Low-carbon
scenario analysis
on urban transport of one
metropolitan in China in 2020
Xiaofei Chen1, Zijia Wang2
1Department Of Mechanical And Industrial Engineering, University Of Toronto (Canada)
2School Of Civil Engineering, Beijing Jiaotong University (China)
Received July 2012
Accepted November 2012
Chen, X., & Wang, Z. (2012). Low-carbon scenario analysis on urban transport of one metropolitan in China in 2020. Journal of Industrial Engineering and Management, 5(2), 344-353. http://dx.doi.org/10.3926/jiem.640
---------------------
Abstract:
Purpose: This paper discussed possible ways of implementing effective energy conservation and GHG emission reduction measures by providing: the forecasts of mid-to-long term city-wide carbon emission rate; and the analysis of potential low-carbon transport solutions.
Design/methodology/approach: According to the characteristics of the transport system in China, based on the review and application analysis of existing transport energy and GHG emission calculation models, the comprehensive carbon emission calculation model established. Existing data were utilized with regression analysis to project the prospective traffic data in the baseline scenario at the target year of 2020 to calculate the emission amount. Four low-carbon scenarios were set in accordance with the goal of “low carbon transportation, green trip”, and the effectiveness of each low-carbon scenario was evaluated by comparing them with the baseline scenario in terms of the respective GHG emission rate.
Findings: Under the current developing trend in policy environment and technical specifications, the total projected GHG (CO2) emissions from transport sector at 2020 of the city will reach 30.085 million ton CO2; private-vehicles are the major contributor among all transport modes at 16.89 million ton CO2.
Practical implications: Limiting the growth in private-vehicle ownership, reducing the frequency of mid-to-long range travel and the average trip distance, and prompting the public transit oriented policies are all possible solutions to reduce carbon emission. The most effective practice involves a shift in public travel behavior.
Originality/value: This paper presents a method to forecast the mid-to-long term city-wide carbon emission rate; and provides some potential low-carbon transport solutions.
Keywords: Low carbon transport, Carbon emission, Scenario analysis, Forecasting, Energy conservation and emission reduction
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1. Introduction
In
China, the negative environmental consequences caused by
GHG emission from transport sector in metropolitan regions grow rapidly
along
with continuous urbanization process. Against the background of
high-speed
economic development, the growing household consumption capability in
metropolitan
results in a dramatic increase of urban residential trips, which
enlarges
carbon emission contribution from urban passenger traffic (He & Huo, 2005).
In
fact, total GHG emission volume stays at a very high
level, despite of recent achievements of traffic and purchase
restrictions upon
household automobile and actions that encourages the utilization of
rail
transportation.
We set up
baseline and relevant low-carbon scenarios to forecast total GHG
emission from
transport industry in one metropolitan in China, and evaluate the
implemented
energy conservation and emission reduction policies, such as
“public
transit priority”. These are of great significance to this metropolitan
and
even to the whole country for coming up with effective strategies to
control
the GHG emission and assume its commitment to emission reduction.
2. GHG emissions calculation model of transport section
In reference to field study, literature review, and
collectable data, the research scope of this paper is as following:
firstly, only
CO2 is taken into account in terms
of types of GHG,; for vehicle genre,
only local registered vehicles are covered; and for transport modes,
this
research mainly involves road transport (including urban road, express
highway
and highway) and rail transit in This metropolitan.
Our research starts
with setting up calculation models for each type
of fuel on roads to examine the overall energy consumption. In
accordance with
terminal consumption of each energy resource, the formulas are as
follows.
For diesel fuel:
Where Ediesel is the consumption of diesel fuel, Qf for the ton kilometer traveled per year (ton·km) (Beijing Municipal Bureau of Statistics, 2010), ef for the fuel economy (L/(ton·km))(Jia, Peng & Liu, 2009), Nd-bus, Sd-bus, ed-bus for the vehicle population, kilometers traveled per year (km), and fuel economy (L/km) of diesel buses, respectively.
For gasoline:
Where Egasoline refers to gasoline consumption, i for relevant vehicle category, including taxis, private cars and cars owned by enterprises and public institutions, Ni for vehicle population of vehicle category i, Si for kilometer traveled per year of vehicle category i, ei for fuel economy of vehicle category i (L/km).
For compressed natural gas:
Where Ng-bus, Sg-bus, eg-bus refers to vehicle the population, kilometer traveled per year (km), and fuel economy (kg/km) of compressed natural gas driven buses, respectively.
While for electricity consumption:
Where Ne-bus, Se-bus, ee-bus denotes the vehicle population, kilometer traveled per year (km), and fuel economy (kg/km) of electricity-based buses, respectively; Eillu for electricity consumed by a road illumination system, which is recorded by power department, and Ej-vehicle, Ejk-station refer to the vehicle energy consumption of rail line j and the station energy consumption of station k in rail line j, respectively.
Using the energy consumption obtained through the above model and the CO2 emission factor of each fuel category from ICPP (Intergovernmental Panel on Climate Change), in combination with the proportion of coal electricity out of overall electricity supply and CO2 emission factor for electricity generation in the grid, the overall GHG emission of this metropolitan’s urban transport can be calculated with the following formula:
Where C denotes the overall GHG emission of urban transport per year; Ei stands for the consumption of fuel category i, including diesel fuel, gasoline, compressed natural gas (L or kg); EFi is the emission factor for fuel category i (kg CO2/L or kg CO2/kg); Ee refers to the electricity consumption of transport, including consumption of electricity-based buses, road illumination system and urban rail transit system (k·Wh); z denotes the sharing of coal electricity out of overall electricity generated in the grid, which is 81.81% (China Electricity Council, 2011); Y is the CO2 emission factor for coal electricity during generation stage in the grid (Ma, 2002)(CO2 kg/k·Wh).
3. Scenario analysis on GHG emissions of Urban Transport in 2020
This paper
examines two major categories of scenarios assumed in the long-term
urban
planning of this metropolitan, including baseline scenario and
low-carbon
scenarios. The plan sets 2020 as its target year. As to baseline
scenarios,
they are introduced in consideration the trend of the transport
infrastructure,socio-economic
features, existing vehicle techniques and transport policies. The GHG
emission is
projected. When it comes to low-carbon scenarios, they are assumed
according to
the concept of "low carbon transportation, green trip", which
applauses for vehicle purchase control, traffic sharing, and
short-distance
trips. We calculate GHG emission of each and make comparison between
them.
3.1. Baseline Scenario
As for road transport, the calculation parameters
including tonnes-kilometres
travelled of freight per year and the vehicle population of buses is
projected
through the regressive relationship with respect to GDP.
In consideration of vehicle population control policy of
this metropolitan, the projected population of taxis and non-operating
cars still
need to be adjusted and controlled. With the projected parameters, the
GHG emission
can be calculated through the model established above.
As for rail transit, due to the difference between the
calculation
methods in the present and the future,the vehicle energy consumption
per ton per kilometer and the
average energy consumption per station of different conditioning type
are excepted
and used as basic parameter for future energy consumption of urban rail
transit
network.
1) Projection of Per capita GDP
According to the Statistical
Yearbook (1999-2011),
in combination with the population supporting capacity of this
metropolitan (Ma
& Hou, 2004),
the population of this urban registered residents in 2020
can be drawn based on model in formula 6. According to the long term
economic
development planning in this metropolitan, annual growth rate of GDP is
assumed
to be 8%. And the GDP of research target in 2020 is able to be
predicted. Then
the GDP per capita in 2020 will be got.
In the formula, R denotes the residential population in year t.
2) Urban Road Transport Energy Consumption Projection
As shown in formula 7 and 8, freight volume and bus
population are in a regressive relationship with GDP per capita. It
freight
volume Qf of
this urban in 2020 can be estimated, so
does the bus population Nbus,
of which the proportion of vehicle driven by diesel fuel, by
electricity and by
CNG is 9.266:1:2.235. In baseline scenario, each bus runs the same
mileage as
that in 2011.
In the formula, denotes Qf the freight volume in ton·km, and G is GDP per capita.
Of which Nbus denotes the bus population, and G is GDP per capita.
As for taxis, according to "Notice on Adjustment of
Small Passenger Car Registration" (Beijing Transport Management Bureau, 2009) issued by
Traffic Management Bureau of this urban, the taxis number will be
strictly
controlled to be the present level, and the mileage per vehicle per
year
remains the present value.
Meanwhile, the increase of non-operating cars should not
exceed 240.000 each year. In consequence, population of non-operating
car in
2020 will be easily drawn, and the mileage per car per year in target
year is
taken as the present value.
As for road illumination system, assuming the energy
consumption
increases by evenly each year, the total consumption is predicted in
2020.
3) Projection Model of the Energy Consumption of Urban Rail Transit
With regard to
vehicle energy consumption, it is can be calculated with the basic index through index method,
which signifies energy consumption of rail transit in unit ton
kilometre. It is
calculated by formula 9 with present energy consumption and line
feature data.
In the
formula, denotes
energy consumption per TKT (k·Wh
/ton·km);
E is energy consumption per year
(kwh); W is
train weight (t); N annual number of train departures; L,
operating mileage(km);P, passenger volume, assumed to have an average
weight of
With the help
of
formula 10, we can come up with total vehicle energy consumption by
including
future rail network planning data into consideration.
Evi suggests vehicle energy consumption of vehicle for line i
per year, h1 and h2 denote energy consumption per TKT for underground
line and above ground line, respectively, while l1
and l2 mileage of underground and surface lines,
respectively.
For station energy consumption, rail stations are
classified
into 3 types according to laying approach and environment control
systems.
Energy consumption per station per month of each type is recorded, and
this
provides the research with data base. With the help of all these,
future
station energy consumption can be forecasted through formula 11:
Esi denotes station energy consumption of line i, and n1, n2, n3 are the number of stations using ground open system, underground open system and platform door system as environment control system, respectively; and E1, E2, E3 are for energy consumption of each type of station as mentioned above, respectively.
Furthermore, the long-term rail transit network planning
of
this metropolitan declares that the mileage of rail transit will reach
to 1,050
kilometers in 2020 with 450 stations. Taking advantage of this
information and
the projection models, total network energy consumption and the
corresponding
GHG emission can be forecasted with formula 5.
3) GHG emission for baseline scenario
To support
the forecasts of GHG
emission, not only does the research make use of the models established
above,
but also takes into account the 2011 data (table 1) (Hao,Wang & Li, 2009; Ministry of Industry and the Ministry of
Information, 2011; Transportation
Research Center, 2011), concerning
energy consumption and emission.
Vehicle types |
Fuel type |
Energy economy |
Emission factor |
Freight |
diesel |
0.1079L/tkm |
2.73kg/L |
Buses |
diesel |
40.12L/100km |
2.73kg/L |
electricity |
0.9646 k·Wh /km |
1.019kg/k·Wh |
|
CNG |
35kg/100km |
2.26kg/L |
|
Taxis |
gasoline |
7.58L/100km |
2.26kg/L |
Social cars |
gasoline |
7.76L/101km |
2.26kg/L |
Table 1. Energy economy and emission factor for various vehicle and fuel types
The results project the GHG emission of baseline scenario for this metropolitan’s urban transport in the target year 2020, which is 30.085 million ton CO2. The GHG emission sharing of freight transport, buses, taxis, non-operating cars, road illuminating system and rail transit are 2.580×106, 1.986x106, 2.112×106, 2.061×107, 4.19×105, 4.19×106 ton CO2, respectively.
3.2. Low carbon scenarios
Scenario 1: Take more strict control on vehicle population growth
The population of non-operating cars will reach 5.96 million in 2020 according to baseline scenario. It is necessary to hold more strict control on the growth of vehicle quantity to ensure the number of private cars and buses not to exceed 5 million in 2020. In that case, the GHG emission of this metropolitan’s transport will be 28.203 million ton CO2, with a reduction of 1.882 million ton CO2, equal to a minus 6.26% compared with the baseline scenario.
Scenario 2: Decrease average trip distance
Optimization of urban
layout will result in gradual decrease in average residential trip
distance. In
the baseline scenario, the average trip distance of non-operating cars
is
Scenario 3: Increase the ridership of public transport
In the baseline scenario, the structure of ridership in
this
metropolitan is: 28.9% by bus, 10.0% by urban rail transit, 7.1% by
taxi, and
34.0% by non-operating cars.
With the introduction of the "public transit priority" policy, significant changes are taking place in the development of public transportation in this metropolitan. Particularly, rail transit network is booming and its ridership increases remarkably. Consequently, the municipal government sets the low-carbon scenario as follows: In 2020, the ridership of public transportation will account for 50%. To be more specific, the ridership of buses will be 20%; urban rail transit, 30%; taxi, 5%; and bicycles, 25%. In contrast, the use of private cars will decrease to 15%, and the rest share of trip is assumed by walking. In terms of the scenario setting, GHG emission in 2020 will be 23.426 million ton CO2, or 24.13% compared with the baseline scenario.
Scenarios |
Baseline scenario |
Low-carbon scenario 1 |
Low-carbon scenario 2 |
Low-carbon scenario 3 |
GHG emissions (million tCO2) |
30.085 |
28.203 |
25.648 |
23.426 |
Emission reduction (million tCO2) |
/ |
1.882 |
4.437 |
6.659 |
Proportion (%) |
/ |
6.26 |
14.75 |
22.13 |
Table 2. Comparison of Scenarios 1, 2 and 3
4. Conclusion and Suggestions
The article completed the following researches and reached the corresponding conclusions:
·
In the current
developing trend in policy environment and technical specifications,
total GHG
(CO2) emission from transport sector in 2020 of the city
will reach
to 30.085 million ton CO2. Private vehicles are the major
contributor among all transport modes, emitting 16.89 million ton CO2.
·
As indicated by the
analysis of low carbon scenarios, restricting the growth of
private-vehicle
ownership, shortening trip distance in average, and promoting "public
transit priority" policies are all solutions to reduce CO2
emission. The most effective way is to prompt a shift in public travel
behaviour.
Meanwhile, the article also likes to provide a number of
suggestions with regard to reducing transport GHG emission:
·
It is believed that it
is necessary to speed up the constructions of urban rail transit,
improve
service quality of public transport, to optimize “P+R” facilities and
public
bicycle rental system, to advocate trip by bicycle and walking, to
promote the
change in transportation mode, and to increase the ridership of public
transit.
·
The success of
emission controlling can’t be reached without contribution of
individuals. We
advocate integrated community development because it can address part
of the
travel demands within the community, and further reduce the need for
mid and
long distance travels. Meanwhile, we should avoid functional
agglomeration
redundancy in urban planning and land use.
·
Except some
traditional policies, e.g. vehicle purchase restrictions, we should
take
further measures to induce more economical use of private cars through
differentiated charges on parking in certain regions.
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Journal of Industrial Engineering and Management, 2008-2024
Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008
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