Supplementary Data for Lopez-Reyes et al. (2026) "Decarbonizing desert greenhouse crop production with direct air capture–based CO₂ enrichment"

This dataset accompanies the manuscript Lopez-Reyes et al. (2026) "Decarbonizing desert greenhouse crop production with direct air capture–based CO₂ enrichment" published in npj Sustainable Agriculture and contains the following files:

• Greenhouse CO2 balance.zip: this dataset contains the co2Balance.py script and the weather data to model the crop photosynthesis, greenhouse CO2 mass balance and break-even CO2 price, in a lettuce and cherry tomato high-tech greenhouse located in hot-desert climate (Jeddah, Saudi Arabia).
• Master TEA.zip: this dataset contains the DAC_for_greenhouses_TEA.py script and input data (Master TEA.xlsx) used to size the DAC-E (TVSA and MSA) and conventional enrichment systems to meet greenhouse CO₂ demand, and to model their capital, operational, and levelized costs. The code also includes functionality to perform sensitivity analyses on selected input parameters.
• Master LCIA.xlsx: This dataset contains the life cycle inventory and results for an ISO 14040/14044–compliant cradle-to-farm-gate LCA comparing on-site adsorption DAC-based CO₂ enrichment (TVSA and MSA) with trucked liquid-CO₂ (ConvE) in high-tech greenhouses located in hot-desert climate (Jeddah, Saudi Arabia). The model was developed in openLCA v2.5 using background data from ecoinvent v3.11 (cut-off) and covers the greenhouse structure, mechanical cooling system, growing system, and CO₂-enrichment subsystems over a 20-year lifetime, with 1 kg marketable tomato or lettuce as the primary functional unit. Scenarios vary DAC cyclic productivity, liquid-CO₂ transport distance, and electricity mix (grid, PV, and grid + PV), with impacts assessed by the IPCC 2021 GWP100 method and uncertainties evaluated through 1,000-run Monte Carlo simulation.