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Supporting Information for
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Life-Cycle Costing of Food Waste Management in
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Denmark: Importance of indirect effects
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Martinez-Sanchez, V. et al., 2016. Life-Cycle Costing of Food Waste Management in Denmark: Importance
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of Indirect Effects. Environmental science & technology, 50(8), pp.4513–4523
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Veronica Martinez-Sanchez*1, Davide Tonini1, Flemming Møller2and Thomas Fruergaard Astrup1
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1 Technical University of Denmark, Department of Environmental Engineering, Miljoevej, Building
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113, 2800 Kgs. Lyngby, Denmark
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2Aarhus University, Department of Environmental Science, Frederiksborgvej 399, 4000 Roskilde,
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Denmark
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*) Corresponding author: Address: Miljoevej, Building 113, 2800 Kgs. Lyngby, Denmark; Phone:
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(0045) 45251602; e-mail:vems@env.dtu.dk
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This supporting information is 36 pages long and contains 16 tables and 3 figures.
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S2
Contents
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I. Food Commodities .................................................................................................................................... 3
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II. Marginal products/technologies ................................................................................................................ 5
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A. Energy.................................................................................................................................................... 5
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B. Fertilizers ............................................................................................................................................... 5
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C. Fodder .................................................................................................................................................... 6
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D. Income effect ......................................................................................................................................... 6
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III. Method ................................................................................................................................................... 6
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A. Income Effect ........................................................................................................................................ 8
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B. iLUC .................................................................................................................................................... 10
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IV. Environmental and Economic inventories ........................................................................................... 16
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V. Upstream food commodities - Factor prices & Ecoinvent names ........................................................... 18
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VI. Accounting prices of emissions ........................................................................................................... 20
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VII. Detailed Results ................................................................................................................................... 21
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A. Environmental LCC: Economic part ................................................................................................... 21
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B. Environmental LCC: Environmental part (LCA) ................................................................................ 23
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C. Societal LCC ....................................................................................................................................... 26
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D. Food composition importance ............................................................................................................. 29
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E. Uncertainties results: ........................................................................................................................... 31
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VIII. References: .......................................................................................................................................... 32
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S3
I. Food Commodities
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To identify the food commodities found in the edible food waste, we used the amounts given by 1,2
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for the different food groups (e.g., fruits or bread/cake). We then calculated the final commodities
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with annual household consumption data from Statistics Denmark.3,4
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The data from Statistics Denmark included: 1) annual consumption of food from (FU5 2010:2012)
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reported in DKK household-1 year-1, 2) variable list of detail commodities for each group reported in
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FU5 and 3) average prices for food commodities in 2011 (DKK/kg). Even if the dataset “2)” was
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really detailed, “3)” was only available for some food commodities, so the food groups were split
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into commodities that were present in “2)” and “3)”. Once the food commodities were selected, we
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found the amounts dividing “2)” by “3)” and then we calculated the ratios.
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The data from 1,2 was simplified by merging: i) processed and un-processed food, ii) cooked and
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uncooked food and iii) packed and unpacked bread. In addition the shares of “breakfast products”,
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“other dry products”, “other”, “ready meals-(un)opened”, “lunch leftover” and “bread with spreads”
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were distributed among the rest of commodities (accordingly with their shares). After this step, we
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excluded food commodities whose contribution in the edible food waste (either vegetable or
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animal) was lower than 10% and again their shares were distributed among the other commodities
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according with their shares. An exception was “fish” whose ratio was 5%, but it was nevertheless
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included to assess its impact. For the modelling, we assumed that: 1) Danish pastry was merged
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with bread and 2) Spreads were assumed butter.
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- Sensitivity analysis on the food commodities composition:
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To assess the importance of the composition of the food commodities, we performed a sensitivity
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analysis with a different composition of food commodities following the approach of 5, i.e.
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multiplying the loss rates reported by 6 and 7 by the average consumption in Danish households
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(same commodities used in the Baseline results), see Table S1.
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S4
Table S1: Columns 1-3: Ratio of edible and inedible fractions for vegetable food waste and animal food waste. Column 4: food
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commodities used in the study. Column 5: food composition assumed in the main results calculated from 1 and the adjustments mentioned
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above. To calculate the alternative food composition discussed in section “importance of the food commodities” and shown in column 9 we
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used: 1) food consumed per household in Denmark from Danish Statistics3,4(column 6), avoidable Losses for each food commodity from 5
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(column 7), the calculated amount of avoidable food waste for each commodity (column 8).
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Edible/Inedible
(%) from 1,2
Food
commodities
Baseline Food
composition (%)
Food consumption
(kg hh-1 y-1) from 3,4
Avoidable (%)
from 5
Avoidable
(kg hh-1 y-1)
Alternative Food
composition (%)
Vegetable food waste
(VFW)
Edible
50
Orange
4.7
30.20
19
5.74
5
Banana
3.4
19.25
19
3.66
3
Apple
3.9
22.55
24
5.41
5
Grapes
2.8
23.12
19
4.39
4
Kiwi
2.3
17.91
19
3.40
3
Carrots
16.5
100.85
14
14.12
13
Potatoes
3.6
56.75
17
9.87
9
Cabbage
8.8
36.31
21
7.62
7
Tomatoes
13.5
40.11
21
8.42
8
Bread
30.4
125.84
33
41.53
38
Rice
5.1
6.03
29
1.75
2
Pasta
5.1
10.19
29
2.96
3
Inedible
50
Animal-derived Food
Waste (AFW)
Edible
75
Eggs
6.7
14.37
9
1.29
4
Beef meat
10.5
23.04
9
2.07
6
Pork meat
8.3
17.76
13
2.33
7
Chicken meat
4.0
11.91
13
1.52
5
Fish - Salmon
1.3
12.60
11
1.32
4
Milk
31.7
177.25
9
15.95
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Yoghurt
8.4
45.68
9
4.11
13
Cheese (45%)
4.4
22.91
14
3.21
10
Butter
24.2
8.06
8
0.65
2
Inedible
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S5
II. Marginal products/technologies
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Table S2 summarizes the marginal technologies/processes considered in this assessment. The
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“marginal” is defined as that technology/process that is more likely to respond to changes induced
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in the demand/supply of a specific service/product (e.g. food waste) following the definition given
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in 8,9.
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Table S2: Marginal technologies/products considered.
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Product/service
Market scope
Marginal technology/product
Electricity
National (DK)
Coal-fired power plants
District heating
National (DK)
Natural gas boilers
Energy-feed
Global
Maize
N-fertilizers
Global
Urea
P-fertilizer
Global
Diammonium phosphate
K-fertilizer
Global
K2O
A. Energy
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It was assumed that in a long-term perspective energy from waste contributes to the
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decommissioning of fossil-based energy production capacities (both electricity and heat) as these
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technologies are generally intended to be phased out in order to comply with political CO2
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reduction targets. Thus, scenarios generating energy from the waste (e.g. incineration and anaerobic
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digestion) were credited with the substitution of fossil fuel-based energy production. Coal-fired
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power plants were assumed as marginal technologies for electricity production, following Danish
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government’s targets to phase-out coal by 2030.10 Marginal heat was assumed to originate from
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natural gas boilers. This choice is supported by projections from11where natural gas clearly appears
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as the preferred fuel for new installations and capacity expansions.
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B. Fertilizers
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The digestate produced from anaerobic digestion was used as organic fertilizer (for N, P, and K),
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which avoided marginal mineral N, P, and K fertilizers to be produced and used, based on the
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content of N, P, and K of the digestate. The marginal N, P, and K fertilizers considered were urea,
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diammonium phosphate (DAP), and potassium chloride as in 12. This choice is supported by 2000-
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2010 trends in demand/consumption(IFA 2014) and by projected capacity installation elaborated
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S6
based on statistics from International Fertilizer Association (IFA).13 The LCI for urea, DAP, and
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potassium chloride was based on Ecoinvent data for the production of these fertilizers.
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C. Fodder
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With respect to the substitution of conventional fodder, only the energy-part of the animal diet was
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considered as the protein-content of vegetable food waste is generally low (ca. 10%, as opposite to
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animal waste which contains ca. 34%). The marginal energy-fodder was assumed to be maize-
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fodder conformingly with 12. The substitution was therefore based upon the energy-feed content of
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the mixed vegetable waste relative to that of maize, both calculated as Scandinavian Feed Units as
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done in 12.
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In this study, the SFU of the mixed vegetable waste was quantified to 1.06 SFU kg-1 Dry Matter
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(DM) food waste, based on the weighted average of the SFU of each individual food commodity
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composing the mix (e.g., tomatoes, bread, potatoes, etc.). The SFU of each commodity was
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calculated based on the composition retrieved from 14. For maize, instead, the SFU equaled 1.21
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SFU kg-1 DM based on its typical composition from15. Thus, 1 kg DM food waste-fodder substitutes
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for 0.87 kg DM maize-fodder (on a wet basis, 1 kg ww food waste-fodder substituted for 0.38 kg
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ww maize-fodder, having vegetable food waste mix and maize a dry matter content of 36% and
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86%, respectively).
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D. Income effect
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To estimate the marginal consumption induced/reduced by the change in household expenses in
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comparison with Sc-IN, we used the approach of 16 and the data from Statistics Denmark on
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households annual consumption (by type), group of households and price unit (FU5) in the year
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2011:2013. See section SIIIA for a description of the method used.
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III. Method
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Table S3 described the activities included in the assessments under Environmental LCC and
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Societal LCC.
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S7
Table S3: Assessment criteria, indicators, cost types, agents involved and “items” included. *including emissions from the material
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provision related to the food commodities associated with the edible food waste.
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Indicators
Cost types
Agents or stakeholders involved in the system
WMS
Food Industry
Energy
Industry
Agriculture
Sector
General
Industry
State
Environmental LCC
Financial
Assessment
Budget costs
Factor prices
of the goods
used in the
WMS
Factor prices of the
food commodities
Factor
prices of
energy
Factor prices of
NPK and maize
Difference in
cost between
the baseline
scenario and
the
alternative
Transfers
Taxes
revenues,
subsidies
and tax
exemptions
LCA
Emissions
related to:
WMS*
- Food production*
- iLUC from food
production
Marginal
energy
generation
Production and
use of mineral
NPK and maize
Marginal
consumption
related to the
Income
effect
NA
Societal LCC
Welfare
Economic
Assessment
Budget costs
= to the “Budget costs” of the Environmental LCC but accounted for in accounting
prices, i.e. factor prices times the Net Tax Factor (NTF).
NA
Externality
costs
- LCA in
accounting
prices
- Transport
externality
costs
- LCA in accounting
prices
- Ecosystem losses
related to iLUC
LCA in
accounting
prices
- LCA in
accounting
prices
- Ecosystem
losses related
to iLUC
LCA in
accounting
prices
Tax
distorsion
loss

S8
A. Income Effect
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The marginal consumption was calculated with the following approach (Figure S1):
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1) Matching the categories used in the Danish expenditure statistics and the Input/Output tables
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from17 regarding households consumptions (Table S4).
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2) Calculating the variation on the expenses for each of the 9 item (good/service) between
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adjacent income groups and divide them by the variation on total expenditure between the
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same income groups. These percentages represent the marginal consumptions of each
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income group. Step 1 of Figure S1.
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3) Calculating the weighted marginal consumption in Denmark, using the marginal
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consumptions and the population in each income group. Step 2 of Figure S1.
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Figure S1: Calculation procedure to identify the marginal consumption from the Danish expenditure
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statistics (Raw Data).
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S9
Table S4: Matching between Danish statistics data and IO tables and the composition of the
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marginal consumption (income effect distribution) estimated and used in this study.
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Items number and
name in the study
Danish Statistics Item4
Input-Output
tables (EU 27)17
Marginal
Consumption
1.Clothing
Clothing and Footwear (categories
3111,3121-3123, 3131, 3141, 3211-
3213, 3221)
133 Household
use, Clothing,
EU27
6%
2.Communication
Purchase of Vehicles (categories 7111,
7121, 7131), Other transport services
and communication (categories 7211-
7221, 7231, 7241, 7251, 7321, 7331,
7341, 7351, 7361, 8111, 8211, 8311)
134 Household
use,
Communication,
EU27
22%
3.Education
Other goods and services (categories:
9721, 9741, 9751 and 9941)
135 Household
use, Education,
EU27
3%
4.Health Care
Medical products, services and
physicians (categories 6111, 6121,
6131, 6211, 6221, 6232-6233 and
6311)
136 Household
use, Health care,
EU27
2%
5.Housing
Rent Housing (categories 4111, 4112,
4121, 4211, 4215, 4221-4222, 4225,
4311, 4321, 4411, 4421, 4425, 4431,
4441), Electricity and fuels (categories
4511, 4521,4522,4531,4541,4551)
Furniture, Furnishings and households
services (categories: 5111, 5121, 5131,
5211, 5311-5317, 5321, 5331, 5411-
5413, 5511, 5521, 5611-5612, 5621-
5622).
137 Household
use, Housing,
EU27
31%
6.Hygiene
Other goods and services (categories
9912-9913)
138 Household
use, Hygiene,
EU27
1%
7.Leisure
Tobacco (categories 2211-2213) and
Other goods and services (9811-9812,
9821, 9911, 9921, 9931-9932)
139 Household
use, Leisure, EU27
16%
8.Meals
Food (categories from 1111 to 1199)
and Beverages (categories 1211-1213,
1221-1224, 2111, 2121-2122, 2131 and
2199).
140 Household
use, Meals, EU27
13%
9.Security
Other goods and services (categories
9951-9955, 9962, 9972, 9981)
(e.g., insurances, financial
intermediation, public services and
security)
141 Household
use, Security,
EU27
5%
138

S10
B. iLUC
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Figure S2 describe the procedure used to calculate costs and impacts of iLUC in both LCCs.
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141
Figure S2: iLUC calculation approach used in the investigation. E/I ratio =
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Expansion/Intensification ratio, LCI= Life Cycle Inventory, CEVua = Combined Ecosystem Value
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per unit area, CF(LCA) = Characterization Factors used in the LCA, AP = Accounting Prices.
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Accounted
in Societal LCC
Accounted
in LCA
1 tonne food
commodity
Global
Yield
Area demanded
(ha)
E/I ratio
Tonini et
al. 2015
Area Intensified
(ha) Area Expanded
(ha)
Tonini
et al.
2015
Location Area
Expanded (ha)
Tonini
et al.
2015 CEVua
Emissions (kg)
CF
(LCA) AP
Emission costs of
Expansion (€)
Impacts
Expansion
Ecosystem loss
(€)
Tonini
et al.
2015
N-fertilizer consumption (kg)
P-fertilizer consumption (kg)
K-fertilizer consumption (kg)
N-emissions
(kg)
LCI
Emissions
(kg)
CF
(LCA) AP
Emission costs of
Intensification (€)
Impacts
Intensification

S11
Several approaches for estimation of emissions from expansion and intensification have been
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proposed in literature 18–22. In this study, we followed the approach outlined in Figure S1 based on
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Tonini et al.12 This included the assumption that demand for land had global effects (reflecting the
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global nature of agricultural commodity trading) and that iLUC effects. Modelling of the iLUC data
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provided in Table S5 included estimation of: 1) the share of intensification and expansion in the
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overall response to changes in demand for land agricultural land, 2) the geographical location of the
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expansion and affected biomes, 3) the changed flows of carbon and nitrogen as a result of the
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expansion, and 4) the increases in use of N, P, K fertilizer for intensification as well as the overall
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emissions associated with fertilizer use. The inventory data in Table S5 were based on deforestation
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data for the period 20002010 from FAO. The data applied here are comparable with other estimates
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in literature (e.g. 23).
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Table S5: Inventory for land-use (1 ha arable land demanded). For intensification N- emissions,
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N2O is reported as the sum of direct and indirect N2O emissions (from 12)
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Expansion
Emission to air
Value per area expanded
Value per area demanded
CO2
2.4
t CO2 ha-1exp y-1
2.2
t CO2 ha-1dem y-1
CO
414
kg CO ha-1exp y-1
104
kg CO ha-1dem y-1
CH4
27
kg CH4 ha-1exp y-1
6.6
kg CH4 ha-1dem y-1
N2O
0.88
kg N2O ha-1exp y-1
0.22
kg N2O ha-1dem y-1
NOx
7.1
kg NO ha-1exp y-1
1.8
kg NO ha-1dem y-1
Intensificatio:
NPK fertilizer
production
Material
Value per area intensified
Value per area demanded
N-fertilizer
166
kg N ha-1int y-1
125
kg N ha-1dem y-1
P-fertilizer
68
kg P2O5 ha-1int y-1
52
kg P2O5 ha-1dem y-1
K-fertilizer
47
kg K2O ha-1int y-1
35
kg K2O ha-1dem y-1
Intensification:
N-emissions
Emission to air
Value per area intensified
Value per area demanded
N2O (dir + ind)
4.4
kg N2O ha-1int y-1
3.4
kg N2O ha-1dem y-1
NH3
4
kg NH3 ha-1int y-1
3
kg NH3 ha-1dem y-1
NOx
6
kg NO2 ha-1int y-1
4.5
kg NO2 ha-1dem y-1
Emission to wáter
Value per area intensified
Value per area demanded
NO3-N
33
kg NO3-N ha-1int y-1
25
kg NO3-N ha-1dem y-1
158

S12
For the Societal LCC, we had two externality costs related to iLUC: one caused by the emissions
159
during expansion and intensification, and the other related to the ecosystem loss. The externality
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costs of the emissions were calculated with the same iLUC emissions of the LCA (Table S5), but
161
converted into accounting prices.
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For the ecosystem loss, we calculated total value of the ecosystem as a sum of all the ecosystem
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services provided by the ecosystem using the following approach:
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1- Calculate the Combined Ecosystem Service Value provided by a unit area of ecosystem that
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resulted from the sum of all the services values provided by the ecosystem, Equation S1.
166




 

 (S1)
167
Where:
168


 = Combine Ecosystem Service Value per unit area (ua) of ecosystem i per year
169
(€/ha/year).
170


  = Ecosystem Service Value of the service j per unit area (ua) of ecosystem i per year
171
(€/ha/year)
172
j = ecosystem services, there are m services provided by this combination
173
174
2- Calculate the Combined Ecosystem Service Value related to the production of commodity a,
175
based on the area of ecosystem i transformed because of the production of food commodity
176
a (
) and the combine ecosystem service value per unit area of the ecosystem i (

),
177
Equation S2.
178


 



 (S2)
179
Where:
180

 = Combined Ecosystem Service Value related to cultivate 1 tonne of product a
181
(€/t of a)
182

 = Area of ecosystem i displaced to cultivate 1 tonne of a (ha/y/ tonne a)
183
i = ecosystem displaced, there are n ecosystem displaced in the production of product a.
184
Table S6 reports the Ecosystem Service Values for each region and service provided as well as the
185
Combined Ecosystem Value per unit area. Due to the uncertainty related to the valuation techniques
186
and the variability between studies, for each combination “biome-region-service” we used the
187
median of the values reported in main results and max and min values only for discussion.
188

S13
Such values were multiplied by the share of ecosystems affected by expansion that are summarized
189
in Table S7.
190

S14
Table S6: Ecosystem use values per region and ecosystem service in € ha-1 y-1. Tropical forest (TRF) includes: Tropical evergreen forest,
191
Tropical seasonal forest, Tropical open forest, Tropical moist forest, Tropical rain forest, Tropical dry forest. Temperate Forest includes:
192
Temperate evergreen forest, Temperate seasonal forest, Temperate deciduous forest. GL=Grassland, DS=dessert, OF= Open forest,
193
PL=Peatland, TEGL= Temperate grassland, SL=Shrubland, MF= Mountain forest, TRGL= tropical grassland, TRW=tropical woodland.
194
TEV was excluded because of double counting
195
Ecosystem values per region and ecosystem services and the Combine Ecosystem Value per unit area (CEVua) in € ha-1 y-1
Region
Ecosystem
Climate
Food
Genepool
Medical
Raw materials
Recreation
CEVua
No Data
Ref.
South
America
TRF
202-14 (89)
912-1 (56)
146-1 (1)
716-0 (6)
230-1 (19)
496-5 (27)
2701-21 (200)
TEF and
DS
24–36
GL
118-68 (68)
118-68 (68)
Central
America
TRF
75
247-0 (6)
84-4 (5)
1520-5 (762)
72
1221-1 (66)
3220-157 (988)
GL and
DS
37–48
TEF
77-15 (42)
247
4
5
5-1 (1)
338-272 (299)
South-
East Asia
TRF
963-0 (454)
144-6 (10)
19-0 (10)
48-0 (3)
98-8 (41)
11
1283-25 (528)
PL
33,41,48–
58
OF
Africa
TRF
117-2 (88)
41-0 (2)
19-2 (4)
180-0 (26)
46-2 (8)
157-1 (13)
559-6 (142)
SL
33,59–63
MF
17
17
Oceania
TEF
4
0
2
3-1 (1)
1
10-8 (8)
TRGL
33,46,64–
66
TRF
11
4
16-5 (12)
711-14 (363)
5
4
751-43 (399)
TRW
2
1.9
196

S15
Table S7: Share of ecosystems affected by expansion from 12.RF=Rain Forest, M= Moist, D= Dry,
197
St= Steppe, Sh= Shrub, Dt= Desert, M= Mountain, C= Coniferous, T= Tundra
198
RF
M
D
St
Sh
Dt
M
C
T
Oceania
Tropical
0.01
0.02
Subtropical
0.01
Temperate + Boreal + Polar
South East
Asia
Tropical
0.07
0.02
0.01
0.01
Subtropical
Temperate + Boreal + Polar
Rest of
Asia
Tropical
Subtropical
0.01
Temperate + Boreal + Polar
Africa
Tropical
0.11
0.09
0.10
0.02
0.01
Subtropical
Temperate + Boreal + Polar
Europe &
North America
Tropical
Subtropical
Temperate + Boreal + Polar
Central
America
Tropical
0.01
0.01
0.01
Subtropical
0.01
Temperate + Boreal + Polar
South
America
Tropical
0.21
0.04
0.04
0.08
0.01
Subtropical
Temperate + Boreal + Polar
199

S16
IV. Environmental and Economic inventories
200
The environmental and economic inventories used in the study stem from 67 and Table S8
201
summarizes specific parameters for this study.
202
- The source segregation included costs of bags and the emissions associated. Scenarios CD
203
and AF included source separation of organic waste and vegetable food waste, respectively.
204
No data was available for the sorting efficiencies of Scenario AF and we assumed 60% in
205
SFH and 40% in MFH.
206
- The collection included: capital costs, operation and maintenance costs (e.g. labor, fuel) and
207
externality costs.
208
- The incineration accounted for all cost/revenues incurred by the incinerator operator. We
209
assumed that all the scenarios used the same incineration plant, but the waste with higher
210
LHV and met thermal capacity with less waste, see 67.
211
- The anaerobic digestion accounted for costs of: pre-treatment of organic municipal waste
212
with screw press and co-digestion of organic waste with manure. We assumed a state-of-the-
213
art plant with annual capacity of 300,000 Mg to treat manure and organic waste and that
214
10% of the total capacity was used for organic waste and the rest for manure, see 67.
215
- The economic and environmental data for: 1) Transportation of Bottom Ash and Fly Ash to
216
final disposal, 2) bottom ash landfill and 3) neutralization of acid waste with fly ash stem
217
from67.
218
- Twenty minutes heating are enough for the required drying (up to 90% dry matter)
219
hygienization to use vegetable food waste as animal fodder68. This can be done with thermal
220
drying or electric heaters. We assumed electric heaters and we calculated the electric
221
consumption (Q) to be 152 kWh t-1 ww based on Eqs. S3-S5.
222
-      (S3)
223
-    
   (S4)
224
-    
   (S5)
225
Where;
226
  640 kg water/tonne food waste (= 260 kg solid/tonne food waste)
227
 = 57.3°C (T input = 7.7°C and T output 65°C)
228
 = 0.33 h (20 min)
229

S17
Table S8: Physical parameters used in the modelling. OW= Organic waste, VFW = Vegetable Food
230
Waste, MW = Mixed waste (without organic source separation), RW = Residual Waste (with
231
organic source separation), hh=households, CP = Collection point, AUR = Annual Usage Rate,
232
AD= Anaerobic Digestion, H=Hauling, wi=waste incinerated
233
Single Family Housing
Multi-Family Housing
IN
CD
AF
PR
IN
CD
AF
PR
Source Separation (%)
OW
75
50
VFW
60
40
Waste Density
(kg m-3)
MW & RW
160
120
120
160
160
120
120
160
OW & VFW
300
300
300
300
Bags
(bag y-1)
MW & RW
82
27
42
37
56
37
46
OW & VFW
52
52
52
52
Volume container
(l)
MW & RW
140
140
140
140
660
660
660
660
OW & VFW
140
140
400
400
HC
(hh container-1)
MW & RW
1
1
1
1
7
7
7
7
OW & VFW
1
1
1
14
25
Coll. frequency
(times y-1)
MW & RW
52
52
52
52
52
52
52
52
OW & VFW
52
52
52
52
Container CP-1
MW & RW
1
1
1
1
7
7
7
7
OW & VFW
1
1
3
2
Time CP-1(min)
0.3
5
Distances
(km)
Between stops
0.02
0.3
H. MW&RW
20
20
H. OW&VFW
40
40
LHV (GJ t-1)
MW & RW
3.56
3.56
5.52
3.00
3.91
3.91
4.89
3.22
AUR (1000 t y-1)
775
706
500
920
706
706
564
857
Ash ¨(kg Mg wi-1)
Bottom Ash
14.5
14.4
20.8
12.6
15.5
15.5
18.3
13.1
Fly Ash
2.1
2.1
3.0
1.8
2.2
2.2
2.6
1.9
AD plant subsidy
(% Capital cost)
20
20
Electricity Subsidy
(€ kWh-1)
0.4
0.4
234

S18
Table S9: Inventory of the items included in the income effect from Ecoinvent version 2.2. in kg of emission or resource per €2003 of each
235
consumption item. Items 1 to 9 correspond to the categories stated in Table S4. To convert this inventory to €2013 we used an inflation rate
236
of 22% as reported by 69 for the Eurozone.
237
Inventory per € of consumption item
Item 1
Item 2
Item 3
Item 4
Item 5
Item 6
Item 7
Item 8
Item 9
Aluminium
Raw
kg
1E-03
2E-03
4E-04
5E-04
9E-04
8E-04
1E-03
7E-04
5E-05
Ammonia
Air
kg
2E-04
4E-05
3E-05
5E-05
6E-05
1E-04
2E-04
1E-03
4E-06
Carbon dioxide, biogenic
Air
kg
1E-01
3E-02
3E-02
3E-02
1E-01
8E-01
8E-02
7E-01
2E-03
Carbon dioxide, fossil
Air
kg
1E+00
1E+00
3E-01
3E-01
8E-01
3E+00
7E-01
8E-01
3E-02
Carbon dioxide, in air
Raw
kg
7E-02
1E-02
1E-02
2E-02
5E-02
2E-01
7E-02
4E-01
1E-03
Carbon monoxide
Air
kg
4E-03
1E-02
1E-03
2E-03
3E-03
1E-02
1E-02
6E-03
2E-04
Coal, hard
Raw
kg
2E-01
9E-02
5E-02
5E-02
1E-01
5E-01
1E-01
1E-01
5E-03
Copper
Raw
kg
2E-04
4E-04
8E-05
2E-04
3E-04
2E-04
2E-04
2E-04
1E-05
Dinitrogen monoxide
Air
kg
2E-04
9E-05
4E-05
9E-05
9E-05
2E-04
3E-04
9E-04
5E-06
Gas, natural
Raw
kg
9E-02
1E-01
2E-02
3E-02
8E-02
3E-01
7E-02
7E-02
2E-03
Iron
Raw
kg
7E-03
1E-02
3E-03
3E-03
8E-03
8E-03
6E-03
7E-03
5E-04
Lead
Raw
kg
2E-06
2E-06
8E-07
2E-06
2E-06
2E-06
2E-06
2E-06
1E-07
Methane
Air
kg
3E-03
1E-03
7E-04
8E-04
2E-03
7E-03
3E-03
1E-02
6E-05
Nickel
Raw
kg
9E-04
1E-03
4E-04
8E-04
8E-04
9E-04
8E-04
8E-04
7E-05
Nitrogen dioxide
Air
kg
2E-03
5E-03
7E-04
8E-04
2E-03
3E-03
2E-03
2E-03
9E-05
NMVOC, unspecified origin
Air
kg
1E-03
3E-03
3E-04
5E-04
8E-04
2E-03
2E-03
2E-03
4E-05
Oil, crude
Raw
kg
1E-01
2E-01
4E-02
5E-02
1E-01
4E-01
1E-01
1E-01
4E-03
Other minerals (extracted for use)
Raw
kg
7E-02
3E-02
1E-02
5E-02
5E-02
6E-02
9E-02
1E-01
2E-03
Other minerals (related unused extraction)
Raw
kg
1E-02
2E-02
5E-03
8E-03
1E-02
1E-02
1E-02
1E-02
8E-04
Sand and clay
Raw
kg
4E-01
4E-01
2E-01
2E-01
9E-01
4E-01
5E-01
4E-01
5E-02
Sulfur dioxide
Air
kg
3E-03
2E-03
7E-04
8E-04
2E-03
5E-03
2E-03
2E-03
7E-05
Zinc
Raw
kg
6E-06
7E-06
3E-06
5E-06
5E-06
6E-06
5E-06
5E-06
5E-07
238

S19
V. Upstream food commodities - Factor prices & Ecoinvent names
239
Table S10: Factor prices from3 and names of the processes used from Ecoinvent.
240
Food
Commodity
Price
Ecoinvent
DKK/kg
Process name
Project name
Beef meat
92.38
Beef (farm type 23)
LCA food DK
Butter
66.72
Spreadable (Kærgården)
Cheese
84.58
Cheese
Chicken meat
96.58
Chicken
Eggs
38.27
Egg
Fish
147.43
Trout (standard), from trout pond farm
Milk
6.90
Milk, conventional, from farm, without quotas
Pork meat
83.45
Pork, from farm
Yoghurt
15.52
Assumed same impact as milk per kilogram 70
Bread
30.63
Bread, wheat, conventional, fresh
Pasta (øko)
30.81
Assumed 1,33 t durum wheat x 1kg pasta 71
Flour, wheat, conventional
Apple
15.43
Apple {GLO}| market for | Conseq, U
Ecoinvent 3 -
consequential -
unit
Banana
14.34
Banana {GLO}| market for | Conseq, U
Cabbage
6.28
Cabbage white {GLO}| market for | Conseq, U
Carrots
8.27
Carrot {GLO}| 335 production | Conseq, U
Citrus
11.92
Citrus {GLO}| production | Conseq, U
Grapes
34.91
Grape {GLO}| production | Conseq, U
Kiwi
14.85
Kiwi {GLO}| production | Conseq, U
Potatoes
9.34
Potato{GLO}| production | Conseq, U
Rice
26.85
Rice {GLO}| production | Conseq, U
Tomatoes
26.35
Tomato {GLO}| production | Conseq, U
Maize
1.45
Maize grain {GLO}| market for | Conseq, U

S20
VI. Accounting prices of emissions
241
The shadow prices of emissions (also so-called accounting prices) should represent the “marginal
242
damage cost” of an emission. Due to the difficulty of this calculation other approaches are applied
243
such as: 1) National reduction costs, 2) Marginal reduction costs of the emission and 3) Tax =
244
highest cost at which a reduction will take place. When the tax is applied in the welfare economic
245
assessment, its real value has to be multiplied by the Net-Tax-Factor. Data available depends on the
246
emission, here we used values reported in Table S11:
247
Table S11: Accounting prices used in the investigation. CF = Characterization factor
248
Emission
Value used
Reference
CO2
Allowance price for 2014, i.e. 155.4 DKK /ton ( 2009 prices), 21 €/t CO2.
72
CH4
CO2- Allowance price * 25 (CF for global warming)
73
N2O
CO2- Allowance price * 298 (CF for global warming)
73
PM 2.5
Damage cost from stationary installation:
105 DKK/kg in urban areas. (14,189€/ton)
72
NOx
Damage cost from stationary installation:
46 DKK/kg in rural and urban areas. (6,216€/t)
72
SO2/SO4
Damage cost from stationary installation:
89 DKK/kg in urban areas (12,027€/t)
72
CO
Rural 8.27 DKK/ton
Urban 23.1 DKK/ton (3.12 €/ton)
74
HC
Rural 2.37 DKK/kg
Urban 2.79 DKK/kg (377 €/ton)
74
Hg
Rural: 1,658 DKK/kg
Urban: 1,906 DKK/kg (257,567 €/ton)
75
Pb
Rural: 10,016 DKK/kg
Urban: 10,406 DKK/kg (1,406,216 €/ton)
75
Dioxins
2,078,729 DKK/kg (280,909 €/ton)
76
249

S21
VII. Detailed Results
250
A. Environmental LCC: Financial Assessment
251
Table S12 summarizes the results of the financial assessment in million € per functional unit (FU).
252
It includes the costs incurred by the 6 actors affected by the system (i.e., WMS, energy sector, food
253
industry, agriculture, general industry affected by income effects and the State). The household
254
expenses are assumed to be the sum of the expenses incurred by the abovementioned sectors minus
255
the state costs. The subtraction relates to the fact that some costs for the households are benefits for
256
the state, e.g. taxes paid to the State are accounted as benefits for the State (negative value), and as
257
costs for the household (positive value).
258
Results are separated are shown with and without indirect effects, i.e. Direct effects and Total
259
Effects. The only difference between these results is on the expenses of the income effect-industry
260
as budget costs and transfers.
261
If only direct effects are included, the households’ expenses differ between scenarios, and some
262
money flows are not considered in the system boundaries. The households expenses of Sc-CD and
263
Sc-AF are 34 and 41 M€ respectively higher than Sc-IN (Baseline), and these money flows are
264
incorporated within the system boundaries without any consequence. In contrary, in Sc-PR some
265
money flows are taken out of the economy without consequences, the households expenses of Sc-
266
PR are 1226 M€ lower than Sc-IN (1255-29).
267
When direct and indirect effects are accounted (total effects), the households expenses are equal for
268
all the scenarios and correspond to 1255 M€. For the financial assessment, it does not matter which
269
is the marginal consumption of the case study because it has been aggregated into a single actor
270
(Income effect- industry).
271
272

S22
Table S12: Financial Assessment of the four scenarios assessed in million € (M€) per functional
273
unit (FU) including only direct effects (direct effects) and including total effects.
274
Financial Assessment (M€ FU-1)
Direct Effects
Total Effects
IN
CD
AF
PR
IN
CD
AF
PR
Budget Cost
Waste Management System (WMS)
58
106
108
33
58
106
108
33
- Source separation:
21
28
31
11
21
28
31
11
- Collection:
28
71
71
18
28
71
71
18
- WTE:
9
3
5
4
9
3
5
4
- Co-Digestion:
0
4
0
0
0
4
0
0
- Food treatment for fodder:
0
0
0
0
0
0
0
0
- Transportation:
0
0
0
0
0
0
0
0
- Bottom Ash Landfill:
0
0
0
0
0
0
0
0
- Fly Ash treatment:
0
0
0
0
0
0
0
0
Energy sector
-8
-5
-5
-3
-8
-5
-5
-3
- Reduced Electricity due to incineration
-4
-1
-2
-1
-4
-1
-2
-1
- Reduced Heat due to incineration
-5
-2
-3
-2
-5
-2
-3
-2
- Reduced Electricity due to Co-Digestion
0
-2
0
0
0
-2
0
0
- Reduced Heat due to Co-Digestion
0
-1
0
0
0
-1
0
0
Food industry
968
968
968
0
968
968
968
0
- Demand for animal food commodities
444
444
444
0
444
444
444
0
- Demand for vegetable food commodities
524
524
524
0
524
524
524
0
Agriculture
0
-2
-18
0
0
-2
-18
0
- Reduced demand of marginal N
0
0
0
0
0
0
0
0
- Reduced demand of marginal P
0
-1
0
0
0
-1
0
0
- Reduced demand of marginal K
0
0
0
0
0
0
0
0
- Reduced demand of marginal fodder
0
0
-18
0
0
0
-18
0
Income effect – Industry
0
-27
-33
980
- Difference on WMS
0
-22
-42
22
- Difference on energy sector
0
-7
-5
-10
- Difference on Food
0
0
0
968
- Difference on Agriculture
0
1
15
0
Transfers
The State
-237
-223
-244
1
-237
-216
-236
-244
- From waste management system:
-6
14
-10
-3
-6
14
-10
-3
- From the energy system:
11
6
7
4
11
6
7
4
- From the food industry
-242
-242
-242
0
-242
-242
-242
0
- From other industries
0
7
8
-245
Household Expenses (Budget Cost and Transfers):
Sum of the expenses related to all the sectors minus the
expenses of the State.
1255
1289
1296
29
1255
1255
1255
1255
275

S23
B. Environmental LCC: Environmental part (LCA)
276
Table S13 summarizes the LCA results of the total effects (direct and indirect effects). The results
277
including only direct effects, represented in Figure 2A (II), can be estimated by excluding the
278
impacts of “income effects” and “iLUC” from Table S13.
279
The impacts related to “income effects” shown in Table S13 results from the product of the impact
280
associated with one € 2015 shown in Table S14 times the budget costs associated with income
281
effects in each scenario from Table S12 (i.e. 0 M€ in Sc-IN, -27 M€ in Sc-CD, -33 M€ in Sc-AF
282
and 980 M€ in Sc-PR). In addition, Table S13 show as well the variation of the net results in the
283
extreme cases of income effects (using the maximum and minimum impacts per € 2015 shown in
284
Table S14. The iLUC impacts are related to the food industry and the agriculture (marginal fodder),
285
but shown separately to be able to evaluate the importance of this indirect effect.
286
Table S13: LCA results as characterized impacts per functional unit.
287
GW (kg CO2)
POF (kg NMVOC)
IN
CD
AF
PR
IN
CD
AF
PR
WMS
1E+07
4E+07
5E+07
8E+06
4E+05
3E+05
2E+05
2E+05
- Source separation:
4E+06
4E+06
5E+06
4E+06
8E+03
9E+03
1E+04
8E+03
- Collection:
4E+06
4E+06
4E+06
2E+06
4E+03
4E+03
4E+03
2E+03
- WTE:
3E+06
1E+06
2E+06
9E+05
4E+05
2E+05
2E+05
2E+05
- Co-Digestion:
0E+00
3E+07
0E+00
0E+00
0E+00
6E+04
0E+00
0E+00
- Food treatment for fodder:
0E+00
0E+00
4E+07
0E+00
0E+00
0E+00
3E+04
0E+00
- Transportation:
4E+06
4E+06
4E+06
2E+06
4E+03
4E+03
4E+03
2E+03
- Bottom Ash Landfill:
4E+04
2E+04
3E+04
2E+04
9E+01
5E+01
6E+01
4E+01
- Fly Ash treatment:
1E+04
7E+03
8E+03
5E+03
3E+01
1E+01
2E+01
1E+01
Marginal Energy
-1E+08
-1E+08
-1E+08
-6E+07
-2E+05
-2E+05
-1E+05
-9E+04
Food industry
3E+08
3E+08
3E+08
0E+00
5E+05
5E+05
5E+05
0E+00
- Demand for animal food
2E+08
2E+08
2E+08
3E+05
3E+05
3E+05
- Demand for vegetable food
1E+08
1E+08
1E+08
3E+05
3E+05
3E+05
Agriculture
0E+00
-5E+06
-3E+07
0E+00
0E+00
-2E+04
-1E+05
0E+00
- Reduced demand of PK
-5E+06
-2E+04
- Reduced demand of maize
-3E+07
-1E+05
Income effect
0E+00
-2E+07
-3E+07
8E+08
0E+00
-4E+03
-5E+03
2E+05
iLUC
1E+08
1E+08
7E+07
0E+00
3E+05
3E+05
2E+05
0E+00
- AFW
7E+07
7E+07
7E+07
2E+05
2E+05
2E+05
- VFW
3E+07
3E+07
3E+07
9E+04
9E+04
9E+04
- Maize
-3E+07
-9E+04
Net Impact:
3E+08
3E+08
3E+08
7E+08
1E+06
9E+05
7E+05
3E+05
Results variation due to income effect distribution:
Net Impact (MAX)
3E+08
3E+08
3E+08
2E+09
7.2E+05
6.1E+05
4.8E+05
4.8E+05
Net Impact (MIN)
3E+08
3E+08
3E+08
1E+09
7.2E+05
6.1E+05
4.8E+05
3.6E+05

S24
Continuation Table S13
288
RD f (MJ)
RD m (kg Sb eq)
IN
CD
AF
PR
IN
CD
AF
PR
WMS
1E+09
2E+09
1E+09
5E+08
2E+00
2E+00
3E+00
2E+00
- Source separation:
0E+00
0E+00
0E+00
0E+00
2E+00
3E+00
3E+00
2E+00
- Collection:
5E+08
5E+08
5E+08
2E+08
4E-05
4E-05
4E-05
2E-05
- WTE:
1E+07
5E+06
5E+06
5E+06
3E-04
1E-04
1E-04
1E-04
- Co-Digestion:
0E+00
5E+08
0E+00
0E+00
0E+00
5E-05
0E+00
0E+00
- Food treatment for fodder:
0E+00
0E+00
4E+08
0E+00
0E+00
0E+00
5E-05
0E+00
- Transportation:
5E+08
5E+08
5E+08
2E+08
3E-04
1E-04
2E-04
1E-04
- Bottom Ash Landfill:
4E+06
2E+06
3E+06
2E+06
-3E-01
-1E-01
-2E-01
-1E-01
- Fly Ash treatment:
4E+05
2E+05
3E+05
2E+05
4E-08
2E-08
2E-08
2E-08
Marginal Energy
-1E+09
-1E+09
-8E+08
-5E+08
-1E-04
-1E-04
-9E-05
-5E-05
Food industry
1E+09
1E+09
1E+09
0E+00
5E+02
5E+02
5E+02
0E+00
- Demand for animal food
4E+08
4E+08
4E+08
2E+02
2E+02
2E+02
- Demand for vegetable food
1E+09
1E+09
1E+09
2E+02
2E+02
2E+02
Agriculture
0E+00
-5E+07
-2E+08
0E+00
0E+00
-6E+02
-6E+01
0E+00
- Reduced demand of PK
-5E+07
-6E+02
- Reduced demand of maize
-2E+08
-6E+01
Income effect
0E+00
-3E+08
-4E+08
1E+10
0E+00
-1E+02
-1E+02
3E+03
iLUC
2E+08
2E+08
2E+08
0E+00
2E+03
2E+03
1E+03
0E+00
- AFW
1E+08
1E+08
1E+08
1E+03
1E+03
1E+03
- VFW
7E+07
7E+07
7E+07
6E+02
6E+02
6E+02
- Maize
-7E+07
-6E+02
Net Impact:
1E+09
2E+09
2E+09
1E+10
2E+03
2E+03
2E+03
3E+03
Results variation due to income effect distribution:
Net Impact (MAX)
1E+09
9E+08
8E+08
3E+10
2E+03
2E+03
2E+03
5E+03
Net Impact (MIN)
1E+09
1E+09
1E+09
2E+10
2E+03
2E+03
2E+03
5E+03
289

S25
Continuation Table S13
290
EU mw (kg N-Eq)
IN
CD
AF
PR
WMS
2E+05
4E+05
9E+04
7E+04
- Source separation:
3E+03
3E+03
4E+03
3E+03
- Collection:
1E+03
1E+03
1E+03
5E+02
- WTE:
2E+05
8E+04
7E+04
7E+04
- Co-Digestion:
0E+00
3E+05
0E+00
0E+00
- Food treatment for fodder:
0E+00
0E+00
1E+04
0E+00
- Transportation:
1E+03
1E+03
1E+03
6E+02
- Bottom Ash Landfill:
4E+01
2E+01
2E+01
2E+01
- Fly Ash treatment:
9E+00
4E+00
5E+00
3E+00
Marginal Energy
-8E+04
-6E+04
-5E+04
-3E+04
Food industry
4E+06
4E+06
4E+06
0E+00
- Demand for animal food
3E+06
3E+06
3E+06
0E+00
- Demand for vegetable food
1E+06
1E+06
1E+06
0E+00
Agriculture
0E+00
-7E+03
-3E+05
0E+00
- Reduced demand of PK
0E+00
-7E+03
0E+00
0E+00
- Reduced demand of maize
0E+00
0E+00
-3E+05
0E+00
Income effect
0E+00
-4E+02
-5E+02
1E+04
iLUC
9E+04
9E+04
6E+04
0E+00
- AFW
6E+04
6E+04
6E+04
- VFW
3E+04
3E+04
3E+04
- Maize
-3E+04
Net Impact:
4E+06
4E+06
4E+06
6E+04
Results variation due to income effect distribution:
Net Impact (MAX)
4E+06
4E+06
4E+06
1E+05
Net Impact (MIN)
4E+06
4E+06
4E+06
7E+04
291
292

S26
Table S14: Characterized environmental impact associated with the input/output process used in
293
the income effects per € 2015. The impact associated with the marginal consumption of the baseline
294
results by income distribution shown in Table S4 and Figure 1 and these characterized impacts per
295
item. The cells colored in red represent the maximum impact in each category given by a specific
296
item whereas the greens represent the minimum impacts.
297
298
Items used in the income effects distribution
Characterized impact per € 2015
GW
kg CO2
POF
kg NMVOC
RD (m)
kg Sb
RD (f)
MJ
EU (m)
kg N
133 Household use, Clothing, EU27
8.9E-01
2.3E-04
3.4E-06
1.3E+01
1.5E-05
134 Household use, Communication, EU27
8.4E-01
1.3E-04
4.8E-06
1.3E+01
2.8E-06
135 Household use, Education, EU27
2.6E-01
5.6E-05
1.6E-06
3.6E+00
2.6E-06
136 Household use, Health care, EU27
2.3E-01
4.9E-05
2.5E-06
3.3E+00
3.3E-06
137 Household use, Housing, EU27
7.4E-01
1.5E-04
3.4E-06
1.1E+01
4.9E-06
138 Household use, Hygiene, EU27
2.2E+00
3.8E-04
3.1E-06
3.5E+01
8.9E-06
139 Household use, Leisure, EU27
8.4E-01
1.9E-04
3.9E-06
1.1E+01
1.7E-05
140 Household use, Meals, EU27
8.9E-01
2.0E-04
2.4E-06
8.5E+00
6.7E-05
141 Household use, Security, EU27
2.5E-01
5.3E-05
2.6E-06
3.5E+00
2.7E-06
Impact of the Marginal Consumption (baseline)
7.7E-01
1.5E-04
3.6E-06
1.1E+01
1.5E-05
299
C. Societal LCC
300
Table S15 summarizes the results of the welfare economic assessment in million € per functional
301
unit (M€ FU-1) including only direct effects (direct effects) and including total effects (direct and
302
indirect). In addition, it shows the variation of the results with respect to the distribution of the
303
income effects. The distribution of the income effects only affect the externality costs associated
304
and have little influence on the total results since the externality costs are two others of magnitude
305
lower than the budget costs. Table S16 shows the externality costs associated with each item
306
included in the income effect.
307
The welfare costs for: the WMS, Energy sector, Food industry, Agriculture and Industry results
308
from the product of the expenses reported in Table S12 and the Net Tax Factor (assumed 1.17 for
309
Denmark).77 The Tax Distorsion Loss resulted from: 1) the difference of the tax received by the
310
State in the baseline scenario (Sc-IN) and the alternative scenarios (Sc-CD, Sc-AF and Sc-PR) from
311
Table S12 and 2) the Tax Distortion factor (assumed 20% for Denmark).77 The emissions
312
consequences are calculated with the emissions from the LCA and the accounting prices of
313
emissions (Table S11). And the Ecosystem losses were calculated as shown in Figure S2 and the
314
tables of SI-IIIB.
315

S27
Table S15: Social Life-Cycle Costing of the four scenarios assessed in € per functional unit
316
(M€/FU) including only direct effects (direct effects) and including total effects (direct and
317
indirect).
318
S-LCC (M€/FU)
Direct Effects
Total Effects
IN
CD
AF
PR
IN
CD
AF
PR
Budget Cost
Waste Management
68
124
126
39
68
124
126
39
Energy sector
-10
-6
-6
-4
-10
-6
-6
-4
Food industry
1132
1132
1132
0
1132
1132
1132
0
Agriculture
0
-2
-21
0
0
-2
-21
0
Rebound effect - Industry
0
32
38
-1147
Externality Costs
Tax Distortion Loss
0
3
-1
48
0
4
0
-1
Emissions (direct effects)
16
24
12
-1
16
24
12
-1
-WMS (w/o avoided)
3
3
3
1
3
3
3
1
- Avoided energy
-3
5
-4
-2
-3
5
-4
-2
- Avoided NPK
0
1
-1
0
0
1
-1
0
- Avoided fodder
0
0
-2
0
0
0
-2
0
- Animal food production
8
8
8
0
8
8
8
0
- Vegetable food production
8
8
8
0
8
8
8
0
Emissions (indirect effects)
11
9
8
47
Emissions income effects
0
-1
-2
47
Emissions iLUC
11
11
9
0
Ecosystem loss - ILUC
3
3
2
0
Ecosystem loss – AFW
2
2
2
0
Ecosystem loss - VFW
1
1
1
0
Ecosystem loss - Maize
-1
Accidents and noise externality
1
3
2
1
1
3
2
1
Collection
1
3
2
1
1
3
2
1
Transportation
0
0
0
0
0
0
0
0
Total
1208
1278
1243
83
1221
1323
1293
-1066
Results variation due to income effect distribution:
Total (MAX)
1221
1321
1291
-997
Total (MIN)
1221
1322
1292
-1021
319
320
321
322

S28
Table S 16: Externality costs associated with the input/output process used in the income effects
323
per € 2015. The impact associated with the marginal consumption of the baseline results by income
324
distribution shown in Table S4 and Figure 1 and these characterized impacts per item. The cells
325
colored in red represent the maximum impact in each category given by a specific item whereas the
326
greens represent the minimum impacts.
327
Externality cost (€ emission / € spend)
CO2
CH4
N20
Nox
SO2
CO
Total
133 Household use, Clothing, EU27
2E-02
1E-03
1E-03
1E-02
3E-02
9E-06
6E-02
134 Household use, Communication, EU27
2E-02
4E-04
5E-04
2E-02
2E-02
4E-05
6E-02
135 Household use, Education, EU27
5E-03
3E-04
2E-04
4E-03
7E-03
3E-06
2E-02
136 Household use, Health care, EU27
5E-03
3E-04
5E-04
4E-03
8E-03
4E-06
2E-02
137 Household use, Housing, EU27
1E-02
7E-04
4E-04
8E-03
2E-02
8E-06
4E-02
138 Household use, Hygiene, EU27
5E-02
3E-03
9E-04
2E-02
5E-02
3E-05
1E-01
139 Household use, Leisure, EU27
1E-02
1E-03
1E-03
1E-02
2E-02
3E-05
5E-02
140 Household use, Meals, EU27
1E-02
5E-03
5E-03
1E-02
2E-02
1E-05
6E-02
141 Household use, Security, EU27
5E-04
3E-05
2E-05
5E-04
7E-04
4E-07
2E-03
Marginal Cost (baseline)
1E-02
1E-03
1E-03
1E-02
2E-02
2E-05
5E-02
328
329

S29
D. Food composition importance
330
Figure S3 illustrate costs/impacts of the different food commodities included in the baseline and
331
alternative composition of the edible food waste. “Beef” appeared to have the highest intrinsic
332
impact on GW, EU (mw) and RD (m), due to methane, dinitrogen oxide, ammonium and carbon
333
dioxide emissions to the air, while “fish” had the highest intrinsic cost and “Tomato” the highest
334
intrinsic impact on RD (f). Other commodities, albeit with lower production impacts (such as bread,
335
carrots, and tomatoes), nevertheless had significant contributions to the total impact of edible food
336
waste due to their significant presence (in weight) in total edible food waste. Such impacts could be
337
prevented by reducing the wastage of such commodities. Even if “Beef” appeared to have the
338
highest intrinsic impact in most of the categories, the greatest benefits of food waste prevention
339
could be obtained by reducing the amounts of bread, butter, tomatoes, rice, and potatoes due to their
340
large presence in weight.
341

S30
342
Figure S3 Intrinsic impact of each food commodity (per tonne of commodity) and the contribution
343
of each commodity into the impact of the functional unit (FU). A) Budget costs (factor prices), B)
344
Global warming (kg CO2 eq), C) Photechemical Ozone Formation (kg NMVOC eq), D) Marine
345
eutrophication (kg N eq), E) Fossil resource depletion (MJ) and F) mineral resource depletion (kg
346
Sb).
347

S31
E. Uncertainties results:
348
Including food transportation and handling (associated with the edible food waste) would
349
correspond to an average driving distance (by car) of 28 km week-1 households-1 and electricity
350
consumption (per meal) of 1 MJ.78,79 This increases the financial costs of the first three scenarios by
351
approximately 4%: 19 M€ related to home transportation (allocating only 54% of the total distance
352
to the edible food waste, i.e. weight percentage of the edible food waste in the total food waste, and
353
assuming 100 km liter diesel-1 and 1€ liter diesel-1) and 10 M€ associated with the electricity
354
consumed for cooking etc. in the extreme scenario in which all the edible food waste is cooked
355
before being thrown (assuming 0.5 kg meal-1 and 20 € GJ-1 electricity). At the same time, the
356
income effects of S-PR adjust to the cost of S-IN, i.e. it increases by 30 M€ (3% of the income
357
effects of this scenario) and all the scenarios shown the same financial costs.
358
This inclusion also increases the GW of all the scenarios, by 100% for the three scenarios and by
359
3% in the S-PR scenario and the ranking between scenarios remains the same but the difference
360
among them decreases. The transportation increases the GW impact by 2.6 108 kg CO2 (assuming
361
132 g CO2 km driving with diesel car) and the electricity consumption by 5.9 107 kg CO2 (assuming
362
0.47 kg CO2 MJ-1 electricity. The ranking of the scenarios in the Societal LCC is not affected by the
363
inclusion, but the difference between them increases (i.e. the first three scenarios adds 35.1 M€ (30
364
M€ * 1.17) while the S-PR increases the social benefit by the same amount in terms of income
365
effects.
366
367

S32
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368
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