilization uncertainties lead to severe economic risks. Clim. Change 2014, 127 (3–4), 463–474; DOI 10.1007/s10584-014-1283-0.

531 532

(14)

Pindyck, R. S. Climate Change Policy: What Do the Models Tell Us? J. Econ. Lit. 2013, 51 (3), 860–872; DOI 10.1257/jel.51.3.860.

533 534 535

(15)

Jakob, M.; Luderer, G.; Steckel, J.; Tavoni, M.; Monjon, S. Time to act now? Assessing the costs of delaying climate measures and benefits of early action. Clim. Change 2012, 114 (1), 79–99; DOI 10.1007/s10584-011-0128-3.

536 537 538

(16)

Vogt-Schilb, A.; Hallegatte, S. Marginal abatement cost curves and the optimal timing of mitigation measures. Energy Policy 2014, 66, 645–653; DOI 10.1016/j.enpol.2013.11.045.

539 540 541 542

(17)

The Cost of Delaying Action to Stem Climate Change; Executive Office of the President of the United States.; Washington, DC, 2014; https://www.whitehouse.gov/sites/default/files/docs/the_cost_of_delaying_action_to_stem _climate_change.pdf.

543 544

(18)

Acemoglu, D.; Aghion, P.; Bursztyn, L.; Hemous, D. The Environment and Directed Technical Change. Am. Econ. Rev. 2012, 102 (1), 131–166; DOI 10.1257/aer.102.1.131.

545 546 547 548

(19)

Krey, V.; Riahi, K. Implications of delayed participation and technology failure for the feasibility, costs, and likelihood of staying below temperature targets—Greenhouse gas mitigation scenarios for the 21st century. Energy Econ. 2009, 31 (SUPPL. 2), S94–S106; DOI 10.1016/j.eneco.2009.07.001.

549 550 551

(20)

Solomon, S.; Plattner, G.-K.; Knutti, R.; Friedlingstein, P. Irreversible climate change due to carbon dioxide emissions. Proc. Natl. Acad. Sci. 2009, 106 (6), 1704–1709; DOI 10.1073/pnas.0812721106.

552 553 554

(21)

Tol, R. S. J. Estimates of the Damage Costs of Climate Change, Part 1: Benchmark Estimates. Environ. Resour. Econ. 2002, 21 (2), 135–160; DOI 10.1023/A:1014539414591.

555 556

(22)

Photovoltaic System Pricing Trends; Feldman, D.; Margolis, R.; Chung, D.; Fu, R.; Nrel, C. D.; Barbose, G.; Bolinger, M.; Seel, J.; Wiser, R.; Berkeley, L.; 2015;


26

Page 27 of 36


557

https://emp.lbl.gov/sites/all/files/pv_system_pricing_trends_presentation_0.pdf.

558 559

(23)

Nykvist, B.; Nilsson, M. Rapidly falling costs of battery packs for electric vehicles. Nat. Clim. Chang. 2015, 5 (4), 329–332; DOI 10.1038/nclimate2564.

560 561 562

(24)

Parson, E. A.; Fisher-Vanden, and K. Integrated assessment models of global climate change. Annu. Rev. Energy Environ. 1997, 22 (1), 589–628; DOI 10.1146/annurev.energy.22.1.589.

563 564 565

(25)

The Role of Integrated Assessment Models in Climate Policy: A User’s Guide and Assessment; Metcalf, G.; Stock, J.; 2015; http://belfercenter.ksg.harvard.edu/files/dp68_metcalf-stock.pdf.

566 567 568

(26)

EPA U.S. National MARKAL Database; U.S. Environmental Protection Agency.; Washington, DC, 2006; http://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryID=150883.

569 570 571

(27)

Loulou, R.; Labriet, M. ETSAP-TIAM: the TIMES integrated assessment model Part I: Model structure. Comput. Manag. Sci. 2008, 5 (1–2), 7–40; DOI 10.1007/s10287-0070046-z.

572 573 574

(28)

Loulou, R. ETSAP-TIAM: the TIMES integrated assessment model. part II: mathematical formulation. Comput. Manag. Sci. 2008, 5 (1–2), 41–66; DOI 10.1007/s10287-007-00450.

575 576 577

(29)

Messner, S.; Schrattenholzer, L. MESSAGE–MACRO: linking an energy supply model with a macroeconomic module and solving it iteratively. Energy 2000, 25 (3), 267–282; DOI 10.1016/S0360-5442(99)00063-8.

578 579 580 581

(30)

van Vuuren, D. P.; Hoogwijk, M.; Barker, T.; Riahi, K.; Boeters, S.; Chateau, J.; Scrieciu, S.; van Vliet, J.; Masui, T.; Blok, K.; et al. Comparison of top-down and bottom-up estimates of sectoral and regional greenhouse gas emission reduction potentials. Energy Policy 2009, 37 (12), 5125–5139; DOI 10.1016/j.enpol.2009.07.024.

582 583

(31)

Ciscar, J.-C.; Dowling, P. Integrated assessment of climate impacts and adaptation in the energy sector. Energy Econ. 2014, 46, 531–538; DOI 10.1016/j.eneco.2014.07.003.

584 585 586

(32)

Kelly, D. L.; Kolstad, C. D. Integrated Assessment Models For Climate Change Control. In International yearbook of environmental and resource economics 1999/2000: a survey of current issues; Folmer, H.; Tietenberg, T., Eds.; Edward Elgar, 1999; pp. 171–197.

587 588

(33)

Bhattacharyya, S. C. Applied general equilibrium models for energy studies: a survey. Energy Econ. 1996, 18 (3), 145–164; DOI 10.1016/0140-9883(96)00013-8.

589 590 591

(34)

Böhringer, C.; Löschel, A. Computable general equilibrium models for sustainability impact assessment: Status quo and prospects. Ecol. Econ. 2006, 60 (1), 49–64; DOI 10.1016/j.ecolecon.2006.03.006.

592 593

(35)

D’Agostino, A. L.; Sovacool, B. K.; Trott, K.; Ramos, C. R.; Saleem, S.; Ong, Y. What’s the state of energy studies research?: A content analysis of three leading journals from


27


594

Page 28 of 36

1999 to 2008. Energy 2011, 36 (1), 508–519; DOI 10.1016/j.energy.2010.10.013.

595 596 597

(36)

Bertram, C.; Luderer, G.; Pietzcker, R. C.; Schmid, E.; Kriegler, E.; Edenhofer, O. Complementing carbon prices with technology policies to keep climate targets within reach. Nat. Clim. Chang. 2015, 5 (3), 235–239; DOI 10.1038/nclimate2514.

598 599 600 601

(37)

Kriegler, E.; Weyant, J. P.; Blanford, G. J.; Krey, V.; Clarke, L.; Edmonds, J.; Fawcett, A.; Luderer, G.; Riahi, K.; Richels, R.; et al. The role of technology for achieving climate policy objectives: overview of the EMF 27 study on global technology and climate policy strategies. Clim. Change 2014, 123 (3–4), 353–367; DOI 10.1007/s10584-013-0953-7.

602 603 604

(38)

Rogelj, J.; Luderer, G.; Pietzcker, R. C.; Kriegler, E.; Schaeffer, M.; Krey, V.; Riahi, K. Energy system transformations for limiting end-of-century warming to below 1.5 °C. Nat. Clim. Chang. 2015, 5 (6), 519–527; DOI 10.1038/nclimate2572.

605 606 607

(39)

Schneider, S. H. Integrated assessment modeling of global climate change: Transparent rational tool for policy making or opaque screen hiding value-laden assumptions? Environ. Model. Assess. 1997, 2 (4), 229–249; DOI 10.1023/A:1019090117643.

608 609 610

(40)

Haim, D.; Plantinga, A. J.; Thomann, E. The optimal time path for carbon abatement and carbon sequestration under uncertainty: The case of stochastic targeted stock. Resour. Energy Econ. 2014, 36 (1), 151–165; DOI 10.1016/j.reseneeco.2013.11.006.

611 612 613

(41)

Gerlagh, R.; Michielsen, T. O. Moving targets—cost-effective climate policy under scientific uncertainty. Clim. Change 2015, 132 (4), 519–529; DOI 10.1007/s10584-0151447-6.

614 615

(42)

Nordhaus, W. D. A Review of the Stern Review on the Economics of Climate Change. J. Econ. Lit. 2007, 45 (3), 686–702;.

616 617

(43)

Baker, E.; Clarke, L.; Weyant, J. Optimal Technology R&D in the Face of Climate Uncertainty. Clim. Change 2006, 78 (1), 157–179; DOI 10.1007/s10584-006-9092-8.

618 619 620

(44)

Peters, G. P.; Andrew, R. M.; Boden, T.; Canadell, J. G.; Ciais, P.; Le Quéré, C.; Marland, G.; Raupach, M. R.; Wilson, C. The challenge to keep global warming below 2 °C. Nat. Clim. Chang. 2012, 3 (1), 4–6; DOI 10.1038/nclimate1783.

621 622 623

(45)

Ackerman, F.; DeCanio, S. J.; Howarth, R. B.; Sheeran, K. Limitations of integrated assessment models of climate change. Clim. Change 2009, 95 (3–4), 297–315; DOI 10.1007/s10584-009-9570-x.

624 625

(46)

Stanton, E. A.; Ackerman, F.; Kartha, S. Inside the integrated assessment models: Four issues in climate economics. Clim. Dev. 2009, 1 (2), 166; DOI 10.3763/cdev.2009.0015.

626 627 628

(47)

Yeh, S.; Farrell, A.; Plevin, R.; Sanstad, A.; Weyant, J. Optimizing U.S. Mitigation Strategies for the Light-Duty Transportation Sector: What We Learn from a Bottom-Up Model. Environ. Sci. Technol. 2008, 42 (22), 8202–8210; DOI 10.1021/es8005805.

629 630

(48)

Jewell, J.; Cherp, A.; Riahi, K. Energy security under de-carbonization scenarios: An assessment framework and evaluation under different technology and policy choices.


28

Page 29 of 36


631

Energy Policy 2014, 65, 743–760; DOI 10.1016/j.enpol.2013.10.051.

632 633 634

(49)

Rogner, M.; Riahi, K. Future nuclear perspectives based on MESSAGE integrated assessment modeling. Energy Strateg. Rev. 2013, 1 (4), 223–232; DOI 10.1016/j.esr.2013.02.006.

635 636 637

(50)

Hainoun, A.; Seif Aldin, M.; Almoustafa, S. Formulating an optimal long-term energy supply strategy for Syria using MESSAGE model. Energy Policy 2010, 38 (4), 1701– 1714; DOI 10.1016/j.enpol.2009.11.032.

638 639 640

(51)

Pfenninger, S.; Hawkes, A.; Keirstead, J. Energy systems modeling for twenty-first century energy challenges. Renew. Sustain. Energy Rev. 2014, 33, 74–86; DOI 10.1016/j.rser.2014.02.003.

641 642 643

(52)

Seebregts, A. J.; Goldstein, G. a; Smekens, K. Energy/Environmental Modeling with the MARKAL Family of Models. In Operations Research Proceedings 2001; Springer Berlin Heidelberg: Berlin, Heidelberg, 2002; Vol. 2001, pp. 75–82.

644 645 646

(53)

Chen, W.; Wu, Z.; He, J.; Gao, P.; Xu, S. Carbon emission control strategies for China: A comparative study with partial and general equilibrium versions of the China MARKAL model. Energy 2007, 32 (1), 59–72; DOI 10.1016/j.energy.2006.01.018.

647 648 649

(54)

Chen, B.; Fu, Y.; Strumolo, M.; Chao, X.; Tamor, M. Potential Natural Gas Impact on Cost Efficient Capacity Planning for Automakers and Electricity Generators in a Carbon Constrained World. SAE Int. J. Mater. Manuf. 2015, 8 (3) DOI 10.4271/2015-01-0466.

650 651 652

(55)

Chen, B.; Klampfl, E.; Strumolo, M.; Fu, Y.; Chao, X.; Tamor, M. A. Optimal investment strategies for light duty vehicle and electricity generation sectors in a carbon constrained world. Ann. Oper. Res. 2016 DOI 10.1007/s10479-016-2119-2.

653

(56)

Green Vehicle Guide; https://www.epa.gov/greenvehicles.

654 655 656

(57)

Greene, D. L.; Chen, C. K. E. Scrappage and survival rates of passenger cars and light trucks in the U.S., 1966–1977. Transp. Res. Part A Gen. 1981, 15 (5), 383–389; DOI 10.1016/0191-2607(81)90144-8.

657 658 659

(58)

Annual Energy Outlook 2015 with projections to 2040; U.S. Energy Information Administration.; Washington, DC, 2015; http://www.eia.gov/forecasts/aeo/pdf/0383(2015).pdf.

660 661 662 663 664

(59)

Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards; Final Rule; U.S. Environmental Protection Agency; U.S. Department of Transportation (NHTSA).; Washington, DC, 2010; http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/CAFE-GHG_MY_20122016_Final_Rule_FR.pdf.

665 666 667 668

(60)

2017 and Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; U.S. Environmental Protection Agency; U.S. Department of Transportation (NHTSA).; Washington, DC, 2012; http://www.nhtsa.gov/staticfiles/rulemaking/pdf/cafe/2017-25_CAFE_Final_Rule.pdf.


29


Page 30 of 36

669 670

(61)

Summary of Fuel Economy Performance; U.S. Department of Transportation (NHTSA).; Washington D.C., 2011.

671 672 673

(62)

Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 Through 2016; U.S. Environmental Protection Agency.; Washington, DC, 2016; https://www.epa.gov/sites/production/files/2016-11/documents/420r16010.pdf.

674 675

(63)

Vehicle Cost Calculator Assumptions and http://www.afdc.energy.gov/calc/cost_calculator_methodology.html.

676 677 678

(64)

NewsRoom: Annual Cost to Own and Operate a Vehicle Falls to $8,698; http://newsroom.aaa.com/2015/04/annual-cost-operate-vehicle-falls-8698-finds-aaaarchive/.

679 680

(65)

ADAC Autokosten 2014; Allgemeiner Deutscher Automobil-Club e.v.; Munich, Germany, 2014.

681 682 683

(66)

Dodds, P. E.; McDowall, W. Methodologies for representing the road transport sector in energy system models. Int. J. Hydrogen Energy 2014, 39 (5), 2345–2358; DOI 10.1016/j.ijhydene.2013.11.021.

684 685 686

(67)

Bahn, O.; Marcy, M.; Vaillancourt, K.; Waaub, J.-P. Electrification of the Canadian road transportation sector: A 2050 outlook with TIMES-Canada. Energy Policy 2013, 62, 593– 606; DOI 10.1016/j.enpol.2013.07.023.

687 688 689

(68)

Gül, T.; Kypreos, S.; Turton, H.; Barreto, L. An energy-economic scenario analysis of alternative fuels for personal transport using the Global Multi-regional MARKAL model (GMM). Energy 2009, 34 (10), 1423–1437; DOI 10.1016/j.energy.2009.04.010.

690 691

(69)

U.S. Car and Truck Sales, 1931-2015; Wards http://wardsauto.com/datasheet/us-car-and-truck-sales-1931-2014.

692 693 694 695

(70)

Number of U.S. Aircraft, Vehicles, Vessels, and Other Conveyances; U.S. Department of Transportation.; Washington, DC, 2015; http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation _statistics/html/table_01_11.html.

696 697 698 699

(71)

Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990 – 2014; U.S. Environmental Protection Agency.; Washington, DC, 2016; https://www3.epa.gov/climatechange/Downloads/ghgemissions/US-GHG-Inventory2016-Main-Text.pdf.

700 701

(72)

Sources of Energy; https://www.eia.gov/energyexplained/index.cfm?page=about_sources_of_energy.

702 703 704

(73)

National Electric Energy Data System (NEEDS) v.5.14; U.S. Environmental Protection Agency.; Washington, DC, 2015; https://www.epa.gov/sites/production/files/201508/needs_v514.xlsx.

705

(74)

Cost Report: Cost and Performance Data for Power Generation Technologies; Black &


Methodology;

Auto.;

2016;

30

Page 31 of 36


706

Veatch.; 2012; http://bv.com/docs/reports-studies/nrel-cost-report.pdf.

707 708 709

(75)

Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants; U.S. Energy Information Administration.; Washington, DC, 2013; http://www.eia.gov/forecasts/capitalcost/pdf/updated_capcost.pdf.

710 711

(76)

Lazard’s Levelised Cost of Energy Analysis (version https://www.lazard.com/media/438038/levelized-cost-of-energy-v100.pdf.

712 713 714

(77)

Chan, H. S. (Ron); Cropper, M. L.; Malik, K. Why Are Power Plants in India Less Efficient than Power Plants in the United States? Am. Econ. Rev. 2014, 104 (5), 586–590; DOI 10.1257/aer.104.5.586.

715 716 717 718

(78)

Supekar, S. D.; Caruso, K. A.; Daskin, M. S.; Skerlos, S. J. Least-Cost Technology Investments in the Passenger Vehicle and Electric Sectors to Meet Greenhouse Gas Emissions Targets to 2050. In Re-engineering Manufacturing for Sustainability; Nee, A. Y. C.; Song, B.; Ong, S.-K., Eds.; Springer Singapore: Singapore, 2013.

719 720 721

(79)

Bertram, C.; Johnson, N.; Luderer, G.; Riahi, K.; Isaac, M.; Eom, J. Carbon lock-in through capital stock inertia associated with weak near-term climate policies. Technol. Forecast. Soc. Change 2015, 90 (PA), 62–72; DOI 10.1016/j.techfore.2013.10.001.

722 723 724

(80)

den Elzen, M. G. J.; van Vuuren, D. P.; van Vliet, J. Postponing emission reductions from 2020 to 2030 increases climate risks and long-term costs. Clim. Change 2010, 99 (1–2), 313–320; DOI 10.1007/s10584-010-9798-5.

725 726 727

(81)

van Vuuren, D. P.; Stehfest, E. If climate action becomes urgent: the importance of response times for various climate strategies. Clim. Change 2013, 121 (3), 473–486; DOI 10.1007/s10584-013-0769-5.

728 729 730

(82)

Vaughan, N. E.; Lenton, T. M.; Shepherd, J. G. Climate change mitigation: trade-offs between delay and strength of action required. Clim. Change 2009, 96 (1–2), 29–43; DOI 10.1007/s10584-009-9573-7.

731 732 733

(83)

Grubb, M. J.; Ha-Duong, M.; Hourcade, J.-C. Influence of socioeconomic inertia and uncertainty on optimal CO2-emission abatement. Nature 1997, 390 (6657), 270–273; DOI 10.1038/36825.

734 735 736

(84)

Friedlingstein, P.; Solomon, S.; Plattner, G.-K.; Knutti, R.; Ciais, P.; Raupach, M. R. Long-term climate implications of twenty-first century options for carbon dioxide emission mitigation. Nat. Clim. Chang. 2011, 1 (9), 457–461; DOI 10.1038/nclimate1302.

737 738 739

(85)

Figueres, C.; Schellnhuber, H. J.; Whiteman, G.; Rockström, J.; Hobley, A.; Rahmstorf, S. Three years to safeguard our climate. Nature 2017, 546 (7660), 593–595; DOI 10.1038/546593a.

740 741 742

(86)

Lackner, K. S.; Brennan, S.; Matter, J. M.; Park, a.-H. a.; Wright, A.; van der Zwaan, B. The urgency of the development of CO2 capture from ambient air. Proc. Natl. Acad. Sci. 2012, 109 (33), 13156–13162; DOI 10.1073/pnas.1108765109.


10.0);

31


Page 32 of 36

743 744 745

(87)

Fuss, S.; Jones, C. D.; Kraxner, F.; Peters, G. P.; Smith, P.; Tavoni, M.; van Vuuren, D. P.; Canadell, J. G.; Jackson, R. B.; Milne, J.; et al. Research priorities for negative emissions. Environ. Res. Lett. 2016, 11 (11), 11; DOI 10.1088/1748-9326/11/11/115007.

746 747

(88)

Vaughan, N. E.; Lenton, T. M. A review of climate geoengineering proposals. Clim. Change 2011, 109 (3–4), 745–790; DOI 10.1007/s10584-011-0027-7.

748 749 750 751

(89)

Hoffert, M. I.; Caldeira, K.; Benford, G.; Criswell, D. R.; Green, C.; Herzog, H.; Jain, A. K.; Kheshgi, H. S.; Lackner, K. S.; Lewis, J. S.; et al. Advanced Technology Paths to Global Climate Stability: Energy for a Greenhouse Planet. Science (80-. ). 2002, 298 (5595), 981–987; DOI 10.1126/science.1072357.

752 753 754

(90)

Greenblatt, J. B.; Saxena, S. Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles. Nat. Clim. Chang. 2015, 5 (9), 860–863; DOI 10.1038/nclimate2685.

755 756 757

(91)

McCollum, D.; Krey, V.; Kolp, P.; Nagai, Y.; Riahi, K. Transport electrification: A key element for energy system transformation and climate stabilization. Clim. Change 2014, 123 (3–4), 651–664; DOI 10.1007/s10584-013-0969-z.

758 759 760

(92)

MacDonald, A. E.; Clack, C. T. M.; Alexander, A.; Dunbar, A.; Wilczak, J.; Xie, Y. Future cost-competitive electricity systems and their impact on US CO2 emissions. Nat. Clim. Chang. 2016, 6 (5), 526–531; DOI 10.1038/nclimate2921.

761 762 763 764

(93)

Zwaan, B. C. C. van der; Rösler, H.; Kober, T.; Aboumahboub, T.; Calvin, K. V.; Gernaat, D. E. H. J.; Marangoni, G.; McCollum, D. A Cross-Model Comparison of Global Long-Term Technology Diffusion under a 2˚C Climate Change Control. Clim. Chang. Econ. 2013, 4 (4), 1340013; DOI 10.1142/S2010007813400137.

765 766 767

(94)

Bartolozzi, I.; Rizzi, F.; Frey, M. Comparison between hydrogen and electric vehicles by life cycle assessment: A case study in Tuscany, Italy. Appl. Energy 2013, 101, 103–111; DOI 10.1016/j.apenergy.2012.03.021.

768 769 770 771

(95)

Offer, G. J.; Howey, D.; Contestabile, M.; Clague, R.; Brandon, N. P. Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system. Energy Policy 2010, 38 (1), 24–29; DOI 10.1016/j.enpol.2009.08.040.

772 773

(96)

Renewable Electricity : Insights for the Coming Decade,; Stark, C.; Pless, J.; Logan, J.; Zhou, E.; Arent, D. J.; NULL: 2015; http://www.nrel.gov/docs/fy15osti/63604.pdf.

774 775 776

(97)

Supekar, S. D.; Skerlos, S. J. Reassessing the Efficiency Penalty from Carbon Capture in Coal-Fired Power Plants. Environ. Sci. Technol. 2015, 49 (20), 12576–12584; DOI 10.1021/acs.est.5b03052.

777 778 779

(98)

The Social Cost of Carbon; United States Government.; Washington, DC, 2013; http://www.whitehouse.gov/sites/default/files/omb/assets/inforeg/technical-update-socialcost-of-carbon-for-regulator-impact-analysis.pdf.

780

(99)

Ross Morrow, W.; Gallagher, K. S.; Collantes, G.; Lee, H. Analysis of policies to reduce


32

Page 33 of 36


781 782

oil consumption and greenhouse-gas emissions from the US transportation sector. Energy Policy 2010, 38 (3), 1305–1320; DOI 10.1016/j.enpol.2009.11.006.

783 784 785

(100) Supekar, S. D. Environmental and Economic Assessment of Carbon Dioxide Recovery and Mitigation in the Industrial and Energy Sectors, Doctoral Thesis, University of Michigan, Ann Arbor, 2015.

786 787 788 789

(101) Jacobson, M. Z.; Delucchi, M. A.; Bazouin, G.; Bauer, Z. A. F.; Heavey, C. C.; Fisher, E.; Morris, S. B.; Piekutowski, D. J. Y.; Vencill, T. A.; Yeskoo, T. W. 100% clean and renewable wind, water, and sunlight (WWS) all-sector energy roadmaps for the 50 United States. Energy Enviromental Sci. 2015, 8 (7), 2093–2117; DOI 10.1039/C5EE01283J.

790 791 792 793

(102) Jacobson, M. Z.; Delucchi, M. A.; Cameron, M. A.; Frew, B. A. Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind, water, and solar for all purposes. Proc. Natl. Acad. Sci. 2015, 112 (49), 15060–15065; DOI 10.1073/pnas.1510028112.

794 795 796 797

(103) Bond, T. C.; Doherty, S. J.; Fahey, D. W.; Forster, P. M.; Berntsen, T.; DeAngelo, B. J.; Flanner, M. G.; Ghan, S.; Kärcher, B.; Koch, D.; et al. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos. 2013, 118 (11), 5380–5552; DOI 10.1002/jgrd.50171.

798 799

(104) Jacobson, M. Z. On the causal link between carbon dioxide and air pollution mortality. Geophys. Res. Lett. 2008, 35 (3), L03809; DOI 10.1029/2007GL031101.

800 801 802 803

(105) West, J. J.; Smith, S. J.; Silva, R. A.; Naik, V.; Zhang, Y.; Fry, M. M.; Anenberg, S.; Horowitz, L. W.; Lamarque, J.-F.; Adelman, Z.; et al. Co-benefits of mitigating global greenhouse gas emissions for future air quality and human health. Nat. Clim. Chang. 2013, 3 (10), 885–889; DOI 10.1038/NCLIMATE2009.

804


33


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)


Page 34 of 36

Page 35 of 36


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)


Page 36 of 36

Analysis of Costs and Time Frame for Reducing CO2 Emissions by 70

Recommend Documents