Effectiveness of Mitigation Measures in Reducing Future Primary

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Effectiveness of Mitigation Measures in Reducing Future Primary Particulate Matter (PM) Emissions from On-Road Vehicle Exhaust Fang Yan, Tami C. Bond, and David G. Streets Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es503197f • Publication Date (Web): 13 Nov 2014 Downloaded from http://pubs.acs.org on November 30, 2014

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Environmental Science & Technology

1

Effectiveness of Mitigation Measures in Reducing Future

2

Primary Particulate Matter (PM) Emissions from On-Road

3

Vehicle Exhaust

4 5 Fang Yan1, 2, 3, Tami C. Bond1*, and David G. Streets2, 3

6 7 8 9

1

Department of Civil and Environmental Engineering, University of Illinois at Urbana–

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Champaign, Urbana, Illinois, United States 2

Decision and Information Sciences Division, Argonne National Laboratory, Argonne,

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Illinois, United States 3

Computation Institute, University of Chicago, Chicago, Illinois, United States

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Revised manuscript submitted to Environmental Science & Technology

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November 8, 2014

18 19 20 21 22 The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government.

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* Corresponding Author. Phone: (217) 244-5277; e-mail: [email protected]

1 ACS Paragon Plus Environment


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ABSTRACT: This work evaluates the effectiveness of on-road primary particulate matter

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emission reductions that can be achieved by long-term vehicle scrappage and retrofit measures

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on regional and global levels. Scenario analysis shows that scrappage can provide significant

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emission reductions as soon as the measures begin, whereas retrofit provides greater emission

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reductions in later years, when more advanced technologies become available in most regions.

31

Reductions are compared with a baseline that already accounts for implementation of clean

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vehicle standards. The greatest global emission reductions from a scrappage program occur 5 to

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10 years after its introduction and can reach as much as 70%. The greatest reductions with

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retrofit occur around 2030 and range from 16–31%. Monte Carlo simulations are used to

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evaluate how uncertainties in the composition of the vehicle fleet affect predicted reductions.

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Scrappage and retrofit reduce global emissions by 31–60% and 15–31%, respectively, within 95%

37

confidence intervals, under a mid-range scenario in the year 2030. The simulations provide

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guidance about which strategies are most effective for specific regions. Retrofit is preferable for

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high-income regions. For regions where early emission standards are in place, scrappage is

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suggested, followed by retrofit after more advanced emission standards are introduced. The early

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implementation of advanced emission standards is recommended for Western and Eastern Africa. (a) Emission reduction by scrappage in 2030 b) Scrappage: 2030

(b) Emission reduction by retrofit in 2030

Global

Global

Pacific

Pacific

Southeast Asia

Southeast Asia

East Asia

East Asia

South Asia

South Asia

Former USSR

Former USSR

Europe

Europe

Middle East

Middle East

Africa

Africa

Latin America

Latin America

North America 0

42

North America

20 40 60 80 Emission reduction (%)

100

0


43


100

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INTRODUCTION

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The transportation sector is one of the major contributors to fine particulate matter (PM)

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emissions now and in the future,1–8 and transportation activity is increasing rapidly.9–12 PM is the

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most harmful vehicle pollutant and exposure to vehicle PM has been associated with a range of

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health impacts such as lung cancer.13,14 It has been reported that 25 million years of cumulative

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life and more than 0.21 million human lives will be lost by 2030 if accelerated clean vehicle and

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fuel policies are not adopted.15 In addition to causing adverse health outcomes, PM emissions

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from vehicles are implicated in climate change.16–21 One of the most important components of

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PM is black carbon (BC) which may be the second largest positive radiative forcing (RF)

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agent.22–24 Reductions in BC emissions have been proposed as a desirable strategy to reduce

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future global warming.25–27

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Diesel vehicles are ideal candidates for PM and hence BC control, and emission reductions

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can achieve substantial co-benefits to air quality28 and human health.13,29–31 This is because diesel

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vehicles are important sources of on-road PM emissions, contributing over 85% of the total.7,32

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Among all anthropogenic source types, exhaust PM emissions from diesel vehicles have the

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highest ratio of BC to organic carbon (OC) and therefore the greatest warming effect per unit

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mass. 33–36 Perhaps most importantly, the technologies and strategies to control vehicle emissions

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are available today.35,37–40

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Despite these known advantages, there remains a lack of studies concerning the emission

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reduction potential from vehicles in the future, especially studies that consider uncertainty.

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General mitigation measures have been proposed,41 but few studies provide an evaluation of their

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effectiveness or quantification of PM emission reductions by world region and by analysis year.

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In this work, we provide an analysis of mitigation measures by incorporating dynamic

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relationships of socioeconomic and technology to future emissions, thus providing the ability to

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relate the changes of primary PM emissions from the exhaust of on-road vehicles to

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technological development and regulatory measures. Mitigation measures may also affect

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secondary PM42,43 – formed from chemical reactions involving primary gaseous emissions; work

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to identify precursor species and emission rates is still underway, and effects on secondary PM

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are not evaluated here.



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APPROACH

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Regulatory steps such as European and U.S. emission standards have been taken to make

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new vehicles cleaner, and these steps have managed to decrease emissions in spite of growth in

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fuel use.5,6,44 Our previous studies7,8,45 have shown that, as the cleaner vehicles take up a greater

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share of the fleet, so-called “superemitters” (defined as vehicles having extremely high emission

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factors) and old vehicles contribute more to total PM emissions. Emissions of vintage vehicles

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are not well monitored or regulated as effectively as those of new vehicles. For these reasons, we

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examine the effects of two important mitigation measures, scrappage46,47 and retrofit,48 that aim

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to reduce PM emissions from the existing stock of old and high-emitting vehicles.

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Scrappage. Vehicle scrappage—also known as accelerated retirement—refers to the

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replacement of old or high-emitting vehicles with newer ones that emit less pollution, before

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their owners would otherwise retire them from use. Several countries, states, and local

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governments have adopted such programs, such as “cash-for-clunkers” in the U.S. and economic

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incentives to phase out “yellow label” vehicles in Beijing.46,47 Scrappage programs at national or

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local levels have been evaluated in several studies.47,49–54 Most of them have examined the

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effects of scrappage on carbon monoxide (CO) and hydrocarbon (HC) emissions in a single

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urban airshed or in a state in less than five years after program initiation,47 and only recently

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have greenhouse gases been considered.51,55 However, few studies have addressed PM

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emissions,49 and none have done so at the global level.

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Retrofit. The term retrofit can be broadly defined as any technology, device, fuel, or system

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that, when applied to an existing vehicle or engine, achieves emission levels lower than those

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required by regulations at the time of the original certification of the vehicle or engine. Diesel

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particle filters (DPF)56 and diesel oxidation catalysts (DOC)57 are two examples of retrofit

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technologies. Retrofit programs have been implemented to reduce PM emissions from diesel

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vehicles.58–61 Though studies have evaluated the effectiveness of specific retrofit

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technologies,56,62,63 only limited research has evaluated their potential contribution to emissions

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from the entire vehicle fleet28,64 and over long time periods.

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The purpose of this work is to provide an understanding of the potential benefits of these two

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types of mitigation measure to global and regional primary PM emissions in a 35-year period

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from 2015 to 2050. We discuss the following questions: 4 ACS Paragon Plus Environment

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(1) What contribution can scrappage and retrofit make to improvements in global emissions over the coming decades?

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(2) Given uncertainties in the parameters required to model the changing vehicle fleet in the future, what is the level of confidence in the projected emission reductions due to these measures?

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(3) In which regions are such measures most and least effective at reducing emissions, and what features of the vehicle fleet govern these results?

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The research reported here focuses on exhaust emissions during vehicle operation. A

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complete treatment including life-cycle analysis would evaluate emissions associated with the

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production of new vehicles or aftertreatment and the disposal of old ones. The life-cycle

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assessments 55,65–67 are not performed here.

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METHOD

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Scenario analysis. We simulate the effects of the mitigation measures by using the scenario

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analysis68, for which we make reasonable assumptions by combining sets of parameters.

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Emission projections are created for a baseline scenario without any application of measures, as

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well as scenarios in which either scrappage or retrofit is imposed. For each measure, we select

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three scenarios (Low, Medium, and High), based on the aggressiveness of implementation and

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the degree of emission reduction from the baseline.

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Scrappage is influenced by four factors: vehicle targets, start calendar year, gap-year (defined

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as the delayed years of introduction of the mitigation measure after adoption of certain emission

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standards), and accelerated retirement rate. We refer to these as “measure-related factors”.

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Retrofit is described by five measure-related factors: vehicle target, start calendar year, gap-year,

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retrofit rate, and the retrofit technology. Table 1 summarizes the main assumptions and values

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for these measure-related factors in Low, Medium, and High scenarios. For example, the High

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scenario with the scrappage measure has the most aggressive implementation. It targets all

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possible vehicles, starts at an early year, applies measures as soon as the technology becomes

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available, and retires vehicles at a high rate. The High scenario therefore results in low future

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emissions and high emission reductions. A detailed description of the measure-related factors,

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choice of values, and emission sensitivities appears in the SI.



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In this work, we present emission projections under the fuel consumption and socioeconomic

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conditions of the A1B scenario, which describes a “middle-of-the-road” scenario. This scenario

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was developed by the Intergovernmental Panel on Climate Change (IPCC) for the Special Report

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on Emission Scenarios.68,69 For consistency with previous studies,7,8,45 we use historical fuel data

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from the International Energy Agency until 2005, and estimate fuel consumption after 2005 by

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growth rates from the A1B scenario.

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Emission projection model. Future PM emissions with and without mitigation measures, as

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well as their uncertainties, are estimated within the framework of the Speciated Pollutant

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Emission Wizard (SPEW)-Trend model.7 Three vehicle groups are included: light-duty gasoline

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vehicles (LDGV), light-duty diesel vehicles (LDDV), and heavy-duty diesel vehicles (HDDV).

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Each group is further sub-divided by emission standards (e.g., European and U.S. emission

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standards). The SPEW-Trend model tracks the vehicle population in each group over time, so

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changes in overall emission factors are represented explicitly by the changes in technology. A

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detailed description of the SPEW-Trend model is given in Yan et al.7 Countries are grouped into

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17 world regions for modeling purposes, but are aggregated into 10 summary regions for

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presentation here.

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Many countries have either already adopted progressively or have set timelines for tighter

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emission standards. Besides income level, other factors, such as trends in neighboring countries

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or prevailing air quality problems, may govern the introduction of standards. As explained in

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Yan et al.,7 we adopt an empirical method of introducing standards. We rely on income level, the

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timing of standards in the population-dominant country, the average of the implementation years

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in each country, and the timing of standards in the leading country plus several years to represent

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the emission standard schedule of a given region. Global emissions are driven by how fast

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stringent standards are implemented in low-income regions. Most low-income regions are

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estimated to implement Euro V after 2015, and Euro VI after 2020. However, two important

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regions, Eastern and Western Africa, are not expected to adopt Euro standards until the 2040s.

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Under our assumptions, these two regions will adopt standards when they reach a level of GDP

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per capita similar to the average of other technology-following world regions. The high sulfur

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content of transportation fuels in these two regions prohibits the introduction of stringent


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standards. Efforts are underway to upgrade fuel quality in the next several years, and tighter

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standards in these two regions might be adopted earlier than we have projected.70

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To simulate mitigation measures, new functionality was added to the model. Without

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implementation of mitigation measures, old vehicles retire at the rate determined by vehicle age

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and income level.7 In the scrappage scenario, an enforced constant retirement rate is applied to

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the vehicles targeted by the policy. In scenarios with retrofit, targeted vehicles are retrofitted to

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ones with cleaner emission standards. A schematic representation of the model results with

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mitigation measures is illustrated in Figure S1.

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One of the important constraints of the ability to retrofit a vehicle is its original technology.

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Verified on-road diesel retrofit technologies promoted by the U.S. Environmental Protection

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Agency and the California Air Resources Board (CARB) are applicable only to vehicles

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produced after 1993 in the U.S.61,71 Older vehicles might have emissions that are high enough to

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overwhelm the control devices, or they may not be able to support operation of the retrofit

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devices for other reasons. Therefore, in this paper, we assume that only diesel vehicles under

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Euro II standards (or equivalent U.S. standards) and cleaner ones can be retrofitted.

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Another constraint on retrofit is the availability of aftertreatment technologies within a region,

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which depends on the stringency of the prevailing emission standards for new vehicles. We

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assume all retrofits involve aftertreatment technologies, which begin to penetrate after Euro IV

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and become prevalent when Euro V and VI (or 2007 U.S. standards) are required. Thus retrofit

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would likely be adopted only when these highly advanced emission standards are already in

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place. In this work, we assume that target vehicles can be retrofitted to Euro V or VI under

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European emission standards, or to 2007 standards under U.S. emission standards.

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Compared to retrofit, scrappage is constrained less by the stringency of emission standards.

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Scrappage is applicable to any old and dirty vehicles, and is aimed at a larger portion of the

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vehicle fleet, tending to produce greater emission reductions. Moreover, scrappage does not need

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to wait for the adoption of highly advanced emission standards involving aftertreatment

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technologies. It can be adopted even when looser standards (e.g. Euro I) are prevalent, as long as

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the replaced vehicles are cleaner than the scrapped ones. This feature means that scrappage is



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less dependent on the timing of advanced standards, and that it produces earlier emission

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reductions.

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Monte Carlo simulations (MCS). Emission projections are uncertain even in the baseline

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scenario. Yan et al. 45 showed that parameters describing vehicle fleet dynamics, including

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vehicle retirement rate and transition to superemitters, produce the largest uncertainty in total

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emissions when fuel consumption is prescribed. We investigate the uncertainty in these

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parameters, which we term “fleet-related,” using MCS.

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For ease of description, we simplify the relationships in the SPEW-Trend model into one

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equation. In MCS, two simulations of emissions under baseline (Ebase,i) and mitigation scenario

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(with either scrappage or retrofit) (Emitig,i) are paired if they share the same set of random fleet-

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related parameters (Vi) and differ only in the mitigation measure (M):

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Ebase,i = f ( FC , EF , Vi )

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(1)

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Emitig,i = f ( FC , EF ,Vi , M )

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where FC is fuel consumption; EF is emission factor; and f stands for the framework of the

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SPEW-Trend model.

(2)

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The emission difference of paired simulations represents the emission reduction by the

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mitigation measure. Five hundred paired simulations (i = 1, 2,…, 500) for emissions under the

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baseline and with each mitigation measure under the Medium scenario are made. Therefore, the

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emission projection and emission reduction by mitigation measure in each year is not a single

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value, but a distribution that reflects uncertainties in the composition of the vehicle fleet.

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RESULTS

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Scenario analysis of scrappage measures. Figure 1a shows global PM emission projections

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under the baseline and scrappage scenarios defined in Table 1, and Figure 1b shows the

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reductions as percentages. The baseline scenario has first a decreasing, then an increasing trend.

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The decline of emissions before 2030 can be explained by the implementation of new and

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advanced emission standards. The increase of emissions after 2030 is mainly due to the fast 8 ACS Paragon Plus Environment

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growth of vehicles without or with poor emission controls in Africa.7 Scrappage scenarios also

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show a minimum that occurs earlier as scenarios become more aggressive. Emissions are

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reduced from the baseline immediately after the scrappage program begins and remain below the

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baseline until 2050.

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Global emissions in 2030 can be reduced by as much as 0.50 Tg (48%) under the High

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scenario. Even under the Low scenario, emissions are reduced by 0.17 Tg (16%) in 2030. For the

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Medium and High scenarios, the largest emission reductions of 53-75% occur 5 to 10 years after

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the adoption of scrappage measures. This happens because a large number of uncontrolled and

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poorly controlled vehicles, as well as superemitters, have accumulated before the scrappage

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measure is introduced, so emissions are reduced quickly and dramatically. Emission reductions

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are lower when old and polluting vehicles have already been replaced by modern vehicles and

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most vehicles are subject to up-to-date emission standards; this is why emission reductions

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decline after 2020 under the High and Medium scenarios. The sharp emission decrease in the

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High scenario after 2045 is caused by the rapid removal of uncontrolled vehicles when Euro

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emission standards begin to be adopted in Eastern and Western Africa7.

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Scenario analysis of retrofit measures. Figures 1c and 1d show emission projection and

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emission reductions with retrofit. Unlike the emission scenario with scrappage, the largest

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emission reductions obtainable by retrofit happen 25 to 35 years after the program begins. The

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emission reduction increases until around 2030–2040, when emissions range from 0.73 Tg to

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0.91 Tg, a reduction of 16–31% compared to the baseline. In contrast to the emission scenarios

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with scrappage, the delayed and smaller emission reductions can be explained by the nature of

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retrofit. A retrofit measure is applicable only when the highly advanced technology (e.g., Euro V

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or VI) is available; most low-income regions are not projected to adopt Euro V or VI until 2015

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or later. Furthermore, retrofits can only work on diesel vehicles with relatively new engines

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under Euro II (or equivalent U.S. standards) and cleaner standards. Vehicles unsuitable for

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retrofit persist in many regions well into the future. In contrast, scrappage provides more

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immediate emission reductions because it is applicable as soon as even moderately advanced

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technology (e.g., Euro II or III) becomes available, and it rapidly removes emissions from

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vintage vehicles without standards or with relatively lax standards.



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Effectiveness of measures in light of fleet uncertainties. Figure 2 shows probability

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density distributions of global emissions and emission reductions with scrappage and retrofit.

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These distributions are produced by MCS with uncertainties from fleet-related parameters. Table

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2 summarizes MCS results for global emissions, including estimate mean, standard deviation,

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and coefficient of variation (CV, defined as the ratio between standard deviation and mean).

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Probability density distributions of emissions with scrappage are narrower than those under the

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baseline and with retrofit, as shown in Figures 2a, 2b, and 2c.The CV of emission projections

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with scrappage in year 2030 is 0.1, while CVs for the other scenarios are around 0.2. As

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mentioned earlier, the uncertainties in emissions are caused by uncertainties in fleet-related

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parameters, including these in retirement rate and superemitter transition rate. The comparison of

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CVs means that the scrappage scenarios are less affected by the uncertain fleet-related

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parameters than are the retrofit scenarios. Scrappage measures force the replacement of vintage

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vehicles and superemitters with new vehicles meeting more stringent emission standards. This

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transition reduces heterogeneity in the vehicle fleet, and the remaining vehicles follow common

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fleet dynamics. In contrast, retrofit measures are applicable only when vehicles with highly

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advanced emission standards are available. Old and high-emitting vehicles persist longer in the

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fleet, yielding diversity similar to the baseline. Vehicle fleets with mixtures of new and old

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technologies have the greatest uncertainty and higher CV values, so the greatest uncertainty

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occurs in fleets in transition. By the year 2050, the CV of emissions with retrofit is similar to

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those with scrappage, because older vehicles under the retrofit scenarios have been phased out as

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a result of retirement.

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The distributions of emission reductions reflect the effectiveness of mitigation measures

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under uncertainties arising from the vehicle fleet. Figures 2d, 2e, and 2f show the probability

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distributions of emission reductions, and Table 3 summarizes their confidence intervals. The 95%

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uncertainty bounds of emission reductions are 0.05–0.86 Tg for scrappage, and 0.09–0.42 Tg for

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retrofit in 2030, which are 31–60% and 15–31% reductions from the baseline, respectively.

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This effectiveness varies considerably by region. Figures 3a and 3b show boxplots of

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regional emission reductions under the scrappage scenario in 2020 and 2030. The Middle East

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and Asian regions benefit most from scrappage, with median emission reductions greater than 70%

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in 2020, followed by the Former USSR and Latin America, which obtain 50-60% emission 10 ACS Paragon Plus Environment

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reductions. Africa achieves the least emission reduction (below 10%) with least uncertainty in

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2020. This is because most vehicles in Africa are not governed by emission standards that are

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stringent enough to allow scrappage to have an effect. Therefore, it will be more effective for

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Africa to force the implementation of stringent emission standards for new vehicles and

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transition the fast growing vehicle fleet into a cleaner one, especially in Eastern and Western

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Africa. Transportation fuels in Africa tend to have high sulfur content, and this is an important

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constraint to impose strict emission standards, because high levels of sulfur limit the use of

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aftertreatment devices.72,73 Europe (47%), North America (22%), and Pacific (27%) obtain more

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emission reduction than Africa, but less than other regions, because of more rapid turnover and

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thus fewer vintage vehicles in the fleet to scrap. Emission reductions in 2030, compared to 2020,

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are similar or lower in most regions. The majority of the target vehicles are replaced with clean

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ones when scrappage measures are first implemented. An additional reason is that the emission

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standards in these regions are adopted before 2030, and these standards are effective in cleaning

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the fleet before that time. In summary, scrappage is most effective in regions where the measure

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is implemented in conjunction with the introduction of advanced emission standards, and where

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vehicle lifetimes are long and a legacy fleet leads to the persistence of emissions.

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Figures 3c and 3d show regional emission reductions gained under the retrofit scenario. In

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2020, retrofit measures reduce emissions only in North America, Europe, and Pacific, with

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median reductions between 20-40%. In 2030, this measure produces the greatest fractional

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emission reductions in the Middle East, East Asia, and Former USSR (over 45%), followed by

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Southeast Asia, South Asia, Europe, and North America with the median reduction in the range

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of 30-40%. Latin America and Pacific achieve less than 30% emission reductions. The retrofit

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measure described in this work does not yield any emission reductions in Africa, because the

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Euro V and VI emission standards are implemented so late that appropriate technology is not

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available by 2030. Lower emission reductions are achieved in North America, Europe, and

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Pacific. In these regions, the earlier adoption of stringent emission standards and higher

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retirement rates cause a cleaner fleet, and the future holds fewer vehicles with high emission

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rates that can benefit from retrofit.

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We compare the simulated reductions in this study with an actual program for in-use diesel vehicles in California. This program requires owners of in-use heavy trucks and buses to reduce 11 ACS Paragon Plus Environment


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PM and nitrogen oxides (NOx) emissions from their fleet by upgrading vehicles to meet specific

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performance standards for these pollutants.61 This requires retrofit or accelerated replacement of

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older in-use vehicles. The impact of this program has been evaluated by Millstein and Harley64

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using emission trends developed by CARB.74,75 They estimate that exhaust PM emissions from

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on-road HDDVs would be reduced by about 70% after implementation of the program for four

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years (in 2014), compared with emissions without the program. Our work estimates PM emission

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reductions of 22% (5-46%, Figure 3a) and 25% (3-47%, Figure 3c) under the Medium scenario

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with scrappage and retrofit, respectively, in North America in 2020. We examine the emission

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reductions with retrofit and scrappage separately, while the California program includes both, but

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even the predicted combination is lower than the 70% estimated by Millstein and Harley.64 One

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reason is that the mitigation scenarios in this work start five years later than the program

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evaluated by Millstein and Harley, and fewer old and heavily polluting vehicles remain in the

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fleet to be scrapped or retrofitted. In the model presented here, the 2010 fleet contains 43%

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HDDVs built before U.S. 2007 emission standards, but the 2015 fleet has only 6% of these

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dirtier vehicles. When measures are applied beginning in 2010 instead, modeled reductions in

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2014 by scrappage and retrofit under the High scenarios are 53% and 51%, respectively. Further,

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if superemitters are controlled so that none develop from normal vehicles after 2010, this High

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scenario with earlier implementation of standards produces 66% reduction in 2014 for scrappage

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alone (Figure S4). Thus, an aggressive control program that begins early approaches the

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reductions of 70% estimated by Millstein and Harley.64

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Discussion. Although the scrappage and retrofit measures analyzed in this work are

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generalized and may differ from the exact implementation of policies, the simulations provide

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guidance about which strategies reduce PM most effectively in specific regions. A

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comprehensive analysis of the mitigation measures could also include climate benefits, health

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co-benefits,76,77 and cost-benefit analysis.78,79 These three aspects are not explored here. However,

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an understanding of the factors that determine the emission outcomes of different choices should

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help policymakers to design emission control policies that have the greatest chances of success.

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Our study suggests that:


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(1) Retrofit is preferable for high-income regions (North America, Europe, and Pacific).

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Even though the emission reductions by scrappage and retrofit are comparable in these

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regions, the cost of retrofit is likely to be less than that of scrappage for the consumer.

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(2) A combination of scrappage and retrofit is preferable for low-income regions, with the

337

exception of Western and Eastern Africa. Scrappage reduces emissions by a greater

338

amount than retrofit. Although the cost of retrofit is limited to the aftertreatment device

339

and is significantly less than that of a new vehicle, emission control standards are not

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currently advanced enough and the fuel sulfur content is too high to permit end-of-pipe

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retrofit. Therefore, these regions may first consider scrappage with high compensation, so

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that owners can afford new vehicles, and then encourage future retrofits at lower cost

343

once standards with aftertreatment devices are introduced.

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(3) The early implementation of advanced emission standards is recommended for Western

345

and Eastern Africa. Less than 10% of emissions are reduced under scrappage scenarios

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and no reduction is achieved by retrofit scenarios in 2020, because most vehicles in these

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regions are not governed at present by emission standards that are stringent enough to

348

allow for either scrappage or retrofit. The most effective measure is stringent emission

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standards that will promote development of clean technology in the fast-growing vehicle

350

fleet. The availability of low-sulfur fuel would enable tighter emission standards to be

351

introduced.

352

ASSOCIATED CONTENT

353

Supporting Information.

354

Assumptions of measure-related factors, sensitivity analysis of these factors, scenario analysis in

355

North America.

356

ACKNOWLEDGMENT

357

This work was funded by the U.S. Environmental Protection Agency under grant RD83428001

358

and the U.S. Department of Agriculture, National Institute of Food and Agriculture, through

359

award number 2012-67003-30192 to the University of Chicago. We are grateful for the support

360

of the USDA Project Officer, Luis M. Tupas. Argonne National Laboratory is operated by 13 ACS Paragon Plus Environment


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UChicago Argonne, LLC, under Contract No. DE-AC02-06CH11357 with the U.S. Department

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of Energy.

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578


Figures a) Scrappage: PM emissions

b) Scrappage: emission reduction 80

1.2 1 0.8 0.6 0.4

Baseline Low Medium High

PM Emission Reduction (%)

PM Emissions (Tg/yr)

1.4

60 50 40 30 20 10 0 2015 2020 2025 2030 2035 2040 2045 2050 Year

0.2 2015 2020 2025 2030 2035 2040 2045 2050 Year c) Retrofit: PM emissions

579

70

d) Retrofit: emission reduction 80 PM Emission Reduction (%)

PM emissions (Tg/yr)

1.4 1.2 1 0.8 0.6 0.4

580 581 582 583

70 60 50 40 30 20 10 0 2015 2020 2025 2030 2035 2040 2045 2050 Year

0.2 2015 2020 2025 2030 2035 2040 2045 2050 Year

Figure 1. Global PM emission projections (Tg/yr) and reductions (%) with scrappage and retrofit measures. They are all under the IPCC A1B energy and economic forecast. The mitigation scenarios (Low, Medium, and High) are defined in Table 1.



Page 22 of 29

Emission reduction distribution

Global emission distribution Probability Density

8 a)2020

Baseline Scrappage Retrofit

6 4

0.3 0.2

2 0 0

0.4 d)2020

0.1 0.5 1 1.5 2 Global PM emission (Tg/yr)

2.5

8

0

20 40 60 Emission reduction(%))

80

0.2 b)2030

e)2030

6

0.15

4

0.1

2

0.05

0 0

0

0.5

1

1.5

2

2.5

8

0

0

20

40

60

80

20

40

60

80

0.1 c)2050

f)2050

6 4

0.05

2 0 0

0.5

1

1.5

2

2.5

0

0

584 585 586 587

Figure 2. Probability density distributions of global PM emissions (a, b, and c), and emission reductions (d, e, and f) in 2015, 2030, and 2050. Measure-related parameters are based on Medium scenarios. The distributions are based on 500 trials in MCS.

588


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a) Scrappage: 2020

b) Scrappage: 2030

Global

Global

Pacific

Pacific

Southeast Asia

Southeast Asia

East Asia

East Asia

South Asia

South Asia

Former USSR

Former USSR

Europe

Europe

Middle East

Middle East

Africa

Africa

Latin America

Latin America

North America

North America

0

589


100

0


c) Retrofit: 2020

d) Retrofit: 2030

Global

Global

Pacific

Pacific

Southeast Asia

Southeast Asia

East Asia

East Asia

South Asia

South Asia

Former USSR

Former USSR

Europe

Europe

Middle East

Middle East

Africa

Africa

Latin America

Latin America

North America 0

590 591 592

100

North America


100

0


100

Figure 3. Distributions of regional emission reduction: (a) and (b) with scrappage, and (c) and (d) with retrofit in the years 2020 and 2030, respectively.

593



594 595

Tables

596

Table 1. Main assumptions in the scenarios with scrappage and retrofit. Measures Scrappage

Retrofit

Measure-related factors Vehicle target Start calendar year Gap-year Accelerate retirement rate Vehicle target Start calendar year Gap-year Retrofit rate Retrofit technology

Low low (only HDDV superemitters) late (year 2020) long (10 years) low (0.2) low (only HDDV superemitters) late (year 2020) long (10 years) low (0.2) low (Euro V)

Medium medium (HDDVs) early (year 2015) medium (5 years) medium (0.5) medium (HDDVs) early (year 2015) medium (5 years) medium (0.5) high(Euro VI)

High high (all vehicles) early (year 2015) short (0 years) high (0.8) high (all diesel vehicles) early (year 2015) short (0 years) high (0.8) high (EuroVI)

597


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Page 25 of 29

598 599 600


Table 2. Summary of MCS results: estimated mean ( µˆ ), standard deviation ( σˆ ), and coefficient of variation of global PM emissions under scenarios with baseline and mitigation measures in 2020, 2030, and 2050. Scenarios Baseline

Scrappage

Retrofit

Year

µˆ (unit: Tg/yr)

σˆ (unit: Tg/yr)

CV( σˆ µˆ )

2020 2030 2050 2020 2030 2050 2020 2030 2050

1.22 1.07 1.27 0.60 0.62 0.93 1.16 0.82 0.97

0.23 0.24 0.24 0.06 0.06 0.09 0.21 0.16 0.11

0.19 0.22 0.19 0.11 0.11 0.10 0.18 0.20 0.12

601



602

Table 3. Distributions of global emission reduction (∆E) caused by mitigation measures Measures Scrappage

Retrofit

603

Page 26 of 29

a

Year

95% CI of ∆Ea(unit: Tg/yr)

95% CI of ∆E/E1

2020 2030 2050 2020

0.61 ± 0.40 0.46 ± 0.41 0.34 ± 0.34 0.05 ± 0.05

49% ± 16% 41% ± 19% 26% ± 16% 4% ± 3%

2030

0.25 ± 0.17

23% ± 8%

2050

0.30 ± 0.27

23% ± 13%

∆E = E1-E2; E1 is the PM emissions under baseline; E2 is PM emissions with mitigation measures


Page 27 of 29

1


Figures b) Scrappage: emission reduction

a) Scrappage: PM emissions 80

1.2 1 0.8 0.6 0.4

Baseline Low Medium High


PM Emissions (Tg/yr)

1.4

60 50 40 30 20 10 0 2015 2020 2025 2030 2035 2040 2045 2050 Year

0.2 2015 2020 2025 2030 2035 2040 2045 2050 Year c) Retrofit: PM emissions

2

70

d) Retrofit: emission reduction 80


PM emissions (Tg/yr)

1.4 1.2 1 0.8 0.6 0.4

3 4 5 6

70 60 50 40 30 20 10 0 2015 2020 2025 2030 2035 2040 2045 2050 Year

0.2 2015 2020 2025 2030 2035 2040 2045 2050 Year

Figure 1. Global PM emission projections (Tg/yr) and reductions (%) with scrappage and retrofit measures. They are all under the IPCC A1B energy and economic forecast. The mitigation scenarios: Low, Medium and High are defined in Table 1.



Global emission distribution

Page 28 of 29

Emission reduction distribution

Probability Density

8 a)2020

Baseline Scrappage Retrofit

6 4

0.3 0.2

2 0 0

0.4 d)2020

0.1 0.5 1 1.5 2 Global PM emission (Tg/yr)

2.5

8

0

20 40 60 Emission reduction(%))

80

0.2 b)2030

e)2030

6

0.15

4

0.1

2

0.05

0 0

0

0.5

1

1.5

2

2.5

8

0

0

20

40

60

80

20

40

60

80

0.1 c)2050

f)2050

6 4

0.05

2 0 0

0.5

1

1.5

2

2.5

0

0

Figure 2. Probability density distributions of global PM emissions (a, b and c), and emission reduction (d, e, and f) in 2015, 2030, and 2050. Measure-related parameters are based on Medium scenarios. The distributions are based on 500 trials in MCS


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a) Scrappage: 2020

b) Scrappage: 2030

Global

Global

Pacific

Pacific

Southeast Asia

Southeast Asia

East Asia

East Asia

South Asia

South Asia

Former USSR

Former USSR

Europe

Europe

Middle East

Middle East

Africa

Africa

Latin America

Latin America

North America

North America

0


100

0


c) Retrofit: 2020

d) Retrofit: 2030

Global

Global

Pacific

Pacific

Southeast Asia

Southeast Asia

East Asia

East Asia

South Asia

South Asia

Former USSR

Former USSR

Europe

Europe

Middle East

Middle East

Africa

Africa

Latin America

Latin America

North America

North America

0

100


100

0


100

Figure 3. Distributions of regional emission reduction: (a) and (b) with scrappage, and (c) and (d) with retrofit in the years 2020 and 2030, respectively.


Effectiveness of Mitigation Measures in Reducing Future Primary

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