Assessment Framework

Building upon the systematic classification and inherent characteristics (e.g. additionality) of identified opportunities and threats (see Section 3.1.2), a quantitative assessment is provided as far as applicable. The general procedural approach is shown in Figure 11. Due to the wide project scope and data availability constraints as well as inherent limitations, only direct effects of the three clusters “Logistics and Transport”, “Packaging”, and “End-of-Life” are suitable for a quantitative assessment. Indirect opportunities and threats of these clusters and all effects of the remaining clusters are described in a qualitative manner, stating trends and expected future developments based on available research.

Figure 11: Assessment framework and potential assessment sequences

Due to the cascading nature of environmental effects arising from E-commerce, opportunities and threats were classified as either direct or indirect (see Table 5 in Section 3.1.2). This classification is not only a methodological choice but also a prerequisite for the in-depth assessment as it provides a rationale, whether quantitative or qualitative information is deemed appropriate. This is because direct and indirect effects entail different degrees of uncertainty (due to data availability, assumptions, etc.), requiring to individually choose, whether a quantitative or qualitative evaluation of the respective opportunity or threat is feasible. The methodological possibilities to either extrapolate quantitative data from available literature or to calculate and estimate certain effects are considered rather limited due to the numerous assumptions involved. As a general rule, scientifically sound quantifications of certain effects can only be made, if the scope and unavoidable uncertainties are clearly defined and discussed (e.g. geographical and temporal conditions, population density, behavioural aspects of consumers, etc.).

Table 11 summarises available data sources and methodological possibilities referring to either direct or indirect effect pathways (opportunities or threats).

Table 11: Overview of preferred data sources and methodological possibilities for the assessment of E-commerce effects (Börjesson Rivera et al., 2014b; Dost and Maier, 2018)

Effect

Pathway Preferred Data Sources Rationale Result Type

Direct

 Techno-scientific literature;

 Environmental Product Declarations (EPDs), LCA studies;

 LCI Databases (e.g. GaBi professional database, European Life Cycle Database (ELCD) of the Joint Research Centre); and

 Complementary LCA calculations / extrapolations and Inventory Analyses.

 Market reports / Grey literature (e.g. from associations);

 Statistics (e.g. Eurostat, Statista); and

 Corporate reports (e.g. annual reports and non-financial reporting). capture certain effect clusters (e.g. consumer needs and behaviour). A quantification of complex and uncertain effects (usually indirect effects) could suggest an accuracy that cannot be achieved in this study. Hence, opportunities and threats which cannot be quantified reliably are assessed qualitatively, based on techno-scientific literature and stakeholder input.

As described in Section 4.1, in order to compare the effects of the opportunities and threats between traditional brick-and-mortar retail and E-commerce for the current state as well as future developments, representative Circular Economy aspects/indicators were identified for each of the selected clusters. All quantitative and qualitative effects within the respective cluster are assessed against those representative indicators relating to a functional unit. The determination of the functional unit and of the representative indicators are explained below and in Section 4.3, respectively.

The in-depth assessment results will be discussed and summarised per cluster. As stated earlier, the following clusters entail a detailed quantitative assessment of the respective direct opportunities and threats:

 End-of-life;

 Logistics and transport; and

 Packaging.

Based on research and evidence from previous studies relating to E-commerce and Circular Economy, these clusters are considered highly relevant for this kind of assessment. Moreover, in comparison to the other clusters, more quantitative data and points of reference for the opportunities and threats contained in these clusters were found in the literature. In contrast, the other clusters contain several opportunities and threats which have been barely addressed in the literature or by any stakeholders thus far. This circumstance renders these other clusters inappropriate for a detailed quantitative assessment under the scope of this study. Nevertheless, quantitative information and data points from literature are included in the discussions and reflected in the results to the extent possible.

For the discussions and presentations of the results from the in-depth assessment, identified opportunities and threats within a given cluster are grouped according to their properties as follows (see also Section 3.1.2):

 Relative effects associated with E-commerce as per today: additional effects of direct opportunities and threats on respective Circular Economy aspects as well as comparison with baseline impacts as long as feasible and sufficient data available;

 Positive influencing factors associated with E-commerce for future development: expected effects of indirect opportunities on respective Circular Economy aspects; and

 Negative influencing factors associated with E-commerce for future development: expected effects of indirect threats on respective Circular Economy aspects.

In order to enable a more nuanced understanding of possible influences within the assessments, influences (i.e.

opportunities and threats) were – wherever possible – ascribed:

 a “plus” (+) for positive influences;

 a “minus” (–) for negative influences; or

 a “zero” (0) where influences were inconclusive.

Furthermore, as a sign of their relevance within their respective cluster, a double sign (i.e. “++” or “- -”) was ascribed for highly relevant influences.

The ascription of signs was done on the basis of circumstances such as occurrence of the influence in literature, its importance within the stakeholder consultations, and expert judgement. The below colour-coding was used to show both sign and relevance of the respective effect.

++ Positive effect deemed to have high relevance for a given CE indicator compared to traditional (brick-and-mortar) channel

+ Positive, effect with low relevance

-- Negative effect with high relevance

- Negative effect deemed to have low relevance

o Inconclusive effect thus deemed to have low relevance

n.a. Not assessed/not applicable (e.g. due to lack of data, non-applicability)

Functional Unit: “One fulfilled unit”

The functional unit for the quantitative assessment needs to be applicable for the traditional brick-and-mortar commerce as well as for the E-commerce value chain, and it shall be precise but also general enough to be used for all product categories. Consequently, “one fulfilled unit” is defined as the functional unit for the assessment. One fulfilled unit is understood as one product that is shipped to or purchased and ultimately kept by the consumer. Products returned by consumers, including all related additional resource usage and emissions, are allocated to the product that is kept.

In reality, a single item or product is often part of a larger and diverse shopping basket, thus associated environmental impacts would need to be allocated (e.g. based on the weight, volume or economic value of an item, number of orders during delivery time frame) to the single item under consideration (Van Loon et al., 2015b). The determination and justification of appropriate allocation factors is, however, associated with several methodological constraints and uncertainties (e.g. limiting factors – time, volume, weight – of a truck load can be diverse and are highly dependent on individual contexts). Therefore, the assessment is conducted under the assumption that every product is packed and shipped individually. Each product category is analysed based on at least one representative product, which then serves as a reference for values such as mass or volume, where required. Representative products are determined depending on

Data Search Method

A focused literature review (= data-pull-principle) was conducted in order to support a determination of both quantitative and qualitative assessment results related to the identified opportunities and threats as well as certain product categories.

Thus, all data was acquired through a secondary analysis. This method involves the utilisation of existing data (e.g. by means of extrapolation or conversion), initially collected and generated for the purposes of other studies, in order to answer above research questions and thereby support the objective of this study. In addition, a dedicated data basis for future research was compiled (see Table 12, Table 24, Table 32 and Table 41).

The secondary analysis was performed by the following steps:

Step 1: Determination of relevant data pools;

Step 2: Capturing of potentially relevant sources;

Step 3: Selection of sources and data points applicable to the respective assessment sequence (quantitative/qualitative); and

Step 4: Linkage of data points/sources to relevant opportunities/threats and calculation (of quantitative) results and estimation (of qualitative) results in terms of proposed indicators.

Assumptions & Limitations

In general, it must be differentiated between (i) assumptions and uncertainties inherent in adopted data points and (ii) assumptions that have been necessary to utilise existing data. Among the latter category of assumptions, the following were deemed a prerequisite of quantitative in-depth assessments:

 only the clusters “Logistics & Transport”, “Packaging”, “End-of-Life” were assessed quantitatively, and the remaining four clusters were selected for a qualitative assessment;

 focus was on environmental CE aspects and corresponding indicators;

 social and economic aspects were considered in a more general discussion, if relevant and available in the literature;

 a product-centric perspective was adopted;

 business models were not the focus of this assessment;

 representative products were identified in order to gather data and insights on certain product categories;

 comparisons between product categories were generally not possible due to different underlying assumptions, scope, etc. of data sources;

 solely single-consumer purchases were accounted for, i.e. an E-commerce purchase is placed by one individual, not by a group of individuals;

 only end-user purchases were assumed, i.e. purchases are not done with the intention to re-sell the purchased product;

 only the first life cycle of products was considered;

 transport systems and technologies in a single country were assumed similar throughout that country;

 the project scope required pragmatic decisions regarding balance of data detail and assumptions.

Further assumptions are mentioned in the respective in-depth assessment sections.