Assessing the Life Cycle Sustainability of Solar Energy Production Systems: A Toolkit Review in the Context of Ensuring Environmental Performance Improvements

Abstract

In order to pursue clean, low-carbon, safe, and efficient energy utilization and accelerate the development of new energy, sustainability is the necessary research. In recent decades, solar power generation has rapidly formed and been widely applied. Sustainability analysis is a key aspect that directly affects the construction of solar power projects when conducting solar power generation construction. This paper attempts to start with life cycle sustainability assessment (LCSA) and study the status quo of its three pillars (These three pillars include life cycle assessment, life cycle cost assessment, and social life cycle assessment) in the field of solar power generation. At the same time, the externality indicators are considered between pillars. In addition, the application of uncertainty analysis was studied during the analysis process to address the impact of various random factors. The conclusion shows that further research is needed to support this transition by integrating life cycle assessment, life cycle cost, and social life cycle assessment into LCSA for the evaluation. To improve the effectiveness of future research, studies should focus on fulfilling major data gaps in the literature such as the lack of detailed documentation for specific materials and background process choices in life cycle assessment databases. The development of solar power generation can be an important alternative in efforts to decrease climate change impacts and pursue cleaner energy sources in countries where solar energy is more easily available by integrating life cycle assessment (LCA), life cycle cost (LCC), and social life cycle assessment (SLCA) into LCSA. The sustainable development of the solar power generation industry in terms of multiple aspects is supported.

1. Introduction

Over the past decades, energy consumption has increased significantly due to rapid economic growth. Traditional energy sources have higher greenhouse gas emissions throughout the life cycle than renewable energy sources, except for nuclear power generation [1]. In addition, human activities such as travel, agricultural activities, and energy consumption will increase pollution emissions through vehicle use and biomass combustion [2]. The combustion of fossil fuels produces more CO₂, CO, NO_x and hydrocarbon emissions. The CO₂ will result in the greenhouse effect, and the CO, NO_x and hydrocarbon emissions will jeopardize people’s health. On current trends, the total world energy consumption will rise from 575 trillion British thermal units (Btu) in 2015 to 736 trillion Btu in 2040, representing an approximately 28% increase [3]. Thus, it is very important for the development of renewable energy and the reduction in carbon emissions [4]. The search for new alternative energy becomes the key to the energy strategy. However, solar energy is considered a renewable and clean energy source in the 21st century due to the environmental friendliness and sustainability. The development of solar power generation can be an important alternative in efforts to decrease climate change impacts and pursue cleaner energy sources in countries.

The growth of renewable energy in the electricity market is a trend, both now and in the future. For example, in 2017, the new installed capacity of renewable energy projects in the power sector is more than that of conventional energy systems [5]. Currently, the total annual solar radiation that the Earth receives is approximately 7500 times more than the world’s total annual consumption of primary energy [6]. All the energy received from the sun in a day could theoretically meet the world’s energy needs for over 20 years [7]. According to the “World Energy Outlook 2022” report [8], Figure 1 shows the solar supply and solar PV generation in 2021. The solar energy supply is expected to grow rapidly in the next 10 years, reaching an estimated 124EJ by 2050. It can be seen that China is leading the world in terms of solar PV generation. For example, China is 326 TWh, the United States is 145 TWh, Brazil is 14 TWh, the European Union is 151 TWh, Africa is 15 TWh, the Middle East is 12 TWh, and Russia is 2 TWh. It is expected that the global solar photovoltaic (PV) generation will experience significant growth, reaching 12,118 TWh by 2050.

Solar energy can be harvested as thermal or electrical energy, and thermal energy harvesting is easier than photovoltaic conversion. Solar power can be generated directly by using PV or indirectly by using concentrated solar power (CSP) technology [9]. The current solar power system structure is shown in Figure 2. The CSP is a collector-type solar power generation system. By using optical principles, a reflector or lens is used to concentrate a large area of sunlight onto a relatively small collector area. When the sunlight in the collector area on the generator is collected, the temperature rises. The CSP is combined with thermal energy storage (TES) to become a renewable energy source. Energy storage is a crucial element of solar thermal power systems [10]. The four currently used types of CSP technologies are as follows: parabolic troughs, power towers, dish/engine systems, and Linear Fresnel reflectors [11]. The development problem of CSP is the need to find heat transfer fluids with a high heat capacity and reduce heat loss [12].

Another type is PV power generation. The working principle of PVs is that a voltage difference will be generated between the two electrodes and generate electricity as the light shines onto a solid or liquid power generation system. PV panels should be installed in areas with no specific constraints but with suitable irradiation. In the process of solar energy conversion, photovoltaic cells can convert sunlight into electrical energy [13]. As can be seen in Figure 2, the components of PV mainly include solar panels, a solar controller, storage batteries, and an inverter. The efficiency of converting solar energy into electricity through photovoltaic cells is between 7% and 40% for the semiconductor material [14,15,16]. Thus, the technological development of photovoltaic cells is very important. This is due to the fact that material and conversion efficiency are directly related [15]. Many scholars are looking for the most efficient and economical materials for PV cells in this area [17,18]. PV power generation has become more of a small-scale, low-cost power generation option.

The solar power generation systems can convert solar energy into usable energy, and there are also many energy consumption and pollutant emissions during the construction of solar systems. Therefore, when solar power generation systems are developed, it is necessary to pay attention to energy consumption and pollution emission. In order to evaluate the full life cycle of solar power generation systems, many scholars have used the life cycle sustainability assessment (LCSA) method. For example, Yu and Halog [19] evaluated the use of LCSA in solar photovoltaics in Australia. The conclusion is also presented in terms of environmental, economic, and social aspects, and the manufacturers and governments should make more efforts to achieve sustainable development. Similarly, Corona and San Miguel [20] summarized its environmental impact, economic loss, and social impact in terms of three aspects and evaluated HYSOL technology (an innovative configuration delivering improved efficiency and power dispatch). The HYSOL power plant can enhance the sustainability of CSP technology and the Spanish electricity sector. In addition, Traverso et al. [21] applied LCSA for the evaluation of PV modules. In the research, all data were concentrated in the sustainable development dashboard. Finally, the scores were considered as a tool for supporting decision-making processes, and sustainable production and consumption were summarized. From this, it can be seen that LCSA has made a great contribution to the sustainable development decision making of solar power generation systems and the determination of the direction that needs to be worked on.

As shown in Figure 3, LCSA is a “cradle-to-grave” assessment of a program, including a range of processes from production to recycling. There are two main forms of LCSA. The first is the LCSA model proposed by Kloepffer et al. [22], which is composed of LCA + LCC + S-LCA. As shown in Figure 4, these three parts are referred to as the three pillars of LCSA. In this model, LCA is an environmental life cycle assessment in accordance with ISO; LCC is an environmental life cycle cost of the LCA type; S-LCA is a social life cycle assessment. Second, LCSA is an evaluation model. The scope of LCSA analysis is extended from product-related issues to broader issues [23]. Similarly, the scope is expanded from product-related issues (product level) to issues related to sectors (sectoral level) or even the whole economy (economic level) [24].

According to the LCSA model, life cycle assessment (LCA) is an environmental assessment of a product’s life cycle. In the 1980s and 1990s, the idea of LCA emerged and was employed to evaluate the life cycle of inputs, outputs, and potential environmental impacts [25,26]. Currently, LCA is internationally standardized by ISO 14040 [27], which involves four different phases: (a) definition of objectives and scope, (b) inventory analysis, (c) life cycle impact assessment, and (d) interpretation of relevant results and impacts. LCA has many applications in multiple industries [28,29]. As part of the LCSA, the LCA can be a good description of the environment or other issues.

The LCC is an economic evaluation method for predicting the life cycle cost of a project from “cradle to grave” [30]. It can estimate all relevant cost relationships using its relevant units’ present values. The life cycle cost assessment (LCC) methodology was first used for the acquisition of high-cost military equipment by the USA Department of Defense in the 1960s [31,32]. Behind the sharp development of the economy, LCC is widely used to estimate and control products and services [33]. In 1996, O’Brien and colleagues began researching the integration of social life cycle assessment (S-LCA) and the environment to investigate the impact of social and political factors on the environment [34]. In addition, the S-LCA is a methodology and is used to assess the social and social-economic aspects of a product and its potential positive and negative impacts. Then, the S-LCA methods were proposed and gradually developed [35]. In the current study, they are used in many ways [36,37] and play an important role in society.

Related researchers have conducted a series of literature reviews on solar power generation, including LCA from environmental analysis [38], LCC from economic effects [39], and S-LCA from social impacts [40]. These reviews provided effective references for environmental impact, economic analysis, and corresponding social effects throughout the full life cycle, including solar energy. However, the existing reviews mainly focused on certain aspects of sustainable development throughout the full life cycle, making it difficult to provide an overall framework for the analysis process of sustainable development throughout the full life cycle. Therefore, it is necessary to sort out the analysis process of sustainable development towards the life cycle. And a coherent and comprehensive life cycle assessment method is needed to meet the development of future solar power generation systems. To meet this requirement, it is necessary to integrate LCA, LCC, and S-LCA into the framework of life cycle sustainability assessment (LCSA) for the sustainable development of the solar power generation industry.

This article mainly studies the application of the three pillars for LCSA in the field of solar energy. The structure of this article is shown in Figure 5. The first section reviews the relevant literature of solar energy. The second section reviews the application of LCSA’s three pillars (LCA, LCC, and S-LCA) in two different forms of solar power generation (PV and CSP). The application issues of the three pillars were discussed in terms of the measurement of pollutant emissions by LCA in the solar energy field, the consideration of economic feasibility by LCC, and the study of local social issues by S-LCA. The third section introduces the research results of low-carbon emission reduction. In addition, the fourth section also analyzes and discusses the interrelationships between the three pillars. Section 5 is the uncertainty analysis. Section 6 is a key overview of the challenges and opportunities faced by the application of LCSA in this field, and some suggestions were put forward for future research directions. Finally, some very useful conclusions were obtained through analysis.

2. Use of LCSA for Solar Power

2.1. LCA Assessment and Environmental Factor Analysis

The LCA is primarily used for the assessment of environmental issues. From the environmental perspective, energy consumption, pollution gas emissions, and other issues are considered very important in the life cycle process for concentrated solar power (CSP) plants [41]. The emission mainly includes production, operation, and disposal processes. Each stage has the potential to produce emissions of pollutants. With the increase in photovoltaic power generation, pollutant emissions and energy consumption increase. LCA’s natural resource impact types include resources (such as minerals and energy), climate change (such as Global Warming Potential), the atmospheric environment (such as acidification and ozone layer depletion), water and soil toxicity (such as fresh water and human health), and other indicators.

From

Table 1. Summary of the research results on solar power in the literature.

Research Object	Location	Evaluation Indicators	Findings	Additional Discoveries/Comments
Solar heliostat; Telsnig et al. [42]	California	GWP (Global Warming Potential)	Conventional solar fields are the main contributors to GWP.	For power generation systems, compared to traditional heliostats, the impact of climate change is reduced by about 10%. The concept of a heliostat with independent renewable energy supply leads to a significant reduction in pollution emissions.
Single-crystalline Si solar cells; Mahmud et al. [43]	/	Multiple impact categories such as global warming potential, human toxicity, ozone depletion, GHG, etc.	Solar panels, batteries, and heat storage systems have a significant impact on the environment.	Solar photovoltaic frames have better environmental performance than solar thermal frames.
Monocrystalline and polycrystalline photovoltaic; Ali et al. [44]	New York State	GWP	Site location, capacity factor and system design (monocrystalline and polycrystalline panels, area power ratio) cause GWP differences.	Engaging in scrap management has a positive impact.
Photovoltaic power generation module; Miller et al. [45]	The United States, India, Australia, Europe, China, etc.	Carbon intensity (gCO2e/kWh)	The reversible temperature effect of the module increases the carbon intensity of silicon photovoltaic power generation installed in warm regions.	Mainstream forms of photovoltaic power generation have significantly lower lifetime greenhouse gas emissions than fossil fuels.
2.7 kWp distributed solar photovoltaic system; Kannan et al. [46]	Singapore	EPBT, GHG	The greenhouse gas emissions generated by solar photovoltaic systems are lower than those of oil-fired steam turbine power plants and gas combined cycle power plants.	There is uncertainty in the environment of solar photovoltaic systems.
Molten salt concentrating solar power plants, oil power plants, and gas power plants; Piemonte et al. [47]	Italy	Multiple impact categories such as global warming potential, human toxicity, ozone depletion, etc.	CSP power plants are more desirable compared to traditional (oil and gas) power plants.	Further development is needed, mainly to improve the conversion efficiency from thermal energy to electrical energy.
Coupling System of Solar Photovoltaic and Wind Power Generation with Batteries; Ian Miller [48]	The United States, Egypt, Australia, Colombia, China, Germany, etc.	GHG	The carbon intensity of photovoltaic and wind power generation is still far lower than that of fossil power generation.	The approach to minimizing greenhouse gas emissions from coupled systems depends on the low-emission power generation given by the generator.
Molten Salt and Synthetic Oil in Trough-Focused Solar Energy; Batuecas et al. [49]	/	Multiple impact categories such as global warming potential, human toxicity, ozone depletion, etc.	Synthetic oil has a greater impact than molten salt.	Choosing a salt system is beneficial for environmental protection.
First- and second-generation photovoltaic cells (crystalline silicon/amorphous silicon); Soares et al. [50]	/	Emissions such as CO2 and nitrogen oxides	Compared to photovoltaic systems obtained from crystalline silicon, PV systems prepared from amorphous silicon exhibit a lower environmental impact and shorter energy recovery periods.	The development of third-generation photovoltaic cells with a high conversion efficiency and low environmental impact has received attention.
Distributed Concentrated Solar Combined Heat and Power (DCS-CHP) System; Norwood and Kammen [51]	America	GWP	Predicted global warming potential in Auckland City, California; any cogeneration system will reduce the demand for cooling while improving the overall solar efficiency of the system.	The prospect of distributed concentrated solar cogeneration coupled with seawater desalination is not optimistic.

, it can be seen that the research in the field of solar energy has mainly focused on the overall evaluation of power generation projects and the evaluation of certain materials. Many studies were concentrated in the United States region. Solar power generation is more developed in these regions. From the perspective of the main evaluation indicators, a portion of them adopt GHG and GWP as two indicators. Greenhouse gases can strongly absorb longwave radiation from the ground, preventing it from scattering into the universe, and have a strong greenhouse effect, resulting in serious environmental impacts. Therefore, these two indicators can effectively evaluate environmental impacts. In fact, current research has found that surface solar power projects can make significant contributions to the environment, and the materials used in the projects can also reduce the environmental issues of the projects themselves through new technologies.

2.1.1. LCA Analysis in Photovoltaics

For photovoltaic modules, many scholars have used LCA to study the energy consumed in the manufacturing process [52,53], but environmental analysis is highly valued in modern society. In photovoltaic solar power plants, photovoltaic modules or photovoltaic systems are the focus of research due to 78–80% of the carbon emissions from PV plant production [54].

Numerous studies have been conducted to evaluate the life cycle environmental impacts of photovoltaic power generation. System boundary conditions and functional metric units need to be defined in these studies. Most LCA studies on PV power systems define the system boundary conditions, including activities such as raw material extraction, manufacturing, operation, and end-of-life, and will also have a transport phase included [55,56,57,58,59]. However, some studies excluded the consideration of the final recovery phase, limiting its system boundaries to raw material extraction, the manufacturing process, transportation, installation, operation, and maintenance [60]. Due to differences in system boundaries, there are also differences in the final emissions.

A significant focus of LCA is life cycle inventory. For inventory analysis, different scholarly studies set different inventories. Kannan et al. [46] used the material inventory, which included solar PV modules, inverters, and aluminum and concrete. These areas include the main elements of the list. In addition, Miller, I. et al. [48] focused on PV solar power, integrating the following elements: PV life cycle inventories (LCIs); emission factors from the Ecoinvent database; known physical correlations; and capacity factors from the software tool PV Watts Version 5. The functional unit is one kilowatt–hour of AC electricity supplied to the grid. The same is true in another study by other authors [45]. Boundaries include the production of chemicals used in the processing, construction, and operation of infrastructure; feedstock crossing system boundaries into power production systems; and transportation in multiple prospective stages. The primary source of LCI for PV systems in the study is the IEA 2015 report “Life Cycle Inventories and Life Cycle Assessments of PV Systems” [61] and the 2012 Report “Life Cycle Inventories of Photovoltaics” [62]. Due to the current detailed list that can be referenced, the settings in the list are usually only slightly adjusted based on the research object.

2.1.2. Concentrating Solar Power in Different Data Source and Regions

During the analysis process, the data source and regions are very important and directly affect the accuracy of the lifecycle analysis. Several studies have assessed the life cycle environmental impacts of CSP power generation systems [63,64,65,66,67]. Most CSP studies defined LCA boundary conditions as including activities such as manufacturing (extraction of raw materials, transportation to manufacturing, forming of final products), construction (transportation of components to the site, performing assembly), operation and maintenance (cleaning, repair, maintenance), disassembly, and disposal (transportation of disassembled waste to landfills). From these studies, it can be seen that the LCA studies of CSP are relatively uniform in their definitions of boundary systems.

Backes, et al. [68] studied boundaries including the production, transportation, assembly, and use of the concentrator towers. Especially, the LCI is based on the raw data and secondary data from the GaBi and Ecoinvent databases. LCA was used to investigate the tower solar power plants in various storage capacities by Gasa [69]. The specific data were obtained from the Ecoinvent database. Among all the methods included in the database, the study used Re Ci Pe 2016 and IPCC 2013. In the study, all components of the CSP power plant, including the construction phase, operation phase, and dismantling phase in the framework, were considered. The framework includes the acquisition and installation of raw materials, the operational phase, and the dismantling and recycling phases, but the transportation phase is not described in detail. Piemonte [70] focused on tank solar systems with molten salt heat carriers (operating temperature up to 550 °C) and double-tank molten salt storage (hot and cold storage). The system boundary includes the use phase, where energy production and electrical energy are also considered.

The LCA study of CSP includes CSP power systems and greenhouse gas emissions (in gCO₂e/kWh). Since the study of CSP applications mainly involves different geographical locations, the geographical location where the CSP system is installed will also be considered. Blanca Corona et al. [71] studied the performance of CSP power plants in different geographical locations (Spain, Chile, the Kingdom of Saudi Arabia, Mexico, and South Africa). The results showed that the geographic location had a significant impact on the environmental profile of plants. In terms of causes, there are some differences in the availability of local solar and water resources and in the composition of the national electricity mix.

In summary, from the perspective of life cycle assessment, PV is mainly different from the actual use of system boundaries and data sources. It is due to different modules and materials. For CSP, there are only geographical differences due to similar construction processes. Thus, the system boundaries are roughly the same, and the data sources are also very similar. This makes them somewhat similar in the evaluation process. In terms of indicators, GWP and GHG are still commonly used.

2.2. LCC Analysis Method of Solar Power

The life cycle cost (LCC) is used to assess all costs during the life cycle. For LCC, design costs for the feasibility and improvement items are not considered. Currently, the full life cycle cost includes the following components: C_inv (initial investment cost), C_o&m (operation and maintenance cost), and C_dor (disposal or recovery cost) [72]. Equation (1) was developed for life cycle costs based on the interpretation of the three cost categories:

$$LCC=C_{inv}+C_{o\&m}+C_{dor}$$

(1)

Initial investment costs include all costs for initial inputs such as materials and construction cycles for the overall project system. Operation and maintenance costs include maintenance procedures and replacement parts (transportation costs for replacement parts or transportation costs for materials required for the repair process). In addition, the disposal or recovery costs include the decomposition cost of the project and the transportation cost after decomposition. The decomposed material is transported to a landfill for disposal.

The actors in the product life cycle have a direct relationship with the costs associated with the product life cycle [73]. Therefore, LCC involves cost aspects; all discussions of cost inevitably include discussions of inputs and outputs, discount rates, benefits, etc. As can be seen in Figure 6, the LCC analysis mainly focuses on the use of economic indicators for cost analysis based on the combination of environmental data.

The purpose of economic analysis is to study the costs and benefits of a project in order to determine its economic feasibility. Based on the diversity of solar power generation projects, some indicators have been widely used in economic analysis, such as NPV [74], PBP [75], IRR [76], PI [77], ROI [78], etc. The comparison of these indicators is shown in

Table 2. Comparison of economic indicators.

Evaluation Indicators	Advantages	Disadvantages
NPV (Net Present Value)	Considering the time value of cash flow; reflecting the degree of investment risk.	NPV is sensitive to discount rates, and different discount rates may lead to different NPV values.
PBP (Payback Period)	Predicting short-term cash flows is relatively easy	Failure to consider the time value of funds and cash flows occurring after the payback period.
IRR (Internal Rate of Return)	Considers all cash flows of the project.	There is an issue with multiple IRRs within the project.
PI (Profitability Index)	Considering all cash flows, cash flows can be reinvested at the lowest rate of return.	Only indicates the relative profitability of the project.
ROI (Return on Investment)	Clearly reflects comprehensive profitability.	Lack of overall concept and consideration of the time value of funds.

. Although there are some differences in the analysis of these indicators in the solar energy economic evaluation, the indicators used are different due to differences in the construction time, operation time, and location of different projects. Nevertheless, these indicators can provide sufficient support for the main feasibility analysis.

2.2.1. Analysis of Multiple Economic Indicators for Photovoltaic

Much research on LCC for photovoltaic power generation already existed and was spread over various aspects. The scale of projects in PV project plans is usually large, so LCC can help makers to make appropriate decisions when approving such projects [79]. Because of the high initial investment in such large-scale projects, the impact on the initial investment should be studied [80]. There are many factors that affect cost. However, solar radiation and environmental temperature have a significant impact on the LCC of PV systems [81]. Meanwhile, the feasibility of LCC in assessing economic sustainability had been questioned by different experts [82,83], who argued that value-added, labor and capital productivity, etc. may more accurately describe the socio-economy.

In order to evaluate the economic performance of PV projects, the time to recover the investment in photovoltaic projects is considered a major economic indicator. Li and Liu [84] proposed an improved life cycle cost method coupled with the pixel method and used the cost payback periods contour to investigate the optimal range for constructing potential PV projects with a desired payback period over the full lifetime. This study is beneficial for designers and construction managers in determining the best solution. However, the traditional approach for LCC is the net present value (NPV). Based on the full life cycle cost, Amini et al. [85] investigated the thermal energy storage (TES) of photovoltaic systems by LCC. The results showed that the optimal TES size improved by 7.1% over 30 years of the building life. Because the discount rate was not easy to determine, Akinsipe et al. [86] developed a mathematical model by LCC and assessed the costs of PV modules, battery systems, inverter systems, charge controllers, installation, operation, and maintenance in Nigeria. In order to improve the feasibility of PV power generation, Akinsipe offered some improved suggestions based on the LCC model. Although these methods are different, they all effectively reflect the full life cycle cost of the project.

2.2.2. Cost Analysis of Concentrating Solar Power

Cost combination calculation is commonly used in CSP cost analysis. For example, minimizing the life cycle cost was usually used in a novel hybrid system consisting of wind turbines [87], which was very complicated for calculating the cost. This is due to the fact that the life cycle cost and energy management strategy optimization minimizing the loss of supply probability are integrated. In addition, the conventional LCC method is frequently used to evaluate the Dish-Stirling concentrating solar power plant [88]. The role of discount rates is not reflected in the study, but rather, the costs are directly used for statistical calculations. An economic comparison of concentrating tower solar, photovoltaic power systems, collapsible vertical axis wind turbines, and diesel generators is performed using the LCC dynamic economic approach for the best choice between the different options.

Some scholars’ research still focused on the overall evaluation. Based on the LCC theory, Hernández-Moro and Martínez-Duart [89] used a mathematical model to evaluate the solar thermal power-based electricity LCC for 2010–2050 in terms of the present value, discount rate, and useful life. The present value was also used to evaluate the economic performance of a 50 MW trough solar thermal power plant operating in mixed mode with different natural gas inputs (between 0% and 30%) [71]. It was concluded that solar energy was the best choice. Although there are many evaluations of the overall situation, many studies focus on components. Among the relevant components, the reflector in CSP accounts for 50% of the total installation cost, so the huge initial investment and high lifecycle cost are key considerations [90].

In related components, thermal energy storage (TES) and electrical energy storage (EES) are the most common energy storage systems in CSP and PV. Okou et al. [91] found that flywheel storage systems could provide significant cost savings compared to lead-acid batteries in solar homes by LCC analysis. Raj and Ghosh [92] studied hydrogen storage. They compared PV-diesel systems with PV-H2 systems, which would be more accepted as fossil fuels were depleted. Zakeri and Syri [93] analyzed the LCC and levelized power cost of electric energy storage. Similarly, Marchi et al. [94] studied the LCC model, which considered the cost components that could be linked throughout the operation of the battery energy storage system. Gil et al. [95] developed an LCC model and studied thermal storage materials and high-temperature thermal storage power generation. The LCC cost was obviously improved. In addition, Rezaei et al. [96] used the present value to study the economic analysis, which helped to select the lowest LCC for TES. There are many components associated with PV and CSP, such as EES and TES, where the investment contributes to the photovoltaic conversion as well as to the heat gain but increases their initial investment cost. However, if all costs are considered from a life cycle perspective, the overall technological improvement can still reduce costs throughout the full life cycle.

In summary, the LCC method has been well applied in the field of solar energy to evaluate economic issues related to products and systems. The application of specific indicators is determined based on the initial investment, project scale, and discount rate of the specific project. The economic feasibility of the project can be effectively evaluated. However, LCC is one of the three pillars, and the integration of economic and environmental elements in sustainability analysis has also become a focus of research in the past. The main research has been achieved by integrating LCC and LCA to achieve the integration of the two pillars in LSCA, making LSCA a more tightly integrated whole. Bierer et al. [97] carried out some research, and some aim had been achieved, except for the environmental LCC incorporating explicit market prices such as GHG emissions [98]. However, as part of the lifecycle sustainability assessment, LCC mainly includes the economic pillars of sustainability [99,100]. In the future, how to link the two pillars of LCA and LCC for better development is the focus of subsequent development, and more research should be carried out.

2.3. Two Analysis Paths of S-LCA

As the energy system transitions to low-carbon energy, its environmental and economic sustainability must be assessed, along with the potential social impacts. A social life cycle assessment framework can be used to comprehensively address the concerns of different stakeholder groups regarding the social nature of energy across all life cycle stages associated with low-carbon energy systems. S-LCA does not yet have a standardized methodology. Currently, the guidance published by the UNEP/SETAC Life Cycle Initiative can help to further refine the standardization process and methodology [101,102,103]. In general, these indicators of the social lifecycle assessment framework mainly include four types of stakeholder organizations within the power system: workers, power consumers, local communities/companies, and society. The social LCA framework identifies generation and waste management through multiple points in the life cycle of the same energy system (from raw material extraction to manufacturing, transportation, operations, and power transmission).

The S-LCA, as a new method of evaluation, is a cut-through evaluation of LCSA from a social impact perspective. The S-LCA method is a technical framework that can benefit from a wider range of stakeholders when evaluating the lifespan of goods and services. The indicators in S-LCA are different from many other factors, including sustainable behavior, health, socio-economic impacts, and labor policies [104]. In addition, S-LCA involves stakeholders in the design of the entire framework, unlike LCA and LCC. The S-LCA inventory data contain social relationships. Most scholars and experts have divided social inventory data into two different levels: (1) the national level, which includes social issues at the level of national activities, and (2) the company, business, or organizational level, which includes the range of data involving that activity such as companies [105]. The national level data sources come from the national level, considering the overall society. This requires the integration of multiple aspects of society. Second, the company-level data sources focus on the impact of activities on society and on data from each company. Unlike LCA and LCC, some indicators in S-LCA are not expressed quantitatively but more qualitatively: for example, workers work longer hours. Assessing social impact is very specific and culturally dependent. Certain indicators are highly dependent on the location and culture in which the project is evaluated. For example, absenteeism leading to insufficient hours may have different economic or social impacts [106]. S-LCA results are stakeholder-dependent; S-LCA allows for the assessment of negative and positive impacts with stakeholders, while LCA evaluations are primarily negative.

In S-LCA analysis, there are usually two types, one is qualitative analysis and the other is quantitative analysis. Qualitative data are mainly descriptive data in social impact analysis. For example, there are many indicators such as the longer working hours of workers or the need to answer whether the working environment of workers has a significant impact. At this point, qualitative analysis is needed. In quantitative analysis, S-LCA can rely on the digital representation of the data list, mainly using research indicators for digital quantification and finally obtaining research results through scores. For example, 107. Tang et al. [107] proposed seven rules for assigning points for the research on concentrated solar power generation. Qualitative analysis and quantitative analysis are equally important. When conducting sustainability assessments, combining the two will make the entire process and results more credible.

Figure 7 shows the two analysis paths of S-LCA: (1) Quantify the importance of collected data by using project data points and the weight according to the weight of each unit in the -roduct lifecycle; (2) Evaluate the impact of the project through impact path analysis, such as whether workers’ work is often too long, whether there is an impact on certain specific indicators, etc. These analyses, including these two views, do not fully include all of S-LCA but are applicable to most analysis scenarios.

2.3.1. Impact Categories in Photovoltaic

In previous studies, the impact of sustainability evaluation had been generally divided into three categories: techno-economic, environmental, and social [108]. Specifically, the social impacts of solar power technologies are divided into two categories: (1) fuel poverty alleviation: the contribution of energy technologies to fuel poverty reduction is assessed through the reduction in energy consumption achieved by the technology; (2) the ability to provide employment, the main social impact expected from solar power due to its employment creation effect. The significant social impacts are analyzed and qualitatively assessed from the perspectives of different stakeholders [19]. This research approach is a qualitative analysis approach, which belongs to the second type of analysis, assessing the impact of the project through impact path analysis. The obtained results show that its social performance is not as good as expected, its social contribution is insufficient, and the government needs more support. There are many research indicators on social impact, divided into five areas: (1) Trina Solar, (2) research and development, (3) government, (4) electricity distribution network, and (5) customers (local community).

In order to find better ways to solve the above problems, many scholars chose different indicators to analyze the project. For example, Stamford and Azapagic [109] chose the following indicators when conducting their social evaluation: the provision of employment, human health impacts, large accident risk, local community impacts and human rights and corruption, energy security, nuclear proliferation, and intergenerational equity. It was found that PV projects could help increase employment. The impact on work-related injuries was significant, but the mortality rate was low. It provided diversity in energy supply, but ultimately, the recovery of the project was even more difficult. Since there are multiple evaluation indicators, a single indicator can be explicitly analyzed for a specific project, such as workers in the chain of participating projects, who, as an essential link, are an important part of the social evaluation. Traverso et al. [21] evaluated the indicators in terms of workers, who were classified as workers involved in the product chain (direct workers) and other workers; then, they were further grouped as ML (management level) workers and other workers, and workers’ working hours, rest time, wages, etc. were studied, and dashboard presentations were made. The comprehensive analysis and comparison concluded that the German module of 2009 had the worst social impact.

2.3.2. Quantitative Analysis of S-LCA in Concentrating Solar Power

Qualitative analysis can be considered as an analytical method based on non-numerical data. It focuses on the qualitative characteristics of the problem, such as social hot topics. The impact of the problem on the project can be obtained deeply. In addition, quantitative analysis is also based on digital and numerical data. In qualitative analysis, the researchers usually used methods such as case studies for analysis and focused on the quantity and degree of the problem, such as the frequency, proportion, mean, standard deviation, etc., to objectively reflect the quantitative characteristics of the problem. Similarly, the researchers usually also used methods such as questionnaire surveys, experiments, and statistical analysis to collect and analyze data. In the field of CSP, most scholars used a combination of qualitative and quantitative analysis. For example, Corona et al. [110] investigated workers, local communities, society, and value chain participants. First, the study evaluated the importance of the issue through the first research method, as well as the positive or negative social manifestations of identified social issues and their potential risks at the company level. Second, based on the life cycle theory of specific locations, qualitative evaluation methods were used to evaluate the social impact performance of participants, workers, and other participating organizations. The final evaluation indicated that the highest contribution to social risks was the operation and maintenance stages.

In addition, Rey-García et al. [111] investigated the quantified data on the social dimension of CSP and selected three indicators: job creation, social risk, and initiator social performance. These indicators promoted decision making in this field. Job creation includes direct and indirect employment from solar power stations. Social risk is a quantitative indicator that represents the social problems caused by the economic relationships generated by each functional unit on the value chain. However, the importance of indicators is determined through qualitative analysis. The above can be used as a social hotspot assessment for specific sites. Backes et al. [68] conducted a detailed risk analysis for the key raw materials in order to quantify social impacts through the social hotspots database, which contained a comprehensive list of indicators on labor rights and community infrastructure. The study was divided into five directories: Labor Rights and Decent Work, Health and Safety, Human Rights, Governance, and Community. At the national analysis level, there is no quantitative analysis, but subjective qualitative analysis is used to determine the risk situation. The research results indicate differences between workers with different levels of proficiency.

2.3.3. A Multi-Level Analysis Perspective of S-LCA

Many scholars have chosen to combine social life cycle evaluation with economic life cycle evaluation, which facilitates the evaluation of economic factors involving social impacts. The LCSA study of PV modules developed by Traverso et al. [21] showed the difficulty of comparing economic and social impacts. Whether a product is beneficial or harmful depends on both the impact on the company and the impact on society, the company’s workers, or the local community. It is crucial to integrate economic and social impacts when conducting evaluations, but there is a lack of a unified standard when aligning the two pillars.

In order to achieve validity and accuracy in sustainability assessment, additional macro-level systems need to be studied to complete a multi-level perspective analysis through the quantitative analysis of micro-units. In this process, it helps to define the boundaries of the whole system and facilitates a comprehensive assessment of the sustainability of the power plant. In order to fully understand what the social impact of the project is, the selected social hotspot research cases and the stakeholders involved in the interest chain should cover different locations, different scales, and different levels, and a perfect system of research subjects can make the cases more representative and the research more convincing. Indeed, there is a strong link between assessing social impacts closer to human well-being through research pathways and ensuring that our assessments have a certain balance regarding being closely related to the object of study (the product system), and both need to be further explored. S-LCAs mostly perform quantitative model calculations to document the relevant causal chains in this path. However, a complete distinction is not made between whether these models are used at the firm level or at the country level, which is often the way to go.

In summary, there are two analytical methods for S-LCA: qualitative and quantitative. The specific reasons for using different analysis methods in the project are as follows: (1) Different data sources (mostly in databases); (2) Different evaluation indicators (work hours, social performance, risk level, etc.); (3) No clear guidelines for handling methods. The commonly used indicators still focus on workers and social performance. They are mainly focused on participants in the value chain.

3. Contribution of the Life Cycle Theory Approach in Achieving Low-Carbon Emission Reduction

CSP and PV are deployed on a large scale worldwide, and their environmental assessment remains an open issue. There are many ways in which the LCSA approach can be used to reduce carbon emissions. Some improvements in assessment techniques have been carried out. In addition, some scholars have evaluated it by comparing it with other power generation projects. In general, the evaluation of power generation systems is aimed at verifying the potential of solar power plants to reduce environmental pollution and evaluating the emissions of the power system. The solar energy low-carbon emission reduction is shown in Figure 8. In the process of promoting low-carbon emission reduction, a continuous reduction in pollution emission is achieved through the improvement and development of technology.

In the field of photovoltaic power generation, because greenhouse gases are a significant focus of research on environmental issues, many scholars have carried out the investigation on greenhouse emissions by the LCA method. The photovoltaic power generation is also mainly affected by geographical factors, so the power generation and the corresponding emissions are different depending on the geographical location. For example, Kannan et al. [46] found that carbon dioxide (CO₂) was an important component of greenhouse gases. Meanwhile, CO₂ emissions were estimated by Kurtz et al. [112] and Ho [113].

Table 3. Summary of photovoltaic modules on GHG in the literature.

Photovoltaic Modules	Greenhouse Gas Emissions (gCO2e/kWh)	Location	Scholars
mc-Si	18	Japan	Kabir et al. [114]
	189	Switzerland	Dones and Frischknecht [115]
	50	Netherlands	Alsema [116]
	12	China	Ito et al. [117]
	32	Europe	Alsema et al. [118]
	37	Europe	Fthenakis and Alsema [119]
	37	Europe	Fthenakis and Kim [120]
	72.4	USA	Pacca et al. [121]
	42	China	Ito et al. [122]
	180.3	Italy	Bravi et al. [59]
	30	Europe and USA	Fthenakis and Kim [123]
	88.74	Italy	Desideri et al. [56]
a-Si	47	Netherlands	Nieuwlaar et al. [124]
	15	Japan	Kato et al. [114]
	187.8	USA	Lewis et al. [60]
	50	Netherlands	Alsema [116]
	39	USA	Meier [125]
	34.3	USA	Pacca et al. [121]
	15.6	China	Ito et al. [126]
	43	China	Ito et al. [122]
sc-Si	114	Switzerland	Dones and Frischknecht [115]
	200	Italy	Frankl et al. [127]
	60	Netherlands	Alsema [116]
	35	Europe	Alsema et al. [118]
	165	Singapore	Kannan et al. [46]
	45	Europe	Fthenakis and Kim [120]
	131	Spain	García-Valverde et al. [128]
	51	China	Ito et al. [122]
	98.9	Italy	Bravi et al. [59]
	38	Europe	Fthenakis and Kim [123]
CdTe	23.6	USA	Fthenakis and Kim [129]
	25	Europe	Alsema et al. [118]
	16	Europe	Fthenakis and Kim [120]
	48	Europe	Raugei et al. [130]
	12.8	China	Ito et al. [126]
	51	China	Ito et al. [122]
	19	Europe and USA	Fthenakis and Kim [123]
	18.7	Portugal	Held and Iig [131]
CIS	95	Europe	Raugei et al. [130]
	10.5	China	Ito et al. [126]
	46	China	Ito et al. [122]

shows the greenhouse gas emissions of different photovoltaic module materials studied by different scholars. As the specific indicator studied is greenhouse gas, it can effectively explain the pollution situation of different material components. Figure 9 shows that most greenhouse gas values are around 50 gCO₂e/kWh for PV modules. Specifically, the average emission of a-Si is 53.96 gCO₂e/kWh, which is a medium level, the average emission of mc-Si is 65.7 gCO₂e/kWh, the average emission of sc-Si is the highest at 93.79 gCO₂e/kWh, the average emission of CdTe is 26.76 gCO₂e/kWh, and the average emission of CIS is 50.5 gCO₂e/kWh. Due to the different material structures and costs of PVs, there are differences in emissions generated during production, operation, and other processes. However, the existence of significant differences can make people re-examine their efforts in environmental pollution. The European and U. S. regions have comparable emissions at around 74 gCO₂e/kWh and 58 gCO₂e/kWh, respectively. In addition, Oceania is 52 gCO₂e/kWh, and Asia has the least, at around 40 gCO₂e/kWh. From the values, more developed regions have more emission values compared with developing countries. This is due to the economic development and energy needs. But with the development of technology and the control of pollution, the future is expected to narrow the differences in terms of regional emissions. These values provide support for solar energy project decision making and future environmental pollution prediction.

3.1. Low-Carbon Emission Reductions in Internal Driving Factors

In the research on evaluating solar power technologies, the environmental contribution of solar power is usually demonstrated by evaluating the environmental emissions. For example, Ehtiwesh et al. [132] evaluated the environmental impact of the full life cycle of the plant using a 50 MW parabolic line focus unit, with the highest impact of 69% for the human health damage category and 7% for the ecosystem quality damage category. Through in-depth research on driving factors, Backes et al. [68] conducted a life cycle sustainability assessment of a Disc-Stirling plant located in Italy. The main drivers of emissions were electronic components (16%) and structural steel (37%), for which this specific low-carbon study provided concrete data. Miller et al. [45]. conducted a life cycle assessment of photovoltaic power as well as wind power, which remained far less carbon-intensive than fossil power. After a detailed internal analysis, the effect of the reversible temperature of the modules on the carbon intensity of silicon PV installed in warm regions increases, but Chinese polysilicon module manufacturing emits 25% more GHG than European manufacturing, and the overall PV has lower life cycle GHG emissions than fossil energy [56]. Overall, taking a certain location as an example, a solar power project indicates that the solution can reduce environmental performance and is therefore attractive [133].

3.2. Low-Carbon Emission Reduction in Program Comparisons

In general, a comparison should be carried out and show the priority of different solar power solutions. Compared with other energy sources, solar power solutions demonstrate a unique level of contribution to low-carbon emission reduction. Piotrowska et al. [134] used life cycle theory to study wind farms and photovoltaic plants. The result showed that wind farms had a higher potential total negative environmental impact and had a greater impact on the environment. In addition, Kannan et al. [46] performed the energy payback period (EPBT) analyses on solar PV systems by using the metric from LCA. The steam turbines were used as a reference, and the greenhouse gas emissions had been compared. The GHG emissions of solar PV systems for electricity generation are lower than those of oil-fired steam turbine power plants and gas-fired combined cycle power plants. Piemonte et al. [47] performed a life cycle evaluation for high-temperature molten salt-enriched solar power plants. The results show that the power plant has a smaller and better impact on the environment compared with the traditional (oil and gas) power plants. Thus, this new technology deserves further development. From the available studies [135], the current power generation system using a more parabolic trough and central receiver CSP emits about 50% more GHG than the parabolic dish, solar chimney, and solar pond CSP power generation systems.

In order to obtain the superior abatement option, many scholars have studied the different solar power types. Magrassi et al. [136] used a life cycle assessment method to investigate the GHG emissions of a 100 kWp PV and a 100 kW hybrid solar–gas turbine power system. The result showed that PV was the best environmental option, with GHG emissions of 0.043 kg CO₂eq/kWh. Mahmud et al. [43] studied solar photovoltaic systems with monocrystalline Si solar cells and solar thermal systems containing vacuum glass tube collectors. The result showed that it had a lower impact on climate change and ecosystems and lower emissions compared to solar thermal systems.

3.3. Internal System Improvements for Low-Carbon Emission Reduction

Others are technical innovations or process improvements within the system. Ali et al. [44] assessed the impact of the electricity generation on the environment for the distributed solar PV systems in New York State, with a mean net climate change impact of 45.6 g CO₂eq/kWh and a standard deviation of 11.9 g CO₂eq/kWh. The average GHG emission from solar PV systems was 13% higher than that when EoL processs were considered. Similarly, the average GHG emission from solar PV systems was 26% higher than that when EoL processes were not considered. Piemonte et al. [137] presented a life cycle assessment of a new hybrid plant for EM production in a solar steam reformer reactor. The result showed that it was an innovative plant with a lower environmental impact compared to traditional conventional plants.

Oró et al. [138] investigated the impact of three different solar thermal power systems (sensible thermal storage in solid and liquid thermal storage media and latent thermal storage using phase change materials) on the environment. Similarly, Gasa et al. [139] also investigated a CSP tower plant with molten salt storage in a base-load configuration and a reference CSP plant without storage. The results showed that the plant with storage had a lower environmental impact due to a lower operational impact. In addition, storage is an important element in reducing the impact of a CSP plant on the environment. Ameri and Mohammadzadeh [140] evaluated a novel solar combined cycle system by the life cycle evaluation. The result showed that the use of a parabolic trough solar cycle in an online conventional combined cycle power plant reduced the impacts of the combined solar plant on the environment. Corona et al. [141] also studied the effects of the geographic location of the CSP power plant on the environment for a combined cycle configuration consisting of a 100 MWe turbine and an 80 MWe biomethane gas turbine by the LCA method. The result showed that the life cycle theory could effectively optimize the generation process and reduce carbon emissions.

In summary, the LCA method can optimize the generation process and significantly reduce carbon emissions in the application of solar power generation systems, thereby reducing pollution emissions. This is because the life cycle theory can comprehensively consider various reasons. For solar power generation systems, it not only reduces the pollution emissions during the construction process but also reduces the pollution emissions after completion. And through a technological revolution, the emissions can be further reduced.

4. Monetization of Environmental Externality and Internal Connections within the Pillars

Externality is the costs from environmental and social impacts, which are not directly borne by product life cycle participants [142]. The monetization of externalities has inherent uncertainties and is related to methodological issues such as discounting, geography, and time preferences [143]. In the research, many factors have an impact on the final evaluation results, so it is necessary to quantify and analyze the external factors well. In the assessment process of the environmental impact of solar power technologies, the monetization of environmental externalities is a hot research point. The study can combine environmental and economic factors and make comparisons between direct economic costs and external costs. Thus, monetization in the environmental research process can manage the costs in order to make better decisions. In previous studies, the environmental externalities should be monetized as much as possible to be associated with economic costs to be assessed together [144,145].

The impact of human activities on the environment is also very important. When quantifying environmental factors from a social and human point of view, an overall measure is needed to determine which are more severe and which can be ignored. The monetization of environmental impact is one of the ways to solve this problem and is an important embodiment of the combination of LCA and LCC. Environmental impact monetization is the conversion of environmental impacts caused by environmentally harmful substances into monetary units. Environmental impact monetization is mainly applied in the field of the cost analysis of society, etc. Therefore, the relevant analysis can also be performed in S-LCA.

Monetization is the main presentation in LCA presented in a weighted form, which is mainly to measure the balanced relationship between different impacts [146]. Due to the fact that LCA is one of the tools for quantifying the environmental impacts of the life cycle, the monetization of life cycle assessment can be used to evaluate ecological performance [147]. The advantage of monetizing environmental impacts is that it overcomes the trade-offs between the many impact categories. Vogtl änder et al. [148] developed a single sustainability indicator and an eco–costs–value ratio model for economic allocation. The research for monetization is very important. However, it usually provides a single score for intra-organizational communication.

4.1. Levelized Cost of Electricity

The most common application of monetization is the levelized cost of electricity (LCOE) [149]. The International Energy Agency [150] shows the eighth edition of generation cost projections, which provides an in-depth study of the LCOE of all major generation technologies including solar PV. When exploring the study of solar PV externalities, the diurnal and seasonal variations will also be used as an object of study, which describe the average external cost of purchased power for local electric utility fuel mixtures [151]. The LCOE includes input costs, operation and maintenance (OM) costs, financing costs, and utilization rates in its calculations. Due to the use of different technologies, the above costs may vary significantly in the implementation process. Generally, due to the low OM costs for solar power technologies, the variation in LCOE is almost proportional to the input costs of the technology. According to “Renewable Power Generation Costs in 2021” from IRENA, Figure 10 shows that the LCOE decreases year by year in PV and CSP as the power generation costs reduce [152].

The LCOE, which describes the cost of solar power over a period of time, is a metric that can be considered when conducting a life cycle assessment of solar power. At the same time, LCOE and LCA can also be coupled together. Gasa et al. [69] established correlations between LCOE and LCA results by comparing the impact of tower CSP plants with different storage capacities (3, 6, 9, and 17.5 h at nominal conditions, i.e., 17.5 h in plant operation) in a comparative study. The LCOE is calculated as the LifeCycleCost to LifeCycleEnergy ratio. The LCOE can be obtained by Equation (2) [153]:

$$LCOE=\frac{LifeCycle\mathrm{Cos}t(LCC)}{LifeCycleEnergy(LCE)}$$

(2)

The LCC can be simply described as a combination of the costs: feasibility analysis costs at the beginning of the project, procurement and installation costs, operation and maintenance costs, and recovery and disposal costs. The LCOE can usually be validated as an economic indicator. Ranganath et al. [153] conducted a study on six different solar power plants in India and assessed the feasibility of local solar power plants by using LCOE, including economic plausibility, and cash flow analysis. They confirmed the project return linked to the initial investment and found the economic feasibility of six power plants.

4.1.1. Techno-Economic Evaluation of LCOE for Concentrating Solar Power

In general, the LCOE is employed to evaluate the economics of CSP technology. It can effectively quantify and showcase solar energy technology economically. Wagner and Rubin [154] found that LCOE increased with the increase in reserve capacity. Specifically, the LCOE increased from USD 190/MWh to USD 240/MWh. This is due to the upgrade of the two-tank molten salt TES system. Similarly, Hernández-Moro and Martínez-Duart [89] developed an LCOE model and assigned 12 independent variables: the solar resource, the discount rate, the initial cost of the system, operation and maintenance costs, etc. The obtained results showed that the LCOE increased from USD 270/MWh (no storage) to USD 295/MWh (storage 7.5 h) for CSP systems over the period 2010–2050. The CSP technology was more suitable for locations in relatively arid regions with low latitudes. However, some studies have shown that even if the cost is high, it may result in lower LCOE values [155]. Therefore, the LCOE evaluation should be conducted in terms of multiple aspects.

Spping et al. [156] developed an optimization algorithm for improving the dynamics of a pure solar combined cycle power plant with an average cost of electricity of 12–24 UScts/kWhe, which depended on the size of the initial investment. The system was competitive with current solar thermal technologies. Based on the LCOE theory, the solar energy collected by CSP technology is easy to store compared with other renewable energy technologies, such as PV. Thus, it is critical to analyze CSP systems with economic metrics-related characteristics.

4.1.2. The Impact of Loans on LCOE for the Photovoltaic

In terms of electricity prices, the tipping point for solar PV adoption is considered grid parity. Due to the increase in fossil fuel costs and increasingly strict environmental requirements, the price of traditional electricity is rising, while the price of PV devices is decreasing with the continuous development of material technology. “Grid parity” means that the cost of PV systems has fallen to the point where solar power can compete with grid power without any enabling policies [157,158,159]. When considering technologies such as PV or when making comparative grid parity, the most used method is the LCOE [160,161]. The financeable life of solar PV systems is usually considered to be typically 20–25 years. During the life cycle, the loan approach has an impact on LCOE. Singh [162] pointed out that the conclusion of grid parity was incorrect due to the static LCOE value and the increase in actual electricity costs. Similarly, they pointed out that lifetime was correlated with confidence in the loan guarantee. Adjusting the annualized loan cost over the life of the loan will result in a significant reduction in the LCOE for each year after the life of the loan [163]. In the full life cycle calculation, the loan term is different from the working life of the PV system. Usually, the LCOE will be lower than the traditional loan method, which will change with economic and social development. Based on the economic structure of the society, whether the results of the data measurement are feasible in the society also needs to be based on the local policy.

4.2. Indicators That Create Linkages between Pillars

Some indicators will not only appear in one of the pillars but also in multiple pillars. For example, Bachmann proposed the damage costs and abatement costs based on the individual willingness to pay (WTP) [164]. One important datapoint in environmental economics is the WTP and willingness to accept (WTA). There has been a lot of research between WTP and WTA, but WTA tends to have a higher value. Thus, the research on WPT is more accepted by people in environmental economics. Nduka [165] found that the WTP was higher when the solar photovoltaic completely replaced generators. From a cost perspective, the solar photovoltaic was beneficial for implementing policies for energy transformation.

Area of Protection (AoP): S-LCA considers any social impact, including both negative and positive impacts. Different impact categories in LCA are linked to AoP. Some AoPs are human health, resource scarcity, and ecosystem quality [166]. Conversely, some distinguish four AoPs: human health, social assets, biodiversity, and primary production [167]. Linking climate change impacts to human health, ecosystems, and resources uses abatement costs to derive monetary factors of climate change. In fact, AoP is a focal point and a guaranteed theme that must be maintained for future generations—for example, human welfare.

Discounting: Discounts also exist in LCA. Heierweg et al. [168] studied the discounting of LCA and concluded that the impact of LCA was not discounted. Whether the discounting of values associated with non-market goods is fair for the present and future is a topic for discussion. Impacts that occur after a point in the future are also discounted to some extent, but one cannot assume that they are discounted in the present. It is important to obtain the discount rate and, thus, the possible differences in the valuation results. When using LCC to analyze energy technologies, the costs and benefits generated over the life of the project need to be discounted to present value. The discount rate is used to realize external costs, which is a controversial issue [169]. It determines the importance of preserving the quality of the environment for future generations. Currently, there is not a specific and well-developed discounting method in the discounting process.

4.3. Multiple Redundant Input/Output

Multiple redundant input/output (MRIO) analysis aggregates economic, social, and environmental data recorded by a specific number of regions and sectors in a specific year or time frame. Those value chain data measurements allow for the assessment of a broad environmental and socio-economic footprint [170]. These footprints are scattered throughout the three pillars of LCSA. Based on the social life cycle evaluation, the socio-economic analysis and MRIO methods were combined and employed to study the impact of economic evaluation on social factors and estimate the production of goods and services, value-added creation, and employment opportunities. Garraín et al. [171] evaluated the sustainability performance of a naturally ventilated photovoltaic curtain wall system, analyzing the socio-economic and environmental impact of an innovative plant. The socio-economic effects were assessed by using the MRIO approach and were related to G & S production, value-added creation, and employment creation. Cabernard and Pfister [172] used the extended environmental MRIO analysis to investigate the effect of the main social factors involved in biodiversity and labor on global value chains.

Importantly, the application of this method allows the study to describe the footprint of the impact factors. The context of MRIO allows for the study of multiple aspects in terms of economic, social, and environmental footprints. In general, most of the research on solar PV mainly focused on installation projects and employment generation [173,174]. Zafrilla et al. [175] investigated domestic and foreign employment and conducted an analysis of educational qualifications. It can be seen that the deployment of a new solar PV capacity provides new stable, high-quality jobs. Overall, the above studies involve all three pillars of LCSA.

In summary, the LCOE can help us control the core points in the investment process of power projects. In addition, it can be used to determine the profitability and investment value of industries and projects in the growth stage, such as the solar energy industry. In comparison with the status and project competitiveness of mature industries such as thermal power, wind power, etc., the LCOE has a better evaluation advantage in evaluating solar power generation. The internal connections between pillars are strengthened through indicators such as the WTA. At present, the connection between the pillars is not enough, and the area should be developed in the future.

5. Uncertainty Analysis

Uncertainty analysis is the estimation and investigation of the changes caused by various external factors, which cannot be controlled beforehand in the production and operation process. Uncertainty exists when the LCSA is employed to evaluate solar power generation. In order to make a correct decision, the probability of the occurrence of each factor and its impact on the decision solution need to be calculated, from which the best solution is selected.

Currently, there is some analysis for uncertainty in LCA and LCC. In LCA, uncertainty analysis should be required for various emission factors and environmental impact factors. This is due to the fact that the life cycle inventories (LCIs) are heavily developed based on stoichiometric relationships, and some parameters are inherently unstable. Similarly, the distributions of all types of economic factors depend on the location, time, and other circumstances in LCC. Thus, there is a link between the allocation of economic factors and uncertainty. Uncertainty analysis is usually conducted using three types of methods: breakeven analysis [176], sensitivity analysis [177], and probability analysis [178]. The advantages and disadvantages of these methods are shown in Figure 11.

5.1. Uncertainty Analysis in LCA

Uncertainty analysis is feasible in LCA. Many experts and scholars have conducted relevant research. For example, Pomponi et al. [179] proposed a Monte Carlo analytical method, which took uncertainty random modeling as the input and incorporated uncertainty analysis into the life cycle assessment. In addition, Xiao et al. [180] performed uncertainty analysis for the life cycle evaluation of solar thermal power plants with an integrated air-cooled supercritical CO₂ Brayton cycle and investigated the effects of the solar irradiation, ambient temperature, turbine inlet temperature, lifetime, and TES capacity on the LCA results.

To ensure the reliability of the study comparison, an uncertainty analysis of the environmental impacts of solar photovoltaic systems and solar thermal systems was carried out by Mahmud [43] in the framework of LCA. LCA analysis is mainly conducted on the sustainability of environmental impact indicators through the method of life cycle data systems, describing the probability distribution of the two systems. Moreover, Gong et al. [181] developed a Monte Carlo model and studied the uncertainty analysis for the life cycle assessment of two types of solution-processed chalcogenide solar modules. Due to the lack of life cycle inventory data, Mohr et al. [182] conducted an uncertainty analysis and evaluated the environmental impact of two types (thin-film GaInP/GaAs tandem modules and polycrystalline silicon) of PV modules by using the economic allocation method. The uncertainty analysis greatly improved the practicality of LCA.

5.2. Uncertainty Analysis in LCC

There are many uncertainties in the process of economic analysis, and these can lead to a large gap between the analysis results and reality. Solar investment decisions are uncertain due to the ambiguity of production levels and energy prices. So, Ranganath and Sarkar [153] used the life cycle cost to analyze six solar power plants in India and identify their economic viability. Finally, financial cash flow analyses were carried out for different sensitivity scenarios, and feasibility conclusions were obtained.

In general, three types of methods are commonly used for uncertainty analysis, but most of them are sensitivity analysis. In 1979, Jones and Lior [183] first developed a model for LCC and investigated the cost sensitivity of solar systems. Similarly, Zhou et al. [184] conducted a sensitivity analysis of the system performance by varying the solar collector array. The results showed that there was an optimal value of the solar collector area that resulted in the lowest whole LCC system. Yaman and Arslan [185] took the solar energy assurance rate of 40% in different regions of Türkiye as the constraints of the life cycle cost analysis and examined the impact of possible operating parameters on the collectors by setting a sensitivity index. The results showed the season had the greatest impact on it.

In order to improve CSP, some researchers also focused on PV. Azzopardi et al. [186] developed an improved LCC model coupled with sensitivity analysis and investigated the cost boundary of new technology for photovoltaic solar cells. Finally, through sensitivity analysis, the accuracy of PV was proved. In addition, Nicholls et al. [187] conducted a life cycle economic assessment with a rooftop PV system in Australia, considering multiple life cycle factors and performing a sensitivity analysis of the electricity price and feed-in tariff. Oğuz and Şentürk [188] performed a sensitivity analysis for each system in order to determine the most feasible recovery strategy, separately for PV plants and onshore wind farms. During the study, sensitivity analysis was required to study the actual lifetime of the system by fixing the economic lifetime.

In the process of analysis, there are many analytical tools that come into play. Pythagorean fuzzy sets and computer simulations are the tools for dealing with system uncertainty. Uncertainty is taken into account in the economic analysis of solar power plants by Çoban and Onar [189]. The Pythagorean fuzzy sets are used to deal with solar investments with fuzzy parameters to more accurately compare the economic value of various energy systems. Some scholars perform sensitivity analysis in the LCOE approach to account for any uncertainty [144]. Hernández-Moro and Martínez-Duart [89] used a computer simulation program to generate a graph of the evolution of the LCOE in the comparison of PV and CSP, sensitivity analysis curves, and, more intuitively, the investment of power plants for decision-making recommendations.

In summary, uncertainty analysis is very important when studying the impact of environmental factors and financial data. The reliability of the data must be ensured. Based on uncertainty analysis, we conducted a series of the most influential parameter sensitivity analysis and identification, discovering more sustainable solar systems.

6. Discussions

The solar life cycle analysis method has been widely applied in the construction of solar energy projects. LCSA is a synthesis of LCA, LCC, and S-LCA. From the analysis process, all three pillars have a quantitative analysis process, and conclusions are drawn through numerical analysis. The qualitative analysis also runs through it, and the degree of importance is determined by the impact interpretation. In specific evaluations, specific indicators will be used to analyze (GHG, GWP, NPV, PI, social performance, etc.). It includes environmental, economic, and social impact assessments. These indicators are applied throughout the analysis process of LCSA. So, overall, LCSA is a combination of qualitative and quantitative analysis. However, with the changes in the geographical environment and the application of updated data, the applicability of existing analysis methods should be studied.

6.1. Limitations of the LCSA

6.1.1. Limitations of Data Sources

According to the source of the data, the data can be divided into original data and secondary data. From the perspective of research, very few research data are derived from original data. On the contrary, many studies use secondary data, which are derived from existing literature documents or from the Ecoinvent LCA database. In terms of data sources, in the extraction and manufacturing phase of raw materials, it will be more conducive for us to use more original data for accurate evaluation. Due to the qualitative nature of the evaluation and the very limited availability of data, complex social assessments are subjective to some degree. By selecting only key stakeholders, the information collected is neither complete nor unbiased. Therefore, all conclusions require further knowledge of the data. All data are case-specific, and the interpretation of the results depends heavily on the analysis of the specific case.

Since there are three aspects of research in LSCA, when collecting and counting the indicators of each stage of solar power generation, the different importance of the indicators is not considered. Due to the lack of the same value standard and weight calculation method, the overall evaluation results cannot be reflected in the form of a total calculation of data. But the results of a certain part are summarized and analyzed. It is a direction of research on strengthening the link between aspects.

In S-LCA, some indicators can only exist in individual cases and are difficult to use for investigating the other cases. For example, there are data levels at the national and company/organization levels in S-LCA, and it is still questionable whether this indicator is sufficient to provide a comprehensive social impact assessment. At the same time, when assessing the social impact, it should ensure that there is a close correlation between the assessment and the research object. In the processes of models or data collection, it is often necessary to strictly determine whether it is applicable to the macro level (regional and national) or the company level. However, some data will involve two levels at the same time, without a clear distinction standard. The evaluation of social pillars is still in the development stage, as S-LCA lacks common standard indicators and cannot be applied across different projects. Researchers have ignored important core social issues because they have concentrated on worker- and health-related metrics and have not delved deeply into some other metrics. Some indicators are overlooked, as they focus on indicators related to workers and health.

6.1.2. Selection of Research Indicators

The limitations of LCA are mainly the same as those of LCA alone. The biggest limitations are the application of the LCI methodology and the documentation of the inventory. Sustainability assessments are conducted with all dimensional assessment indicators available but with a very narrow view of ecology. In these studies, for example, only one or two specific emission indicators or environmental impact categories were assessed [190,191]. However, this is not the only important environmental impact that needs attention. Therefore, more factors and indicators of environmental pollution should be considered [192].

In the case of S-LCA, a major limitation is the selection of indicators for social impact. Since S-LCA is an assessment method developed from LCA derivation, two types of analysis exist for this method, one quantitative and one qualitative, but the current research approach mainly favors quantitative data analysis. Due to the limited data availability, it is extremely difficult to adequately portray complex social activities. Many researchers recognize this limitation, but sometimes it is difficult to combine qualitative and quantitative research. The complexity, uncontrollability, and high cost of studying the interest chain of social impacts often hinder their implementation.

There are many limitations related to LCC, and one of the main issues is the use of static time points in the time domain for research. The life cycle of solar energy is usually between 10 and 25 years, and the entire economic indicators may change within this assumed time frame.

The monetization of externalities is also an object to be considered. Many researchers considered the translation of externalities into monetary values to be controversial. In the study of solar power, it is also an issue whether the price of electricity is taken into consideration, such as in Nguyen et al. [193]. Although they also suggest ways to potentially monetize the externalities of the product, it is difficult to monetize the externalities by attaching a monetary value to the production of electricity.

6.1.3. The Degree of Wholeness and Perfection of the Framework

The overall maturity level is not yet high enough when considering the integration of environmental, economic, and social issues in the use of the solar power sector. A major challenge in the assessment is the need to develop a comprehensive and truly circular view of the life cycle phases, including the use phase and the reuse/recycling phase. As different power generation systems exist in different configurations. Specific cases differ in manufacturing, installation, transportation, operation, and recovery, and there are inconsistencies in the consideration of system boundaries.

Different studies define the main research stages based on their research content. Their choices will vary depending on their research object and purpose. Therefore, there may be significant differences in the research background of the same field. For example, Oro et al. [138] studied the heat storage system of a solar power station. The analysis mainly focused on the manufacturing stage and, to a lesser extent, on the operation stage, mainly due to the difference in material media.

For S-LCA, a complete and detailed framework is provided for taking stakeholder impacts into consideration at the time of the study. Stakeholder-related data may best illuminate a more accurate picture of the potential impact of projects on social impacts within the system boundaries. However, the focus on stakeholders within the framework is not always easy to represent. Especially in the qualitative and weighting phases, the voices of affected stakeholders cause the existence of differences in the assessment models.

6.2. Outlook and Potential Improvements

Currently, LCSA is still a commonly used analysis method for solar power generation and is widely used globally. In the evaluation process, the descriptions of LCA, LCC, and S-LCA are often combined. In the analysis process, it is necessary to establish an evaluation framework and analyze the data indicators of different models. On this basis, specific regional variables were studied, or circulation data from their respective pillars were used.

In terms of the assessment framework, the transportation and final recovery stages are easily overlooked. Whether equipment transportation is considered in the system unit needs to be seen in the context of the study. Sometimes, equipment transportation is included in the manufacturing phase or the construction phase. Since these differences can have different impacts on the results, whether to include transportation impacts in a particular phase construction phase requires further consideration. For the dismantling and recycling phase, the series of fuel and energy consumed in this process needs to be considered—for example, in the proportional calculation of the site area, as in Viebahn et al. [194].

Life cycle sustainability assessment is a complex and systematic research model framework. Due to the many aspects to be considered, it is necessary to establish an evaluation system that can communicate between different evaluations. Thus, the LCA-LCC-SLCA can be interlinked to obtain a systematic result that reflects the commonality of values. It is recommended to consider the interrelationship of the system as well as the dynamic connectivity. Balasbaneh and Marsono [195] applied the three pillars of LSCA to evaluate the environmental problems of concrete and stone walls used for residential buildings in Malaysia, with different sustainability performances. Economic and social aspects are evaluated by considering the above criteria and implementing the Multi Criteria Decision Analysis (MCDA) method. This is a holistic approach to studying the three pillars, and it requires in-depth experimentation in solar energy research.

The full life cycle cost models are simpler in estimating costs for the final disposal phase. There is no uniform standard for estimating the cost of the disposal stage and the potential benefits after disposal. Among the available studies, a few separate studies have been conducted on the end-of-life (EoL) recycling phase. It is crucial to properly handle photovoltaic waste to recycle valuable materials and comply with regulatory requirements. The impact of EoL on environmental emissions is equally important. Aryan et al. [196] studied the end-of-life (EoL) phase. A comparative analysis of the environmental impacts from EoL treatment of non-fluorinated and fluorinated back sheet materials presented in PV modules was conducted using a life cycle assessment study. Twelve impact categories were studied in two EoL scenarios. The life cycle study of Tian et al. [197] examined the impact of recycling strategies on emissions, focusing on materials and processes and examining recycling as an end-of-life scenario. The results showed that recycling strategies could reduce the energy recovery time by 72.6% and greenhouse gas emission factors by 71.2%. It is recommended to conduct research based on different recycling mechanism conditions to further achieve low-carbon emissions.

Due to the fact that solar power generation may occur in different situations, it depends on the location of the site. Complex geographical and even climatic environments may affect production, transportation, installation, and usage costs. There is a need to develop cost models that are suitable for different geographical conditions. Similarly, due to the different geographical locations of power generation, political and market factors (taxes, interest rates, subsidies) in the region may have a significant impact on the economic analysis process. These factors are rarely considered in existing economic analysis. It is recommended to consider these factors simultaneously and optimize the existing model. When using prediction algorithms for economic analysis, especially when using climate, economic, and political factors, more data sources are needed for prediction. This can improve the accuracy of economic analysis when using future data. However, due to the need for LCC, LCA, and SLCA when analyzing future data, current predictions are difficult in covering the full lifecycle and require further development.

In terms of data, the study should focus on project-specific data collection. Currently, most of the data collected are based on secondary data such as research literature or secondary data such as semipros. Very few studies will be conducted to collect primary data. Different databases have different impacts. For example, Yu and Halog [19] studied the development of solar energy in Australia, and the data collected are mostly based on the European context. Therefore, it is important to improve the Australian life cycle database. In this way, the higher the degree of local relevance of the data collection, the better it is for the evaluation of the project, and it makes the results more reliable.

Compared to other LCSA reviews [198,199], the paper focuses more on applications in the field of solar energy. Firstly, it applies the three pillars separately in the field of solar energy and analyzes the application of different indicators in different situations. The life cycle sustainability assessment summarized in this article is applicable to various types of solar power generation worldwide. For the evaluation of LCA, LCC, and S-LCA, the integration of the three pillars can provide a universal model framework reference for solar life cycle assessment in different regions. Overall, for the evaluation of a certain type of solar power generation in a certain area, it is necessary to comprehensively consider factors such as environmental impact, economic feasibility, and local social impact to determine a detailed plan. Although most studies have only been validated in a few specific regions, these models provide references for solar power generation under different regional conditions through different data indicators. Therefore, when applying these models, it is necessary to modify the model framework and structure according to local conditions. It can be seen that with the joint efforts of numerous researchers, LCA, LCC, and S-LCA have developed a relatively complete framework that can be applied to different types of solar power generation in different regions. However, the indicators and structural details of the model lack the ability to flow between different pillars. There is a need to establish a detailed and universally applicable solar life cycle cost model that comprehensively considers the environmental, economic, and social impacts of the different regions involved. This will be beneficial for the development of solar power generation.

7. Conclusions

This article summarizes the LCA, LCC, and S-LCA of solar power projects and integrates them into LCSA. From the perspective of life cycle assessment, PVs mainly have differences in pollution emissions due to differences in components and materials. The system boundaries of CSP are roughly the same, and the data sources are also similar, which makes them similar in the evaluation process. The improvement of heat storage technology has reduced costs from the perspective of the full life cycle. S-LCA can be analyzed using both quantitative and qualitative approaches based on indicators. In terms of sustainable development, compared to other fossil fuel and wind power projects. The entire solar energy project has better low-carbon emission reduction effects and can provide more feasible foundations in terms of the environment. In addition, the maturity of using LCSA is still not high enough when considering the comprehensive challenges of the environment, economy, and society. In order to make the LCSA system more complete and strengthen the connection between the three pillars, comprehensive evaluation indicators such as LCOE and MRIO should be carried out. The limitations and recommendations were dedicated to emphasizing the existing gaps and providing opportunities for different aspects discussed in the literature to fulfill the intended accuracy and transparency. Developing conversion factors is worth emphasizing given their importance to enabling the benchmarking of the results obtained globally and cumulating solid unified knowledge globally.

Overall, further research is needed on the use of LCA, LCC, and S-LCA in LCSA. There is a need to develop LCSA suitable for the field of solar power generation. This model needs to be suitable for different environments and participants. In addition, efforts should be made to combine these three methods. Considering the quantitative and qualitative data connections between the three pillars, data indicators can be developed that can flow between these three methods. This ensures that LCA, LCC, and S-LCA are integrated into the LCSA model framework of a system. In addition, the life cycle technology is reported to achieve minimal environmental impacts when applied in upstream treatment for PV and solar energy, while incineration techniques were associated with minimal environmental impacts in downstream treatment for most scenarios. We also examined the various LCA stages (goal and scope, life cycle inventory, life cycle impact assessment, and interpretation) to identify methodological gaps, which, besides the limited data availability, included a lack of studies on PV management, especially regarding reuse options, a lack of uncertainty analyses, and a lack of regionalized site-specific characterization factors (CF) for non-developed impact categories. The study concludes by proposing a systematic LCSA model framework for addressing these gaps and improving the sustainable management of industrial PV.