lecture: advanced environmental assessments · calorific value of burned coal 27.7 mj/kg 15.7 mj/kg...
TRANSCRIPT
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Spatial LCA
20.01.2017Stefanie Hellweg 1
Lecture:
Advanced Environmental Assessments
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Learning Goals
1. Understand when site-dependent models are needed
2. Understand the computational structure of regionalized
LCA (inventory analysis and impact assessment)
3. Get to know examples of regionalized LCA case studies
Advanced Environmental Assessments
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Why regionalization in LCA?
• Background inventory data may change as a function of site
(e.g. different technologies).
• Emissions and resource use flows may have a different impact,
depending on the site (e.g. different sensitivities, background
concentrations, …).
Regionalization (LCI and LCIA data) can reduce uncertainties
due to spatial variability.
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Spatial differences in LCI
• Example of electricity from coal power plants
Germany India
Calorific value of burned coal 27.7 MJ/kg 15.7 MJ/kg
Emission abatement Desulphurisation,
denitrification, and dedusting
operating in most power
plants
In most power plants only
dedusting
CO2 92 g/MJ coal 96 g/MJ coal
NOx 0.06 g/MJ coal 0.63 g/MJ coal
SO2 0.07 g/MJ coal 0.89 g/MJ coal
PM2.5 0.005 g/MJ coal 0.202 g/MJ coal
Net efficiency of power plant 36 % 32 %
ecoinvent 3.1
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0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
India Germany
ReCiPe points/kWh
Remaining substances
Nitrogen oxides
Sulfur dioxide
Particulates, < 2.5 um
Hard coal in ground
Carbon dioxide, fossil
Differences in impact due to spatial variability in LCI data
• Example of electricity from coal power plants
ecoinvent 3.1
20.01.2017 5Advanced Environmental Assessments
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Source: Ecoinvent 2.2
Differences in impact due to spatial variability in LCI data
• Climate change effects of electricity supply mixes in Europe
20.01.20176
Advanced Environmental Assessments
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Mutel et al. Env Sc & Technology, 2011, Hellweg & Mila y Canals, Science, 2014
• 558 coal power plants
• 1,322 gas power plants
• 1,296 hydropower plants
• 51 nuclear power plants
• 1,230 other plants
(mainly renewables)
Differences in impact due to spatial variability in LCI data
• Climate change effects of US electricity supply technologies
20.01.2017 7Advanced Environmental Assessments
||www.ifu.ethz.ch/ESD 20.01.2017Bernhard Steubing 8
Spatial differences in LCI: new approach
followed by ecoinvent v3
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Geographical impact distribution (climate
change)
Steubing B, G Wernet, J Reinhard, C Bauer, E Moreno, The ecoinvent database version 3 (part II):
analyzing LCA results and comparison to version 2. accepted by Int J LCA, 2015
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Electricity related impacts (climate change) in
global and Euorpean datasets
v2.2
v3.1
Global Europe
Steubing B, G Wernet, J Reinhard, C Bauer, E Moreno, The ecoinvent database version 3 (part II):
analyzing LCA results and comparison to version 2. accepted by Int J LCA, 2015
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How can spatial inventory data and impact
assessment be combined?
*
Spatial characterization factorsSpatial LCI
Spatial Impact scores
2015Advanced Environmental Assessments 11
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Recap: Computational structure of site-generic LCA
Output
Inte
rmedia
te
pro
ducts
as
inputs
Environm
enta
l
inte
rventions
A = demand matrix (nxn)
Technosphere (economical
system)
B = biosphere matrix (mxn)
(environmental interventions)
a11 a12 … a1n
a21 a22 … a2n
. A .
. .
an1 an2 … ann
b11 b12 … b1n
b21 b22 … b2n
. B .
. .
bm1 bm2 … bmn
For background reading: R. Heijungs et al., The Computational Structure of Life
Cycle Assessment, Springer Science+Business Media Dordrecht 2002
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Recap: Computational structure of site-generic LCA
For background reading: R. Heijungs et al., The Computational Structure of Life
Cycle Assessment, Springer Science+Business Media Dordrecht 2002
Slide source: Chris Mutel 2013
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Recap: Computational structure of site-generic LCA
For background reading: R. Heijungs et al., The Computational Structure of Life
Cycle Assessment, Springer Science+Business Media Dordrecht 2002
Slide source: Chris Mutel 2013
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Recap: Computational structure of site-generic LCA
For background reading: R. Heijungs et al., The Computational Structure of Life
Cycle Assessment, Springer Science+Business Media Dordrecht 2002
)()( 1 fdiagAIB
h: total environmental impact
diag(c): diagonal matrix constructed from the characterization vector c
diag(f): diagonal matrix constructed from the demand vector f
I : identity matrix,
)()( 1 fdiagAIBcdiagh Impact:
Inventory:
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How can spatial inventory data and impact
assessment be combined (computational structure)?
Mutel et al. Environmental Science & Technology, 2011
• R is the characterization matrix, which has rows of spatial
units and columns of environmental flows.
• M is the inventory mapping matrix with rows of technological
processes, and columns of inventory spatial units. It indicates
where a technological process occurs.
• If the inventory spatial scale is not the same as the LCIA spatial
scale, we need a geographic transform matrix G; G has rows
of inventory spatial units and columns of LCIA spatial units. Row
values represent the proportional area of an inventory spatial unit
that is located in each LCIA spatial unit (rows sum to 1).
2015Advanced Environmental Assessments 16
ℎ = (𝑀𝐺𝑅)𝑇°𝐵 𝐼 − 𝐴 −1𝑑𝑖𝑎𝑔(𝑓)
“◦”: element-wise multiplication (Hadamard product)
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How can spatial inventory data and impact
assessment be combined (computational structure)?
Picture source: C. Mutel 2013; Mutel et al. Environmental Science & Technology, 2011
• M : inventory mapping matrix
ℎ = (𝑀𝐺𝑅)𝑇°𝐵 𝐼 − 𝐴 −1𝑑𝑖𝑎𝑔(𝑓)
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How can spatial inventory data and impact
assessment be combined (computational structure)?
• R : characterization matrix; example:
Picture source: C. Mutel 2013; Mutel et al. Environmental Science & Technology, 2011
ℎ = (𝑀𝐺𝑅)𝑇°𝐵 𝐼 − 𝐴 −1𝑑𝑖𝑎𝑔(𝑓)
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How can spatial inventory data and impact
assessment be combined (computational structure)?
• G : geographic transform matrix
Picture source: C. Mutel 2013; Mutel et al. Environmental Science & Technology, 2011
ℎ = (𝑀𝐺𝑅)𝑇°𝐵 𝐼 − 𝐴 −1𝑑𝑖𝑎𝑔(𝑓)
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How can spatial inventory data and impact
assessment be combined (computational structure)?
ℎ = (𝑀𝐺𝑅)𝑇°𝐵 𝐼 − 𝐴 −1𝑑𝑖𝑎𝑔(𝑓)
5.075.0
75.21
75.21
525.1
5.075.0
01
5.05.0
5.05.0
525.1
5.075.0
01
5.05.0
10
01
01
i ii
A
B
C
em1 res1 em1 res1
A
B
C
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How can spatial inventory data and impact
assessment be combined (computational structure)?
Picture source: C. Mutel 2013; Mutel et al. Environmental Science & Technology, 2011
…
Technical processes
Em
issio
ns/r
esourc
es
…
Technical processes
Em
issio
ns/r
esourc
es
AmountsCharacterization factors
(process related)
◦
ℎ = (𝑀𝐺𝑅)𝑇°𝐵 𝐼 − 𝐴 −1𝑑𝑖𝑎𝑔(𝑓)
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Case study I: US power plants
Results for ecosystem damage of water consumption
Mutel et al. Environmental Science & Technology, 2011
2015Advanced Environmental Assessments 22
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Mutel et al. Env Sc & Technology, 2011, Hellweg & Mila y Canals, Science, 2014
2015Advanced Environmental Assessments 23
Case study I: US power plants
Results for ecosystem damage of water consumption
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Mutel et al. Env Sc & Technology, 2011, Hellweg & Mila y Canals, Science, 2014
2015Advanced Environmental Assessments 24
Case study I: US power plants
Results for ecosystem damage of water consumption
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Acidification
Emission sources: power plants, traffic, industry, animalraising
Excess inflow ofprotons (into soil orwater)
Ecosystem impacts
Examples:
• SOx H2SO4
• NOx HNO3
• NHx NH3
2015Advanced Environmental Assessments 25
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Spatial aspects and variability
Air emissions are transported over large distances
Soil and water systems have different buffer capacitiesand respond differently: carbonate buffered soils hardlychange while sensitive soil systems react strongly toacidification
2015Advanced Environmental Assessments 26
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Case study I: US power plants
Results for acidification
Mutel et al. Environmental Science & Technology, 2011
2015Advanced Environmental Assessments 27
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Mutel et al. Env Sc & Technology, 2011, Hellweg & Mila y Canals, Science, 2014
2015Advanced Environmental Assessments 28
Case study I: US power plants
Results for acidification
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Case study II: Worldwide land-occupation
impacts from agriculture and forestry
20.01.2017Advanced Environmental Assessments
2
9
Chaudhary et
al. 2016, ES&T
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Impacts of land use due to Swiss food
consumption
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• Forage, soybean, wheat and wine are the products imported most by
amount.
• Import share from Germany, France, Italy and Austria and the European
ports of Belgium and Netherlands large, but biodiversity impacts are
rather low.
• Cocoa beans and coffee imports were responsible for the biggest land-
use related biodiversity impact in South and Central America and
Southeast Asia.
• The total imported impacts are respectively 25, 52, 500 and 430
times higher than biodiversity loss due to domestic crop land use
for Swiss consumption in Switzerland.
• By contrast: the ratio of total land embodied in imported products
and net domestic agricultural land used for consumption was
approximately 5.
PhD project A. Chaudhary 2015
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Regionalization makes LCA more complex, but it can
Reduce uncertainties due to spatial variability (e.g. see
variability in impact assessment results of US power plants)
Identify regional hotspots of impact and possibly provide an
incentive to relocate production to less sensitive regions (see
case studies on US power plants and global agricultural
production and forestry)
Increase our understanding of regionally distributed value
chains (see Swiss food consumption case study)
Understand better the relation between the location(s) of
impacts and the responsible actors (see Swiss food
consumption case study)
Conclusions from the case studies
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Conclusions and Take-home messages
• Regionalization in LCI is necessary when e.g. technologies
vary as a function of space
• Regionalization in LCIA is needed when emissions or resource
use flows have a different impact, depending on the site
• There are computational methods to consider and combine
regionalized LCI and LCIA data, even if the spatial support is
different.
• Only some few software systems (OpenLCA, Brightway) are
currently able to deal with regionalization in LCIA
• LCI data is often not available in a properly regionalized manner
• Regionalized LCIA methods exist for several impact categories
(e.g. www.lc-impact.eu); development is ongoing