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Integration of Waste Supply and Use Data into Regional Footprints: - - PowerPoint PPT Presentation

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CIRP Life Cycle Engineering Conference 2018 Integration of Waste Supply and Use Data into Regional Footprints: Case Study on the Generation and Use of Waste from Consumption and Production Activities in Brussels Vanessa Zeller, Edgar Towa,

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SLIDE 1

01/05/2018

Integration of Waste Supply and Use Data into Regional Footprints:

Case Study on the Generation and Use of Waste from Consumption and Production Activities in Brussels

Vanessa Zeller, Edgar Towa, Marc Degrez, Wouter M.J. Achten

CIRP Life Cycle Engineering Conference 2018

This research is conducted in the frame of the BRUCETRA project funded by the Brussels’ capital region – Innoviris (2015-PRFB-3a)

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SLIDE 2

Context- Case Study

2

Context Method Results Conclusions

Research scale

  • at city/region level
  • economy/system-wide
  • → political decision-making

Application fields

  • Waste management/CE
  • Transport, energy system
  • Household consumption

Data framework/method

  • Input-output (IO)data
  • (EE)- IOA

Data requirement

  • Physical waste flows

Material flows & environmental impacts?

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SLIDE 3

Context- Case study

3

Context Method Results Conclusions Flanders Wallonia Brussels Flanders Wallonia Brussels PREC 2016 Waste Plan

Brussels

  • Comparison of

environmental footprints

  • →Waste flows & footprint
  • New waste plan & CE
  • Demand to monitor waste

performance

  • Support decision-makers

with IA of future scenarios

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SLIDE 4

Method – Theoretical Framework of the WIO Model

4

Context Method Results Conclusions Waste treatment allocation table Waste supply table T=products W=Waste Lenzen & Reynolds 2014

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SLIDE 5

Method – Model Construction

5

Context Method Results Conclusions

Waste supply table (Brussels)

  • 1. Statistical data (xt of residual waste)
  • 2. Conversion (xt of organic in residual waste)
  • 3. Linkage with economic sectors (xt of organic

waste in HORECA)

Waste supply table (Brussels 2014) Economic activities (j) Total Final demand Total 1 2 3 … 81 1-81 HH Imp. EA+HH Waste type (k) (ton per year) Glass 14,099 24,998 39,097 Inert 556,608 96,260 652,869 Metals 167,232 33,084 200,317 Organic waste 55,000 105,014 160,014 Paper & cardboard 126,315 60,586 186,900 Plastic 57,517 45,108 102,625 Textile 11,132 14,528 25,660 Wood 42,789 20,487 63,276 Other 39,634 26,924 66,558 Garden waste 14,856 26,449 41,305 Total 1,070,327 426,988 1,538,620

EA= Economic activities; HH=Households

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SLIDE 6

Method – Model Construction

6

Context Method Results Conclusions

Waste treatment allocation table

  • 1. Data collection
  • 2. Data conversion (waste type)

Economic activities (j) Total Final demand Total 1 2 3 …

  • Mat. rec.

… Inci. Comp. … 1-81 HH Exp. EA+Exp. Waste type (k) (ton per year) Glass 9,806 9,806 29,291 39,097 Inert 177,500 11,132 188,632 464.236 652,869 Metals 90,493 11,504 101,997 98,320 200,317 Organic waste 153,337 153,337 6,677 160,014 Paper & cardboard 51,603 64,599 116,202 70,699 186,900 Plastic 12,226 64,899 77,126 25,499 102,625 Textile 1,481 21,677 23,158 2,502 25,660 Wood 13,359 13,359 49,917 63,276 Garden waste 16,111 17,839 33,950 7,355 41,305 Other 56,175 56,175 10,382 66,558 Total 333,302 422,600 17,839 773.741 764.879 1,538,620

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SLIDE 7

Method – Model Construction

7

Context Method Results Conclusions Waste treatment allocation table

  • 1. Data collection
  • 2. Data conversion (waste type)
  • 3. Waste allocation x waste

supply table Example: Food waste

Waste treatment allocation table (S1) for economic activities Glass Inert Metals Food waste Paper & cardboard Plastic Textile Wood Garden waste Other

  • Mat. recovery

0.00 0.26 0.45 0.00 0.16 0.00 0.00 0.00 0.00 0.00 Reuse 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Incineration 0.35 0.01 0.07 0.89 0.30 0.75 1.00 0.33 0.30 0.85 Composting 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21 0.00

  • Ex. to mat. rec.

0.65 0.42 0.49 0.03 0.55 0.02 0.00 0.45 0.00 0.15

  • Ex. to inc.

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.00 0.00

  • Ex. to comp.

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.50 0.00

  • Ex. to landf.

0.00 0.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

  • Ex. to AD.

0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00

  • Ex. to unk.

0.00 0.18 0.00 0.00 0.00 0.22 0.00 0.16 0.00 0.00

  • Ex. to reuse

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

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SLIDE 8

Results – Urban Waste Flows

8

Context Method Results Conclusions Local reuse rate

  • f 1.4%

Local WT: 50% Local rec.: 20% MSW recycling rates: 23% Global:60%

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SLIDE 9

From flow analysis to impact assessment

9

Context Method Results Conclusions

Emissions to air

B41

(t)

B42

(t)

BF

(t)

Emissions to water B41

(t)

B42

(t)

BF

(t)

Resource uses B41

(t)

B42

(t)

BF

(t)

Environmental extension B Impact assessment method: ReCiPe, hierachist

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SLIDE 10

10

0,5 1 1,5 2 2,5 3 3,5 4

Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Br. Fl. W. Climate change Terrestrial acidification Freshwater eutrophication Marine eutrophication Human toxicity Photochemical

  • xidant

formation Particulate matter formation Marine ecotoxicity Agricultural land occupation Metal depletionFossil depletion

Midpoints per cap. (2010)

Direct household impact 1: Food 3: Clothing and footwear 4: Housing, water, electricity, gas and other fuels 5: Household equipment & maintenance 6: Health 7: Transport 8: Communication 9: Recreation and culture 10: Education 11: Restaurants and hotels 12: Miscellaneous goods and services

Context Method Results Conclusions

Impact assessment- regional footprint

=12 ton CO2-eq. /capita

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SLIDE 11

11

Context Method Results Conclusions

Impact assessment- Brussels waste treatment sector

5.000 10.000 15.000 20.000 25.000 30.000 35.000 40.000 45.000

Human Health Ecosystems Resources

Normalised impacts (points)

Waste incineration Recycling Biological Waste treatment

Existing situation Evaluation of new scenarios

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SLIDE 12

12

Context Method Results Conclusions

Access to

  • Regional IO data
  • National physical IO data
  • Waste statistical data
  • Waste composition data
  • Waste treatment

statistics

Conclusions

  • Development of WIO model feasible at

city/regional level

  • → High data requirements
  • →Integration into MRIO model
  • Environmental impacts & waste performance
  • f Brussels deviate from other regions
  • →city/region scale is needed
  • Powerful, because flexible tool (MFA &

impacts)

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SLIDE 13

Vanessa Zeller, PhD

Postdoctoral Researcher

Université Libre de Bruxelles (ULB) IGEAT-GESTe

Avenue F.D. Roosevelt, 50 (CP 165/63), 1050 Brussels (Belgium) Tel : +32 (0)2 650 4333 Mail : vzeller@ulb.ac.be

CONTACT INFORMATION

Thank you for your attention

This research is conducted in the frame of the BRUCETRA project funded by the Brussels’ capital region – Innoviris (2015-PRFB-3a)