Between Green Hills and Green Bills: Unveiling the Green Shades of Sustainability and Burden Shifting through Multi-Objective Optimization in Swiss Energy System Planning

Abstract

The Paris agreement is the first-ever universally accepted and legally binding agreement on global climate change. It is a bridge between today's and climate-neutrality policies and strategies before the end of the century. Critical to this endeavor is energy system modeling, which, while adept at devising cost-effective carbon-neutral strategies, often overlooks the broader environmental and social implications. This study introduces an innovative methodology that integrates life-cycle impact assessment indicators into energy system modeling, enabling a comprehensive assessment of both economic and environmental outcomes. Focusing on Switzerland's energy system as a case study, our model reveals that optimizing key environomic indicators can lead to significant economic advantages, with system costs potentially decreasing by 15% to 47% by minimizing potential impacts from operating fossil technologies to the indirect impact related to the construction of the renewable infrastructure. However, a system optimized solely for economic efficiency, despite achieving 63% reduction in carbon footprint compared to 2020, our results show a potential risk of burden shift to other environmental issues. The adoption of multi-objective optimization in our approach nuances the exploration of the complex interplay between environomic objectives and technological choices. Our results illuminate pathways towards more holistically optimized energy systems, effectively addressing trade-offs across environmental problems and enhancing societal acceptance of the solutions to this century's defining challenge.

Figures (8)

• Figure 1: Graphical representation of the methodology followed integrating indicators into . The green steps at the bottom of the figure illustrate the adaptation of the to the technologies to be split into variable and constant impact to allow the optimization of economic (red) and environmental (green) variables.
• Figure 2: Matrix representation of the coupling between and . The background-background matrix $A_{bb}$ is the original technology matrix from ecoinvent 3.8. The latter is extended as we create new processes to model technologies, thus creating the overall technology matrix $A$. $A_{bb}$ is a square matrix if size $n$, where $n$ is the number of processes within ecoinvent 3.8 database. Each mapped technology generates an additional column and row. The foreground-foreground matrix $A_{ff}$ denotes the inputs (flows and construction) and outputs (flows) among technologies and flows. $A_{ff}$ is a square matrix of size $n'$, where $n'$ is the number of technologies and flows in EnergyScope. Consequently, the overall technology matrix $A$ is a square matrix of size $n+n'$. $A_{bf}$ is composed of a set of columns of $A_{bb}$, identified through the mapping of technologies with ecoinvent life-cycle inventories. $A_{bf}$ columns are further multiplied by conversion factors contained in the transformation matrix $T$. In a nutshell, $A_{bf} = A_{bb} \times T$. Some entries of $A_{bf}$ are set to zero when their equivalent is positive in $A_{ff}$ to avoid double-counting.
• Figure 3: OF values comparison for . Each sub-figure corresponds to an individual optimization. The height of the segments corresponds to the 's relative variation to the 2020 reference scenarios values [%]. CF: CF, Cost: Total Cost, FNEU: FNEU, REQD: REQD, RHHD: RHHD, WSF: WSF
• Figure 4: Overall cost composition of energy systems for single-objective optimizations. The secondary axis highlights installed storage capacity. The 2020 scenario represents the current Swiss energy system and the other six represent hypothetical scenario for an energy-independent Switzerland 2020 with single objective optimization.
• Figure 5: Pearson correlation coefficient matrix based on the . The upper triangle depicts the correlation factor $r$ with the color gradient and the significance $p$ with the transparency. The diagonal depicts the distribution of the appearance of the individual variables. The lower triangle represents the observation distribution with the corresponding trend line and confidence interval. Each point corresponds to one distinct configuration. The Pareto-front between the can be observed.