After offhand remarks Bret Victor reflects deeply on the climate problem bearing upon us and the many ways that our technological attention could be better spent. site 
Victor
This is aimed at people in the tech industry, and is more about what you can do with your career than at a hackathon.
(much about the problem)
If you believe that language design can significantly affect the quality of software systems, then it should follow that language design can also affect the quality of energy systems.
One effective approach to addressing climate change is contributing to the development of Julia. It has beautiful foundations, enthusiastic users, and a lot of potential. I say this despite the fact that my own work has been in much the opposite direction.
Drag the dot to forecast acceptable emissions. Zero in 2050 is not good enough but zero in 2035 works. The target conveniently moves out thanks to earlier savings.
Claims with numbers rarely provided context to interpret those numbers. And never were readers shown the calculations behind any numbers. Readers had to make up their minds on the basis of hand-waving, rhetoric, bombast.
Readers [of models] are thus encouraged to examine and critique the model. If they disagree, they can modify it into a competing model with their own preferred assumptions, and use it to argue for their position. Model-driven material can be used as grounds for an informed debate about assumptions and tradeoffs.
In order for model-driven material to become the norm, authors will need data, models, tools, and standards. If data is hard to find, models are virtually impossible.
Even the most modern writing tools are designed around typing in words, not facts. These tools are suitable for promoting preconceived ideas, but provide no help in ensuring that words reflect reality, or any plausible model of reality. They encourage authors to fool themselves, and fool others.
Authors will need to figure out journalistic standards around model citation, and readers will need to become literate in how to “read” a model and understand its limitations.
There are at least 100,000 people, every year, looking for an engineering problem to solve. Climate change is the problem of our time. It’s everyone’s problem, but it’s our responsibility — as people with the incomparable leverage of being able to work magic through technology.
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Serious models as one would write in R, Matlab, or Julia can be a life's work. The internal complexity of each tool and the models they host are justified by scientific quest.
The interpreters of science can draw correct conclusions from simple calculations effectively rendered from these models. For example, Bret 'kills calculus' in the 'drag the dot' illustration where integration over time just happens.
Wiki aspires to make aggregate data and simplified calculations intrinsically shared. I expect science to help with these abstractions while my obligation is to make the vehicle that carries them equally transparent and mutable.
See Wikipedia as the New Front Matter to Science.