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19 th International Input-Output Conference 14 June 2011 Hiroaki Shirakawa Graduate School of Environmental Studies, Nagoya University, Japan In collaboration with Xin Zhou Institute for Global Environmental Strategies, Japan Manfred Lenzen - PowerPoint PPT Presentation
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Aggregation Effect in Carbon Footprint Accounting by the Multi-
Region Input-Output Model
19th International Input-Output Conference14 June 2011
Hiroaki ShirakawaGraduate School of Environmental Studies, Nagoya
University, Japan
In collaboration with
Xin ZhouInstitute for Global Environmental Strategies, Japan
Manfred LenzenIntegrated Sustainability Analysis, University of
Sydney, Australia
Motivations
Conventional EIA and emissions accounting at firm, project or product levels.
Useful applications of IO analysis to account for both direct and indirect environmental impacts.
Limitations of IO tables for practical environmental assessment due to the aggregation of similar products, processes and sampled firms.
Aggregation error in IO analysis and its importance in environmentally extended IO analysis.
Purpose
To examine the aggregation effects in carbon footprint accounting using MRIO
To analyse the range of aggregation errors by Monte Carlo simulations; and
To find major factors influencing the size of errors.
Methodology 1
1.Carbon footprint calculation
c: carbon intensity; es: carbon footprints in region
s;: element multiplication.
Methodology 2-1
2. Sectoral aggregation scheme
sectors. n)( )( into aggregated are Region in sectors
sectors; n)( )2( into aggregated are 2Region in sectors
sectors; n)( )1( into aggregated are 1Region in sectors
2
1
2
1
21
22221
t11211
2
1
tmtn
mn
mn
g
g
g
x
x
x
AAA
AAA
AAA
x
x
x
s
ts
s
ss
s
ttttt
t
t
Methodology 2-2
)(000
000
00)2(0
000)1(
ts
s
s
S
s(1), s(2),…, and s(t) are block summation matrices for region 1, 2, …, and t, with the size m(1)n, m(2)n, …, and m(t)n, respectively.
s’(1), s’(2),…, and s’(t) are the transposed matrices of s(1), s(2),…, and s(t), with the size nm(1), nm(2),, …, and nm(t), respectively.
Each column of the block summation matrix has one and only one number “1”. However each row can have more than one “1”, which determines which sectors to be aggregated.
)('000
000
00)2('0
000)1('
'
ts
s
s
S
Methodology 2-3
Variables Before aggregation After aggregation
Intermediate demand X Y = S X S’
Final demand g h = S g
Total output x y = S x
Carbon intensity c d = S (c x) y-1
rsijrs xXX rsijrs yYY rir ggg rir hhh rir xxx rir yyy rsijrs aAA rsijrs bBB rsijrs lLL rsijrs mMM rir ccc rir ddd sis eee sis fff
Methodology 3
3. Aggregation error and measurement
Define: aggregation error as aggregation error rate (in %) as
Method 2Method1 (Reference)
Carbon footprint calculation using the large-sized MRIO and carbon intensity
Aggregation of the large-sized MRIO table and the carbon intensity
Summation matrix S
Aggregation of regional carbon
footprints
Summation matrix S
Carbon footprint calculation using the aggregated MRIO and carbon intensity
sif
sif
)( si
si ff
si
si
si fff /)(
Data and Simulations
AIO2000 (IDE, 2006): 76 sectors and ten Asian-Pacific regions (IDN, MYS, PHL, SGP, THA, CHN, TWN, ROK, JPN, USA);
GTAP-E database on emissions intensity: 57 sectors;
Sector matching;
Determination of the summation matrix S randomly by Monte-Carlo simulations for 100,000 times
(i) Randomly determine the number of selected regions; (ii) Randomly determine which regions to be
selected; (iii) Randomly determine the number of sectors to
be aggregated for each selected region; (iv) Randomly determine which sectors to be selected
for each selected region.
Results 1-1
Aggregation error rates: Aggregated sectors (in %)
Region Minimum Average Maximum Standard Deviation
IDN -0.734 0.107 2.677 0.140MYS -12.176 0.037 11.471 0.162PHL -8.139 0.057 29.343 0.271SGP -0.438 0.066 1.885 0.162THA -2.941 0.044 165.589 0.713CHN -478.753 0.167 119.134 2.149TWN -1.384 0.072 8.798 0.119ROK -0.527 0.029 6.869 0.100JPN -7.846 0.065 4.558 0.100USA -0.355 0.092 1.574 0.088
Results 1-2
Aggregation error rates: Non-aggregated sectors (in %)
Region Minimum Average Maximum Standard Deviation
IDN -0.733 -0.023 16.585 0.162MYS -0.758 -0.040 5.779 0.118
PHL -0.669 -0.042 5.309 0.100
SGP -0.876 -0.049 3.349 0.127
THA -0.772 -0.030 6.237 0.107
CHN -0.806 -0.036 3.548 0.115
TWN -0.595 -0.031 5.072 0.094
ROK -0.681 -0.025 4.338 0.066
JPN -0.694 -0.038 2.906 0.074USA -0.826 -0.043 3.596 0.106
Results 1-3
Fig. 1 Distribution of error rates in ten economies
Fig.1 Distribution of error rates in ten economies
Indonesia Thailand Japan
Malaysia China Mainland USA
Philippines Taiwan
Error rate Error rate
Error rate
Error rate
Error rate
Error rate
Error rate
Error rate
Density (
%)
Density (
%)
Density (
%)
Density (
%)
Density (
%)
Density (
%)
Density (
%)
Density (
%)
-2 -1 0 1 2
0 5 10 15 20 25 30
Singapore Korea
Density (
%)
Density (
%)
Error rate Error rate
Results 2-2
Factors influencing the size of aggregation errors by ranking top 300 aggregation errors:
High concentrations in particular regions: CHN (140), PHL (105), IDN (22), MYS (14), ROK (13), and JPN (3) and SGP (3).
High concentrations in particular sectors: CHN (“Iron and steel”/87 times, “Chemical fertilizers and pesticides”/25 times; PHL (“Crude petroleum and natural gas”/104 times); IDN (“Iron and steel”/20 times); MYS (“Non-metallic ore and quarrying”/13 times); ROK (“Timber”/5 times); JPN (“Cement and cement products”/2 times); SGP (“Electricity and gas”/2 times, “Building construction”/2 times).
Characteristics of these sectors: relatively higher carbon intensity in their specific regions, but a less contribution to the final demand of their relevant aggregated sectors.
Conclusions
Large range of error rates (-479, 166), indicating sector aggregation has large effects on carbon footprint accounting using MRIO;
More aggregation effects on aggregated sectors than on non-aggregated sectors.
High concentration of top errors in specific regions and specific sectors, indicating their greater impacts on the size of aggregation errors. For practitioners, exclusion of these sectors in their aggregation schemes will greatly decrease the size of errors.
Relatively higher carbon intensity and relatively lower contributions to the final demand of the aggregated sectors will make a sector distinguished in terms of its effects on the size of aggregation error. For practitioners, pre-examination of this potential relationship can help find distinguished sectors during the design of aggregation scheme.
Thank you for your attention!
Contact:
Xin Zhou at [email protected] Shirakawa at [email protected] Lenzen at [email protected]