Lecture: Advanced Environmental Assessments€¦ · Saner D, Vadenbo C, Steubing B, Hellweg S,...
Transcript of Lecture: Advanced Environmental Assessments€¦ · Saner D, Vadenbo C, Steubing B, Hellweg S,...
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Assessing the environmental impacts of
consumption and optimizing municipal
energy supply
15.11.2015Stefanie Hellweg 1
Lecture:
Advanced Environmental Assessments
||www.ifu.ethz.ch/ESD 15.11.2015Housing Case Stury 2
Example: Swiss consumption
• On average 12 t CO2-eq. per capita and year (Jungbluth et al. 2011)
More than 2/3 of total
climate change impact
from housing, mobility
and food consumption
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• To asses environmental impacts of housing, mobility, food
of all households in a region
• To characterize consumption patterns that lead to high,
medium and low-impact
• To compare actual CO2-eq emissions to the Swiss societal
goal of 1 t CO2-eq emissions/capita
• To optimize the system and set a theoretical benchmark
for improvement
15.11.2015Housing Case Stury 3
Study goals
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Spatial LCA Household-Consumption-Model
Slide: Andi Frömelt 2015
Modelling and comparing the environmental impacts from mobility, housing
and food consumption of individual households
Housing EnergyDemand per Household
Environmental Impacts from Housing per
Household
Mobility Demand per Household
Environmental Impacts from Mobility per
Household
Housing EnergyDemand Model
MATSim-Simulations
Housing EnergyDemand per
Building
Mobility Demand per Agent
National Census
ecoinvent
Food Demand per Household
Environmental Impacts from Food per Household
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Building model
Material /
component
service life l
Material /
component
service life l
Energy demand
Q(t)
Space heat
demand Qh(t)
Hot water
demand QDHW(t)
Electricity
demand Qapp(t)
building
data
service life l
Occupation
Ob
Geometry
Geo-reference
(x,y,z)
Type of material
i
Surface areas
Ak
Surface
composition
dk,b
Energy carrier
j
Occupation
Ob
Geometry
Geo-reference
(x,y,z)
Type of material
i
Surface areas
Ak
Surface
composition
dk,b
Energy carrier
j
Occupation
Ob
Geometry
Geo-reference
(x,y,z)
Type of material
i
Surface areas
Ak
composition
dk,b
Energy carrier
j
Occupation
Ob
Geometry
Geo-reference
(x,y,z)
Type of material
i
Surface areas
Ak
composition
dk,b
Occupation
Ob
Geometry
Geo-reference
(x,y,z)
Type of material
i
Age class
Yb
Surface areas
Ak
Composition
dk,b
household
data
climate
Final demand
(energy)
𝑓𝐸 =⋯
⋮ ⋱ ⋮⋯
final
demand
Final demand
(material)
𝑓𝑀 =⋯
⋮ ⋱ ⋮⋯
LCA
dataMaterial
volume
Vi
material
data
Database
management
Service life l
1 Database 2 Building model 3 Processing 4 LCIA
Material mass
Mi(t)
Initial
construction M0
Demolition Mrem
Maintenance
Mren(t)
Slide: Niko Heeren
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Heat, warm water, electricity, infrastructure
Heat balance for every apartment in Switzerland
15.11.2015Housing Case Stury 6
Housing
, , , , ,
end
begin
t
h T t V t g s t iP t iEl t
t t
Q Q Q Q Q Q
Transmission losses Ventilation losses
Solar gains Internal gains Waste heat electricity use
Heat demandHeat storage capacity of
building
Source: Saner et al., ES&T
2013
, ,
1,
, ,0 0
n
i t a t j j
jT t
i t a t
T T A UQ
if T T
Ti: ambient room temperature (e.g. 20°C)
Aj: area of building component j
Uj: heat transfer coefficient
: air exchange rate
density of air
ca: specific heat capacity of air.j
, ,
,
, ,0 0
i t a t a a
V t
i t a t
T T V cQ
if T T
V
a
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Evaluation of model results (case study of
Zernez)
Andi Frömelt, submitted to JIE, 2015
Model
Empirical
data
Buildings, sorted by
heating demand (n=133)
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Results: Per-capita climate change impact of
housing (example of the municipality of Wattwil)
Cumulative relative impact
(%) for housing and mobility
Saner D, Heeren N,
Jäggi B, Waraich BA,
Hellweg S, Housing
and Mobility
Demands of
Individual Households
and their Life Cycle
Assessment,
Environmental
Science and
Technology 47 (11),
5988-5997, 2013
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• Census data matched to buildings
• Cluster analysis household characteristics like number of household members, income, age of household members, education, average apartment area per person.
10% of households emit less than 1 t CO2/capita: typically old people or families with young children, small living space, renewable energy supply
20% of households emit more than 50% of the total emissions of the municipality: fossil heating systems and large living space
Impact was independent of income in this case
15.11.2015Housing Case Stury 9
Characteristics of high- and low-impact
households
Saner D, Heeren N, Jäggi B, Waraich BA, Hellweg S, Housing and Mobility Demands of Individual Households and their Life
Cycle Assessment, Environmental Science and Technology 47 (11), 5988-5997, 2013
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• Minimize environmental impacts
• Meeting the demand of heated
living space, warm water and
electricity
• Subject to constraints (resource
availability, capacity restrictions,
regulations…)
• Single and multiple objective
optimization (Tan et al. 2008)
15.11.2015Housing Case Stury 10
Optimization
Saner D, Vadenbo C, Steubing B, Hellweg S, Regionalized LCA-based optimization of building energy supply: method and
case study for a Swiss municipality", Environmental Science and Technology 48 (13), pp 7651–7659, 2014
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Heating systems optimization: Current heating
Saner et al., ES&T, 2014
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Heating systems optimization
h: Impact score
I: Characterization factors
A: Technology matrix
B: Biosphere matrixSaner et al., ES&T, 2014
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• Assumption of best available standards for
refurbishment
• Space heat demand calculated for each individual
building with and without each refurbishment measure
(i.e. refurbished windows, refurbished roof, etc.)
difference is amount of space heat that could be
saved (i.e. supplied) from each renovation measure.
If buildings were recently renovated this difference is
close to zero, while for old buildings with poor
insulation the savings would be substantial.
15.11.2015Housing Case Stury 13
Modeling of refurbishment
Saner et al., ES&T, 2014
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• Heat: Oil, gas, wood (chips, logs, pellets), heat pumps (brine-water, air-water), district heat (wood chips), and polymer electrolyte membrane (PEM) fuel cell systems (co-generation of heat and electricity fueled by gas).
• Hot water could additionally be supplied by solar collector panels.
• Electricity: photovoltaic (PV) panels (ribbon-Si) mounted on roof tops, PEM fuel cells or electricity from the grid.
• Only account for usable fraction of heat from solar systems in summer
• Refurbishment as an option to reduce heat demand
15.11.2015Housing Case Stury 14
Energy supply options
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• PV: less than 50% of the roof tops
• No competition for roof area between PV panels and solar collectors (PV only occupy the residual roof area after subtracting the area of the solar collectors from the total roof area)
• Ground source heat pump systems only allowed for buildings situated in designated areas (low risk of groundwater contamination by leaking)
• District heat grid only in suitable areas
• Locally available wood chips less than 10,000 m3/year and that of wood logs 2,500 m3/year
• Supply with wood from outside the region assumed to be zero
• PEM fuel cells only run with natural gas or purified biogas (only in buildings with a connection to the gas network)
15.11.2015Housing Case Stury 15
Heating systems optimization: constraints
Saner et al., ES&T, 2014
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Single-objective optimization (carbon footprint)
• Only some refurbishment
Source: Saner et al. ES&T, 2014
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Single-objective optimization (respiratory
effects)
• Refurbishment almost everywhere
Source: Saner et al., ES&T, 2014
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• Carbon footprint and respiratory impacts (particulate
emissions)
• Lower and upper bounds determined from single-objective
optimization
15.11.2015Housing Case Stury 18
Multi-objective optimization
Source: Saner et al., ES&T, 2014
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Heating systems optimization:
Multi-objective optimization
Source: Saner et al., ES&T, 2014
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Refurbishment of buildings:
Multi-objective optimization
Windows Roof
Floor Wall
Saner et
al., ES&T,
2014
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Hot water supply:
Multi-objective optimization
Source: Saner et al., ES&T, 2014
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Heating systems optimization: global warmig
potential (multi-objective optimization)
Climate change impact
from housing could
(theoretically) be
lowered by > 75%
Source: Saner et al., ES&T, 2014
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Model:
MATSim: agent based
transport model
Car, public transportation,
bikes, walking
Travel distance and used
vehicle for all inhabitants
15.11.2015Housing Case Stury 23
Mobility (only land-based transport)
Validation:
Meister et al. (2008)
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Results: Per-capita climate change impact of
land-based mobility (example of Wattwil)
Source: Saner et al. 2013
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Housing and mobility –
spatial representation of results
Source:Saner et al., ES&T, 2013
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Based on household budget enquiry
Generalized linear models
15.11.2015Housing Case Stury 26
In-house food consumption
Validation (example of chocolate consumption)
Saner et al., International Journal of LCA, 2015
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Results: Per-capita climate change impact of in-
house food consumption (example of Wattwil)
Cumulative relative impact
(%)
Meat
and
fish
4
3
2
1
Dairy
products
and eggs
Saner et al., International Journal of LCA, 2015
||www.ifu.ethz.ch/ESD 15.11.2015Housing Case Stury 28
Improvement potentials for food consumption
• Less animal products
• Reduction of food waste
Beretta C, Stössel F, Baier U, Hellweg S, Quantifying food losses and the potential for reduction
in Switzerland, Waste Management 33 (3), 2013, 764–773
Animal
produc
tion
Hu-
man
in-
take
Crop
pro-
duction Post-
harvest
hand-
ling
and
trade
Proces
sing
Retailin
g
House-
holds
Restau-
rants
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• We are not even close to the 1 t CO2-eq/capita goal
• Significant improvement potentials exist:
• Housing: renewable energy supply and efficiency gains can cut down greenhouse gas emissions significantly
• Mobility: large spread in current impact between different households; 50% of mobility for leisure
• Food: reduction in meat consumption and food waste
• Outlook: complete and further valuate models; follow-up studies with further municipalities with stakeholder involvement
15.11.2015Housing Case Stury 29
Conclusions
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Waste and resource management
?
Waste DResource D
Resource C
Waste C
Waste B
Resource B
Waste AResource A
Picture: C. Vadenbo 15.11.2015Stefanie Hellweg 30
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1. Depart from MFA of status quo
2. Define availabilities of resources and their potential uses
3. Define required functionalities of the system and system constraints
4. Run optimization algorithm and/or scenario analysis, minimizing
environmental impacts (and costs)
5. Output: set of material flows (if dynamically implemented also
stocks) that minimizes environmental impacts while satisfying the
defined needs
15.11.2015Stefanie Hellweg 31
Approach – combine MFA and LCA
Stefanie Hellweg
P P
Picture: B. Steubing 3115.11.2015
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Allows to use synergies of MFA and LCA: consider restrictions
concerning resource, technology and capacity limitations (capability
of MFA) and use environmental criteria (capability of LCA) for finding
optimal scenarios
Allows to combine various objectives, e.g. several environmental and
economic objectives
Analyzes a much larger set of scenarios than typically done in MFA or
LCA studies and returns pareto-efficient solutions
Combination of mathematical optimization, MFA and
LCA