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Aggregation Effect in Carbon Footprint Accounting by the Multi-Region Input- Output Model 19 th International Input-Output Conference 14 June 2011 Hiroaki.

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Presentation on theme: "Aggregation Effect in Carbon Footprint Accounting by the Multi-Region Input- Output Model 19 th International Input-Output Conference 14 June 2011 Hiroaki."— Presentation transcript:

1 Aggregation Effect in Carbon Footprint Accounting by the Multi-Region Input- Output Model 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 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; e s : 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 VariablesBefore aggregationAfter aggregation Intermediate demandXY = S X S’ Final demandgh = S g Total outputxy = S x Carbon intensityc d = S (c  x) y -1

8 Methodology 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

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 %) RegionMinimumAverageMaximum Standard Deviation IDN-0.7340.1072.6770.140 MYS-12.1760.03711.4710.162 PHL-8.1390.05729.3430.271 SGP-0.4380.0661.8850.162 THA-2.9410.044165.5890.713 CHN-478.7530.167119.1342.149 TWN-1.3840.0728.7980.119 ROK-0.5270.0296.8690.100 JPN-7.8460.0654.5580.100 USA-0.3550.0921.5740.088

11 Results 1-2 Aggregation error rates: Non-aggregated sectors (in %) RegionMinimumAverageMaximum Standard Deviation IDN -0.733-0.02316.5850.162 MYS -0.758-0.0405.7790.118 PHL -0.669-0.0425.3090.100 SGP -0.876-0.0493.3490.127 THA -0.772-0.0306.2370.107 CHN -0.806-0.0363.5480.115 TWN -0.595-0.0315.0720.094 ROK -0.681-0.0254.3380.066 JPN -0.694-0.0382.9060.074 USA -0.826-0.0433.5960.106

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@iges.or.jpzhou@iges.or.jp Hiroaki Shirakawa at sirakawa@urban.env.nagoya-u.ac.jpsirakawa@urban.env.nagoya-u.ac.jp Manfred Lenzen at m.lenzen@physics.usyd.edu.aum.lenzen@physics.usyd.edu.au

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