Citation: Bossek, D.; Rudolph, C.;
Bach, V.; Finkbeiner, M. Life-LCA:
Impacts of a German Human Being
in the Old Adulthood Stage.
Sustainability 2023,15, 11447.
https://doi.org/10.3390/
su151411447
Academic Editor: Gianpiero Greco
Received: 16 June 2023
Revised: 20 July 2023
Accepted: 21 July 2023
Published: 24 July 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sustainability
Article
Life-LCA: Impacts of a German Human Being in the Old
Adulthood Stage
David Bossek * , Caroline Rudolph, Vanessa Bach and Matthias Finkbeiner
Department of Sustainable Engineering, Institute of Environmental Technology, Technische Universität Berlin,
Straße des 17. Juni 135, 10623 Berlin, Germany
*Correspondence: [email protected]
Abstract:
Life-LCA studies, which assess the environmental impacts of human beings, focused so far
on the span from conception to 50 years. This case study extends the analysis to an “old adulthood
stage”, including a retirement (65–75 years) and end-of-life phase (75–80 years), thus complementing
the assessment gap in the life cycle of a human being. The Life-LCA method is applied to a fictional
study object representing an average German adult using mainly secondary data. Over both life
phases, impacts result in 1.2
×
10
2
t CO
2
-eq for climate change, 9
×
10
−5
CTUh for human toxicity
cancer, 2
×
10
−3
CTUh for human toxicity non-cancer, 1.35
×
10
0
kg Sb-eq for abiotic depletion for
elements, and 1.55
×
10
0
TJ for fossil fuels. Across all impact categories, “transport” is a hotspot,
contributing 41% to GWP, followed by “Energy and water” (39%) and “food” (20%). For abiotic
depletion for elements, “Electronics” shows a share of 50%. The “retirement phase” causes a higher
environmental impact than the “EoL phase” across all impact categories due to restricted mobility
with higher age. A study with primary data collection is suggested to check the plausibility of
the results.
Keywords:
life cycle assessment (LCA); Life-LCA; old adulthood; sustainable lifestyles; sustainable
consumption; carbon footprint; personal environmental footprint
1. Introduction
Germany is expecting demographic changes and an aging population in the coming
years. In 2021, older adults (65+ years) will make up around a quarter of the total population;
by 2050, this share is expected to increase to 28% [
1
]. Therefore, the role of older adults in
consumer markets and their associated environmental impacts are becoming increasingly
important. Research shows that spending on food, transportation, personal care, and
fashion is decreasing for individuals in the “old adulthood stage” (OAS) while spending
on medical expenses is surging [
2
,
3
]. This shift in consumption behavior can be attributed
to the health and physical limitations that typically affect older adults [2].
In the first Life-LCA case study of a human being, Bossek et al. [
4
] calculated the
environmental impacts of a real-life study object, “Dirk”, from his birth to 49 years (his
age at the time of performing the analysis). Further, the previously assessed “childhood
and youth”, “early adulthood”, and “middle adulthood” stages were based on primary
data collection of individuals [
4
,
5
]. The “old adulthood stage” (60 years to the end of life),
defined by Goermer et al. [6] was excluded until now.
Therefore, by applying the Life-LCA method [
6
], this study aims to close this gap
by collecting data for the “old adulthood stage”. In addition, for the first time in the
context of the Life-LCA, a fictional study object representing the average German old
adult was chosen mainly using secondary data (e.g., publicly available statistics, pieces of
the literature) instead of primary data collection, which was resource- and time-intensive
(e.g., monitoring phases up to six months).
Further, to broaden the scope of the Life-LCA application and gain new insights, the
impact categories of abiotic depletion for elements (ADP-e), abiotic depletion for fossil
Sustainability 2023,15, 11447. https://doi.org/10.3390/su151411447 https://www.mdpi.com/journal/sustainability
Sustainability 2023,15, 11447 2 of 18
fuels (ADP-f), human toxicity cancer (HT cancer), and human toxicity non-cancer (HT
non-cancer) are assessed for the first time in the Life-LCA. Additionally, to provide a
comparative perspective with prior Life-LCA studies, the impact categories climate change
(GWP), acidification (AP), eutrophication (EP), and photochemical ozone creation (POCP)
are analyzed to see further if the use of secondary data provides plausible results.
Another motivation for this study was to analyze how environmental impacts change
with increasing age, beginning from the statistically average retirement age in Germany
(65 years) to extending beyond death to include aspects such as funeral and grave mainte-
nance. To better show these changes in consumption patterns and associated impacts, a
subdivision of the life stage into the life phases “retirement age” (65–75 years) and “end of
life” (75–80 years) is made. (See Section 2.2 for further explanation.)
In the following sections, the applied materials and methods (see Section 2), Life-
LCA results (see Section 3), a discussion with a sensitivity analysis (see Section 4), and a
conclusion and outlook (see Section 5) are presented.
2. Materials and Methods
This chapter presents the applied materials and methods, including the goal and scope
(see Section 2.1), system boundaries (see Section 2.2), selected impact assessment method
(see Section 2.3), and life cycle inventory (see Section 2.4).
2.1. Goal and Scope
This study aims to quantify the environmental impacts of an average German adult in
the old adulthood stage by applying the Life-LCA method [6].
The reporting unit combines the reporting individual, period, and flow for an “Individual
Life-LCA”. It relies on the individual monitoring and recording their consumption over a
defined period and providing these data to the Life-LCA practitioner [6].
However, in this study, a fictional study object is determined to embody the statistically
average German adult aged 65 and over. While the term “reporting unit” is typically used
in such studies, it is not applicable here due to the subject’s fictional nature. Instead,
we propose using “reporting unit (average)” to reflect this scenario more accurately. The
reference flow is set to be all products consumed within the period in the “old adulthood stage”
(65–80 years). The “old adulthood stage” comprises two subdivisions: the “retirement
phase” (65–75 years) and the “end-of-life phase” (75–80 years). (See Section 2.2 for further
explanation.)
A “baseline scenario” (BS) provides Life-LCA results of the fictional study object
over a one-year period, within the “retirement phase” (see Section 3.2) and the “end-of-
life phase” (see Section 3.3). Further, the difference in the annual consumption impacts
between these two phases is compared and evaluated (see Section 3.3). As this study uses
mainly secondary data, the consumption behavior of the fictional study object strives to
represent the statistical average of an older adult in Germany as far as possible. Therefore,
no differentiation is made between the sexes, and where appropriate, the median of both
sexes is taken. For some of the product categories (see Supplementary Materials (SMs)),
the yearly consumption inventory provided in the first Life-LCA case study [
4
] is based on
the 49-year-old entrepreneur Dirk Gratzel and subsequently scaled based on the income
and consumption survey conducted by the Federal Statistical Office of Germany (see SMs
for an overview of the scaling rates and the selected products) [7].
Further, a sensitivity analysis is carried out for selected product categories (e.g., influ-
ence on transportation or different burial options) (see Section 4.2).
2.2. System Boundaries
Two dimensions are assessed: Dimension one, referred to as “the human life cycle”,
includes changing product consumption behaviors over an individual’s life and considers
different life stages (childhood and youth stage, early adulthood stage, middle adulthood
Sustainability 2023,15, 11447 3 of 18
stage, and old adulthood stage). Dimension two, or “product life cycle”, assesses the
individual’s life cycle of consumed products.
This study focuses on the “old adulthood stage”. The Life-LCA method [
6
] defines
the “old adulthood stage” from 60 to death. However, it can be assumed that entering
retirement leads to a more significant change in consumption behavior than age does [
8
].
Therefore, this study quantifies the environmental impacts from the average retirement age
in Germany of 65 years to death and beyond [9].
Life stages were first subdivided in a Life-LCA case study, which assessed the environ-
mental impacts of an infant [
5
] to reduce uncertainty. The same principle is implemented
in the presented case study to effectively assess the changing and time-varying consumer
behavior and increase the specificity of the results [
5
]. The two phases of the “old adulthood
stage” are defined as follows:
•Retirement phase: 65 to 75 years.
#
The average retirement age in Germany is 64.2 years [
9
]. Therefore, the “old
adulthood stage” is set to start at age 65. It includes ages 65 to 75, as there
are apparent differences in physical activity, expenditures, and medical needs
compared to individuals over 75 years [10].
•EoL phase: 75 to 80 years.
#
The average death age at which a male dies in Germany is 78. A female indi-
vidual’s average life expectancy is 83 years [
11
]. Therefore, an average of both
life expectancies is used, and the “EoL phase” is defined as the period between
75 and 80 years. All products “consumed” after death (grave maintenance,
funeral) are included in the EoL phase; however, they are shown in the new
product category “after-life”.
As shown in Figure 1, for assessing the Life-LCA of a fictional study object in the “old
adulthood stage”, the temporal system boundary for dimension one is set to the “retirement
phase” and “EoL phase”. The system boundaries for dimension two include all services
and products consumed during the two life phases considering all impacts from “cradle to
grave” (raw material extraction, production, and use and end-of-life phase of the product).
Sustainability 2023, 15, x FOR PEER REVIEW 4 of 18
fictional study objects for both life phases across the most impactful product categories—
transport, energy and water, housing, food, as well as hobbies and leisure.
Figure 1. Two-dimensional view in Life-LCA for the “old adulthood stage” based on Goermer et al.
[6].
2.3. Impact Assessment
In order to broaden the scope of the Life-LCA application and generate new insights,
the impact categories abiotic resource depletion for elements (ADP-e) and fossil fuels
(ADP-f) and human toxicity (HT) cancer and non-cancer are analyzed. Further, for com-
parability purposes with previous Life-LCA case studies, climate change (GWP), acidifi-
cation (AP), eutrophication (EP), and photochemical ozone creation (POCP) are assessed
and discussed separately (see Section 4.3).
HT cancer and HT non-cancer are chosen as they assess the effects on human health
from exposure to toxic substances due to inhalation of air, consumption of food/water,
and dermal penetration [13]. However, it must be stated that this impact assessment
method has a lower maturity than established ones like GWP [14].
ADP-e and ADP-f refer to the use of abiotic resources (elements) and fossil fuels.
The LCIA method “CML 2001- IA. 2016” is used for the product categories ADP-e,
ADP-f, GWP, ADP, EP, and POCP, as it is one of the most applied methods for assessing
environmental impacts [15]. The LCIA method ‘Environmental Footprint 3.0 [16] is used
for the impact category HT cancer and non-cancer.
2.4. Life Cycle Inventory
This chapter outlines the method of data collection and allocations made (see Section
2.4.1). Furthermore, the modeled product clusters with their underlying datasets are pre-
sented (see Section 2.4.2).
2.4.1. Data Collection and Allocation
An annual consumption inventory for all product categories is established for the
“retirement phase” and the “EoL phase”. Each product cluster is allocated to a pre-defined
product category (e.g., product cluster “shoes” allocated to the category “clothes and jew-
Figure 1.
Two-dimensional view in Life-LCA for the “old adulthood stage” based on Goermer et al. [
6
].
Sustainability 2023,15, 11447 4 of 18
The Life-LCA of the “old adulthood stage” spans over 15 years. Therefore, the results
can be determined by adding ten times the one-year results of the “retirement phase”
(spanning over ten years) to five times the one-year results of the “EoL phase” (spanning
over five years) (see Equation (1)).
Life-LCA results of “old adulthood stage” = 10 ×baseline scenario of
“retirement phase” + 5 ×baseline scenario of the “EoL phase (1)
In addition, as a second scope, the temporal system boundary is set to a one-year
period to compare the two phases and scenarios.
Further, the fictional study object’s death and funeral are included in the EoL phase.
Therefore, an additional “after-life” product category is added. (See Section 2.4.2 for an
overview of all product categories.)
Moreover, it is assumed that the fictional study object lives in a 2-person household,
as more than 60% of older adults (60+) live in an average household size of 1.7 per-
sons [
12
]. The consumption of shared products is allocated 1:1 to the fictional study object
(e.g., electricity and thermal energy consumption of a 2-person household).
Financial investment, insurance, and products consumed by the children and grandchil-
dren of the fictional study object are excluded. Figure 1shows the key facts of the fictional
study objects for both life phases across the most impactful product categories—transport,
energy and water, housing, food, as well as hobbies and leisure.
2.3. Impact Assessment
In order to broaden the scope of the Life-LCA application and generate new insights,
the impact categories abiotic resource depletion for elements (ADP-e) and fossil fuels (ADP-
f) and human toxicity (HT) cancer and non-cancer are analyzed. Further, for comparability
purposes with previous Life-LCA case studies, climate change (GWP), acidification (AP),
eutrophication (EP), and photochemical ozone creation (POCP) are assessed and discussed
separately (see Section 4.3).
HT cancer and HT non-cancer are chosen as they assess the effects on human health
from exposure to toxic substances due to inhalation of air, consumption of food/water, and
dermal penetration [
13
]. However, it must be stated that this impact assessment method
has a lower maturity than established ones like GWP [14].
ADP-e and ADP-f refer to the use of abiotic resources (elements) and fossil fuels.
The LCIA method “CML 2001- IA. 2016” is used for the product categories ADP-e,
ADP-f, GWP, ADP, EP, and POCP, as it is one of the most applied methods for assessing
environmental impacts [
15
]. The LCIA method ‘Environmental Footprint 3.0’ [
16
] is used
for the impact category HT cancer and non-cancer.
2.4. Life Cycle Inventory
This chapter outlines the method of data collection and allocations made (see
Section 2.4.1). Furthermore, the modeled product clusters with their underlying datasets
are presented (see Section 2.4.2).
2.4.1. Data Collection and Allocation
An annual consumption inventory for all product categories is established for the
“retirement phase” and the “EoL phase”. Each product cluster is allocated to a pre-defined
product category (e.g., product cluster “shoes” allocated to the category “clothes and
jewelry”). (See Section 2.4.2 for an overview of all product categories.) An Excel sheet
is created to collect data and document the yearly consumption of products and their
corresponding units. (See SM for an overview of the consumption inventory.)
Where available, data for the consumption inventory are obtained from the secondary
literature; otherwise, they are calculated by scaling the annual consumption values of a
49-year-old human being from the first Life-LCA case study [
4
]. The scaling rates (see SMs
for an overview of scaling rates and examples for calculation) are determined considering
Sustainability 2023,15, 11447 5 of 18
the expenditure of certain products in different life phases and how they change over time
(before 65 years, between 65 and 69 years, and after 69 years). The expenditures for the
different product clusters are based on the income and consumption survey of private
households by the Federal Statistical Office in Germany [7].
Further allocation procedures and assumptions for selected product categories are
presented in the following:
•After-Life
Concerning funeral choices in Germany, cremation is the most popular choice (76%),
followed by traditional burial (24%) [
17
]. Hence, it is assumed that the fictional study object
in the “baseline scenario” would choose this method and that the urn would be made of
the commonly used material stainless steel [
18
]. On average, a single cremation process
requires approx. 200 kWh of natural gas and 50 kWh of electricity [
19
]. In a separate
sensitivity analysis, the environmental impacts of both funeral choices are compared (see
Section 4.2), assuming that the grave surface area is 3 m
2
[
20
] and that the water demand of
a cemetery is 0.1 L per m
2
and day [
21
]. Further, it is assumed that the grave plot is sold for
25 years [22].
•Energy and water
An RWTH report [
23
] shows that the average electricity consumption of a 2-person
household in the “old adulthood stage” is approx. 3545 kWh/a, whereas the total energy
consumption of a 2-person household is assumed to be 18,817 kWh per year [
24
]. Half of
the environmental impacts caused by energy consumption are allocated to the fictional
study object.
Moreover, per capita water usage in Germany is approx. 47.085 L p/a [
25
]. The current
breakdown of renewable energy sources for thermal heating in Germany is 86% biomass
and -gas, 4.2% solar energy, and 9.8% geothermal energy [
26
]. Hence, it is assumed that
biomass and -gas represent the primary renewable energy sources to meet the thermal
energy demand of the fictional study object.
•Food
The annual consumption values for “food” are based on the study by Heuer et al. [
27
].
The report gives an overview of the average daily food consumption by gender and age
group. It is assumed that there are no changes in consumption behavior for “food” between
the “retirement phase” and “EoL phase”, as the difference in average daily energy intake
of an individual between the ages of 65 and 74 years (2095 kcal/day) and 75 and 84 years
(2060 kcal/day) is negligibly small [28].
•House
For the product category “house”, the living space of the fictional study object is
assumed to be 92.6 m
2
. This value represents the average living space of an older adult over
65 in Germany (approx. 111 m2for homeowners and 69 m2for tenant households) [29].
•Transport
From the retirement age onwards, the daily distance traveled by different modes of
transport decreases significantly. The main travel purposes for individuals aged 60–69 in
Germany are accompanying purposes (5%) (e.g., doctor appointment of a spouse), leisure
(28.5%), errands (18.5%) (e.g., short travels to the post office, dry cleaners, car wash),
shopping (23.5%), and commute-/work-related travel (21%) [
30
]. Due to the scope of this
case study, all work-related travel and commuting are neglected.
A study by the Federal Ministry of Transport and Digital Infrastructure [
30
] states
that individuals aged 60–69 travel a daily distance of approx. 36 km. For the age group
70–79 years, this value is 25 km. No differentiation is made between the “driver” and “pas-
senger” for the daily traveled distance by motorized private transportation (e.g., privately
owned cars). The total daily distance is combined and half is allocated to the fictional
study object as all car rides are assumed to be shared with their partner. Furthermore,
Sustainability 2023,15, 11447 6 of 18
distances covered by “other transport” methods (e.g., taxi and car-sharing) are allocated to
the product cluster “diesel car”, as 85% of all German taxis are run by diesel [30,31].
2.4.2. Modeling of Product Clusters
The two common LCA databases GaBi (content version 2022.2) [
32
] and Ecoinvent
3.5 [
33
] are used for modeling all product clusters. (See SMs for an overview of data source
types and datasets used for modeling a cluster.) In the case of similar datasets in both
databases, GaBi is preferred according to the hierarchy of datasets for modeling [4].
Figure 2shows an overview of the relative shares of data sources for each product
category. “Reference materials” are chosen for product clusters where the average material
composition is unknown or the product datasets are unavailable in the databases. Where a
product is predominantly composed of one material, it is modeled based on that specific
material dataset. The term “Proxy, own model” refers to products that are modeled based
on their average material composition. Additionally, some proxies are taken from the first
Life-LCA case study (referred to as “Proxy, Bossek et al., (2021)”) as information from the
secondary literature is lacking.
Sustainability 2023, 15, x FOR PEER REVIEW 6 of 18
(28.5%), errands (18.5%) (e.g., short travels to the post office, dry cleaners, car wash), shop-
ping (23.5%), and commute-/work-related travel (21%) [30]. Due to the scope of this case
study, all work-related travel and commuting are neglected.
A study by the Federal Ministry of Transport and Digital Infrastructure [30] states
that individuals aged 60–69 travel a daily distance of approx. 36 km. For the age group
70–79 years, this value is 25 km. No differentiation is made between the “driver” and “pas-
senger” for the daily traveled distance by motorized private transportation (e.g., privately
owned cars). The total daily distance is combined and half is allocated to the fictional
study object as all car rides are assumed to be shared with their partner. Furthermore,
distances covered by “other transport” methods (e.g., taxi and car-sharing) are allocated
to the product cluster “diesel car”, as 85% of all German taxis are run by diesel [30,31].
2.4.2. Modeling of Product Clusters
The two common LCA databases GaBi (content version 2022.2) [32] and Ecoinvent
3.5 [33] are used for modeling all product clusters. (See SMs for an overview of data source
types and datasets used for modeling a cluster.) In the case of similar datasets in both
databases, GaBi is preferred according to the hierarchy of datasets for modeling [4].
Figure 2 shows an overview of the relative shares of data sources for each product
category. “Reference materials” are chosen for product clusters where the average mate-
rial composition is unknown or the product datasets are unavailable in the databases.
Where a product is predominantly composed of one material, it is modeled based on that
specific material dataset. The term “Proxy, own model” refers to products that are mod-
eled based on their average material composition. Additionally, some proxies are taken
from the first Life-LCA case study (referred to as “Proxy, Bossek et al., (2021)”) as infor-
mation from the secondary literature is lacking.
Figure 2. Relative shares of data sources for each product category.
“Proxy, own model” has a significant share, especially for the product categories
“food”, “health and medical equipment”, as well as “living, household, and accessories”.
The high percentile is because general mixes are modeled for certain food products (e.g.,
meat is modeled based on the general meat consumption mix in Germany, which consists
of beef (17.5%), pork (58%), and poultry (24.5%)). General mixes are created either to sim-
plify future studies or because available secondary data are based on generic values. As
Figure 2. Relative shares of data sources for each product category.
“Proxy, own model” has a significant share, especially for the product categories
“food”, “health and medical equipment”, as well as “living, household, and accessories”.
The high percentile is because general mixes are modeled for certain food products
(e.g., meat is modeled based on the general meat consumption mix in Germany, which
consists of beef (17.5%), pork (58%), and poultry (24.5%)). General mixes are created either
to simplify future studies or because available secondary data are based on generic values.
As yearly food consumption was based on the study of Heuer et al. [
28
], new products had
to be modeled.
A total of 11 product categories are defined based on the first Life-LCA case study [
4
],
which covered 124 product clusters.
Overall, seven “Proxy, Bossek et al., (2021)” are used. Forty-seven product clusters are
newly modeled (“Proxy, own model”). The clusters “clothes”, “shoes”, “sanitary ware”,
Sustainability 2023,15, 11447 7 of 18
“furniture”, and “tableware” represent the average material composition of the global
market. Cereal represents the European mix and consists of wheat, maize, barley, rye,
and oat. Finally, the clusters “electricity, conventional energy”, “electricity, renewable
electricity”, “fruit”, “meat”, and “vegetable” represent the German market.
3. Results
The following chapter presents the results for the impact categories GWP, ADP-e,
ADP-f, HT cancer, and HT non-cancer for the “old adulthood stage” (see Section 3.1), the
“retirement phase” (see Section 3.2), and the “EoL phase” (see Section 3.3). The results for
AP, EP, and POCP can be found in detail in the Supplementary Materials and are briefly
discussed in comparison with the results of the first Life-LCA case study in the discussion
part (see Section 4.3).
3.1. Life-LCA in the Old Adulthood Stage
Figure 3shows the Life-LCA results in the “old adulthood stage” over 15 years. The
results for GWP add up to 120 t CO
2
-eq, for ADP-e to 1.35 kg Sb-eq, for ADP-f to 1.55 TJ, for
HT cancer to 0.00009 CTUh, and for HT non-cancer to 0.002 CTUh. For GWP, this equates
to an annual impact of 8 t CO2-eq.
Sustainability 2023, 15, x FOR PEER REVIEW 7 of 18
yearly food consumption was based on the study of Heuer et al. [28], new products had
to be modeled.
A total of 11 product categories are defined based on the first Life-LCA case study
[4], which covered 124 product clusters.
Overall, seven “Proxy, Bossek et al., (2021)” are used. Forty-seven product clusters
are newly modeled (“Proxy, own model”). The clusters “clothes”, “shoes”, “sanitary
ware”, “furniture”, and “tableware” represent the average material composition of the
global market. Cereal represents the European mix and consists of wheat, maize, barley,
rye, and oat. Finally, the clusters “electricity, conventional energy”, “electricity, renewable
electricity”, “fruit”, “meat”, and “vegetable” represent the German market.
3. Results
The following chapter presents the results for the impact categories GWP, ADP-e,
ADP-f, HT cancer, and HT non-cancer for the “old adulthood stage” (see Section 3.1), the
“retirement phase” (see Section 3.2), and the “EoL phase” (see Section 3.3). The results for
AP, EP, and POCP can be found in detail in the Supplementary Materials and are briefly
discussed in comparison with the results of the first Life-LCA case study in the discussion
part (see Section 4.3).
3.1. Life-LCA in the Old Adulthood Stage
Figure 3 shows the Life-LCA results in the “old adulthood stage” over 15 years. The
results for GWP add up to 120 t CO2-eq, for ADP-e to 1.35 kg Sb-eq, for ADP-f to 1.55 TJ,
for HT cancer to 0.00009 CTUh, and for HT non-cancer to 0.002 CTUh. For GWP, this
equates to an annual impact of 8 t CO2-eq.
Figure 3. Life-LCA old adulthood stage (15 years) results and relative shares by product categories.
Sustainability 2023,15, 11447 8 of 18
In total, the fictional study object traveled 166,000 km using different transporta-
tion modes and consumed 141 MWh energy and 706 t of water during the entire “old
adulthood stage”.
The share of transport is a top contributor for all impact categories (41% for GWP
and ADP-e, 46% for ADP-f and HT cancer, and 36% for HT non-cancer) due to traveling
166,000 km using public transportation (15%) as well as private vehicles (85%). As shown
in Figure 3, the product categories energy and water (30%) and food (20%) also have a
significant share for most impact categories.
In contrast to the other impact categories, electronics showcases a significant share
for ADP-e, as it is strongly influenced by resource stocks and their extraction rate [
34
].
Furthermore, the most contributing clusters for each product category are explained in
detail for the “retirement and EoL phase” (see Section 3.3).
3.2. Baseline Scenario of the Retirement Phase
The baseline scenario of the “retirement phase” provides results over a one-year period
between the ages of 65 and 75 (see Figure 4).
Sustainability 2023, 15, x FOR PEER REVIEW 8 of 18
Figure 3. Life-LCA old adulthood stage (15 years) results and relative shares by product categories.
In total, the fictional study object traveled 166,000 km using different transportation
modes and consumed 141 MWh energy and 706 t of water during the entire “old adult-
hood stage”.
The share of transport is a top contributor for all impact categories (41% for GWP and
ADP-e, 46% for ADP-f and HT cancer, and 36% for HT non-cancer) due to traveling
166,000 km using public transportation (15%) as well as private vehicles (85%). As shown
in Figure 3, the product categories energy and water (30%) and food (20%) also have a
significant share for most impact categories.
In contrast to the other impact categories, electronics showcases a significant share
for ADP-e, as it is strongly influenced by resource stocks and their extraction rate [34].
Furthermore, the most contributing clusters for each product category are explained in
detail for the “retirement and EoL phase” (see Section 3.3).
3.2. Baseline Scenario of the Retirement Phase
The baseline scenario of the “retirement phase” provides results over a one-year pe-
riod between the ages of 65 and 75 (see Figure 4).
Figure 4. Baseline scenario results of the “retirement phase” on a yearly basis and relative shares of
product categories.
Transport is one of the largest contributors across all impact categories. Transport by
diesel and petrol cars have a combined relative impact share for ADP-e, ADP-f, GWP, HT
cancer, and HT non-cancer of between 96% and 99.5%. It is assumed that the fictional
study object travels a total of 12,500 km by various means of transport, 85% by car.
Transport by car contributes 20% to the overall kg CO
2
-eq emissions.
Energy and water is the second largest contributor to ADP-f (30%), GWP (30%), and
HT non-cancer (22%).
Food is also a dominant contributor to GWP (20%), mainly caused by cheese and
meat consumption. Both clusters have an impact of 42% on the total kg CO
2
-eq emissions
for food. Moreover, coffee has among the highest relative impact shares for HT cancer
(17%) and HT non-cancer (15%) within the product category “food”. Applying fertilizers
Figure 4.
Baseline scenario results of the “retirement phase” on a yearly basis and relative shares of
product categories.
Transport is one of the largest contributors across all impact categories. Transport by
diesel and petrol cars have a combined relative impact share for ADP-e, ADP-f, GWP, HT
cancer, and HT non-cancer of between 96% and 99.5%. It is assumed that the fictional study
object travels a total of 12,500 km by various means of transport, 85% by car. Transport by
car contributes 20% to the overall kg CO2-eq emissions.
Energy and water is the second largest contributor to ADP-f (30%), GWP (30%), and
HT non-cancer (22%).
Food is also a dominant contributor to GWP (20%), mainly caused by cheese and meat
consumption. Both clusters have an impact of 42% on the total kg CO
2
-eq emissions for
food. Moreover, coffee has among the highest relative impact shares for HT cancer (17%)
and HT non-cancer (15%) within the product category “food”. Applying fertilizers and
pesticides (e.g., zinc and chlorpyrifos pesticides) commonly used during the cultivation of
coffee beans is the leading cause. Also, the distribution of coffee (e.g., the transportation of
coffee beans to the end user) is responsible for 25% of human toxicity impacts [34].
Moreover, the category “living, household, and accessories” has a small impact on
the total ADP-e with a share of only 5% due to the cluster furniture, which has a relative
impact share of 76%.
Sustainability 2023,15, 11447 9 of 18
“Electronics” (Computers and notebooks (38%) as well as small electrical appliances
(26%)) has a noticeably high impact on ADP-e (46%) due to the use of rare earth metals.
Further, electronics, including hard drives, loudspeakers, and headphones, depend on
neodymium contributing to ADP-e [
35
]. Electronics also contributes significantly towards
HT non-cancer with a total impact share of 14%, with the clusters of large electrical ap-
pliances (35%), computers (21%), and TV (18%) being the main contributors. Electronic
products contain harmful substances such as lead, mercury, and certain phthalates and are
therefore clustered as hazardous [36].
The category hobbies and leisure has a small impact on HT cancer, with a share of
8%. Gym balls are the leading causes (relative impact share of 95%), as they are commonly
made of anti-burst PVC plastic, which contains chemical additives such as phthalates,
aliphatics, epoxy, terephthalates, trimellitates, polymerics, and phosphates [37].
The product categories “cosmetics, hygiene and cleaning”, “clothes and jewelry”, and
“health and medical equipment” have a minor impact (0–1%) (see SMs).
3.3. Baseline Scenario of the End-of-Life Phase and Comparison with the Retirement Phase
The baseline scenario of the “EoL phase” provides results over a one-year period of
the fictional study object’s life between the ages of 75 and 80. Figure 5visualizes relative
shares of each product and impact category and the comparison with the baseline results
of the “retirement phase”.
Sustainability 2023, 15, x FOR PEER REVIEW 9 of 18
and pesticides (e.g., zinc and chlorpyrifos pesticides) commonly used during the cultiva-
tion of coffee beans is the leading cause. Also, the distribution of coffee (e.g., the transpor-
tation of coffee beans to the end user) is responsible for 25% of human toxicity impacts [34].
Moreover, the category “living, household, and accessories” has a small impact on
the total ADP-e with a share of only 5% due to the cluster furniture, which has a relative
impact share of 76%.
“Electronics” (Computers and notebooks (38%) as well as small electrical appliances
(26%)) has a noticeably high impact on ADP-e (46%) due to the use of rare earth metals.
Further, electronics, including hard drives, loudspeakers, and headphones, depend on ne-
odymium contributing to ADP-e [35]. Electronics also contributes significantly towards
HT non-cancer with a total impact share of 14%, with the clusters of large electrical appli-
ances (35%), computers (21%), and TV (18%) being the main contributors. Electronic prod-
ucts contain harmful substances such as lead, mercury, and certain phthalates and are
therefore clustered as hazardous [36].
The category hobbies and leisure has a small impact on HT cancer, with a share of
8%. Gym balls are the leading causes (relative impact share of 95%), as they are commonly
made of anti-burst PVC plastic, which contains chemical additives such as phthalates, al-
iphatics, epoxy, terephthalates, trimellitates, polymerics, and phosphates [37].
The product categories “cosmetics, hygiene and cleaning”, “clothes and jewelry”,
and “health and medical equipment” have a minor impact (0–1%) (see SMs).
3.3. Baseline Scenario of the End-of-Life Phase and Comparison with the Retirement Phase
The baseline scenario of the “EoL phase” provides results over a one-year period of
the fictional study objects life between the ages of 75 and 80. Figure 5 visualizes relative
shares of each product and impact category and the comparison with the baseline results
of the “retirement phase”.
Figure 5. Comparison of the baseline scenarios between the “retirement phase” and “EoL phase”
based on relative shares of product categories on a yearly basis.
The results show that in the “EoL phase”, “transport” remains one of the largest and
most significant contributors across all impact categories. When comparing the baseline
scenario results of both phases, it is evident that the “retirement phase” causes a higher
environmental impact than the “EoL phase” across all impact categories (see Figure 5).
The average yearly GWP in both considered phases is 30% lower than that of an average
German adult (11 t CO2-eq) [38]. In the case of GWP, the total emissions decrease by 17%
Figure 5.
Comparison of the baseline scenarios between the “retirement phase” and “EoL phase”
based on relative shares of product categories on a yearly basis.
The results show that in the “EoL phase”, “transport” remains one of the largest and
most significant contributors across all impact categories. When comparing the baseline
scenario results of both phases, it is evident that the “retirement phase” causes a higher
environmental impact than the “EoL phase” across all impact categories (see Figure 5).
The average yearly GWP in both considered phases is 30% lower than that of an average
German adult (11 t CO
2
-eq) [
38
]. In the case of GWP, the total emissions decrease by 17%
in the “EoL phase” (see Table 1). The relative impact of transportation by car (diesel and
petrol) is >91%. While in the baseline scenario of the “retirement phase”, 110,600 km is
driven by car, the traveled distance is reduced by 38% in the “EoL phase”. Consequently,
transport impacts are lower in the “EoL phase”.
Sustainability 2023,15, 11447 10 of 18
Table 1.
Changes in the GWP results on a yearly basis for the baseline scenario of the retirement and
EoL phase.
Product Category Retirement Phase
GWP [kg CO2-eq]
EoL Phase
GWP [kg CO2-eq] Changes in %
clothes and jewelry 29.90 27 −9.7%
cosmetics, hygiene, and
cleaning 38.30 38 0%
electronics 334 256 −23.4%
energy and water 2450 2450 0%
food 1590 1590 0%
after-life 0 35 0.4%
health and medical equipment 0.62 0.84 34.9%
hobbies and leisure 75 65 −13%
house 162 162 0%
living, household, and home
office 52 47 −9.8%
transport 3770 2400 −36.3%
sum 8502 7072 −16.8%
Besides transport, changes in GWP between the two phases for other product cate-
gories are small.
For the product categories “electronics”, “health and medical equipment”, “hobbies
and leisure”, “living, household and accessories”, and “clothes and jewelry”, changes
in consumption for selected product clusters are assumed. As shown in Table 1, for
example, the product cluster clothes decreases by 13% and shoes by 10% in the “EoL phase”
compared to the “retirement phase”. For electronics, the reduction ranges from 7% (housing
infrastructure) to 80% (audio and hifi). These changes have a small impact on the overall
results. For GWP, clothes and jewelry result in an impact of 30 kg CO
2
-eq in the “retirement
phase” and 27 kg CO
2
-eq in the “EoL phase”, which equates to a reduction of approx. 10%
for this specific product category (see Table 1). However, regarding total GWP, the changes
in consumption of clothes and jewelry only result in an overall reduction of 0.04%. For
the product category electronics, the decrease in GWP in the “EoL phase” equates to only
0–1%.
The product category after-life part of the “EoL phase” consists of three product
clusters: urn, casket, and cremation process. “After-life” impacts the five chosen categories
little, as the share remains below
≤
0.5%. In the case of GWP, “after-life” adds an impact
of 35.2 kg CO
2
-eq and increases the overall GWP of the “retirement phase” by 0.4%.
Furthermore, the product cluster urn is the main contributor with a relative share of 92%
towards ADP-e due to stainless steel.
4. Discussion
This chapter presents the case study results (see Section 4.1), its limitations, challenges,
and future perspectives (see Section 4.2), a sensitivity analysis (see Section 4.3) and presents
a comparison of the results with the optimized scenario of the first Life-LCA case study
(see Section 4.4).
4.1. General Discussion of the Results
Comparing the results of the “retirement phase” and “EoL phase” shows that con-
sumer behavior changes per life phase. Especially for the product category “transport”, the
changes significantly impact the overall Life-LCA results within the “old adulthood stage”.
Therefore, splitting the life stage into two separate phases was essential to determine the
most accurate results.
Further, while the “after-life” product category broadens the system boundary and
contributes to a more holistic understanding of the “old adulthood stage”, it was found
to have inconsequential impacts on all evaluated impact categories. These results suggest
Sustainability 2023,15, 11447 11 of 18
that Life-LCA studies of the “old adulthood stage” could feasibly exclude the “after-life”
category, thereby streamlining the process. Moreover, refining the necessary product
categories for analysis, particularly in studies primarily using secondary data, can further
simplify the study while reducing the demands of data collection and modeling. For
instance, considering the results within a German context, a simplified assessment could
solely focus on the product categories of transport, energy and water, and food. These
categories account for approx. 80–90% of the environmental impacts across all considered
impact categories, except for ADP-e, where electronic products predominantly contribute
to the impacts.
However, it is crucial to consider the influence of specific regional consumption
patterns. A product category with a minor impact in one region may have a significant
impact in another. For instance, in the transportation category, private vehicle use might
have a more substantial environmental footprint in countries with less developed public
transportation systems compared to countries with efficient public transit. Similarly, in
the food category, a shift towards a plant-based diet might have a more pronounced
environmental impact in countries where meat consumption is high compared to countries
with lower meat consumption rates. Therefore, tailoring Life-LCA studies to reflect local
consumption patterns is critical for ensuring accuracy and relevance.
Also, the results show that alterations in consumption habits may result in the redistri-
bution of burdens among the considered impact categories, indicating that optimization
measures should consider more than just GWP. For example, replacing meat with plant-
based foods could reduce GWP but may unintentionally increase burdens in other impact
categories such as EP.
Moreover, the chosen consumption values for “energy and water” represent the
statistical average in Germany but do not differentiate between ages. For this reason,
assumptions were established (e.g., that the fictional study object lives in a two-person
household) to reduce uncertainty when obtaining the energy consumption values.
As mentioned in Section 2.4.2, only GaBi and Ecoinvent databases were used as sources
for the modeling of product clusters. The LCA reports, and case studies are not chosen
as sources, as in the previous Life-LCA case studies [
4
,
5
]. Therefore, result conversions
did not occur, and associated uncertainties could be reduced. Additionally, this study
does not apply any strict allocation rules. For some product categories (e.g., electricity and
thermal energy consumption of a two-person household), a ratio of 1:1 was allocated to
the fictional study object. Therefore, long-lasting and shared products (e.g., house, televi-
sion) with a high burden per product unit show low impacts. Nevertheless, an adequate
allocation of burdens in the two-person household is generally missing for most product
categories (e.g., allocation of car rides if only one family member runs errands such as
grocery shopping).
4.2. Case Study Challenges, Limitations, and Future Perspectives
The major limitation and challenge of the study is that it is mainly based on secondary
data. In some cases, the necessary secondary data were unavailable (e.g., average con-
sumption values of products within “living, household, and accessories”), and consumed
quantities of specific products had to be obtained from different sources. For instance, for
some product categories (see SMs), data were taken from the yearly consumption inventory
provided in the first Life-LCA case study [
4
] based on the 49-year-old entrepreneur Dirk
Gratzel and were subsequently scaled based on the income and consumption survey con-
ducted by the Federal Statistical Office of Germany (see SMs for an overview of the scaling
rates and the selected products) [
7
]. Therefore, the values provided for these product
categories do not represent the average adult in Germany. Further, the same uncertainties
apply to selected products as in the previous Life-LCA case study.
Nevertheless, this study has demonstrated that secondary data collection is more
efficient and resource-saving compared to primary data collection. It eliminates the need
for familiarization of a study object with the goal and scope of the study, long monitoring
Sustainability 2023,15, 11447 12 of 18
phases, ongoing supervision during data collection (e.g., feedback sessions), and primary
data evaluation. Furthermore, it suggests the potential value of creating a centralized
database with average consumption rates, for example, the average distance traveled per
transport means and life stage, as implemented in this study. Such a database would
enhance both the precision and efficiency of environmental impact studies and broaden
the Life-LCA methodology’s application. Readily accessible data could reduce resources
spent on primary data collection, simplify comprehensive cradle-to-grave assessments, and
ultimately lead to a more nuanced understanding of environmental impacts.
However, while creating a centralized database would bring numerous advantages, it
would also present its challenges, notably ensuring the data remain up-to-date to accurately
reflect the changes in consumption patterns across different life stages. This could be
particularly challenging given the dynamic nature of consumer behaviors, which can shift
due to technological advancements, societal changes, policy interventions, and economic
factors. On a broader scale, this would require cooperation and coordination among various
national and international entities to gather and share the necessary data. In general, the
presented approach based on secondary data can serve as a blueprint for similar studies
in other countries or regions, not only focusing on studies with similar socio-economic
and demographic contexts. Primary data collection should be conducted additionally
for specific product categories where existing data are insufficient or lacking. It is also
suggested to evaluate the impact of regional discrepancies (e.g., metropole vs. rural areas)
as the region in which a person lives also impacts consumer behavior, living space, and
energy consumption [39].
Further, existing research often bases macro-scale impact studies of human consump-
tion patterns on Life Cycle Assessments (LCAs) for specific products. The results from these
LCAs are then extrapolated to represent overall consumption patterns through various
scaling techniques [
40
,
41
]. However, most of these studies primarily investigate the impacts
of citizens’ lifestyles [
42
,
43
], with a predominant focus on greenhouse gas emissions using
input–output analysis [
44
,
45
], or they concentrate on a different regional context or broader
scope (e.g., Europe instead of Germany) [
40
,
46
]. They overlook the specific product clusters
and consumption patterns associated with different stages of the human life cycle, such as
the “old adulthood stage” included in this case study.
Another limitation is that gender was not considered, even though it can be an in-
fluencing factor affecting the overall consumption pattern and Life-LCA results. (See
Section 4.3 for a gender sensitivity analysis on “transport”.) Therefore, it is suggested
that future studies should carry out a Life-LCA for both genders in all considered product
categories to assess and compare the differences in results.
While the presented case study is partly empirical, focusing on data drawn from
national statistics and the first Life-LCA case study, the theoretical persuasion lies in
extending the system boundaries of the Life-LCA method to cover an unexamined life
stage and introducing additional impact categories. Further, the Life-LCA method got
adapted with a subdivision into two separate life phases and introduced a new product
category, “after life”. This can be viewed as a practical contribution to the theory of Life-
LCA, expanding its application to make a comprehensive cradle-to-grave assessment for
future studies possible.
Moreover, by using the insights from this study and previous Life-LCA case stud-
ies, it is now possible to examine unexplored life phases such as adolescence and early
childhood or conduct a more detailed analysis of the early adulthood stage (which was
estimated using data from a 49-year-old study subject, Dirk, from the initial Life-LCA
study) to carry out a comprehensive cradle-to-grave case study. It is suggested to base this
comprehensive study on a German context, as all previous case studies were conducted in
this regional background. Therefore, the existing datasets and consumption inventories
from the previous Life-LCA case studies can be utilized.
Nevertheless, the application of the Life-LCA method and the general framework
for assessing a human being’s environmental impacts through the LCA is not limited to
Sustainability 2023,15, 11447 13 of 18
Germany. Exploring new regions could reveal interesting new challenges as consumption
patterns and environmental impacts differ based on local socio-economic factors, cultural
differences, and policy measures. Each region’s unique characteristics would require
adapted data collection methods, modeling of specific product clusters, and redefining
dimension one, the human life cycle, adding complexity to the research.
Furthermore, by setting benchmarks for the environmental impacts of humans across
various regions, targeted intervention strategies could be developed for product categories
where reduction measures would have the greatest impact. Such information would be
particularly useful for policymakers as well as interesting for the general public.
Beyond the typical scenario of linear, healthy aging until death assumed in this
study, it is suggested to explore other potential life events that could significantly alter
consumption patterns and environmental impacts. Health crises, for example, could lead
to hospitalization or necessitate a shift to a care home resulting in changes in consumption
behaviors, such as transport or food choices and, therefore, environmental implications. An
interdisciplinary approach encompassing gerontology, LCA, and health sciences, among
others could provide a holistic view of the environmental implications of life events in the
“old adulthood stage”.
4.3. Sensitivity Analysis
In the following, the results of the sensitivity analysis for selected product categories
and clusters are presented.
•“After-life”: traditional burial vs. cremation
In the baseline scenario, the fictional study object is assumed to be cremated after his
death, as cremation is Germany’s most popular funeral choice, followed by the traditional
burial [
18
]. Thus, product clusters for a traditional burial were modeled, which include
“grave maintenance, gardening” and “grave maintenance, irrigation”. Results show that a
cremation causes a GWP of 35.2 kg CO
2
-eq in the EoL phase, while a traditional burial has
22.5 kg CO
2
-eq. This equates to a 56% higher impact of cremation than a traditional burial
due to the “cremation process” itself, specifically the use of fossil fuels and the high energy
needs. Cremation has an overall GWP of 0.15% in the total Life-LCA result for the old
adulthood stage, while traditional burial only impacts around 0.1%. Land use, an impact
category not yet considered, could potentially present significant impacts in the case of
traditional burials compared to cremation.
•“After-life”: alternative urn materials
Due to the underlying dataset of “stainless steel” in the “baseline scenario”, the
product cluster “urn” is the main contributor with a share of 90% of the total ADP-e for the
product category “after-life”. Alternatively, urns can be made from other materials with
lower impact. Thus, the urn was modeled with ceramic and stone. If the urn was to be
made from ceramics, it would only have an ADP-e impact with a share of 30%, and the
product category “after-life” would have a total ADP-e impact of 0.012 g Sb-eq (on a yearly
basis) in the “EoL phase”.
Moreover, if the urn was made from stone, “after-life” would only have an ADP-e
impact of 0.009 g Sb-eq. Nevertheless, in all cases, the overall contribution of “after-life”
towards the total ADP-e in the “EoL phase” is negligibly small (0–1%). Therefore, if the
fictional study object was preserved in an urn, the choice of different material compositions
of the product would make an insignificant change towards the total environmental impact.
•“Electronics”: impact of consumption reduction
Compared to other product categories in the baseline scenario, “electronics” has a
contribution of 49% to the total ADP-e impact. When reducing the overall consumption
by 30%, the impact reduces by 15% (from 1.35 kg Sb-eq to 1.15 kg Sb-eq). These results
showcase the importance of reducing the consumption of electronic devices.
•“Energy and water”: conventional energy vs. renewable energy
Sustainability 2023,15, 11447 14 of 18
The energy consumption of the fictional study object is based on the current electric-
ity and thermal energy mix in Germany. In the “baseline scenario”, it is assumed that
47% of electricity consumption (778.28 kWh) derives from renewable energy, while 53%
(874.12 kWh) stems from conventional energy sources [
47
]. However, if the current yearly
electricity demand for the fictional study object (1652.39 kWh) in Germany was entirely cov-
ered by conventional energy, energy impacts would increase their GWP by 21%, amounting
to an overall contribution of 35% (44.7 t CO
2
-eq) towards the total GWP in the “baseline
scenario” of the Life-LCA. In contrast, if the current electricity demand for the fictional
study object was covered only by renewable energy, “energy and water” could decrease
their GWP by 24% compared to the “baseline scenario”. Consequently, “energy and water”
would only contribute 25% towards the total GWP. Furthermore, if 100% of the thermal
energy consumption was based on renewable energy carriers, the GWP of “energy and
water” could also decrease by 24% compared to the “baseline scenario”.
In a further step, if both electricity and thermal energy demands were covered by
renewable energy, then “energy and water” would only have a GWP of 12.9 t CO
2
-eq. The
impact can be reduced by 65% for “energy andwater”, and the total GWP in the Life-LCA
could decrease by 20%.
•“Transport”: public transport vs. car transport
If the fictional study object travelled all distances by public transport (local train)
instead of petrol and diesel car, GWP for “transport” would decrease by 73%. The GWP
would have an impact of 84.3 t CO
2
-eq, which is 30% lower than the “baseline scenario”.
However, there are constraints and limitations to only using public modes of transportation
in Germany. Especially in rural regions, the infrastructure for trains and buses is insufficient.
Many communities have inadequate connections to the nearest small or midsized cities,
and there is a clear need to improve access to public transportation [
48
]. Approx. 90% of all
households own at least one car, and more than 60% of all trips are made by private car [
49
].
The current infrastructure significantly limits achieving climate goals in the transportation
sector, as the urban form and transportation systems are among the primary obstacles to
achieving decarbonization within this sector [50].
•“Transport”: influence of gender
Furthermore, gender can influence the Life-LCA result. For example, women aged
60–69 travel on average 30% less km than men [
30
]. Therefore, if the fictional subject
object was male, the GWP for “transport” would be 59 t CO
2
-eq. In contrast, if the subject
was female, the impact would be 38 t CO
2
-eq, representing a reduction of approx. 35%
compared to the male subject. As a result, a fictional female study subject would have a
total GWP of 16% lower than a male subject during the “old adulthood stage” (spanning
15 years).
4.4. Comparison of GWP, AP, EP, and POCP Impacts of the Old Adulthood Stage with the
Optimized Scenario Results of the First Life-LCA Case Study and Plausibility Check
The combined average CO
2
-eq emissions on a yearly basis for the two life phases
under consideration in the old adulthood stage is 7.75 tons, around 20% lower than Dirk’s
emissions in the optimized scenario (9.5 tons), mainly due to restricted mobility with
ongoing age. In comparison with the optimized scenario, all considered impact categories
(GWP, AP, EP, and POCP) in the old adulthood stage have lower impacts, except for “food”
and “health and medical equipment” (see supplementary materials). The combined average
impacts for AP add up to 33 kg SO
2
-eq, for EP to 15 kg PO
4
-eq, and for POCP to 2.7 kg
C2H4-eq.
For AP and POCP, the results from the old adulthood stage are 50%, and for EP, they
are 35% lower. The relative shares for all product categories show similarly across all
considered impact categories. Hence, the difference in the optimized scenario from the first
Life-LCA case study will be briefly explained using GWP as an example.
Sustainability 2023,15, 11447 15 of 18
The consumption values for food in the old adulthood stage are based on the average
dietary intake of a German who does not follow any particular diet and are twice as high
as in the optimized scenario as Dirk adheres to a vegan diet. As a result, the average values
still account for meat and dairy products, significant contributors to CO2-eq emissions.
As age progresses, health and medical equipment’s impacts for GWP increase by
about 15% in the end-of-life phase. Further, Dirk’s living area is twice as large, leading to a
30% higher energy consumption. Additionally, more furniture is present, which increases
“living, household, and home office” by approx. 75%.
Dirk owns about twice as many clothes as the average in the old adulthood stage
resulting in double the impacts. There is also a substantial reduction (
−
85%) in “hobbies
and leisure” in the old adulthood stage since Dirks’s dog was included in the first Life-
LCA case study. His hobbies, such as hunting or tennis, are also captured and increased
overall emissions.
Transport in the average old adulthood stage scenario is reduced by only 10% for GWP.
Although Dirk travels five times as many kilometers as the average, the values almost
equalize since he mainly uses public transportation.
The observations suggest that the comparison results and the 20% lower impacts in
the old adulthood stage relative to Dirk’s results are plausible. However, conducting an
additional study with real-life individuals from both life phases and primary data collection
would further validate these findings and corroborate the effectiveness of secondary data
use in such studies.
5. Conclusions and Outlooks
In the context of the Life-LCA, the impact categories ADP-e, ADP-f, HT cancer, and
HT non-cancer were selected for the first time showing valuable insights. Any measures
for reduction should consider different impact categories to prevent trade-offs. Further,
the Life-LCA database was expanded by modeling specific datasets for the old adulthood
stage, adding value and data quality and offering a solid foundation for future case studies.
For the first time, secondary data and a fictional study object were used to apply the
Life-LCA approach, reducing the assessment’s resources and time effort. However, a study
with primary data instead of secondary data could be conducted to check the plausibility
of the results. Moreover, to further reduce uncertainties, the previously defined “old
adulthood stage” was divided into two subdivisions (retirement and EoL phase), enabling
a more accurate Life-LCA. A new product category, “after life”, expanded the Life-LCA
system boundaries. This enhances the practical contribution to the theory of Life-LCA,
expanding its application to make a comprehensive cradle-to-grave assessment for future
studies possible.
The results underscore the significance of the transport sector across all impact cate-
gories in the Life-LCA, indicating the importance of environmentally friendly transport
alternatives. In addition, the results showed that the older a person gets, the smaller
their footprint becomes, attributable mainly to the individual’s restricted mobility. Future
research on different influencing factors that affect the overall consumption patterns of an
individual (e.g., gender and regional discrepancies) is suggested to face new challenges
since regional and socio-economic conditions influence the variations in consumption be-
haviors and environmental impacts. The unique features of each region would necessitate
custom data collection methods and the redefinition of the human life cycle model of
dimension one, adding another level of intricacy to the research. In addition, establishing a
central database of average data on consumption rates (e.g., yearly traveled distance by
diesel car or clothes consumption in kg) and having pre-modeled product mixes (e.g., meat,
cereal) for each life stage are recommended to facilitate future modeling efforts and allow
for an easier benchmarking.
Sustainability 2023,15, 11447 16 of 18
Supplementary Materials:
The following supporting information can be downloaded at:
https://www.mdpi.com/article/10.3390/su151411447/s1. The Supplementary Materials provide
LCIA results (1), the consumption inventory (2), data source type and dataset used for product cluster
modeling (3), scaling rates (4), and LCIA results II (5).
Author Contributions:
Conceptualization, D.B., C.R., V.B. and M.F.; data curation, D.B. and C.R.;
investigation, D.B. and C.R.; methodology, D.B. and C.R.; supervision, V.B. and M.F.;
writing—original draft, D.B. and C.R.; writing—review and editing, V.B. and M.F. All authors
have read and agreed to the published version of the manuscript.
Funding: We acknowledge the support of the Open Access Publication Fund of the TU Berlin.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data presented in this study are available in the article and
Supplementary Materials.
Conflicts of Interest: The authors declare no conflict of interest.
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