Geothermal energy could play a crucial role in the European energy market and future scenarios focused on sustainable development. Thanks to its constant supply of concentrated energy, it can support the transition towards a low-carbon economy. In the energy sector, the decision-making process should always be supported by a holistic science-based approach to allow a comprehensive environmental assessment of the technological system, such as the life cycle assessment (LCA) methodology. In the geothermal sector, the decision-making is particularly difficult due to the large variability of reported results on environmental performance across studies. This calls for harmonized guidelines on how to conduct LCAs of geothermal systems to enhance transparency and results comparability, by ensuring consistent methodological choices and providing indications for harmonized results reporting. This work identifies the main critical aspects of performing an LCA of geothermal systems and provides solutions and technical guidance to harmonize its application. The proposed methodological approach is based on experts’ knowledge from both the geothermal and LCA sectors. The recommendations cover all the life cycle phases of geothermal energy production (i.e., construction, operation, maintenance and end of life) as well as a selection of LCA key elements thus providing a thorough base for concerted LCA guidelines for the geothermal sector. The application of such harmonized LCA framework can ensure comparability among LCA results from different geothermal systems and other renewable energy technologies.

Life cycle assessments (LCAs) quantify environmental impacts of systems and support decision-making processes. LCAs are however time-consuming and difficult to conduct for nonexperts, thus calling for simplified approaches for multicriteria environmental assessments. In this paper, a five-step protocol is presented to generate simplified arithmetic equations from a reference parametrized LCA model of an energy system and its application illustrated for an enhanced geothermal system for heat generation with very low direct emissions in continental Europe. The simplified models estimate seven environmental impacts (climate change, freshwater ecotoxicity, human health, minerals and metals, and fossil resources depletion, and acidification) based on six technological parameters: number of injection and production wells, power of the production and injection pump, average well length, thermal power output, and eight background parameters defining the European electricity mix. A global sensitivity analysis identified these parameters as influencing the variance of the environmental impacts the most. Ensuring the representativeness of the reference LCA model and the validity of the simplified models requires thorough assessment. This protocol allows to develop relevant alternatives to detailed LCAs for quick and multicriteria environmental impact assessments of energy systems, showing that LCAs can be simplified to system-specific equations based on few, easily quantified, parameters.

Life cycle assessment (LCA) is a standardized, holistic, and multi‐criteria approach to estimate the environmental impacts of a system over its entire lifecycle. Modeling the environmental impacts of future scenarios and evolving systems in LCA is challenging due to lack of knowledge around technological evolutions and changing backgrounds influencing the foreground system. The method proposed here tackles these issues by combining parameterized life cycle inventories (LCIs) of foreground and background systems to assess how background changes, for example in the energy sector, affect the environmental impacts of the foreground. First, parameterized LCIs for the heating and energy sector were developed to flexibly estimate the environmental impacts of heating, gas, and electricity trajectories. Assessing their environmental impacts is essential to avoid any burden shifting to other impacts. Thanks to their parameterization, these models can represent different geographical, temporal, or technological contexts. Second, the combination of these parameterized LCIs was implemented using the Python library lca_algebraic , designed to enhance computational efficiency and calculation speed of LCAs. lca_algebraic ’s features were extended to link parameters to specific LCIs and, hence, ease their import and export and facilitate teamwork. In a final step, the developed LCIs and lca_algebraic library were used to estimate the environmental impacts of French heating scenarios from 2018 until 2050, as a test of the approach. The parameterized models developed within this work are available on a Git together with published reference datasets for comparison, so that the method can be adapted to evaluate similar energy trajectories for other countries or regions. This article met the requirements for a gold–gold JIE data openness badge described at http://jie.click/badges .

A reliable quantification of the potential effects of chemicals on freshwater ecosystems requires ecotoxicological response data for a large set of species which is typically not available in practice. In this study, we propose a method to estimate hazardous concentrations (HCs) of chemicals on freshwater ecosystems by combining two in silico approaches: quantitative structure activity relationships (QSARs) and interspecies correlation estimation (ICE) models. We illustrate the principle of our QSAR-ICE method by quantifying the HCs of 51 chemicals at which 50% and 5% of all species are exposed above the concentration causing acute effects. We assessed the bias of the HCs, defined as the ratio of the HC based on measured ecotoxicity data and the HC based on in silico data, as well as the statistical uncertainty, defined as the ratio of the 95th and 5th percentile of the HC. Our QSAR-ICE method resulted in a bias that was comparable to the use of measured data for three species, as commonly used in effect assessments: the average bias of the QSAR-ICE HC50 was 1.2 and of the HC5 2.3 compared to 1.2 when measured data for three species were used for both HCs. We also found that extreme statistical uncertainties (>10⁵) are commonly avoided in the HCs derived with the QSAR-ICE method compared to the use of three measurements with statistical uncertainties up to 10¹². We demonstrated the applicability of our QSAR-ICE approach by deriving HC50s for 1,223 out of the 3,077 organic chemicals of the USEtox database. We conclude that our QSAR-ICE method can be used to determine HCs without the need for additional in vivo testing to help prioritise which chemicals with no or few ecotoxicity data require more thorough assessment.