**2. Materials and Methods**

The goal of the present study is the Life Cycle Assessment (ISO 14040 [3]) of electricity generation scenarios in Italy at 2030. In particular, two scenarios developed for the INECP are taken into consideration: the Baseline scenario (2030 BASE) that describes an evolution of the Italian energy system with current policies and measures and the INECP scenario (2030 INECP) that quantifies the strategic objectives of the plan [2].

In this study each scenario is defined by:


For future mixes, the technological progress in the electricity conversion technologies was taken into account through enhanced conversion efficiencies and load factors.

Background system evolution in time (as for example global production market of main materials for power plant construction) has not been considered in this study. This can be a critical issue especially for studies on long term scenarios [14]. Nevertheless the results of the present study could help to identify the most relevant background processes and materials production markets, with a role in environmental potential trade-offs which can be investigated in future studies, with a much longer perspective (2050, 2070).

In order to evaluate the evolution of the electricity mix, and consequently the evolution of the environmental profile associated with it, the LCA of the current electricity mix is first presented, taking into consideration years 2016 and 2017.

The functional unit is 1 kWh of electricity Gross National Consumption (GNC) which includes the total gross national electricity generation from all sources (excepted pumped hydro generation), plus electricity imports, minus exports. As regards system boundaries, all phases of the life cycle, from cradle to grave, are included in the analysis: fuel supply, power plant construction, power plant operation and power plant end-of-life.

Since the functional unit refers to GNC and not to the final consumption, transmission and distribution network, as well as the losses associated with it, are excluded from the boundaries of the analyzed system. The impact categories and assessment methods are selected on the basis of Impact Assessment guidelines [15] drawn up by Joint Research Center—European Commission—JRC. Only impact categories reported in the guideline with the level of recommendations I (recommended and satisfactory) and II (recommended but in need of some improvements) are utilized (see Table 1). For assessment methods description and reference, refer to [15].


**Table 1.** Impact categories taken into consideration in the LCA of the Italian electricity scenarios.

Regarding data quality, primary data (statistical data and environmental declarations from Italian power plants) and secondary data (Ecoinvent LCI database [16]) were used, as better specified below. For elaborations the LCA software SimaPro (v8, PRè Consultant, Amersfoort, The Netherlands) was used.

As regards the allocation of impacts between the main product and by-products, the "cut-off" [16] approach was adopted. To the secondary (recycled) materials, only the impacts of the recycling process are assigned (no impact from the primary production of the material). In the case of electricity from wastes, all the impacts of incineration are allocated to the waste treated in the plant (electricity production is burden free). For allocation between heat and electricity in the cogeneration power plants, allocation proportional to the output was used. The fuel is attributed proportionally to the amount of the output product (electricity and heat). This approach was chosen since it is coherent to the method used in Eurostat energy balance.

Statistical data have been elaborated in order to obtain electricity mix detailed by power plant typology. A power plant typology is defined by a combination of fuel in input and transformation technology (e.g., natural gas combined cycle power plant). Since available official data and life cycle inventories present aggregated data for Italian electricity mix, a combination of different official energy statistics has been analyzed and elaborated, in order to consider a complete set of fuels and technologies. As regards thermal power plants, technologies taken into consideration are those reported in the statistical reports published annually by TERNA (the Italian system transmission operator) [17]:


Fuels taken into consideration are all those reported in the energy balance published by Eurostat [18]. In addition to thermoelectric (fossil and renewable), the mix includes hydroelectric (reservoir and runoff), wind and photovoltaic plants.

Tables A1–A4, in Appendix A, show Italian current and future electricity mixes, with the details of the power plant typology. The 2016 and 2017 mixes are very similar: natural gas accounted for 39–42% of the total electricity production in Italy. Among renewable sources, hydropower ranks first, covering 11–13% of the total production, followed by solar and wind energy. A share of about 11% of the electricity is imported. For scenario BASE at 2030 a greater penetration of renewable is foreseen especially for hydropower (from 10% to more than 15%), wind power (from 5% to 7.5%) and solar power (from 7% to almost 10%). In the INECP Scenario a phase out of coal is included, with zero contribution at 2030, while the penetration of solar power rises up to more than 20%.

To build the life cycle inventory, both primary and secondary data have been taken into account. For future mixes, the technological progress in the electricity conversion technologies was taken into account through conversion efficiencies and load factors, resulting from the scenarios described in the INECP [2]. Table 2 contains power plant efficiencies taken into consideration for current and future mixes.

Primary data include:


the values based on environmental declarations of the Italian thermoelectric plants registered to the Community eco-management and audit system—EMAS (Regulation 1221/2009) [23]. Average emission factors per unit of fuel in input (used in this study for current and future scenarios) are reported in Table 4.


**Table 2.** Power plant electrical efficiencies taken into consideration for current and future mixes.

**Table 3.** Wind and Photovoltaic load factors for current and future mixes.



**Table 4.** CO2 average emission factors per unit of fuel in input, in operation phase of power plants.


**Table 5.** Description of the sample of power plants used for the calculation of the average emissions.


<sup>1</sup> Sum electricity production of the sample/total electricity production.

**Table 6.** Calculated average emissions per unit of fuel in input, in operation phase of power plants.


Exceptions are coke oven gas and blast furnaces gas for which secondary (Ecoinvent 3.3 [16]) data have been used.

Secondary data from database Ecoinvent [16] have been used for power plants construction and dismantling, as well as for all background systems.

#### **3. Results**

The effects of policies in the INECP scenario are very evident compared to the baseline scenario. Both 2030 scenarios lead to an increase in renewables, but the strategic objectives of the plan in INECP scenario make the transition to renewables decidedly evident, bringing to zero the electricity production from coal and driving wind plus photovoltaic share in the mix to more than 30%.

In the following graph (Figure 1) the percentage contributions of the different power plants to the mix are highlighted and the electricity mix by the European Commission scenario PRIMES 2016 (2030 EU-REF IT) [24] is also added for comparison. The EU Reference Scenario is one of the European Commission's key analysis tools in the areas of energy, transport and climate action. It uses the PRIMES model for energy and CO2 projections [24].

**Figure 1.** Contribution of different power plants to current and future electricity mixes.

Table 7 shows the results of the impact assessment. For each category, the impact along the entire life cycle of the electricity mix is reported, referred to the functional unit, 1 kWh of GNC.


**Table 7.** Life Cycle Impact Assessment results per 1 kWh of electricity GNC.

For the sake of completeness, a comparison is also provided with the 2030 scenario developed by the European Commission for Italy (2030 EU-REF IT).

The INECP scenario is the one with the best environmental performance, resulting in the least impact for almost all categories. The only notable exception is the impact category "Mineral, fossil & ren resource depletion": the strong increase in photovoltaic (more than double in percentage compared to the baseline scenario) is the main reason for the greater impact and is essentially due to the metals present in the inverter and to the aluminium frame and the support structures of the modules. This impact could be significantly reduced in the future thanks to the diffusion of innovative photovoltaic solutions, as for example double-sided glass-glass modules. It should be reminded that in the present study only the evolution of power plant efficiencies and load factors is taken into consideration in the scenarios, and no hypothesis is formulated about changes in the background system, as for example global production market of main materials such as aluminium. This could be an interesting task for future insights, above all on wind and photovoltaic technologies, for which background processes have a higher impact than operation and maintenance phase.

Results (Figure 2) show a general decrease from 2016 to 2030 of the impacts of the Italian electricity mix. The most marked decrease is observed for Climate Change (−46% compared to 2016 in the INECP case) and Water Eutrophication (−51% compared to 2016 in the PNIEC case) impact categories. The decrease is driven by the transition to renewables (mainly wind and photovoltaic).

**Figure 2.** Comparison between Life cycle impact assessment of current and future mixes. Results in percentage respect to 2016.

The decrease of Ionizing Radiation impact is due to the lowest share of imported electricity, since for this impact category, nuclear energy (absent in the Italian mix but present in the European import mix) has the greatest effect.

For all the impact categories, BASE scenario results are similar to the results coming from EU-REF IT scenario.

Table 8 highlights the category of power plants that most contributes to the impact, for both scenarios at 2030.


**Table 8.** Main contributor to the impact for Base and INECP scenarios.

Each technology contribution is determined by two factors: the specific impact of the single source/production technology and the share of the single source/production technology in the electricity mix. Natural gas power plants contribution is in general due to the high share in the mixes.

More in detail, for both scenarios at 2030, BASE and INECP:


Figures 3 and 4 put in evidence, for each impact category, the relative contribution of each energy source.

**Figure 3.** Contribution of different typologies of power plant to the overall impact of the 2030 BASE scenario electricity mix for Italy.

**Figure 4.** Contribution of different typologies of power plant to the overall impact of the 2030 INECP scenario electricity mix for Italy.

More in detail, regarding climate change, annual CO2 eq emissions due to Italian electricity mix amount to 136–138 Mt/year in 2016–2017, 122 Mt/year in 2030 for the BASE scenario, and 76 Mt/year in 2030 for the INECP scenario.

The main driver of this trend is the decrease in the share of electricity produced from coal and, to a lesser extent, from oil and imported electricity. Finally the increase in the average efficiency of the generation mix also plays a role in reducing CO2 eq emissions.

The contribution of natural gas power plants to the CO2 eq emissions is strongly influenced by the fact that the share in the mix of electricity produced by natural gas power plant stands at high values (from 34% to 42%). On the other hand, coal plants with a much lower share in the mix, 9–12% (in the case of the INECP scenario it is equal to 0%), cover from 25% to 35% of the impact. In no case do wind, photovoltaic and hydropower contribute significantly to the CO2 eq emissions of the mix, despite of shares in the mix far from negligible (10–15% for hydroelectric, 5–11% for wind, 7–21% for photovoltaic) (Figure 5).

**Figure 5.** CO2 eq/kWh for current and future electricity mixes. The contribution of different typologies of power plant is highlighted.

## **4. Discussion and Conclusions**

According to ISO 14040, LCA results can be elaborated trough two optional steps: normalization and weighing. Both can help in interpretation of the results, but can add elements of subjectivity to the evaluation.

In this study, it was decided to leave out weighting, which should involve decision-makers in the process. Conversely, normalization was carried out, which allows to rank and compare the different impacts of a system and, although not devoid of limitation, is a great aid in looking at scenarios environmental profile as a whole.

Even though the limitations reported in [25] have to be considered, normalisation has a relevant role when LCA is aimed at supporting policy makers to ensure that the focus is put on most relevant aspects and for communication purposes.

In normalization JRC's recommendations [25] have been taken into consideration. In view of the international nature of energy supply chains, it was decided to use normalization factors on a global scale. The normalisation factors represent the total impact of a reference region for a certain impact category (e.g., Climate Change, Eutrophication, etc.) in a reference year.

The standardization factors used are those implemented in the Simapro v.8 software and referring to the JRC table version 0.1.1-15/12/2015 (available from: https://eplca.jrc.ec.europa.eu/LCDN/developerILCD. xhtml), developed as the first version in the context of the work described in [25].

For each impact indicator, the impact assessment result is divided by the global value (i.e., deriving from all human activities) of the same indicator, on a per capita basis. In order to apply the normalization, the Italian gross national electricity consumption per capita per year (electricity yearly GNC divided by the Italian population) was calculated for current and future scenarios. The following graphs show normalized LCA results (dimensionless).

In all scenarios (current, i.e., 2016 and 2017 results are very similar. In the graph only 2016 results are shown, 2030 BASE and 2030 INECP), most of the impact categories present similar values (lower than 0.10). It is notable that, also in the case of water eutrophication impact categories for which life cycle impact assessment showed the higher reduction from current to INECP scenario, the normalized results are lower than 0.10. On the other hand the Climate Change category and the Ionizing Radiation category present definitely higher values (0.18–0.33 and 0.45–0.66 respectively).

The impact on resource depletion, the only category which increase along the time horizon of the assessment, remains, as normalized value, under 0.20, also in the case of INECP scenario (Figures 6–8)

**Figure 6.** Normalized results for current Italian gross national (2016) electricity consumption per capita per year.

**Figure 7.** Normalized results for 2030 BASE Italian gross national electricity consumption per capita per year.

**Figure 8.** Normalized results for 2030 INECP Italian gross national electricity consumption per capita per year.

In conclusion, the present study confirms that LCA can be a powerful tool for supporting energy planning and strategies assessment. As a matter of fact, results put in evidence not only the improvement of the environmental profile from the current to the future mix, but also underline the difference between the baseline scenario and the INECP one, providing an evaluation of the effect of different energy policies. According to LCA results, the impacts of the Italian electricity mix decrease from 2016 to 2030 due to the transition to renewables, mainly wind and photovoltaic. Climate Change impact decreases by about 46% compared to 2016. Most important for policy implication, the scenario that includes the strategic objectives of the Integrated National Plan for Energy and Climate to 2030 is the one with the best environmental profile (INECP scenario CO2 eq/kWh are 37% lower than 2030 BASE scenario). Moreover, considering not only climate change but a set of different impact categories, allowed to identify potential environmental trade-off, thus obtaining a more complete environmental assessment of energy policies.

The only potential environmental trade-off (even if slight looking at normalized results), seems to occur between climate change and the impact category related to resources depletion. The impact on resource depletion is mainly associated with the metals present in the inverter and especially with the aluminium frame and structure of the photovoltaic modules. This finding can address subsequent studies and insights. First of all, a big effort is demanded for improving and updating the inventory data relating to the photovoltaic modules (in the present study, only secondary data were used for the construction and end-of-life phase). Particular attention must also be paid to the aspects relating to the recycling processes (especially for aluminum) also from a methodological point of view (allocation of impact on primary or secondary materials), since a significant reduction in resource depletion impact may depend on recycling. Photovoltaic only in relatively recent years deeply penetrates energy mixes and for this reason, data on recycling and on the secondary products market are not widely available. Sensitivity analysis on various recycling hypotheses could be useful [26].

Finally, in the context of improving inventory data, the technological evolution towards new photovoltaic solutions (for example heterojunction modules [27]) should also be taken into consideration especially in the case of longer-term scenarios, like those in 2050, a horizon identified by Italy for a deep decarbonisation of the energy sector. As mentioned, beside assessing the environmental effect of the energy policy, the study demonstrates that LCA is a powerful tool in supporting decision makers especially dealing with future national energy plan. Nevertheless, although well beyond the scope of the present study, it should be underlined that also economic and social impacts must be taken into account in policy and decision making in energy sector [6]. Sustainable development of energy systems, in fact, requires that all three pillars of, environmental, economic and social, are taken into consideration [28].

**Author Contributions:** Conceptualization, A.G. and P.G.; methodology, A.G.; validation, A.G. and P.G.; formal analysis, A.G. and M.L.C.; investigation, A.G.; data curation, A.G.; writing—original draft preparation, A.G.; writing—review and editing, A.G., P.G. and M.L.C.; visualization M.L.C.; supervision, P.G.; project administration, P.G.; All authors have read and agreed to the published version of the manuscript.

**Funding:** This work has been financed by the Research Fund for the Italian Electrical System in compliance with the Decree of Minister of Economical Development 16 April 2018.

**Acknowledgments:** This work has been financed by the Research Fund for the Italian Electrical System in compliance with the Decree of Minister of Economical Development 16 April 2018.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **Appendix A**


**Table A1.** Current (2016) Italian electricity mix.

**Table A1.** *Cont.*


