How the Green Context Engine Works
What is this?
The Green Context Engine is an automated system that monitors energy and climate news, pulls real data from scientific and government databases, and uses AI to write data-grounded summaries. Every number in every brief traces back to a named, verifiable source. A human reviews and approves every post before it goes live.
The goal is to take the flood of daily energy and climate news and add context that is often missing: how do the numbers in a news story compare to global benchmarks? What does the data actually show? What are the trade-offs?
How it works
- 1
Find relevant stories
The system scans 7+ energy and climate news sources for new articles matching topics like solar, wind, coal, deforestation, and grid decarbonization.
- 2
Decide what data to look up
An AI reads the article and decides which databases to query and for which countries or regions. For example, a story about Indonesian deforestation triggers lookups for Indonesia's electricity grid data and forest cover loss.
- 3
Pull real data
The system queries government and scientific databases for actual numbers: electricity generation by fuel type, carbon intensity, tree cover loss, threatened species counts. It also pulls global and regional benchmarks for comparison.
- 4
Write a brief
An AI writer creates a concise summary that connects the news story to the data, comparing countries to global benchmarks and identifying key trade-offs.
- 5
Fact-check
A separate AI editor reviews the brief and checks every number against the source data. If it finds errors, the brief is revised and re-checked (up to two rounds).
- 6
Human approval
A human reviews the final brief and approves it before it is published. Nothing goes live without this step.
What makes this different
Unlike typical AI-generated content, every brief is grounded in data from named, verifiable sources. Country-level data is always compared to global or regional benchmarks. A dedicated AI editor verifies every factual claim against the actual source data before a human approves publication.
Data sources
Real numbers are pulled from these scientific and government databases.
Global electricity generation, carbon intensity, and emissions data for ~200 countries and economic groups.
US electricity generation by fuel type at national and state level, from the Energy Information Administration.
Satellite-derived tree cover loss data by country, covering 2000-2024 at 30-meter resolution.
Threatened species counts by country and threat category, from the International Union for Conservation of Nature.
Monthly temperature and precipitation data from the Global Historical Climatology Network, covering 180+ countries.
News sources
Stories are monitored from these energy and climate journalism outlets.
Environmental journalism covering energy, biodiversity, and climate.
UK-based outlet specializing in climate science and energy policy.
Global solar industry news and market analysis.
Clean energy and electric vehicle news.
US energy industry news covering utilities and regulation.
Electric vehicles, energy storage, and solar news.
Australian clean energy news and analysis.
Limitations
- Data from sources may be weeks or months behind real-time events.
- AI can misinterpret nuanced trends or draw connections that oversimplify complex dynamics.
- Coverage is limited to the data sources listed above — the pipeline cannot verify claims against sources outside its dataset.
- Briefs are short summaries, not comprehensive analyses. They are meant to add context, not replace deep reporting.