[OC] China's fossil vs zero-carbon electricity generation, 2015–2025 — a redesign of an S&P Global chart

What This Visualization Shows

This data visualization displays "[OC] China's fossil vs zero-carbon electricity generation, 2015–2025 — a redesign of an S&P Global chart" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on S&P Global is a reputable company, and they recently produced a chart about China's power sector that looks impressive. When I actually tried to read it, though, it turned out to be almost unreadable. So I redesigned it. You can see the data and the code in [this gist](https://gist.github.com/bgbg/ed7119d41c74332ddc9268e747994ea0).

**So, what is the problem?**

As I argue in ["C for Conclusion"](https://gorelik.net/2018/06/25/c-for-conclusion), the title of a graph should *be* its conclusion: a short, declarative claim that tells the reader what they are supposed to see. In the S&P chart, the title takes about two minutes to read, and once you're done, it is still completely detached from the graph itself.

A separate, very important problem is the **double y-axis**. This is a big no in data visualization. For example, the point where the red emissions line crosses the brown coal area visually implies that the two values are equal. They are definitely not. They are on different scales and in different units entirely (TWh vs. GtCO₂). There are many more problems with double y-axes (spurious visual correlations, arbitrary choice of scaling, etc.), but that is the cleanest example in this chart.

**What I did**

The key was to use the title as the guiding force towards the visualization.

1. I asked Claude to restate the title, making it much shorter, so its meaning is obvious in one read. 2. Then I asked Claude to digitize the data from the chart pixels. I admit digitization probably introduced some errors (\~±3–5%). 3. Then I asked it to create a better version following standard dataviz principles. The result was already much better. My prompt was

​

this graph is bad in many ways. First - extract the data from the graph, then, shorten the title to still be a conclusion, next - create a static graph that shows the conclusion in the graph, adhering to best data visualization principles. Pay special attention to data-ink ratio and to useful redundancy and information layers

4. Then I asked Claude to simplify it even more. **This is what you see right now.**

**To sum up**

A chart's title should be its conclusion, and the chart should back that conclusion up at a glance. If the reader has to squint at a three-line title, reconcile two y-axes in their head, and distinguish between three near-identical shades of blue before they can extract the point — the chart is doing far less work than it should. Fixing this does not require fancy tools. It requires deciding what claim you are making, and then letting the visual serve that claim.

**Data source:** Digitized from S&P Global, *Look Forward: Energy Futures* (Feb 2026). Values are approximate (±3–5%) due to pixel-level reading of the original chart. For rigorous use, substitute numbers from Ember's Global Electricity Review or the IEA World Energy Outlook.

**Tools:** Python, matplotlib, Claude (Anthropic).

**Code & data:** [https://gist.github.com/bgbg/ed7119d41c74332ddc9268e747994ea0](https://gist.github.com/bgbg/ed7119d41c74332ddc9268e747994ea0)

\----- Boris Gorelik Data visualization lecturer and consultant, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

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