Lifestyle-LCA: Challenges and Perspectives

Abstract

Former Life-LCA case studies provided valuable insights into the environmental impacts associated with specific human beings. However, they were resource and time intensive due to primary data collection. Therefore, this study’s goal is to develop a generic yet comprehensive framework, which is called “Lifestyle-LCA” making an environmental impact assessment of human beings more accessible to the general public. The methodology consists of (1) the development of the conceptual framework, (2) its application in a first case study, (3) refinement and validation of the approach based on the case study results, and a practical user (4) application guidance. Regionality, income, and life stage were identified as key influencing factors on a person’s lifestyle. The “Lifestyle-LCA” inventory provides a framework for determining baseline consumer values per defined lifestyle (ranging from eco-enthusiast to disinterested) and distributing product clusters based on their emission profile. A case study based on the average German consumer shows a range from 4 to 14 t CO₂-eq. per year for the defined lifestyles. Two presented application pathways allow users to choose the most appropriate approach depending on their available resources, time, goal, and scope. Future studies should test the framework across various cultural backgrounds, including new societal, economic, and personal factors.

1. Introduction

The life cycle assessment (LCA) approach plays a critical role in evaluating the environmental impacts of products and organizations, enabling the identification of hotspots, and potential improvements toward sustainability [1,2]. Recent studies have begun to focus on assessing human beings as study objects [3,4,5], resulting in the emergence of the Life-LCA method [6]. This innovative approach seeks to quantify the environmental impacts of individuals and has the potential to raise environmental awareness and promote more sustainable consumer choices.

Two previous Life-LCA case studies provided insights into the environmental impacts associated with specific human beings at various life stages. These include a middle-aged German man (0–49 years) [5], a German infant (0–3 years), and its mother [4]. These studies demonstrate the applicability of the Life-LCA method. However, they focused on particular individuals, their consumer choices, and specific life stages, which implies limited representability. Furthermore, former Life-LCA case studies were time-consuming due to extensive primary data collection over several months. It was also resource-intensive as it was necessary to model a whole spectrum of individual consumer choices (e.g., hunting equipment or baby bottle warmer) in detail without pre-defined modules for certain consumption patterns.

Therefore, the goal of this study is the development of a more accessible, generic, and comprehensive methodology and framework to assess the environmental impacts of human beings called “Lifestyle-LCA,” where the studied consumption patterns are more generalized and based on average consumer data. The framework is a starting point for various applications discussed separately. However, the main application is to provide individuals with a framework for calculating their emissions through LCA without extensive primary data collection, making an environmental impact assessment of human beings more accessible to the general public.

This study addresses the following research questions:

• How can a conceptual framework for Lifestyle-LCA be defined?
• How to analyze the environmental impacts of different lifestyles based on average consumption?
• How can an individual practically apply “Lifestyle-LCA”?

To address these research questions, this paper is organized as follows: The “Lifestyle-LCA” methodology is introduced in Section 2. Section 3 presents the developed conceptual framework, a case study of the average German consumer, and guidance for applying Lifestyle-LCA for individuals. Lastly, the paper concludes with a discussion of the results (see Section 4) and a conclusion and outlook (see Section 5).

2. Methodology

The methodology of this research is structured into four major steps (see Figure 1).

The first step in developing a conceptual framework for Lifestyle-LCA involves conducting a comprehensive literature screening, desk research, and considering Life-LCA insights. Therefore, previously published Life-LCA studies [4,5,6], relevant journal articles, books, and reports are screened to gather essential information as well as data on average consumer choices and values in a particular region. This procedure ensures that the conceptual framework is grounded in established research and considers the current best practices in Life-LCA. The developed conceptual framework is explained in detail in the results section of this paper (see Section 3.1).

The second step tests the applicability of the conceptual framework through a case study (see Section 3.2), which is modeled with the LCA-software GaBi (content version 2022.2) [7] using the statistical data representative of an average German complemented by data from former Life-LCA studies to obtain values indicative of each lifestyle. The same ten product categories (“clothes”, “cosmetics, hygiene and cleaning”, “electronics,” “energy and water”, “food”, “health and medical equipment”, “hobbies and leisure”, “house/apartment”, “living, household, and accessories”, and “transport”) employed in the initial Life-LCA studies were retained for this analysis to ensure consistency with previous research. A comprehensive list of assumptions and data sources for each product category is provided in the (supplementary material Supplementary Materials, Sections S1.2 Consumption Inventory and S1.3 Clusters and Datasets). The case study considers the impacts Global Warming Potential (GWP), Acidification Potential (AP), Eutrophication Potential (EP), and Photochemical Ozone Creation Potential (POCP) according to CML to be consistent with previous Life-LCA case studies and compare results [8].

By testing the “Lifestyle-LCA” framework within this context, insights into its performance and potential for refinement (step 3) are obtained by considering the limitations identified during the case study, the incorporation of new data, or modifications of the underlying model.

The last step, as shown in Figure 1, is to provide practical application guidance for individuals on how to use the Lifestyle-LCA approach based on the findings from steps 1 to 3 and former Life-LCA case studies. It consists of a questionnaire for classification and step-by-step instructions that help consumers apply the Lifestyle-LCA framework to their specific context (see Section 3.3).

3. Results

3.1. Conceptual Framework for Lifestyle-LCA

Figure 2 shows the conceptual framework for Lifestyle-LCA, starting with the identification of the influencing factors for lifestyle choices and consumption patterns (step 1) (see Section 3.1.1).

The second step is about defining the lifestyles (see Section 3.1.2) before establishing the Lifestyle-LCA inventory (steps 3 to 6) (see Section 3.1.3). This inventory involves determining baseline consumer values for each lifestyle (step 3), evaluating the emissions profiles of all considered product clusters (step 4), distributing these product clusters to each lifestyle (step 5), and making selective adjustments to account for specific preferences (step 6). These steps are explained in more detail in the following sections.

By applying the conceptual framework (see Section 3.2), the environmental impacts of various lifestyles can be determined.

3.1.1. Identification of Influencing Factors on Lifestyle-LCA

While many factors influence lifestyle choices and their associated environmental impacts, it is essential to consider three key dimensions: societal, economic, and personal [9].

The societal dimension includes the cultural and social context in which individuals live, while the economic dimension focuses on the financial aspects that influence lifestyle choices. The personal dimension contains all individual factors of a person.

The following factors for the societal, economic, and personal dimensions are considered within Lifestyle-LCA:

• Societal factor regionality: regional context determines the availability and accessibility of product options (e.g., locally produced food) and infrastructure (e.g., public transportation) [10].
• Personal factor “Life stage,” or “age”: different life stages correspond with distinct consumption patterns, preferences, and priorities [5,11].
• Economic factor income: salary significantly impacts consumption patterns and purchasing power [12].

Multiple factors can be considered in a “Lifestyle LCA” among the respective dimensions. However, to ensure that the framework remains practical for its intended application, it is necessary to focus on the most influential factors for each dimension before defining lifestyles in a subsequent step.

3.1.2. Definition of Life-LCA Lifestyles

The Department for Environment Food and Rural Affairs (DEFRA) [13] developed an environmental segmentation model, which divides the general public into seven segments. These different segments have a set of characterizing attitudes and beliefs towards environmental issues and consumer behavior patterns, which overlap with those proposed by Gaiser et al. [14] and a study from the Mafowerk [15].

Gaiser et al. [14] described the behaviors of adolescents regarding sustainability-related attitudes. The focus is on consumption, references to climate change, and starting points for promoting sustainability. Four distinct consumption types are outlined: sustainable consumers, rhetorical delegators, inconsistent mixed type, and doubters. These types reflect different levels of commitment to sustainability and vary in terms of the value attitudes of the consumers. The study from Mafowerk [15] identifies four types of consumers: sustainability activists, the interested, the indifferent, and sustainability deniers. While the types are similar in terms of their overall commitment to sustainability, Gaiser et al. provided more detail on the motivations and attitudes of each type. For instance, sustainability activists actively promote sustainable practices, while the interested express some interest in sustainability but may not be as committed as sustainability activists.

The definitions of the different lifestyles presented in the following represent a synopsis of the three studies listed above. Based on DEFRA, two of the defined segments share the same values as the four lifestyles from Gaiser et al. and Mafowerk. For instance, DEFRA’s “concerned consumers” and “waste watchers” (segments two and three) share the same values as the “sustainable interested.”

In the following, short definitions of each lifestyle are presented:

• The “eco-enthusiast”

This lifestyle type is strongly committed to environmental issues and gears its activities (e.g., purchases, mobility) accordingly. This type informs themselves about the environmental impacts of the purchased products. The “eco-enthusiast” are willing to pay more for products if convinced of their reduced environmental impacts. Additionally, their strong commitment to environmental issues sometimes translates into political engagement, as they advocate for policies supporting sustainable practices.

• The “eco-conscious”

This lifestyle type demonstrates a moderate commitment to environmental matters, making some eco-friendly purchase decisions and seeking information about products’ sustainability aspects. The “eco-conscious” are aware of their actions and influence on the environment and try to reduce their footprint.

• The “eco-indifferent”

This lifestyle type is indifferent regarding environmental aspects. They are not willing to spend more money on sustainably produced products. The “eco-indifferent” also do not feel personally responsible for environmental problems and instead “delegate” finding solutions to other stakeholders, e.g., industries, states, and associations.

• The “eco-disinterested”

These consumers are not interested in environmental aspects and some even consciously oppose climate-smart practices and measures, occasionally coupled with political activity. They firmly base their purchases on price and do not reduce personal consumption. Convenience is more important than the environment.

3.1.3. Establishing the Lifestyle-LCA Inventory

The Lifestyle-LCA inventory is developed through a step-by-step process considering four steps (from 3 to 6), as shown in Figure 2 (background color blue).

• Step 3: Determination of baseline consumer values

The “Lifestyle-LCA” inventory determines average consumer values for product clusters based on the defined product categories in Life-LCA [4,5]. Considering the extensive diversity of products, a bottom-up clustering scheme can be utilized for simplification, as suggested by the previous Life-LCA studies. Similar products are grouped and placed under appropriate product categories. When specific LCA data for a product were unavailable, the dataset of a comparable product was used. For example, various types of cheese could be modeled under the ‘dairy products’ product cluster using a generic cheese dataset.

The product clusters are fewer, more generalized, and adapted to the average consumer compared with individual Life-LCA studies because the focus shifts to a broader understanding of lifestyle choices. For instance, in the first Life-LCA case study, the study object’s hunting equipment in “hobbies and leisure” was modeled, which is not a common hobby for the average consumer. Further, in the food category, “sushi vegetarian” was modeled specifically for the study object, whereas the food consumption for Lifestyle-LCA would be based on annual consumption values of the statistics for the average consumer. Compared with the first case study, about half fewer product clusters were modeled due to a more generic approach.

The approach is based on available secondary data in literature and consumer statistics. By establishing average consumer values for each product cluster, a comprehensive baseline of an average consumer considering the key factors (e.g., regionality) for assessing the environmental impacts across the defined lifestyles is created.

• Step 4: Determination of an emission profile per product cluster

In this step, each product cluster is allocated to an emission profile based on its average emissions. For instance, biking is classified as having a low emission profile (indicated in green in Figure 2), while air travel would have a high emission profile (indicated in red in Figure 2). In some cases, the average consumer value is taken if the environmental impacts of product clusters do not significantly differ per lifestyle (e.g., drinking water consumption).

• Step 5: Distribution of product emission profile to lifestyles

Next, baseline consumer values based on the emission profiles of the considered product clusters are distributed (see Figure 3) to quantify the environmental impacts of different lifestyles.

The following generic example shows the distribution for air travel, a high-emission profile product cluster with a fictional average value of 2000 km traveled distance.

• Eco-enthusiast (50%—1000 km): it is assumed that the eco-enthusiast has half the environmental impact of the average consumer due to their strong commitment to sustainable practices and reduced consumption of high-emission products and services.
• Eco-conscious (75%—1500 km): it is assumed that eco-conscious individuals have a 25% lower environmental impact than the average consumer because they actively make more environmentally friendly choices but may not be as strict in their practices as eco-enthusiasts.
• Eco-indifferent (125%—2500 km): it is assumed that eco-indifferent individuals have a 25% higher environmental impact than the average consumer as they consume products and services with higher emission profiles.
• Eco-disinterested (150%—3000 km): it is assumed that eco-disinterested individuals have an environmental impact 50% higher than the average consumer because they are not considering sustainability aspects in their consumption choices.

• Step 6: Selective Lifestyle Adjustment

The last step to establish the Lifestyle-LCA inventory involves selective lifestyle cluster adjustments, for which the general approach of step 5 does not seem to provide representative values. For example, if the eco-enthusiast chooses not to use air travel as a means of transportation, the inventory value in this product cluster is set to zero. The excluded inventory value is then allocated to the other lifestyle groups by applying the same methodology as outlined in step 3. This approach ensures that the Lifestyle-LCA inventory reflects each lifestyle group’s consumption patterns while maintaining the overall average value.

3.2. Case Study of an Average German Consumer and Different Lifestyles

The case study considers a range of diverse product categories: “clothes and jewelry,” “cosmetics, hygiene, and cleaning,” “electronics,” “energy and water,” “food,” “health and medical equipment,” “hobbies and leisure, “house, apartment,” “living, household, and accessories” and “transport.” Each category is further divided into specific product clusters to capture consumption patterns better. A comprehensive overview of all product clusters within these categories is available in the Supplementary Materials (see Section S1.2 Consumption Inventory).

Figure 4 shows the GWP results for all considered product categories, including Step 6, with selective product cluster adjustment and without adjustment (see Supplementary Materials for a detailed consumption inventory and results). Adjustments were only made for the eco-enthusiast, exemplarily for some product clusters (e.g., no air travel or no meat).

Therefore, it is noticeable in the results that the percentage ratio of the product categories for the eco-enthusiast is different from the other lifestyles. For instance, for GWP, the eco-enthusiast has the largest share of 41% for “energy and water,” followed by “food” (22%) and then “transport” (14%). For the other lifestyles, transport is the product category with the highest impact on GWP, with a 40–50% share, followed by energy and water (25–30%) and food (15–20%). The other product categories have a small share of 0–5%.

Generally, the values range between 4 t (eco-enthusiast) and 14 t CO₂-eq. (Eco-disinterested) for GWP across all lifestyles. The average German has an impact of 9.5 t CO₂-eq. and ranges between the eco-conscious (8.5 t CO₂-eq.) and the eco-indifferent (11.5 t CO₂-eq.)

Within the Supplementary Materials (see Section S1.1 Case Study Results), the results for the impact categories AP, EP, and POCP are shown in detail. The values range for AP from 19 to 61 kg SO₂-eq., for EP from 9 to 25 kg PO₄-eq., and POCP from 1.3 to 5 kg C₂H₄-eq. Like for GWP, “transport” has the largest share of total emissions for all lifestyles for AP, EP, and POCP, except for the eco-enthusiast (transport share is 15–20% for the considered impact categories). “Transport” share for AP and EP ranges from 35% (eco-conscious) to 45% (eco-indifferent and eco-disinterested) and for POCP around 60% (eco-conscious to eco-disinterested).

“Energy and water” has the second largest share for GWP, “food” has the second highest share for all considered lifestyles except the eco-enthusiast for AP (35%), EP (40%), and POCP (20%), followed by “energy and water” with a share between 5 and 10%.

For the eco-enthusiast, the second and third highest share for AP and POCP is “energy and water” and “food,” with similar shares of 20–25%. However, for EP, the share of emissions for food is around 40%.

3.3. Application Guidance of Lifestyle-LCA for the Individual

This chapter provides guidance for two distinct pathways for the application of Lifestyle-LCA for individuals.

3.3.1. Pathway 1: Lifestyle Classification: Selection of the Most Representative Lifestyles

Lifestyle-LCA aims to provide individuals with a simple and accessible method to estimate their environmental footprint without requiring extensive data collection or input, as required in former Life-LCA case studies [4,5]. Pathway 1 (see Figure 5) offers the simplest approach for applying the Lifestyle-LCA. The primary step in this pathway is completing a questionnaire, which draws on the mentioned studies for defining Life-LCA lifestyles (see Section 3.1.2) [13,14,15] to help individuals identify their representative lifestyle cluster. The interactive questionnaire (see Supplementary Materials) comprises five questions about values and consumption choices, purchases, and general actions concerning sustainability.

• For example, one question is: “Do you base your purchases on sustainability aspects?”
• The eco-enthusiast would select: “I consciously inform myself about products in terms of sustainability and also would pay more for them.”
• The eco-disinterested answers: “I base my purchases mainly on price and not sustainability.”
• The eco-conscious replies: “I already base some of my purchases on sustainability aspects.”
• The eco-indifferent states: “I would only spend a little money on sustainably produced products.”

This approach aims to provide users with a clear and easily understandable classification system. After completing the questionnaire, the individual obtains the result of which of the four lifestyles represents its consumption patterns best. For each of the lifestyles, the average results are available, and the respective environmental profiles are selected to represent the individual as a proxy.

The values for the questionnaire in the Supplementary Materials are based on the average consumer from Germany, as outlined in the case study from the previous Section 3.2.

3.3.2. Pathway 2: Lifestyle Customization: A Modular Product Category-Based Approach

Pathway 2 offers the next sophistication level by offering a more flexible and customizable approach to Lifestyle-LCA, allowing individuals to select their lifestyle preferences at different levels of granularity, ranging from product categories (level 1) to product clusters (level 2) and even specific products (level 3) (see Figure 6).

In its simplest form, on the first level, users can choose their lifestyle from ten product categories (e.g., food), each assigned with average consumption values from their respective regions, resulting in a fixed value. This is represented exemplarily in Figure 6 with product category a. For instance, a person might be environmentally indifferent when choosing transportation but align with an eco-enthusiast’s consumption pattern in the food category.

Proceeding to the second level, users focus only on the clusters with the highest emission profile within each chosen category instead of making decisions across all product clusters, which would result in too many decisions. This process significantly streamlines decision-making by reducing the number of choices while focusing on key emission contributors (see Figure 6). For instance, on the second product cluster level, for meat, the consumer selects a lifestyle as well. For the eco-enthusiast, the average consumption of poultry would be considered as background data, whereas beef, with a greater environmental impact, would be assigned to the eco-disinterested [16]. Advancing to the third level, a similar methodology is applied. Here, the selection process focuses only on the products with the highest emission profile within each chosen product.

For example, different types of beef can be considered: for the eco-enthusiast conventional minced beef, and for the eco-disinterested organic beef [16].

Considering the first Life-LCA study as a baseline, the user can choose from ten product categories at the first level.

It is important to note that all three levels are not meant to be successive stages, but rather separate and independent options for granularity that a user can select in the Lifestyle-LCA approach.

4. Discussion

In the following section, the six conceptual steps of Lifestyle-LCA and their challenges (see Section 4.1), the case study results in comparison with the first Life-LCA case study (Section 4.2), and the proposed application pathways (Section 4.3) are discussed.

4.1. Discussion of the Methodological Challenges of Lifestyle-LCA

The conceptual framework identified income, life stage, and regionality as the most influencing factors for Lifestyle-LCA. However, further analysis of lifestyles concerning solely different income levels (low, medium, and high) or life stages (early adulthood to old adulthood) should be made. Further, the database and analysis should be expanded, focusing on the defined lifestyles but within different regions (e.g., by continents).

An exemplary table listing further potential factors within each dimension can be found in the Supplementary Materials (see Supplementary Materials: Section S2.3 Factors). Combinations of these factors can be considered in future studies. For example, if a maximum of three factors were used, with four potential factors per dimension, as shown in the table, this results in around 200 different combinations. Consequently, while adding more factors can provide a more accurate reflection of the diversity of consumer lifestyles and their environmental impacts, it presents challenges in data collection, analysis complexity, and interpretation. Further, interdisciplinary collaboration might be necessary. For instance, an in-depth analysis of lifestyles concerning different income levels, life stages, or regional aspects might require sociologists and economists to parse the complex interplay of societal norms, economic constraints, and individual choices.

The terminology used in the presented studies [13,14,15] to describe these groups, such as “sustainable activist” or “sustainable denier”, were not ideally suited for the context of this study due to their potentially polarizing or political connotations. Thus, the alternative terminology of “eco-enthusiast”, “eco-conscious”, “eco-indifferent”, and “eco-disinterested” was chosen for the lifestyle groups. The eco-prefix unifies the language across all four groups, emphasizing their belonging to the outlined research.

As the lifestyles outlined in step 2 of the framework are broadly defined, they may not be entirely appropriate in different geographical contexts. They need to be adapted to the regional understanding of consumption and sustainability. Further, in the proposed methodology, opting for a specific lifestyle does not necessarily imply reduced consumption levels. Instead, it involves the preferences for a more environmentally friendly product cluster, considering that average regional consumption values are taken into account in step 3. Future studies should explore reducing consumption for additional product clusters (e.g., clothes) for eco-friendly lifestyles and vice versa.

In determining baseline consumer values in step 3 of the conceptual framework (see Section 3.1.3), challenges regarding data collection occur. For example, in most regions, data are available for certain product categories, such as energy, food, and transport, whereas data quality might be lower for other clusters (e.g., hobbies and leisure). Therefore, first, it should be investigated which product categories have sufficient data. The overall complexity can be reduced by excluding categories with rather low environmental impacts. However, in different regions, product clusters might have differing impacts, e.g., due to a lack of infrastructure or environmental conditions such as climate [17]. For instance, developing public transport infrastructure can replace non-green travel methods (e.g., air traffic on short distances) and private car ownership, reducing carbon emissions [18,19]. Studies have shown that building rail lines significantly eases traffic congestion and lessens car exhaust emissions [20]. Furthermore, establishing a green-energy infrastructure for electric cars can notably decrease greenhouse gas emissions [21], given that electric cars emit 10 to 26 times less CO₂ than their fuel-powered counterparts [22]. Moreover, environmental conditions, such as regional climate, are crucial in defining the energy demand for housing infrastructure [23]. For example, the demand for cooling systems may increase in hotter regions. Further, depending on the climate, houses may be built with materials with high thermal mass, like bricks or concrete, to help maintain indoor temperatures despite high outdoor temperatures, thus reducing the need for mechanical cooling [24]. These variations affect households’ overall energy consumption and carbon footprint [25].

In addition, it is recommended to rely on national statistics for data collection related to consumption values. This ensures the representativeness of the data. Primary data collection and targeted consumer studies (e.g., data collection for certain product categories) might extend the Life-LCA database.

Determining a product cluster’s emission profile, as described in step 4, is simple for some categories (e.g., transport: biking vs. air travel) but ambiguous for others. Analyzing products’ environmental footprints can be time-consuming if the information is hard to find in the literature.

Average consumer values for product clusters are multiplied in step 5 with an assigned percentage, depending on the lifestyles, which can be applied to other product categories as well. However, the environmental impact can be overestimated or underestimated for certain individuals within these groups as not all eco-enthusiasts may limit their air travel as assumed. Further, the distribution approach might encourage a binary view of sustainability, implying that all eco-enthusiasts are ‘good’ and all eco-disinterested individuals are ‘bad’. Each lifestyle category has the potential to contribute to sustainability in several product categories, a concept further reinforced by the application guidance of pathway 2.

Step 6, the “Selective Lifestyle Adjustment,” may not always be required due to the insights gained from previous steps. This step, which adjusts specific inventory values based on lifestyle beliefs, can introduce bias and oversimplify the nuanced consumer behaviors within each lifestyle group. Furthermore, reallocating excluded inventory values to other lifestyle groups might blur the defined lifestyle distinctions, potentially misrepresenting consumption patterns and results. Furthermore, step 6 can be adapted in future iterations to accommodate the specific requirements of high-emission profile product clusters. This adaptation can allow for substituting generic values of average consumers, which are the basis for calculating lifestyles, with user-specific consumption data to reflect actual consumption better.

4.2. Discussion of the Case Study Results and Comparison with the First Life-LCA Case Study

For the presented case study, average baseline consumer data for the product clusters in the categories of “health and medical equipment,” “hobbies and leisure,” “house,” and “living, household, and accessories” were taken without establishing an emission profile as described in step 4. This is due to the challenges in distinguishing different product clusters in these product categories based on their environmental performance and their relatively minor environmental impact on overall results, as demonstrated in prior Life-LCA case studies. For future Lifestyle-LCA studies, practitioners may consider excluding specific product categories that align with the study’s goal and scope. The exclusion of product categories should be performed carefully, taking into account regional lifestyle and consumption patterns. For instance, the category “hobbies and leisure” may seem minor for some regions but are relevant in others.

The results emphasize not only focusing on GWP results for reduction measures as this can lead to neglecting other significant environmental impacts and potential trade-offs across different impact categories.

In the first Life-LCA case study [5], the study object’s (Dirk) consumption pattern was analyzed and optimized. In the optimized scenario, Dirk avoided air travel and followed a vegan diet, likewise to the eco-enthusiast (see Supplementary Materials Section S1.2 Consumption Inventory). However, his yearly impacts result in 9.5 t kg CO₂-eq., matching the average German (see Figure 4). The key difference in Dirk’s case is that he travels a distance more than five times longer than the average German, using diesel and hybrid vehicles besides public transportation. In addition, Dirk’s energy consumption is nearly double due to his living area, which is approximately twice the size of the average German. In the baseline scenario before lifestyle optimization, Dirk emitted 27 tons of CO₂-eq. annually, which raises the question of whether the chosen range of average values to be multiplied (step 4 of the conceptual framework) is too narrow. It would thus be necessary to not only consider product category omissions among eco-enthusiasts but also to denote excessive consumption in certain product categories (e.g., frequent air travel due to business activity). Such correction factors can be part of the proposed step 6.

4.3. Review of the Proposed Application Pathways, Limitations, and Perspective of the Approach

Regarding the application guidance of Lifestyle-LCA for the individual, pathway 1 may offer a simple and accessible approach for users to classify their lifestyle and estimate their environmental footprint without extensive data collection. However, this simplicity may lead to oversimplification and generalization, as the complex reality of individual consumption choices and behaviors is reduced to average values for each lifestyle category. This can result in inaccurate or incomplete estimations of environmental impacts. It is important to note that the questionnaire (see Supplementary Materials Section S2.1 Questionnaire) is a preliminary exemplar designed for Pathway 1 in its current state. However, it can be further refined, e.g., including regional and cultural contexts, more product specificity, and challenging assumptions such as an eco-enthusiast presumptively not consuming meat to support Pathway 2. More specific queries about consumption at each of the three levels can be included from the product category level (Level 1) to the individual product level (Level 3). This would allow for more granular, personalized environmental footprints and can improve the accuracy of the Lifestyle-LCA approach.

Pathway 2 also presents several challenges. The increased granularity requires more data and a more complex calculation process, which may hinder its accessibility and ease of use for the average user. However, by offering insights into the environmental impact of different lifestyles at various levels of detail, users can better understand the consequences of their decisions and make more informed choices to reduce their environmental footprint. As users select their lifestyles at a more detailed level (level 3), the final step can become a reversed individual Life-LCA (see Figure 6), which may reintroduce the need for extensive data collection and reduce the primary advantage of the Lifestyle-LCA, to simplify the assessment process by relying on average consumer values.

For both pathways, there is the challenge of monitoring and updating the database of consumer values for various regions, product categories, and lifestyles as consumption patterns evolve and new alternatives emerge. These metrics can show how far the regions under consideration are decoupling environmental impacts from economic growth, the advantages of transitioning to a circular economy, and advancements toward the Sustainable Development Goals [26,27]. Ongoing evaluation of emerging technologies and integration of their data that aligns with average consumer statistics can facilitate monitoring shifts in environmental impacts over time.

It is recommended to test both pathways in a different region with a sample of potential users to assess the approach’s effectiveness.

Further, the primary application of the “Lifestyle-LCA” framework is to offer an easily accessible and generic way for individuals to benchmark their emissions through LCA, thus making environmental impact assessments easily accessible. However, the results and underlying database on the average consumers can also be used by policymakers to devise regulations and guidelines that foster sustainable living and reduce overall emissions, an approach that can be particularly effective when targeting product clusters with high emission profiles. For instance, this study can help monitor Sustainable Development Goals 12 [26] and addresses the 7th European Environmental Action Program [28] request for methodologies to benchmark resource efficiency and related impacts such as greenhouse gas emissions. The framework was applied in the presented case study on four impact categories. However, future research can expand it to other impact categories (e.g., land use, water).

In existing research, macro-scale impact studies on consumption often rely on LCAs for special products. These results are then magnified to depict overall consumption patterns using various scaling techniques [27,29,30]. Certain studies also explore the impacts of citizens’ lifestyles [31,32], mainly focusing on greenhouse gas emissions and applying input–output analysis [25,33].

In contrast, the Lifestyle-LCA approach synthesizes data from national statistics, applying a bottom-up approach that analyzes products from cradle to the grave. Notably, the end user’s primary responsibility is to select a lifestyle, thus bypassing the need for intensive data collection. Additionally, the framework shows flexibility and can be applied to any regional or cultural context. Exploring a hybrid approach, combining elements of input–output analysis with Lifestyle-LCA can offer possibilities for future research, including the strengths of comprehensive, sector-wide assessments with the detailed, product-specific insights of LCAs.

Additionally, this framework provides information on how specific lifestyle choices contribute to distinct climate and environmental impacts. The insights gathered through the Lifestyle-LCA process can facilitate the systematic identification of potential improvements, contributing to a comprehensive understanding of behavioral strategies influencing consumer choices at different supply chain stages. This sets the stage for exploring critical issues surrounding the role of consumers in promoting climate change mitigation [34].

Moreover, the Lifestyle-LCA framework can examine a range of scenarios within the established dimension, including those with greater granularity, as illustrated in Pathway 2. This versatility presents potential for future research, enabling the investigation of consumption patterns through establishing the database for average consumer goods, benchmarking with lifestyle results from other countries, and policy interventions stimulating innovative strategies for environmental impact reduction.

Moreover, the current Lifestyle-LCA approach represents a foundational version that can be developed and tailored to create a holistic Lifestyle-LCA assessment tool and database. Several footprint calculators were developed and implemented over the past years by different institutions, e.g., non-governmental organizations like World Wide Fund For Nature [35], governmental entities such as the German Environmental Agency [36], and even on the European level [27,37] to assess consumption patterns and different lifestyles as well as to define options to reduce environmental impacts. Unlike conventional footprint calculators, the Lifestyle-LCA approach is designed more as an assessment tool, focusing on defining a person’s lifestyle. This definition then leads to fixed environmental impact values based on the average consumer data at different levels of granularity. Users of the Lifestyle-LCA approach do not specify exact consumption quantities, which significantly minimizes the demand for data collection. The presented framework utilizes an underlying set of average consumer data providing a streamlined and efficient pathway to calculate the environmental impact values for each lifestyle in ten product categories and therefore differs from existing methodologies.

5. Conclusions and Outlook

The Lifestyle-LCA approach introduces a streamlined method for estimating environmental impacts associated with different lifestyles within a specific region. Its six-step process reduces the need for primary data collection, making it more time- and resource efficient than previous Life-LCA case studies. The framework’s product clusters align with general consumption patterns due to utilizing average consumer data and are less in quantity, reducing modeling efforts for future case studies. The defined lifestyles provide the baseline for evaluating environmental impacts, ranging in the presented case study of average German consumers from the ‘eco-enthusiast’ lifestyle, with environmental impacts of 4 t CO₂-eq., to the ‘eco-denier’ with 13 t CO₂-eq. The case study confirmed the frameworks’ applicability, with transport energy, water, and food emerging as hotspots.

While the presented questionnaire offers guidance to determine the user’s lifestyle, it should be seen as a preliminary tool. The questionnaire can be adjusted based on the specific needs, system boundaries, and goals of the LCA practitioner.

The Lifestyle-LCA approach features several levels of detail, as demonstrated through the two proposed pathways. This flexibility enables users to tailor the framework to their needs, better understand the consequences of their decisions, and make more informed choices to reduce their environmental footprint.

The current Lifestyle-LCA framework serves as a baseline that can be expanded and tailored to develop a comprehensive Lifestyle-LCA assessment tool to benchmark lifestyle results across different regions and analyze policy interventions’ impact on environmental impact reduction. In addition, continuous evaluation of new technologies and their alignment with consumer statistics can help track environmental impact trends over time.

The framework can be applied in different cultural and regional contexts to benchmark average consumer lifestyles. This expansion can enrich the database on a broader scale, considering region-specific product clusters. In addition, future research can explore the interplay of different societal, economic, and personal factors within this framework, offering comprehensive insights into consumption patterns and their associated environmental impacts.