This study shows that the choices of scenario, system boundary, and evaluation method to some extent decide the eco-design drivers for the present VR headset. As a further matter, the study suggests that it could become awkward to agree on product category rules (PCR) to satisfy all settings which are seemingly reasonable. PCR will only be valid for a specific region, such as the one represented by the European Union.
4.1. ILCD Evaluations
For S1 in
Figure 2, it is surprising that the distribution of the VR headset (≈29% of the total score) is more important than the use stage (≈7%). The share of the production of the electronics at ≈36% is rather high, as expected for consumer electronics [
6,
7,
15,
16], and all other parts such as plastic and screws are ≈17%. The ICs (including 13% gold production and 12.5% Wafer Processing (WP) and final IC assembly) are 26% of the total score, which make them important but not dominant as hypothesized. Primary gold production is 18% of the total score. In comparable ILCD scores for smartphones and tablets, the electronics (especially ICs and screens) are ≈90% and distribution is ≈2%.
For S2, the metals gold, silver, and copper are assumed to originate from metal scrap. For the present VR headset, ensuring the recycled content will in theory be an effective strategy for reducing the environmental damage cost by ≈20%.
Choosing S3, in which 5% of the products are reused, reduces the total score less than 5%. One of the reasons for this is the increasing use stage power consumption.
For S4, using LIEP could reduce the environmental impacts to the same degree as S2. The explanation is that LIEP has ≈20 times lower environmental impact than HIEP per kWh. This difference magnitude might not be the case for all sources of low impact electric power compared to all sources of higher impact electricity.
4.3. EPS2015 Evaluations
EPS is a long-lasting weighting method for LCA that was introduced in 1999 [
17,
18]. EPS2015 is a complete evaluation system for the pathways of many LCI flows, including mid-point categories (e.g., crop growth capacity and Years of Life Lost (YOLL) both called “state indicators”), damage categories (e.g., human health and ecosystem services, both called “safeguard subjects”), and weighting factors for each mid-point category. The mid-point indicator for one YOLL pathway (heat stress) for the LCI flow CO
2 emission to air is 1.35 × 10
−7 person-years/kg, whereas the GWPI for CO
2 is 1 kg CO
2-eq./kg [
10]. The bearing idea of EPS2015 [
10] is the cost per LCI flow of reaching sustainability in 2100. As such, EPS2015 addresses long-term costs, but not the long-term market effect which is the goal of consequential LCA. The cost is the one for protecting so-called safeguard subjects of which abiotic resources is one example and ecosystem services is another.
For S1 in
Figure 3 and
Figure 5, the electronics are 59%, ICs are 49% (43% primary gold production, 7% WP and IC), and primary gold production is 58% of the total EPS2015 score. Hence, the significance of ICs is here due to their gold content and not so much caused by WP and IC. EPS2015 favors S2 because primary gold production stands for a much larger share of the total EPS2015 score than that of corresponding ILCD and LIME2 scores, and naturally the potential is larger when using secondary/recycled gold.
S1, S3, and S4 have similar total scores for EPS2015, implying that reuse (S3) and LIEP (S4) will not lead to a significant difference compared to S1. This trend is similar to the one derived from ILCD for S1 and S3.
The use of secondary metals instead of ore metals, especially gold, is highlighted as S2 shows a 60% reduction compared to S1 (
Figure 5). According to EPS2015, it seems more effective to use secondary metals (especially gold) than reuse the VR headset or use LIEP in the upstream. Primary gold here has ≈9800 times higher environmental damage cost than secondary gold (2.28 × 10
6 versus 230 Environmental Load Units {ELU}/kg).
4.4. LIME2 Evaluations
LIME was developed in Japan between 1998 and 2003 [
8,
9]. LIME is a complete evaluation system for many LCI flows including mid-point categories (e.g., air pollution and resource consumption), damage categories (e.g., human health and biodiversity), normalization factors for each damage category, and weighting factors for each damage category based on conjoint analyses. The first edition (LIME1) laid the foundation of a damage-oriented life cycle impact assessment method for Japanese industry. LIME1 was updated to LIME2 in 2012 [
11]. LIME2 uses weighting factors for four different areas of protection (human health, social assets, primary productivity and biodiversity) that reflect environmental awareness among the Japanese public [
11]. Here, the weighting factors for G20 nations are used from Table 5 in Reference [
11].
LCAs using LIME2 are often driven by human health costs that people want to avoid, such as those related to particulate matter. Therefore, it mostly emphasizes the benefits of LIEP and consequently S4 is 36% lower than S1 (
Figure 5).
For S1 (
Figure 4 and
Figure 5), the electronics are 44%, ICs are 28% (9% primary gold production, 19% WP and IC), and primary gold production is 12% of the total LIME2 score. Here, the share of WP and IC is relatively large as LIME2 puts a larger emphasis on electric power production than e.g., EPS2015.
Unlike the ILCD and EPS2015 evaluations, the total scores for S1–S3 are more alike than S4.
VR headsets show a somewhat different pattern for emissions and energy footprints than e.g., smartphones, in which the manufacturing of electronic parts (using gold) usually dominates more at the expense of final assembly and distribution [
6,
7,
15,
16]. The present VR headset e.g., has no touchscreen, which makes it different from smartphones and tablets as seen from emission and energy footprints perspectives. A smartphone is necessary for the present VR headset to work. Moreover, smartphones and tablets have larger environmental damage costs per piece than VR headsets (
Figure 6). Game consoles, which also could be used together with VR headsets, use around 32–500 kWh/piece/year [
19]. This suggests that the system boundaries for VR headset LCAs should be set larger than in the present study, as the indirect environmental impacts are higher than for just one headset. One could argue that extended system boundaries for the studied product system would lead to different insights and conclusions.
The End-of-Life Treatment modeling is not particularly precise; however, reuse is still probably a measure which could avoid more life cycle impacts than material and energy recovery strategies. Theoretically, it would be more effective to actually use recycled metals in product design than to use ore metals and then recycle them. The issue of the actual benefits obtained by material recycling in LCA is still somewhat equivocal. However, hopefully the Product Environmental Footprint (PEF) Guidance [
12,
20] can streamline the process for LCA practitioners.
The alternate weighting methods in EPS2015 and LIME2 show the core of the challenge of sustainability evaluations. Clearly, several methods should be used to obtain a comprehensive understanding of the system at hand. The weighting methods (especially EPS2015) are moreover highly sensitive to the precision of the material content of sub-parts and matching of the LCI flows with the weighting indices in the LCA tools.
For most devices, a new LCA is necessary for each market condition. Here just one market condition is investigated with four different scenarios. The VR headset, however, demonstrates totally different emission footprints, and thereby LCA scores, depending on the production place and the final market in which it is used. The number of combinations and scenarios for the production and use of VR devices are huge, but optimum conditions might be found. Despite the weighing of environmental impacts, a universal eco-design strategy occasionally cannot be derived for specific products due to the large number of possible scenarios.
Moreover, the absolute uncertainty ranges of end-point weighed scores are likely very large. Based on the appendices of EPS2015 [
10], the uncertainty for the environmental cost (0.13 Environmental Load Unit (ELU) ≈ Euro) of emitting 1 kg CO
2 to air could be around 169% (coefficient of variance), i.e., −0.255 to 0.6 ELU/kg CO
2 in a 95% confidence interval, (mean value = 0.13 ELU/kg CO
2; standard deviation = 0.219 ELU/kg CO
2). The probability density function for this interval is shown graphically in
Figure 7.
CO2 is just one of many inventory flows contributing to the total score in LCAs of VR headsets; gold resources is another. Still, end-point weighed scores give more interesting indications of directions for eco-design than would just one mid-point indicator such as “Minerals and fossil resource depletion.”