Life-cycle assessment and techno-economic analysis of a grid-connected solar photovoltaic system: Environmental benefits and economic feasibility

Abstract

This study presents a comprehensive life-cycle assessment and techno-economic analysis of a 270.665 kWdc solar photovoltaic (PV) system installed at Aswaq Al Salam in Jordan. Using OpenLCA with the Ecoinvent database, greenhouse gas emissions were evaluated across major components including solar panels, steel structures, cables, and inverters. Results show that the total system impact is 232,367.84 kg CO₂-eq over its 25-year lifetime, with the majority (74.6%) originating from solar panel manufacturing due to an energy-intensive silicon purification process. When compared to grid electricity, which would have produced 7,915,520.10 kg CO₂ over the same period, the PV system achieves a 97.06% reduction in emissions, corresponding to only 15.59 g CO₂/kWh. An off-grid alternative with a 3 MWh battery storage was also assessed, yielding a higher footprint of 431,929.63 kg CO₂-eq, primarily due to emissions from lithium-ion battery production. In addition to the environmental assessment, a discounted techno-economic evaluation was performed. The total investment cost was estimated at USD169,200, yielding first-year savings of USD92,891. When accounting for discounting, operation and maintenance costs, and electricity price escalation, the system achieves a net present value of approximately USD979,950 over 25 years and a levelized cost of electricity of USD0.024/kWh, significantly lower than the prevailing grid electricity tariff. The simple payback period remains short at 1.82 years, confirming both the short-term attractiveness and long-term economic robustness of the grid-connected PV system.

Keywords

Solar photovoltaic system life-cycle assessment greenhouse gas emissions techno-economic analysis renewable energy

Introduction

The global energy system has continued to grow in scale and complexity, and persistent reliance on fossil fuels has produced both large life-cycle greenhouse gas (GHG) emissions and serious local air pollution (CO₂, CO, NO _x , PM, and hydrocarbons) that damage public health and ecosystems. Historic projections suggested that total world energy consumption could increase substantially without aggressive mitigation, underscoring the need for large-scale uptake of low-carbon electricity technologies (Al Asfar et al., 2023; Pimentel Pincelli et al., 2024). Evidence from multiple sectoral assessments indicates that decarbonizing electricity generation is a high-leverage intervention for cutting national GHG inventories because electricity production remains a major source of CO₂ emissions globally (Abu Qadourah, 2022).

Solar photovoltaics (PVs) have become a cornerstone of global electricity decarbonization strategies, driven by their rapidly declining installed costs, modular and scalable nature, and negligible direct GHG emissions during operation (Amer Ali Amer et al., 2025; Elmnifi et al., 2025). Extensive global and regional energy system analyses demonstrate that high levels of PV penetration can be achieved by mid-century, contributing a substantial share of electricity generation in low-carbon energy scenarios (El-Khozondar et al., 2025c). Beyond standalone applications, PV solar fields play a vital role in hybrid energy systems, where they are integrated with other renewable and conventional sources—such as PV–grid, PV–wind, PV–battery, PV–diesel, and PV–concentrated solar power configurations (El-Khozondar et al., 2023; Nassar et al., 2022a). These hybrid systems enhance operational flexibility, improve supply reliability, reduce fuel consumption and emissions, and strengthen overall energy security, particularly in regions with high solar resource availability (Aqila et al., 2025). Consequently, large-scale PV deployment represents a key pathway for achieving both environmental sustainability and resilient power systems (Al-Dahidi et al., 2019; Virtuani et al., 2023). Growth in PV capacity has had a strong impact on avoided fossil generation in many countries, motivating rigorous life-cycle accounting to quantify net emission benefits (Stanbery et al., 2023; Tawalbeh et al., 2021). Driven by climate change concerns, the global renewable energy market has grown rapidly. By the end of 2024, cumulative solar PV capacity reached about 2.2 TW, while battery energy storage exceeded 150 GW (363 GWh), reflecting a global shift toward low-carbon energy systems and underscoring the importance of assessing the environmental and economic performance of PV technologies (Aqila et al., 2025; Nassar et al., 2025b).

Despite their low operational emissions, PV systems are not environmentally burden-free when evaluated over their entire life cycle. Cradle-to-grave life-cycle assessment (LCA) studies consistently show that the dominant share of GHG emissions associated with PV electricity is embodied in upstream manufacturing processes rather than during operation. These emissions arise mainly from energy-intensive stages such as polysilicon production, ingot casting and wafering, as well as cell fabrication and module assembly (El-Khozondar et al., 2021; El-Khozondar and El-batta, 2022).

The magnitude of embodied emissions is strongly influenced by several factors, including the energy intensity of manufacturing processes, the carbon intensity of the electricity mix used during production, and the geographic location of manufacturing facilities. In addition, transportation logistics between raw material extraction, manufacturing sites, and installation locations contribute to the overall carbon footprint (El-Khozondar et al., 2025a). End-of-life (EoL) treatment, including recycling or disposal pathways, further affects life-cycle impacts by enabling the recovery of energy-intensive materials such as aluminum, glass, and silicon. Consequently, accurate environmental evaluation of PV systems requires a comprehensive life-cycle perspective that accounts for upstream production, transportation, operation, and EoL stages (El-Khozondar et al., 2025b). Such an approach is essential for identifying emission hotspots, improving manufacturing practices, and ensuring that large-scale PV deployment delivers genuine and sustained climate benefits (Polverini et al., 2023; Shin et al., 2017). Consequently, accurate, location-sensitive LCA models are required to report PV life-cycle impacts in g CO₂-eq/kWh and to compare them fairly with fossil alternatives (Al-Ghussain et al., 2025; Khan et al., 2024).

Meta-analyses and harmonization exercises reduce methodological scatter and provide robust reference ranges: de Wild-Scholten's harmonized review reported energy payback times (EPBTs) for commercial modules in the range of ∼0.68–1.96 years and carbon footprints between about 15.8 and 38.1 gCO₂-eq/kWh under high-irradiance scenarios, demonstrating that modern utility-scale PV often yields EPBTs below 2 years under favorable conditions (De Wild-Scholten, 2013). National Renewable Energy Laboratory (NREL) and other recent harmonized studies similarly document that modern utility-scale crystalline silicon systems can achieve life-cycle GHGs in the low tens of gCO₂-eq/kWh (e.g. central tendencies near 10–36 gCO₂-eq/kWh in low-carbon manufacturing scenarios), illustrating the strong role of manufacturing grid intensity and site irradiance in final results (Life Cycle Assessment Harmonization | Energy Systems Analysis | NREL, n.d.; Pimentel Pincelli et al., 2024).

Regionally specific studies confirm the sensitivity of results to local parameters: Tawalbeh's review synthesized many LCAs and reported PV carbon footprints spanning 14–73 gCO₂-eq/kWh across studies, noting that assumptions about module lifetime, performance ratio, and local grid mixes usually drive much of the variability (Tawalbeh et al., 2021). Similarly, Akinyele et al. (2017). reported life-cycle emission rates varying from 37.3 to 72.2 gCO₂-eq/kWh for specific small-scale systems, reinforcing that smaller systems or low-irradiance locations typically record higher per-kWh embodied emissions.

Several authors have quantified the benefits of supply-chain decarbonization and recycling: for example, studies show that lowering the carbon intensity of the electricity used in polysilicon and module manufacture can reduce module-level GHGs by tens of percent, whereas implementing effective material recovery and recycling can cut embodied emissions substantially—literature estimates indicate recycling might reduce GHGs by up to ∼30–42% in some scenarios depending on recovery rates and technology maturity. Méndez et al. (2021) demonstrated that using lower-energy silicon feedstocks (e.g. upgraded metallurgical-grade silicon) and optimized wafering reduces climate impacts by ∼20–25% and can bring EPBTs below 1 year under ideal conditions.

Technology and design choices also matter: module design (glass thickness, frame materials, module area) and BOS (balance of system) configuration influence both embodied energy and the per-kWh allocation of these burdens; Polverini et al. (2023) and Virtuani et al. (2023) have quantified that optimally oriented rooftop PVs in European contexts can produce mean carbon intensities ∼36 gCO₂-eq/kWh, while utility-scale systems sited in high-irradiance regions achieve substantially lower values per delivered kWh. This variation underscores that a direct transfer of LCA results from one geography to another is unreliable without contextual adjustments for irradiance and supply-chain specifics (Al-Ghussain et al., 2024).

Specific case studies provide concrete numerical anchors for Jordan-relevant planning: Abu Qadourah et al. (2022) analyzed rooftop PVs in Jordan and found schemes to be economically feasible across climate zones, while Alrwashdeh's Amman residential PV study reported EPBT and levelized cost of electricity (LCOE) metrics contextual to Jordanian solar conditions and tariff structures, offering useful baseline data for system performance and payback assumptions in national LCA scenarios (Alrwashdeh, 2023). These national and regional assessments provide localized performance and economic values which, when combined with OpenLCA process inventories, enable credible gCO₂-eq/kWh estimates for Jordanic PV deployment (Ho et al., 2025).

EoL treatment is another decisive factor: studies on recycling processes (e.g. Shin et al., (2017) on silicon wafer recovery) show that wafer reclamation and material reuse can recover significant embodied energy and material value, thereby lowering net life-cycle burdens; lifecycle studies incorporating advanced recycling report meaningful reductions in both GHGs and resource-depletion indicators when high recovery rates are assumed (Chen et al., 2019). However, current infrastructure and regulatory frameworks in many countries lag behind the technical potential for recycling, limiting real-world impacts.

Despite substantial international literature, two central gaps remain for Jordan and similar Middle East contexts: first, many public LCA inventories reflect European, Chinese, or North American manufacturing footprints and electricity mixes, limiting their direct applicability to Jordan where imported modules, local transport distances, and the national electricity generation mix differ materially; second, while individual LCAs exist, integrated life-cycle sustainability assessments that combine environmental LCA, life-cycle costing, and social LCA at the national or sectoral level are scarce, constraining policy appraisal of trade-offs across environmental, economic, and social dimensions. The present study therefore constructs a transparent OpenLCA process inventory adapted to Jordanian system boundaries, quantifies gCO₂-eq/kWh for multiple scenarios (varying manufacturing electricity mix, module lifetime, recycling rate, and transport logistics), and situates these results against representative fossil generation baselines to inform decarbonization policy and circular economy planning.

Methodology

This research adopts a comprehensive methodological framework that integrates an LCA approach with techno-economic evaluation to assess the performance of a mid-scale PV system located in Amman, Jordan. The methodology follows the ISO 14040/44 guidelines for conducting LCAs, while economic modeling and environmental emission accounting are applied to ensure a holistic representation of both sustainability and feasibility of the project.

Study site

The case study site is situated at Aswaq Al Salam, Al-Jandawil, Amman, Jordan, where a rooftop PV project is installed. Amman's geographical position makes it one of the most suitable cities in the Middle East for solar energy generation, with a global horizontal irradiance averaging $5.5 kWh / m^{2} / day$ and annual sunshine duration exceeding 3000 h. These conditions provide a robust basis for evaluating the performance of PV systems under realistic field operation. The installed system is characterized by an alternating current (AC) output capacity of 250 kW and a direct current (DC) input capacity of 270.655 kWp, ensuring that the DC/AC ratio of ∼1.08 is within the industry's optimal range for maximizing yield while preventing inverter clipping losses.

System configuration and data collection

The PV system consists of 407 HiKu7 modules (665 W each) manufactured by Canadian Solar, covering a total area of $1261.7 m^{2}$ . Two high-efficiency Sungrow SG/125CX inverters, each rated at 125 kW, are employed to convert DC power into grid-compatible AC power. The mounting structure, sourced locally, consists of ∼ $11, 000 kg$ of aluminum and steel, while cabling infrastructure includes $2000 m$ of $1 \times 6 mm$ DC cabling, 40 m of $4 \times 150 mm$ AC cabling, and the associated grounding conductors. These data (shown in Tables 1 and 2) form the basis of the life-cycle inventory (LCI), ensuring that both foreground (primary) and background (secondary) processes are adequately represented.

Table 1.

Data collected from the operator of the case study site.

Case study: Aswaq Al Salam
AC system capacity: 250 kWac
DC system capacity: 270.655 kWp
Item	Model	Quantity	Unit	Manufacturing
PV modules	Hiku7-665W	407.0	PC	Canadian Solar
Inverters	SG/125/CX	2.0	PC	Sungrow
Structure	Roof top	11,000.0	kg	Local
DC cable	1 × 6 mm	2000.0	m	Local
AC cable	4 × 150 mm	40.0	m	Local
Cable earth-AC	1 × 150 mm	40.0	m	Local
Cable earth-DC	6 mm	800.0	m	Local

AC: alternating current; DC: direct current; PV: photovoltaic.

Table 2.

Materials of panels.

Process	Amount	Unit
Solar panel production	407.00	Item (s)
Silicon solar grade production	2971.00	kg
Aluminum, ingot, primary	1017.50	kg
Market for solar glass, low iron	9361.00	kg
Market for electricity, medium voltage	1,139,193.00	MJ
Silver production	8.14	kg
Ethylene vinyl acetate production	325.60	kg
Polyethylene terephthalate	407.00	kg
Treatment of used cable copper cathode	81.40	kg

The monthly average electricity output of the system was estimated using the below equation (Nassar et al., 2023a Nassar et ):

E_{month} = P_{DC} \times H \times P R \times D

(1)

where

P_{DC}

is the installed DC capacity (270.655 kWp), H is the average daily solar irradiation (

5.5 kWh / m^{2} / day

), PR = system performance ratio (dimensionless), accounting for losses due to inverter efficiency, temperature, shading, and wiring, and D is the number of days in a month (30).

Materials inventory and embedded energy

A detailed breakdown of material requirement was carried out to capture the environmental footprint of system manufacturing. The module production requires 2971 kg of solar-grade silicon, 9361 kg of low-iron solar glass, and $1017.5 kg$ of aluminum (Nassar et al., 2025a). The use of 8.14 kg of silver in the conductive layers significantly contributes to both cost and environmental impact due to the high-energy demand in silver mining. The energy input for the module production process was estimated at 1,139,193 MJ, which corresponds to ∼ $316, 443 kWh$ . This embedded energy is crucial in determining the EPBT, calculated as

E P B T = \frac{E_{emb}}{E_{annual}}

(2)

where

E_{emb}

is the embedded energy in manufacturing and

E_{annual}

is the annual energy output of the system.

LCA framework

The life cycle of a PV module was considered from cradle-to-grave, including raw material extraction, polysilicon production, wafering, cell and module manufacturing, transportation, installation, operation, and maintenance. The system boundary includes all material and energy flows associated with these stages. Energy requirements and carbon emissions for each phase were determined using OpenLCA and the Ecoinvent database. The module manufacturing stage contributes the highest embodied energy and GHG emissions, while operation produces negligible direct emissions, highlighting the importance of upstream processes in the PV life cycle. The environmental performance of the PV system was evaluated using a process-based, attributional LCA approach, following ISO 14040/14044 and International Reference Life Cycle Data System (ILCD) guidelines. The analysis was conducted using OpenLCA v1.11 coupled with the Ecoinvent v3.9 database, ensuring comprehensive coverage of all upstream and downstream supply-chain processes. The PV system was disaggregated into four main subsystems: modules, inverters, support structures, and cabling, with associated material and energy flows. The functional unit was defined as 1 kWh of electricity generated over the system's 25-year operational lifetime, capturing the full life-cycle performance of the system (Nassar et al., 2024).

The LCI involved quantifying all relevant inputs (raw materials, components, and energy flows) and outputs (emissions to air, water, and soil, as well as waste) for each subsystem. The LCI is expressed using the below equation:

I = \sum_{i = 1}^{n} Input_{i} - \sum_{j = 1}^{m} Output_{j}

(3)

where

I n p u t_{i}

includes raw materials and energy flows, and

O u t p u t_{j}

includes emissions and waste. All material and energy flows for modules, inverters, support structures, and cables were modeled using Ecoinvent datasets, assuming standard global manufacturing processes. Transportation from manufacturing sites to the installation location was included based on typical logistics distances and modes.

The environmental impact assessment was conducted using the TRACI 2.1 methodology, covering categories such as global warming potential (GWP100), acidification, eutrophication, and resource depletion. The total impact for category k is

E_{k} = \sum_{i = 1}^{n} (L C I_{i} \times C F_{k, i})

(4)

where

C F_{k, i}

is the characterization factor for flow i in category k. The cumulative energy demand (CED) was calculated as

C E D = \sum_{i} (Energy Flow_{i} \times C F_{i})

(5)

To provide an aggregated environmental score, normalization and weighting followed ISO and ILCD recommendations, using reference values for each impact category. Normalized impacts were calculated as shown below:

Normalized Impact = \frac{Impact_{Category}}{Reference_{Category}}

(6)

and aggregated through the below equation:

Weighted Score = \sum_{i} (Impact_{i} \times Weight_{i})

(7)

EoL treatment and recycling of PV components were not explicitly included in the life-cycle model. This choice was made due to the lack of region-specific data on PV recycling infrastructure, recovery efficiencies, and waste management pathways in Jordan. In addition, EoL modeling involves significant uncertainty related to future recycling technologies, material recovery rates, and energy mixes at the time of decommissioning, which could introduce speculative assumptions into the analysis. Therefore, the system boundary was defined as cradle-to-gate with the inclusion of the operational phase.

Ecoinvent database

The LCA was performed using OpenLCA in conjunction with the Ecoinvent v3.9 database, which provides comprehensive, peer-reviewed inventory data for PV modules, inverters, steel mounting structures, and associated components. Ecoinvent was selected due to its international recognition, transparency, and compatibility with widely used LCA software, facilitating reproducibility and comparison with prior studies.

The system boundary includes material extraction, manufacturing, transportation, installation, and the operational phase (electricity generation) of a PV system, defined as cradle-to-gate plus use-phase. EoL recycling and disposal were not included due to the lack of reliable, region-specific data on PV module recycling in Jordan and the inherent uncertainty in predicting future recovery rates and associated energy inputs. This approach provides a conservative estimate of the system's life-cycle environmental impacts and aligns with common practice in PV LCA studies.

The Ecoinvent database was indispensable for providing high-resolution background data with more than 17,000 interconnected datasets covering materials, processes, and emissions. Ecoinvent ensures robust modeling of upstream processes such as silicon purification, aluminum extrusion, and polymer production. For example, the dataset on “silicon solar grade production” provided energy inputs of $180 – 220 MJ / kg$ , consistent with industry data. Similarly, aluminum production was modeled with an energy intensity of $155 – 200 MJ / kg$ , while silver mining was estimated at $7500 MJ / kg$ . These datasets allowed precise quantification of indirect environmental impacts, which would otherwise be underestimated if only primary data were used.

The linkage between OpenLCA and Ecoinvent ensured that all supply-chain emissions were captured, including GHGs ( ${CO}_{2}, {CH}_{4}, N_{2} O$ ), acidifying gases ( ${SO}_{2}, {NO}_{x}$ ), and resource-depletion metrics. This approach also enabled cross-comparison with conventional electricity generation pathways in Jordan, where the grid mix is dominated by natural gas and oil with an average emission factor of $0.712 kg {CO}_{2} / kWh$ . Based on this modeling framework, the following assumptions were adopted:

The LCA was performed using OpenLCA software with background processes sourced from the Ecoinvent database, assuming that these datasets are representative of current global PV manufacturing practices.

Generic (average) datasets were used for PV modules, inverters, mounting structures, cables, and BOS components due to the unavailability of site- or manufacturer-specific inventory data.

Manufacturing energy mixes embedded in Ecoinvent datasets were assumed to reflect global or regional averages, rather than country-specific electricity mixes for individual component suppliers.

The functional unit was defined as 1 kWh of electricity generated by the PV system over a 25-year lifetime, assuming stable system operation over the assessment period.

Transportation stages (raw material to manufacturing, manufacturing to installation site) were modeled using typical transport distances and modes available in Ecoinvent, assuming standard logistics pathways.

Environmental impacts were assessed using 100-year global warming potential (GWP100) characterization factors, consistent with Intergovernmental Panel on Climate Change (IPCC) recommendations.

Economic evaluation

The economic performance of the PV system was evaluated using net present value (NPV), LCOE, and payback period (PBP) to provide both short- and long-term economic insights. The analysis was conducted over a project lifetime of 25 years and accounts for discounting and operational costs.

The LCOE was calculated using the below equation:

L C O E = \frac{\sum_{t = 0}^{N} C_{t} / {(1 + r)}^{2}}{\sum_{t = 0}^{N} E_{t} / {(1 + r)}^{2}}

(8)

where

C_{t}

represents annual costs including operation and maintenance (O&M),

E_{t}

is the annual electricity generation, r is the discount rate (assumed as

5 %

), and N is the system lifetime (25 years). Annual electricity generation accounts for module performance degradation, assumed at 0.7% per year, consistent with manufacturer warranties and literature (Yuan et al., 2024).

The NPV was calculated using the below equation:

N P V = \sum_{t = 1}^{N} \frac{(S_{t} - C_{O & M, t})}{{(1 + r)}^{t}} - C_{inv}

(9)

where

S_{t}

denotes the annual electricity cost savings,

C_{O & M, t}

is the annual operation and maintenance cost, and

C_{inv}

is the initial investment cost.

The PBP was determined using the below equation:

P B P = \frac{C_{inv}}{S_{annual}}

(10)

where

S_{annual}

represents the average annual electricity cost savings. Annual O&M costs were assumed to be

1.5 %

of the initial investment cost (Lemes and Hvam, 2019), consistent with standard PV industry practice. Electricity price escalation was assumed at

2 %

annually (Alrbai et al., 2025) to reflect historical trends in Jordan's electricity tariffs. Additional assumptions include a single inverter replacement at year 12, with replacement costs included within O&M expenditures. Transportation impacts and costs associated with component delivery were incorporated in the LCA using generic Ecoinvent datasets and were assumed to have a negligible effect on the overall economic results due to their relatively small contribution compared to capital and operational costs.

A local one-at-a-time sensitivity analysis was conducted by independently varying each input parameter $x_{j}$ around its baseline value $x_{j, 0}$ , while all other parameters were held constant. The sensitivity coefficient $S_{j}$ is defined as

S_{j} = \frac{Δ I / I_{0}}{Δ x_{j} / x_{j, 0}}

(11)

where

I_{0}

represents the baseline value of the output indicator (e.g. life-cycle emission intensity, LCOE, or NPV), and

Δ I

is the corresponding change in the indicator resulting from a perturbation

Δ x_{j}

in parameter

x_{j}

. For environmental analysis, I corresponds to the life-cycle GHG emission intensity (

kg {CO}_{2}

-eq or

g {CO}_{2} / kWh

), while for economic analysis I represents techno-economic indicators such as LCOE, NPV, or PBP. This normalized formulation enables direct comparison of the relative influence of heterogeneous parameters across both environmental and economic domains.

Results and discussion

In this section, the outcomes of the OpenLCA simulations are presented and analyzed. The assessment focused on estimating GHG emissions, expressed in kilograms of ${CO}_{2}$ -equivalent, during the manufacturing stage of a solar PV system. The analysis covered raw material extraction, processing, cutting, and assembly, all of which contribute to the environmental footprint of the final solar installation. Due to the lack of precise data on transportation distances and methods for all components, the transportation phase was excluded from the analysis. Thus, emphasis was placed on the manufacturing stage, which represents the most significant share of life-cycle emissions.

LCA simulation for all components

Solar panels

Solar panels represent the dominant contributor to the system's environmental footprint. Their production requires energy-intensive processes, particularly the purification of silicon, which must reach a purity level of ∼ $99 %$ to ensure adequate PV efficiency (Nassar et al., 2022c). The reduction of quartz to metallurgical grade silicon occurs in electric arc furnaces operating at temperatures above $1900 \circ C$ (Nassar et al., 2022b). This is followed by further purification through a Siemens process, involving additional high-temperature chemical reactions. These processes alone account for the majority of the emissions associated with solar-grade silicon. The complete process is shown in Figure 1.

Figure 1.

Solar PV life-cycle flow chart. PV: photovoltaic.

As shown in Table 3, the total carbon footprint of the 407 solar panels used in the Aswaq Al Salam system amounted to $173, 338.26 kg {CO}_{2}$ -eq. Silicon purification alone contributed $73.40 %$ of the total, while aluminum production and solar glass contributed $12.30 %$ and $8.43 %$ , respectively. Other components, including silver, ethylene-vinyl acetate, and polyethylene terephthalate, contributed smaller shares but still formed an integral part of the emission profile.

Table 3.

CO₂ emissions associated with solar panel manufacturing.

Contribution (%)	Process	Required amount	Unit	Total result (kg CO₂-eq)
100.00	Solar panel production	407.00	Item (s)	173,338.26
73.40	Silicon solar grade production	2971.10	kg	127,230.28
12.30	Aluminum production	1017.50	kg	21,320.61
8.43	Solar glass, low-iron	9361.00	kg	14,612.42
3.24	Electricity, medium voltage	1,139,193.00	MJ	5616.16
1.19	Silver production	8.14	kg	2062.73
0.88	EVA production	325.60	kg	1525.38
0.55	PET production	407.00	kg	953.36
0.22	Copper cable	81.40	kg	381.34

EVA: ethylene-vinyl acetate; PET: polyethylene terephthalate.

In addition to PV modules, BOS components—including mounting structures, electrical cables, and auxiliary equipment—were explicitly included in the LCI. Although these components contribute a smaller share of the total system mass compared to PV modules, they play an important role in embodied emissions due to the energy-intensive production of materials such as steel and copper (Life Cycle Assessment Harmonization | Energy Systems Analysis | NREL, n.d.). Mounting structures contribute primarily through steel manufacturing, while cables contribute through copper extraction and processing. Furthermore, BOS components influence system performance by affecting electrical losses, particularly resistive losses in cabling, which slightly reduce net electricity output over the system lifetime. Including these components ensures a more comprehensive and realistic representation of the PV system's environmental performance and avoids underestimation of life-cycle GHG emissions (Méndez et al., 2021). The analysis of these components is as follows.

Steel structure

The steel structure, which serves as the mounting frame for the solar panels, also contributes significantly to the overall system emissions (Table 4). The total weight of the structure was $11, 000 kg$ , manufactured from galvanized steel with a zinc coating thickness of $32.5 μ m$ to enhance durability against corrosion. The analysis estimated the total ${CO}_{2}$ impact at $37, 607.8 kg$ , with low-alloyed steel production contributing $87.70 %$ of emissions, followed by zinc coating (7.76%).

Table 4.

${CO}_{2}$ emissions associated with the steel structure.

Contribution (%)	Process	Required amount	Unit	Total result (kg CO₂-eq)
100.00	Steel structure	11,000	kg	37,607.80
87.70	Steel production, low alloyed	10,836.55	kg	32,991.00
7.76	Zinc coating	704.00	$m^{2}$	2916.90
4.52	Section bar rolling, steel	10,836.55	kg	1699.90

Cables

The cable component of the system consisted of DC, AC, and grounding cables, with copper being the dominant material. As summarized in Table 5, the total GHG impact from all cables was 4421.09 kg CO₂-eq. The DC cables contributed $1329.19 kg {CO}_{2}$ -eq, with copper production accounting for $83.10 %$ of the total. The AC cables, more complex in design due to their steel armoring and multiple conductors, contributed $1745.41 kg {CO}_{2}$ -eq.

Table 5.

${CO}_{2}$ emissions associated with system cables.

Contribution (%)	Process	Required amount	Unit	Total result (kg CO₂-eq)
100.00	DC cables (1 × 6 mm²)	2000	m	1329.19
83.10	Copper production process	104.00	kg	1104.90
9.31	Insulation layer polyvinylchloride production	44.00	kg	123.77
6.62	Wire drawing, copper	104.00	kg	87.94
0.95	Extrusion, plastic pipes	44.00	kg	12.58
100.00	AC cables (4 × 150 mm²)	40	m	1745.41
69.80	Copper production process	114.68	kg	1218.36
8.04	Steel production	46.08	kg	140.28
6.85	Polyvinylchloride production	45.88	kg	119.48
5.57	Wire drawing, copper	114.68	kg	97.27
4.92	Metal working, average for steel armoring	46.08	kg	85.85
3.69	Market for polyethylene, low density, granulate	22.92	kg	64.47
1.13	Extrusion, plastic pipes	68.8	kg	19.67
100.00	AC earth cables (1 × 150 mm²)	40	m	644.15
89.10	Copper production process	54.00	kg	573.70
7.09	Wire drawing, copper	54.00	kg	45.66
3.49	Market for polyethylene, low density, granulate	8.00	kg	22.50
0.36	Extrusion, plastic pipes	8.00	kg	2.29

DC: direct current; AC: alternating current.

Inverters

Inverters, which convert DC electricity from solar panels into grid-compatible AC electricity, contributed $8056.95 kg {CO}_{2}$ -eq in total (Table 6). Two 125 kW inverters were modeled based on Ecoinvent datasets.

Table 6.

${CO}_{2}$ emissions associated with inverters.

Contribution (%)	Process	Required amount	Unit	Total result (kg CO₂-eq)
100.00	Inverters	2.00	Item (s)	8056.95
100.00	Market for inverter, 125 kWac	1.00	Item (s)	4028.475

Replacement panels

Given the 25-year operational life, it was estimated that 5% of panels (∼21 units) would need replacement due to potential physical damage or weather-related impacts. The replacement panels added an additional $8943.74 kg {CO}_{2}$ -eq to the total system emissions.

Total GHG impact for the Aswaq Al Salam system

The aggregated emissions of all components, including replacement panels, amounted to $232, 367.84 kg {CO}_{2}$ -eq (Table 7). As expected, solar panels’ contribution is the highest share ( $74.6 %$ ), followed by the steel structure (16.18%), with inverters, cables, and replacements contributing smaller proportions.

Table 7.

Total GHG impact of the Aswaq Al Salam solar system.

Contribution [%]	Item	$kg {CO}_{2}$ result
74.60	Solar panels	173,338.26
16.18	Steel structure	37,607.8
3.85	Replacement panels	8943.74
3.47	Inverters	8056.95
1.90	Cables	4421.09
100	Total impact	232,367.84

GHG: greenhouse gas.

The majority of emissions were associated with silicon purification and panel production, consistent with results from previous LCA studies.

Validation with literature

The calculated carbon footprint per square meter of PV module in this study (137.38 kg CO₂/m²) falls within ranges reported in recent LCAs of crystalline silicon modules. Life-cycle studies have shown that monocrystalline silicon PV modules typically exhibit carbon footprints between ∼100 and 230 kg CO₂/m² depending on manufacturing processes and electricity mixes used during production, supporting the consistency of the present result (Yuan et al., 2024). Similarly, the system-specific emission intensity of 15.59 g CO₂/kWh is consistent with values reported for modern PV systems. According to comprehensive LCA reviews and recent fact sheets, cradle-to-grave GHG emissions for monocrystalline silicon PV systems commonly range from about 14 to 44 g CO₂-eq/kWh depending on location, lifetime assumptions, and energy mix (Stucki et al., 2024). These comparisons demonstrate that the environmental impact results obtained via OpenLCA in this work are in good agreement with published ranges for similar PV technologies and system configurations. It should be noted that the short PBP and favorable economic indicators reported here are specific to the Jordanian context, which combines high solar irradiance, relatively high electricity tariffs, and supportive local policies. In regions with lower insolation, lower tariffs, or different subsidy structures, the economic performance of similar PV systems would differ. Nevertheless, the methodological framework applied in this study—including discounted cash-flow analysis, NPV, and LCOE calculations—can be directly applied to assess site-specific economic feasibility in other geographic and policy contexts.

Comparison with grid electricity

Over its 25-year lifespan, the solar system is projected to generate 11,114,181.56 kWh of electricity. If this same amount of energy were supplied by the Jordanian grid, with an emission factor of $0.7122 kg {CO}_{2} / kWh$ , the total emissions would reach $7, 915, 520.10 kg {CO}_{2}$ . By comparison, the PV system resulted in $232, 367.84 kg {CO}_{2}$ , representing a $97.06 %$ reduction in emissions. This confirms the substantial environmental benefit of transitioning to solar PVs as an alternative to conventional gas- and diesel-fired power plants.

Off-grid system scenario

For the off-grid scenario, additional battery storage of ∼3 MWh with 2 days of autonomy was required. Incorporating lithium-ion battery manufacturing emissions resulted in an additional 199,561.79 $kg {CO}_{2}$ -eq (Table 8). When combined with the baseline system, the off-grid configuration produced a total of $431, 929.63 kg {CO}_{2}$ -eq.

Table 8.

${CO}_{2}$ emissions from battery storage (3 MWh system).

Contribution (%)	Process	Required amount	Unit	Total result (kg CO₂-eq)
100.00	Battery	1	Item	199,561.79
36.35	Aluminum	4875	kg	72,533.50
15.87	Lithium production	390	kg	31,674.62
14.73	Cobalt sulfate production	975	kg	29,393.50
14.66	Copper production	1365	kg	20,775.19
10.41	Anode, graphite	2925	kg	18,375.87
9.21	Nickel sulfate	1950	kg	14,957.04
3.09	Polyethylene	1950	kg	6168.53
1.22	Electrolyte production	1170	kg	3253.51
0.98	Steel, low-alloyed	2925	kg	2430.04

The off-grid configuration, while enabling independence from the grid, significantly increased emissions compared to the grid-connected option. This highlights the importance of balancing autonomy and sustainability in PV system design.

Overall, the comparative analysis of the three scenarios—conventional grid electricity, grid-connected PV, and off-grid PV with battery storage—reveals pronounced differences in environmental performance. The grid-connected PV system exhibits the lowest life-cycle emissions at 232,367.84 kg CO₂-eq (15.59 g CO₂/kWh), achieving a 97.06% reduction relative to grid electricity. This substantial mitigation potential highlights the effectiveness of grid-connected PV in decarbonizing electricity supply while avoiding the additional material and energy burdens associated with large-scale storage. The off-grid PV configuration, although environmentally superior to the conventional grid, nearly doubles the footprint of the grid-connected case due to emissions from battery manufacturing, reaching 431,929.63 kg CO₂-eq. In contrast, continued reliance on the Jordanian grid would result in 7,915,520.10 kg CO₂ over the system lifetime. Collectively, these findings underscore that grid-connected PV represents the most environmentally sustainable pathway, offering deep emission reductions while maintaining system reliability and minimizing life-cycle environmental impacts. Although EoL recycling was not included in the quantitative LCA, existing studies indicate that incorporating PV module recycling can further reduce life-cycle GHG emissions by recovering energy-intensive materials such as aluminum, glass, and silicon. Consequently, the environmental impacts reported in this study can be considered conservative estimates. Future work should integrate detailed EoL scenarios and recycling credits once region-specific data and recycling pathways for PV systems in Jordan become available, enabling a more comprehensive circular economy assessment.

Techno-economic performance

The grid-connected PV system not only achieves a significant reduction in GHG emissions (232,367.84 kg CO₂-eq, 15.59 g CO₂/kWh) but also generates substantial electricity over its 25-year lifetime, directly translating into economic savings. The high-energy output ensures that the avoided emissions from conventional grid electricity correspond proportionally to the financial benefits. To complement the environmental LCA, a discounted techno-economic evaluation was conducted to assess the long-term financial feasibility of the grid-connected PV system. The total capital investment was estimated at USD169,200, resulting in first-year electricity cost savings of USD92,891 under current tariff conditions. The economic analysis was performed over a 25-year project lifetime and explicitly accounts for the time value of money through discounting, annual O&M costs, and electricity price escalation. Using a constant discount rate, the NPV of the project was estimated at approximately USD979,950, indicating a highly profitable investment over the system lifetime. Furthermore, the LCOE was calculated as USD0.024/kWh, which is substantially lower than the prevailing grid electricity tariff in Jordan, underscoring the strong cost competitiveness of the system. Although the simple PBP remains short at 1.82 years, the inclusion of discounted economic indicators confirms that the financial attractiveness of the project extends well beyond rapid capital recovery, demonstrating the long-term economic robustness of grid-connected PV deployment under local market conditions.

Sensitivity analysis

To enhance robustness, uncertainty ranges for key inputs were defined based on literature: PV module embodied carbon (≈800–1200 kg CO₂-eq/kWp), grid emission factor (±15–20%), performance ratio (0.70–0.85), Capital Expenditure (CAPEX) and electricity tariff (±20–30%), annual energy yield (±10–15%), discount rate (6–10%), and degradation rate (0.3–0.8%/year). These ranges are consistent with International Energy Agency (IEA), IPCC, NREL, and International Renewable Energy Agency (IRENA) benchmarks (PVPS Task, 2020). A uniform ±20% variation was therefore adopted in the sensitivity analysis as a conservative envelope capturing the dominant real-world uncertainty of all parameters.

Figure 2 illustrates the sensitivity of GHG emission intensity to ±20% variations in key PV system components and performance parameters. It is evident that the most influential factors are the module embodied carbon and the grid emission factor, as they produce the largest positive and negative deviations in the GHG intensity. This indicates that the carbon footprint of PV modules and the carbon intensity of the electricity grid are the dominant drivers of lifecycle emissions. Conversely, components such as cables, inverters, and steel structures have a much smaller impact, suggesting that their variations contribute relatively little to the total emissions. The chart also highlights the asymmetric effect of the performance ratio, where a 20% decrease substantially increases GHG intensity, while a 20% improvement reduces it proportionally. The base intensity line confirms the relative magnitude of the baseline system emissions. Overall, the trends emphasize that efforts to reduce lifecycle emissions should prioritize low-carbon modules and sourcing electricity from cleaner grids, while minor optimizations in BOS components have limited effect.

Figure 2.

Sensitivity analysis of life-cycle carbon intensity (±20% variations).

Figure 3 presents a combined normalized sensitivity analysis for NPV, LCOE, and PBP under ±20% variations in seven techno-economic parameters. The chart shows that annual energy output and electricity tariff are the most influential inputs across all metrics. In particular, increases in annual energy or electricity tariff strongly improve NPV and reduce PBP, highlighting the system's revenue dependence on energy production and market pricing. Conversely, LCOE is more sensitive to CAPEX and discount rate, which directly affect the cost per unit of electricity. Other parameters such as O&M cost, degradation rate, and tariff escalation show moderate effects, while the ranking of sensitivities varies across the three metrics. For example, CAPEX has a larger impact on PBP and LCOE than on NPV, whereas tariff escalation mostly affects NPV. These trends demonstrate that financial performance is governed by a combination of revenue-related and cost-related parameters, and the sensitivity analysis provides clear guidance on which variables require careful estimation and management in project planning.

Figure 3.

Economic sensitivity analysis of the system (±20% variations).

Finally, Table 9 shows that the environmental and economic performance of the present PV system is consistent with, and in some aspects superior to, comparable systems reported in the literature. The life-cycle GHG intensity obtained in this study (15.59 g CO₂-eq/kWh) lies below the ranges reported by Hsu et al. (2012) and Hou et al. (2016) which mainly reflects higher solar irradiation and longer effective energy yield at the Jordanian site. Economically, the achieved LCOE (USD0.024/kWh) is very close to the value reported by Ali et al. (2023) and lower than that reported by Saifeddin et al. (2023). In addition, the simple PBP of 1.82 years is shorter than or comparable to those reported in studies by Saifeddin et al. and Ali et al., indicating faster cost recovery. Overall, the comparison confirms that the present results are realistic and fall well within the performance envelope of real PV systems reported worldwide.

Table 9.

Comparison of the environmental and economic performance of the current study with representative photovoltaic systems reported in the literature.

Study	System size (kW)	Reported GHG intensity	Reported LCOE (USD/kWh)	Reported simple payback (years)
This study	270.665	15.59 g CO₂-eq/kWh	0.024	1.82
Hsu et al. (2012)	1700	44–48 g CO₂-eq/kWh	–	–
Hou et al. (2016)	660	60.1–87.3 g CO₂-eq/kWh	–	1.6–2.3
Saifeddin et al. (2023)	134.55	141.71 tons per year	∼0.03–0.036	∼3.4
Ali et al. (2023)	1500	∼1320 tons per year	0.023	2.2–3.2

GHG: greenhouse gas; LCOE: levelized cost of electricity.

Conclusion

The results of this study highlight the dual environmental and economic advantages of solar PV adoption in Jordan. The LCA confirmed that the total GHG footprint of the grid-connected system is 232,367.84 kg CO₂-eq, which translates to 15.59 g CO₂/kWh, aligning with international benchmarks for mono-Si PV modules. In contrast, grid electricity would have generated 7,915,520.10 kg CO₂ over 25 years, while the off-grid system increased emissions to 431,929.63 kg CO₂-eq due to battery production. From an economic perspective, the grid-connected PV system demonstrates both short- and long-term viability. The total investment cost of USD169,200 results in first-year savings of USD92,891. When accounting for discounting, annual O&M costs, and electricity price escalation, the system achieves an NPV of approximately USD979,950 over 25 years and an LCOE of USD0.024/kWh, which is significantly lower than the prevailing grid electricity tariff. The simple PBP remains short at 1.82 years, confirming rapid capital recovery alongside long-term financial robustness. When comparing the three scenarios, the grid-connected PV system stands out as the most sustainable option, achieving the highest emission reductions while delivering rapid cost recovery. The off-grid configuration, although environmentally superior to the conventional grid, carries a significantly higher footprint due to battery manufacturing. The analysis clearly demonstrates that transitioning to grid-connected PV systems in Jordan can drastically reduce national carbon emissions while ensuring strong economic returns, supporting both climate goals and energy security.

The integration of environmental and economic indicators underscores the dual benefit of grid-connected PV deployment in Jordan. High electricity generation ensures substantial GHG emission reductions, while simultaneously driving significant cost savings and a short PBP. This clear linkage between energy output, environmental performance, and financial returns reinforces the importance of PV systems as a sustainable and economically viable solution for the country's energy transition. Also, this study provides evidence to accelerate grid-connected solar deployment, strategically phase-in energy storage, and establish a circular economy framework through PV module recycling standards in Jordan. Finally, this study is subject to several limitations. The LCA relies on generic LCI data and does not explicitly model EoL treatment or recycling due to data limitations, which may lead to conservative impact estimates. In addition, the economic analysis assumes constant discount rates and representative tariff escalation, whereas future market and policy conditions may vary. Future research should incorporate region-specific recycling pathways, dynamic economic parameters, and sensitivity analyses to further refine the environmental and economic performance of PV systems.

Footnotes

ORCID iDs

Mohammad Alrbai

Jamil AlAsfar

Muhmmad Saeed

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

References

Abu Qadourah

(2022) Energy and economic potential for photovoltaic systems installed on the rooftop of apartment buildings in Jordan. Results in Engineering 16: 100642.

Abu Qadourah

Alfalahat

Alrwashdeh

(2022) Assessment of solar photovoltaics potential installation into multi-family building’s envelope in Amman, Jordan. Cogent Engineering 9(1).

Akinyele

Rayudu

Nair

NKC

(2017) Life cycle impact assessment of photovoltaic power generation from crystalline silicon-based solar modules in Nigeria. Renewable Energy 101: 537–549.

Al Asfar

Alrbai

Qudah

(2023) Energy analysis of a hybrid parabolic trough collector with a steam power plant in Jordan. Energy Exploration and Exploitation 41(6): 1850–1868.

Al-Dahidi

Ayadi

Alrbai

, et al. (2019) Ensemble approach of optimized artificial neural networks for solar photovoltaic power prediction. IEEE Access 7: 81741–81758.

Al-Ghussain

Alrbai

Al-Dahidi

(2025) Comprehensive techno-economic and life cycle greenhouse gases analysis of green ammonia production utilizing PV and wind energy: Jordan as a case study. Renewable Energy 249: 123249.

Al-Ghussain

Alrbai

Al-Dahidi

, et al. (2024) Integrated assessment of green hydrogen production in California: Life cycle greenhouse gas emissions, techno-economic feasibility, and resource variability. Energy Conversion and Management 311: 118514.

Ali

Sarker

Hossain

, et al. (2023) Techno-economic feasibility study of a 1.5 MW grid-connected solar power plant in Bangladesh. Designs 7(6): 40.

Alrbai

Al-Ghussain

Al-Dahidi

, et al. (2025) Techno-economic assessment of residential PV system tariff policies in Jordan. Utilities Policy 93: 101894.

10.

Alrwashdeh

(2023) Energy profit evaluation of a photovoltaic system from a selected building in Jordan. Results in Engineering 18: 101177.

11.

Amer Ali Amer

Irhouma

Hamdan

, et al. (2025) Economic-environmental-energetic (3E) analysis of photovoltaic solar energy systems: Case study of mechanical & renewable energy engineering departments at Wadi AlShatti University. Wadi Alshatti University Journal of Pure and Applied Sciences 51–58. doi:10.63318/waujpasv3i1_09

12.

Aqila

Nassar

El-Khozondar

, et al. (2025) Design of hybrid renewable energy system (PV/wind/battery) under real climatic and operational conditions to meet full load of the residential sector: A case study of a house in Samno Village—southern region of Libya. Wadi Alshatti University Journal of Pure and Applied Sciences 168–181. doi:10.63318/waujpasv3i1_23

13.

Chen

Morita

, et al. (2019) Boron removal for solar-grade silicon production by metallurgical route: A review. Solar Energy Materials and Solar Cells 203: 110169.

14.

De Wild-Scholten

(2013) Energy payback time and carbon footprint of commercial photovoltaic systems. Solar Energy Materials and Solar Cells 119: 296–305.

15.

El-Khozondar

El-batta

El-Khozondar

, et al. (2021) Optimal design of a standalone hybrid energy system for a Covid-19 quarantine center energization. SSRN Electronic Journal 9.

16.

El-Khozondar

El-batta

(2022) Solar energy implementation at the household level: Gaza Strip case study. Energy, Sustainability and Society 12(1): 17.

17.

El-Khozondar

El-batta

El-Khozondar

, et al. (2023) Standalone hybrid PV/wind/diesel-electric generator system for a COVID-19 quarantine center. Environmental Progress & Sustainable Energy 42(3): e14049.

18.

Elmnifi

Khaleel

Yusupov

, et al. (2025) Economic and environmental benefits of recycling electrical and electronic waste products in North African countries. Environmental Science and Engineering Part F 636: 55–93.

19.

KHY

AWL

Son

, et al. (2025) Environmental impact assessment of solar panel production and recycling in Southeast Asia. Journal of Cleaner Production 522: 146277.

20.

Hou

Sun

Jiang

, et al. (2016) Life cycle assessment of grid-connected photovoltaic power generation from crystalline silicon solar modules in China. Applied Energy 164: 882–890.

21.

Hsu

O’Donoughue

Fthenakis

, et al. (2012) Life cycle greenhouse gas emissions of crystalline silicon photovoltaic electricity generation. Journal of Industrial Ecology 16(Suppl.1): S122–S135.

22.

Khan

Reichel

Molina

, et al. (2024) Global warming potential of photovoltaics with state-of-the art silicon solar cells: Influence of electricity mix, installation location and lifetime. Solar Energy Materials and Solar Cells 269: 112724.

23.

Lemes

Hvam

(2019) Maintenance costs in the process industry: A literature review. In: IEEE International conference on industrial engineering and engineering management, pp.1481–1485. IEEE Computer Society.

24.

Life Cycle Assessment Harmonization | Energy Systems Analysis | NREL (n.d.). Available at: https://www.nrel.gov/analysis/life-cycle-assessment (accessed 6 October 2025).

25.

Méndez

Forniés

Garrain

, et al. (2021) Upgraded metallurgical grade silicon and polysilicon for solar electricity production: A comparative life cycle assessment. Science of The Total Environment 789: 147969.

26.

Mohammed

Nassar

El-Osta

, et al. (2023) Carbon and energy life cycle analysis of wind energy industry in Libya. Journal of Solar Energy and Sustainable Development. 2023.ajol.info. Epub ahead of print 2023.

27.

Nassar

Alsadi

El-Khozondar

, et al. (2022a) Design of an isolated renewable hybrid energy system: A case study. Materials for Renewable and Sustainable Energy 11(3): 225–240.

28.

Nassar

Alsadi

Miskeen

, et al. (2022b) Mapping of PV solar module technologies across Libyan territory. In: 2022 Iraqi international conference on communication and information technologies, IICCIT 2022, pp.227–232. Institute of Electrical and Electronics Engineers Inc.

29.

Nassar

Amer

El-Khozondar

, et al. (2023a) Thermoelectrical analysis of a new hybrid PV-thermal flat plate solar collector. In: 8th International engineering conference on renewable energy and sustainability, ieCRES 2023. Institute of Electrical and Electronics Engineers Inc. Epub ahead of print 2023. DOI: 10.1109/IECRES57315.2023.10209472.

30.

Nassar

El-Khozondar

Alsadi

, et al. (2022c) Atlas of PV solar systems across Libyan territory. In: Proceedings—2022 International conference on engineering and MIS, ICEMIS 2022. Institute of Electrical and Electronics Engineers Inc. Epub ahead of print 2022. DOI: 10.1109/ICEMIS56295.2022.9914355.

31.

Nassar

El-Khozondar

El-Osta

, et al. (2024) Carbon footprint and energy life cycle assessment of wind energy industry in Libya. Energy Conversion and Management 300: 117846.

32.

Nassar

El-Khozondar

Khaleel

, et al. (2025a) Sensitivity of global solar irradiance to transposition models: Assessing risks associated with model discrepancies. e-Prime—Advances in Electrical Engineering, Electronics and Energy 11: 100887.

33.

Nassar

Irhouma

Salem

, et al. (2025b) Towards green economy: Case of electricity generation sector in Libya. Solar Energy and Sustainable Development Journal 14(1): 334–360.

34.

Pimentel Pincelli

Hinkley

Brent

(2024) Carbon, materials and energy footprint of a utility-scale solar plant in Aotearoa New Zealand. Solar Energy 273: 112535.

35.

Polverini

Espinosa

Eynard

, et al. (2023) Assessing the carbon footprint of photovoltaic modules through the EU Ecodesign Directive. Solar Energy 257: 1–9.

36.

PVPS Task I (2020) Trends in PV applications 2020. Available at: https://iea-pvps.org/trends_reports/trends-in-pv-applications-2020/ (accessed 16 December 2025).

37.

Saifeddin

Jallad

, ‫ جالد

الحفيظ

et al. (2023) Performance evaluation, economic assessment and environmental impact of a 134.55 kWp grid connected solar photovoltaic (PV) power plant in Palestine. Palestine Technical University Journal—Kadoorie 11 (4): 1–23.

38.

Shin

Park

(2017) A method to recycle silicon wafer from end-of-life photovoltaic module and solar panels by using recycled silicon wafers. Solar Energy Materials and Solar Cells 162: 1–6.

39.

Stanbery

Woodhouse

van de Lagemaat

(2023) Photovoltaic deployment scenarios toward global decarbonization: Role of disruptive technologies. Solar RRL 7(12): 2300102.

40.

Stucki

Götz

De Wild-Scholten

(2024) Fact Sheet: Environmental life cycle assessment of electricity from PV systems. Epub ahead of print, 2 May 2024. DOI: 10.69766/ALGS2169.

41.

Tawalbeh

Al-Othman

Kafiah

, et al. (2021) Environmental impacts of solar photovoltaic systems: A critical review of recent progress and future outlook. Science of The Total Environment 759: 143528.

42.

Virtuani

Borja Block

Wyrsch

, et al. (2023) The carbon intensity of integrated photovoltaics. Joule 7(11): 2511–2536.

43.

Yuan

Nain

Kothari

, et al. (2024) Material intensity and carbon footprint of crystalline silicon module assembly over time. Solar Energy 269: 112336.