GID team reveals policies targeting ‘super-polluting’ units could substantially reduce pollutant emissions

2018-01-08 | Dan Tong

An international joint research team led by Professor Zhang Qiang from the Department of Earth System Science at Tsinghua University and Associate Professor Steven J. Davis at the University of California, Irvine, published a paper entitled ” Targeted emission reductions from global super-polluting power plant units ” in Nature Sustainability. This study built the first global thermal power air pollutant emission database (Global Power Emissions Database, GPED) at the level of individual generating units, and pointed out that policies targeting a relatively small number of ‘super-polluting’ units could substantially reduce pollutant emissions and thus the related impacts on both human health and global climate. Nature Sustainability also published a review entitled “small and bad” to highlight this work.

Fossil fuel power plants, is one of the major sources of carbon dioxide (CO2) emissions, also produce a large amount of harmful air pollutants such as SO2 and PM2.5, which cause serious impacts on both climate change and human health. It is a striking fact that the consumption of fossil fuel in the power sector accounts for about 50% of the total global consumption. However, due to the opacity of basic information from different countries and regions and the difficulties of identify point-source power plants, most of the research on global power emissions and their related environmental impact can hardly support high-resolution or unit-based analysis, which hinder further policy making.

The research team led by Qiang Zhang overcame the above-mentioned difficulties and for the first time established the global thermal power air pollutant emission database, GPED. The researchers obtained the basic information of more than 70000 thermal power units around the world by big data mining, developed a unified method for air pollutant emissions estimation, and built the comprehensive air pollutant emissions database. The newly developed global power emission database comprises the capacity of the power plants, fuel type, age, location and installed pollution-control technology of about 72000 units from more than 30000 thermal power plants.

Estimated percentage of emissions from each age cohort

The study presented some important and interesting findings. For example, the results showed that a large proportion of power plants were new — more specifically, in 2010, 34% of the plants operating worldwide were less than 12 years old. Many of the new coal-fired operating units are located in emerging economies, particularly China and India, primarily due to rapid economic growth and industrialization, whereas in advanced economies like the United States and Europe, coal-fired units tend to be older than gas-fired units — a reflection of the transition to cleaner energy production. Additionally, the study pointed out that the environmental and health impacts of power generation differed across countries as a result, among other issues, of different degrees of stringency in environmental regulations with most of the emissions reductions achieved in developed countries.

The most important finding of this study is that a large proportion of the air pollutants from the power sector is emitted by a disproportionately small fraction of power generating capacity. For example, retiring or installing emission control technologies on units representing 0.8% of the global coal-fired power plant capacity could reduce levels of PM2.5 emissions by 7.7–14.2%. In India and China, retiring coal-fired plants representing 1.8% and 0.8% of total capacity can reduce total PM2.5 emissions from coal-fired plants by 13.2% and 16.0%, respectively. This is mainly due to small units not being equipped with the most advanced and effective emissions control technologies and not being able to achieve significant operating efficiencies. Hence policies targeting a small number of ‘super-polluting’ units could substantially reduce pollutant emissions and the associated impacts on human health.

This study is a major step towards building a consistent, global, spatially resolved dataset for different air pollutants emissions based on the best available information and statistics, and shows potential for using big data to further understand facility-level emissions, identify super-emitters and investigate regional and global emission reduction options. Importantly, the database will inform the next generation of public health policies and increase the chances of effective decision making in the fight against air pollution.

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