An efficient and sustainable use of resources as well as ways to achieve climate neutrality are central topics of the FfE. Not least because of the EU’s Sustainable Finance Strategy, methods of sustainability assessment are becoming more relevant. In the following series of articles, components and criteria of sustainability assessment are presented. The focus is on available methods, their field of application and differences. This is the third post of the following topics, which are now successively appearing on our website.
Future-oriented Life Cycle Assessment – What is it all about?
The term “Life Cycle Assessment” (LCA) refers to the assessment of the environmental impact of a product, process or service over its entire life cycle – from raw material extraction to disposal. How an LCA is carried out is described in detail in the first part of the series. Classic LCAs often refer to existing products. They use data from the past or present. However, this view is not enough to make decisions that are as environmentally friendly as possible for the future. Future products and developments must also be taken into account.
With regards to a future-oriented LCA, the exact method is defined slightly differently: According to Arvidsson et al. 2018, an LCA is considered prospective if the (emerging) technology under investigation is in an early stage of development […], but the technology is modeled in a future, more advanced phase [ 1]. Guinée et al. evaluate various definitions in the field of life cycle assessment: They refer to prospective LCAs as the determination of future environmental impacts over the entire life cycle based on scenarios . For the purposes of this article, we will talk about future-oriented life cycle assessments and include all life cycle assessments that take into account future environmental impacts.
Differences between classic and future-oriented life cycle assessments
Figure 1 shows the main differences in execution between classical and future-oriented LCA. Primary data is used in the foreground system. If, for example, the life cycle assessment of an electric car is modelled, all the information about the materials used to build the vehicle, the construction of the battery or the energy consumption during the journey flows into the foreground system. The data of the foreground system refer to the time under consideration and are adjusted for the future depending on predicted developments. For example, a future increase in the efficiency of the car or the use of other, new materials for the production of the battery can be taken into account.
Secondary data from LCA databases, such as ecoinvent , are used for the background system . These include, for example, environmental impacts for the production of a material, the transport of goods or the provision of electricity for charging the electric car. The data sets from such LCA databases also need to be adapted to reflect future developments.
Subsequently, the life cycle assessment can be modelled and evaluated by linking the foreground and background system. When interpreting the results of prospective LCAs, it should be noted that the uncertainty of the results increases.
Adaptation of background data to future developments
The ecoinvent database contains more than 18,000 records. For prospective LCAs, these all need to be adapted to future developments. This can be done, for example, with the tool premise . The basic procedure is shown in Figure 2 .
At the beginning, assumptions have to be made: On the one hand, one of the five so-called “Shared Socio-economic Pathways (SSPs)” must be chosen, which determine fundamental developments in society. In addition, pathways for the development of greenhouse gas concentrations in the atmosphere (Representative Concentration Pathway; RCP) need to be defined. These determine, for example, whether the temperature increase in the climate scenario under consideration is below 1.5 °C or whether the climate targets are not met. An IAM combines knowledge from different scientific disciplines to understand climate change, quantify uncertainties, and evaluate policy options. The assumptions from the SSPs and RCPs are used. For example, global expansion rates of renewable energies, the extraction of fossil fuels or global greenhouse gas (GHG) emissions are calculated.
The results of the IAM are used to customize the ecoinvent database. The processes in ecoinvent are adapted based on the results of the IAM and other assumptions. For example, it takes into account how the electricity mix will develop in the future or what efficiency gains and technology changes can be expected. The resulting background databases can then be used for the modelling of pLCA.
Exemplary results – greenhouse gas emissions of the German electricity mix
The following example shows how the THE emissions of the German electricity mix up to 2050 can be determined using a prospective LCA. The foreground data comes from the eXtremOS project. The development of the electricity mix assumed for the solidEU scenario is shown in Figure 3 .
The figure shows that the electricity mix is becoming increasingly greener, as the share of wind and photovoltaics will increase sharply by 2050. For the calculation of GHG emissions, the entire life cycle of the plants is taken into account. This means that in addition to the direct, combustion-related emissions (e.g. from the combustion of coal or natural gas), all upstream chains of energy sources and plants (e.g. natural gas production or construction of a wind turbine) are also taken into account. The emissions from the supply of electricity and from all upstream and downstream processes come from the ecoinvent database.
If, for the example of the German electricity mix described above, the resulting GHG emissions are calculated once without and once with background adjustment, the result shown in Figure 4 is obtained.
For the calculation without background adjustment, the composition of the electricity mix is taken into account for each year according to the developments in Figure 3, but without an adjustment of the background database ecoinvent. When considering the background fit, the scenario “SSP2- Peak Budget 1150” of the MIND model is used. As described above, a version of the ecoinvent database is created for each year considered, in which future developments according to this scenario are taken into account. The background processes adapted in this way are then combined with the prevailing electricity mix from the solidEU scenario in the respective year.
The results illustrate that the adjustment of the background leads to a significant reduction in the GHG emissions of the electricity mix, especially in later years. This is because the use of a climate protection scenario for the background leads to a reduction in emissions from the upstream and downstream processes.
Figure 5 shows a more detailed breakdown of emissions per energy source for the case with background adjustment.
The positive effect of the decline in fossil fuels on the GHG intensity of the electricity mix can be seen. However, the figure also illustrates that even in 2050, i.e. after the implementation of the energy transition and an adaptation of the background database to a climate protection scenario, emissions will arise in the upstream chain of renewable energies. These residual emissions are due to various effects. On the one hand, the decarbonization of industry and the energy sector is progressing at different rates in different regions of the world. On the other hand, not all sectors of the economy and processes are mapped in premise, so that some processes are not adapted to future developments.
Solving the environmental problems of the future or just clairvoyance?
With the help of the method, statements can be made about a possible development of environmental impacts. The methods and approaches are important for making decisions for a greener world today. This is the only way, for example, to further develop emerging technologies in an environmentally friendly way, to make climate-friendly decisions for the future or to develop long-term climate targets. Companies can also benefit from the scientific methods and calculate, for example, how upstream and downstream emissions in Scope 3 will develop in the future. However, as with classic life cycle assessments, future-oriented life cycle assessments do not provide absolute truth about the environmental impact of a product. Numerous assumptions are made for the calculation, scenarios are used and sometimes hidden background data can have a significant influence on the result. A critical approach to the results is therefore very important and should not be neglected.
How can we help you?
Prospective LCAs are currently still the focus of science, but the methods and data sets will also become increasingly relevant in practice in the future: For example, prospective LCAs can be used to calculate future Scope 3 emissions. In addition, the consideration of future developments is also important for the well-founded derivation of company-specific climate targets and transformation paths, e.g. in the course of the Science Based Targets Initiative (SBTi).
- Project “Circular Energy Transition” to balance the reduction of greenhouse gases through future material cycles in the life cycle of energy systems and components
- Project Accompanying Research Energy Transition in Transport (BEniVer) Head of the sub-project “Ecological Assessment”
We are also happy to support your company with:
- Training and development of methodological expertise in the field of prospective consideration of environmental impacts
- Support in the calculation of prospective emissions, e.g. for the determination of science-based climate targets in Scope 3
Contact us without obligation at: email@example.com, +49 (0)89 158121-45 or firstname.lastname@example.org
 Arvidsson, R., Tillman, A. M., Sandén, B. A., Janssen, M., Nordelöf, A., Kushnir, D., & Molander, S. (2018). Environmental Assessment of Emerging Technologies: Recommendations for Prospective LCA. Journal of Industrial Ecology, 22(6), 1286–1294. https://doi.org/10.1111/jiec.12690
 Guinée, J. B., Cucurachi, S., Henriksson, P. J. G., & Heijungs, R. (2018). Digesting the alphabet soup of LCA. International Journal of Life Cycle Assessment, 23(7), 1507–1511. https://doi.org/10.1007/s11367-018-1478-0
 ecoinvent Version 3; Wernet, G., Bauer, C., Steubing, B., Reinhard, J., Moreno-Ruiz, E., and Weidema, B., 2016. The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment, [online] 21(9), pp.1218–1230.
 R. Sacchi, T. Terlouw, K. Siala, A. Dirnaichner, C. Bauer, B. Cox, C. Mutel, V. Daioglou, G. Luderer, PRospective EnvironMental Impact asSEment (premise): A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models; Renewable and Sustainable Energy Reviews, Volume 160, 2022 https://doi.org/10.1016/j.rser.2022.112311.