Simplified models for predicting the environmental impacts of geothermal power generation

Abstract

Geothermal energy is a renewable source of base-load power that could facilitate decarbonising the power generation sector. This work proposes novel simplified models based on Life Cycle Assessment (LCA) that enable rapid but accurate estimates of the environmental impacts of geothermal power. The proposed approach not only reduces the variability of LCA estimates due to methodological choices, but also substantially facilitates data collection by identifying the most important input parameters. These parameters are selected using Sobol’ total order indices from Global Sensitivity Analysis to a general parametric model. The models are applicable to both conventional and enhanced geothermal technologies, and cover numerous environmental impact categories. We determine the level of correlation between the simplified models and the general model. Our analysis shows that the simplified models correlate well with the general model, with correlation coefficients above 0.75 for both types of geothermal technologies and for all environmental categories. We also evaluate the performance of the simplified models by comparison with literature data. The results are positive, especially for conventional technologies where the relative difference with literature data on climate change impacts averages 14%. Finally, we identify the most appropriate model for each technology archetype and environmental category. • Simplified geothermal models rely on 1–5 influential parameters. • Influential parameters are obtained from Sobol’ total order indices. • Models cover conventional/enhanced technologies and multiple environmental categories. • Carbon footprint estimates are validated against literature data. • Open-source python script for easy updates or customisation.