A Novel Approach of Environment Impact Assessment and Emission

Measurement on the Inter-city Transportation in the Greater Bay

Area (GBA) of China using a Modified Gravity Model

Eugene Yin Cheung Wong

, Danny Chi Kuen Ho

, Stuart So

Eve Man Hin Chan

and Chi-Wing Tsang

Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Hong Kong

Department of Design, Faculty of Design and Environment, Technological and Higher Education

Institute of Hong Kong (THEi), Hong Kong

Department of Construction Technology and Engineering, Technological and Higher Education

Institute of Hong Kong (THEi), Hong Kong

Keywords: Carbon Footprint, Emission, EV, GBA, Gravity Model, HFCV, LCA, Linear Regression.

Abstract: The Guangdong-Hong Kong-Macau Greater Bay Area (GD-HK-MO) also referred as Greater Bay Area

(GBA), is a megalopolis, consisting of nine cities and two special administrative regions, i.e., Hong Kong and

Macao in South China. GBA has a total population of approximately 71.2 million people representing about

5% of China’s total population with a combined regional GDP at USD 1642 billion in 2018, i.e. about 12%

of GDP for the whole mainland China. Hong Kong acting as a window of China, plays a critical role in

contributing to the growth of the GDP. Given the enormous scale of this regional economy and increasing

collaboration among these GBA cities, it is utmost important to design a novel environmental impact

assessment and emission measurements of the cross-border transportation among Hong Kong and various

GBA cities with the aim of proposing countermeasures on carbon emissions of vehicles in the transport and

logistics sector of the GBA. In the study, two modified gravity models are designed by considering social,

economic, and other variables affecting the carbon

emission of vehicles travelling within and across cities in

the GBA. Further study will be pursued using decomposition analysis based on the modified gravity model

to analyse the crucial contributors and determinants of carbon emission among the GBA cities.

1 INTRODUCTION

The need for sustainable low-carbon transport and

logistics has become a top priority in many countries

since emission targets were set at the Conference of

Parties to the United Nation Framework Convention

on Climate Change (COP21), and a number of

countries have set policies to ban new petrol and

diesel cars by 2030 or 2040 (Clover, 2017;

McKinnon, 2018; Vaughan, 2018). Carbon

mitigation in the transport and logistics sector was

emphasised at the recent World Economic Forums

because this sector is the world’s second-largest

carbon emitter, growing from 22% of global carbon

emissions in 2011 to 23% in 2015 (IEA, 2012; IEA,

2017). The 2018 Policy Address of Hong Kong

promoted the development of renewable energy as an

integral part of mitigating climate change (Lam,

2018). Hong Kong has set a target of reducing carbon

intensity by between 65% and 70% by 2030

compared with the 2005 level, which is equivalent to

an absolute reduction of 26% to 36%, or a reduction

in per-capita emissions from about 5.7 tonnes in 2015

to about 3.3 to 3.8 tonnes in 2030. Thus, exploring the

use of renewable energy for major vehicles is critical

for sustaining Hong Kong’s long-term

decarbonisation strategy as a smart city with low

carbon transport and logistics (Alaswad, 2016;

Environment Bureau, 2017). Currently, there are only

11,417 road-use electric vehicles (EV) in Hong Kong,

including private cars and goods vehicles; as these

account for less than 1.5% of all licensed vehicles, the

challenges of achieving decarbonisation and

renewable energy for sustainable transport and

logistics are huge. There are studies that hydrogen-

powered vehicles have lower carbon emissions, faster

686

Wong, E., Ho, D., So, S., Chan, E. and Tsang, C.

A Novel Approach of Environment Impact Assessment and Emission Measurement on the Inter-city Transportation in the Greater Bay Area (GBA) of China using a Modiﬁed Gravity Model.

DOI: 10.5220/0010500806860692

In Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2021), pages 686-692

ISBN: 978-989-758-513-5

refuelling speeds and lower maintenance costs over

EVs, and a number of countries, including Germany,

France, Japan and South Korea, have introduced

hydrogen-powered fuel cells with government-

supported pilot runs on the road (Cano et al., 2018;

Ryall, 2018; Schoentgen, 2018; Topel, 2018; UNDP,

2017). Hydrogen-powered vehicles suit the emerging

needs of long-range and high-utilisation

transportation vehicles for logistics cars and trucks

(Cano et al., 2018). With the large number of licensed

vehicles in Hong Kong and cities in the Greater Bay

Area (GBA; e.g., 3.18 million in Shenzhen, 2.34

million in Guangzhou, 62 thousands in Zhuhai, and

78 thousands in Hong Kong), achieving zero-carbon

emissions in transport and logistics requires effective

measurement of the carbon footprint of a vehicle and

its usage in addition to the development of renewable

energy for motor cars and trucks, especially given the

increasing traffic flow between Hong Kong and

Zhuhai across the Hong Kong–Zhuhai–Macau Bridge

(HZMB) (National Bureau of Statistics of China,

2018). Thus, structured and systematic measurement

of the carbon footprint of a vehicle is necessary, both

from cradle-to-gate and cradle-to-grave, as is the

exploration of renewable energy to mitigate carbon

emission in transport and logistics in GBA cities. The

car-manufacturing process is complex, involving

component manufacturing and assembly, and there is

a lack of research on the product carbon footprints of

automotive, in particular cars and trucks (Berners-Lee

& Clark, 2013; Zhao et al., 2012). In addition,

because the hydrogen-oxygen combustion product is

the cleanest substance (water and heat) and has a fast-

refuelling speed, it has great potential for use in

automobiles and automotive products. Replacing

fossil fuels in the transport sector by renewable

energy will help combat climate change through

reducing the carbon footprint significantly within the

vehicle operation cycle. The economic and

environmental impact of the use of renewable energy

vehicles travelling both within and amongst Hong

Kong and GBA cities should thus be analysed.

The proposed project aims to develop an

environmental impact assessment and product carbon

footprint measurement for selected EV used in

transport and logistics and to design a renewable

energy hydrogen-powered fuel cell prototype for

motor cars and trucks. A novel method for EV carbon

footprint measurement and a renewable energy fuel

cell will be developed. It is the objective to incoproate

these carbon footprint measurements into the graviity

model which is not covered by previous studies which

usually emphasise on economic mass. A hybrid

product carbon footprint will be adopted that

integrates process-based and organisation-level

approaches. Upon measuring the current emission

level of vehicle automotive products in pilot

companies, a solid-state hydrogen-powered fuel cell

will be explored. A hydrogen fuel cell prototype will

be developed and applied to vehicles’ automotive

system. The carbon emissions from conventional,

hybrid, electric and hydrogen fuel cell vehicles will

be evaluated and compared, and a sensitivity analysis

and cost and benefit analysis will be conducted. A

model analysing the economic and environmental

impact on Hong Kong and cities in the Greater Bay

Area (GBA) will be evaluated. A database platform

with the carbon emissions of vehicles in the transport

and logistics sector will be established that includes

emission factors and carbon emitted by various types

of energy-consumed vehicles. This database will

facilitate measurement and research analysis

activities to mitigate carbon emissions in transport

and logistics in Hong Kong and GBA cities.

2 LITERATURE REVIEW

2.1 Gravity Model

The concept of a center of gravity of a material body

is derived from physics and was first applied to

analyze the spatial distribution of the population in

the U.S. in 1872 (Hilgard, 1872). A center of gravity

represents the point that can balance all of the gravity

produced by the fulcrum that pushes back into the

gravitational field, regardless of the location of the

object (Kumler and Goodchild, 1992). In terms of the

economic centers of gravity, the assumption is that

economic forces, such as gross domestic product

(GDP) and population, balance the regional economy

(Kumler and Goodchild, 1992). Over the years, a large

number of research studies in economics have widely

applied the gravity model (Kandogan, 2014) to study

the impact of geographic distribution on trade (Chan et

al., 2018), immigration (Karemera et al., 2000, Lewer

and Berg, 2008), population (Kumler and Goodchild,

1992, McKee et al., 2015; Yang and He, 2017), and

land utilization (Chen and Zhou, 2011, Xiaolin and Fei,

2011). Existing studies have also used the gravity

model to analyze the spatiotemporal distributions of

carbon dioxide (CO

)

emissions and energy

consumption (Zhang et al., 2012; Wang and Feng,

2017), urbanization (Fu et al., 2015), energy supply

and demand (Zhang et al., 2012), and trajectory of CO

emissions of gravity centers as well as study their

spatial and temporal differences (Song et al., 2015;

Wang and Feng, 2017; Zhang et al., 2012). However,

A Novel Approach of Environment Impact Assessment and Emission Measurement on the Inter-city Transportation in the Greater Bay Area

(GBA) of China using a Modiﬁed Gravity Model

687

there have yet to be studies that use the model to

investigate CO

emissions among the Guangdong-

Hong Kong-Macau Greater Bay Area (GBA) cities.

2.2 Transportation in Greater Bay

Area

The Great Bay Area has a great potential for

developing a comprehensive delivery network on the

road as they are so near, the distance is not far away.

There are roads to connect the Great Bay Area Cities.

Each city has well infrastructure to fit The Great Bay

Area development. As the city between cities is not

far, using trucks to deliver is the most efficient, it is

cheaper than barge and plane, so they will use trucks

as their major transportation to deliver goods.

However the carbon emissions of trucks are very

high, it will cause serious pollution problems. Hence,

it needs to replace traditional trucks by renewable

energy vehicles. Also there are more truck EVs

developing. It is a great opportunity to change the

traditional trucks to renewable energy vehicles, thus,

to improve the air pollution problem.

Huang, Guo and Xu (2020) issued a study that

aims to develop a new method for examining the

regional integration and the spatial connection that

affect vehicle emission via crowdsourced traffic data

and an emission model because GBA lacks an

effective framework for accurately estimating the

real-time transportation performance. The research

selected the AutoNavimo and OpenStreetMap as

sources to collect detailed traffic conditions of GBA

including vehicle types, time periods, geographical

areas, pollutants, vehicle operating characteristics,

and road types. Next, creates a project-level database

for the data input. Characteristic traffic data from the

directions service and site-specific data (e.g. fuel

information and meteorology conditions) are

imported and stored in this database. The third step

runs the developed model to calculate emission

inventories. The final step is to establish different

strategies to reduce the emission of GBA. The result

found that vehicle energy consumption and emission

have a difference between the expressway and other

urban roads based on the speed . Based on the article,

the project can consider using AutoNavimo and

OpenStreeMap as sources to collect detailed traffic

conditions of GBA cities.

2.3 Development of Hydrogen

Fuel-cell Vehicles in the GBA

The development of the renewable-energy of

hydrogen fuel cell vehicles in Hong Kong and South

China is still in the starting phase; its impact,

particularly on traffic flow and emissions, has yet to

be evaluated. A growing number of studies have

examined carbon emissions in this context, but a

number of research gaps remain. First, previous

studies examined this issue in various geographical

settings at the national (Gambhir et al., 2015),

provincial (Zheng et al., 2015) and city levels (Wang

et al., 2017; Zeng et al., 2016) and in connected

metropolitan areas (Du et al., 2017). However,

research on cities in the GBA is inadequate. Second,

studies at the city level have examined the carbon

emissions of transport services within specific

geographical boundaries, whilst carbon emissions

from intercity road transport flow across city

boundaries have received less research attention.

Third, the potential of hydrogen fuel cells for carbon

emission reduction in GBA cities remains

underexplored, as the adoption of hydrogen-powered

fuel cell vehicles remains in its infant stage. As such,

road transport carbon emissions both within and

between the 11 cities in the GBA and the carbon

emission reduction potential of alternative fuel types,

including a hydrogen fuel cell, will be analysed in this

project. A modified gravity model will be designed

and developed based on theoretical analysis,

considering social, economic and other variables

affecting the CO2 emission of vehicles travelling

within and across cities in the GBA (Zhou et al., 2018;

Anderson, 2011; Jung et al., 2008).

3 METHODOLOGY

3.1 Research Model

In response, the use of the gravity model will be

extended in this study to analyze the crucial

contributors of CO

emissions among the GBA cities.

Until recently, this model has been mainly applied to

aggregated data with cross-sectional or time-series

estimation techniques to analyze traffic flow

statistics. Here, the conventional gravity model is

applied to determine the volume of CO

emissions in

GBA cities and extended to cover other factors not

considered in previous studies, such as number of

domestic and cross border vehicles, type of energy

source, traffic mix, etc. In addition, the determinants

used are related to road transportation and policies

including qualitative variables (as the dummy

variables). The model will be subjected to a panel

data analysis to investigate the fixed effects over time

for each scenario, thus exploring the changes and

increasing the manipulation of the data quality and

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688

quantity which would otherwise not be possible with

the use of cross sectional or time series estimation

alone. The model will be utilized to investigate the

impacts of energy emissions, city-specific social and

political determinants as well as economic indicators

that affect the CO

emissions between Hong Kong

and the GBA cities. In studies on traffic flow and

population migration (Anderson, 2011), the gravity

model is regarded as a common method to evaluate

the strength of the flows. The conventional gravity

model is based on Newton's Law of Gravitation and

shown as follows:

where T

is the expected strength of the traffic flow;

is the quantity of generated traffic; P

is the

attracted quantity of traffic; D

is the distance

between different traffic zones; and k is a gravity

constant, which is usually set to 1 (Jung et al., 2008).

The model in this study will include various

factors of the conventional explanatory variables,

such as the distances between the 11 cities, GDP,

population, and factors related to road transport, such

as the number of domestic vehicles, number of cross

boarder vehicles that post the impact to the traffic

flow. A modified gravity model using a hierarchical

factor approach is derived based on the conventional

model by taking the logarithm as follows:

ln (Tj) = α0 + α1ln(GDPi) + α2ln(GDPj) +

α3ln(Li) + α4ln(Lj) + α5ln(Dij)+ α6(TRKij)

+ εij

(1)

where GDPi and GDPj are the gross domestic product

for cities i and j, Li and Lj are the populations of cities

i and j, TRKij represents the number of registered

vehicles of cities i and j. εij is a random error term,

usually taken to be normally distributed. This formula

is applied in the analysis.

In order to analyze the crucial contributors of CO

emissions,

a second modified gravity model is

constructed based on the conventional model as

follows with the consideration of other factors on

transportation and energy including the influence

from the type of energy source and traffic mix, that

contribute to CO2 emissions.:

ln (CO

ij) = α0 + α1ln(Yi) + α2ln(Yj) +

α3ln(Li) + α4ln(Lj) + α5ln(Dij) + α6(Aij) +

εij

(2)

where CO

ij are the CO

emissions from GBA city i

to j, Yi and Yj are the income values for cities i and j,

Li and Lj are the populations of cities i and j, and Dij is

the distance between cities i and j. Aij represents the

factors that contribute to CO

emissions between the

pairs of cities. Likewise, εij is a random error term.

Model (2) in this study is targeted to include

various factors of conventional explanatory variables,

such as the distance between 5 cities, GDP,

population as well as the factors that are related to

road transport, for example, the number of domestic

and cross border vehicles, type of energy used, traffic

mix, etc., all of which contribute to CO

emissions.

On the other hand, Model (1) is an alternative

approach to quantifying the impact of environmental

impact of the cross-border traffic in the GBA region.

Owing to the research work is still in progress, only

Model (1) is used to demonstrate the significance of

our theoretical framework in this study.

3.2 Data Analysis Method

3.2.1 Panel Data Estimation Approach

The data were analyzed by using the panel data

estimation approach with an econometric and

statistical software – EViews, which is designed for

econometric analysis. The findings will demonstrate

the impacts of the crucial elements of CO

emissions

on the GBA cities.

A pool cross sectional (PCS) or cross sectional

(CS) ordinary-least-square (OLS) is often applied in

the gravity model. Unfortunately, Cheng and Wall

(2005) showed that these estimation approaches

create biased results. Since there is no heterogeneity

allowed in the error term for standard CS regression

equations, the gravity model overestimates the

results. In order to solve the problem of using OLS,

the panel data estimation method will be utilized to

determine the variables that affect the CO

emissions

among the GBA cities over time. As Baltagi (2013)

noted, the advantages of using this method will

increase the volume of informative data in variability

but with less collinearity among the variables.

Moreover, the method will allow for more degrees of

freedom and efficiency.

3.2.2 Data Collection

A panel dataset of 5 cities over the period of 2015 to

2020 is used in this study. Data of CO2 emissions in

China are obtained from the China Emissions

Accounts and Datasets (http://www.ceads.net/),

which can be found and referenced in Shan et al.

(2016). The other explanatory variables will be

collected from the China Statistical Yearbook. The

A Novel Approach of Environment Impact Assessment and Emission Measurement on the Inter-city Transportation in the Greater Bay Area

(GBA) of China using a Modiﬁed Gravity Model

689

socioeconomic indexes of 5 cities including the

annual population, GDP (at 2015 constant prices) as

well as other socioeconomic data will be sourced

from the statistical yearbooks, population and GDP

(at 2015 constant prices) time series data of Hong

Kong and Macao respectively, and obtained from the

database of Census and Statistics Department of

Hong Kong and Statistics and Census Service of

Macao. The exchange rates of the Hong Kong dollar

(HKD) and Macao pataca (MOP) to renminbi (RMB)

will be collected from the China Statistics yearbook

and China Foreign Exchange Trade System (CFETS,

2021). Emissions from Hong Kong and Macao will

be collected from the Emissions Database for Global

Atmospheric Research (EDGAR, 2021).

4 ANALYSIS AND DISCUSSION

In order to be able to use the Gravity model, we first

collected the data of two major cities in the GBA in

order to demonstrate the significance of the modified

gravity model in terms of the preliminary assessment

on environmental impact by the cross-border traffic.

We collected data from the Census & Statistics

Department of the Governments in Hong Kong and

Shenzhen between 2003 and 2019, i.e., the period

after China joined the World Trade Organization

(WTO). Data includes traffic flow between the two

places, GDP of the two cities, Population of two

cities, number of registered vehicles of two cities and

the distance between two HK and SHZ.

Model 1 was tested with multiple regression

analysis in the SPSS 17.0 software, while the

resulting regression statistics are shown in Table 1,

the regression coefficients of various predictors ae

shown in Table 2. The approach enables the study to

obtain the explanatory power of each independent

variable separately as well as the significance of the

hypothesised relationships for determining the fitness

of the proposed conceptual model through evaluating

the significance of multiple correlation coefficients

and the beta values.

Table 1: Regression statistics of gravity model 1 (first

iteration).

Regression Statistics

0.858

Adjusted R

0.794

p-value 0.000

Table 2: Regression coefficients of gravity model 1 (first

iteration).

Regression Coefficients (Sig.)

GDP SHZ 0.982

GDP HK 0.384

Population SHZ 0.074

Population HK 0.075

Total Vehicles 0.009

The results in Table 1 shows how good the model

explanability is, i.e., model prediction, while Table 2

shows how good the coefficient estimates are, i.e., the

predictability of independent variables (IVs). The

results indicate that the regression model is

significant, but the coefficient estimates of the model

are insignificant except the IV, total vehicles.

According to the results of correlation analysis, the

correlation among the GDPs and other IVs exceeds

0.5 (the correlation between variables is becoming

more significant where the range of values of

correlation coefficient is between 0 and 1 ) which has

a poor multicollinearity. Then, we exclude the GDP

variables and re-run the test and summarized in the

results in Table 3 and Table 4 below.

Table 3: Regression statistics of gravity model 1 (second

iteration).

Regression Statistics

0.848

Adjusted R

0.812

p-value 0.000

Table 4: Regression coefficients of gravity model 1 (second

iteration).

Regression Coefficients (Sig.)

Population SHZ 0.048

Population HK 0.016

Total Vehicles 0.001

The results in Table 3 remains significant, while

the coefficient estimates summarized in Table 4

indicates that the results are significantly improved.

Hence, we obtained the following regression model

based on the results in the second iteration of the test.

In(T

) = (28.589) + (0.3995) In (L

) +

(-2.486) In (L

) + (0.102) In(TRKi

) +

(0) In (D

)

(3)

We can use the model to forecast traffic flow. With

more time-series based CO

emission obtained later on,

we will include the total life cycle emission of each

VEHITS 2021 - 7th International Conference on Vehicle Technology and Intelligent Transport Systems

690

vehicle in Model 2 with the aim to find the total

emission in the further of different types of vehicle.

5 CONCLUSIONS

The modified gravity model is developed in this

regard. The model will analyse the crucial

contributors of CO2 emission among the GBA cities.

Until recently, gravity model has been mainly applied

to aggregated data with cross-sectional or time-series

data estimation techniques to analyse traffic flow

statistics. Here, the novel model is applied to CO2

emissions from GBA cities and extended to cover

other factors not considered in previous studies. In

addition, the determinants which are related to road

transportation and policies including qualitative

variables. The model will be subjected to a panel data

analysis to investigate the fixed effects over time for

each scenario, thus exploring the changes and

increasing the manipulation of the data quality and

quantity which would otherwise not be possible with

the use of cross sectional or time series estimation

alone. The model will be utilized to investigate the

impacts of energy emission, city-specific social and

political determinants as well as economic indicators

that affect the CO

emission between Hong Kong and

the GBA cities.

ACKNOWLEDGEMENTS

The work described in this paper was partially

supported by a grant from the Research Grants

Council of the Hong Kong Special Administrative

Region, China (UGC/IDS(C)14/B01/19).

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