The ecoinvent database version 3 (part II): analyzing LCA results and comparison to version 2

We distinguish and discuss improvements for selected existing activities according to the following classification:

In v2.2, several products resulting from electrolysis were mass allocated. For example, the activity “Electrolysis of lithium chloride (GLO)” produces 0.15 kg lithium (reference product) and 0.75 kg gaseous chlorine (by-product). Consequently, 83 % of the environmental impact followed the by-product of the activity. This approach was revised for v3.1 in the relevant system models. Since the atomic mass of the lithium and the chlorine cannot be considered as the driver for the electricity demand of the electrolysis, mass allocation was replaced with economic allocation; the allocation approach which is preferred according to ISO (2006) when physical relationships offer no realistic possibility. This causes a large deviation in GWP as the revenue from lithium is significantly higher than from gaseous chlorine. As a result, lithium is now associated with around six times higher environmental impacts than before and the impacts associated with gaseous chlorine drop significantly.

A similar case can be made for all spatial and technological variants of the chlor-alkali electrolysis which produces gaseous chlorine (reference product), sodium hydroxide, and liquid hydrogen in co-production. The adaptation in allocation of the mentioned activities increases the impacts associated with liquid hydrogen roughly by a factor of 10, while the impacts of gaseous chlorine and sodium hydroxide decrease somewhat.

A notable improvement in this category concerns capital equipment, e.g., “port facilities construction” and “airport construction.” In v2.2, these activities merely included the construction, but not the maintenance of the infrastructure. Consequently, the improvement focused on the supplement of all interventions associated with the maintenance over their lifetime (e.g., 100 years). The consistent consideration of maintenance increased the GWP by a factor of roughly 1500 (port facilities) and 36 (airport). To put it differently, the maintenance efforts of the modeled facilities are much larger than the actual interventions associated with their construction. This affects the downstream supply chains for air and ship transport, albeit to a limited extent only.

Another noteworthy improvement in completeness can be observed for many agricultural datasets requiring an input of irrigation. For example, the GWP of the activity “Coconut production, husked, (PH)” increases by a factor of roughly 5000 because crop and country-specific irrigation requirements have been added. The addition of irrigation has a large impact because the activity does not record many other interventions. Such improvements in completeness are based on the new availability of country-specific irrigation activities. In v2.2, irrigation in agricultural activities was usually modeled with a direct input of water from nature ignoring the interventions associated with the provision of the water. V3.1 offers country-specific irrigation activities that represent the average applied technologies within a particular country. This allows for a more realistic modeling of the interventions associated with irrigation.

A far-reaching improvement in this category concerns the general revision and harmonization of the emission models used for the modeling of N₂O, NH₃, and NO₃ emissions in agricultural activities and the development and cultivation of a superstructure for the consistent modeling of emission from land use change. Nemecek et al. (2014) offer a detailed discussion of the improvements and their consequences in terms of climate change impacts.

Another example is the implementation of a new model for transport in version 3. Transport is now accounted for within market datasets and was revised throughout the database based on sector transportation statistics (Wernet et al. 2016). In addition, road freight transport activities were updated.

Due to space restriction, we focus on the most important additions per sector. For a complete overview of new activities, the interested reader may refer to Moreno Ruiz et al. (2013, 2014). New electricity datasets make up a large share of new datasets across all continents, as shown in Table 3 (Treyer and Bauer 2013, 2014). In total, more than 1800 datasets in v3.1 Cut-off are related to electricity production, while it was around 600 datasets in v2.2. With this, more than 80 % of global power generation is now covered by local datasets in the ecoinvent database.

In addition, the structure of the wood sector was revised and expanded. Roughly 150 new activities covering forestry (country- and species-specific wood production), forest machinery (new operation datasets such as chipping, forwarding, harvesting, skidding, and yarding), sawmill, wood boards, and wood preservatives allow a more fine-grained modeling of the production and processing of wood products. The food sector was complemented with 30 new fruit and vegetable datasets (Stoessel et al. 2012) as well as industrial data for dairy products (milk, yogurt, cheese, butter, cream) and soya derivatives (tofu and soybean beverage). Passenger transport coverage was expanded by new size classes and types (such as e-mobility and alternate fuel sources) (Del Duce et al. 2014), and road freight transport was complemented with new data on EURO 6. The chemical sector was expanded by 90 chemicals including, among others, citric acid, glycine, sodium amide, acrolein, lactic acid, and iron chloride. Tap water provision operates on a higher level of detail as the infrastructure data and the technologies used for tap water production activities were expanded. In addition, new datasets for building materials were developed, including, among others, cement, concrete, bricks, and soapstone. Finally, the metal sector was complemented with new datasets for primary and secondary aluminum production including power generation. New datasets on magnesium, ferrosilicon, iron pellet, gold and silver mining, and steel processing were added as well.

A central aim of version 3 was to start the development of a global LCI database (Wernet et al. 2016). To this end, new modeling principles for the geographical coverage of activities and their spatial linking were introduced. Global activity coverage means that every process is represented either by an activity with a global (GLO) geographical scope, or by activities of local scope that cover together the entire global production volume, usually including a Rest-of-the-World (RoW) dataset. Consistent spatial linking means, e.g., that local activities receive inputs from their own geography, if available, whereas global activities receive inputs from all geographies (see Wernet et al. 2016 for a complete explanation and graphical illustration). Market activities, i.e., consumption mixes, play a major role in this context, as they bundle the inputs of producers within their geographical scope based on market shares that correspond to annual production volumes. While markets do not cause any direct environmental interventions, they act as connectors between producers and downstream consumers. As the production of every product is covered globally, activities that receive inputs from global markets (which is currently the case for many products) are connected to supply chains from all geographies, depending on production volumes and the degree of regionalization. In version 2, consumption mixes have also existed, but were not implemented consistently for every product in the database. It is important to realize that the implementation of these concepts in version 3 results in spatially consistent supply chains across the ecoinvent database, which can differ substantially from those modeled in v2.2.

For example, since country-specific data for South Africa (ZA) was missing in v2.2, the electricity requirement of the activity “Platinum group metal mine operation, ZA” was approximated with the European UCTE¹ electricity mix. In v3.1, the input of UCTE electricity was deleted. Since ZA electricity producers have been added during the electricity sector update, ZA electricity consumers are now linked to the ZA electricity market by default if not specified otherwise by direct links (Wernet et al. 2016). This influences the platinum group metal production, as the GWP of the ZA electricity mix is seven times larger than the UCTE mix, mainly due to the large proportion of hard coal based electricity. That is, the improved modeling results in increased environmental impacts for all metals produced by the mine operation (an exception being palladium, which shows reduced environmental impacts due to updated price data used in the economic allocation). Alternative examples for such improvements can be found in “Sanitary ceramics production, in Switzerland (CH)” where the input of the UCTE electricity and heat mix was replaced with CH-specific electricity and heat consumption mixes.

An analysis by the number of datasets per continent (Table 3, and Table S3 (ESM) by ecoinvent geographies) shows that version 3 features more regionalized datasets for every continent than v2.2. The strongest increases can be observed in North America, Australia, and Asia. The notable increase in datasets with global coverage is, to a large part, due to the introduction of global markets. Still, the number of global production datasets has almost doubled. Despite only a small increase of European datasets, Europe continues to be the region that is best covered within the database, both by local productions and local markets. In addition, RoW activities have been introduced to cover all geographies where a local activity is not yet available. Overall, 71 % of all datasets are production datasets and 29 % are markets. While some products are distributed via local markets (e.g., electricity (Treyer and Bauer 2014)), most products are distributed via global markets. Thus, the regionalization in version 3 so far takes place mostly at the level of regionalized production datasets, which then contribute to global markets. One reason for this is that regionalized production datasets must exist before regionalized markets can be created.

To assess the consequences of the increased global coverage, we have analyzed the environmental impacts that arise along the supply chains of each product in v2.2 and v3.1 Cut-off by geography. Figure 4 shows that significant differences can be observed between the databases: while most impacts in v2.2 occur in Europe, in v3.1 Cut-off Asia, North America, and RoW gain in importance. The importance of European datasets drops drastically in version 3. It is important to note that both databases are not directly comparable, as they do not contain the same datasets (e.g., version 3 has more datasets with non-European geographies than version 2). Nevertheless, Fig. 4 clearly shows that version 3 is developing towards a global LCI database. When the geographies are disaggregated to the underlying ecoinvent geographies, the top five contributing geographies across supply chains in v2.2 are RER (Region Europe) and CH (Switzerland), followed by Global, Germany, and Italy (Fig. S3, ESM). In v3.1 Cut-off, those geographies are RoW, China, Global, Russia, and RER. The reason why RoW is that important in version 3 is that in many cases, local production datasets only cover a small fraction of global production volumes. The impact fraction arising in RoW datasets is therefore an indicator of how well the ecoinvent database covers important processes with regionally appropriate data.

The impact contributions by geography in Fig. 4 should not be mistaken for actual global distributions of environmental impacts. Instead, the figure reflects an inward perspective on the ecoinvent database, which does not cover all economic sectors equally well (Majeau-Bettez et al. 2011; Reinhard et al. submitted).

The direct contribution of transportation across the database has not changed significantly from v2.2 to v3.1 Cut-off, as shown in Fig. 5 (see ESM, Fig. S4 for ReCiPe and ES’06). In both versions, road transport is the most important, followed by ship, rail, and air transport. The mean contribution of transport to all matched datasets is in both versions around 5 %. Across all datasets, i.e., within each database separately, the mean contribution of transport rises slightly from 5.5 to 5.8 %.

As electricity production datasets cover 50 countries (and due to partitioning of these 71 geographies), the electricity sector is a prime example to study the combined effect of global geographical coverage and global background data on LCIA results. In order to do so, the upstream impact contributions from electricity production were compared for v2.2 and v3.1 Cut-off for two distinct geographies: for global and European datasets. In order to remove the bias of having different datasets in each of the geographies across database versions, only matched datasets were considered (n = 262 for GLO and n = 947 for RER). In comparison to the total number of datasets, the share of electricity datasets remained constant at about 15 % in both database versions.

Results show that in v2.2, impacts from electricity consumption along the supply chains of global and European products occur in both cases mainly within Europe (shown for GWP in Fig. 6), since non-European processes for electricity generation were hardly available in 2. 2 and production processes in general often neglected non-European supply chains. In contrast, in v3.1 Cut-off, electricity impacts arise across all world regions for global datasets. For European datasets, a high fraction arises within Europe; however, as inputs along the supply chain of European datasets are partly produced in other world regions, an important share of electricity-related impacts also arises outside of Europe. Results for ReCiPe and ES’06 are similar, although the contribution of the electricity sector to the overall impacts is lower (ESM, Figs. S5–S7). The electricity sector therefore demonstrates that the introduction of a global coverage of activities in version 3 effectively results in geographically more diverse (and more realistic) supply chains than in version 2, especially for global and non-European datasets.

It could be assumed that the drastic changes observed in the electricity sector are a driver for the differences in LCIA results in versions 2 and 3 (Cut-off). In order to test this hypothesis, copies of both databases were made where electricity inputs across all datasets were set to zero. When LCIA results were compared, the median database deviation of GWP dropped to 1.1 % (before 6.5 %) (ESM, Fig. S8, Table S4). The median database deviations for ReCiPe and ES’06, on the other hand, remained similar (6.8 % instead of 8.4 %, and 18.9 % instead of 17.4 %; higher numbers can be explained by an increased relevance of other sectors in the context of overall lower impacts in the databases, as they exclude electricity-related impacts). This result indicates that while the combined effect of global activity coverage and availability of regionalized data for electricity production leads, at average, to increased GWP for all datasets, the median database deviation cannot be explained by changes in electricity supply chains alone.