The Sustainable Development Goals (SDGs) are a universal call to action to end poverty, protect the environment and climate, and ensure that people everywhere can enjoy peace and prosperity [1]. For the countries to achieve the SDGs, different sectors should participate in this quest. Considering that the power sector is one of the engines of a country's development, it is also responsible for participating in this pursuit. Implementing the SDG philosophy in the energy sector is a significant challenge for achieving efficient and sustainable production systems [2]. Access to electricity is increasingly recognised as a critical enabler of economic growth and poverty reduction in developing countries, driving economic and social development by enhancing productivity and enabling new types of job-creating enterprises [3]. According to Mastoi et al. [4], renewable energy is currently argued as the most prominent solution to environmental pollution, the energy crisis, and social sustainability, being also key element to support sustainable development and a social contributor to people living in isolated communities [5].

While many studies have focused on the environmental and economic assessment of power generation [6]–[9], or power generation related [10], fewer articles address the social life-cycle performance of energy supply systems [11]–[14]. A clear research gap exists in considering social issues in such assessments. Despite deep decarbonisation being a critical pillar in the power sector for a carbon-neutral energy system, its socioeconomic benefits remain unexplored [15]. In this context, Social Life Cycle Assessment (S-LCA) emerges as a tool to evaluate the social aspects associated with the life cycle of goods and services and to identify the hotspots of social risks in the energy value chain [16]. Life Cycle Assessment (LCA) is the compilation and evaluation of inputs, outputs, and the potential environmental impacts of a product system throughout its life cycle [17]. Hence, S-LCA can be considered a methodology to assess the social impacts of products and services across their life cycle, employing the Environmental Life Cycle Assessment (E-LCA) combined with social sciences methods [18]. Additionally, S-LCA has linkages with international initiatives and can monitor progress in ten SDGs (especially SDGs 8 and 12) [2].

The utilisation of S-LCA as a social sustainability assessment tool is still being developed due to the complex nature of social impacts [19]. Currently, those impacts are understood as the positive or negative consequences of the causal relationship between an activity and an aspect relating to human well-being, as covered by impact subcategories [18]. These subcategories must, indirectly, be related to the stakeholders, i.e., individuals or group that has an interest in any decision or activity of an organisation [20], while the stakeholder category is a cluster of stakeholders having common interests due to their similar relationship to the investigated product system [18]. The main stakeholder categories considered in the S-LCA are Workers, Local communities, Value chain actors (e.g., suppliers), Consumers, Children, and Society. Because impact categories are broad themes, a life-cycle initiative project group created by UNEP/SETAC in 2004 has focused its initial effort on identifying and building consensus around subcategories that describe more precisely social areas of interest [21]. Social and socio-economic subcategories of impact have been defined according to international agreements and international best practices, presented in Table 1 and published in the Guidelines for Social Life Cycle Assessment of Products [22]. In Table 1, Fair Salary, a subcategory of the impact category Working Conditions, relates to SDGs 1 and 8 [18].

A Fair Wage is a topic that influences all stakeholder groups identified within the S-LCA guidelines. However, studies considering the wage issue are rare in the electricity sector in S-LCA scopes. Fortier et al. [23] discuss how social LCA can address energy justice for stakeholder categories across the life cycle of electrical energy systems and analyse whether wages are docked by companies for reasons beyond a worker's control, wage gaps between sex, gender, nationality, and race; and the percentage of workers earning a living wage based on their location. Traverso et al. [24] report the sustainability assessment of the assembly step of photovoltaic (PV) modules production by Life Cycle Sustainability Assessment (LCSA) and included indicators like the average wage of male and female workers and the minimum wage of a worker. Contreras et al. [25] assessed the impacts of the bagasse cogenerated bioelectricity using LCSA and encompassed, among the indicators, Lowest Paid Workers, compared to the country's Minimum Wage. Prasara-A et al. [26] identify the environmental, socioeconomic, and social hotspots of products within the Thai sugar industry (e.g., bagasse-based electricity) using LCA and S-LCA, including the indicators Range of Wage Received by Workers, and Percentage of Workers Satisfied with Wage.

Considering this background, a Fair Wage is a concept that goes beyond the notion of a minimum wage enabling needs satisfaction and including the fair remuneration of work according to its quality [27]. It has already been listed as one of the meaningful aspects to be considered in assessing labour rights and decent working conditions, being highly relevant for the future development of human beings and, consequently, of regions and countries, as the basis for prosperity and wealth [28].

A “fair” remuneration along the life cycle of a product can serve as one powerful measure to estimate related social impacts on involved workers. In this context, Neugebauer et al. [29] proposed Fair Wage as a new midpoint impact category and developed a characterisation model to convert inventory data on workers' remuneration along a product's life cycle into category indicator results, creating the Fair Wage Potential (FWP) indicator. FWP considers the actual wage paid at each process step, compared to a minimum living wage, and relates wage to the effective working time, including a factor to account for income inequalities.

The method proposed by Neugebauer et al. [29] is a distance-to-target impact pathway [18] and can be summarised according to eq. (1):

$FW{P}_n=\frac{R{W}_n}{ML{W}_n}\times \frac{CW{T}_n}{RW{T}_n}\times \left(1-IE{F}_n^2\right)$ (1)

where FWP_n is the Fair Wage Potential (expressed in FW_eq) representing the n^th process within a product's life cycle at a defined location or sector; RW_n is the Real (average) Wages (€/month calculated over one year), which are paid to the worker(s) employed in the n^th process; MLW_n is the Minimum Living Wage (€/month), which has to be paid to the worker to enable an adequate living standard for an individual and/or family in the respective country or region/sector where the n^th process is performed; CWT_n is the Contracted Working Time per country or sector (hours/week) for workers performing the n^th process (including vacation days); RWT_n is the Real Working Time (hours/week) of workers performing the n^th process (including vacation days and unpaid overtime); IEF_n is the (squared) Inequality Factor (expressed in percentage) of the organisation region, country or sector, where the n^th process is performed. For RW_n and MLW_n, the national currencies are used in eq. (1). FWP depends on mainly three country/region-specific and/or product-specific parameters: 1) living wages, 2) working time, and 3) income (in-)equality [18].

If the RW_n value is smaller than the MLW_n value, the resulting FWP_n will be < 1; hence the greater the distance from the (minimum) targeted state, the lower the FWP_n value is. Also, if the Real Working Time is equal to the CWT value, then no effect on the FWP_n occurs. On the other hand, if the RWT value is greater than the CWT value (which indicates overtime work), the resulting FWP_n will also be < 1 (smaller FWP_n values indicate more overtime the worker does). A FWP_n equal to 1 (one) is the reference value for determining a Fair Wage; values >1 mean the salary is fair. An accumulation of FWP values <1 may indicate regular annual underpayment [29]. Thus, a determined distance from Fair Wage is a category indicator for the impact category Fair Wage. Excessive working hours may additionally contribute to cases of underpayment through time lost to replace the lack of income.

The methodology by Neugebauer et al. [29] allows for consistently determining Fair Wage impacts along a product's life cycle. However, the characterisation model does not foresee a direct relation to the functional unit. Vitorio Junior & Kripka [30] propose a weighted Fair Wage Potential method to assess building typology and relate material inventory to the social data of the construction sector. However, a knowledge gap remains in the methodology to assess the energy sector, linking the FWP to electricity production.

The present work aims at fulfilling this gap by proposing an Employment-Weighted Fair Wage Potential (E-WFWP) indicator based on the characterisation model presented by Neugebauer et al. [29]. It differs from the existing social assessment methods by relating the electricity production alternatives to social data, allowing the consideration of social aspects in selecting the best choice among a set of analysed options. Additionally, the study performs an S-LCA of ten power generation technologies, considering their E-WFWP to identify the wage situation of workers involved in the electricity system, searching for social hotspots (well-being threats). It proposes and applies a decision-support indicator based on Fair Wages, in alignment with SDGs 1 and 8, underpinned by a life cycle approach.

This section presents the premises for selecting electricity technologies, the S-LCA parameters, and the data-gathering procedure. The methodology used is described as follows.

Social life cycle assessment

S-LCA presents a systematic assessment process like that of the E-LCA. This subsection presents the definition of objective and scope, life cycle inventory, and premises.

Definition of objective and scope.

The present S-LCA aims to assess the potential of alternative power generation technology to offer a Fair Wage to the workers' category along the life cycle of electricity generation. The Functional Unit (FU) is 1 TWh of produced electricity. The scope of the power plant analysis is cradle-to-grave, and encompasses the stages presented by Rutovitz et al. [31], i.e., the power station construction and installation, manufacturing of parts, operation & maintenance (O&M), decommissioning, and fuel extraction and processing.

A product system is a collection of unit processes with elementary and product flows, performing one or more defined functions, and which models the life cycle of a product [32]. Figure 1 presents the product system of the study, as well as its system boundary. It can be observed that the extraction of primary resources and waste treatment and disposal are outside the scope of this analysis.

Figure 1.

Product system and system boundary of the study

Life-cycle inventory data.

Primary and secondary data are gathered for the Life Cycle Inventory (LCI) phase. Table 2 displays the technical assumptions for each power technologies alternative. The installed capacity values presented in Table 2 are the theoretical necessary capacities, considering the efficiencies also presented in Table 2, to meet the production of 1 TWh/year (the functional unit). The data of the world installed capacity for each power technology is based on the world breakdown of the technology’s installed capacity in 2020 [33]– [35].

Table 2.

Summary of life cycle inventory data and assumptions

Power options	Power plant assumptions
	Lifetime	Efficiency	Installed capacity	Breakdown of the world installed capacity
Solar PV	30 years [36]	25% [37]	456.6 MW	China - 36.0%, USA - 10.7%, Japan - 9.5%, Germany - 7.6%, Italy - 3.1%, Australia - 2.5%, South Korea - 2.1%, Spain - 2.0%, RoW ¹ - 26.7%
Hydro (Reservoirs)	150 years [36][12]	78% [36]	146.4 MW	Brazil - 9.5%, USA - 7.3%, Canada - 7.0%, Russia - 4.4%, India - 4.0%, Norway - 2.9%, Turkey - 2.7%, Japan - 2.4%, France - 2.1%, RoW - 57.8%
Hydro (R-o-R)	80 years [36][12]	82% [36]	139.2 MW	USA - 7.3%, Canada - 7.0%, Russia - 4.4%, India - 4.0%, Turkey - 2.7%, Japan - 2.4%, France - 2.1%, RoW - 70.1%
Onshore wind	20 years [36]²	20% [36]	570.8 MW	China - 46.4%, USA - 20.0%, Germany - 9.3%, India - 6.6%, RoW - 17.8%
Offshore wind	20 years [36][13]	30% [13]	380.5 MW	UK - 30.2%, China - 26.2%, Germany - 22.5%, Netherlands - 7.3%, RoW - 13.8%
Oil	30 years [38]	40%[13]	285.4 MW	China - 28.2%, USA - 16.2%, India - 7.4%, Japan - 4.9%, Russia - 4.2%, RoW - 39.0%
Gas	30 years [39], [40]	38% [36]	300.4 MW	China - 28.2%, USA - 16.2%, India - 7.4%, Japan - 4.9%, Russia - 4.2%, RoW - 39.0%
Coal	30 years [36], [39]	36.5% [39]	312.8 MW	China - 50%, USA - 13%, India - 11%, RoW - 25%
Nuclear	40 years [36]	80.4% [36]	142.0 MW	USA - 25.0%, France - 16.1%, China - 11.6%, Japan - 8.1%, RoW - 39.2%
Biogas	25 years [41]	33% [41]	345.9 MW	Germany - 37%, USA - 11.4%, UK - 9.2%, Italy - 7.1%, Turkey - 3.7%, RoW - 31.6%

1

RoW – Rest of the World;

2

lifetimes of the moving parts

This section presents the results of each step of the assessment process.

Employment

Using the Employment Factors (EF_i) for the selected power technologies presented in Table 3, the data from Table 2, the functional unit, and applying eq. (2), the employment results are depicted in Figure 2. The complete data is presented in Table A4 in the Appendix. The results suggest biomass provides the highest employment, equivalent to 1118 jobs-years/TWh. The second-best option is run-of-river with 734 jobs-years/TWh, followed by solar PV at 659 jobs-years/TWh. For this indicator, the reservoir provides the lowest life-cycle employment (41 jobs-years/TWh), possibly due to its relatively high efficiency (see Table 2) and lower labour requirements per unit of electrical output. With the results presented in Figure 2, it is possible to estimate the percentage of work positions for different electricity technologies in each life cycle stage (Table 6).

Figure 2.

Employment provided by different electricity options.

Table 6.

Percentage of work positions in each life cycle stage for different electricity technologies

Power technology	Percentage of work positions
	Construction & installation	Manufacturing	O&M	Fuel extraction & processing	Decommissioning
Solar PV	30.0%	15.5%	48.5%	NA	6.0%
Hydro (Reservoir)	17.5%	8.3%	70.8%	NA	3.5%
Hydro (R-o-R)	3.7%	2.6%	92.9%	NA	0.7%
Onshore wind	22.0%	32.3%	41.3%	NA	4.4%
Offshore wind	27.4%	53.4%	13.7%	NA	5.5%
Oil	8.8%	6.3%	28.3%	54.9%	1.8%
Gas	8.8%	6.3%	28.3%	54.9%	1.8%
Coal	18.4%	8.9%	6.9%	62.2%	3.7%
Nuclear	29.7%	3.3%	60.4%	0.7%	5.9%
Biomass-biogas	17.3%	3.6%	46.4%	29.2%	3.5%

As outlined in Table 6, the O&M stage presents the highest percentage of work positions for run-of-river (92.9%), reservoir (70.8%), nuclear (60.4%), solar PV (48.5%), biomass-biogas (46.4%), and onshore wind (41.3%), showing the importance of this life cycle stage on the employment for the analysed technologies. Manufacturing emerges as a significant employment stage for offshore wind (53.4%), while the fuel extraction and processing stage presents the highest percentage of work positions for coal (62.2%), oil and gas (both with 54.9%).

Employment-Weighted Fair Wage Potential

The E-WFWP results for each option were estimated using Table 4, Table 5, Table 6, and eq. (3) and are displayed in Figure 3. The results indicate that run-of-river has the fairest wage potential option (3.33). Reservoir is ranked second best (2.80), followed by nuclear (2.56) and gas (2.17), the latter followed closely by oil (2.16). Solar PV technology presents the lowest E-WFWP value (1.16) but is still above the considered fair wage line. The relatively low value found for solar PV can be explained by the significant contribution of China's work positions on the weighting process (59%), with C&D, manufacturing, and O&M FWPs of 0.60, 0.68 and 0.92, respectively. Hydropower technologies also presented a low C&D FWP (0.99) due to China's C&D FWP (0.60) and Brazil's C&D FWP (0.76). However, the E-WFWPs were the highest, mainly because of the high FWP values of the O&M stage (3.49) and high work position rates: 92.9% for run-of-river and 70.8% for reservoir (see Table 6).

Figure 3.

Employment-Weighted Fair Wage Potential of the different electricity options

The results suggest, within the assumed premises, that the run-of-river option provides a higher social benefit concerning fair wages in the electricity generation sector. Considering the life cycle stages analysed, run-of-river could generate more positive social impacts than the other power technologies because, besides being the second most employing option (see Figure 2), the workers of the involved sectors, especially the O&M stage, have high incomes. The wage level of an individual or a family directly relates to the living situation and nutritional status, which can be linked with life expectancy, and thus to human health and social well-being [30]. In the company scope, according to the United Nations [60], beyond fulfilling a duty of care, ensuring the payment of decent wages to workers can be translated into an investment in human capital, bringing returns, such as a reduction of absenteeism and an increase of retention and motivation.

The results presented in the S-LCA, considering the impact subcategory within the stakeholder category under concern, support the selection of the electricity options with the best potential social impacts, promoting the sustainability of the power sector. Through E-WFWP, decision-makers can choose among electricity generation options considering the potential upgrades to the worker's category. The results indicate the potential use of the indicator E-WFWP as a useful tool for assessing the social benefits of power technologies besides being potentially applicable to other industries. It is worth mentioning that the reference wage of the sector/region/country significantly influences the results of a fair or unfair wage.

This paper originally proposes an employment-weighted fair wage assessment that aims to identify and implement socially sustainable electricity generation options. Recognizing the close relationship between fair salaries and SDGs 1 and 8, the study focuses on the power sector and adopts a life cycle approach to evaluate the Fair Wage Potential of ten power technologies.

At a life cycle stage level, the findings for each stage are the following:

• C&D and manufacturing: Spain presents the highest FWP values (3.03 and 3.89, respectively), and China presents the lowest (0.60 and 0.68, respectively).

• Fuel extraction and processing:

• Agriculture: Italy has the highest FWP value for agriculture (1.78), while Germany has the lowest (0.79).

• Mining: India presents the greatest value (3.47), and China the lowest (1.02).

The study's outcomes on employment assessment reveal that biomass-biogas is the option with the highest employment potential, presenting 1118 jobs-years/TWh. R-o-R ranks second with 734 jobs-years/TWh, and solar PV follows closely with 659 jobs-years/TWh. On the other hand, reservoir-based power generation is identified as the least favorable option in terms of employment, with only 41 jobs-years/TWh.

Based on the results of this work, hydro options emerge as the fairest wage potential options presenting values around three times greater than the target (3.33 for R-o-R, and 2.80 for reservoir), followed by nuclear (2.56). Solar PV technology presents the lowest E-WFWP value (1.16) but is still above the fair wage line.

According to the findings of this study, it is possible to realize that the method described in this paper incorporates the social dimension into the assessment of power options' sustainability and can also be adapted to different industries and countries, which has particular significance from a community standpoint. By considering an additional social aspect when implementing new power plants, this approach can enhance power sector policies. The E-WFWP sets itself apart from existing social sustainability indicators by linking the electricity generated by power options to social data, allowing decision-makers to move beyond technical and environmental issues.

However, there are certain limitations to the method. One drawback is the challenge of obtaining sector-specific primary data such as working hours and real wages, especially if the companies being analysed do not publish annual reports. Additionally, Minimum Living Wage and Real Wage values used in the assessment need to be updated annually. Finally, it is important to note that this study did not consider unjustifiably high wages, such as managerial salaries, which warrants further discussion. Future research should expand the methodology to other stages of the power sector, such as transmission and distribution systems, as well as explore its applicability to other industries. Another issue that should be addressed is the capacity of powerful companies (ex: from oil and gas industry) to intentionally increase their employees' wages to impact the public perception as more socially sustainable than competitive low workforce power technologies.