2030 Climate Challenge
The team will jointly deploy a proven and tested data-oriented framework across residential and commercial buildings, industrial facilities and transportation in major communities across the United States. Here is an example city with granular emissions assessments for each building and industrial facility within the community, as well as transportation.
With the 2030 Climate Challenge, we are looking for proven, data-driven solutions ready to serve as a model for change for communities across the United States.
Today Dynamhex uses real-data and inventory frameworks to assess reduction potential and matches actual solutions across (1) residential and commercial buildings, (2) industrial facilities and (3) transportation. The software calculates the necessary reduction needed from each sector in each city, to meet the IPCC goals. Measurement happens with data evidence, every calendar year and when new data is available.
We are climate scientists, engineers and policy advocates, fully aware of the rebound effects of having a single-sector or single-solution approach that may jeopardize the grant investments. Climate targets can only be received when ALL sectors are being tracked. Emissions reduction due to vehicle electrification, may increase emissions in power generation with no net-benefit.
Our approach tracks and verifies that impact in one sector does not equate to higher emissions elsewhere through displacement.
The assembled team has shown a track record of working in diverse capacities on domestic energy policy, both at the federal, state and local level. Policy is just a start we also need project deployments at the industrial (utility) and corporate levels, as well as project validations for a multitude of energy projects for the U.S. government. Each partner has deep expertise in the framework design and implementation, including prior testimony to our U.S. Congress, helping the U.S. government divisions and other major stakeholders in implementing complex multi-year energy and emissions projects across industry, transportation and buildings with complex stakeholder relationships over decades.
Dynamhex is proven in various socioeconomic and geopolitical contexts and local communities in the United States, with support from leading non-profit organizations such as NRDC, Fortune 100 companies and utilities, as well as being directly programmed into municipal resolutions and ordinances. In certain cases, the local economy is mostly residential with passenger vehicles, whereas others it is most commercial and industrial with heavy fleet vehicles – which dictates which technical and behavioral solution should be deployed in which community.
With thousands of communities across the U.S., the permutation of possibilities of more than a billion steps introduces sheer complexity in implementing climate action policies on the ground. Our approach overcomes this very complexity with tailored data driven solutions by location and uniqueness, for true scalability.
Climate action is one of the longest marathons, made up of thousands of sprints. Making sure the sprints count in the marathon is the challenge. We are focused on the extending longevity of the many emissions-reduction projects that are happening haphazardly.
Dynamhex has data going back to the1960s and the mobile first processes will allow future technologies to build on a solid foundation till 2030 and beyond. Data driven solutions based on verifiable models will stand the test of time – Incorporating local policy as code will allow us to benchmark cities and companies, enabling people to form groups unbound by boarders to visualize impact using data.
Some of the risks to implementing climate-positive solutions are organizational, while others have been policy-centric and socio-economic in nature. As a result top-down, one-size-fit-all approaches have not worked. By doing bottom-up climate planning and action, we are guiding and de-risking climate action, by identifying, evaluating and collaboratively implementing “impactful” strategies with a long-term orientation.