Through the looking glass

Country level emissions data are (probably) not what they seem

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“…the most basic metric of development , GDP, should not be treated as an objective number but rather as a number that is the product of a process in which a range of arbitrary and controversial assumptions are made. As a result the metric should be used with the utmost care.” - Morten Jerven

Uncertainty over the true level of greenhouse gases (GHG) emitted by individual countries is likely to become an increasingly important issue. While multi-national companies are looking to cut their carbon risk, climate orientated investors are seeking to allocate capital to those opportunities with the best carbon abatement returns.

As it is with economic data, researchers lament the dearth of credible country-level emissions data across much of the world, at least outside of the most advanced economies. A lack of transparency, mediocre and outdated methodologies, and the opportunity for data manipulation are common criticisms.

China offers a perfect example of some of the challenges involved. Its emissions estimates come with a health warning, bigger even than the air quality alerts familiar to residents of the country’s most polluted cities.

Estimates by the Centre for Research on Energy and Clean Air (CREA) indicate that Chinese energy sector emissions are likely to have increased by 5.2% in 2023, matching official estimates of Chinese GDP growth. CREA’s recent report highlights “preliminary official data” that shows Chinese total energy consumption increased by 5.7% in 2023, with coal demand growing by 4.4%.¹

Most economic forecasters take a more pessimistic (read realistic) view of recent trends in Chinese economic growth. For example, analysis by the Rhodium Group, a research firm with a focus on China, suggests that actual growth in 2023 was more like 1.5%, highlighting the “realities of a still-shrinking property sector, limited consumer spending, falling trade surplus, and battered local government finances,” for the poor performance.²

The government may say that GDP expanded by 5.2% in 2023, but it’s long been an open secret that Chinese economic data is just for show. Li Keqiang – the late Chinese premier, but previously known as the head of Liaoning province’s communist party – admitted over dinner with the US ambassador to China in 2007 that the country’s GDP figures were “man-made” and therefore unreliable. Mr Li went on to say that when evaluating his province’s economic progress he instead concentrated on just three data points – electricity consumption, rail cargo volume and bank lending.

Analysts quickly compiled a compilation of the metrics, coining the group of data points the “Li Keqiang index'“. Although the switch to a more services-based economy has meant that traditional indicators have not been as reliable in predicting GDP, there was something more nefarious going on. Businesses are often asked to misreport data including electricity consumption by local government officials, while others are also nudged to keep electricity intensive machines idling away in order to bump up the consumption figures. It seems that once an indicator becomes widely used, its worth as a gauge of economic activity rapidly diminishes through manipulation.

The statistical window on Chinese economic activity became even more opaque following Xi Jinping’s election as general secretary of the Communist Party in 2012. In 2022, the Financial Times reported that the annual number of economic indicators made available by China’s National Bureau of Statistics (NBS) dropped from over 80,000 to less than 20,000 in the early 2020’s. Long running statistical series that shed some light on far corners of the Chinese economy have been quietly discontinued.³

The lack of robust official data sources has forced analysts into relying on ever more esoteric means of gauging activity. For example, analysis of night-time light intensity from satellite imagery over China found that annual economic growth may have been one-third slower than official GDP estimates. The study, which was published in 2018, found that annual GDP growth rates were inflated by 15%-30% in the most authoritarian regimes. If correct it suggests that China’s economy is both much smaller than previously believed, but also responsible for a lower level of GHG emissions (see How much should we trust the dictator’s CO2 estimates?).

Even if the official energy and economic data could be relied upon, getting a reliable gauge on carbon intensity is an enormous challenge, and one that can have a major implications for country level emissions estimates. CREA base their China carbon intensity estimates on the calorific values embedded in different fuels, according to estimates provided by NBS, while also using default emissions factors from the Intergovernmental Panel on Climate Change (IPCC). This reliance on default estimates fails to capture significant underlying changes in emissions intensity.

For example, the quality of the coal is an important determining factor in many countries such as China. The lower the quality of the coal, the higher the carbon intensity, all else being equal. A study published in the journal Nature in 2015 found that the carbon intensity of thermal coal burned in Chinese coal-powered plants was on average 40% lower than IPCC recommended values. Rather than using global default values the study looked at the actual carbon content found in over 4,000 coal mines in China and in lab tests using 602 coal samples. A deterioration in the quality of the coal (and a corresponding increase in carbon intensity) typically occurs when state controlled mining companies are told to boost domestic output, at the expense of cleaner imports of thermal coal.⁴

So, how should companies and investors assess the quality of a country’s emissions data? The first step is to understand how good the economic data is. Without sound macroeconomic and industrial statistics there is no foundation upon which to build reliable GHG emission estimates. World Economics has reviewed the usefulness of individual countries official GDP data using five factors to judge the quality of their data.