Exploring spatiotemporal variation characteristics of China's industrial carbon emissions on the basis of multi-source data

Area of Science:

• Environmental Science
• Climate Change Research
• Geospatial Analysis

Background:

• Accurate assessment of industrial carbon emissions (IE) is essential for effective emission reduction strategies.
• Traditional spatialization methods for CO2 emissions are unsuitable for IE due to its unique spatial distribution.
• Understanding spatiotemporal variations of IE is critical for developing targeted environmental policies.

Purpose of the Study:

• To estimate and spatialize industrial carbon emissions (IE) at the city level using satellite data.
• To analyze the spatiotemporal variation characteristics of China's IE from multiple levels.
• To identify key regions and factors influencing IE for future reduction targets.

Main Methods:

• Utilized greenhouse gases observing satellite level 4A products for IE estimation.
• Employed industrial land density to determine IE distribution within administrative districts.
• Validated results using sectoral emission inventories from 182 cities and the MIX inventory.

Main Results:

• Developed a novel method for estimating city-level IE with a mean relative error of 56.11%, showing high consistency with existing inventories.
• Identified distinct IE growth phases in high-emission cities: rapid growth (2009-2012) followed by slower growth (2012-2017).
• Observed shifts in emission hotspots and found that high annual IE growth often correlates with low emission efficiency, particularly in Inner Mongolia and Xinjiang.

Conclusions:

• IE exhibits significant spatiotemporal variations across Chinese cities, necessitating localized sustainable development strategies.
• Medium and high IE grids may indicate high emission efficiency, suggesting targeted industrial development.
• Regions with high annual IE growth or low emission efficiency are critical areas for achieving future IE reduction targets.