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Accounting uncertainty for spatial

modeling of greenhouse gas emissions in the residential sector: fuel combustion and heat production

Olha Danylo, Rostyslav Bun, Linda See, Petro Topylko, Xu Xianguang, Nadiia Charkovska, Przemysław Tymków

Kraków, 9

th

October, 2015

(2)

Agenda

 Introduction

 Methodology

 Inventory results: Poland and Ukraine

 Validation of approach

 Conclusions

(3)

Essence of the approach

3

Disaggregation algorithms and data processing

Emissions СО2, СН4,N2O:

???

Uncertainties: ???

Емісії, екв. CO2, Гг

Кількість попадань в інтервал

Емісії, екв. CO2, Гг

Кількість попадань в інтервал

Uncertainty analisys

Monte-Carlo mthod, 95%, ……

country Mathematical model:

fossil fuels, greenhouse gases, net calorific values....

region Uncertainty

of Input Data

Results of spatial inventory

Database of geo-referenced data

, 0

2 ) exp ln(

2 x , 1

; 2

2

x x

x

f

Visualization of results Statistical data

Parameters Other information

All settlements All regions

Map of emission sources

' ) ( )

(

1 1

1

~ ,

1

~ , ,

, ,

,

I

k O S p

I

k O S p

R i Urb R

i Rur

R i typ R

k typ

Rur Urb

H p Q H

p Q F H

  

SRur s

J

j

G j s R

Rur j O j I

i

G i s R

Rur i O i

G M F EF M F EF

E

~ 1

, Re , 1

, Re ,

2

1 Res

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Introduction: residential sector

4

What determines the amount of GHG emissions in the residential sector at the level of geographical elementary objects?

Spatially

Needs

1. Space heating 2. Cooking

3. Water heating

Energy sources 1. Natural gas 2. Liquefied gas 3. Coal

4. Wood

5. Other fossil fuels

What determines?

1. HDD

2. Access to energy source 3. Population

4. Living area (LA) 5. Living conditions

6. Urban/rural areas (!!!) 7. Other indicators

IPCC -1A4b

(5)

Step 4: GHG emission estimation

Methodology

Step 1: Input data collection

Step 2: Energy demand assessment

Step 3: Fossil fuel disaggregation

Spatial inventory of GHG emissions:

households

Algorithm

(6)

Input data collection

Energy demand assessment

Fossil fuel disaggregation

GHG emission estimation

Input data

(1) official statistical information (2) country-specific emission factors

(3) digital maps of investigated area

• population density map

raster data on population density disaggregated with CLC (Gallego, 2010)

a) update of the map (2010 data)

b) urban/rural characteristics were added

• Heating-Degree Days map (HDD)

6

Step 1:

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Energy demand assessment

GHG emission estimation

Energy demand assessment Input data

collection

cooking for families cooking for livestock water heating

space heating

Energy demand structure

h w

c

Q Q

Q

Q   

Cooking:

The average energy demand for :

• cooking per person,

• feed cooking,

• water heating for drinking and sanitary per 1 head of cattle.

agri c rs

c

c

Q Q

Q

,

,

Water heating:

Average hot water consumption (norms):

• 48 dm3 – dwelling,

• 35 dm3 - detached house (55℃ per person).

wint w summ

w

w

Q Q

Q  

Space heating:

• relative change of HDD

• living area (LA) per person

• energy demand per sq m of LA

• characteristics of living area

• efficiency coefficient

Q , , LA,

f k

QhHDDh sqm

Fossil fuel disaggregation

7

Step 2:

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Fossil fuel disaggregation

Energy demand assessment

GHG emission estimation

Disaggregation algorithm Input data

collection

, , 1

, ,

,

, M F n N

Mi ni Rtypen i

- consumed fossil fuel i in region R, - characterizes affiliation of

elementary object to urban or rural area,

- disaggregation coefficient.

Statistical data:

fossil fuel consumption

N

1

2 ....

Disaggregation algorithm

Regions (or municipalities) Country (or region) R fossil fuel i

i

R

M

i,

n i

F

type,

Elementary objects n

i

F

type, R

Mi,

type

8

Step 3:

N

-1

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GHG emission estimation

GHG emission estimation

Fossil fuel disaggregation Energy demand

assessment Input data

collection

9

, , 1

,

,

,

,

M EF n N

E

iGn

in

iGn

- emission factor of greenhouse gas G

СО2

CH4

N2O

СО

2

-equivalent

G n

EFi,

Step 4:

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Specific GHG emissions in residential sector (mln kg/sq.km., CO2-eq., Poland, 2010)

Structure of GHG emissions by type of fossil fuel for administrative regions

(mln kg, CO2-eq., Ukraine, 2010)

10

Inventory results: Poland

32,2 16,0 1,5 0,4 0,3 0,2 0,1 0,03

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11

Specific GHG emissions in residential sector (mln kg/sq.km., CO2-eq., Ukraine, 2010)

Structure of GHG emissions by type of fossil fuel for administrative regions

(mln kg, CO2-eq., Ukraine, 2010)

Inventory results: Ukraine

(12)

N

Inventory results: Ukraine (Lviv region)

Prosm-map of specific GHG emissions in residential sector (mln kg/sq.km., CO2-eq., Lviv region, Ukraine, 2010)

(13)

Specific GHG emissions in residential sector (mln kg/sq.km., CO2-eq.,

South-Eastern Poland, Western Ukraine, 2010)

13

Comparison of GHG inventory results:

South-Eastern Poland and Western Ukraine

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0 0,5 1 1,5 2

Свентокшиське Підкарпатське Малопольське Люблінське

0 0,5 1 1,5

Чернівецька Тернопільська Рівненська Львівська Ів.-Франківська Закарпатська Волинська

ngas coal wood lgas other

Fig. 1. Structure of GHG emissions per capita by type of fossil fuel (thousands kg per capita, СО2 –eq., South-Eastern Poland, 2010)

Fig. 2. Structure of GHG emissions per capita by type of fossil fuel (thousands kg per capita, СО2 –eq., Western Ukraine, 2010)

Comparison of GHG inventory results:

South-Eastern Poland and Western Ukraine

14

Lubelskie

Małopolskie

Podkarpackie

Świętokrzyskie

Volyn

Zakarpattya Iv.-Frankivsk

Lviv

Rivne

Ternopil

Chernivtsi

(15)

GHG emissions from the heat production

Greenhouse gas emissions from heat production in Poland (thousands tons, СО2-equivalent, 2010)

(16)

16

Fuel

95%

U- U+

E

2,5%

Total emission/uncertainties:

СО2, CH4, N2O, CO2-eq.

Iterative process

Number of realization…

Fuel types (coal, brown coal, nat. gas, oil,…) Types of GHG

СО2, CH4, N2O Result

Inventory level

Settlement Region Country

f g

f

En C

K , ,

( )

,

QEn,f En,np

( )

,

QEn,f En,np

f g

f

En C

K , ,

( )

,

QEn,f En,np KEng,f,Cf

( )

,

QEn,f En,np

f g

f

En C

K , ,

Uncertainty analysis: Monte-Carlo method

(17)

Voivodeship СО2, Gg (uncertainty, %)

CH4, Gg (uncertainty, %)

N2O, Gg (uncertainty, %)

Total emission Gg (uncertainty, %)

Lower Silesian 2635,8 5,4 0,03 2780,50

(-12,9 : +14,9) (-21,4 : +25,5) (-19,7 : +23,2) (-13,2 : +15,2)

Kuyavian-Pomeranian 1741,5 4,0 0,02 1848,54

(-14,5 :+16,7) (-21,5 : +25,5) (-19,9 : +23,4) (-14,7 : +16,9)

Lublin 1982,9 4,5 0,03 2103,56

(-14,3 : +16,5) (-21,5 : +25,6) (-19,8 : +23,4) (-14,5 : +16,8)

Lubusz 700,4 1,3 0,01 735,77

(-11,8 : +13,6) (-21,3 : +25,4) (-19,3 : +22,7) (-12,1 : +14,0)

Łódż 2451,2 5,8 0,03 2606,73

(-15,0 : +17,3) (-21,6 : +25,6) (-20,0 : +23,6) (-15,2 : +17,5)

Lesser Poland 3091,0 6,3 0,04 3258,20

(-12,7 : +14,7) (-21,4 : +25,5) (-19,7 : +23,3) (-13,0 : +15,0)

... ... ... ... ...

... ... ... ...

Warmian-Masurian 900,1 1,9 0,01 949,97

(-13,0 : +15,0) (-21,4 : +25,5) (-19,5 : +23,0) (-13,2 : +15,3)

Greater Poland 3013,4 5,9 0,04 3172,27

(-12,4 : +14,3) (-21,3 : +25,4) (-19,5 : +22,9) (-12,7 : +14,6)

West Pomeranian 1163,7 1,8 0,01 1210,98

(-9,6 : +11,0) (-21,0 : +25,1) (-18,6 : +21,9) (-9,9 : +11,3)

Uncertainty analysis: Monte-Carlo method

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18

Statistical data devided by disaggragated data (black dots – forest cover)

Validation of the approach: Ukraine, wood combustion

(19)

Validation of the approach: Poland, natural gas

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Conclusions

 A new understanding of the residential sector

 Lack of detailed data on FF combustion -> dissagregation ->

spatial uncertainty

 Validation and uncertainty analysis are important

components of spatial inventory

(21)

Thank you for your attention!

Olha Danylo

Interntional Institute for Applied Systems Analysis email: olha.danylo@gmail.com

danylo@iiasa.ac.at

(22)

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