Dynamic nowcast of the New Zealand greenhouse gas inventory (2402.11107v1)
Abstract: As efforts to mitigate the effects of climate change grow, reliable and thorough reporting of greenhouse gas emissions are essential for measuring progress towards international and domestic emissions reductions targets. New Zealand's national emissions inventories are currently reported between 15 to 27 months out-of-date. We present a machine learning approach to nowcast (dynamically estimate) national greenhouse gas emissions in New Zealand in advance of the national emissions inventory's release, with just a two month latency due to current data availability. Key findings include an estimated 0.2% decrease in national gross emissions since 2020 (as at July 2022). Our study highlights the predictive power of a dynamic view of emissions intensive activities. This methodology is a proof of concept that a machine learning approach can make sub-annual estimates of national greenhouse gas emissions by sector with a relatively low error that could be of value for policy makers.
- [dataset] Energy Market Services “Current Carbon Intensity API (em6 API)” Version 1.9, https://www.ems.co.nz/em6-api-integration-guide/, 2022
- [dataset] Ministry for the Environment “Time series emissions data by category (National inventory report)” ME 1635, https://environment.govt.nz/publications/new-zealands-greenhouse-gas-inventory-1990-2020/, 2022
- [dataset] Ministry of Business, Innovation and Employment “Data tables for coal (Energy statistics)”, https://www.mbie.govt.nz/building-and-energy/energy-and-natural-resources/energy-statistics-and-modelling/energy-statistics/coal-statistics/, 2022
- [dataset] New Zealand Meat Board “Provisional Export Livestock Processing Data”, https://www.nzmeatboard.org/the-industry/production-data/, 2022
- [dataset] New Zealand Transport Agency “Motor Vehicle Register (Waka Kotahi Open Data)”, https://nzta.govt.nz/resources/new-zealand-motor-vehicle-register-statistics/new-zealand-vehicle-fleet-open-data-sets/#data, 2022
- [dataset] New Zealand Transport Agency “TMS daily traffic counts API (Waka Kotahi Open Data)”, https://opendata-nzta.opendata.arcgis.com/datasets/tms-daily-traffic-counts-api/api, 2022
- [dataset] New Zealand Transport Agency “Traffic data booklets and state highway traffic volumes (Waka Kotahi Open Data)”, https://www.nzta.govt.nz/resources/state-highway-traffic-volumes/, 2019
- [dataset] Statistics New Zealand “AGR001AA (Infoshare)”, http://infoshare.stats.govt.nz/, 2022
- [dataset] Statistics New Zealand “EXP012AA (Infoshare)”, http://infoshare.stats.govt.nz/, 2022
- [dataset] Statistics New Zealand “IMP033AA (Infoshare)”, http://infoshare.stats.govt.nz/, 2022
- [dataset] Statistics New Zealand “LSS025AA (Infoshare)”, http://infoshare.stats.govt.nz/, 2022
- Chukwuemeka Amaefule, Igwe J. Ibeabuchi and Akeem Shoaga “Determinants of Greenhouse Gas Emissions” In European Journal of Sustainable Development Research 6.4 Modestum Ltd, 2022, pp. em0194 DOI: 10.21601/ejosdr/12176
- “Random Search for Hyper-Parameter Optimization” In Journal of Machine Learning Research 13.10, 2012, pp. 281–305 URL: http://jmlr.org/papers/v13/bergstra12a.html
- Leo Breiman “Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)” In Statistical Science 16.3 Institute of Mathematical Statistics, 2001 DOI: 10.1214/ss/1009213726
- “2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories” Published: IPCC, Switzerland, https://www.ipcc-nggip.iges.or.jp/public/2019rf/index.html, 2019
- Shisheng Chen, Kuniaki Mihara and Jianxiu Wen “Time series prediction of CO2, TVOC and HCHO based on machine learning at different sampling points” In Building and Environment 146 Elsevier BV, 2018, pp. 238–246 DOI: 10.1016/j.buildenv.2018.09.054
- Daniela Debone, Vinicius Pazini Leite and Simone Georges El Khouri Miraglia “Modelling approach for carbon emissions, energy consumption and economic growth: A systematic review” In Urban Climate 37 Elsevier BV, 2021, pp. 100849 DOI: 10.1016/j.uclim.2021.100849
- Donald E. Farrar and Robert R. Glauber “Multicollinearity in Regression Analysis: The Problem Revisited” In The Review of Economics and Statistics 49.1 JSTOR, 1967, pp. 92 DOI: 10.2307/1937887
- Peter Filzmoser, Bettina Liebmann and Kurt Varmuza “Repeated double cross validation” In Journal of Chemometrics 23.4 Wiley, 2009, pp. 160–171 DOI: 10.1002/cem.1225
- “Current and future global climate impacts resulting from COVID-19” In Nature Climate Change 10.10 Springer ScienceBusiness Media LLC, 2020, pp. 913–919 DOI: 10.1038/s41558-020-0883-0
- Pierre Geurts, Damien Ernst and Louis Wehenkel “Extremely randomized trees” In Machine Learning 63.1 Springer ScienceBusiness Media LLC, 2006, pp. 3–42 DOI: 10.1007/s10994-006-6226-1
- Hamid Ghorbani “Mahalanobis distance and its application for detecting multivariate outliers” In Facta Universitatis, Series: Mathematics and Informatics University of Nis, 2019, pp. 583 DOI: 10.22190/fumi1903583g
- Mariano González-Sánchez and Juan Luis Martín-Ortega “Greenhouse Gas Emissions Growth in Europe: A Comparative Analysis of Determinants” In Sustainability 12.3 MDPI AG, 2020, pp. 1012 DOI: 10.3390/su12031012
- “Highly Resolved Dynamic Emissions of Air Pollutants and Greenhouse Gas CO2 during COVID-19 Pandemic in East China” In Environmental Science & Technology Letters 8.10 American Chemical Society (ACS), 2021, pp. 853–860 DOI: 10.1021/acs.estlett.1c00600
- Yue Huang “Linear Calibration Methods” In Chemometric Methods in Analytical Spectroscopy Technology Springer Nature Singapore, 2022, pp. 237–254 DOI: 10.1007/978-981-19-1625-0˙7
- “Nowcasting CO2 emissions” Nordisk Ministerråd, 2022 DOI: 10.6027/temanord2022-537
- Vaishnavi Jayaraman, Saravanan Parthasarathy and Arun Raj Lakshminarayanan “Forecasting the Emission of Greenhouse Gases from the Waste using SARIMA Model” In 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI) IEEE, 2022 DOI: 10.1109/icoei53556.2022.9777119
- Ali Mehmandoost Kotlar, Jasdeep Singh and Sandeep Kumar “Prediction of greenhouse gas emissions from agricultural fields with and without cover crops” In Soil Science Society of America Journal 86.5 Wiley, 2022, pp. 1227–1240 DOI: 10.1002/saj2.20429
- “Analyzing the impact of three-dimensional building structure on CO2 emissions based on random forest regression” In Energy 236 Elsevier BV, 2021, pp. 121502 DOI: 10.1016/j.energy.2021.121502
- “Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production” In Scientific Data 7.1 Springer ScienceBusiness Media LLC, 2020 DOI: 10.1038/s41597-020-00708-7
- “Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic” In Nature Communications 11.1 Springer ScienceBusiness Media LLC, 2020 DOI: 10.1038/s41467-020-18922-7
- Ministry for the Environment “Measuring Emissions: A Guide for Organisations: 2020 Detailed Guide. Wellington: Ministry for the Environment.” Accessed 12 Oct. 2022., https://environment.govt.nz/assets/Publications/Files/Measuring-Emissions-Detailed-Guide-2020.pdf, 2022
- Ministry for the Environment “New Zealand’s Greenhouse Gas Inventory 1990–2019, National inventory report. Wellington: Ministry for the Environment” Accessed 13 Oct. 2022., https://environment.govt.nz/assets/Publications/Greenhouse-Gas-Inventory-1990-2019/New-Zealands-Greenhouse-Gas-Inventory-1990-2019-Volume-1-Chapters-1-15.pdf, 2020
- Ministry for the Environment “New Zealand’s Greenhouse Gas Inventory 1990–2020, National inventory report. Wellington: Ministry for the Environment” Accessed 13 Oct. 2022., https://environment.govt.nz/assets/publications/GhG-Inventory/New-Zealand-Greenhouse-Gas-Inventory-1990-2020-Chapters-1-15.pdf, 2021
- Ministry for the Environment “New Zealand’s Interactive Emissions Tracker” Accessed 8 Nov. 2021., https://emissionstracker.mfe.govt.nz/, 2022
- Ministry of Transport “A vehicle scrappage trial for Christchurch and Wellington: May 2009” Accessed 16 Jun. 2021., https://www.transport.govt.nz//assets/Uploads/Report/Scrappage-Report-FINAL.pdf, 2009
- Mihai Mutascu “CO2 emissions in the USA: new insights based on ANN approach” In Environmental Science and Pollution Research 29.45 Springer ScienceBusiness Media LLC, 2022, pp. 68332–68356 DOI: 10.1007/s11356-022-20615-1
- Andrew Y Ng “Preventing “overfitting” of cross-validation data” In ICML 97, 1997, pp. 245–253 DOI: 10.5555/645526.657119
- “Errors and uncertainties associated with the use of unconventional activity data for estimating CO2 emissions: the case for traffic emissions in Japan” In Environmental Research Letters 16.8 IOP Publishing, 2021, pp. 084058 DOI: 10.1088/1748-9326/ac109d
- “Regional and Longitudinal Estimation of Product Lifespan Distribution: A Case Study for Automobiles and a Simplified Estimation Method” In Environmental Science & Technology 49.3 American Chemical Society (ACS), 2015, pp. 1738–1743 DOI: 10.1021/es505245q
- “Scikit-learn: Machine Learning in Python” In Journal of Machine Learning Research 12, 2011, pp. 2825–2830
- Francesco Del Pero, Massimo Delogu and Marco Pierini “The effect of lightweighting in automotive LCA perspective: Estimation of mass-induced fuel consumption reduction for gasoline turbocharged vehicles” In Journal of Cleaner Production 154 Elsevier BV, 2017, pp. 566–577 DOI: 10.1016/j.jclepro.2017.04.013
- “Temporary reduction in daily global CO2 emissions during the COVID-19 forced confinement” In Nature Climate Change 10.7 Springer ScienceBusiness Media LLC, 2020, pp. 647–653 DOI: 10.1038/s41558-020-0797-x
- “Reply to Comment on ‘Unintentional unfairness when applying new greenhouse gas emissions metrics at country level”’ In Environmental Research Letters 16.6 IOP Publishing, 2021, pp. 068002 DOI: 10.1088/1748-9326/ac02ec
- Philip B Stark and Robert L Parker “Bounded-variable least-squares: an algorithm and applications” In Computational Statistics 10 PHYSICA-VERLAG GMBH, 1995, pp. 129–129
- Statistics Netherlands “Quarterly estimates of greenhouse gas emissions” Accessed 8 Nov. 2021., https://www.cbs.nl/en-gb/custom/2020/37/quarterly-estimates-of-greenhouse-gas-emissions, 2021
- Statistics New Zealand “Greenhouse gas emissions (industry and household): March 2021 quarter.” Accessed 8 Oct. 2021., https://www.stats.govt.nz/experimental/greenhouse-gas-emissions-industry-and-household-march-2021-quarter, 2021
- Ilayda Ulku and Eyup Emre Ulku “Forecasting Greenhouse Gas Emissions Based on Different Machine Learning Algorithms” In Lecture Notes in Networks and Systems Springer International Publishing, 2022, pp. 109–116 DOI: 10.1007/978-3-031-09176-6˙13
- Guido Van Rossum and Fred L. Drake “Python 3 Reference Manual” Scotts Valley, CA: CreateSpace, 2009
- “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python” In Nature Methods 17, 2020, pp. 261–272 DOI: 10.1038/s41592-019-0686-2
- “Forecasting CO2 emissions in China’s commercial department, through BP neural network based on random forest and PSO” In Science of The Total Environment 718 Elsevier BV, 2020, pp. 137194 DOI: 10.1016/j.scitotenv.2020.137194
- “Using decision tree analysis to identify the determinants of residents’ CO2 emissions from different types of trips: A case study of Guangzhou, China” In Journal of Cleaner Production 277 Elsevier BV, 2020, pp. 124071 DOI: 10.1016/j.jclepro.2020.124071
- “Refining national greenhouse gas inventories” In Ambio 49.10 Springer ScienceBusiness Media LLC, 2020, pp. 1581–1586 DOI: 10.1007/s13280-019-01312-9
- “Satellite-based estimates of decline and rebound in China’s CO2 emissions during COVID-19 pandemic” In Science Advances 6.49 American Association for the Advancement of Science (AAAS), 2020 DOI: 10.1126/sciadv.abd4998