Carbon Market Trading and the Urgent Call for Coal Phase-Out Mandates in China

Sunday, 22 September 2024, 23:00

Carbon market trading must be complemented by mandates to phase out coal to achieve a clean energy transition by 2045. Experts highlight the urgency of this approach. A strong mandate could allow China to reach its zero-emission targets sooner.
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Carbon Market Trading and the Urgent Call for Coal Phase-Out Mandates in China

The Necessity of Mandates in Carbon Market Trading

Implementing strong mandates to phase out coal power could accelerate China's clean energy transition, according to experts. Simon Sharpe from S-Curve Economics emphasizes that a mandate holds more power than mere carbon market trading.

Current State of Coal Power in China

China, the world's largest coal consumer, has announced plans to phase down coal usage from 2026. However, to successfully transition towards zero-emission goals by 2060, the government must enforce surer policies rather than relying solely on market mechanisms.

  • Phase-out of coal power in developed economies by 2035
  • 2045 deadline for developing economies
  • Targeted policies for zero-emission industrial production

Challenges to Clean Energy Transition

Despite significant strides, including reaching solar and wind capacity goals early, coal still makes up to half of China’s energy consumption. Experts point out that significant changes to existing power structures and practices are essential.

  1. Reallocation of investments from fossil fuels to clean energy
  2. Development of energy storage systems
  3. Introduction of a carbon floor price to make coal less competitive

Tim Lenton from the Global Systems Institute highlights that the right mandates could make clean energy cheaper than fossil fuels by 2045. Without prompt action, the costs associated with climate inaction will continue to escalate.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


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