The path to carbon neutrality: a time series approach

Haoran Zhang; Yuchen Dong

doi:10.1117/12.2676311

4 October 2023 The path to carbon neutrality: a time series approach

Haoran Zhang, Yuchen Dong

Proceedings Volume 12675, Applications of Machine Learning 2023; 126750V (2023) https://doi.org/10.1117/12.2676311
Event: SPIE Optical Engineering + Applications, 2023, San Diego, California, United States

Conference Poster

Abstract

Achieving carbon neutrality has become the United Nation’s most urgent mission, but the lack of data, evaluation criteria and associated techniques presents a challenge. Moreover, the energy crisis in 2022 has unexpectedly complicated carbon dioxide (CO₂) data, and existing research focuses primarily on CO₂ absolute emissions. Policymakers have established milestones on carbon reduction roadmap but have failed to meet them. Therefore, we adopt the new CO₂ emission and sink data released in November 2022. Our approach leverages Time Varying Parameter Vector Auto Regression (TVP-VAR) model and Monte-Carlo simulation to monitor the dynamics of net-zero emission roadmap. This approach provides insights into the global pathway towards The United Nations Framework Convention on Climate Change (UNFCCC).

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Haoran Zhang and Yuchen Dong "The path to carbon neutrality: a time series approach", Proc. SPIE 12675, Applications of Machine Learning 2023, 126750V (4 October 2023); https://doi.org/10.1117/12.2676311

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