Project description:

China is the biggest unknown element in the fight against climate change. The world’s top polluter has more sway than any other country over whether the world succeeds at slowing the rise in global temperatures. Yet few companies in China publish data on their emissions, and those that do don’t say how they arrived at those figures. 

 

Karoline Kan and Jin Wu spent months poring over corporate financial statements and sustainability reports to produce the first-ever emissions estimates of some of China’s biggest corporate polluters. The results were remarkable: some state-run companies emit as much as entire developed nations. 

Impact reached:

The story was the first attempt to estimate how China’s emissions breakdown at the company level. What stood out was the huge role that the construction sector, including steel and cement firms, have in driving China’s pollution. These industries will have to deliver the bulk of reductions needed to achieve China’s climate goals, requiring a drastic shift in the economy so it no longer relies on property development and heavy industry as major growth drivers. 

 

After laying out the scope of the problem, we produced one of the most detailed sectoral breakdowns of China’s greenhouse gases to show how China can tame its biggest emitters. This calculation required a whole different collection of datasets including multiple government statistics yearbooks, academic papers and industry reports. Interviews with experts and additional visual elements helped illustrate specific solutions that can be deployed. 

 

The project garnered positive feedback from climate experts and other journalists, who called the research “stunning,” a “fantastic investigation,” “beautiful visualization and “important work.” The data added to debate heading into COP26 climate talks in Glasgow about what countries should do to cut their emissions. 

Techniques/technologies used:

We worked with researchers from the Centre for Research on Energy and Clean Air to come up with our own methodology in order to calculate greenhouse gas emissions from major corporate polluters in China. With help from the researchers, we decided what were the best published data out there that can help us reach best estimation.

 

Data was collected into Google sheets for smooth collaboration with the researchers. We used R for formatting and analyzing data as well as generating drafts for some of the charts, then later polished them in Illustrator and exported them with ai2html for better accessibility on the web.

 

To make invisible emissions visible, we created moving particles with HTML5 Canvas to help our audience better connect to the topic. In addition to that, for the top as well as the grid graphic comparing company emissions, we used a multi-layer visual treatment to add motion to static photo/graphics with ai2html and photoshop, making the style consistent throughout the piece. 

 

The scrolly top was built with HTML, CSS and JavaScript. GSAP, a Javascript animation library was used to create the smooth transitions, visually guiding our audience into the more data-heavy part of the story.

 

For the maps showing expanding construction in China’s cities, we used QGIS to map the built-up areas in different time periods, exported them as transparent png, then overlaid on top of satellite images. 

What was the hardest part of this project?

The biggest challenge of the project was compiling the datasets from scratch,and figuring out how to drive home the scope of China’s true emissions through the visual elements. 

 

Our goal was to collect enough data to be able to make a best-possible estimation of greenhouse gas emissions. Sources of pollution vary from industry to industry, requiring us to mine different base data and make independent calculations. The researchers we worked with reviewed academic papers, industry reports and government statistics to determine the best way to translate the data we could find (such as tons of steel used or number of cars produced) into their equivalent carbon footprints. 

 

Once we had those numbers, we had to find a way to get across to readers just what they meant. We experimented with various ideas, eventually settling on using different equivalences, such as the emissions for countries, or barrels of oil and numbers of trees that have to be planted to absorb that CO2. 

What can others learn from this project?

When there’s an important story we want to tell but there’s no published ready-to-use data on it, a close collaboration between journalists and domain experts (in our case, the researchers from CREA) to design a specific “study” could bring all sorts of possibilities.

 

Most of the time, we work with data that already exists, which is also true when it comes to research data. As journalists, we constantly have ideas that we hope there’s data for, but we lack the expertise to either collect that data, or do meaningful analysis and calculations by combining different datasets. So it’s becoming increasingly effective for a group of journalists to bring an idea to domain experts, brainstorming what could be done.

 

Be cautious with companies’ claims on “low carbon”, “carbon neutrality” and “sustainability” as ESG has become an area where a lot of greenwashing is happening. Many companies are not serious about their ESG reports, and often nobody will hold them accountable for whatever they reveal or not reveal in their ESG reports. Be careful with company emission data, and always try to be clear what scopes are included. 

Project links:

www.bloomberg.com/graphics/2021-china-climate-change-biggest-carbon-polluters/