IA idea · Environment & climate

Is deforestation in [a country] slowing down?

AI SLAI HLAA SL Accessible Also in: Statistics, Modelling

Research question

Is there a significant trend in annual tree-cover loss in [a country] since 2001, and did loss change after a specific policy or event?

Adapt it: change the place, the data or the comparison until the question is yours.

Why it makes a good exploration

Satellite data now measures forest loss every year. Testing a trend, and whether a policy made a difference, is a real environmental-statistics question you can answer with open data.

The mathematics you'll need

  • Time-series plots and linear regression
  • Significance of a trend (correlation test)
  • Comparing means before and after an event (t-test)
  • Percentage change

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

Annual tree-cover loss by country from Global Forest Watch.

  • Global Forest Watch open data — Annual tree-cover loss by country since 2001, from Landsat imagery.
  • 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

  1. Explain what “tree-cover loss” does and does not measure.
  2. Plot and describe the series.
  3. Test for a trend.
  4. Compare before and after a policy.
  5. Reflect on regrowth, plantations and satellite limitations.

Pitfalls that cost marks

  • Equating tree-cover loss with deforestation without comment.
  • Choosing a policy date after seeing the data.
  • Ignoring outlier years (fires).

Showing personal engagement

  • Choose a country connected to you or your diet (soy, palm oil, beef).
  • Compare two countries.
  • Discuss what would count as success.

See Criterion C: personal engagement for what examiners look for.

Taking it further

Model cumulative loss and estimate remaining forest under different scenarios.

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