Want to reduce carbon emissions?Start with these power plants


The world seems Fire and flood occurred at the same time, and Latest expert report Show that we have little time to avoid more serious climate changeAll of this should allow us to find ways to reduce carbon emissions as quickly and economically as possible.

A recently published paper brought some good news in this regard, which examined the contribution of individual power plants to global emissions.This learn It has been found that facilities in many countries emit carbon dioxide at a rate much higher than the national or global average. Closing the worst 5% of factories will immediately wipe out about 75% of them. carbon emission Generated by power generation.

Revisit CARMA

It is easy to think of power generation in simple terms, such as “renewable energy is good, coal is not good”. To some extent, this statement is accurate. But it also compresses all power generation, from “a bit bad” to “very bad”, into a single category. It is clear from various studies that the situation is more complicated. Depending on their age, different factories convert fossil fuels into electricity with varying degrees of efficiency. Some of the least efficient factories only go online during periods of very high demand; the rest of the time, they are idle and produce no emissions at all.

The interaction between these factors determines whether a given power plant is the main contributor to emissions or just a part of the background noise of a country’s carbon output. If we have a global inventory of the emissions and production of each power plant, we can use this data to identify the most serious offenders and develop a list of targets that effectively reduce carbon emissions.

In fact, we do have one-emphasizing the past tense. Using data from 2009, someone put together the Carbon Monitoring Action Database (CARMA). Now, nearly ten years later, Don Grant, David Zelinka, and Stefania Mitova of the University of Colorado Boulder have used 2018 data to construct an update to CARMA, and the emissions data provided may be the latest.

This task is much more difficult than it seems. Some countries provide detailed emission data at each plant level, so their data can be directly imported into CARMA. But many others did not. For these countries, researchers rely on everything from the production data obtained by the International Energy Agency to the engineering specifications of individual factories.

When the researchers identified the largest source of uncertainty in the data, they found that it was mainly concentrated in smaller factories that had the least impact on overall emissions. For large facilities that may be major contributors, the data is usually very good.

Worst worst

There is no doubt that all the worst offenders are coal-fired power plants. But the distribution of the most polluted plants may contain some unexpected things. For example, despite its reputation as the land of coal, China There is only one factory among the top 10 most serious offenders. In contrast, South Korea has three on the list and India has two.

In general, there are not many factories in China that are particularly bad, partly because many of its factories were built during the great industrialization boom. Therefore, in terms of efficiency, there is not much difference between factories and factories. In contrast, countries like Germany, Indonesia, Russia, and the United States have seen big differences, so they are likely to have some very inefficient outlier factories.

In other words, the author studied how much pollution is generated by the worst 5% of power plants in a country, sorted by carbon emissions. In China, the worst 5% accounts for about a quarter of the country’s total emissions. In the United States, the worst 5% of factories generate about 75% of carbon emissions in the power sector. South Korea‚Äôs figures are similar, while Australia, Germany, and Japan have the worst conditions, with 5% of factories accounting for about 90% of their carbon emissions in the power sector.


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