Asia-Pacific Integrated Modelhttp://www-iam.nies.go.jp/aim/index.html

Impacts of Economic Development and Urbanization in South/Southeast ASIA

for Estimating Future GHGs Emissions- CH4 Emissions from Landfill -

0

Land Cover/Land Use Change SARI International Regional Science Meeting in South/Southeast Asia Chaing Mai, Thailand

17-19 July 2017

Tatsuya HANAOKA

Center for Social and Environmental SystemsNational Institute for Environmental Studies

Japan

MOEJ-S12: Promotion of climate policies by assessing environmental impacts of SLCP and seeking LLGHG emission pathways (FY2014 – FY2018)

1

Goal: To develop an integrated evaluation system for LLGHG and SLCP mitigation policy, by interconnecting emission inventory, integrated assessment models, and climate models.

Theme 1: Air quality change event analysis・Analysis on regional AQ change・Development of emission inventory ・Inversion algorithms of emission

estimation

Theme 2: Integrated model and future scenarios・Global socio-economic scenarios・National & regional emissions

scenarios・Urban & household emissions AQ

assessment

Theme 3: SLCP impacts on climate& environment・Impact assessment of aerosols & GHG・Assessment of health, agriculture,

water cycle, sea level rise

SLCP emissions scenariosImproved emission inventory

Feedback of impactsAssessment of activities/policies

Regional EmissionInventories and

Chemical Transfer Model

Integrated Assessment Model (AIM)

Climate and Environment

Model

Chemical transfer model and emission inventory in Asia

AIM/Enduse modelSocio-economical & emissions scenario

Climate model, earth system model Climate change impact & adaptation

Theme 4: Integrated operation system (Toolkits, data archive)

MDG・SDG・Future Earth

StakeholdersPolicy makers

Society

Information transmissionSystem utilization

CCAC, UNFCC, IPCC, EANETProposal and assessment of climate and

air pollution policies

Regional strategy

⇅Global

strategy

Science

Experiment setupDatabase development

Metric definitions

Model improvement

Overview of Key Research Topics

2

Urbanization

ElectrificationIndustrializationFuel Energy

Consumption

Electricity Consumption

IndustrialProduction

Dietary HabitChange

EconomicDevelopment

BehavioralChange

AgriculturalProduction

CH4, N2OCO2

IndustrialWastewater

Discharge

IndustrialSolid Waste

Disposal

DomesticSolid Waste

Disposal

DomesticWastewater

Discharge

PassengerTransport

Volume

Residential Service

Demand

Commercial Service

Demand

FreightTransport

Volume

CH4, N2O

CO2, PM, BC, SO2, NOx, etc

Research Motivations- how to achieve emissions pathways of the 2℃ target? -

3

Source）UNEP (2011), Figure ES-2

How about air pollutants reductions and their benefits and trade-offs, when achieving 2 degree?

What kinds of SLCP measures can be? How drastic it should be?

If SLCPs reductions are fully implemented by 2030, it reduces global warming between 2010 to 2040 by about 0.4 – 0.5 ℃

Source) IPCC AR5 Synthesis Report (2015) Figure SPM.5

This study aim to seek for balanced emissions scenarios of LLGHGs, SLCPs, air pollutants and evaluate cobenefits and tradeoffs of mitigation measures

Remaining cumulative CO2 emissions (i.e. carbon budgets) staying below 2℃ are around 1000 GtCO2

Remaining around1000 GtCO2

additional 1℃

Short-Lived Climate Pollutants- How much can we reduce SLCPs? -

4

Source） Climate and Clean Air Coalition http://www.ccacoalition.org/en/science-resources

Today’s topic

Backgrounds- Sources of CH4 emission from Asia

5

Source） EDGER4.2

25%

5%

17%

47%

1%5%

2005 Asia140.9 TgCH4

Power

Mining

Industry

Transport

Buildings

Waste

Agriculture

Agriculture waste burning

Savannah burning etc

relation with economic development

relation with lifestyle

relation with landuse (i.e. landfill)

We have mitigation measure for waste.We can reduce landfill waste. But how?

AIM/Enduse[Global] Model and Element Models

6

0

5

10

15

20

25

30

35

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050GH

G e

mis

sion

s in

Asia

(Gt C

O2e

q)

Cement production

Value added of 2nd industry

Agricultural production

Fluorocarbon emission

Transport volume (Freight)

Energy service (Residential)

Municipal solid waste generation

Energy service (Commercial)

Transportation Demand Model

Household Lifestyle Model

Municipal Solid Waste Model

Cement Production Model

Building sector

Energy Supply sector

Socio-economicscenario

Agricultural Prod & Trade model

FluorocarbonEmission Model

Agriculture sector

Model DatabaseVariable

Solid waste management sector

Transport volume (Passenger)

Crude steelproduction

Steel Production & Trade Model

Gas fuel

Heat

Liquid fuel

Solid fuel

Hydrogen

Energy balance

Primaryenergy

Energy price

Emission factor

EnergyDB

Nuclear Hydro Geothermal

Solar Wind Biomass

Emissions

Energy mining sector

GasCoal Oil

Bottom-up model (i.e. AIM/Enduse)

Macro Economic

frame Model

Population & Household number

GDP & Sector value added

Macro-economic model

Iron and steelsector

Cementsector

Other industries sector

Transport sector

Fluorocarbon sector

Energy Resource DB

Cost

Lifetime

Technology DB

Efficiency

Diffusion rate

Service demand models

Electricity

Today’s topic

Sub-Sectors in the Waste Sector

7

GHG emissions in the Waste Sector

4A Solid Waste DisposalCH4 emissions from landfilled municipal, industrial and other solid waste are reported. Generally, this category is the largest source of GHG emissions in the Waste Sector.

4B Biological Treatment of Solid WasteCH4 and N2O emissions from Composting andAnaerobic digestion of organic waste are reported.

4C Incineration and Open Burning of WasteCO2 and CH4 and N2O emissions from waste incineration are reported. GHG emissions from incineration with energy recovery are reported in the Energy Sector.

4D Wastewater Treatment and DischargeCH4 and N2O emissions from the wastewater treatment and discharge are reported.

4E Other

The 2006 IPCC Guidelines for National Greenhouse Gas Inventories

GHG Emissions from Waste Sector in Asian Countries

8

Japan (2012)Landfill (CH4)

IndustrialWastewater (CH4)Domestic wastewater (CH4)Domestic wastewater (N2O)Incineration(CO2/CH4/N2O)Others(CO2/CH4/N2O)

China (2005)Landfill (CH4)

IndustrialWastewater (CH4)

Domestic wastewater (CH4)

Domestic wastewater (N2O)

Incineration(CO2/CH4/N2O)

Others(CO2/CH4/N2O)

India (2000)Landfill (CH4)

IndustrialWastewater (CH4)

Domestic wastewater (CH4)

Domestic wastewater (N2O)

Incineration(CO2/CH4/N2O)

Others(CO2/CH4/N2O)

Indonesia (2005)

Landfill (CH4)

IndustrialWastewater (CH4)Domestic wastewater (CH4)Domestic wastewater (N2O)Incineration(CO2/CH4/N2O)Others(CO2/CH4/N2O)

Thailand (2000)

Landfill (CH4)

IndustrialWastewater (CH4)

Domestic wastewater (CH4)

Domestic wastewater (N2O)

Incineration(CO2/CH4/N2O)

Others(CO2/CH4/N2O)

Vietnam (2010)

Landfill (CH4)

IndustrialWastewater (CH4)

Domestic wastewater (CH4)

Domestic wastewater (N2O)

Incineration(CO2/CH4/N2O)

Others(CO2/CH4/N2O)

Note) In Indonesia and India, methodology of CH4 emissions from industrial wastewater need to be carefully investigated whether emissions are over-estimated.

GHG Emissions from Waste Sector from Asian Countries

9

SoureJapanInventory（2012）

ChinaSNC

(2005)

IndiaSNC

（2000）

IndonesiaSNC

(2005)

ThailandSNC

(2000)

VietnamJICA

(2010)

Landfill※２

(CH4)2,928 46,284 10,252 24,409 4,864 5,005

Industrial waste water (CH4)

103 25,620 23,163 124,673 1,902 1,617

Domestic waste water （CH4）

1,361 8,400 15,036 10,298 1,504 6,827

Domestic waste water （N2O）

1,175※１ 28,830 4,101 2,366 1,023 1,838

Incineration・Open burning（CO2, CH4, N2O）

28,068 2,658 NE 4,886 23 65

Others（Compost etc）

826 0 --- 199 0 0

Total 34,460 111,792 52,552 166,831 9,316 15,352

Unit：ktCO2(CO2eq)※１：N2O emission from domestic sewage includes industrial waste water※２：Solid waste includes industrial solid waste in Japan and Viet Nam. Solid waste in China, India, Indonesia, Thailand

are supposed not to include industrial solid waste.

Methodology in the IPCC Guideline- how to estimate CH4 from solid waste in landfill -

10

The 2006 IPCC Guidelines for National Greenhouse Gas Inventories

( )4 4 , 1x T T Tx

CH Emissions CH generated R OX

= − • − ∑

( )4 16 / 12fCH generated potential W DOC DOC MCF F= • • • • •

T : inventory year, x : waste category or type/material, RT : recovered CH4 in year T, OXT : oxidation factor in year T, W : mass of waste deposited, DOC : degradable organic carbon in year of deposition, DOCf: fraction of DOC that can decompose, MCF : CH4 correction factor for aerobic decomposition in the year of deposition, F : fraction of CH4 in generated landfill gas.

Where

How to estimate MSW generation is one of keys for the future GHG

emissions scenarios

Research Questions and Methodologies

11

What kinds of socio-economic variables can explain the historical trends of MSW generation per capita?

① Urbanization ratio ② Energy consumption per capita③ GDP per capita④ Income per capita

[ Research question 1 ]

Note) We need to choose the type of socio-economic variables by considering data availability.

Research Questions and Methodologies

12

[Research question 2 ]

Y aX b= +ln( )Y a X b= +

2Y aX bX Z= + +

Single linear approximation

Logarithmic approximation

Quadratic approximation

Y : MSW generation per capita, X : Socio-economic variables a, b : constant value Z : intercept coefficient.

Where

Environmental Kuznets curve

What kinds of equations can be applied, in order to explain MSW generations increase?

If the linear regression is used for estimating MSW generations, it means that generated MSW amounts will keep increasing as explanatory variables increase. But, after reaching a certain level, will it be saturated and/or declined ?

Historical Municipal Solid Waste (MSW) Generation- China: rapid growing and urbanizing country -

13Source) provincial data from 2003 to 2012 in Chinese Statistical Yearbook

Large provinces have linear correlations between GDP per capita and MSW generated per capita in each provinces.

But difficult to see correlations among provinces. Size and growth rate of MSW per capita are different in large provinces

Outlier(Tibet)

Is there correlation between “provincial GDP per capita” and “provincial MSW generated per capita”, to comprehend features of urbanization ?

Excludingoutlier

If data in the whole China is compared with provincial data, its characteristic is different because of provincial diversities.

No superior correlations between linear or logarithm functions, in China’s data. Thus It is better to investigate features including data in other countries, too.

[ Question ]

The more urbanization increase, the more MSW per capita are

generated ?

The more energy use per capita increase, the more MSW per

capita are generated ?

[ Question ]

Verification of Research Questions- Correlation with energy use and urbanization -

14Source ) Panel data include Japan, China, Thailand, USA, EU27

y = 31.612x + 368.11

R² = 0.12710

100 200 300 400 500 600 700 800 900

1000

0 2.0 4.0 6.0 8.0 10.0 Energy use per capita (toe per capita)

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r]

Answer: there is a weak correlation.(maybe because energy consumption data

includes industries as well as residential ?)

y = 4.1215x + 185.95R² = 0.1481

0 100 200 300 400 500 600 700 800 900

1000

0 20 40 60 80 100 Urban population ratio (%)

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r]

Answer: there is a weak correlation.(because urban ratio may be one element but

not a major for waste generation? )

Verification of Research Questions- Correlation with GDP per capita and income per capita -

15

The more GDP per capita increase, the more MSW per capita are generated?The more income per capita increase, the more MSW per capita are generated?

y = 128.05ln(x)-783.85

R² = 0.5406

0 100 200 300 400 500 600 700 800 900

1000

0 20000 40000 60000 80000 100000

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r]

GDP per capita (US$2005 per capita)

y = 129.04ln(x) -770.54

R² = 0.567

0 100 200 300 400 500 600 700 800 900

1000

0 20000 40000 60000 80000 National income per capita (US$2005 per capita)

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r]

Answer: Coefficient of determinations are almost similar between these two cases,

and it can be reasonable to use GDP per capita as an explanatory variable. However, R2 is not high enough, thus it is necessary to check outliers carefully

and also to include more reliable data especially in lower-income countries.

Source ) Panel data include Japan, China, Thailand, USA, EU27

Verification of Research Questions- Correlation with GDP per capita -

16

R2 become increased and reliable if data in the lower-income level is added. However, it is necessary to carefully discuss methodologies when GDP per

capita reaches the level beyond 5000 US$ per capita, because the increasing trends are different, which trends developing countries will follow.

Source ) Panel data include Japan, China, Thailand, USA, EU27, China provincials

0100200300400500600700800900

1000

0 20000 40000 60000 80000 100000

MSW

gen

erat

ed p

er ca

pita

(k

g M

SW p

er ca

pita

/yea

r]

GDP per capita (US$2005 per capita)

Eastern Europe Western Europe USAJapan China(Provinces)China(Country)Thailand

y = 139.7ln(x)-904.65 R² = 0.7938

Western-style trend

Developed countries average trend

y = 184.4ln(x )- 1243.3 R² = 0.8302

Asian-style trendy = 100.94ln(x)- 617.02

R² = 0.7445

Future MSW Generation per capita- Example in Asian Developing Countries -

17

0

100

200

300

400

500

19801990200020102020203020402050

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r] Thailand

0

100

200

300

400

500

19801990200020102020203020402050

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r] VietNam

0

100

200

300

400

500

19801990200020102020203020402050

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r] India

0

100

200

300

400

500

19801990200020102020203020402050

MSW

gen

erat

ed p

er c

apita

(k

g M

SW p

er c

apita

/yea

r] China

Historical Asian-style trend equation functionDeveloped countries averaged trend equation functionWestern-style trend equation function

MSW generated per capita will increase as GDP per capita increase in all countries.

Future MSW Generation- Example in Asian Developing Countries -

0

10

20

30

40

50

60

1980 1990 2000 2010 2020 2030 2040 2050

MSW

gen

erat

ed

(Mt M

SW/y

ear] Thailand

0

10

20

30

40

50

60

1980 1990 2000 2010 2020 2030 2040 2050

MSW

gen

erat

ed

(Mt M

SW/y

ear] VietNam

0

100

200

300

400

500

600

19801990200020102020203020402050

MSW

gen

erat

ed

(Mt M

SW/y

ear] China

0

100

200

300

400

500

600

19801990200020102020203020402050

MSW

gen

erat

ed

(Mt M

SW/y

ear] India

18

Historical Asian-style trend equation functionDeveloped countries averaged trend equation functionWestern-style trend equation function

MSW generated amount = MSW generated per capita × population

Future MSW to Landfill- Example in Asian Developing Countries -

19

0

5

10

15

20

25

30

2000 2010 2020 2030 2040 2050

MSW

to la

ndfil

l (M

t MSW

]

Thailand

0

5

10

15

20

25

30

2000 2010 2020 2030 2040 2050

MSW

to la

ndfil

l (M

t MSW

]

Viat Nam

0

100

200

300

400

500

600

2000 2010 2020 2030 2040 2050

MSW

to la

ndfil

l (M

t MSW

]

China

050

100150200250300350400

2000 2010 2020 2030 2040 2050

MSW

to la

ndfil

l (M

t MSW

]

India

Landfill ratios are set at 97%, 70%, 80% and 60% in China, India, Thailand, Viet Nam respectively based on national specific values and the IPCC guideline default values

Historical Asian-style trend equation functionDeveloped countries averaged trend equation functionWestern-style trend equation function

Future CH4 emissions from Landfill MSW- Example in Asian Developing Countries -

20

0

5

10

15

20

25

2000 2010 2020 2030 2040 2050

CH4

emis

sion

from

land

fill M

SW(M

t CO

2eq]

Thailand

0

5

10

15

20

25

30

2000 2010 2020 2030 2040 2050

CH4

emis

sion

from

land

fill M

SW(M

t CO

2eq]

Viat Nam

050

100150200250300350400

2000 2010 2020 2030 2040 2050

CH4

emis

sion

from

land

fill M

SW(M

t CO

2eq]

China

0

50

100

150

200

250

300

2000 2010 2020 2030 2040 2050

CH4

emis

sion

from

land

fill M

SW(M

t CO

2eq]

India

Parameters such as waste compositions, degradable organic carbon, aerobic decomposition, etc, are set at based on national specific values and the IPCC guideline default values

Historical Asian-style trend equation functionDeveloped countries averaged trend equation functionWestern-style trend equation function

0100200300400500600700800900

1000

2000 2010 2020 2030 2040 2050

CH4

emis

sion

from

land

fill M

SW(M

t CO

2eq]

Asia

0100200300400500600700800900

1000

2000 2010 2020 2030 2040 2050

CH4

emis

sion

from

land

fill M

SW(M

t CO

2eq]

Asia

Effects of Reducing Landfill and Introduce Incineration- Example in Asian Developing Countries -

21

Historical Asian-style trend equation functionDeveloped countries averaged trend equation functionWestern-style trend equation function

Considering a scenario if the whole Asia countries take waste policy to cut landfill ratio by half by 2030 and to increase incineration.

After 2030, keep the same landfill ratio as same as the 2030 level.

Reduction of landfill can reduce CH4 emission largely However, if the same landfill ratio continues after 2030, CH4 emission from landfill will

increase again due to waste generation growth. Thus necessary to keep accelerating landfill reduction or recovering CH4 from landfill, to peak out CH4 emission from landfill.

waste policy

120MtCO2eq

86MtCO2eq

403MtCO2qe

Note) CO2 emission in Thailand in 2010 was around 220 MtCO2

Without waste polity With waste polity

ご清聴ありがとうございました

Asia-Pacific Integrated Modelhttp://www-iam.nies.go.jp/aim/index.html

Scenario Dimensions - SSP (Shared Socioeconomic Pathways) -

23

(O’Neill, 2012)

Details quantitative data and qualitative stories:https://secure.iiasa.ac.at/web-apps/ene/SspDb/dsd?Action=htmlpage&page=aboutPeer-reviewed paper: Riahi, K. et al (2017) Global Environmental Change, 42:153-168http://www.sciencedirect.com/science/article/pii/S0959378016300681

Western-style trendequation function

Developed countries averaged trend equation function

Asian-style trendequation function

This study applied three different equation functions to the three different concepts of socio-economic scenarios.

Population and GDP in Asia in SSP Scenarios

2424

01020304050607080

1990 2010 2030 2050

GDP

per

cap

ita

(100

0 U

S$20

05/p

erso

n) Japan

0

2

4

6

8

10

12

1990 2010 2030 2050

GDP

per

cap

ita

(100

0 U

S$20

05/p

erso

n) India

0

5

10

15

20

25

30

1990 2010 2030 2050

GDP

per

cap

ita

(100

0 U

S$20

05/p

erso

n) China

0

2

4

6

8

10

1990 2010 2030 2050

GDP

(Tril

lion

US$

2005

)

Japan

0

5

10

15

20

1990 2010 2030 2050

GDP

(Tril

lion

US$

2005

)

India

05

10152025303540

1990 2010 2030 2050

GDP

(Tril

lion

US$

2005

)

China

80

90

100

110

120

130

140

1990 2010 2030 2050

Popu

latio

n (m

illio

n)

Japan800

1000

1200

1400

1600

1800

2000

1990 2010 2030 2050

Popu

latio

n (m

illio

n)

India

1000

1100

1200

1300

1400

1500

1990 2010 2030 2050

Popu

latio

n (m

illio

n)

China

Historical SSP1 SSP2 SSP3 SSP4 SSP5

02468

10121416

1990 2010 2030 2050

GDP

per

cap

ita

(100

0 U

S$20

05/p

erso

n) ASEAN

0

2

4

6

8

10

1990 2010 2030 2050

GDP

(Tril

lion

US$

2005

)

ASEAN

400

500

600

700

800

900

1000

1990 2010 2030 2050

Popu

latio

n (m

illio

n) ASEAN

POP

GDP

GDP/POP

Characteristics of GDP per capita will effects on MSW per capita are generated