Populating Climate Technology Database for Multiple Technology Types

Project Overview

Project Description

Rho Impact, in collaboration with Prime Coalition, has created CRANE™, an open-access and free tool built for anyone assessing the emissions reduction potential of innovative technologies or companies. Rho Impact is now gearing up for a major push to further populate CRANE’s databases for multiple technology types. These new models will be trustworthy, robust, and open. Currently, there are approximately 200 public models. In 2024, Rho Impact is planning to further increase the number of solutions by at least double, ensuring comprehensive industry and solution coverage, and making accessible a deeper data set relevant to avoided emissions for screening, due diligence, and reporting. The project will leverage a forthcoming commercial database of high-resolution factors in development at Rho Impact.

Primary responsibilities include:

Designing core taxonomic definitions at the level of granularity appropriate for CRANE’s use cases
Ensuring the taxonomy system is compatible with third party data to facilitate inclusion of external data into CRANE
Targeting and populating data of approximately 500 additional technology types that map to existing and planned CRANE models, through secondary research and utilization of Rho Impact’s datasets
Reviewing technical and scientific papers for the purposes of numerical modeling – focused on the life cycle environmental impacts of industrial and other systems

Impact

This project squarely aligns with Rho Impact’s goal of removing barriers for investors and operators to incorporate emissions reduction potential (aka. avoided emissions, scope 4 emissions, expected emissions reductions) into their core decision-making, reporting, and other existing processes. By enhancing the leading open-access software tool and dataset for evaluating new climate technologies focused on investors and founders (CRANE), this project will be a critical contributor to the mission of finding efficiencies to direct capital to the most promising solutions.

Eligibility Criteria

Skills / Experience:

Required:
- Data Analysis + Statistics
- Research
- Technical Writing and Documentation
Desired:
- Process Design
- Chemical process design, fabrication, and analysis
- Energy Systems design + analysis
- Demonstrated passion for sustainable technologies and exploring complex systems.
- Experience with performing scientific literature reviews and aggregating data/findings from various sources to develop new insights.
- Familiarity with life cycle analysis work and concepts.
- Comfortable working with prototype software and data science tools (data science experience is nice to have but not required).

Software needs:

Excel and/or Google Sheets

Discipline:

An advanced degree (or working towards one) in a STEM program with research experience. Exceptional candidates with a humanities background will also be considered. A bachelor’s degree may be considered if the candidate has research experience and meets all other qualifications.

Preferred fellow local experience (all projects can be completed remotely):

Time zone compatibility (when the Fellow should be available for meetings):

PDT/EDT (UTC-7 – UTC-4)

Project Outcomes

This project aimed to enhance the integration of emissions modelling data from Koi into Rho Impact’s CRANE platform, thereby advancing the efficiency and accuracy of climate technology assessments. CRANE, developed by Rho Impact and Prime Coalition, is a free, open-access tool to evaluate innovative technologies’ emissions reduction potential. Koi complements this by transforming authoritative energy and environmental datasets into actionable models, simplifying complex methodologies into decision-useful insights. The project involved developing a Python-based solution to automate the transformation of Koi’s data into CRANE-compatible formats to achieve seamless integration. The process began with extracting essential fields from Koi’s dataset to define key parameters for CRANE’s input. The script aggregated emissions data by calculating annual values for incumbent and solution scenarios, leveraging interpolated component GHG values from Koi’s dataset, enabling a detailed calculation of emissions reductions over the specified timeframe. The script addressed uncertainties in the solution scenario to capture potential variability. Market size was calculated by interpolating Koi data, key for assessing themarket capture potential of climate technologies. Additionally, the script computed the unit impact by comparing baseline and solution emissions, incorporating upper and lower uncertainty bounds to provide a nuanced view of emissions reduction estimates. The final output was a CRANE-compatible JSON file, including transformed data on incumbent unit emissions, solution unit emissions, market size, and unit impact. The project successfully automated the transformation of Koi data into CRANE format, enabling the rapid and accurate population of the CRANE database with high-resolution emissions models. This significant advancement supports CRANE users in more effectively assessing and reporting on climate technologies.