Carbon Monitor: a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production (2006.07690v1)

Abstract: We constructed a near-real-time daily CO2 emission dataset, namely the Carbon Monitor, to monitor the variations of CO2 emissions from fossil fuel combustion and cement production since January 1st 2019 at national level with near-global coverage on a daily basis, with the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including: hourly to daily electrical power generation data of 29 countries, monthly production data and production indices of industry processes of 62 countries/regions, daily mobility data and mobility indices of road transportation of 416 cities worldwide. Individual flight location data and monthly data were utilised for aviation and maritime transportation sectors estimates. In addition, monthly fuel consumption data that corrected for daily air temperature of 206 countries were used for estimating the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 7.8% decline of CO2 emission globally from Jan 1st to Apr 30th in 2020 when compared with the same period in 2019, and detects a re-growth of CO2 emissions by late April which are mainly attributed to the recovery of economy activities in China and partial easing of lockdowns in other countries. Further, this daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making.

Analysis of Carbon Monitor: A Daily Dataset of Global CO2 Emissions

The paper titled "Carbon Monitor" introduces a comprehensive dataset designed to deliver near-real-time tracking of global CO2 emissions stemming from fossil fuel combustion and cement production. This dataset provides daily emissions data at a national level, facilitating frequent updates encompassing a broad geographic scope. The dataset draws from a diverse array of activity data sources, including hourly electricity generation reports from 29 countries, monthly industrial production statistics from 62 regions, daily mobility data from 416 cities worldwide, aviation and maritime transportation data, and temperature-corrected fuel consumption records from 206 countries. The primary objective is to monitor the dynamic nature of CO2 emissions, influenced by workdays, holidays, and situational events such as the COVID-19 pandemic.

Key Findings and Numerical Results

The dataset covers emissions from major sectors, including power generation (39%), industrial production (28%), ground transportation (18%), aviation (3%), maritime transport (2%), and residential consumption (10%). One pivotal finding of the paper is the observed global reduction in CO2 emissions by 7.8% from January 1 to April 30, 2020, when compared to the same period in 2019. This decline is attributed predominantly to the global economic downturn induced by the COVID-19 pandemic. However, emissions showed signs of resurgence by the late April, primarily due to the economic recovery in China and partial easing of lockdowns elsewhere.

Implications and Future Directions

From a practical standpoint, the Carbon Monitor dataset offers significant value for policymakers, enabling better-informed decisions concerning climate action and air quality management. Theoretically, the dataset serves as an empirical basis for understanding temporal and spatial variability in CO2 emissions, offering a deeper insight into the anthropogenic impact on climate change. Moreover, the proposed framework can pave the way for advancements in emissions modeling, allowing for refined resolution both spatially and temporally.

Looking forward, the systematic approach outlined in the paper can be expanded to include other sectors and sources of emissions, such as land use changes and emissions from non-fossil industrial processes. Moreover, integrating machine learning models to analyze patterns and predict future emissions could augment the dataset's utility in global carbon budget analysis.

Uncertainty and Technical Validation

The paper also explores the uncertainties involved in emissions estimation, noting the variability across different sectors. For instance, power sector emissions are associated with ±1.5% uncertainty, while transport sectors show higher uncertainties, primarily due to discrepancies in activity data proxies, such as congestion levels. The overall uncertainty is estimated at ±6.8%, with a detailed uncertainty analysis following IPCC Guidelines for National Greenhouse Gas Inventories.

In conclusion, the Carbon Monitor dataset embodies a significant stride in the continuous monitoring and assessment of global carbon emissions. Its comprehensive scope and methodological robustness make it an instrumental tool for both scientific research and policymaking in the pursuit of climate change mitigation.