Developed by Lawrence Berkeley National Laboratory in collaboration with the Resource Innovation Institute, the Controlled Environment Agriculture (CEA) Screening Tool is part of a two-year project funded by the U.S. Department of Energy to promote water- and energy- efficient controlled environment agriculture facilities. The goal of the tool is to aid in early stage feasibility assessments by visualizing data relevant for CEA site selection and providing site-specific baseline estimates of water, energy, and carbon intensity. The tool also demonstrates how implementing different sets of technologies can reduce resource consumption and, therefore, economic burden on growers.

The data included in the tool was curated based on conversations with industry stakeholders regarding pertinent information for CEA site selection. Because not all of the data layers suggested were publicly available at the national level at the time of tool development, some data sources (e.g., water and wastewater lines) are available for only five community partners (Riverside, California; Kaua'i, Hawaii; York County, Pennsylvania; Augusta, Georgia; and the Western Lake Erie Region). If you have interest and means of improving the data availability in your region, please contact us at ceascreeningtooldev@gmail.com.

The data included in the tool represents, to the best of our knowledge, the most reliable, publicly available data at the time of tool development (2024). However, more accurate and/or complete data sources may have become available since then. Additionally, many of the datasets included herein have limitations (overviewed in the "Data Sources" tab of this website and detailed in the source documentation) that users should familiarize themselves with prior to using them for analysis, decision-making, or any other application. The U.S. Department of Energy assumes no responsibility for the accuracy or completeness of this data.

To approximate the baseline energy and water demands shown in the tool interface, we developed a simplified model that integrates fundamental lighting, heating, cooling, and evapotranspiration principles. Validated using empirical data from the Resource Innovation Institute, this model can provide order-of-magnitude estimates of resource demand and be used to compare different facility configurations (e.g., location, sizes, type, crop, and technological complexity). More detail on this model can be found in Hodson et al., (2025), in Review). For more information on the technology implementation pathways that impact resource consumption, please see Aghajanzadeh et al. (2025).

Our facility size categories are as follows:
Small (< 10,000 sq ft)
Medium (10,000 - 25,000 sq ft)
Large (> 25,000 sq ft)

The data displayed in the tool is up-to-date as of September 2024. While we do not currently have plans to perform tool updates, the schedule will be posted on this website should funding for tool maintenance and/or expansion become available.

Questions or feedback? We'd love to hear from you at ceascreeningtooldev@gmail.com!

No. This tool is designed for early-stage feasibility assessments and site selection. The estimates provided are high-level and intended to help users compare relative impacts of different locations, facility types, and technologies. For detailed design, financial projections, or investment decisions, you should consult with qualified engineering and financial professionals.

The tool's model is generalized for common CEA crops including tomatoes, zucchini, basil, broccoli, cucumbers, lettuce, and roses which have well-documented resource requirements. Users can select one or multiple crop types for modeled facilities in the tool which influences the evapotranspiration and lighting demand calculations. While it can provide a general estimate for other similar crops, the accuracy may vary. The underlying model assumptions are detailed in "A Simplified Approach to Energy-Water-Carbon Modeling for Controlled Environment Agriculture" (Hodson et al., 2025).

Though the tool includes local utility rates for electricity and, in some cases, potable water as a data layer, our baseline estimates do not include cost or account for rebates/incentives for energy efficiency or water conservation. Subsequently, users will need to obtain this information locally to translate the resource consumption estimates into operational costs and assess economic feasibility.

The tool allows users to select between different conceptual CEA facility types, primarily distinguishing between greenhouses, hoop-houses and plant factories (indoor vertical farms). The choice of facility type significantly influences the assumptions made about lighting, HVAC, and water systems in the baseline model.

These pathways represent predefined packages of common energy and water-efficient technologies (e.g., LED lighting, high-efficiency HVAC, water recirculation systems, advanced glazing). When a user selects a technology pathway, the tool adjusts the baseline resource consumption estimates to reflect the potential savings achievable with that set of technologies. These adjustments are based on research detailed inAghajanzadeh et al. (2025).

The baseline model assumes typical operational practices for a standard CEA facility of the selected type, size, and crop. This includes generalized assumptions about factors like lighting hours and intensity (for supplemental or sole-source lighting), target temperature and humidity setpoints, air exchange rates, and baseline irrigation efficiency before advanced technologies are applied. These are generalized assumptions intended for comparative purposes. More specific details on these assumptions can be found in Hodson et al. (2025).

The tool is primarily intended for prospective CEA growers, investors, policymakers, local government planners, and researchers who are in the early stages of exploring CEA development. It can help them understand the resource implications of different CEA configurations, identify potentially suitable regions or sites, and appreciate the impact of technology choices on sustainability and operational efficiency.