IPCC Expert Meeting: Application of the 2006 IPCC Guidelines to Other Areas
1-3 July 2014, Sofia, Bulgaria

National Greenhouse Gas Inventory for
Thailand’s Second National Communication and
Mitigation Aspects

Jakapong Pongthanaisawan, PhD
Senior Policy Researcher
National Science Technology and Innovation Policy Office
Ministry of Science and Technology, Thailand

Outline• History of Thailand’s National GHG Inventory• Second National Communication (SNC) andNational GHG Inventory• Mitigation Assessment
 National Level – Energy Sector
 Subsector Level – Road Transport Sector• Conclusion and Recommendation

2

History of Thailand’s National GHG Inventory
1st National GHG Inventory• As a part of Thailand’s Initial
National Communication (INC)• Using the Revised 1996 IPCC
Guidelines to estimate theemissions in 1994
• Prepared by Office ofEnvironmental Policy andPlanning (OEPP), Ministry of
Science and Technology(MOST)• Submitted to UNFCCC on
November 13, 2000
3

History of Thailand’s National GHG Inventory
2nd National GHG Inventory••

As a part of Thailand’s Second
National Communication (SNC)
Followed the guidelines:– Revised 1996 IPCC Guidelines toestimate the emissions– 2000 IPCC Good Practice Guidanceand Uncertainty Management in
Nation Greenhouse Gas Inventories– 2003 Good Practice Guidance forLand Use, Land-use Change andForestry••

Prepared by Office of Natural
Resources and Environmental
Policy and Planning (ONEP),
Ministry of Natural Resources andEnvironment (MORE)
Submitted to UNFCCC on March 24,
20114

SNC and National GHG Inventory
Institutional Framework of SNC

Source: TGO

5

Structure of SNC Organization

JGSEE – the Joint Graduate School of Energy and Environment, King Mongkut’s University of Technology Thonburi
CU – Chulalongkorn University

Source: JGSEE, KMUTT

6

Conceptual Framework of National GHG Inventory
Guideline
1. Revised 1996 IPCC
Guidelines for National
Greenhouse Gas Inventories
2. 2000 IPCC Good PracticeGuidance and Uncertainty
Management in National
Greenhouse Gas Inventories
3. 2003 Good PracticeGuidance for Land Use,
Land-Use Change andForestry

DEDE = Department of Alternative Energy Development and Efficiency
EGAT = Electricity Generating Authority of Thailand
PTT = Petroleum Authority of Thailand
OTP= Office of Transport and Traffic Policy and Planning
EPPO = Energy Policy and Planning Office DIW = Department of Industrial WorkDLD = Department of Land Development OAC = Office of Agriculture Economics
RFD = Royal Forest Department
BMA= Bangkok Metropolitan Administrative
PCD = Pollution Control Department

Source: Thailand’s Second National Communication

7

Framework of GHG Inventory Methodology

Type of GHG
Carbon Dioxide: CO2
Methane: CH4
Nitrous Oxide: N2O
Hydrofluorocarbon: HFC
Perfluorocarbon: PFC
Sulfur Hexafluoride: SF6

Source: Thailand’s Second National Communication8

Total GHG Emission=229.08 Mt CO2eq

Source: Thailand’s Second National Communication
9

GHG Emission from Energy Sector
Emission in 2000 by 'Energy Sector' (Mt CO2 eq, %)1A2 Manufacturingindustries andconstruction, 30.78,
19.3%

Waste, 9.32, 4.1%
LULUCF, -7.90, -3.4%

Agriculture,
51.88, 22.6%

1A1 Energy
Industries, 66.44,
41.7%
Energy, 159.39,
69.6%
1A3 Transport,
44.70, 28.0%

1B2 Oil and naturalgas, 4.56, 2.9%
Industrial processes,
16.39, 7.2%

1B1 Solid fuels, 0.67,
0.4%

Total GHG Emission with LULUCF = 229.08 MtEq

1A4c
Agriculture/Forestry/
Fishing, 6.67, 4.2%

1A4b Residential,
5.58, 3.5%

Source: Thailand’s Second National Communication
10

Mitigation Assessment :National Level

Mitigation Assessment : National Level
Concept, Structure and Steps
1.
2.

3.

4.
5.
6.
7.
8.
9.

Source: Module2 mitigation concept, UNFCCC

Collect data.
Assemble base year/historical data on
activities, technologies, practices andemission factors.
Calibrate base year to standardized
statistics such as national energybalance or emissions inventory.
Prepare baseline scenario(s).
Screen mitigation options.
Prepare mitigation scenario(s) andsensitivity analyses.
Assess impacts (social, economic,
environmental).
Develop Mitigation Strategy.
Prepare reports.

12

Estimation of GHG Emission
Methodology
Macroeconomic – Econometric Model
AD = f (GDP, Pop, Price, Irrigation Area,
Crop Area, etc.)

Default EF from 2006 IPCC
Guideline

𝐸 = 𝐴𝐷 ∙ 𝐸𝐹
E = Emissions or Removals
AD = Activity Data - data of a human activity resulting in emissions or removals
EF = Emission Factor - coefficients which quantify the emissions or removals
per unit activity

Sectors
Energy, Industrial Processes and Product Use (IPPU), Agriculture, Forestry andOther Land Use (AFOLU), and Waste
13

Scenario Analysis• Baseline Scenario (Business-as-usual)– Socio-economic Assumptions• Population growth rate
• GDP growth rate
• Irrigation area
• Crop area, etc.

• Mitigation Scenario (Energy Sector)– Electricity Generation• Promotion of technologies for electricity generation from renewableenergy and low-carbon fossil fuel• Improve efficiency of generation and transmission system– End-use Sectors• Introduction of high energy efficiency technology and renewableenergy for heat in industrial sector, building sector and transport
sector.
14

Mitigation Scenario

Screening Cost Curve for Mitigation Technology Selection
Priority-

Ton of CO2 Avoided
Cost of Saved Carbon ($/tCO2)

Criteria• Potential for large impact on greenhouse gases (GHGs)• Consistency with national development goals• Consistency with national environmental goals• Potential effectiveness of implementation policies• Sustainability of an option• Data availability for evaluation• Institutional considerations

15

Mitigation Scenario – Energy Sector

16

GHG Emission Projection – BAU Scenario

Mt of CO2eq
SectorEnergy
IPPU
AFOLU
Waste
Total

2010
211.5
32
88.1
13.8
345.4

2020
315.5
57.1
109.4
16.6
498.6

2030
463.6
96.1
135
20.4
715.1

2040
687.2
106
166.8
25.7
985.7

2050
1036.9
120.7
208.1
33.1
1398.8Assumptions: average GDP growth rate ~4% per year (NESDB, 2008)17

GHG Emission Mitigation

Mt of CO2-eq

2010 2020
BAU

0

0

2030

2040

2050

0

0

0

Electricity Generation -1.4

-38.6 -107.7 -161.6 -239.9

Industry

-0.3

-5.3

-5.5

-5.8

-6.4

Building

-4.5

-14.2

-24.4

-26.6

-28.7

Transport

-1.5

-4.9

-8.4

-9.6

-10.8

-7.7

-63.0 -146.0 -203.6 -285.8

Total% Reduction-2.2% -12.6% -20.4% -20.7% -20.4%

18

Mitigation Assessment :Road Transport Sector

Mitigation Assessment : Road Transport Sector
20-Year Energy Efficiency Development Plan (EEDP 2011-2030)
Target: reducing “energy intensity”(the amount of energy used per unit ofGDP) by 25% by 2030 compared with2005 as base year, accounting for totalenergy saving of 30,000 kilotons of oil
equivalent (ktoe) in 2030
Energy saving targets by sector

SectorTransportation
Industry
Large Commercial Building
Small Commercial Building& Residential
Source: Energy Policy and Planning Office (2011)

EnergySaving % share(ktoe)
13,400 44.7%
11,300 37.7%
2,300
7.6%
3,000

10.0%
20

Mitigation Assessment : Road Transport Sector
10-Year Alternative Energy Development Plan (AEDP 2012-2021)

Target: using 25% ofrenewable energy for totalenergy consumption (heatand electricity generation)by the year 2021

Note: MW=Megawattktoe = kilotons of oil equivalent
ML = Million liters
Source: Department of Alternative Energy Development and Efficiency (2012)21

Energy Policies in Thailand•

Alternative Energy Development Plan
(2012-2021)– Switch conventional fossil fuel,such as gasoline and diesel, tobiofuels, i.e. bioethanol andbiodiesel•

Energy Efficiency Development Plan
(2011-2030)– Promotion of high energy
efficiency vehicle technologies
for private vehicles, such as ecocar, hybrid car and electricvehicle22

Overview of Methodology
End-Use Energy Demand Model
Scenarios Analysis:
Alternative
Technology Options

Legend:Exogenous Inputs

Vehicle Sale by Vehicle Type [Vehicles]
Fuel Share of Vehicle Sale by Fuel Type [%]Survival Rate of Vehicle by Age [%]Vehicle Stocks by Vehicle Type[Vehicles]

Model Calculations
Final Outputs

Vehicle Stock Profile by Age [%]Technology Penetration [%]Fuel Share of Vehicle Sale [%]% Share of Alternative Fuels [%]Fuel Economy of Vehicle [km/liter]

Annual Average VKT by
Vehicle Type [km/year]
VKT Degradation by Age [%]Stock Average VKT by
Vehicle Type [Veh-km]

Total VKT by Vehicle Type[Vehicle-kilometer]

On-Road Fuel Economy by
Vehicle and Fuel Type [km/liter]
Fuel Economy Degradation by
Age [%]

Stock Average Fuel
Economy by Vehicle
and Fuel Type [km/liter]

End-use Energy Consumption[MJ]

Tank-to-Wheel GHG Emission Factors[kg/MJ]

Tank-to-Wheel GHG Emissions[kg]

Well-to-Tank GHG Emission Factors[kg/MJ]

Well-to-Tank GHG Emissions [kg]Calibration andVerification

GHG Emissions Estimation

Life Cycle GHG
Emissions [kg]23

End-use Energy Demand Model
𝑽𝒔𝒕𝒐𝒄𝒌,𝒊,𝒋 (𝒕) × 𝑽𝑲𝑻𝒔𝒕𝒐𝒄𝒌,𝒊,𝒋 (𝒕) × 𝑭𝑬−𝟏𝒔𝒕𝒐𝒄𝒌,𝒊,𝒋 (𝒕)𝑬𝑫(𝒕) =
𝒊

Where

𝒋

𝐸𝐷(𝑡) is the total energy demand in a calendar year t (MJ)
𝑉𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡) is the total stock of vehicle type i , which use fuel type j, in acalendar year t (vehicles)
𝑉𝐾𝑇𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡) is the stock’s average annual vehicle kilometer of travel ofa given vehicle type i, which use fuel type j, in a calendar year t (kilometers)
𝐹𝐸𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡) is the stock’s average fuel economy of that given vehicle type i,which use fuel type j, in a calendar year t (vehicle-kilometer per MJ)
t is the calendar year of consideration for a vehicle stock estimation
i is the type of vehicles

j is the type of fuels.
24• Stock Turnover Analysis
𝒗=𝒕𝑽𝒔𝒕𝒄𝒐𝒌,𝒊,𝒋 (𝒕) =𝑽𝒔𝒂𝒍𝒆,𝒊 (𝒗) × 𝝋𝒊 𝒌 × 𝜳𝒊,𝒋 (𝒗)
𝒗=𝒗′

Where

𝑉𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡) is the number of vehicle stock type i which use fuel type j in a calendar year t(vehicles)

𝑉𝑠𝑎𝑙𝑒,𝑖 (𝑣) is the number of new vehicle type i that sold in vintage year v (vehicles)
𝑉𝑠𝑎𝑙𝑒,𝑖 (𝑣) is the number of new vehicle type i that sold in vintage year v (vehicles)
𝜑𝑖 𝑘 is the survival rate of vehicles type i with age k (%)
𝛹𝑖,𝑗 (𝑣) is the percentage share of fuel type j within the sales of vehicle type i in
the vintage year v (%)

v is the vintage year of vehicles, of which v < t
v’ is the oldest vintage year of vehicles in the stock.
k is the age of vehicle, where k = t – v (years)25 Survival Rate of Vehicles (𝜑𝑖 𝑘 )• Survival rate of vehicles is a probability that vehicles are survived withincreasing ages after they entered the market.
Observed data
Weibull Function
Modified Weibull Function

𝒌 + 𝒃𝒊𝝋𝒊 𝒌 = 𝒆𝒙𝒑 −𝑻𝒊𝒃𝒊, 𝝋𝒊 𝟎 ≡ 𝟏

Where 𝜑𝑖 𝑘 is the survival rate of vehicle
type i with age kk is the age of vehicles

bi is the failure steepness for vehicles
type i (bi>1, i.e. failure rate increase
with age)

S-shaped Gompertz scrapping curve

Ti is the characteristic service life forvehicle type i.

Data sources: Department of Land Transport (DLT)

26

Age
(Years)

PC

PU

TAXI

COMC

3WL

MC

BUS

TRK

bi

4.02

2.04

7623.24

2.66

2.22

2.18

2.80

2.22

Ti

39.70

55.17

7634.32

18.09

15.68

15.42

24.46

30.05

R2

0.79

0.91

0.94

0.77

0.90

0.97

0.84

0.8427𝛿𝑖,𝑗 (𝑘) = 𝛼𝑖,𝑗 𝑘𝛽𝑖,𝑗Where 𝛼𝑖,𝑗 and 𝛽𝑖,𝑗 are coefficients of variable k (vehicleage) of vehicle type i which uses fuel type j.
𝑉𝐾𝑇𝑣𝑒ℎ,𝑖,𝑗 𝑘 = 𝛿𝑖,𝑗 𝑘 × 𝑉𝐾𝑇𝑣𝑒ℎ,𝑖,𝑗 0= 𝛼𝑖,𝑗 𝑘 𝛽𝑖,𝑗 × 𝑉𝐾𝑇𝑣𝑒ℎ,𝑖,𝑗 (0)Where 𝛿𝑖,𝑗 (𝑘) is the degradation factor of VKT of vehicle type i

𝑉𝐾𝑇𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡) =𝑣=𝑡𝑣=𝑣′ 𝑉𝑟𝑒𝑚𝑎𝑖𝑛,𝑖,𝑗 𝑡, 𝑣

× 𝑉𝐾𝑇𝑣𝑒ℎ,𝑖,𝑗 𝑘
𝑉𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡)Where 𝑉𝐾𝑇𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡) is the stock’s annual average vehicle
kilometer of travel of vehicles type i which use fuel type jin a calendar year t (kilometers per vehicle)
𝑉𝑟𝑒𝑚𝑎𝑖𝑛,𝑖 (𝑡, 𝑣) is the number of vehicle type i that sold inwhich use fuel type j with age k

𝑉𝐾𝑇𝑣𝑒ℎ,𝑖,𝑗 (𝑘) is the annual average VKT of vehicle type iwhich use fuel type j with age k (kilometers)
𝑉𝐾𝑇𝑣𝑒ℎ,𝑖,𝑗 (0) is the annual average VKT of new vehicle

type i which use fuel type j (kilometers)
𝛼𝑖,𝑗 and 𝛽𝑖,𝑗 are coefficients of variable k (vehicle age) of
vehicle type i which use fuel type j.

vintage year v, which still remains on road in a calendar
year t (vehicles)
Data sources: King Mongkut’s Institute of Technology Thonburi (KMITT)28

Vehicle TypeVKT of New Vehicle(km/year)
αi
βi
R2

PC

PU

TAXI

23,24837,955

72,1540.907-0.2020.98

0.900-0.2150.98

0.953-0.1060.99

COMC
26,7580.939-0.132
0.99

3WL

MC

BUS

TRK

13,766

14,69098,395

98,111

0.946-0.1200.99

0.853-0.3070.98

0.811-0.3870.98

0.689-0.5940.97

29

 Fuel Economy of Vehicles (𝐹𝐸𝑠𝑡𝑜𝑐𝑘,𝑖,𝑗 (𝑡))
•

•
•

•

Fuel economy is an average vehicledistance travelled per unit of fuelused. It is generally presented in termof vehicle-kilometer per liter.
The efficiency of a vehicle is normallyreducing when the vehicle get older.
According to a survey data of KMITT,
relationship between the degradationof vehicle efficiency and its age could
not be found.
Therefore, in this study, we assumed
that age of vehicle does not affect tothe fuel economy of vehicles. The fueleconomy of vehicles was also assumed
to be constant.

Fuel Economy(Vehicle-kilometer per liter)

VehicleType

Gasoline

Diesel

LPG

CNG*

PC

12.27

11.31

10.69

10.86

PU

11.82

11.93

11.06

10.78

TAXI

13.50

10.00

9.66

11.16

COMC

9.37

8.34

11.22

8.71

3WL

17.68

15.37

10.80

10.25

MC

28.71

-

-

-

BUS

-

3.91

-

2.26

TRK

-

4.14

-

1.67

Note: * Unit of CNG fuel economy is veh-km per kg.

Data sources: King Mongkut’s Institute of Technology Thonburi (KMITT) and
Energy Policy and Planning Office (EPPO)
30

GHG Emissions Estimation• Default GHG Emission Factors
CO2 Emission
𝐶𝑂2 = 𝐹𝐶𝑗 × 𝐸𝐶,𝑗

Non-CO2 Emissions

𝐶𝐻4 = 𝐹𝐶𝑗 × 𝐸𝐶𝐻4,𝑗
𝑁2 𝑂 = 𝐹𝐶𝑗 × 𝐸𝑁2𝑂,𝑗Where 𝐶𝐻4 is the methane emission (kg)
Where 𝐶𝑂2 is the carbon dioxide emission (t CO2)
𝑁2 𝑂 is the nitrous oxide emission (kg)𝐹𝐶𝑗 is the fuel consumption of fuel type j (ktoe)
𝐸𝐶𝐻4,𝑗 is the methane emission factor
𝐸𝐶,𝑗 is the carbon emission factor of fuel type j (t C/TJ)
of fuel type j (kg/TJ)
𝐸𝑁2𝑂,𝑗 is the nitrous oxide emission
factor of fuel type j (kg/TJ).

31

Long-range Energy Alternatives Planning (LEAP) System

32

Life Cycle of Fuel Supply-Demand of Road Transport Sector• Well-to-Wheel Analysis for Transport Fuel Systems– Well-to-Tank: Fuel Production– Tank-to-Wheel: Fuel Utilization

Well-to-Tank

Tank-to-Wheel

Well-to-Wheel

33

Supply Chain of Road Transport Fuels
Primary Energy

Storage/Transport

Conversion

Storage/
Distribution

Final Energy- Palm plantation, cultivation,and transportation- Crude Palm Oil Extraction- Palm Oil Production

Crude Palm Oil

Storage/Transport

Production of
Biodiesel

Storage/
Distribution

Biodiesel- Sugar cane farming, cultivationand transportation- Sugar Production(Molasses as by product)

Molasses

Storage/Transport

Production of
Bioethanol

Storage/
Distribution

Bioethanol
DieselCrude Oil Extraction andTransportation

Crude Oil

Storage/Transport

Refining to
Gasoline/Diesel/
LPG/Fuel Oil

Storage/
Distribution

Gasoline

Coal Mining, Processing andTransportation

Natural Gas

Coal

Storage/Transport

Seperation/
Purifying

Electricity
Power Plant

Diesel/Fuel Oil

LPG
Natural Gas Extraction andTransportation

Storage/
Distribution

Transmission

CNG

Electricity

Hydropower

Renewable Energy

34

Life Cycle GHG Emissions Estimation• Life Cycle (well-to-wheel) GHG Emissions
𝑤𝑡𝑡
𝑡𝑡𝑤
𝐺𝐻𝐺𝑤𝑡𝑤,𝑗 = 𝐺𝐻𝐺𝑤𝑡𝑡,𝑗 + 𝐺𝐻𝐺𝑡𝑡𝑤,𝑗 = 𝐸𝐷𝑡𝑡𝑤,𝑗 × 𝑓𝐺𝐻𝐺,𝑗+𝐸𝐷𝑡𝑡𝑤,𝑗 × 𝑓𝐺𝐻𝐺,𝑗

Where 𝐺𝐻𝐺𝑤𝑡𝑤,𝑗 is the well-to-wheel greenhouse gas emission of fuel type j (g of CO2 eq.)
𝐺𝐻𝐺𝑤𝑡𝑡,𝑗 is the well-to-tank greenhouse gas emission of fuel type j (g of CO2 eq.)

𝐺𝐻𝐺𝑡𝑡𝑤,𝑗 is the tank-to-wheel greenhouse gas emission of fuel type j (g of CO2 eq.)
𝐸𝐷𝑡𝑡𝑤,𝑗 is the tank-to-wheel energy supply to end-use (or consumption at end-use) of
fuel type j (MJ)
𝑤𝑡𝑡
𝑓𝐺𝐻𝐺,𝑗is the corresponding factor of well-to-tank GHG emission of fuel type j(g of CO2 eq./MJ of fuel use)𝑡𝑡𝑤
𝑓𝐺𝐻𝐺,𝑖is the corresponding factor of tank-to-wheel GHG emission of fuel type j(g of CO2 eq./MJ of fuel use).
35

Life Cycle GHG Emission per Unit of Energy
Consumption by Fuel Type36

Scenarios Analysis• Business-as-usual scenario• Government’s plans scenarios– Alternative Energy Development Plan (AEDP)(Fuel Switching Option)– Energy Efficiency Development Plan (EEDP):(Energy Efficiency Option)– Combination of REDP and EEDP• Maximum potential scenario
37

Business-as-usual (BAU) Scenario• Assumptions– Socio-economic Parameters• Average GDP growth rate 3.5% per year,• Average population growth rate 0.6% per year, and• Average crude oil price constant during 2009 to 2015
and growth with 1.5% per year after 2016.

– Fuel Share and Fuel Economy• Assumed to be constant to 2030.
References:• GDP and Population: the Office of National Economic and Social Development Board (NESDB)• Crude Oil Price: International Energy Agency (IEA)

38

Scenarios Analysis (cont.)
Alternative Energy Development Plan (AEDP) •– Promotion of biofuels (ethanol andbiodiesel) to substitute for conventional
gasoline and diesel.

•

Energy Efficiency Development Plan (EEDP)– Promotion of high energy efficiency vehicles
technology, such as eco-car, hybrid car, andelectric motorcycle.

Year

Penetration Rate for Vehicle Sale (%)
HEV for PC

ECO for PC

EMC for MC

2010

0.4

0.4

0.8

2015

3.6

4.6

8.2

2020

14.6

18.3

32.9

2025

19.4

24.3

43.7

2030

20.0

25.0

45.0

Type of Vehicle/Model

Fuel Economy

Hybrid Car

14.14 km per liter15.77km per liter

Plug-in Hybrid CaroCombination of AEDP and EEDP (COMB)Electric Car

and 4.41 km per kWh4.74 km per kWh

Eco-Car20 km per liter

Electric Motorcycle

24.27 km per kWh

Sources: Department of Alternative Energy Development and Efficiency (2008) and Energy Policy andPlanning Office (2011)39

Scenarios Analysis (cont.)
Maximum Potential (MAX) Scenario•

Fuel Switching Option Assumptions– Bioethanol is expected to substitute for gasoline 9.72 million liters per day by 2030.
– Biodiesel is expected to substitute for diesel 7.85 million liters per day by 2030.

•

Energy Efficiency Option Assumptions– High energy efficiency ICE (Eco-car)

Penetration Rate for Vehicle Sale (%)– Hybrid Electric Vehicle (HEV)Year– Plug-in Hybrid Electric Vehicle (PHEV)HEV forPC

ECO forPC

PHEV forPC

EV forPC

EMC for
MC

2010

0.5

0.5

-

-

1.8

2015

5.5

5.5

-

-

18.2

2020

21.9

21.9

-

-

73.1

2025

29.1

29.1

3.6

4.3

97.1

2030

30.0

30.0

7.3

8.6

100.0

– Electric Motorcycle (EMC)– Electric Vehicle (EV)

Soures: International Energy Agency (IEA) and Nagayama, H. (2011)40

Projection under BAU Scenario• Vehicle Stocks

53.1 Mil. Vehicles

2 times ofcurrent status26.8 Mil. Vehicles

In 2030
Vehicle Stocks:
PC 9.0 Mil. Vehicles
PU 11.6 Mil. Vehicles
MC 30.1 Mil. Vehicles
Others 2.35 Mil. Vehicles
Vehicle Ownership:
PC 119 veh./1,000 persons
PU 153 veh./1,000 persons
MC 397 veh./1,000 persons
Other 31 veh./1,000 persons
41

Projection under BAU Scenario (cont.)• End-use Energy Demand
35,096 ktoe
2 times ofcurrent status

17,060 ktoe

42

Projection under BAU Scenario (cont.)• GHG Emissions
2 times ofcurrent status

WTT: 7.2 Mt of CO2 eq.
TTW: 49.4 Mt of CO2 eq.
WTW: 56.6 Mt of CO2 eq.

WTT: 14.0 Mt of CO2 eq.
TTW: 100.1 Mt of CO2 eq.
WTW: 114.1 Mt of CO2 eq.

43End-use Energy Demand Reduction

BAU: 35,108 ktoe
AEDP: 35,108 ktoe
EEDP: 33,258 ktoe
COMB: 33,258 ktoe
MAX: 32,384 ktoe

By 2030
AEDP: 0 ktoe
EEDP: -1,850 ktoe
COMB: -1,850 ktoe
MAX: -2,724 ktoe

By 2030
AEDP: 0 %EEDP: -5.3%
COMB: -5.3%
MAX: -7.8%

44

Life Cycle GHG Emissions Mitigation

By 2030
AEDP: -9.3 Mt of CO2eq
EEDP: -5.2 Mt of CO2eq COMB:-12.2 Mt of CO2eq
MAX: - 13.7 Mt of CO2eq

Scenarios

GHG Emissions (Mt of CO2eq.)
Well-to-Tank

Tank-to-Wheel

Well-to-Wheel

BAU

13.8

100.3

114.1

AEDP

14.0

90.8

104.8

EEDP

14.3

94.5

108.8

COMB

14.6

87.3

101.9

MAX

15.2

85.2

100.3

By 2030
AEDP: -8.1 %EEDP: -4.6%
COMB: -10.7%
MAX: -1.37%

45

Conclusion• GHG inventories are complied for both scientific activity
and policy planning Use for modeling activities Use for future projections and setting targets for emission
reduction
 Use for policy and measures planning and their monitoring Use for mitigation measure and technology assessment• Previous national GHG inventories have done with tier 1
level, because lacking of activity data and country-specificemission factors in higher tier level in most sectors.
• For some subsector, i.e. road transport sector, data isavailable to develop end-use energy demand model for
activity data (energy consumption) estimation, but stilllacking of country-specific emission factors.
46

Recommendation
Greenhouse Gas Inventory
Areas that need further technical support to improve inventory activities for
Thailand are as follows: Local emission factors in major sectors and those sectors that areimportant to economic development. The priority sectors are agriculture
and forestry.
 Develop appropriate activity data to support the estimation ofgreenhouse gas inventory. The priority sectors are energy, agriculture,
forestry and waste management.
 Develop estimation method for key sectors to higher tier. These are theenergy, agriculture, and forestry sectors.
 Train relevant officials and agencies to carry out the estimation regularly.
 Develop technical personnel in specific areas to develop appropriate
estimation methodologies or techniques for Thailand.
 Develop techniques in greenhouse gas emission forecast.
47

Recommendation
Greenhouse Gas Mitigation
Techniques, know-how and technologies to mitigate GHGs are needed, as
follows:
 Analytical techniques to prioritize mitigation options for energy
conservation and renewable energy Advanced technologies for energy conservation for electricity production
and consumption
 Efficient technologies and systems for traffic and mass transport,
especially for logistics
 Technologies for biomass and biogas energy production appropriate for
local conditions
 Environment-friendly technologies for cement production
 Development of knowledge and infrastructure for innovation of clean
technologies
 Technologies to mitigate GHG from rice paddy fields
48

THANK YOU FOR YOUR ATTENTION

Jakapong Pongthanaisawan, PhD
Senior Policy Researcher
National Science Technology and Innovation Policy Office
319 Chamchuri Square Building 14th Fl.,
Phayathai Rd. Patumwan, Bangkok 10330 THAILAND
T: +66 2160 5432-37 ext. 302
F: +66 2160 5438
E: jakapong@sti.or.th49