Data for: Solar bike path feasibility study in California

This project explores the feasibility of integrating solar-powered infrastructure into bike pathways as a sustainable energy and transportation solution for California. Using advanced tools like ArcGIS (for analysis), PVWatts, SAM, and JEDI, this study evaluates the economic, environmental, and technical implications through a conceptual case study based in Riverside. Insights drawn from global case studies and stakeholder feedback highlight challenges such as financial constraints, regulatory complexities, and technical design considerations, while also identifying opportunities for renewable energy generation, greenhouse gas emission reductions, and enhanced urban mobility. The conceptual case study serves as a framework for assessing potential benefits and informing actionable strategies. Recommendations address barriers and align implementation with California’s climate action and sustainability goals, offering a roadmap for integrating renewable energy with active transportation systems.

Methods
The data collection and processing methods for this project utilized a combination of publicly available tools and resources to ensure accuracy and usability. Key geospatial, energy modeling, and economic analysis data were gathered using reliable tools such as ArcGIS, SAM, JEDI, and PVWatts, with outputs systematically processed into accessible formats. This approach enabled comprehensive analysis of bike path integration, energy performance, and economic impacts.

Data Collection:

BikePaths_Riverside.qgz: Geospatial data detailing bike paths in Riverside was gathered from publicly available sources and initially analyzed using ArcGIS Pro. To ensure open access and reusability, the data has been converted to a .qgz project file compatible with QGIS (version 3.42), a free and open-source GIS platform.

SAM_Input_Variable_Values.csv: Input parameters were collected based on standard system specifications, financial assumptions, and default or adjusted inputs available in the System Advisor Model (SAM), a widely used tool for energy performance and financial modeling.

SAM_Results_Summary.csv: The simulation outputs from SAM were generated after running the model with the collected input variables, providing details on energy production, financial metrics, and economic performance.

JEDI_Results_Summary.csv: Job and economic impact data were derived using the Jobs and Economic Development Impact (JEDI) model, which relies on predefined multipliers and region-specific data.

PVWatts_Monthly.csv: Energy production and irradiance data were generated using NREL’s PVWatts Calculator for a fixed-tilt, premium-module PV system located in Riverside, California (ZIP code 92507), using typical meteorological year (TMY) data and standard system configuration inputs.

Data Processing:

BikePaths_Riverside.qgz: The original geospatial data was preprocessed and analyzed using ArcGIS Pro to support spatial measurements and site selection. For the purpose of public data sharing, the file has been converted and exported to QGIS format .qgz to ensure it is viewable with open-source software.

SAM_Input_Variable_Values.csv and SAM_Results_Summary.csv: Data inputs and outputs were processed and summarized in spreadsheets for clarity and usability, focusing on key energy and financial metrics.

JEDI_Results_Summary.csv: Results from the JEDI model were organized and documented in a spreadsheet to provide a clear summary of job creation and economic impacts.

PVWatts_Monthly.csv: Simulation results were exported directly from PVWatts into a structured CSV file, replacing the original PDF summary and organizing monthly values for AC output, DC production, and plane-of-array irradiance for use in model validation and greenhouse gas reduction analysis.