# Energy Return on Investment of Major Energy Carriers: Review and Harmonization

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. EROI Definition

_{in,i}is the amount of money invested in global energy sector i, and EI

_{i}is the average energy intensity of energy sector i, in exajoules per 1 USD. However, not only is this a much more descriptive formulation than that provided by Equation (1), it is also a fundamentally different calculation that opens the door to potential distortions, such as those which arise due to the elasticity of the money-to-energy relationship or the fact that this value is really a measure of the power flow, i.e., energy per unit time.

#### 2.2. Supply Chain Boundary Mismatch

_{2}in Figure 1) are often larger than the initial energy investment for resource extraction (i.e., Inv

_{1}in Figure 1) [15,20,21,22,23,24,25].

#### 2.3. Temporal Boundary Mismatch

#### 2.4. Focusing on Net Energy and the “Cliff”

_{PE-eq}.

_{PE-eq}inconsequential, in light of the inevitable uncertainties that these life-cycle calculations entail. In fact, the global average value of (1/X) for all fossil fuels combined (oil + coal + gas) can be estimated to be 0.96, by using the information in the latest IEA World balance Sankey diagram [32].

_{PE-eq}explicit [10,11]:

_{G}is the life-cycle efficiency of the grid mix.

_{PE-eq}calculated according to Equation (6) above, and subject to a sensitivity analysis on the value of η

_{G}(which may vary significantly depending on the proportion of thermal vs. renewable energy resources used to generate electricity).

_{PE-eq}trends over time are to be interpreted, as discussed in some of the literature [34,35,36]. In simple terms, the value EROI

_{PE-eq}becomes closer and closer to the “straight” EROI (calculated as the ratio of the output electricity to the investments), as the primary-to-electric energy conversion efficiency of the grid mix as a whole improves. This is consistent with the replacement logic that underpins the definition of “primary energy equivalent”; in fact, asymptotically, if a grid mix achieved η

_{G}= 1, then one unit of electricity would become equivalent to one unit of primary energy.

_{max}= 1 − T

_{C}/T

_{H}). However, in the coming decades, “a massive cross-sector electrification and a concomitant shift away from thermal processes […] may open the door to achieving the required services with much lower demand for primary energy, which in turn entails that a significantly lower EROI than previously assumed may suffice” [37]. In other words, all else being equal, the reduced reliance on thermal power plants in electricity grids will tend to increase η

_{G}, and therefore decrease the EROI

_{PE-eq}of electricity, but at the same time, the resulting lower EROI

_{PE-eq}will still suffice to clear the correspondingly reduced “minimum EROI” threshold required for the support of an increasingly electrified society.

#### 2.5. Literature Review

#### 2.5.1. Literature on EROI of Thermal Fuels

#### 2.5.2. Literature on EROI of Electricity

#### 2.6. EROI Harmonization

#### 2.6.1. Harmonization of EROI Values of Thermal Fuels

#### 2.6.2. Harmonization of EROI Values of Electricity

- (i)
- all “straight” EROI ratios were consistently multiplied by the same fixed 1/η
_{G}value, thereby calculating the corresponding EROI_{PE-eq}, as per Equation (6). Given the critical sensitivity associated to η_{G}(as discussed in Section 2.3), a sensitivity analysis was carried out by repeating such calculation twice, first by setting η_{G}= 0.3 (representative of deployment in most grid mixes dominated by conventional thermal generators), and then by setting η_{G}= 0.7 (representative of deployment in a typical “decarbonized” grid mix with a significant penetration of renewable energies [7]). - (ii)
- All “weighted” EROI ratios were first divided by whatever weighting factor had originally been assumed by the authors, thereby essentially undoing any such weighting and reverting to the corresponding “straight” EROIs where the numerator is simply the electricity output. Then, the same procedure as for (i) was applied, so as to once again arrive at two sets of EROI
_{PE-eq}values, respectively based on assumed η_{G}= 0.3 and η_{G}= 0.7 life-cycle primary-to-electricity conversion factors.

_{G}values of 0.3 or 0.7, respectively, as described at point (i) above, to convert them to “primary energy equivalent” (EROI

_{PE-eq}). It is noted that, technically, this process fails to account for the additional energy investment for power plant construction and maintenance, but the data has shown that the latter is negligible for large thermal power plants when such investment is spread out over their long service life. For the specific case of gas-fired electricity, the more modern and efficient combined-cycle operation was assumed; additionally, the energy investment for gas distribution (stage 6 in Table 2) was omitted.

## 3. Results

- -
- A total of 113 papers were found reporting EROI values, but, after screening them, the harmonization used only 31 papers.
- -
- Most thermal fuels, including biofuel, oil, and natural gas have EROIs well below 10 after accounting for the entire production chain to the point-of-use.
- -
- EROIs from electricity production from hydro, wind, and PV are all at or above 10, once they are consistently expressed as “primary energy equivalent” (EROI
_{PE-eq}).

#### 3.1. Literature Screening

#### 3.2. Harmonization Analysis

#### 3.2.1. EROIs of Thermal Fuels at Point of Use

#### 3.2.2. EROIs of Electricity at Point of Use

_{PE-eq}ratios are reported, since these were calculated from the harmonized EROIs at point of use for the respective fuels (cf. Section 2.6.2). Methodologically consistent internal comparability is thus made possible among each of the individual sets of harmonized EROI

_{PE-eq}values.

- Hydroelectricity exhibits the highest EROI
_{PE-eq}results by far. The second highest-ranking group of technologies in terms of harmonized EROI_{PE-eq}comprise: nuclear, wind, and—in some cases—PVs (see point 2. below for caveats on the latter). CSP and geothermal electricity can then be grouped together as the third “block” of results in descending order of EROI_{PE-eq}. Broadly speaking, all electricity generation technologies listed thus far are characterized by harmonized EROI_{PE-eq}values greater than 10, when calculated assuming a primary energy to electricity life-cycle conversion factor η_{G}= 0.3. Oceanic electricity straddles this symbolic EROI_{PE-eq}= 10 line. - The EROI
_{PE-eq}values for PV electricity (and to a lesser extent also for geothermal electricity) span a fairly wide range. This appears to be primarily due to intrinsic differences in the assessed supply chains and technologies. Specifically, for the case of PV, the technological differences among the various technologies (sc-Si, mc-Si, CdTe and CIGS) are compounded by the large effect of variations in assumed solar irradiation (from approximately 1000 kWh·m^{−2}·yr^{−1}for northern latitudes e.g., Germany, to over 2300 kWh·m^{−2}·yr^{−1}for southern latitudes e.g., Chile). However, the deliberate choice was made not to attempt any harmonization for the latter, since it represents a real-world variable and not a methodological inconsistency per se. The important take-home message in these cases is that it is unreasonable to expect to arrive at a single value (or a very tight range of estimates) for the EROI_{PE-eq}of these technologies, due to the intrinsic variability ranges that characterize them. - The EROI
_{PE-eq}values for thermal electricity from the combustion of fossil fuels (coal and natural gas) are both in the range of 10–12, when calculated using η_{G}= 0.3. - Thermal electricity from biogas and BECCS is characterized by comparatively low EROI
_{PE-eq}values of 2–5, when calculated using η_{G}= 0.3. While these results may appear to contradict some higher estimates in the previous literature, it seems likely that in those earlier studies some of the supply chain investments identified in Table 2 may have been missed. For instance, Raugei et al. [23] caveated their results for biomass- and biogas-fired electricity by stating that “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”. - Finally, the calculated EROI
_{PE-eq}values for biomass-fired electricity using wood chips is comparatively high at 16 (assuming η_{G}= 0.3). However, as discussed in Section 3.2.1 for wood chips as a fuel stock, this result is only valid for this particular biomass fuel, whereas it would be considerably lower if a blend of woodchips and wood pellets were employed instead (as is the case in the UK, for instance [22]).

_{PE-eq}values for all technologies, when calculated using η

_{G}= 0.7 should not surprise nor be a reason for concern. This is simply the consequence of assuming deployment in a grid mix that is itself on average significantly more efficient at converting primary energy into electricity over its whole life cycle. As discussed in Section 2.4, while the individual EROI

_{PE-eq}for all technologies would be reduced in such conditions, at the same time it is reasonable to expect that, in the future, the same widespread deployment of low-cost renewable energies that will lead to a higher η

_{G}= 0.7 in the first place will also enable a higher degree of electrification across multiple sectors and end uses, thereby essentially lowering the “minimum EROI” threshold to above that which a healthy societal energy metabolism may be sustained.

_{PE-eq}values obtained by setting η

_{G}= 0.3 may still be considered the more representative ones, as the use of thermal technologies to generate electricity is still prevalent globally. These values were therefore selected to be reported vs. the corresponding NTG ratios (i.e., superimposed on the “net energy cliff”) in Figure 8. This latter figure allows a clearer visualization of which electricity generation technologies can be expected to generate sufficient net energy over their life cycles. Once again, the results show that most renewable technologies actually lead to NTG > 0.9, meaning that over 90% of the equivalent primary energy returned by them remains available for societal uses other than supporting the energy sector itself. Overall, this is a reassuring result that should put to rest many often-voiced concerns about the net energy viability of non-conventional and renewable electricity.

## 4. Discussion and Conclusions

_{G}= 0.7, i.e., 1 unit of electricity per 1.4 units of primary energy). This means that greater than 90% of the energy produced by these technologies is delivered to society as net energy.

_{PE-eq}of the resulting electricity mix [7,11].

## Supplementary Materials

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Conflicts of Interest

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**Figure 1.**Streamlined energy systems diagram of the exploitation of a primary energy resource (PES) for the production of a useful energy carrier (EC). Inv1 = energy investment for resource extraction (E); Inv2 = energy investment for resource processing and delivery (P&D). S = energy sink (thermodynamic losses). Energy system diagram following the symbolic conventions introduced by Odum [18].

**Figure 2.**EROI “at point of extraction” vs. EROI “at point of use” for three fictional primary energy resources, highlighting the strong non-linearity in the relation between the two.

**Figure 3.**Net Energy Cliff diagram relating EROI and net energy expressed as proportion of the Gross Energy Output that is delivered to society. Arrows shows how the estimates of EROI and net energy change when extending from point-of-extraction to point-of-use.

**Figure 5.**EROI values for thermal fuels, respectively, as originally published (Harmonization = “None”) and post-harmonization at point of use (Harmonization = “POU”). CSG = coal seam gas; CTL = coal to liquids; LNG = liquified natural gas. Note use of logarithmic scale on horizontal axis, for a more meaningful representation of the significance of the relative differences in terms of net energy (cf. Section 2.4). * = gaseouas fuel; ο = liquid fuel; □ = solid fuel.

**Figure 6.**Harmonized EROI values for thermal fuels at point of use, plotted against their corresponding net-to-gross energy output ratios (“net energy cliff”). CSG = coal seam gas; CTL = coal to liquids; LNG = liquified natural gas. Note use of logarithmic scale on horizontal axis, for a more meaningful representation of the significance of the relative differences in terms of net energy (cf. Section 2.4).

**Figure 7.**EROI values for electricity, respectively, as originally published (Harmonization = “None”), and post-harmonization in terms of equivalent primary energy output, respectively assuming deployment in a thermal-dominated electricity grid mix (Harmonization = “PE eta = 0.3”), and deployment in a de-carbonized electricity grid mix (Harmonization = “PE eta = 0.7”). BECCS = bioenergy with carbon capture and sequestration; CSP = concentrating solar power; PV = photovoltaics. Note use of logarithmic scale on horizontal axis, for a more meaningful representation of the significance of the relative differences in terms of net energy (cf. Section 2.4).

**Figure 8.**Harmonized EROI values for electricity (assuming deployment in a thermal-dominated electricity grid mix, η

_{G}= 0.3), plotted against their corresponding net-to-gross energy output ratios (“net energy cliff”). BECCS = bioenergy with carbon capture and sequestration; CSP = concentrating solar power; PV = photovoltaics. Note use of logarithmic scale on horizonal axis, for a more meaningful representation of the significance of the relative differences in terms of net energy (cf. Section 2.4).

**Table 1.**Calculation of EROI “at point of extraction” vs. EROI “at point of use” for three fictional primary energy resources, highlighting the strong non-linearity in the relation between the two.

Energy Resource | Inv_{1} | PE | EROI “at Point of Extraction” = PE/Inv _{1} | Inv_{2} | EC | EROI “at Point of Use” = EC/(Inv _{1} + Inv_{2}) |
---|---|---|---|---|---|---|

PES1 | 1 | 100 | 100 | 9 | 100 | 10 |

PES2 | 2 | 100 | 50 | 9 | 100 | 9.1 |

PES3 | 4 | 100 | 25 | 9 | 100 | 7.7 |

**Table 2.**For each stage (i) of the supply chain for each thermal fuel beyond extraction, the following values are reported: energy investment required at that stage (Inv

_{i}), cumulative investment in the upstream chain up to that stage, excluding the investment for extraction (${{\displaystyle \sum}}_{2}^{i}In{v}_{j}$), and maximum EROI at that stage (EROI

_{i,MAX}), disregarding the investment for extraction (i.e., assuming infinite EROI at point of extraction). Data taken from Ecoinvent v3.7 and v3.8 [73]. All investments are expressed as % relative to the final energy “return” (i.e., the net available energy in the output fuel at point of use). A value of 0 means that that specific supply chain stage does not apply to that fuel.

Supply Chain Stage | (2) Preparation | (3) Transmission | (4) Refining | (5) Purification | (6) Distribution | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Fuel | Inv_{2} | ${{\displaystyle \sum}}_{2}^{2}\mathit{I}\mathit{n}{\mathit{v}}_{\mathit{j}}$ | EROI_{2,MAX} | Inv_{3} | ${{\displaystyle \sum}}_{2}^{3}\mathit{I}\mathit{n}{\mathit{v}}_{\mathit{j}}$ | EROI_{3,MAX} | Inv_{4} | ${{\displaystyle \sum}}_{2}^{4}\mathit{I}\mathit{n}{\mathit{v}}_{\mathit{j}}$ | EROI_{4,MAX} | Inv_{5} | ${{\displaystyle \sum}}_{2}^{5}\mathit{I}\mathit{n}{\mathit{v}}_{\mathit{j}}$ | EROI_{5,MAX} | Inv_{6} | ${{\displaystyle \sum}}_{2}^{6}\mathit{I}\mathit{n}{\mathit{v}}_{\mathit{j}}$ | EROI_{6,MAX} |

Oil | 0 | 0 | ∞ | 1.5% | 1.5% | 67 | 8.9% | 10.4% | 9.6 | 0 | 0 | 9.6 | 1.1% | 11.5% | 8.7 |

Gas | 0 | 0 | ∞ | 7.7% | 7.7% | 13 | 0 | 7.7% | 13 | 0 | 7.7% | 13 | 10.2% | 17.9% | 5.6 |

Coal | 4.2% | 4.2% | 24 | 5.6% | 9.8% | 10 | 0 | 9.8% | 10 | 0 | 9.8% | 10 | 0 | 9.8% | 10 |

Bioethanol (Maize) | 0 | 0 | ∞ | 0 | 0 | ∞ | 61% | 61% | 1.7 | 2.2% | 62.6% | 1.6 | 1.5% | 64.1% | 1.6 |

Bioethanol (Sugarcane) | 0 | 0 | ∞ | 0 | 0 | ∞ | 2.5% | 2.5% | 39 | 2.2% | 4.7% | 21 | 0 | 4.7% | 21 |

Bioethanol | 0 | 0 | ∞ | 0 | 0 | ∞ | 33.6% | 33.6% | 3.0 | 2.2% | 35.8% | 2.8 | 0 | 35.8% | 2.8 |

Biogas | 0 | 0 | ∞ | 0.2% | 0.2% | 420 | 0 | 0.2% | 420 | 15.3% | 15.5% | 6.4 | 0.4% | 15.9% | 6.3 |

Biodiesel | 3.3% | 3.3% | 31 | 1.7% | 4.9% | 20 | 5.4% | 10.3% | 10 | 0 | 10.3% | 10 | 0 | 10.3% | 10 |

Wood Pellets | 51% | 51% | 2.0 | 0 | 51% | 2.0 | 0 | 51% | 2.0 | 0 | 51% | 2.0 | 11.7% | 63% | 1.6 |

**Table 3.**Number of papers returned by the literature search, per resource type. The total number of papers in this table is more than that reported in Table 2 because some papers estimated EROI values for more than one technology or resource. BECCS = bioenergy with carbon capture and sequestration; CSG = coal seam gas; CTL = coal to liquids; LNG = liquefied natural gas.

Papers by Resource Type | Initial Tally | Post-Screening Tally |
---|---|---|

Thermal fuels | ||

Biofuels (including biodiesel, bioethanol, biogas) | 8 | 3 |

Coal (including CSG and CTL) | 9 | 2 |

Natural Gas (including shale gas and LNG) | 10 | 5 |

Oil (including Oil Sands) | 11 | 9 |

Electricity | ||

BECCS | 3 | 2 |

Biogas | 4 | 0 |

Concentrated Solar Power | 2 | 1 |

Geothermal | 5 | 2 |

Hydropower | 7 | 2 |

Oceanic | 1 | 1 |

Nuclear Power | 3 | 1 |

Photovoltaics | 11 | 4 |

Wind Power | 10 | 5 |

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**MDPI and ACS Style**

Murphy, D.J.; Raugei, M.; Carbajales-Dale, M.; Rubio Estrada, B. Energy Return on Investment of Major Energy Carriers: Review and Harmonization. *Sustainability* **2022**, *14*, 7098.
https://doi.org/10.3390/su14127098

**AMA Style**

Murphy DJ, Raugei M, Carbajales-Dale M, Rubio Estrada B. Energy Return on Investment of Major Energy Carriers: Review and Harmonization. *Sustainability*. 2022; 14(12):7098.
https://doi.org/10.3390/su14127098

**Chicago/Turabian Style**

Murphy, David J., Marco Raugei, Michael Carbajales-Dale, and Brenda Rubio Estrada. 2022. "Energy Return on Investment of Major Energy Carriers: Review and Harmonization" *Sustainability* 14, no. 12: 7098.
https://doi.org/10.3390/su14127098