Lecture: Advanced Environmental Assessments€¦ · Saner D, Vadenbo C, Steubing B, Hellweg S,...

||www.ifu.ethz.ch/ESD

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


• 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


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


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


Heat, warm water, electricity, infrastructure

Heat balance for every apartment in Switzerland


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


Evaluation of model results (case study of

Zernez)

Andi Frömelt, submitted to JIE, 2015

Model

Empirical

data

Buildings, sorted by

heating demand (n=133)


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


• 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


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


• 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)


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


Heating systems optimization: Current heating

Saner et al., ES&T, 2014


Heating systems optimization

h: Impact score

I: Characterization factors

A: Technology matrix

B: Biosphere matrixSaner et al., ES&T, 2014


• 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.


Modeling of refurbishment



• 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


Energy supply options


• 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)


Heating systems optimization: constraints



Single-objective optimization (carbon footprint)

• Only some refurbishment

Source: Saner et al. ES&T, 2014


Single-objective optimization (respiratory

effects)

• Refurbishment almost everywhere

Source: Saner et al., ES&T, 2014


• Carbon footprint and respiratory impacts (particulate

emissions)

• Lower and upper bounds determined from single-objective

optimization


Multi-objective optimization



Heating systems optimization:




Refurbishment of buildings:


Windows Roof

Floor Wall

Saner et

al., ES&T,

2014


Hot water supply:




Heating systems optimization: global warmig

potential (multi-objective optimization)

Climate change impact

from housing could

(theoretically) be

lowered by > 75%



Model:

MATSim: agent based

transport model

Car, public transportation,

bikes, walking

Travel distance and used

vehicle for all inhabitants


Mobility (only land-based transport)

Validation:

Meister et al. (2008)


Results: Per-capita climate change impact of

land-based mobility (example of Wattwil)

Source: Saner et al. 2013


Housing and mobility –

spatial representation of results

Source:Saner et al., ES&T, 2013


Based on household budget enquiry

Generalized linear models


In-house food consumption

Validation (example of chocolate consumption)

Saner et al., International Journal of LCA, 2015


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


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


• 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


Conclusions


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


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


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

Lecture: Advanced Environmental Assessments€¦ · Saner D, Vadenbo C, Steubing B, Hellweg S,...

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