0
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
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 17: FC Gap for sample vehicles certified 6.9L/100km, 2008-2015 ...................................... 30
Figure 18: FC Gap for the sample vehicles certified 6.9L/100km ....................................................... 31
Figure 19: FC Gap for sample vehicles certified 7.9L/100km, 2008-2015 ...................................... 32
Figure 20: FC Gap for the sample vehicles certified 7.9L/100km ....................................................... 32
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/
6
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
7
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%
9
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
10
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
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
Sample size:546,217
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
Model year:2008-2015
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
Model year:2008-2015
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
Model year:2008-2015
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
Model year:2008-2015
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
Model year:2008-2015
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
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%
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
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rea
l-w
orl
d F
C (
L/1
00
km
)
Time (throughout a year)
Beijing
24
9.29
10.46
9.80
9.0
9.4
9.8
10.2
10.6
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rea
l-w
orl
d F
C (
L/1
00
km
)
Time (throughout a year)
Shanghai
9.01
10.50
9.91
8.6
9.0
9.4
9.8
10.2
10.6
11.0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rea
l-w
orl
d F
C (
L/1
00
km
)
Time (throughout a year)
Shenzhen
25
8.59
9.19
8.82
8.5
8.6
8.7
8.8
8.9
9.0
9.1
9.2
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Rea
l-w
orl
d F
C (
L/1
00
km
)
Time (throughout a year)
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%
Sample size:561,082
Model year:2008-2015
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.
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 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%.
Figure 17: FC Gap for sample vehicles certified 6.9L/100km, 2008-2015
111%
118%
110%
114%
122%
127%
100%
105%
110%
115%
120%
125%
130%
2008 2009 2010 2011 2012 2013 2014 2015
FC G
ap
Model year Sample size:20,786
31
Figure 18: FC Gap for the sample vehicles certified 6.9L/100km
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.
The curb weight of vehicles for which actual FC data was retrieved is concentrated in the range of
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)
6.9 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
32
Figure 19: FC Gap for sample vehicles certified 7.9L/100km, 2008-2015
、
Figure 20: FC Gap for the sample vehicles certified 7.9L/100km
112% 114%
114%
139% 140%
100%
110%
120%
130%
140%
150%
160%
2008 2009 2010 2011 2013 2014 2015
FC G
ap
Model year Sample size:8,300
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)
7.9 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
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
Sample size:14,700
Model year:8 of MY2015, 13 of MY2014
FC gap Sales growth
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