Based on the following strategies and plans: National Green Growth Strategy (1393/QD-TTg) approved by the Prime Minister in September 2012,
Green Growth Action Plan (403/QD-TTg) approved by the Prime Minister in March 2014, and the Green Port City strategy (72-KL/TW) of the Communist Party Politburo, Hai Phong formulated the Green Growth Strategy Action Plan of the City of Hai Phong (1463/QD-UBND) in July 2014. With thetarget of the Intended Nationally Determined Contributions (INDCs) to the United Nations Framework Convention on Climate Change UNFCCC, whichaims to reduce 8-25% of total emissions in 2030 compared to Business as Usual (BaU), major cities in Vietnam are required to develop Climate
Change Action Plans (CCAP). The CCAP is necessary and should be integrated with the middle- and long-term master plan of socio-economic development, specific sectoral development plans.
This study is one of the results of the research collaboration between Asian-Pacific Integrated Model (AIM) team in Japan including Ritsumeikan University, Kyoto University, E-konzal, National Institute for Environmental Studies (NIES), Mizuho Information and Research Institute (MHIR), Institutefor Global Environmental Strategy (IGES), and Institute of Strategy and Policy on natural resources & environment (ISPONRE), Department of Natural
Resources and Environment of Hai Phong. We expect this brochure is useful for researchers and policy-makers who are interested in developing or
updating their own CCAP.
We developed two scenarios with the projection of energy consumption and CO 2 emission in energy-related categories such as Residential, Commercial, Transportation, and Industry. They are 2030BaU (Business as Usual) and 2030CM (CounterMeasures). The 2030BaU scenario, where countermeasures for GHG emission reduction are not introduced, reflects the situation in which both, the levels of commitments to climate-friendly-energy
production and technological breakthroughs are relatively low. Specially, countermeasures are assumed the same level as in 2013. On the other
hand, the 2030CM scenario, which additional low carbon countermeasures are introduced in order to assess the reduction effects of GHG emissions.
The socioeconomic assumptions about population, industrial structure, and economic growth are common to both scenarios. Information from many
domestic sources is used to calibrate the parameters for base year 2013. In target year 2030, Extended Snapshot Tool (ExSS) is applied for the projection of future energy consumption and CO2 emission in energy-related categories.
Hai Phong is expected under the rapid growth of driving forces such as population, transport demand, and especially industrial activities; the total
GHG emissions increases 4.01 times, from 6,675 ktCO2eq in 2013 to 33,494 ktCO2eq. The total GHG emissions reduction is 14%, accounting for
ktCO2eq, Hai Phong can reduce such emissions reductions by implementing 30 projects grouped in four actions (Green Industry, Green Building,
Energy Efficiency and Clean Transport). Since the national reduction target has been shown in the Green Growth strategy (10-20% reduction in
2030CM) and in the Vietnam’s INDC (8-25%), Hai Phong is expected to achieve such target by 2030 (14% reduction by 2030CM compared to2030BaU)
Table 1 GHG emissions by sectors (ktCO2eq) in Hai Phong city
2030

2013
GHG emissions
Agricultural energy-related
Industry
Commercial
Residential
Passenger transport
Freight transport
Total GHG emissions

ktCO2eq

BaU%

ktCO2eq

2030
CM

%

ktCO2eq

BaU/2013

CM/BaU

%

2

0.1

10

0.1

10

0.1

4.83

1.00

2,483

37.2

15,92047.5

14,413

50.0

6.41

0.91

221

3.3

1,470

4.4

1,170

4.1

6.64

0.80

1,291

19.3

2,672

8.0

2,291

7.9

2.07

0.86

604

9.1

1,634

4.9

1,350

4.7

2.70

0.83

2,075

31.1

11,797

35.2

9,626

33.4

5.69

0.82

6,675

100.0

33,494100.0

28,850

100.0

5.02

0.86

GHG emissions per GDP (tCO2eq/bil.Dongs)

63.2

58.0

49.9

0.92

0.86

GHG emissions per capita (tCO2eq/person)

3.5

11.2

9.6

3.22

0.86

1

Hai Phong socio-economic scenario
Table 2: Main socio-economic indicators in Hai Phong city

Population
Table 2 shows the results of main socio-economic driving forces forbase year 2013 and target year 2030.
In this study, population in 2030 is referred to the projection of population in the report of Master Plan of Construction in Hai Phong up to2025 and reach 3 million people (1.56 times higher than that in 2013).
Household size in 2030 is assumed by 3 persons/household. Therefore, the total number of households is about 1 million, increases 1.81
times compared to that in 2013.

Transport demands
Due to the increasing population and industrial activities, thetransport demand in Hai Phong in 2030 increases dramatically from10236 to 22490 million person per kilometer (mil.per.km) of passenger transport and 8470 to 48158 million ton per kilometer(mil.ton.km) of freight transport compared to 2013.
Regarding to passenger transport, there is a rapid increase of demandon car to 8.25 times. In 2030BaU, transport demand of car and motorbike is similar. According to the projection of Department of Transportof Haiphong, the share of public transport such as bus by trip increases from 0.26% in 2013 to 10.0% in 2030BaU and reaches 15.0% in
2030CM. Freight transport relies predominantly on roads and followed by waterway. The share of freight transport mode is assumed
unchanged in 2013 and 2030, in which road is still dominates andcontributes to 91%, followed by waterway with 8% contribution.

2013

2030BaU 2030CM BaU/2013
22,49022,453

2.20

1.00

84
988

130
1,540

130
1,540

1.56
1.56

1.00
1.00

7,908
1,204

9,232
9,932

8,367
9,932

1.17
8.25

0.91
1.00

52

1,655

2,483

31.68

1.50

8,470

48,15848,158

5.69

1.00

Car

7,174

40,78940,7895.69

1.00

Waterway

1,296

7,369

7,369

5.69

1.00

Walk
BicycleMotorbike
Car
Bus
Freight transport(mil.ton.km)

2030

1,925,217

3,000,000

2030/2013
1.56

No. of households

household553,406

1,000,000

1.81

GDP per capita

mil. Dongs

55

193

3.51

GDP

bil. Dongs

105,651

577,829

5.47

Outputsbil. Dongs

282,310

1,595,478

5.65

Final consumptionbil. Dongs

67,644369,309

5.46

Gross fixed capitalbil. Dongs
formation

38,607

210,777

5.46

Exportbil. Dongs

111,247

607,360

5.46

Importbil. Dongs

111,847

609,616

5.45

Passenger transportmil.per.km
demand10,23622,4902.20

Freight transport

8,470

48,1585.69

mil.ton.km

According the projection by Department of Investment and Planning ofHaiphong, GDP growth rate up to 2030 is 10.5% per year. The GDP in
2030 will increase 3.51 times compared to 2013. Service sector still
dominates in the economic structure of Hai Phong city, accounting forabout 55.4%, following by industrial sector (about 37.4%).

CM/BaU

10,2362013

persons

EconomyTable 3: Transport demand in Hai Phong city

Passenger transport(mil.per.km)

Unit
Population

Industrial output is expected to increase by 5.65 times in 2030
(1,595,478 billion Dongs) compared to 2013 (282,310 billion Dongs)
The GDP per capita of Hai Phong in 2013 is around 55 mil. Dongs , andincreases 3.51 times by 2030. The GDP per capita in 2030 reaches 193
mil. Dongs due to the rapid GDP growth (10.5% per annual).
In 2030, export and import in Hai Phong expected to similarly increase
5.46 and 5.45 times compared to those in 2013, respectively. Finalconsumption increases from 67,644 (in 2013) to 369,309 billion Dongs(in 2030), in which main consumption is from households for industrial
commodities and services while the government consumption is mainly for science, technology, and other services.

Figure 1: Modal share of transportation in Hai Phong

100%

90%

Modal share in freight transport

Modal share in passenger transport

100%
Bus

80%
70%

Car

60%

50%

Motorbike

40%
Bicycle

30%
20%

Walk

10%

90%
80%

70%
60%

Waterway

50%

Car

40%
30%
20%
10%
0%

0%
2013

2013

2030BaU 2030CM

2

2030BaU 2030CM

Final energy consumption and GHG emissions
Table 3: Final energy consumption by sectors (ktoe)

Energy consumption

GHG emissions

Final energy consumption is protected to increase significantly in
2030 of 9308 ktoe (1904 ktoe in 2013). In 2030CM, final energy consumption is about 8090 ktoe.

Total GHG emission in 2030BaU is projected about 33.5 MtCO 2eq,
accounting for fivefolds higher than base year 2013 (6.7 MtCO2eq).
Main contributors to GHG emissions in the 2030BaU scenario areindustrial sector (15.7 MtCO2eq), followed by transport, residential
and commercial sectors which account for 13.4, 2.7 and 1.5 MtCO2eq,
respectively.

The energy intensity by GDP reduces from 18.0 toe/bil. Dongs in 2013
to 16.1 toe/bil. Dongs in 2030BaU and 14.0 toe/bil. Dongs in 2030CM
due to the lower increasing rate of energy consumption compared to
the rapid growth of GDP.

GHG emissions in 2030CM, is estimated to reduce by 14% from the2030BaU emissions. Breakdown of GHG emission reduction is shownin Figure 3.

Table 4: Relationship of energy consumption and GHG emissions
2013

2030BaU

2030CM

Energy intensity (toe/bil. Dongs)

18.0

16.1

14.0

GHG emissions per GDP (tCO2eq/bil.
Dongs)

63.2

58.0

49.9

GHG emissions per capita (tCO2eq/
person)

3.5

11.2

9.6

Final energy consumption (ktoe)

1,904

9,308

8,090

GHG emissions (ktCO2eq)

6,675

33,49428,850As can be seen from table 4 that per capita GHG emissions in 2013 in
Hai Phong was only 3.5 tCO2eq, though, in BaU scenarios, it isprojected to increase up to 11.2 tCO2eq. In 2030CM scenario it can be
reduced to 9.6 tCO2eq.
Regarding to GHG emission intensity, in 2013, estimated GHG
emission intensity is 62.3 tCO2eq/bil.dongs. In 2030BaU, emission
intensity decreased to 58.0 tCO2eq/bil.dongs mainly because of
greater share of tertiary industry in GDP. In 2030CM scenario, which
introduces implementation of the projects and actions, emission
intensity is estimated to 49.9 tCO2eq/bil.dongs. More details of the
projects and actions are described in the following pages of thisreport.

14%

GHG emission reduction by sector in 2030

33.5

Freight transport

28.9

2.2

Passenger transport

0.3

Residential

0.4

Commercial

0.3

Industry

6.7

1.5
0.0

Figure 2: GHG emissions in Hai Phong

Total

%

1,904 100.0

Table 6: GHG emissions and reduction

Unit: ktoe

2030BaU%

9,308 100.0

2030CM%

8,090 100.0

Year

BaU/2013 CM/BaU
4.89

0.87

By sector
0.6

43

0.5

Agriculture

11

0.5

3.98

0.97

Industry565 29.7

Commercial

47

2.5

313

3.4

3,309 40.9
290

3.6

6.43

0.91

6.64

Residential

373 19.6

772

8.3

728

0.93

9.0

2.07

Passenger transport

205 10.8

554

5.9

458

0.94

5.7

2.70

0.83

Freight transport

703 36.9

3,998 42.9

3,262 40.3

5.69

0.82

Coal
Oil

280 14.7

1,753 18.8

1,542 19.1

6.27

0.88

1,147 60.3

5,354 57.5

4,406 54.5

4.67

0.82

Gas

41

2.2

257

2.8

253

3.1

Solar

0

0.0

0

0.0

79

1.0

6.24

0.98

Biomass

167

8.8

643

6.9

632

7.8

3.85

0.98

Electricity

269 14.1

1,178 14.6

4.84

0.91

3,628 39.0

2.0

2.5

42

3

Unit: ktCO2eq

SectorAgriculture

Coal
1.2

Oil
24.5

Industry

1,074.9

126.1

96.8

1,142.2

2,440.0

36.8
37.2
0.0
0.0
1,150.2
4.9
6,883.0
244.6
76.9
0.0
0.0
7,209.4
4.7
6,082.9
199.8
53.8
0.0
0.0
6,341.3

66.7
489.1
604.3
2,074.8
3,385.4
97.5
816.3
443.1
1,011.9
1,634.3
11,797.4
15,800.5
94.3
743.1
346.7
903.8
1,289.4
9,626.4
13,003.6

0.0
0.0
0.0
0.0
96.8
0.0
603.6
0.0
0.0
0.0
0.0
603.6
0.0
548.9
0.0
0.0
45.3
0.0
594.2

117.9
765.2
0.0
0.0
2,042.4
68.4
7,446.0782.7
1,583.3
0.0
0.0
9,880.3
66.1
6,872.9623.7
1,333.3
15.3
0.0
8,911.3

221.4
1,291.4
604.3
2,074.8
6,674.8
170.8
15,749.0
1,470.3
2,672.1
1,634.3
11,797.4
33,493.8
165.1
14,247.7
1,170.2
2,290.9
1,349.9
9,626.4
28,850.3

Commercial
2013 Residential
Passenger transport
Freight transport
Total
Agriculture
Industry
Commercial
2030
Residential
BaU
Passenger transport
Freight transport
Total
Agriculture
Industry
Commercial
2030
Residential
CM
Passenger transport
Freight transport
Total

By energy type1,301 14.0

1.0
1.5
MtCO2eq

Figure 3: Breakdown of GHG emission reduction by sector in 2030

Table 5: Energy consumption
2013

0.5

Gas
Electricity
0.0
17.2

Total
42.9

Five Low Carbon Actions in Hai Phong[III] Reduction in Transport: 816.0 ktCO2eq

Action 1. Green Industry
Action 2. Green Urban
Low carbon projects related to diffusion of low-energy households andbuildings are grouped into this Action. Action 2 is expected to reduce
262 ktCO2eq. Introduction of insulating solar water heater to households is expected to reduce thelargest GHG emission in this action.
Diffusion ratio of this project in2030 is assumed by 20%. Energy
demand in households and buildings is saving through the diffusionof energy management system andinstallation of insulated glasses.

Action 1 is comprising all low carbon projects regarding promotionof energy efficient equipment and fuel shift in the industry sector.
Total reduction of GHG emissions by this action is 1477 ktCO2eq.
Energy saving in factories is one of the main project in Action 1,
which contributes to reduce about 602 ktCO2eq with assumption ofdiffusion ratio is 30%.

Action 1
32%
Action 4
55%
Action 2
6%
Action 3
8%

Action 3. Energy Efficiency
Action 3 is expected to reduce 363 ktCO2eq,
which including all projects of promoting
energy efficient device and appliance likelighting and air conditioners for houses andbuildings. Promotion of energy-efficient
appliances like refrigerator and others contributes the biggest amount of CO2 reductionof 172 ktCO2eq. Replacement of conventional lighting by high efficient one such as LED
lighting in houses and buildings also contributes to reduce significant amount of CO2
emissions.

Action 4. Clean Transport
Action 4 is expected to reduce the largest amount of CO2 emission (2541 ktCO2eq), which includes variety of projects regarding both passenger and freight transportation such as improvement of fuel efficiency of vehicles, promotion of modal shift to
public transportation and deployment of EV bus. Projects for
freight transport such as promotion of energy-efficient trucks
have large potential to reduce GHG emissions, because GHG
emission from freight transport sector is very high (more thanfourfold of passenger transport). This project is expected toreduce 2060 ktCO2eq (more than 80% of total contribution inthis Action)

Table 7: Climate change actions (ktCO2) for Hai Phong city

Industry
Action 1. Green Industry
Promotion of energy efficient equipment and fuel shift

Commercial Residential

Passenger
Transport

Freight
Transport

1,477

Total
(ktCO2eq)
1,477

Action 2. Green Building
Diffusion of low-energy building (EMS, Insulation, Fuel shift)

199

63

262

Action 3. Energy Efficiency
Promotion of energy efficient device/appliance

130

233

363

Action 4. Clean Transport
Energy efficient vehicle and modal shift
Total (ktCO2eq)

1,477

4

329

296

284

2,257

2,541

284

2,257

4,643

Mitigation projects in Hai Phong to achieve Low Carbon City
Table 6: Detail emission reduction by projects in each category (ktCO2eq)
Table 7: Emission reductions by project

Action1 Green Industry

Project

Sector

1-01

Energy savings in factory

Industry

601.9

1-02

Installation high energy efficiency facilities (such as compressors and motors)

Industry

93.4

1-03

Regional energy supply system

Industry

514.8

1-04

Improvement of kiln and furnace technology

Industry

266.6

Total
2 Green Building

1,476.8

2-01

Installation of insulated glasses to commercial buildings

Commercial

19.5

2-02

Installation of insulated glasses to households

Residential

35.5

2-03

Introduction of incentive to low energy buildings

Commercial

3.5

2-04

Introduction of insulating material to houses

Residential

13.4

2-05

Energy efficiency technology applied to buildings

Commercial

9.7

2-06

Introduction of solar water heater to commercial buildings

Commercial

44.5

2-07

Introduction of solar water heater to households

Residential

102.4

2-08

Introduction of photovoltaic power generation to commercial buildings

Commercial

29.2

2-09

Introduction of photovoltaic power generation to households

Residential

4.2

Total
3 Energy Efficiency

Emission
reduction(ktCO2eq)

3-01

262.0

Energy savings in commercial facilities

Commercial

35.4

Commercial

3.2

Conversion of street lights to LED lighting
3-02
High efficiency lighting in public lighting
3-03

High efficiency lighting in commercial buildings

Commercial

43.0

3-04

High efficiency lighting in households

Residential

36.4

3-05

High efficiency air conditioners (such as air conditioners with inverter controllers) in
Commercial
commercial buildings

22.7

3-06

High efficiency air conditioners (such as air conditioners with inverter controllers) in
Residentialcommercial households

48.8

3-07

Promotion of energy-efficient appliances (refrigerator and other appliances)

Residential

172.2

3-08

Promotion of energy-efficient appliances (cooking appliances)

Residential

1.1

Total
4 Clean Transport

362.8

4-01

Promotion of eco-driving with digital tachographs

Transport

169.7

4-02

Smart trafic management

Transport

5.4

4-03

Expansion of frequencies and routes of bus transportation

Transport

7.6

4-04

Development of Bus Rapid Transit (BRT)

Transport

3.8

4-05

Introduction of EV buses

Transport

7.8

4-06

Introduction of electric motorbikes

Transport

39.9

4-07

Promotion of energy-efficient vehicles (cars for passenger)

Transport

160.2

4-08

Promotion of energy-efficient vehicles (motorbikes)

Transport

87.0

4-09

Promotion of energy-efficient vehicles (trucks)

Transport

2,060.1

Total

2,541.3

Total

4,642.9

5

Methodology

Team members

In order to identify the necessary actions, an “integrated modeling”
based on “back-casting” approach is used.

HAIPHONG DEPARTMENT OF NATURAL RESOURCES & ENVIRONMENT (DONRE)
Pham Quoc Ka
Nguyen Van Can
Vu Thi Thu Huong
INSTITUTE OF STRATEGY AND POLICY ON NATURAL RESOURCES &ENVIRONMENT (ISPONRE)

The back-casting approach sets a vision of the future society as a goal,
and then seeks a pathway towards achieving that goal.
We used ExSS (Extended Snapshot tool) for socio-economic indicators,
energy related sectors. Information collection is the first step in themodeling work.
Socio-economic information as well as environmental information forthe base year (2013) was collected and analyzed in order to estimate
current carbon emissions. Besides this, feasible low carbon measuresfor Hai Phong low carbon 2030 were also collected.

NGUYEN Tung Lam
RITSUMEIKAN UNIVERSITY (RU)NGUYEN Thai Hoa*
Koji SHIMADAKYOTO UNIVERSITY (KU)
Yuzuru MATSUOKA
E-KONZAL

For the future projection, information is based on planned developments, as the model estimates socio-economic activity levels including
population, number of households, land area, transport demand andother variables. Based on the collected information, GHG emissions
are calculated with or without countermeasures.

Yuki OCHI
Tomoki EHARAINSTITUTE FOR GLOBAL ENVIRONMENTAL STRATEGIES (IGES)ExSS estimates future GHG emissions and reductions based on two
approaches, which are top-down and bottom-up approaches.
- Top-down approach is used to estimate socio-economic activities(such as population, number of household, economic development,
industrial structure and transport demand) and energy demand and
GHG emissions from a macroscopic point of view.

Junichi FUJINONATIONAL INSTITUTE FOR ENVIRONMENTAL STUDIES, JAPAN(NIES)- Bottom-up approach is used to assume the technology-related information (such as diffusion rate and energy saving rate) and to estimate
GHG emission reduction and GHG absorption capacity by project.

Toshihiko MASUIMIZUHO INFORMATION AND RESEARCH INSTITUTE (MHIR)We improve estimation by repeating model simulation through information sharing and exchanging with Hai Phong city.

Kazuya FUJIWARA

Acronyms and Abbreviations
Extended Snapshot Tool (ExSS)ExSS is a simplified simulation model for low carbon study and projects
socio-economic activity, energy demand and supply, GHG emissions
and emission reduction by measures. While each part of the tool isrelatively simple, it can describe whole picture of future society as a LCS
in a quantitative and consistent manner with a greater flexibility thanmany other models. It also can consider most of the low carbon
measures existing and expected in near future. In this simulation
model, population is decided by demand from outside of the region,
labor participation ratio, demographic composition and relationship ofcommuting with outside of the region. To determine output ofindustries, input-output approach is applied. Passenger transportdemand is estimated from the population and freight transportdemand whereby it is a function of output by manufacturing industries.
Floor area of commerce is determined from output of tertiaryindustries. Other than driving force, activity level of each sector, energy
demand by fuels determined with three parameters. One is energy
service demand per driving force, energy efficiency and fuel share.
Diffusion of counter measures changes the value of these parameters,
and so GHG emissions.

AIM
BaU
CC
CCAP
EBT
ExSS
GDP
IEA
IGES
INDC
IOT
ktCO2eq
KU
LCC
MHIR
NIES
RU
SYB
toe

Acknowledgement*Contact address:

This research is supported by the Ministry of the Environment, Japan.

NGUYEN Thai Hoa: envi.jp@gmail.com
Yuki OCHI: ochi.ekonzal@gmail.com

November 2016

6

Asia-Pacific Integrated ModelingBusiness as Usual scenario
Climate Change
Climate Change Action Plan scenario
Energy Balance Table
Extended Snapshot Tool
Gross Domestic ProductInternational Energy Agency
Institute for Global Environmental Strategies
Intended Nationally Determined Contributions
Input-Output Tablekilo-ton Carbon dioxide
Kyoto University
Low Carbon City
Mizuho Information and Research InstituteNational Institute for Environmental Studies
Ritsumeikan University
Statistical Yearbook
ton of oil equivalent