TY  - JOUR
AU  - Raygoza-Limón, María E.
AU  - Trujillo-Hernández, Gabriel
AU  - Garcia, José Alejandro Amezquita
AU  - Mercado-Herrera, Abelardo
AU  - Ling-López, Juan Carlos
AU  - Murrieta-Rico, Fabian N.
PY  - 2026
DA  - 2026/09/11
TI  - Scenario-Based Assessment of Life-Cycle GHG Emissions from Grid-Connected Photovoltaic Systems under Different Electricity Mixes
JO  - Journal of Energy and Power Technology
SP  - 018
VL  - 08
IS  - 03
AB  - This study evaluates the carbon-related performance of grid-connected photovoltaic (PV) systems under different electricity-generation mixes using a literature-based, scenario-oriented life-cycle GHG emissions assessment. A 1 MWp utility-scale crystalline-silicon PV reference system was defined with an annual electricity generation of 1,640 MWh, a 30-year lifetime, and an annual degradation rate of 0.5%, resulting in a projected cumulative electricity output of 45,793.99 MWh. The analysis considered three benchmark electricity-mix scenarios: a high-carbon grid, a transitioning grid, and a low-carbon benchmark grid. The gross life-cycle GHG burden of the PV system was interpreted using a literature-based range of 10-36 g CO2e/kWh, with 19 g CO2e/kWh adopted as the median reference case. Results show that scenario-estimated avoided emissions and net GHG benefits are highest in carbon-intensive electricity systems and decrease progressively as grid carbon intensity declines. Nevertheless, the net GHG benefit remains positive across all benchmark scenarios and across the tested PV emission-factor range. The findings indicate that PV deployment provides the greatest immediate GHG mitigation in fossil-intensive grids, while long-term improvements depend on cleaner manufacturing, low-carbon supply chains, circular economy strategies, and responsible end-of-life management. Because the analysis relies on literature-derived PV emission factors and average grid carbon-intensity benchmarks, the results should be interpreted as comparative scenario estimates rather than as outputs from a full process-based LCA model.
SN  - 2690-1692
UR  - https://doi.org/10.21926/jept.2603018
DO  - 10.21926/jept.2603018
ID  - Raygoza-Limón2026
ER  -