IA idea · Environment & climate
Is solar power growing logistically?
Research question
Is the share of electricity from solar in [a country] better described by an exponential or a logistic model, and what saturation level does the logistic model suggest?
Adapt it: change the place, the data or the comparison until the question is yours.
Why it makes a good exploration
New technologies often spread along S-curves. Ember's electricity data lets you test whether solar is still in its exponential phase or already slowing — with a real policy question at the end.
The mathematics you'll need
- Exponential and logistic models
- Fitting with technology and interpreting parameters
- Doubling time
- Out-of-sample testing
Course labels show where a technique sits; using maths from outside your course is fine if you explain it clearly and say it is new to you.
Where the data comes from
Yearly generation by source from Ember's electricity data.
- Ember electricity data — Yearly generation by source, emissions and capacity for 200+ countries (CC BY 4.0).
- Our World in Data — Clean country-level time series (CO₂, population, health, energy, education) with CSV download on every chart.
Cite every source in a footnote where you use it and in your bibliography. Check the licence of any dataset you download.
A possible outline
- Explain why adoption might be logistic.
- Fit both models to early years.
- Compare predictions with recent years.
- Interpret the saturation level.
- Reflect on policy, storage and grid limits.
Pitfalls that cost marks
- Fitting a logistic before the inflection point (very unstable).
- Ignoring changes in total demand.
- Undated data.
Showing personal engagement
- Choose your own country and a contrasting one.
- Compare solar and wind.
- Relate to solar panels you see locally.
See Criterion C: personal engagement for what examiners look for.
Taking it further
Investigate how the fitted saturation level changes as each new year of data is added.
Turn this idea into your IA
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