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Aggregation Effect in Carbon Footprint Accounting by the Multi-Region Input-Output Model

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Aggregation Effect in Carbon Footprint Accounting by the Multi-Region Input-Output Model 19th International Input-Output Conference 14 June 2011 – PowerPoint PPT presentation

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Title: Aggregation Effect in Carbon Footprint Accounting by the Multi-Region Input-Output Model


1
Aggregation Effect in Carbon Footprint Accounting
by the Multi-Region Input-Output Model
  • 19th 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
  • Integrated Sustainability Analysis, University of
    Sydney, Australia

2
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.

3
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.

4
Methodology 1
  • Carbon footprint calculation
  • c carbon intensity
  • es carbon footprints in region s
  • ? element multiplication.

5
Methodology 2-1
  • Sectoral aggregation scheme

6
Methodology 2-2
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 n?m(1), n?m(2),, , and n?m(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.
7
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
8
Methodology 3
  • Aggregation error and measurement
  • Define aggregation error as
  • aggregation error rate (in ) as

9
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.

10
Results 1-1
  • Aggregation error rates Aggregated sectors (in )

Region Minimum Average Maximum Standard Deviation
IDN -0.734 0.107 2.677 0.140
MYS -12.176 0.037 11.471 0.162
PHL -8.139 0.057 29.343 0.271
SGP -0.438 0.066 1.885 0.162
THA -2.941 0.044 165.589 0.713
CHN -478.753 0.167 119.134 2.149
TWN -1.384 0.072 8.798 0.119
ROK -0.527 0.029 6.869 0.100
JPN -7.846 0.065 4.558 0.100
USA -0.355 0.092 1.574 0.088
11
Results 1-2
  • Aggregation error rates Non-aggregated sectors
    (in )

Region Minimum Average Maximum Standard Deviation
IDN -0.733 -0.023 16.585 0.162
MYS -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 0.074
USA -0.826 -0.043 3.596 0.106
12
Results 1-3
  • Fig. 1 Distribution of error rates in ten
    economies

13
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.

14
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.

15
Thank you for your attention!
  • Contact
  • Xin Zhou at zhou_at_iges.or.jp
  • Hiroaki Shirakawa at sirakawa_at_urban.env.nagoya-u.a
    c.jp
  • Manfred Lenzen at m.lenzen_at_physics.usyd.edu.au
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