**4. Results and Discussion**

Figure 7 illustrates the calculated GWP of the domestic grid mix in California, respectively in 2018 (based on historical data) and in 2030 (when the future grid mix is modelled as described in Section 3.1). The first and foremost result is that the carbon intensity of electricity is expected to be almost halved over the course of a single decade. Such remarkable drop is almost entirely due to the combination of two key factors. Firstly, the massive deployment of PV and energy storage allows a substantial phasing out of gas-fired electricity (and SCGTs in particular). Secondly, the up-front carbon emissions due to the manufacturing and installation of the PV and LIB systems are low enough that, when discounted over the total amount of electricity that they deliver in their combined service lives, they result in comparatively negligible GWP contributions to the grid mix.

**Figure 7.** Global warming potential (GWP) results for California domestic grid mix in 2018 and in 2030. The pie charts underneath each bar refer to the corresponding grid mix composition, and are included to aid the interpretation of the results. SCGT = single cycle gas turbines; NGCC = natural gas combined cycles; PV = photovoltaics; CSP = concentrating solar power; LIB = lithium-ion batteries; HV = high voltage.

These results are put in even starker relief when considering that in 2018, gas-fired power plants generated 29% of total domestic electricity while being responsible for 93% of the grid's GWP; conversely, in 2030 PV + LIBs are expected to generate 52% of total domestic electricity while only causing 10% of the grid's total carbon emissions.

In terms of life-cycle energy results, the same planned energy transition results in an overall 31% reduction in the CED of domestic electricity in California (Figure 8), and a corresponding increase in the life-cycle primary-to-electric energy conversion efficiency (ηG) of the grid mix, from 48% to 69%.

The improvement becomes even more significant when specifically focusing on the life-cycle demand for non-renewable primary energy (Figure 9), given that most of the primary energy harvested from the environment to power the grid mix in 2030 is actually renewable (i.e., solar, and to a lesser extent wind, hydro, geothermal and biomass).

**Figure 8.** Cumulative energy demand (CED) results for California domestic grid mix in 2018 and in 2030. The pie charts underneath each bar refer to the corresponding grid mix composition, and are included to aid the interpretation of the results. SCGT = single cycle gas turbines; NGCC = natural gas combined cycles; PV = photovoltaics; CSP = concentrating solar power; LIB = lithium-ion batteries; HV = high voltage.

**Figure 9.** Non-renewable cumulative energy demand (nr-CED) results for California domestic grid mix in 2018 and in 2030. The pie charts underneath each bar refer to the corresponding grid mix composition, and are included to aid the interpretation of the results. SCGT = single cycle gas turbines; NGCC = natural gas combined cycles; PV = photovoltaics; CSP = concentrating solar power; LIB = lithium-ion batteries; HV = high voltage.

As a result, the nr-CED of domestic electricity in California drops by a factor of three, from 5.2 to 1.8 MJ(oil-eq)/kWh. To this effect, it is noteworthy that phasing out nuclear, as well as natural gas, is also beneficial (while nuclear energy is a low-carbon technology, it still obviously relies on non-renewable stocks of fissile fuel, which are also not available domestically in California, adding further meaning to these results in terms of improved energy sovereignty).

Finally, when shifting the viewpoint to the one characteristic of NEA, and thus focusing only on the energy investment per unit of electricity delivered, while excluding the primary energy that is directly harvested and converted to electricity, Figure 10 shows that the planned massive deployment of PV and LIB storage in 2030 does result in significant shares of the total grid mix energy investment being required for these technologies (respectively, 36% and 9%). Even so, the overall energy investment per unit of delivered electricity in 2030 is still reduced with respect to the historical value for 2018.

**Figure 10.** Primary energy investment results for California domestic grid mix in 2018 and in 2030. The pie charts underneath each bar refer to the corresponding grid mix composition, and are included to aid the interpretation of the results. SCGT = single cycle gas turbines; NGCC = natural gas combined cycles; PV = photovoltaics; CSP = concentrating solar power; LIB = lithium-ion batteries; HV = high voltage.

As illustrated in Figure 11, this results in a 10% increase in the EROIel of the California domestic grid mix as a whole. At the same time, however, because of the larger penetration of PV and the phasing out of nuclear and, partially, gas-fired electricity, the life-cycle primary-to-electric energy conversion efficiency of the grid mix (ηG) increases by as much as 44% in relative terms, from η<sup>G</sup> = 0.48 in 2018 to η<sup>G</sup> = 0.69 in 2030. Therefore, the trend in EROIPE-eq = EROIel/η<sup>G</sup> ends up being dominated by the latter change in ηG.

In order to provide additional detail on these NEA calculations, the specific EROI results (in terms of both electricity and equivalent primary energy) for the individual electricity generation technologies comprising the California domestic grid mix in 2018 and 2030 are reported in Table 2. Once again, it is noteworthy that the η<sup>G</sup> values for California in 2018 and 2030 are significantly higher than typically assumed for electricity grids with higher percentages of thermal technologies (η<sup>G</sup> = 0.30–0.35), resulting in comparatively lower values of EROIPE-eq. This showcases how any specific EROIPE-eq values are only valid for the actual conditions considered in each study (such as grid mix composition, year, and location).

Specifically, changes in η<sup>G</sup> are at the root of the differences in EROIPE-eq results for PV in California vs. those previously reported by the same authors when considering a more generalised thermal grid mix (η<sup>G</sup> = 0.30) [50].

Finally, by way of sensitivity analysis, an alternative scenario for 2030 was also analysed, in which more efficient and longer-lasting PV systems were assumed (*cf.* Section 2.2.9). The ensuing variations in the calculated energy and carbon emission indicators for the California domestic grid mix are reported in Table 3. As can be seen, this sensitivity analysis proves that the main results of this study are very robust and not likely to be affected significantly by alternative future PV developments.

**Figure 11.** Energy return on (energy) investment (EROIel and EROIPE-eq) of the California domestic grid mix.



(a) EROI results for these technologies are affected by a larger margin of uncertainty, due to a combination of older inventory data and (for biomass and biogas) possible inaccuracies in the modelling of the feedstock supply chains. However, given the corresponding small grid mix shares of these technologies, such uncertainty does not significantly affect the overall grid mix EROI results presented in the main manuscript. (b) "Conservative" future PV assumptions, assuming only modest module efficiency improvements (to 21% for sc-Si and CdTe PV, and 20% for mc-Si PV), and no improvements in material utilization or BOS [53].

**Table 3.** Sensitivity Analysis on California domestic grid mix results in 2030, resulting from alternative assumptions on future PV systems.


(a) "Conservative" assumptions for PV systems in 2030: 21% system efficiency for sc-Si and CdTe PV, and 20% for mc-Si PV [53]. All PV system lifetimes = 30 years [53]. Capacity Factor = 27% (calculated assuming 43.7 GW installed capacity and 103.7 TWh net generation, the latter arrived at as detailed in Section 3.1 of the main manuscript). (b) "Optimistic" assumptions for PV systems in 2030: 23% system efficiency for sc-Si and CdTe PV, and 22% for mc-Si PV [53]. All PV system lifetimes = 40 years [53]. Capacity Factor = 27% (calculated assuming 43.7 GW installed capacity and 103.7 TWh net generation, the latter arrived at as detailed in Section 3.1 of the main manuscript). All other modelling parameters (including material usage efficiency and foreground energy inputs per m2 of PV module) were kept constant in both scenarios.
