README file
Data Set Title: “An open access dataset on alternative energy scenarios in a commercial vertical farm in Italy”
Data Set Author: Teresa Piovano (University of Bologna), ORCID 0009-0007-9486-1184; Verónica Arcas Pilz (University of Bologna), ORCID 0000-0002-6854-3138; Giuseppina Pennisi (University of Bologna), ORCID 0000-0001-9377-4811; Giorgio Gianquinto (University of Bologna), ORCID 0000-0002-6548-5526; Michale Martin (KTH Royal Institute of Technology, Stockholm / IVL Swedish Environmental Research Institute), ORCID 0000-0003-3014-8930; Francesco Orsini (University of Bologna), ORCID 0000-0001-6956-7054 
Data Set Contact Person: Verónica Arcas Pilz (University of Bologna), ORCID 0000-0002-6854-3138, veronica.arcaspilz2@unibo.it
Data Set License: this data set is distributed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/  
Publication Year: 2026
Project Info: 
•	Svenska Forskningsrådet Formas project “Improving the resource-efficiency and environmental performance of urban-vertical farms through circular-based production methods”, Grant Number: 2021-02447, https://www.vr.se/english/swecris.html#/project/2021-02447_Formas;
•	Project MADLoop (Material circularity for sustainable soilless Agriculture systems in Developing countries). Funded by the European Union – NextGenerationEU through the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2, Investiment 1.2, Young Researchers 2024 SOE programme. MUR Grant Number SOE2024_0000136, CUP ID J33C24003160001; https://site.unibo.it/madloop/it, https://sites.google.com/view/madloop/home. 
Data set Contents
The data set consists of: 
•	1 quantitative data file saved in .xlsx format
“Dataset_Energy_Scenarios_in_VF.xlsx”
•	1 README file saved in .rtf format
“README_Energy_Scenarios_in_VF.rtf”

Data set Documentation
Abstract
The following dataset contains data used for the publication "The Power Behind the Plants: Energy Scenarios in Vertical Farming" (DOI 10.1002/fes3.70234). The study reported in this paper aims to evaluate alternative energy strategies for the production of lettuce and it is based on a cradle-to-grave life cycle assessment (LCA) of a large-scale commercial vertical farm company producing lettuce, located in northern Italy (in the Lombardy region). The data obtained from the private company were used to elaborate the LCA inventory, this data was then further elaborated using Ecoinvent databases to generate the subsequent LCA results. The dataset includes a glossary for the terms and acronyms used in the LCA methodology, both referring to the impact categories as well as the inputs and processes used to describe the system. It also contains key tables relevant for the calculation or representation of the manuscript results. It indicates the impact on the climate change indicator of alternative energy sources for the production of lettuce as well as the specific sensitivity on the incoming feed when considering Biomethane as energy source. Finally, nine tables have been included which detail the contribution of all inputs and processes to the production of lettuce for all the impact categories in different energy scenarios: 1) Baseline - Biomethane; 2) Grid; 3) Natural Gas; 4) Mix Renewables; 5) Hydro-Run-off; 6) Hydro Reservoir; 7) Wind; 8) Photovoltaic.
More details about the effect of the different energy strategies can be found in the related paper. The data were generated in the framework of the European Union - Next Generation EU, Mission 4 Component 2 “MADLoop” project (CUP J33C24003160001) as well as Svenska Forskningsrådet Formas project “Improving the resource-efficiency and environmental performance of urban-vertical farms through circular-based production methods” (Grant Number: 2021-02447).
Content of the file
The file “Dataset_Energy_Scenarios_in_VF.xlsx” contains thirteen sheets reporting: 
•	the table of contents (sheet “Table of contents”);
•	the glossary for the terms and acronyms used in the LCA methodology, both referring to the impact categories as well as the inputs and processes used to describe the system (sheet “A1”);
•	a detailed account of all materials and processes from the Ecoinvent database, added to allow for a full transparency of the life cycle inventory (LCI) reported in the related paper (sheet “A2”);
•	the different figures and graphs presented in the related paper and the data used to creates the graphs (sheet “A3”. For more information see the related paper); 
•	the different tables presented in the related paper (sheet “A4”, “Table 1” to “Table 6”. For more information see the related paper); 
•	the different tables with the detailed data on climate change impact and overall contribution of inputs and processes to all the impact categories with different energy scenarios (sheets “A5” to “C5”).
All tables have been labeled for better understanding and units for each indicator have been added.
File specifics
The file is in .xlsx format and can be easily used.
Notes 
The data are presented in the following publication:
T.Piovano, V.Arcas-Pilz, G.Pennisi, G.Gianquinto, M.Martin, and F.Orsini. 2026. “The Power Behind the Plants: Energy Scenarios in Vertical Farming”. Food and Energy Security 15, no. 2: e70234. https://doi.org/10.1002/fes3.70234.
Methodologies 
This study reports a cradle-to-grave life cycle assessment (LCA) of a large-scale commercial vertical farm producing lettuce in the Lombardy region of northern Italy (the vertical farm company information and exact location have not been disclosed). 
The data obtained from the private company were used to elaborate the LCA inventory, this data was then elaborated to generate subsequent tables within the dataset and in the result section of the paper. The data elaboration required the use of Ecoinvent databases version 3.8 which can be accessed under the appropriate license. Still input and process amounts have been detailed to be reproduced. Sheet “A2” provides the list of chosen materials and processes in the Ecoinvent database for the LCI reported in the related paper.
The functional unit was defined as 1 kg of edible lettuce. Environmental impacts were assessed applying the Environmental Footprint (EF) 3.1 characterization method across nine impact categories: climate change, acidification, terrestrial ecotoxicity, freshwater eutrophication, marine eutrophication, land use, water use, and abiotic depletion of fossil and mineral resources. The current operational configuration, based on biomethane-fueled combined heat and power (CHP) cogeneration, was evaluated against four alternative energy pathways: the Italian national electricity grid, natural gas cogeneration, renewable electricity procurement, and biomethane production from alternative feedstock compositions.
List of Scenarios analyzed
Parameter	Description	
Scenarios	Biomethane CHP powered by Biomethane (from average biomethane - Ecoinvent default) + Italian energy grid)
Biomethane S1 CHP powered by Biomethane (from 50% cattle manure + 50% organic waste) + Italian energy grid
Biomethane S2 CHP powered by Biomethane (from 50% cattle manure + 50% sorghum waste) + Italian energy grid
Biomethane S3 CHP powered by Biomethane (from 50% cattle manure + 50% organic waste -transport only) + Italian energy grid
Biomethane S4 CHP powered by Biomethane (from 100% organic waste) + Italian energy grid
Natural Gas CHP powered by Natural gas + Italian energy grid
Grid powered by 100% Italian energy grid
Biomethane Hydro reservoir CHP powered by Biomethane + hydro grid
Biomethane Hydro run off river CHP powered by biomethane + run-of-river hydro grid
Biomethane Wind CHP powered by biomethane + wind grid
Biomethane Photovoltaic CHP powered by biomethane + Photovoltaic grid
Biomethane Mix renewable energy CHP powered by biomethane + grid electricity from aggregated renewables
Grid Hydro reservoir powered by 100% hydropower grid
Grid Hydro run off river powered by 100% run-off-river hydropower grid
Grid Wind powered by 100% wind grid
Grid Photovoltaic powered by 100% photovoltaic grid
Grid Mix renewable energy powered by 100% grid electricity from aggregated renewables