Summary of Dynamic Nowcast Of the New Zealand Greenhouse Gas Inventory, by Malcolm Jones et al.
Dynamic nowcast of the New Zealand greenhouse gas inventory
by Malcolm Jones, Hannah Chorley, Flynn Owen, Tamsyn Hilder, Holly Trowland, Paul Bracewell
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Atmospheric and Oceanic Physics (physics.ao-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research presents a novel machine learning-based approach to nowcasting (estimating) New Zealand’s national greenhouse gas emissions in near real-time, achieving a latency of just two months. The model leverages current data availability and is shown to be effective in estimating gross emissions with low error. Key findings include a 0.2% decrease in national gross emissions since 2020 as of July 2022. This proof-of-concept study highlights the potential value of machine learning in informing policy makers on sub-annual estimates of national greenhouse gas emissions by sector. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary New Zealand needs to accurately track its greenhouse gas emissions to meet international and domestic targets. Right now, the country’s emissions reports are outdated – taking 15 to 27 months to finalize. Scientists have developed a new way to predict these emissions in real-time, just two months behind the current reporting schedule. This approach uses machine learning algorithms and current data to estimate emissions. The results show that national greenhouse gas emissions decreased by 0.2% since 2020 as of July 2022. This study shows that machine learning can help policymakers make more informed decisions about reducing emissions. |
Keywords
* Artificial intelligence * Machine learning
