1 Introduction
2 Materials and methods
2.1 Goal and scope
2.2 System boundaries and allocation
2.3 Life-cycle inventory
2.3.1 Data collection and calculation
2.3.2 Modelling of product clusters
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“clothes and jewellery” (N = 23),
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“cosmetics, hygiene, and cleaning” (N = 17),
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“electronics” (N = 15),
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“energy and water” (N = 6),
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“food” (N = 41),
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“health and medical equipment” (N = 5),
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“hobbies, leisure, and pet” (N = 29),
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“house” (N = 16),
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“living, household, and home office” (N = 49),
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“transport” (N = 12).
2.4 Impact assessment
3 Results
3.1 Life-LCA
3.2 Baseline scenario
Impact categories/contributing product clusters | Global warming potential | Acidification potential | Eutrophication potential | Photochemical ozone creation potential | ||||||||
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Category | Product cluster | (kg CO2-eq.) | Category | Product cluster | [kg SO2-eq.] | Category | Product cluster | (kg PO4-eq.) | Category | Product cluster | (kg C2H4-eq.) | |
1 | Transport | Diesel car, large | 16,383 | Transport | Diesel car, large | 64.47 | Transport | Diesel car, large | 23.84 | Transport | Diesel car, large | 6.64 |
2 | Transport | Aircraft | 2,046 | Energy and water | Thermal energy renewable gas | 38.27 | Energy and water | Thermal energy renewable gas | 8.45 | Energy and water | Thermal energy renewable gas | 1.30 |
3 | Energy and water | Thermal energy renewable gas | 1,800 | Food | Dairy products | 17.14 | Food | Dairy products | 7.62 | Hobbies, leisure and pet | Pet, wet food | 1.18 |
4 | Food | Dairy products | 1,606 | Transport | Aircraft | 7.80 | Food | Bakery products | 2.03 | Transport | Aircraft | 0.74 |
5 | Energy and water | Thermal energy conventional gas (work and hotel) | 1,333 | House | Wall, stone | 5.36 | Transport | Aircraft | 2.01 | Food | Dairy products | 0.73 |
6 | Energy and water | Conventional energy (work and hotel) | 870 | Hobbies, leisure and pet | Pet, wet food | 4.84 | Hobbies, leisure and pet | Pet, wet food | 1.45 | Food | Marine fish | 0.38 |
7 | Hobbies, leisure and pet | Pet, wet food | 608 | Energy and water | Conventional energy (work and hotel) | 1.34 | Food | Meat, beef | 1.42 | Food | Coffee | 0.31 |
8 | House | Wall, stone | 552 | Food | Meat, beef | 3.76 | Electronics | Computer and notebooks | 1.20 | House | Wall, stone | 0.30 |
9 | Food | Meat, beef | 283 | Energy and water | Renewable energy | 1.95 | Food | Coffee | 1.01 | Food | Wine | 0.20 |
10 | Transport | Train, high speed | 200 | Food | Meat, pork | 1.76 | Energy and water | Conventional energy (work and hotel) | 0.21 | Food | Meat, beef | 0.17 |
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In the product category “hobbies, leisure, and pet,” his current dog is responsible for 76% of the CO2-eq. emissions. Especially pet food (industrial meat and wet dog food) shows a significant contribution. More than half (57%) of Dirk’s total meat consumption is due to his dog. Further, the product cluster hunting has high impacts (10%) due to ammunition (highest share with 95%), weapons, several accessories, and a pair of binoculars. The relative share of this product category in regard to the baseline results is 3.3%.
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In the product category “house,” the product cluster “wall stones” has the major contribution with 86%, followed by “flooring carpets” (5%). The relative share in regard to the baseline results is 2.3%.
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In the product category “electronics,” devices used for interaction (computer and notebooks) have the highest impact (33%). The relative share is 1.4%.The following four product categories have a share of around 0–1% in regard to the overall baseline results:
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Product category “clothes and jewellery”: polyester and cotton clothes contribute one-third to GWP, followed by his sports shoes (4%).
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Product category “living, household, and home office”: furniture (including the product cluster “soft furniture,” “hard furniture metal,” hard furniture, “wood”) has the largest share with approximately 44%.
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Product category “cosmetics, hygiene, and cleaning”: tissue papers have the highest impact (46%), followed by Dirk’s toothpaste (26%) and detergents (solid and laundry = 15%).
3.3 Optimized scenario
Main category | Product cluster | Yearly consumption—BS | Yearly consumption—OS | Difference (%] | Unit |
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Transport | Diesel car, medium | 0 | 742 | Not applicable | km |
Transport | Diesel car, large | 40,534 | 0 | Not applicable | km |
Transport | Hybrid car | 0 | 7,246 | Not applicable | km |
Transport | Train, local | 0 | 7,497 | Not applicable | km |
Transport | Train, high speed | 11,445 | 28,901 | +153% | km |
Transport | Bus | 0 | 72 | Not applicable | km |
Transport | Aircraft | 18,559 | 0 | Not applicable | km |
Energy and water | Renewable energy | 2,097 | 614 | − 71% | kWh |
Energy and water | Conventional energy (work and hotel) | 4,385 | 0 | 0% | kWh |
Energy and water | Thermal energy renewable gas | 80,287 | 59,512 | − 26% | MJ |
Energy and water | Thermal energy conventional gas (work and hotel) | 20,072 | 0 | 0% | MJ |
Energy and water | Water use | 52,894 | 22,379 | − 58% | kg |
Food | Dairy products | 82 | 6 | − 93% | kg |
Food | Meat, beef | 14 | 0 | Not applicable | kg |
Food | Coffee | 15 | 12 | − 21% | kg |
Food | Wine | 48 | 37 | − 23% | L |
Food | Marine fish | 17 | 10 | − 40% | kg |
Food | Vegetables (domestic) | 43 | 117 | +171% | kg |
Food | Bakery products | 66 | 56 | − 16% | kg |
Food | Fruit (domestic) | 3 | 10 | +239% | kg |
Hobbies, leisure and pet | Pet, wet food | 365 | 144 | − 61% | kg |
Hobbies, leisure and pet | Pet, dry food | 35 | 21 | − 40% | kg |
Hobbies, leisure and pet | Pet, vegetables | 0 | 30 | Not applicable | kg |
Hobbies, leisure and pet | Pet, rice | 0 | 6 | Not applicable | kg |
GWP (kg CO2-eq) | AP (kg SO2-eq) | EP (kg PO4-eq.) | POCP (kg C2H4-eq.) | |
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Baseline scenario | 27,600 | 164 | 56 | 13.5 |
Optimized scenario | 9,500 | 67 | 22 | 5.4 |
Difference (%) | -66 | -59 | -60 | -60 |
Product category | GWP (kg CO2-eq.) (baseline scenario) | GWP (kg CO2-eq.) (optimized scenario) | Difference (%) |
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Transport | 18,628 | 3,484 | − 81 |
Food | 2,560 | 738 | − 71 |
Hobbies, leisure, and pet | 921 | 469 | − 49 |
Clothes and jewellery | 96 | 70 | − 28 |
Cosmetics, hygiene, and cleaning | 88 | 70 | − 20 |
Energy and water | 4,214 | 3,418 | − 19 |
House | 641 | 641 | 0 |
Electronics | 374 | 381 | + 2 |
Health and medical equipment | 0,6 | 0.75 | + 20 |
Living, household, and home office | 95 | 230 | + 59 |
Sum | ≈ 27,600 | ≈ 9,500 | − 66 |
4 Discussion
4.1 General discussion of the results
4.2 Discussion of the methodological challenges and uncertainties of Life-LCA
Uncertainty | Consideration in this study | Potential solutions |
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Accounting for work-related emissions (direct and indirect) | - Applied: direct impacts due to energy and water consumption at work (estimated based on home consumption) and business trips - Excluded: indirect impacts due to work-related decisions | - It is suggested that the methodological boundaries of Life-LCA’s focus on (private) consumer behaviour, while indirect work-related impacts are more in the scope of organizational LCAs. A hybrid approach could be an option |
Past consumption: based on memories and documentation is highly subjective | - Applied: 50% lower yearly consumption of the baseline year for the childhood stage - Information for the main contributor, “transport,” “housing,” and “food” available - Excluded: transport from birth to 18 years | - Conduct case studies with younger participants from birth to 18 years considering different allocation scenarios - Subdividing childhood stage into several stages as consumption rates differ between birth and 18 years (e.g., considering socio-psychological models of child development or consumer studies) |
Future consumption: old adulthood stage | - Excluded | - Continuation of the Life-LCA for Dirk - Derivation of possible scenarios (e.g., under consideration of socio-psychological models of human development or consumer studies) - Conduct case studies with older participants to find suitable consumption rates (60 years to end of life) |
Allocation of Dirk’s children on his Life-LCA | - Applied: boundaries of Life-LCA are set around Dirk with a total allocation of his childhood to himself. Accordingly, his children were not allocated to him | - Allocate the burden of each child partly to Dirk - Case studies with younger participants from birth to 18 years - Consideration of socio-psychological child development models (e.g., consider the formation of an own will) |
Data quality; missing aggregated, and quality approved data for many consumer goods | - Applied: establishment of a product cluster system and use of different data sources with an underlying hierarchy | - Development of a consistent database for consumer goods - Conduct more case studies on human beings of different ages, cultural backgrounds or lifestyles - Data quality depends on the accuracy and motivation of the person for data collection |
Financial investments (e.g., stocks) | - Excluded | -It is suggested to keep this optional and declare as additional impacts but not to make this obligatory for Life-LCAs |
Allocation of goods received as inheritance or presents | - Excluded | - An allocation methodology is currently missing. The question would be if an inheritance is burden-free or partly allocated to the receiving person. Often, it is also not under the direct control of the receiving person |
Allocation of pets | - Applied: pets were fully allocated to Dirk | - Allocation based on all different owners, if applicable |
Creation | - Excluded | - Test different allocation options: for instance, burdens are equally shared between the natural parents or fully allocated to the child - Research needed for possibilities to measure different creation options and associated impacts - Pre-natal burdens (pregnancy–medical checks) and their allocation should be considered |
Death | - Excluded | - Should be included if the system boundaries are set to a whole life with future projections and the study object decides on one funeral option - In case no funeral option is chosen by the study object, to model the most likely burying methods (e.g., due to confession or culture) could be a solution |
Evaluation and calculation of shared products | - Applied: documentation of an estimated product use ratio and modelling accordingly | - Assessing the effective usage of a shared product, if the share regarding the overall results is high (e.g., shared car) |
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Consideration of human development and changing consumption behavioursModelling Dirk’s whole life and especially his past consumption leads to high uncertainties as a distinguished consumer behaviour can also be assumed for other product categories, besides the ones described in Sect. 2.3.1 (“transport”, “house”, “energy and water”, “food” and “hobbies, leisure and pet”) and included in this case study.Whereas the “childhood and youth stage” (0–17 years) was taken into account with a 50% lower yearly consumption as in the baseline scenario (see Sect. 2.2), the “old adulthood stage” (60 years to end of life) was excluded in this case study, as it would have involved further uncertainties. A sensitivity analysis with 25% lower and 75% higher yearly consumption as in the baseline scenario for the “childhood and youth stage” results in a 2–5% difference (compared to the overall results) from the 50% lower yearly consumption scenario applied in this study.For the “childhood and youth stage”, where possible, e.g., due to past documentation or the memory of Dirk, more specific data were considered, which face, however, the challenge of subjectivity (e.g., different memories). Thus, a new methodological concept needs to be established in the future. The authors acknowledge that the childhood phase assumption is a very simplified approach and recommend performing additional case studies covering different life stages to fill this gap. The reconstruction of past life consumption behaviour (retrospective) or future years (prospective) remains a general challenge for Life-LCA. Therefore, future research should focus on case studies (including different scenarios) with younger or older participants to consider in detail other life stages to gain further knowledge in regards to appropriate consumption rates and develop the methodology and underlying database.
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Determination and delimitation of potential Life-LCA applicationsThis case study considered the analysis of an individual human being rather than a particular lifestyle. Consequently, all results presented cannot be generalized due to Dirk’s individual variables, such as his living situation, country of origin, or his job as a CEO of a company. As soon as several Lifestyle-LCA case studies have been carried out, Dirk could be classified or integrated into one of these approaches based on criteria such as his age, income class, or eating behaviour.Further, it can be assumed that an individual Life-LCA is more time-consuming than a Lifestyle-LCA concerning data collection and evaluation, as it covers all specific parts of consumption of the study object and does not just focus on specific parts of a particular lifestyle.
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Definition of cut-off criteria for life stages and consumed productsSo far, there are no guiding rules or general cut-off criteria, as practical experience and other case studies in the field of Life-LCA are currently missing. The decision to exclude or include a specific product or life stages (e.g., “old adulthood stage” in this case study) can be a question of (a) goal and scope of the study; (b) data availability, uncertainty, and resources for data collection, modelling, and evaluation; and (c) expected contribution to the overall results.Concerning the life cycles of Dirk’s consumed products, one of the difficulties was the acquisition of specific electricity and water data for individual devices. Energy and water were recorded in total values using the electricity/water meter as it is almost impossible to record the electricity and water consumption of individual devices separately, which would even further increase the effort of data collection. However, this means that (1) the use phase of some products has to be cut off from the modelling to avoid double counting and (2) that the optimization potential of some products is “hidden” in other product categories.
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Allocation of the childhood phase and other socio-psychological interactions between human beingsA more interdisciplinary approach is needed to tackle allocation for childhood and adolescence, which allows determining external influences on consumption patterns. For instance, the products consumed by his children, which Dirk at least partly influences, were excluded in this case study. However, Dirk’s childhood was also fully allocated to him. When allocating part of the impacts from his five children’s childhood phase to his consumption, his environmental impact would increase. The challenge is to find an appropriate interface between the responsibility of consumption choices of children and their parents. Again, representative case studies with younger participants could provide insights and help to define a reasonable allocation.
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Consideration of creation and deathBoth creation and death were excluded from this case study, although these aspects need to be considered in a comprehensive Life-LCA study. There was no specific information on Dirks creation available. Generally, this life cycle stage could be expected to be of minor relevance. Regarding Dirk’s death, the ceremony, biological decay, emissions (e.g., cremation or mercury to air emissions (teeth)), and land use of the funeral could be considered. Keijzer (2017) analysed the environmental impacts of the two funeral options, burial, and cremation through a life-cycle assessment. The results showed no significant difference in the five investigated impact categories. So far, Dirk has not decided on any of these options.
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Collection of complex and diverse consumption data and development of an applicable bottom-up product clustering schemeData collection sheets (see TU Berlin website) and a bottom-up product clustering scheme were developed, which can be used in future Life-LCAs. These sheets and schemes are individualized and suit Dirk’s consumption behaviour. Thus, they need to be adapted when they are applied to a different human being. Different product categories and clusters related to the respective defined goals and scopes might need to be integrated. For instance, the current product categories represent products consumed by a specific European, but it will not be directly applicable to study subjects of another cultural or social background.An essential aspect for LCA studies, in general, but also for the new scope Life-LCA, is the time-consuming process for data collection to ensure adequate results. Data collection needs to be integrated over several months (in this case study: 4 months in total) in the study object’s daily routine. Data quality depends on the accuracy and motivation of the person. Establishing a manageable balance between necessary detail and the effort of data collection is a crucial factor.Further, due to the detailed bottom-up approach applied in this study, it is possible to consider burden-shifting effects already at the product/service level to derive strategies about individual consumption patterns.
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Definition of new data quality requirements and rules for inventory calculationsDue to the complexity of consumer behaviour, it was not possible to perform individual product LCAs for all consumed products. Many data on product level were available, as mentioned in Sect. 2.3.1, but only on a generic level (e.g., we know Dirk uses a television, but the brand, company, or origin of all components could not be considered). For some consumed products or product categories, using data on a generic level might be sufficient (e.g., the television) as their overall consumption and associated impact are less significant, whereas for others, even more specific data are needed (e.g., transport means: car).