2016 real-world passenger vehicle fuel consumption analysis · 2016-09-23 · january 1st, 2016,...

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2016 Real-world Passenger

Vehicle Fuel Consumption Analysis

Innovation Center for Energy and Transportation

September 2016

1

Acknowledgements

We wish to thank the Energy Foundation for providing

us with the financial support required for the

execution of this report and subsequent research

work. We would also like to express our sincere

thanks for the valuable advice and recommendations

provided by distinguished industry experts and

colleagues.

Authors

Lanzhi Qin, Maya Ben Dror, Hongbo Sun, Liping Kang,

Feng An

Disclosure

The report does not represent the views of its funders

nor supporters.

The Innovation Center for Energy and Transportation (iCET)

Beijing Fortune Plaza Tower A Suite 27H

No.7 DongSanHuan Middle Rd., Chaoyang District, Beijing 10020

Phone: 0086.10.6585.7324

Email: [email protected]

Website: www.icet.org.cn

mailto:[email protected]

http://www.icet.org.cn/

2

Glossary of Terms

LDV Light Duty Vehicles; Vehicles of M1, M2 and N1 category not

exceeding 3,500kg curb-weight.

Category M1 Vehicles designed and constructed for the carriage of passengers

comprising no more than eight seats in addition to the driver's seat.

Category M2 Vehicles designed and constructed for the carriage of passengers,

comprising more than eight seats in addition to the driver's seat,

and having a maximum mass not exceeding 5 tons.

Category N1 Vehicles designed and constructed for the carriage of goods and

having a maximum mass not exceeding 3.5 tons

Real-world FC FC values calculated based on BearOil App users’ data inputs.

Certified FC Prior to sale in China (either domestic produced or imported cars),

the vehicle is certified according to the “light duty vehicle FC testing

method” standard (GB/T19233). The fuel consumption result

combines, urban and rural fuel consumption tests.

Entity vehicle Vehicle registered by companies and/or government.

Effective figure While each of the models assessed in this study has an actual

average FC based BearOil App users’ inputs (calculated as

), an average variance is used for deciding

whether or not the average figure is robust enough to be used

(calculated as ).

In this study we only use data in the range M-2s2 <data<M+2s2.

Private vehicle Vehicle registered for private use.

Commercial vehicle Freight vehicles and vehicles with over 9 seats (including driver’s

seat); see GB/T3730.1-2001 for more details.

Passenger vehicles All vehicles with up to 9 seats (including drivers’ seat); see

GB/T3730.1-2001 for more details.

NEDC New European Driving Cycle

MIIT Ministry of Industry and Information Technology

IEA International Energy Agency

3

Table of Contents

Glossary of Terms ............................................................................................................... 2

Executive Summary ............................................................................................................ 5

1. Background ...................................................................................................................... 9

2. Methodology ................................................................................................................. 10

2.1 Certified and reported FC data .......................................................................................................... 11

2.2 Actual fuel consumption ....................................................................................................................... 13

2.3 Real-world and reported FC comparison ...................................................................................... 14

3. FC gaps analyses results and assessment ........................................................... 16

3.1 By-transmission type FC gap analyses results ............................................................................ 16

3.2 By-segment FC gap analyses results ................................................................................................ 18

3.3 By-province FC gap analyses results ............................................................................................... 20

3.4 By-model FC gaps .................................................................................................................................... 26

3.5 A typical actual FC range for a certified range ............................................................................. 27

3.6 Best selling passenger vehicles actual and certified FC gap .................................................. 33

3.7 Fuel saving technologies gaps ............................................................................................................ 34

4. National and provincial passenger vehicles emissions gap ......................... 36

4.1 FC gap from a national standard perspective .............................................................................. 36

4.2 FC gap from a national perspective .................................................................................................. 36

4.3 Case Study: Guangdong Province Passenger vehicles’ carbon emissions gap ............... 38

5. Conclusions ................................................................................................................... 41

Bibliography ...................................................................................................................... 43

Appendix ............................................................................................................................. 46

4

List of Figures

Figure 1: China's type test driving conditions.............................................................................................. 11

Figure 2: FC reporting on MIIT website and FC label ............................................................................... 12

Figure 3: Snapshot of BearOil App’s FC data calculation method........................................................ 13

Figure 4: China's 2008-2015 actual vs. real-world FC .............................................................................. 17

Figure 5: The proportion of manual transmission cars between 2008-2015 ................................ 17

Figure 6: By-segment FC gap analyses results – combined .................................................................... 18

Figure 7: By-segment FC gap analyses results – detailed........................................................................ 19

Figure 8: Hover H6 (1.5L, MT) average FC over different cities by data-sample size ................. 21

Figure 9: Hover H6 (1.5L, MT) average FC over different cities ........................................................... 22

Figure 10: FC yearly changes of GreatWall Hover 6 in different cities – combined ..................... 22

Figure 11: FC yearly changes of GreatWall Hover 6 in different cities – detailed ......................... 23

Figure 12: Selected models' FC gap distribution ........................................................................................ 26

Figure 13: Comparative FC gap of Top 5 least and best performing models .................................. 27

Figure 14: Comparative FC gap of Top 27 brands ...................................................................................... 27

Figure 15: FC Gap for sample vehicles certified 5.9L/100km, 2008-2015 ...................................... 29

Figure 16: FC Gap for the sample vehicles certified 5.9L/100km ....................................................... 29





Figure 21: FC gap of the Top100 best-selling models in 2015 .............................................................. 33

Figure 22: FC gap of China's fastest growing models (by sales) ........................................................... 34

Figure 23: Technology impact of FC - Turbocharger 1.4 T on a 1.8L engine .................................. 35

Figure 24: Actual FC development versus Corporate Average Fuel Consumption (CAFC)

standard .............................................................................................................................................................. 36

List of Tables

Table 1: China's FC type test divide .................................................................................................................. 12

Table 2: 2016 Actual FC Data .............................................................................................................................. 14

Table 3: China’s type approval cycle requirements – some ‘loose ends’ may increase

real-world and certified FC gaps .............................................................................................................. 15

Table 4: By-segment FC gap development ..................................................................................................... 20

Table 5: China's CAFE Phase IV Standard; by-weight FC limits and targets for MY2016 .......... 28

Table 6: National average passenger vehicles’ carbon emission estimation .................................. 37

Table 7: Passenger vehicles’ carbon emissions gap estimation impacting factors....................... 38

Table 8: By-segment reported vehicles registration in Guangdong Province ................................ 39

Table 9: By-segment vehicles information provided by Xiaoxiong APP ........................................... 39

5

Executive Summary

In recent years, China's rapid economic development drove a constantly increasing

demand for oil, over half of which is accounted for by transportation1. As car ownership

rates gain pace and pose a threat to urban air quality and climate change, the

automotive industry has been required to advance its technological energy-saving

competitiveness and meet gradually increasing fuel consumption standards. As of

January 1st, 2016, the forth phase of China’s passenger car fuel consumption standards

started implementation aimed at establishing a national average of 5L/100km by 2020

or 120g/km (for more details: 2016 Annual CAFE Report, iCET2).

Current standard governance builds on the lab-based test cycle in accordance with

the “light vehicle fuel consumption test method" (GB/T19233-2008) that has been used

since February 2008 and has been made publically available through the “light vehicle

labeling regulation” (GB19578-2004) since 2010. However, this lab-based FC

measurement test method is prone to bias stemming from: (1) its reliance on the new

European driving cycle (NEDC) for measuring local fuel consumption in its type

approval test, (2) the test is conducted several times on select vehicles with the best

result reported, (3) the test does not account for external variations in altitude and

temperature, nor local driving styles (assuming there are differences in driving styles

between locations within China).

The study reinforces the observation that fuel consumption depends on driving

conditions related to both (i) anthropogenic driving (e.g. acceleration, air conditioning

usage, load, tires pressure etc.) and (ii) external driving conditions (road elevation,

outside temperatures, traffic congestion etc.). Both of these groups of factors highly

influence the actual FC level of a car, creating FC variations between different locations

for various vehicle models and segments.

This study aims to assess the gap between reported and real-world fuel

consumption (FC). It therefore uses the reported FC data available on the MIIT’s

website3 and a bottom-up actual FC data collection App, BearOil App (小熊油耗)4.

BearOil App data includes nearly 600,000 owners and over 15 million data inputs

inserted between 2008 and 2015, covering 16,000 vehicle models in 31 cities in China

(as opposed to much more limited data inputs available in last year’s feasibility study

report). The data inputs are separated by vehicle model and brand, region and user

demographics.

1 CAFC Phase IV Standard Interpretation. http://www.chinaequip.gov.cn/2015-01/26/c_134023946.htm 2 Kang Liping, Maya Ben Dror, Ding Ye et al., Annual report 2015 of China’s passenger vehicle fuel consumption, iCET. http://www.icet.org.cn/admin/upload/2015112559385769.pdf 3 MIIT-Vehicle fuel consumption website. http://chinaafc.miit.gov.cn/n2257/n2280/index.html

4 Xiaoxiong APP. http://www.xiaoxiongyouhao.com/

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The study reveals the following FC gap insights:

1. FC gap has been growing from 2008 to 2015 with a quicker pace for

Automated transition (AT) cars; China’s FC gap is expected to grow as AT

shares of new vehicle sales grow.

2. By-segment FC gaps have increased at different paces from 2008 to 2015, MPV

and SUV data are the most accurate with about a 10% FC gap increase, 120% and

128% gap respectively, and large vehicle data are the least accurate with a 22%

gap increase, reaching a 13% gap.

112%

116%

122%

125%

127%

107%

113%

119% 120% 121%

118%

121%

125%

127% 131%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

FC

Ga

p

Model Year

Combine MT AT

105%

115%

125%

135%

145%

2008 2009 2010 2011 2012 2013 2014 2015

FC

Gap

Model Year

Small Compact Mid-size Large MPV SUV

Sample size：546,217

3% 2013-2105

6% 2013-15

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3. By-brand comparisons reveal major FC gaps among manufacturers; BMW least

accurate in their FC reporting with 139% and FAW most accurate with 106% FC

gap.

4. The top 100 selling cars in China for 2015 have an average FC gap of 130%, with

outliers reaching 156% and 103%.

139%

106%

134%

112%

133%

113%

133%

114%

133%

114%

100%

110%

120%

130%

140%

BMW FAW Luxgen Venucia Volvo SGMW Audi Skoda BYD Baojun

103%

123%

115%

156%

100%

110%

120%

130%

140%

150%

160%

1000 1100 1200 1300 1400 1500 1600

FC

Gap

Vehicle Weight（kg）

8

5. Location–specific driving conditions impact FC; a GreatWall Motor’s Hover H6

(1.5L engine MT) case study indicates Shanghai, Beijing and Shenzhen tend to

have larger by-season FC gaps and northern cities tend to have higher FC in

general (likely temperature-related).

By flushing out the gap between reported (certified) and real-world fuel

consumption, a more informed design and enforcement mechanism of standards is

advocated for. This study attempts to contribute to more effective policy framework

towards low-carbon vehicle growth and better air-quality urban development. It does so

by challenging the credibility and effectiveness of current traditional vehicle fuel

economy governance.

7.2 L/100km

7.00

8.00

9.00

10.00

11.00

12.00

13.00

14.00

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Re

al-

wo

rld

FC

(L

/1

00

km

)

Time

Harbin Beijing Shanghai Shenzhen Kunming

138% 132% 136%

154% 122%

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1. Background

According to the International Energy Agency (IEA), global carbon emissions from

the transport sector will increase from 23% in 2007 to 41% by 20305. In China, road

traffic emissions grew by 45% over the last 20 years, accounting for 17% of global

emissions and evolving into a major target of national air quality improvement and

carbon mitigation efforts6. Moreover, China committed to capping its oil capacity and the

inclusion of 20% non-fossil fuels in its energy mix by 2030 as part of the recent Paris

Climate agreement, increasing the urgency for effective action such as standards and

regulations. In particular, the national passenger fuel consumption standards are

increasing in stringency7.

China initiated its vehicle fuel consumption standards and policy research in 2001.

In 2004, the State Committee for Standardization and the State Administration of

Quality Supervision Inspection and Quarantine (AQSIC) jointly issued China’s "light

vehicle fuel consumption test method" (GBT19233-2003), in which the reference test

cycle used the EC European Union test cycle (2004/EC/3) and came into effect in

November 2004. During the same year, the implementation of China's first mandatory

"passenger car fuel consumption limits" (GB 19578-2004) began. Fuel consumption

certification requirement--a useful FC standard management method--was finally

announced in 2010.

Four phases of passenger car fuel consumption standards implementation have

occurred since 2004. During the third phase, average passenger car fuel consumption

declined from 7.77L/100km in 2009 to 7.22L/100km in 2014. The fourth phase of the

standard, "Fuel consumption limits for passenger cars" (GB 19578-2014) and "Fuel

consumption evaluation methods and targets for passenger cars" (GB 27999-2014), was

released on December 22, 2014 and came into effect this last January. A

manufacturer-based average annual fuel consumption limit and target has also been set

up on top of the per-vehicle weight bin limits and targets, targeting a national FC average

of 5L/100km by 2020, which translates to 120g/km8. In order to meet the standards’

goal and internalize its local and global emissions implications, fuel consumption

measurements need to be reliable.

iCET’s 2013 “Survey Report on Light-Duty Vehicle Fuel Consumption Labeling” 9, was

5 Zhang TaoXin. Research on urban transportation emissions during urbanization of China. Chinese Population and Environment, 2012, 22(8): 3-9. 6You should know such numbers about global carbon emissions. China Energy http://www.china5e.com/news/news-930525-1.html 7 China submitted its self-contribution file on replying climate change. National Development and Reform Commission. http://www.sdpc.gov.cn/xwzx/xwfb/201506/t20150630_710204.html 8 CAFC Phase IV Standard Interpretation. http://www.chinaequip.gov.cn/2015-01/26/c_134023946.htm 9 Kang Liping, An Feng, Robert Early. Survey Report on Light-duty Vehicle Fuel Consumption Labeling, iCET. http://www.icet.org.cn/admin/upload/2015061847871009.pdf

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conducted in 16 cities across China and included 114 4S dealers surveys and during

2012 found that 93% of car purchasers are concerned about fuel consumption levels;

however, the majority lack confidence in the accuracy of the official reporting system.

Consumers stated that through conversations with car dealers and friends as well as

consulting online opinion posts (e.g. Bitauto10, AutoHome11), they expect to get a better

grasp of actual fuel consumption levels.

BearOil App (小熊油耗) is China’s first independent mobile application aimed at

collecting actual voluntary fuel consumption data across China and among various

vehicle model drivers and publicizes its results to inform decision-making at the

consumer, manufacturer, and policy-making levels. Since its creation in 2008, over

600,000 drivers have downloaded the App in 31 cities, representing over 16,000

different vehicle models, covering over 1,400 auto brands and based on the analysis of

15 million data inputs. iCET first joined forces with BearOil App to conduct a feasibility

study of actual versus reported FC gaps in 201512. The initial study analyzed over

210,000 valid samples of fuel consumption levels reported by drivers from various

locations in China between 2008 and 2014 and concluded that the average FC gap in

China was 127%.

This report attempts to flush out actual and certified FC gaps in China based on

official FC certified data available and voluntary actual FC data collected from drivers

across China through BearOil App and to suggest and assess reasons for causing the gap

(e.g. by-model and by-location). Similar initiatives have been occurring in Europe and in

the use in recent years, and have even cumulated to a new suggested Real Driving

Emissions (RDE) based on Portable Emissions Measurament System (PEMS) test

advocated for by environmental NGOs.

2. Methodology

The study is aimed at analyzing gaps between actual and certified FC levels across

vehicles and locations in China, and assessing potential reasons for these gaps. Vehicle

FC was limited to cars of categories M1, M2 and N1 not exceeding 3,500kg manufactured

between 2008 and 2015. The study therefore uses two sources of data: (i) certified FC

collected from the Ministry of Industry and Information Technology website (MIIT)13,

which is also mandated to be displayed on a car front window upon purchase; and (ii)

BearOil App data sources by nearly 600,000 drivers across 31 cities in China covering

over 16,000 car models.

10

Bitauto, http://beijing.bitauto.com/ 11

AutoHome, http://www.autohome.com.cn/ 12 Xiaoxiong APP, http://www.xiaoxiongyouhao.com/ 13 http://chinaafc.miit.gov.cn/

11

2.1 Certified and reported FC data

In 2010, China began implementing the light vehicle fuel consumption labeling

regulation requiring every M1, M2 and N1 category vehicle sold in China fueled by either

gasoline or diesel and with a curb-weight not exceeding 3500kg to be labeled with its

type approved fuel consumption test results 14 . Domestic automobile production

enterprises and imported car dealers are required to follow the "light vehicle fuel

consumption test method" (GB/T19233) performed by certified testing sites across

China15 to confirm vehicles’ projected fuel consumption data.

Fuel consumption test results conducted by the vehicle manufacturer or its

representative are submitted to the testing agency responsible for the type test. Through

a test with simulated urban and suburban driving conditions representative of typical

driving conditions, carbon dioxide (CO2), nitric oxide (CO) hydrocarbon (HC) emissions

as well as fuel consumption are calculated through a carbon balance method16 by the

authorized test site. The figure and table below demonstrate China’s typical driving (test

cycle speed per second divide), which is based on the EU test cycle (NEDC). The labeling

system is tolerant of 4% fuel consumption gap between company reported and

conformity test fuel consumption results. All M1 vehicles with similar vehicle

curb-weight and vehicle components produced by the same manufacturer are

authorized to use the same FC level.

Figure 1: China's type test driving conditions

14 Light vehicle labeling regulation. Baidu Baike. http://baike.baidu.com/link?url=wnlq8kE1YketxI8ll2Y_fwGQXDe5DTXgkvjIpocbvzeDtHOc-1241_qDbzyfdMLcwAnoEWSGhgqJrRprKVc3DK 15 Capacity of national vehicle test organizations authorized by MIIT. Vehicle Technique Service Center of China. http://www.cvtsc.org.cn/cvtsc/zhxx/572.htm 16 GB19233-2008 Light vehicle fuel consumption test method. MIIT-vehicle fuel consumption website. http://chinaafc.miit.gov.cn/n2257/n2340/c79073/content.html

0

20

40

60

80

100

120

140

1 101 201 301 401 501 601 701 801 901 1001 1101

Spee

d:

km

h

Time: seconds

Urban cycle Rural cycle

Certified FC results

12

Table 1: China's FC type test divide

Test information Suburban Urban Combine % of total

test time

Idling (S) 40 240 280 24%

Clutch disengagement (S) 10 36 46 4%

Shift (S) 6 32 38 3%

Acceleration (S) 103 144 247 21%

Cruise (S) 209 228 437 37%

Brake (S) 32 100 132 11%

Max. speed (km/h) 120 50 N/A N/A

Average speed (km/h) 62.6 19 33.8 N/A

Max. acceleration (km/h/s/) 3.7 3.0 3.2 N/A

Average Acceleration (km/h/s) 1.4 2.7 2.2 N/A

With variations in driving conditions depending on both the driver and external

elements (road elevation, outside temperatures, congestions etc.), real-world vehicle

fuel consumption will vary between vehicles of the same model and may no longer be

well represented by the labeled FC level. Figure 2 illustrates China’s fuel consumption FC

test cycle results as reported on the Ministry of Industry and Information Technology

website and on the official labels meant to be placed on the front window of the vehicle

for sale.

Figure 2: FC reporting on MIIT website and FC label

MIIT website: http://chinaafc.miit.gov.cn/n2257/n2263/index.html

http://chinaafc.miit.gov.cn/n2257/n2263/index.html

13

2.2 Actual fuel consumption

BearOil App is an independent organization devoted to the collection of voluntary

FC data across China since 2008. The BearOil App currently has nearly 600,000 users

covering over 16,000 vehicle types in mainland China. The FC data is collected through

the recording of fuelling volumes and mileage by the App owners (vehicle drivers). The

user receives an immediate FC calculation for his or her own benefit, while the App

stores this information in a large pool of data, which is meant to be available for the

general public through periodic reports and an analyses option part of the App itself

(available for the App user). It is hoped that the real-world FC data collected by this

method will inform more sustainable decision-making at the corporate, consumer, and

policy levels.

For the initial use, after the empty tank warning light turns on, App users will fill

their vehicle tank until it is full. The user records (i) the fuel price paid (it then calculates

the volume based on China’s united fuel cost; e.g. 50L) and (ii) distance driven (e.g.

2000km). From the second time onwards, the App uses stored user data to calculate the

user’s fuel consumption, based on simple insertions of fuel cost and distance (e.g.

2464km), as demonstrated in Figure 3. For example:

50L/(2464km-2000km)*100=10.78L per 100km driven.

Figure 3: Snapshot of BearOil App’s FC data calculation method

BearOil App user can compare his or her own vehicle FC performance with the FC

results of users that drive the same vehicle model, or any other vehicle model that has

the same engine displacement. Since each driver and App user is dependent on his or

her unique actual driving conditions, including anthropogenic and external factors, the

App enables the performance of simple comparisons between FC scores of the same

model or engine displacement in various locations in China.

While each model has a real-world average FC calculated based on user-data inputs

14

of BearOil App: , an average variance is used for deciding

whether or not the average figure is effective:

. This study only uses data that is

between the following range: M-2s2 <data<M+2s2 to exclude potential over-statements of

FC gaps. The average variance used to screen all 575,293 user-data has limited the pool

of “effective figures” that will be used in this study (accounting for 92% of the entire

BearOil data sample size). The data extracted from BearOil represent 0.47% of new car

sales.

Table 2: 2016 Actual FC Data

Model Year Total vehicle models covered % of annual passenger

vehicle sales

2008 20,317 0.30%

2009 27,801 0.27%

2010 42,384 0.37%

2011 67,610 0.47%

2012 110,204 0.71%

2013 139,162 0.78%

2014 102,938 0.52%

2015 64,877 0.31%

Summary Total 575,293

The 2015 BearOil App added driving conditions to their App and received a total of

over 18,000 users’ feedback. The feedback was digested to indicate that over 62% of

respondents use routes with heavy traffic over 60% of driving time. This information

perhaps is rather limited on its own, however in the context of the study it characterizes

the urban driving habits of the users of BearOil App based on which actual FC is being

calculated.

2.3 Real-world and reported FC comparison

The FC testing method provides a detailed driving conditions description followed by

the test performing entity. There are two potential issues with test driving conditions: (i)

some factors allow for high gaps in test conditions, such as vehicle mileage and outside

temperatures, which may result in different FC scores for the same vehicle model; and (ii)

under real-world circumstances, driving conditions may not be well reflected in the test

conditions, mainly given the fact that China is a large country with varying temperatures,

15

topography and urban densities to which averaging does no justice.

To date, very limited studies have been conducted to evaluate the representation of

real-world conditions in the test requirements for average driving or by-location driving in

China. Given the absence of reliable information, Table 3 simply highlights loose testing

requirements for driving condition elements that may increase real-world and certified FC

gaps.

Table 3: China’s type approval cycle requirements – some ‘loose ends’ may increase

real-world and certified FC gaps

Type of test Chassis dynamometer in laboratory

Test cycle NEDC test cycle

Max. speed 120km/h

Max. acceleration 3.7(km/h)/s

Idling 24%

Vehicle weight Curb weight+100kg

Temperature 20-30 °C

Tested vehicle`s driving

distance

3000km~15000km

State of charge starter battery Fully charged battery

Air conditioning Off

Tires pressure Following suggested tires pressure provided by

manufacturer

Transmission shift schedule Following the test regulation

According to the feasibility study conducted in 201517, the gap between reported

real-world and certified fuel consumption during 2008-2014 has increased from 12% to

27%, with an annual average increase of 2.5%. The increase in these gaps may be a

result of data sources quantity and quality variations over time, however it is also likely

to be the result of gaps between real-world driving and certification test conditions.

This study will attempt to provide reference for the claim that the FC gap is not

negligible when attempting to pursue health and environmental goals at either the

model and brand level, nor the national and urban levels. Shenzhen was chosen as a case

study for examining local level impacts, mainly due to data accessibility constraints at

other cities (presented in Chapter 4). The GreatWall Hover 6 (1.5L) was selected as a

by-geography comparison model because of the relatively high volumes of respondent

data, as part of a more general FC gap analysis (Chapter 3).

17

Ding Ye, Maya Ben Dror et al., Real-world and Certified Fuel Consumption Gap Analysis. iCET. http://www.icet.org.cn/admin/upload/2015080439650285.pdf

16

3. FC gap analysis results and assessments

This chapter first examines the difference between real-world and certified fuel

consumption levels based on the methodology presented in Chapter 2. The gap analysis

results are introduced from four major perspectives: transmission (gear shaft type),

vehicle segment, selected model over different locations (GreatWall Hover 6 AT 1.5L).

Then, the possible impact of different aspects of driving conditions as a partial

explanation to fuel consumption gaps is assessed.

Brand-dependent assessment as variable of fuel consumption gap requires a

complex analysis that holds driving conditions equal among drivers, and is therefore

beyond the scope of this work.

3.1 By-transmission type FC gap analysis results

As illustrated in Figure 4, between 2008 and 2015, the real-world and reported fuel

consumption gap has increased for both automated and manual transmission passenger

vehicles by 13% and 14% respectively. Automated vehicles tend to have higher fuel

consumption gaps than manual transmission vehicles, and the difference between types

of transmissions has overall remained the same. That said, manual transmission FC gaps

have fluctuated more 2008 and 2012.

Although the majority of vehicles sold and operated in China are manual

transmission, automated transmission vehicles accounted for 66.5% of vehicles sold in

2015 and have been steadily increasing their share of the market since 201218 (see

Figure 5). The real-world fuel consumption data collected through BearOil’s App is

represented by both automated and manual transmissions cars (with AT vehicles

contributing to 56.4% of the data sample), and as the results of the study favors MT

vehicles in terms of accuracy displayed in a smaller FC gap as demonstrated in Figure 4,

the cumulative FC gap reads as more subtle when MT and AT vehicles are isolated.

Furthermore, the current test procedure is not reflective of the changes in gear shaft

advancements in recent years (hydro-shaft, CVT etc.).

18 CAAM and CATARC, China Auto Development Annual Report. http://max.book118.com/html/2015/0725/21954233.shtm

17

Figure 4: China's 2008-2015 actual vs. real-world FC

Figure 5: The proportion of manual transmission cars between 2008-2015

Note: AT/MT ratio data is retrieved from CATARC’s China Auto Industry Development Annual Report19

19 http://max.book118.com/html/2015/0725/21954233.shtm

112%

116%

122%

125%

127%

107%

113%

119% 120% 121%

118%

121%

125%

127% 131%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

Re

al-

wo

rd F

C/

Ce

rtif

ied

FC

Model Year

Combine MT AT

14%

13%

45.3%

44.1%

38.4%

56.5%

54.2%

58.3%

62.2%

66.5%

30.5%

28.3% 29.8% 32.1%

33.0%

34.9%

38.6%

33.3%

25.0%

35.0%

45.0%

55.0%

65.0%

75.0%

2008 2009 2010 2011 2012 2013 2014 2015

Model Year

AT Proportion (BearOil APP users)

AT Proportion (National passenger vehicle sales)

2.8%

21.2%

18

3.2 By-segment FC gap analysis results

A by-segment assessment of real-world versus reported fuel consumption gaps,

presented in Figure 6 demonstrates three interesting points: (i) the large vehicle segment

have seen greater increase in gap measurements between 2008 and 2015 than other

segments, totaling an overall increase of 22% and an annual average increase of 2.8%; (ii)

Multi-purpose vehicles (MPVs) have the lowest gaps of all other vehicle segments, totaling 10%

with an annual average increase of 1.2%; (iii) All segments displayed an overall increase in

their FC gaps between 2008 and 2015, with some segments experiencing occasional slight

annual decreases.

Figure 6: By-segment FC gap analyses results – combined

105%

115%

125%

135%

145%

2008 2009 2010 2011 2012 2013 2014 2015

Re

al-

wo

rld

FC

/C

ert

ifie

d F

C

Model Year

Small Compact Mid-size Large MPV SUV


19

Figure 7: By-segment FC gap analyses results – detailed

113%

135%

100%

105%

110%

115%

120%

125%

130%

135%

140%

2008 2009 2010 2012 2013 2014 2015

Sample size：2724

Model year：2008-2015

Large segment FC gap

+22%

111%

125%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

Sample size：95031


Small segment FC Gap

+14% 112%

127%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

Sample size：250315


Compact segment FC gap

+15% 118%

132%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

Sample size：60917


Mid-size segment FC gap

+14%

110%

120%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

Sample size：9532


MPV segment FC gap

+10%

117%

128%

100%

105%

110%

115%

120%

125%

130%

135%

2008 2009 2010 2011 2012 2013 2014 2015

Sample size：127698


SUV segment FC gap

20

Table 4: By-segment FC gap development

Year/

Segment 2008 2009 2010 2011 2012 2013 2014 2015 Annual

average

Total

Small* 111% 110% 117% 117% 120% 121% 123% 125% 1.8% 14%

Compact 112% 117% 115% 119% 123% 121% 127% 127% 1.9% 15%

Medium 118% 120% 120% 125% 122% 128% 125% 132% 1.8% 14%

Large 113% 130% 127% ---- 132% 130% 138% 135% 2.8% 22%

MPV 110% 112% 113% 118% 121% 119% 116% 120% 1.2% 10%

SUV 117% 111% 118% 118% 122% 125% 123% 128% 1.4% 11%

* Weighted average calculation includes both small and mini-size vehicles.

3.3 By-province FC gap analyses results

The BearOil App added a by-geography feature to its list of data analyses capabilities in 2014,

namely the Fuel Consumption Index (FCI)20. This new feature enables a snapshot of fuel consumption

levels for a particular vehicle model at different locations, indicating the by-geography condition

impacts on FC differentiation or driving style “areas” (should driving conditions for the compared

location be similar).

This section enables (i) an overview of a single model real-world FC compared with the total

average and certified FC, and (ii) a comparison of by-province FC variations throughout the year (see

Figure 11). The former demonstrates the high volatility in FC levels for the same car if driven at

different provinces, shedding light mainly on the external driving conditions at each province, while the

latter demonstrates the annual variations in FC arguably impacted by various external sources.

The 1.5L AT Hover 6 model year 2013 was selected because it has one of the largest sets of data

inputs of the data samples and is relatively evenly divided between 31 cities. The model has a reported

FC of 7.2L/100km, while the actual FC as indicated by BearOil App is 32% higher. The actual fuel

consumption reported by BearOil users in Tibet, Yunnan, Ningxia, Gansu, Sichuan, Guangxi, Shanxi,

Hainan, Jiangsu, Zhejiang, Shandong, Xinjiang, Shaanxi, Beijing (14 locations, detailed in Appendix I) is

lower that the average of the total 31 locations in the data pool. Jilin, Heilongjiang and Liaoning

provinces, on the other hand, reported an average of 10.51, 10.47 and 10.01L/100km, respectively.

Overall, the remote southwest regions are characterized by relatively low levels of fuel

consumption which corresponds with their subtle road/cars (load factor) volumes, while the Northeast

regions, Guangdong, and Shanghai have the highest FC levels, arguably corresponding well with their

high load factor. Perhaps not surprisingly, Beijing shows a small gap between reported and actual FC.

20 FCI Map, Xiaoxiong APP. http://www.xiaoxiongyouhao.com/dashboard/FCImap.php

21

Interestingly, Harbin, Beijing, Shanghai and Shenzhen had a similar annual fuel consumption variations

curve. While the latter three clearly have economic development as a reason for this similarity, Harbin

may have had an aligned FC due its cold winter (over which it performed at 11.06L/100km, stemming

from the mere -20ºC on average). As oppose to these vehicles poor FC performance during Harbin

winters, vehicles in other cities displayed poor FC performances during summer: Shanghai and

Shenzhen for example. The summer heat often creates overheating of the engine as cooling systems fail

to perform and air conditioning further exhausts the vehicles’ fuel consumption. Vehicles in Kunming

experience the least variation over the year, and maintain FC close to its 8.84L/100km average, a 122.8%

gap from the officially reported FC value.

Figure 8: Hover H6 (1.5L, MT) average FC over different cities by data-sample size

Note: circle size is illustrative of the volume of respondents.

22

Figure 9: Hover H6 (1.5L, MT) average FC over different cities

Figure 10: FC yearly changes of GreatWall Hover 6 in different cities – combined

7.2 L/100km

7.00

8.00

9.00

10.00

11.00

12.00

13.00

14.00


Re

al-

wo

rld

FC

(L

/1

00

km

)

Time

Harbin Beijing Shanghai Shenzhen Kunming

138% 132% 136%

154% 122%

23

Figure 11: FC yearly changes of GreatWall Hover 6 in different cities – detailed

12.60

10.13

11.08

9.0

10.0

11.0

12.0

13.0

14.0


Rea

l-w

orl

d F

C (

L/1

00

km

)

Time (throughout a year)

Harbin

8.99

10.21

9.52

8.4

8.8

9.2

9.6

10.0

10.4


Rea

l-w

orl

d F

C (

L/1

00

km

)


Beijing

24

9.29

10.46

9.80

9.0

9.4

9.8

10.2

10.6


Rea

l-w

orl

d F

C (

L/1

00

km

)


Shanghai

9.01

10.50

9.91

8.6

9.0

9.4

9.8

10.2

10.6

11.0


Rea

l-w

orl

d F

C (

L/1

00

km

)


Shenzhen

25

8.59

9.19

8.82

8.5

8.6

8.7

8.8

8.9

9.0

9.1

9.2


Rea

l-w

orl

d F

C (

L/1

00

km

)


Kunming

26

3.4 By-model FC gaps

Through a selection of 47 brand’s FC actual results (at least 1,000 actual-FC data inputs for each

brand based on various models), this study shows an average gap of 121.9%. Brands with the highest

fuel consumption gaps were BMW (139%), Luxgen (134%), Volvo (133%), Audi (133%) and BYD

(133%), while models with the lowest fuel consumption gaps were FAW (106%), Venucia (112%),

SGMW (113%), Skoda (114%) and Baojun (114%).

It is evident that large cars, SUVs and luxury cars are prone for higher FC gaps, which is likely due

to their large-displacement and tendencies towards aggressive driving (e.g. acceleration). Smaller auto

brands, have a different production mix than bigger brands, mainly focused on mini, small and compact

cars as well as small MPVs, resulting in lower by-brand FC gaps.

Figure 14 presents the comparative FC gap of 29 brands over 2014 and 2015, revealing how

different brands improved or worsened their performances in the past year. One possible

model-related factor increasing fuel consumption gap in the BMW series, for example, is the 8 AMT

gearbox which although impacts FC performance is not properly reflected in the type-approval test

procedures.

It is worth noting that the sample is derived from different drivers at different location and under

different driving conditions in China, which may incur high variations that impact the by-model

averages between models.

Figure 12: Selected models' FC gap distribution

121.9%

100%

110%

120%

130%

140%



27

Figure 13: Comparative FC gap of Top 5 least and best performing models

Figure 14: Comparative FC gap of Top 27 brands

3.5 A typical actual FC range for a certified range

This section aims at examining the typical range of variations in actual FC for three selected

certified FC values, 5.9L/100km, 6.9L/100km (China’s average FC target for 2015) and 7.9L/100km.

Table 5 lists the FC limit and target set forth in China’s Phase IV FC standards (model year 2016

onwards), showing the weight-bins for which the selected three FC values pose a limit. FC targets and

limits have been reduced over time, therefore 2008-2015 model year (MY) vehicles could be certified

with these three selected FC values (all weight-bins above the weight-bin that was limited by each of

these three FC values) and going forward there are limited passenger vehicle weight-groups that can

139%

106%

134%

112%

133%

113%

133%

114%

133%

114%

100%

110%

120%

130%

140%

BMW FAW Luxgen Venucia Volvo SGMW Audi Skoda BYD Baojun

100%

110%

120%

130%

140%

VW

Fo

rd

To

yota

Ch

evy

Bu

ick

Nis

san

Ch

ery

Ho

nd

a

Hyu

nd

ai

HA

VA

L

Jeel

y

Maz

da

Ch

anga

n

Gre

atW

all

Peu

geo

t

Sko

da

BY

D

Kia

Suzu

ki

Cit

roen

Ro

ewe

Mit

sub

i…

Bes

turn MG

Bao

jun

Au

di

Hai

ma

BM

W

Zh

on

ghu

a

Re

al-

wo

rld

FC

/C

ert

ifie

d F

C

2014 FC Gap Average 2015 FC Gap Average

28

exceed these FC values.

Table 5: China's CAFE Phase IV Standard; by-weight FC limits and targets for MY2016

Curb Mass (CM)

(kg)

Phase IV Limits Phase IV

Targets

MT and/or

<3 seat rows

AT and/or

>= 3 seat row

3 seat row and cw <= 1090kg 3 seat rows and cw> 1090kg

or > 3 seat rows

CM≤750 5.2 5.6 4.3 4.5

750<CM≤865 5.5 5.9 4.3 4.5

865<CM≤980 5.8 6.2 4.3 4.5

980<CM≤1090 6.1 6.5 4.5 4.7

1090<CM≤1205 6.5 6.8 4.7 4.9

1205<CM≤1320 6.9 7.2 4.9 5.1

1320<CM≤1430 7.3 7.6 5.1 5.3

1430<CM≤1540 7.7 8.0 5.3 5.5

1540<CM≤1660 8.1 8.4 5.5 5.7

1660<CM≤1770 8.5 8.8 5.7 5.9

1770<CM≤1880 8.9 9.2 5.9 6.1

1880<CM≤2000 9.3 9.6 6.2 6.4

2000<CM≤2110 9.7 10.1 6.4 6.6

2110<CM≤2280 10.1 10.6 6.6 6.8

2280<CM≤2510 10.8 11.2 7.0 7.2

2510<CM 11.5 11.9 7.3 7.5

Figure 15 shows the range of actual FC for the sample vehicles certified 5.9 L/100km between 2008

and 2015. The highest gap between the models’ actual FC and certified value was 133% (7.85

L/100km), however there was a slight decrease in the last year to a current gap of 131%. Figure 16

illustrates the distribution of actual FC value of the sample of cars certified 5.9L/100km, showing

clearly that the average is above the certified value at 7.83L/100km. The best performing model has a

gap of 0.9 L/100km (6.08 L/100km), 3% of the certified value.

The curb weight of vehicles for which actual FC data was retrieved is concentrated in the range of

870-1410kg although only the cars weighing below 980kg are required to meet the 5.8L/100km FC

limit. However, over 85% of the cars with actual FC data have had higher FC levels than not only the

ambitiously reported 5.9L/100 but also above their own weight-bin limit.

29

Figure 15: FC Gap for sample vehicles certified 5.9L/100km, 2008-2015

Figure 16: FC Gap for the sample vehicles certified 5.9L/100km

118%

124% 124%

124%

133%

110%

115%

120%

125%

130%

135%

140%

2008 2010 2011 2012 2013 2014 2015

FC G

ap

Model year Sample size：17,957

5.9 L/100km

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

750 850 950 1050 1150 1250 1350 1450 1550

FC

(L

/10

0k

m)

Curb weight (kg)

5.9 L/100km Models' Real-world FC

CAFC Phase III Limits, MT CAFC Phase III Limits, AT or >= 3 seat row CAFC Phase IV Limits, MT CAFC Phase IV Limits, AT or >= 3 seat row CAFC Phase IV Targets, MT CAFC Phase IV Targets, AT or >= 3 seat row

30

The sample of cars that were certified 6.9L/100km (the national average passenger vehicles FC

target for the year 2015) on average had an actual FC 118.6% higher, of 8.18L/100km, as demonstrated

in Figure 18. Over the years, the gap grew starting from a minimum gap of 111% evidenced in 2010, as

shown in Figure 17.


870-1410kg although only the cars weighing below 980kg are required to meet the 6.9L/100km FC

limit. Vehicles with eight of between 1060-1120kg the FC gap is of about 103-123%; while for vehicles

with weight range of 1550-1605kg the FC gap if of between 115-156%.


111%

118%

110%

114%

122%

127%

100%

105%

110%

115%

120%

125%

130%

2008 2009 2010 2011 2012 2013 2014 2015

FC G

ap


31


Figure 19 shows the sample of cars that are certified with 7.9 L/100km had an actual FC average

some 129.7% higher, of 10.25L/100km. Over the years, the gap fluctuated quite a bit, reaching 134% in

2015, as shown in Figure 20.


870-1410kg although only the cars weighting below 1320 are required to meet the 7.9L/100km FC

limit. However, over 95% of the cars with actual FC data have had higher FC levels than not only the

ambitious reported 7.9L/100 but also above their own weight-bin limit.

6.9 L/100km

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

900 1000 1100 1200 1300 1400 1500 1600 1700 1800

FC

(L

/10

0k

m)

Curb weight (kg)


CAFC Phase III Limits, MT CAFC Phase III Limits, AT or >= 3 seat row

CAFC Phase IV Limits, MT CAFC Phase IV Limits, AT or >= 3 seat row CAFC Phase IV Targets, MT CAFC Phase IV Targets, AT or >= 3 seat row

32


、


112% 114%

114%

139% 140%

100%

110%

120%

130%

140%

150%

160%

2008 2009 2010 2011 2013 2014 2015

FC G

ap


7.9 L/100km

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

13.0

14.0

1100 1200 1300 1400 1500 1600 1700

FC

(L

/10

0k

m)

Curb weight (kg)


CAFC Phase III Limits, MT CAFC Phase III Limits, AT or >= 3 seat row CAFC Phase IV Limits, MT CAFC Phase IV Limits, AT or >= 3 seat row CAFC Phase IV Targets, MT CAFC Phase IV Targets, AT or >= 3 seat row

33

3.6 Best selling passenger vehicles actual and certified FC gap

The sales of the top 100 selling passenger vehicles of 2015 reached 14.6 million, accounting for 69%

of the entire vehicle market. This section attempts to shed light on the incremental FC gap of the rapidly

growing national passenger vehicle market. The smallest gap achieved by best selling models was 111%

while the largest gap was 153%, indicating a variation of 42%. The average actual and reported FC gap

was 126%, with 61 models below. 80% of the best selling models (82 models) achieved an actual gap of

less than 130% from the certified FC value.

Figure 21: FC gap of the Top100 best-selling models in 2015

While the best-selling models often drive vehicle market performance, it is interesting to look at

the best-selling models as an indicator for future market performance, including fuel consumption21.

The average gap of China’s fastest growing vehicle models (by sales) was 127%. The majority of

vehicles (67%) achieved a smaller gap than the average, however BMNZ C-Class reached a gap as large

as 153%, followed by Peugeot 408 with 142% and BYD S7 with 140%.

21 Among the 22 models that saw sales increases of 60% and beyond, SVW Lamando and Honda XR-V have increased their

sales 20 fold; however as their 2014 sales were no more than 1 thousand units each, they were excluded from this analysis.

34

Figure 22: FC gap of China's fastest growing models (by sales)

3.7 Fuel saving technologies gaps

Typical automotive fuel-saving technologies include hybrid vehicle technologies, more efficient

gasoline engines, diesel technology (direct injection, turbocharging, variable valve timing technology,

etc.), and lightweight vehicle technology22. This section will attempt to assess the turbocharger

technology impact on actual FC performance, performing as a feasibility study for technology impact

assessment through novel data source utilization.

Turbocharger is a turbine-driven forced induction device that increases the internal combustion

engine's efficiency and power output by forcing additional air into the combustion chamber as oppose

to the amount of air inserted through atmospheric pressure alone. Turbocharger is said to typically

improve performance by 40%23, so that a 1.4 T turbocharged engine car reaches the same power output

as that with a 1.8 L naturally aspirated engine24. The study therefore compares a 1.4 T turbocharged

22 Energy Saving Technologies of Vehicles. Baidu Baike. http://baike.baidu.com/link?url=zzvjC_XKB_u9pxj3LE-MZjwC9yNOEX8rzjJRXv1AZAjUj6OCNgS9uY_rbhwpFeTw5L6adb3KAnDONiiYGDdqrq 23 Turbo Technology. Baidu Baike. http://baike.baidu.com/link?url=EGnyqs_3E2z1zubQEQaPtCLsje1vgiQQhzq2BuW19T0SLGwoXBOFHMPm3k_YWmuw0kD3txl0x2QG8pp2evLkwK 24

Difference between 1.4 L and 1.4 T，AutoHome: http://www.autohome.com.cn/dealer/201604/56143056.html

113%

153%

127%

50%

1050%

2050%

3050%

4050%

5050%

100%

110%

120%

130%

140%

150%

160%

Sa

les

Gro

wth

Re

al-

wo

rld

FC

/C

ert

ifie

d F

C


Model year：8 of MY2015, 13 of MY2014

FC gap Sales growth

https://en.wikipedia.org/wiki/Turbine

https://en.wikipedia.org/wiki/Forced_induction

35

engine with 1.8L naturally aspirated engine through 7 passenger vehicle models representing each of

the engine types on which over 6k FC data inputs were collected by BearOil between 2014 and 2015.

Figure 23 shows that the average gap between actual and reported FC of the 7 selected 1.4T

models was 123%, while the 1.8L engine average FC gap was 127%. While the 1.4T FC gap is

distributed well around the average (excluding the Audi with 137%), the 1.8L FC gap was more volatile.

The turbocharger seems to have only a slight difference on fuel consumption reduction impact.

Figure 23: Technology impact of FC - Turbocharger 1.4 T on a 1.8L engine

36

4. National and provincial passenger vehicles emissions gap

This chapter attempts to demonstrate the impact of FC measurement gap on national and urban

level emissions benchmarks and regulatory planning for meeting low-carbon development goals. It

therefore tracks FC standards development in comparison to actual FC, and then compares reported

passenger transportation induced FC emissions with real-world fuel consumption (and resulted carbon

emission) volumes on the national (chapter 4.1) and provincial (chapter 4.2) levels based on top-down

and bottom-up methods25.

4.1 FC gap from a national standard perspective

As demonstrated in Figure 24, while the national Corporate Average Fuel Consumption (CAFC)

standard, which is aimed at reducing FC with a gradually increasing pace26, seems to be advancing well

based on reported average corporate FC (blue line) and reached 14% reduction from 2009, actual FC

data collected through the 575k samples of BearOil App users indicates that actual FC levels improved

by only 1.5% over the past 9 years. Moreover, the CAFC target which is based on average corporate FC

data according to its engine-size (and corresponding FC requirement) mix, has presumably been met

since 2013; in fact, actual FC data reveals that the gap between current actual FC and the target FC is of

1.5L/100km.

Figure 24: Actual FC development versus Corporate Average Fuel Consumption (CAFC) standard

4.2 FC gap from a national perspective

According to data provided by the Development Research Center of the State Council, China’s 2014

25 2014 data was chosen as the authors of this work were authorized access. 26 See iCET’s 2016 Annual CAFC Analysis Report for more information.

8.16 8.11 7.99 7.88 7.97 7.71 7.53

7.33 7.22 7.02

9.16 9.14 8.71 8.73 8.65 8.73 8.73 8.98 8.68 9.02

4.0

5.0

6.0

7.0

8.0

9.0

10.0

2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

FC（

L/

10

0k

m）

Model Year

National CAFC Average

Real-world FC (Xiaoxiong)

CAFC Targets

Real-world FC decreased by

1.5% between 2006-2015

National CAFC Average

decreased by 14.0%

between 2006-2015

37

national fuel consumption levels reached 102.66 million tons (Mt) while passenger vehicles accounted

for 84% of that amount. By the end of 2014, China’s vehicles volume reached 264 million while

passenger vehicles volumes were 105 million, of which 19.7 million were newly registered. We assume

that new passenger vehicles were registered during the year in a relatively steady pace; therefore, the

average number of vehicles registered for 2014 that can be accounted for by our calculations should be

the number of vehicles at the year-end minus half of the vehicles added during the year. Thus, vehicle

volume for 2014 is 95.1497 million (105-19.7006/2=95.1497).

Table 6: National average passenger vehicles’ carbon emission estimation

Conversion factor of

gasoline’s volume

and mass

CO2 emission factor

for gasoline

Passenger

vehicles’

gasoline

consumption

proportion

Average real-world fuel

consumption of

passenger vehicles

based on BearOil App

(L/100km)

Average annual driving

distance of passenger

vehicles based on BearOil

App

1,355 L/t27 2.361 kg CO2/L28 84% 9.2 L/100km 13,000 km

Step I: Bottom-up national passenger vehicles’ carbon emissions estimation

I(a) Passenger vehicles’ FC volume = ∑[(Model FC) × (vehicle km traveled, VKT) × (Model

volume)] since lack of detailed data, we will use the average as a proxy:

(9.2 L/100km) × (13,000km) × (95.1497) = 113,799.04 Million L = 83.984 Mt

I(b) Passenger vehicles’ (use-phase) emissions volume = ∑[(per gasoline type FC volume) × (CO2

emission factor)]=113,799.04×2.361 = 268.679 Mt

Step II: Top-down national passenger vehicles’ carbon emissions estimation

Passenger vehicles’ carbon emissions estimation = (National gasoline consumption×conversion

factor from mass to volume) × (passenger vehicles’ gasoline consumption proportion) × (CO2

emission factor for gasoline) = (102.66×1,355) × (84%)×(2.361) = 275.877 Mt

Results show a (275.877/268.679*100=)102.7% gap between the bottom-up ‘real-world’

data-based and the top-down reported data-based national emissions from use-phase of passenger

vehicles. While the gap is small, many factors affect the estimation result. Table 7 lists some of these

potential factors.

27 Average density of gasoline is 0.738 kg/L, thus 1 t gasoline accounts for 1,355 L. 28 Commonly used calculation formula and coefficient of carbon emissions. Carbon Emission Exchange. http://www.tanpaifang.com/tanjiliang/2013/0324/18316.html

38

Table 7: Passenger vehicles’ carbon emissions gap estimation impacting factors

Factor How to influence Effects on Real-world Carbon Emissions

Clunkers Retire By Vehicle Aver. FC；

By Vehicle Number

Decrease

Government cars reform By Vehicle VKT Decrease

Taxi By Vehicle VKT Increase

Stopover vehicles

By Vehicle Number

National-level，no effects；

Single area-level，Increase

Network Trip booking

(Didi/Uber) By Vehicle VKT Increase

Database Matching (include.

Reference data and Xiaoxiong APP

vehicle models’ database)

—— Uncertain

4.3 Case Study: Guangdong Province Passenger vehicles’ carbon emissions gap

While at the national level the gap between real-world and reported-based estimations of FC and

the equivalent carbon emissions was rather small, it is useful to zoom in into a province-based case

study for assessing the potential local gap resulting from different measurement methods. The province

of Guangdong will be used in this case study, for several reasons: first, data access was gained; second,

as a province of 179,700 square kilometers and home to over 107 million residents (by end of 2014),

Guangdong’s car market accounts for nearly 10% of the national market29; last, Guangdong Province

was selected as one of the low-carbon pilot areas in 2010, paving the way for low-carbon transportation

policies and practices.

According to the Guangdong Statistics Yearbook of 2015, the provinces’ passenger vehicle volume

reached 11,441,806, of which 1,517,859 was newly registered30. Therefore, we will use 10,682,876

(11,441,806-1,517,859/2=10,682,876.5) in estimating passenger use-phase carbon emissions of the

province. Gasoline consumption of Guangdong Province was 13.81 Mt31, 84% of which was consumed

by passenger vehicles based on the situation nationwide. Table 8 shows the by-segment vehicle

proportions of Guangdong Province between 2014 and 201532. Table 9 provides the by-segment data of

real-world fuel consumption, annual diving distance and sample size of Guangdong Province.

29 TOP10 provinces of vehicle ownership in China. http://auto.sohu.com/20151105/n425423718.shtml 30 Guangdong Annual Statistics Yearbook 2015. http://www.gdstats.gov.cn/tjnj/2015/directory/content.html?14-11-0 31 Huang Yixin, Gasoline and Diesel Supply-Demand Status and Prediction of Guangdong Province. Shandong Chemical Engineering Journal, 2015（44）：50-52. 32 Auto registration data, http://news.16888.com/spsj/index_4.html

39

Table 8: By-segment reported vehicles registration in Guangdong Province

Segment 2015 2014 Average

Micro (A00) 0.5% 0.7% 0.6%

Small (A0) 6.4% 7.6% 7.0%

Compact (A) 36.2% 39.6% 37.9%

Mid-size (B) 11.3% 14.0% 12.6%

Large (C) 3.5% 4.3% 3.9%

SUV 35.3% 26.1% 30.7%

MPV 6.8% 7.5% 7.2%

Table 9: By-segment vehicles information provided by Xiaoxiong APP

Segment Aver. Real-world Fuel

consumption

（L/100km）

Aver. Annual

Driving Distance

（km）

Sample size

Micro (A00) 6.49 11,396 1,421

Small (A0) 7.68 13,600 11,220

Compact (A) 8.45 14,182 33,842

Mid-size (B) 10.06 14,570 8,564

Large (C) 10.70 18,830 529

SUV 10.10 14,770 17,330

MPV 10.09 15,485 2,427

Total 75,333

Step I: Bottom-up Guangdong passenger vehicles’ carbon emissions estimation

Passenger vehicles’ FC volume = ∑[(Model FC)×(vehicle km traveled, VKT)×(Model volume)×

(Model % as part of the entire fleet)]/1,355 = 107.654 Mt

Therefore the emissions derived from the bottom-up calculations are: 1076.54×1,355×2.361=344.402

Mt

Step II: Top-down Guangdong passenger vehicles’ carbon emissions estimation

= [(Guangdong gasoline consumption) × (conversion factor from mass to volume)]× (passenger

vehicles’ gasoline consumption proportion) × (CO2 emission factor for gasoline) = (1,381×1,355) ×

(84%)× (2.361)/1000 = 371.115 Mt

Results show that passenger vehicles’ carbon emissions accounted for 11.23% of the total

emissions nationwide while the proportion of passenger vehicles of Guangdong Province of the total

number nationwide was 12.82%, providing some indication of the reliability of this calculation method.

Inconsistencies between BearOil App’s users model distribution and national/regional vehicle

models distribution may result in data biases. Other factors may also create bias in the calculation’s

40

results versus real-world carbon emissions. For example, taxies in Shenzhen have an average annual

driving distance of 136,344 km, nearly 10 times that of private vehicles.

41

5. Conclusions

Taking the initial feasibility FC Gap study of 201533, this study is based on a larger bottom-up data

set provided by BearOil App and therefore enables addressing new research questions, such as the

reported versus actual FC gap national and local impact. This report affirms and expands the results of

last year’s research:

The FC gap has been increasing over time from an average of 112% in 2008 to 127% in the last

couple of years. This trend reaffirms FC gaps growth recently assessed by research institutions

in Europe (the international council of clean transportation, ICCT, states the growth in FC gap in

Europe increase from 10% to 40% between 2003 and 201434).

Automated transmission (AT) vehicles typically have a larger gap between reported and actual

FC values than manual transmission (MT) vehicles, averaging 131% and 121% respectively last

year. As AT vehicles accounted for 38.6% of cars in China in 2015 and their share increase

annually (from 30.5% in 2008), the average FC gap of passenger vehicles is likely to grow faster

over time.

Surprisingly, multi-function vehicles (MPVs) have the lowest average FC gap of all vehicle

segments, a mere 120% FC gap with an increase of 10% between 2008 and 2015 (an annual

average of 1.2%); The large vehicle segment has seen the greatest increase in gap between 2008

and 2015 of 22% (an annual average of 2.8%) reaching a gap of 130%; Compact vehicles and

SUVs, have a similar gap of 127% and 128% respectively, however the increase in FC gaps in

compact cars were higher over the past seven years, 15% as opposed to 11% evidenced by SUVs.

By taking the Hover H6 (MT, 1.5L) as a sample model for comparing FC gaps between 31

locations, the study shows that the FC gap of China’s southwest is typically smaller than that its

of northern and eastern areas. Guangdong province, Shanghai, and three provinces in Northeast

China have the highest FC gaps, some 11% higher than the national average.

The FC gap distribution range among brands is even. The average FC gap is 121.9%. The

medium-large models, SUV, and Luxury models mainly have the largest FC gap (the highest is

BMW amounting 139%). And the small, compact models and small MPV mainly have the lowest

FC gaps (the lowest is FAW amounting to 106%).

Meanwhile, the report has some new interesting results:

Based on the analysis of yearly FC gap of some models in cities in certain latitudes, northern

cities (Harbin and Beijing) and southern cities (Shanghai and Shenzhen) have shown different

FC gap variations trends. The maximum and minimum value of FC gaps, as expected, occurred at

different seasons. Kunming has the lowest FC gap, and the yearly range is the smallest.

The FC gap of models with different certified FC values is a good indicator of the validity of the

FC standard. The study reveals that models with 6.3L/100km certified FC have the lowest FC

gap (126%), while models with 5.9L/100km and 7.9L/100km have a much higher FC gap, 131%

33 Ding Ye, Maya Ben Dror et al., Real-world and Certified Fuel Consumption Gap Analysis. iCET. http://www.icet.org.cn/admin/upload/2015080439650285.pdf 34 From laboratory to road: A 2015 update - http://www.theicct.org/laboratory-road-2015-update

42

and 134% respectively.

The average FC gap of China’s 2015 Top 100 selling models was 126%. The fastest sales growth

models demonstrated a gap of 127%, while the highest gap was of Benz C class (153%), and the

lowest of SVW Lavida (111%).

Models with turbo engines typically have slightly lower FC gaps, indicating the technology is

beneficial in reducing fuel consumption. The average FC gap of a sample of 1.4T Turbo models

was 123%, while that of 1.8L models was 127%.

The regional carbon emission can be estimated according to real world fuel consumption data.

The gap between top-down and bottom-up FC data and resulted carbon emissions calculations

was less than 5% on both the national and regional level (Guangdong province case study).

Policymakers can therefore use bottom-up data as a valid indicator for low-carbon

transportation policies measurements.

There are multiple factors impacting the gap between actual and certified (laboratory-reported)

fuel consumption. Besides adjusting for anthropogenic and location-specific driving conditions

(geographical conditions, urban transport planning etc.), which have an effect on actual FC, China’s

current fuel consumption test-cycle could be improved by utilizing actual driving FC measurements

thereby narrowing the FC gap. Furthermore, location-based FC conversions could be developed for

inspiring the formation of local FC standards that align with national and local FC goals. Last but not

least, this report further highlights the need for independent and accountable third-party scrutiny of

auto standards implementation status35.

35 Refer to iCET Sep 21 workshop news for further information regarding FC gap recommendations: http://www.icet.org.cn/english/newsroom.asp?fid=16&mid=17.

43

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46

Appendix

Annexed table 1 By-Brand models’ FC gap

Brand FC Gap Sample Size

FAW 106% 2,549

Venucia 112% 2,047

Wuling 113% 4,128

Skoda 114% 16,158

Baojun 114% 4,521

Suzuki 115% 14,833

Roewe 116% 7,391

Fiat 116% 3,328

Soueast Motor 116% 2,767

Dong Feng Aeolus 116% 2,446

Zotye 116% 1,128

VW 117% 53,102

Chery 117% 24,164

Mitsubishi 117% 5,664

Nissan 118% 24,847

Pentium 119% 5,226

Zhonghua 119% 3,390

Changan Commercial 119% 2,361

Fxauto 119% 2,203

Subaru 119% 2,055

Honda 120% 23,815

Buick 121% 27,932

Mazda 121% 16,848

Citroen 121% 11,300

Toyota 122% 35,859

47

Chevy 122% 31,541

Haima 122% 4,331

LEXUS 122% 1,105

Kia 123% 15,527

JAC 123% 5,296

GreatWall 124% 16,355

Hyundai 125% 21,208

Changan 125% 16,795

GAC Trumpchi 125% 5,340

Ford 126% 52,277

HAVAL 126% 19,414

Jeep 126% 1,599

Peugeot 127% 16,282

Cadillac 127% 1,154

MG 128% 4,758

Geely 129% 18,693

Benz 130% 1,626

BYD 133% 16,104

Audi 133% 4,512

Volvo 133% 1,424

Luxgen 134% 1,990

BMW 139% 3,689

Average 121.9%

Total Sample 561,082

48

Annexed table 2 Real-world FC Distribution of HAVAL H6 (MT, 1.5T), L/100km

District* Real-world

FC

Of Average

Real-world FC

Of Certified

Combined FC Sample Size

Yunnan 8.66 91.1% 120.3% 47

Ningxia 8.98 94.4% 124.7% 28

Gansu 9.18 96.5% 127.5% 63

Sichuan 9.25 97.3% 128.5% 304

Guangxi 9.25 97.3% 128.5% 99

Shanxi 9.27 97.5% 128.8% 78

Hainan 9.27 97.5% 128.8% 22

Jiangsu 9.42 99.1% 130.8% 474

Zhejiang 9.42 99.1% 130.8% 244

Shandong 9.46 99.5% 131.4% 417

Xinjiang 9.47 99.6% 131.5% 49

Beijing 9.5 99.9% 131.9% 112

Shaanxi 9.5 99.9% 131.9% 68

Inner

Mongolia 9.54 100.3% 132.5% 45

Hubei 9.54 100.3% 132.5% 255

Guizhou 9.54 100.3% 132.5% 36

Fujian 9.57 100.6% 132.9% 150

Hebei 9.58 100.7% 133.1% 180

Tianjin 9.59 100.8% 133.2% 81

Chongqing 9.59 100.8% 133.2% 109

Jiangxi 9.63 101.3% 133.8% 76

Anhui 9.67 101.7% 134.3% 153

Hunan 9.77 102.7% 135.7% 158

Henan 9.87 103.8% 137.1% 222

Qinghai 9.87 103.8% 137.1% 14

49

Guangdong 9.91 104.2% 137.6% 529

Shanghai 9.93 104.4% 137.9% 222

Liaoning 10.01 105.3% 139.0% 165

Heilongjiang 10.47 110.1% 145.4% 72

Jilin 10.51 110.5% 146.0% 45

Average/Total 9.57 133.0%

Annexed table 3 2015 Top100 sales models’ FC Gap

Sales rank Model Segment FC Gap Sample size

1 Wuling Hongguang MPV 122% 960

2 New Lavida Compact 111% 254

3 HAVAL H6 SUV 119% 2,080

4 Sylphy Compact 124% 1,109

5 Baojun 730 MPV 114% 710

6 Sagitar Compact 117% 2,292

7 Jetta Compact 115% 658

8 Elantra Compact 128% 802

9 New Santana Compact 115% 620

10 Tiguan SUV 125% 666

11 Corolla Compact 130% 2,593

12 Cruze Compact 119% 975

13 New Excelle Compact 127% 1,667

14 Escort Compact 127% 1,278

15 Verna Small 127% 1,293

16 New Sail Sedan Small 116% 667

17 Jeely EC7 Compact 146% 1,614

18 Passat Mid-size 117% 493

19 New Bora Compact 124% 386

20 JAC Ferine S3 SUV 122% 124

21 Golf Compact 125% 720

50

22 Changan CS75 SUV 120% 1,579

23 Eado Compact 128% 1,776

24 Excelle Compact 117% 855

25 Changan CS35 SUV 128% 763

26 Haval H2 SUV 113% 213

27 X-Trail SUV 122% 3,403

28 Huansu S3 SUV 128% 347

29 New POLO Small 113% 1642

30 Envision SUV 124% 442

31 CR-V SUV 123% 445

32 Kia K3 Compact 130% 1,053

33 Magotan Mid-size —— ——

34 Mistra Mid-size 127% 1,008

35 Kia K2 Sedan Small 124% 397

36 BMW Series 5 Large 150% 319

37 Octivia Compact 116% 1,582

38 Audi A6 Large 137% 202

39 Baojun 560 SUV 115% 882

40 BYD F3 Sedan Compact 125% 280

41 Changan Honor MPV 118% 477

42 Fengxing MPV —— ——

43 Kuga SUV 135% 1,235

44 Trumpchi GS4 SUV 134% 879

45 Accord Mid-size 114% 355

46 Camry Mid-size 127% 984

47 Vezel SUV 124% 518

48 Zotye T600 SUV 127% 120

49 Levin Compact 128% 2,019

50 Weiwang M20 MPV 118% 174

51

51 Yuanjing Compact —— ——

52 Tiggo 3 SUV 120% 2,396

53 New Mondeo Mid-size —— ——

54 XR-V SUV 122% 418

55 Crider Compact 123% 298

56 RAV4 SUV 120% 535

57 Audi A4 Mid-size 147% 423

58 Vios Small 122% 4,106

59 Audi Q5 SUV 128% 120

60 New Teana Mid-size 131% 107

61 New Focus Sedan Compact 135% 740

62 New Regal Mid-size 127% 492

63 BYD S7 SUV 139% 1,949

64 Peugeot 408 Compact 141% 268

65 Hyundai ix35 SUV 124% 297

66 Lamando Compact 123% 177

67 Hyundai ix25 SUV 126% 290

68 Fengguang 330 MPV —— ——

69 New Fit Small 124% 3,729

70 BMW Series 3 Mid-size 140% 306

71 New Focus

Hatchback

Compact 135% 740

72 Mazda 3 Compact 125% 2,094

73 Gran Lavida Compact 120% 106

74 New Elysee Compact 115% 200

75 New Sunny Compact 115% 415

76 Peugeot 308 Compact 125% 1,302

77 BENZ C-Class Mid-size 153% 141

78 New Lacorsse Mid-size 117% 115

52

79 Joyear SUV SUV 122% 157

80 Encore SUV 120% 692

81 Sportage SUV 128% 316

82 Malibu Mid-size 124% 1,563

83 GL8 MPV 122% 249

84 Zhonghua V3 SUV 127% 393

85 HAVAL H1 SUV 128% 1,167

86 New Highlander SUV 128% 415

87 Alsvin Small 131% 574

88 Audi Q3 SUV 132% 251

89 Tiggo 5 SUV 117% 622

90 Peugeot 301 Compact 115% 931

91 Peugeot 3008 SUV 133% 575

92 C3-XR SUV 125% 202

93 Citroen C4 Sedan Compact 134% 191

94 Haima S5 SUV 126% 390

95 Edge SUV 129% 272

96 Venucia T70 SUV 119% 216

97 Sonata Mid-size 128% 203

98 YARiS L Small 128% 1,445

99 Fengshen AX7 SUV 118% 485

100 Surui Compact 139% 233

Average/Total 126% 76,805

2016 real-world passenger vehicle fuel consumption analysis · 2016-09-23 · january 1st, 2016,...

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