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TitleComments Concerning the Proposed Rulemaking to Revise Light-Duty Vehicle Greenhouse Gas
Author
LanguageEnglish
File Size1.9 MB
Total Pages90
Table of Contents
                            I. Agencies have failed to propose maximum feasible standards
	A. Agencies’ modeling of the standards is overly conservative and does not accurately demonstrate the technological feasibility of stronger standards.
		1. The agencies’ characterization of the current state of technology is overly conservative and inconsistent with previous agency conclusions
			a) Mild hybridization
			b) Cylinder deactivation
			c) Advanced boosted engines
			d) High compression ratio engines
			e) Novel engine designs
			f) Batteries for hybrid and plug-in electric vehicles
			g) Mass reduction
				(1) The agencies assumption that mass reduction can only be applied to 50 percent of the curb weight is unjustified and inaccurate
				(2) Reducing the glider fraction inaccurately limits the potential and applicability of mass reduction
				(3) The agencies have inflated the costs of mass reduction, per their own analysis
				(4) Assessing the impact of the agencies’ inaccurate characterization of mass reduction
			h) High octane fuel
			i) Agencies have incorrectly adjusted for a switch from Tier 2 to Tier 3 gasoline
				(1) Adjustment related to energy content instead of carbon content
				(2) Adjustment related to knock threshold
			j) Arbitrary changes in the way the agencies assessed baseline technology deployment overstates the technology already present in the fleet
				(1) Assessing the impact of this subjective analysis
			k) Agencies have underestimated the potential for future innovation
			l) The agencies have grossly mischaracterized the state of electric vehicles
				(1) ZEV is working to improve consumer choice
				(2) The ZEV program has consumer and economic benefits
		2. The agencies’ compliance modeling is inadequate and irrational
			a) The agencies’ NPRM modeling ignores its own updated modeling efforts
				(1) Full vehicle simulation
				(2) Pathway modeling
			b) The Volpe model does not produce economically rational results
				(1) Flaws in the efficiency algorithm
				(2) Evidence of irrational behavior in the current Volpe model
				(3) Creating a more efficient model
			c) The Volpe model does not accurately reflect the banking and trading provisions for compliance with fuel economy OR emissions standards
				(1) The Published Volpe Model Inexplicably Lets Credits Expire
				(2) Why Manufacturers Do Not Fully Utilize Credits in the Volpe Model
				(3) Conclusions on credit inefficiencies
	B. The agencies have underestimated the social and economic benefits of fuel economy and greenhouse gas emissions standards
		1. Agencies overestimate costs to consumers and underestimate consumer benefits of stronger standards
		2. Agencies inconsistently evaluate and arbitrarily utilize consumers’ willingness to pay for fuel economy technologies to generate predetermined, conservative outcomes
			a) Estimates from the literature
			b) How the agencies have inconsistently evaluated willingness to pay
			c) The “technology cost burden” associated with electrification is biased, inaccurate, and does not reflect the technology included in the rulemaking
		3. The proposed standards will decrease employment and reduce economic output
			a) Current Employment Benefits from Existing Standards
			b) Positive Future Employment Benefits from Existing Standards
			c) Shortcomings of Macroeconomic Assessment by Carley, et al.
				(1) Concerns around IU’s model of vehicle price effects
				(2) Concerns around IU’s treatment of compliance with state zero-emission vehicle regulations
			d) EPA and NHTSA underestimate the negative employment impacts of its Preferred Alternative
		4. The agencies’ cost-benefit model is fundamentally flawed
			a) The agencies’ model goes against basic economic theory
				(1) Total light-duty vehicle fleet size
				(2) Fleet vehicle miles traveled
				(3) Engineering scrappage
			b) The agencies’ model is inconsistent with academic literature
				(1) Literature data on modeling scrappage
				(2) Rebound
			c) Implications of these flaws are virtually the entire purported “benefit” of the standard
	C. Agencies mischaracterize the relationship between fuel economy and greenhouse gas regulations and other federal laws
		1. Agencies have mischaracterized the impact of fuel economy and greenhouse gas emissions standards on safety
		2. Agencies have erroneously ignored the interaction between Zero Emission Vehicle standards and federal requirements of the Clean Air Act
	D. Agencies mischaracterize the need for the nation to conserve energy
II. The agencies’ proposal suffers from critical legal errors
	A. Withdrawal of the California waiver would be unprecedented, unwarranted attack on state authority
	B. Vehicle emission standards are not inherently fuel economy standards and therefore are not pre-empted by EPCA
	C. Zero Emission Vehicles standards are not inherently fuel economy standards and therefore are not pre-empted by EPCA
	D. The technical basis for the Proposal is fundamentally flawed
III. Additional requests for comment
	A. Incentives for autonomous and connected vehicle technologies
		1. Autonomous and connected vehicle technologies do not directly reduce emissions
		2. The agencies have previously appropriately excluded crediting indirect emissions
	B. Incentives for hybrid and alternative fuel vehicles
		1. Hybrid incentives
		2. Natural gas vehicles
		3. Incentives for electric vehicles
		4. Combined impact of incentives
	C. Disclosure of credit trading under the CAFE program
IV. References
APPENDIX A: Modifications to Volpe model source code
                        
Document Text Contents
Page 1

Comments Concerning the Proposed
Rulemaking to Revise Light-Duty Vehicle
Greenhouse Gas Emissions Standards and
Corporate Average Fuel Economy Standards:
Technical Appendix






Referencing docket ID numbers:
EPA-HQ-OAR-2018-0283
NHTSA-2018-0067

Page 2

i


Table of Contents
I. Agencies have failed to propose maximum feasible standards ..................................................................... 1

A. Agencies’ modeling of the standards is overly conservative and does not accurately demonstrate
the technological feasibility of stronger standards. .............................................................................................. 1

1. The agencies’ characterization of the current state of technology is overly conservative and
inconsistent with previous agency conclusions ................................................................................................ 1

2. The agencies’ compliance modeling is inadequate and irrational .................................................... 22

B. The agencies have underestimated the social and economic benefits of fuel economy and
greenhouse gas emissions standards ..................................................................................................................... 46

1. Agencies overestimate costs to consumers and underestimate consumer benefits of stronger
standards .................................................................................................................................................................. 47

2. Agencies inconsistently evaluate and arbitrarily utilize consumers’ willingness to pay for fuel
economy technologies to generate predetermined, conservative outcomes .......................................... 49

3. The proposed standards will decrease employment and reduce economic output ..................... 52

4. The agencies’ cost-benefit model is fundamentally flawed ............................................................... 57

C. Agencies mischaracterize the relationship between fuel economy and greenhouse gas
regulations and other federal laws ......................................................................................................................... 62

1. Agencies have mischaracterized the impact of fuel economy and greenhouse gas emissions
standards on safety ................................................................................................................................................ 62

2. Agencies have erroneously ignored the interaction between Zero Emission Vehicle standards
and federal requirements of the Clean Air Act ............................................................................................... 62

D. Agencies mischaracterize the need for the nation to conserve energy................................................ 63

II. The agencies’ proposal suffers from critical legal errors ........................................................................ 64

A. Withdrawal of the California waiver would be unprecedented, unwarranted attack on state
authority ....................................................................................................................................................................... 64

B. Vehicle emission standards are not inherently fuel economy standards and therefore are not
pre-empted by EPCA ................................................................................................................................................. 65

C. Zero Emission Vehicles standards are not inherently fuel economy standards and therefore are
not pre-empted by EPCA .......................................................................................................................................... 65

D. The technical basis for the Proposal is fundamentally flawed .............................................................. 65

III. Additional requests for comment .................................................................................................................. 66

A. Incentives for autonomous and connected vehicle technologies ......................................................... 66

1. Autonomous and connected vehicle technologies do not directly reduce emissions ................. 66

2. The agencies have previously appropriately excluded crediting indirect emissions .................. 67

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42


Credits dispursed

Manufacturer 2010 2011 2012 2013 2014 2015 2016

CODA 5,524 1,727

HONDA


14,182,329


6,590,901

NISSAN-MITSUBISHI 950,000


1,345,570 250,000


1,000,000

TESLA 35,580 14,192 177,941


1,049,384


1,020,296


1,337,853 2,452,519

TOYOTA


2,507,000 831,358

Total


33,752,174


17,674,909


7,950,663 433,465


2,051,111


1,851,654


1,337,853 2,452,519


Credits acquired

Manufacturer 2010 2011 2012 2013 2014 2015 2016

BMW


2,000,000

DAIMLER


3,985,580 814,192 427,941


1,000,000
FERRARI 265,000

FCA 11,424,329 7,090,901


1,049,384


1,020,296


1,337,853 2,452,519

GENERAL MOTORS 5,524 1,727
JLR 39,063 831,358
McLAREN 6,507

Total


33,752,174


17,674,909


7,950,663 433,465


2,051,111


1,851,654


1,337,853 2,452,519




Just over 30 million Mg worth of credits have been traded to date under EPA’s greenhouse gas program. This represents just
over 10 percent of the total overcompliance with the standards thus far.

SOURCE: EPA 2018d

Table 8 shows the trading that has occurred to date. Table 9 outlines trading of credits that occurs
under the simulated compliance pathways through MY2021, which is when banked credits expire and
is therefore most similar to the scenario to date.

A few things are apparent when looking at the data of modeled credit trading:

1) Though manufacturers carried nearly 300 million Mg of overcompliance into MY2016, the vast
majority of those credits are utilized by the earning manufacturers, with our modeled
manufacturer trading resulting in only 54 million Mg of utilization by manufacturers other than
those who earned the credits through overcompliance (18 percent).




TABLE 8. Actual credit trading through MY2016

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Credits dispursed

Manufacturer 2010 2011 2012 2013 2014 2015 2016

FORD 386,226 751,885 980,697

HONDA 100 911,989


2,346,013


2,297,323


1,881,029


2,052,383

HYUNDAI


4,890,103


4,012,969 348,728 353,607 75,430 54,131

KIA


1,726,043

MAZDA


2,102,696 208,773 14,339

NISSAN-MITSUBISHI


5,353,515 812,460


1,057,202 807,013 668,140 974,284

SUBARU


1,454,845


2,106,676


1,165,481


3,001,354


1,580,583

TOYOTA


9,232,715 1,456

Total 53,610,188

23,033,974


9,778,910


4,152,508


4,623,424


6,377,838


5,643,534 -



Credits acquired

Manufacturer 2010 2011 2012 2013 2014 2015 2016

DAIMLER


1,867,095


1,810,307 14,339

FCA


12,578,370 812,460


1,057,202


2,896,152


2,549,169


3,028,123

GENERAL MOTORS


3,777,463


1,619,296


2,346,013


1,261,523


1,407,588 54,131

JLR


4,191,367


3,122,441 608,923 5,772


2,421,081


2,561,280
VOLVO 93,451 165,389

VWA 526,228


2,414,406 126,031 294,588

Total 53,610,188

23,033,974


9,778,910


4,152,508


4,623,424


6,377,838


5,643,534 -



Our model of manufacturer-to-manufacturer trading results in a level of future trades comparable to that seen to date,
incorporating many of the same manufacturers on both the “dispursed” and “acquired” categories.

SOURCE: EPA 2018d



2) The manufacturers which our modified version of the model suggests would buy credits (Table
9) have nearly all already previously purchased credits from other manufacturers (Table 8). The
two exceptions are Volvo, who is entering 2017 at a slight overall deficit, and Volkswagen,




TABLE 9. Modeled manufacturer-to-manufacturer trading MY2016-2021 using banked credits

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return mfrList;
}

public static void CT_CarryForwardM2M(Industry[] modelData, Scenario scen, ModelYear
year, int mfrIndex, int mfrIndex2,ModelingSettings settings, bool expiringOnly, int
expiringYrIndex)
{

CT_CarryForwardM2M(RC.AllPassengerCar, modelData, scen, year, mfrIndex, mfrIndex2,
settings, expiringOnly, expiringYrIndex);
CT_CarryForwardM2M(RC.LightTruck, modelData, scen, year, mfrIndex, mfrIndex2,
settings, expiringOnly, expiringYrIndex);
CT_CarryForwardM2M(RC.LightTruck2b3, modelData, scen, year, mfrIndex, mfrIndex2,
settings, expiringOnly, expiringYrIndex);

}

/// <summary>
/// Performs a credit carry forward operation into a compliance category defined by
the specified model
/// year, between two manufacturers (mfrSource, mfrReceive), into regulatory class rc.
/// A carry forward operation will only be performed if credit trading settings allow
and the destination
/// compliance category is at a deficit.
/// </summary>
public static void CT_CarryForwardM2M(RC rc, Industry[] modelData, Scenario scen,
ModelYear year, int mfrSource, int mfrReceive, ModelingSettings settings, bool
expiringOnly, int expiringYrIndex)

{
CreditTradingValues ct = settings.Parameters.CreditTradingValues;
if (!settings.OperatingModes.AllowCreditTrading ||
settings.OperatingModes.LastCreditTradingYear < year.Year || !ct.AllowCarryForward)
{ return; }

Manufacturer mfr = modelData[year.Index].Manufacturers[mfrSource];
Manufacturer uMfr = modelData[year.Index].Manufacturers[mfrReceive];

double credits = uMfr.GetNetCO2Credits(rc);

// the fleet has positive credits (is in compliance) for this reg-class
if (credits >= 0) { return; }

// scan each year to see if credits can be carried forward
int minComplianceYearIndex = modelData[year.Index].MinYear - ModelYear.MinYear;
int minBankedCredYearIndex = mfr.Description.BankedCO2CreditsMinYear -
ModelYear.MinYear;
int minYearIndex = Math.Min(minComplianceYearIndex, minBankedCredYearIndex);
//
for (int i = minYearIndex; i < year.Index; i++)
{ // get carry forward years

int carryFwdYears = 0;
carryFwdYears = scen.ScenInfo[rc].CreditCarryFwd[i]; // determined by input file
if (carryFwdYears == 0)
{ // no scenario/year specific value defined -- use global setting
carryFwdYears = ct.CarryForwardYears;

}

// if only expiring credits should be used, skip all other years
if (expiringOnly && (i > expiringYrIndex - carryFwdYears)) { continue; }

// if credits already expired, skip year
if (i + carryFwdYears < year.Index) { continue; }

Manufacturer pMfr = null;
double eCredits = 0;

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bool useBank = false;
if (i >= minComplianceYearIndex)
{ // checking one of analysis years -- check for credits generated during
compliance
pMfr = modelData[i].Manufacturers[mfrSource];

// **note: when computing available credits from previous years,
CO2CreditsIn should not be considered,

// since there is no way of tracking where they came from or their
expiration

eCredits = Math.Max(0, pMfr.ModelData.CO2Credits[rc] -
pMfr.ModelData.CO2CreditsOut[rc]);

}
else
{ // checking a year prior to start of modeling -- check for banked credits

pMfr = mfr;
eCredits = mfr.Description.GetBankedCO2Credits(rc, i + ModelYear.MinYear);

useBank = true;
}


// continue to the next year if did not earn any credits in the "i-th" year
if (eCredits <= 0) { continue; }

// perform a credit transfer between the two manufacturers

Mfr2MfrTransfer(settings, scen, eCredits, -credits, rc,
ModelYear.NewFromIndex(i), pMfr, useBank, rc, year, uMfr);


// update credits; if compliance achieved, return
credits = uMfr.GetNetCO2Credits(rc);


if (credits >= 0) { return; }


} // next i (carry forward credit year)
}

public static double costPerTonCO2(Manufacturer mfr, Industry[] modelData, ModelYear
year, ModelingSettings settings)
{

int minComplianceYearIndex = modelData[year.Index].MinYear - ModelYear.MinYear;

Manufacturer mfr0 = modelData[minComplianceYearIndex].Manufacturers[mfr.Index]; //
clone for baseline year

var mmd = mfr.ModelData;
var mmd0 = mfr0.ModelData;

double BaselineCO2PC = mmd0.CO2RatingSum.CalcSum(RC.AllPassengerCar);
double BaselineCO2LT = mmd0.CO2RatingSum.CalcSum(RC.LightTruck);
double CurrentCO2PC = mmd.CO2RatingSum.CalcSum(RC.AllPassengerCar);
double CurrentCO2LT = mmd.CO2RatingSum.CalcSum(RC.LightTruck);

double MfrTonsRedux = (BaselineCO2PC / mmd0.Sales[RC.AllPassengerCar] -
CurrentCO2PC / mmd.Sales[RC.AllPassengerCar]) * StandardsCO2.GetVMT(settings, year,
RC.AllPassengerCar) * mmd.Sales[RC.AllPassengerCar] + (BaselineCO2LT /
mmd0.Sales[RC.LightTruck] - CurrentCO2LT / mmd.Sales[RC.LightTruck]) *
StandardsCO2.GetVMT(settings, year, RC.LightTruck) * mmd.Sales[RC.LightTruck]; //
calculates tons of reductions to date, relative to baseline and scaled to current
sales

double costPerTon = mfr.ModelData.TechCost.Total / MfrTonsRedux; // divides
manufacturer’s tech cost in current model year by tons of reduction

return costPerTon;

}

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