scholarly journals Satellite-based estimates of decline and rebound in China’s CO2 emissions during COVID-19 pandemic

2020 ◽  
Vol 6 (49) ◽  
pp. eabd4998 ◽  
Author(s):  
Bo Zheng ◽  
Guannan Geng ◽  
Philippe Ciais ◽  
Steven J. Davis ◽  
Randall V. Martin ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Electricity Generation ◽  
Satellite Observations ◽  
Economic Recovery ◽  
Spatially Explicit ◽  
Independent Measurement ◽  
Activity Data ◽  
Industrial Activity ◽  
Bottom Up ◽  
Pollutant Concentrations

Changes in CO2 emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO2 emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO2 to deduce 10-day moving averages of NOx and CO2 emissions over China, differentiating emissions by sector and province. Between January and April 2020, China’s CO2 emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.

scholarly journals Timely estimates of India's annual and monthly fossil CO<sub>2</sub> emissions

2020 ◽  
Vol 12 (4) ◽  
pp. 2411-2421 ◽  
Author(s):  
Robbie M. Andrew
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Paris Agreement ◽  
Seasonal Patterns ◽  
Activity Data ◽  
Industrial Activity ◽  
Significant Events ◽  
Emissions Intensity ◽  
Almost All ◽  
Global Datasets

Abstract. India is the world's third-largest emitter of carbon dioxide and is developing rapidly. While India has pledged an emissions-intensity reduction as its contribution to the Paris Agreement, the country does not regularly report emissions statistics, making tracking progress difficult. Moreover, all estimates of India's emissions in global datasets represent its financial year, which is not aligned to the calendar year used by almost all other countries. Here I compile monthly energy and industrial activity data allowing for the estimation of India's CO2 emissions by month and calendar year with a short lag. Emissions show clear seasonal patterns, and the series allows for the investigation of short-lived but highly significant events, such as the near-record monsoon in 2019 and the COVID-19 crisis in 2020. Data are available at https://doi.org/10.5281/zenodo.3894394 (Andrew, 2020a).

scholarly journals Timely estimates of India's annual and monthly fossil CO<sub>2</sub> emissions

2020 ◽  
Author(s):  
Robbie M. Andrew
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Paris Agreement ◽  
Seasonal Patterns ◽  
Activity Data ◽  
Industrial Activity ◽  
Significant Events ◽  
Global Estimates ◽  
Emissions Intensity ◽  
Almost All

Abstract. India is the world’s third-largest emitter of carbon dioxide and is developing rapidly. While India has pledged an emissions-intensity reduction as its contribution to the Paris Agreement, the country does not regularly report emissions statistics, making tracking progress difficult. Moreover, all global estimates of India’s emissions are for its financial year, not aligned to the calendar year used by almost all other countries. Here I compile monthly energy and industrial activity data allowing the production of estimates of India’s CO2 emissions by month and calendar year. Emissions show clear seasonal patterns, and the series allows the investigation of short-lived but highly significant events, such as the near-record monsoon in 2019 and the COVID-19 crisis in 2020. Data are available at https://doi.org/10.5281/zenodo.3894394 (Andrew, 2020).

scholarly journals A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide

2020 ◽  
Vol 20 (1) ◽  
pp. 99-116 ◽  
Author(s):  
Fei Liu ◽  
Bryan N. Duncan ◽  
Nickolay A. Krotkov ◽  
Lok N. Lamsal ◽  
Steffen Beirle ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Power Plants ◽  
Operating Conditions ◽  
Satellite Observations ◽  
Nox Emissions ◽  
Bottom Up ◽  
Monitoring Instrument ◽  
The Us

Abstract. We present a method to infer CO2 emissions from individual power plants based on satellite observations of co-emitted nitrogen dioxide (NO2), which could serve as complementary verification of bottom-up inventories or be used to supplement these inventories. We demonstrate its utility on eight large and isolated US power plants, where accurate stack emission estimates of both gases are available for comparison. In the first step of our methodology, we infer nitrogen oxides (NOx) emissions from US power plants using Ozone Monitoring Instrument (OMI) NO2 tropospheric vertical column densities (VCDs) averaged over the ozone season (May–September) and a “top-down” approach that we previously developed. Second, we determine the relationship between NOx and CO2 emissions based on the direct stack emissions measurements reported by continuous emissions monitoring system (CEMS) programs, accounting for coal quality, boiler firing technology, NOx emission control device type, and any change in operating conditions. Third, we estimate CO2 emissions for power plants using the OMI-estimated NOx emissions and the CEMS NOx∕CO2 emission ratio. We find that the CO2 emissions estimated by our satellite-based method during 2005–2017 are in reasonable agreement with the US CEMS measurements, with a relative difference of 8 %±41 % (mean ± standard deviation). The broader implication of our methodology is that it has the potential to provide an additional constraint on CO2 emissions from power plants in regions of the world without reliable emissions accounting. We explore the feasibility by comparing the derived NOx∕CO2 emission ratios for the US with those from a bottom-up emission inventory for other countries and applying our methodology to a power plant in South Africa, where the satellite-based emission estimates show reasonable consistency with other independent estimates. Though our analysis is limited to a few power plants, we expect to be able to apply our method to more US (and world) power plants when multi-year data records become available from new OMI-like sensors with improved capabilities, such as the TROPOspheric Monitoring Instrument (TROPOMI), and upcoming geostationary satellites, such as the Tropospheric Emissions: Monitoring Pollution (TEMPO) instrument.

scholarly journals Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 811
Author(s):  
Yaqin Hu ◽  
Yusheng Shi
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Atmospheric Carbon Dioxide ◽  
Power Plants ◽  
Large Scale ◽  
Global Climate ◽  
Gaussian Model ◽  
Point Sources ◽  
Emission Inventories ◽  
Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

scholarly journals CO<sub>2</sub> emissions inventory of Chinese cities

2016 ◽  
Author(s):  
Yuli Shan ◽  
Dabo Guan ◽  
Jianghua Liu ◽  
Zhu Liu ◽  
Jingru Liu ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Electricity Generation ◽  
Construction Materials ◽  
Primary Energy ◽  
Primary Industry ◽  
Emission Mitigation ◽  
Chinese Cities ◽  
Emissions Inventory ◽  
Emissions Inventories

Abstract. China is the world's largest energy consumer and CO2 emitter. Cities contribute 85 % of the total CO2 emissions in China and thus are considered the key areas for implementing policies designed for climate change adaption and CO2 emission mitigation. However, understanding the CO2 emission status of Chinese cities remains a challenge, mainly owing to the lack of systematic statistics and poor data quality. This study presents a method for constructing a CO2 emissions inventory for Chinese cities in terms of the definition provided by the IPCC territorial emission accounting approach. We apply this method to compile CO2 emissions inventories for 20 Chinese cities. Each inventory covers 47 socioeconomic sectors, 20 energy types and 9 primary industry products. We find that cities are large emissions sources because of their intensive industrial activities, such as electricity generation, production for cement and other construction materials. Additionally, coal and its related products are the primary energy source to power Chinese cities, providing an average of 70 % of the total CO2 emissions. Understanding the emissions sources in Chinese cities using a concrete and consistent methodology is the basis for implementing any climate policy and goal.

scholarly journals Key Factors Influencing the Achievement of Climate Neutrality Targets in the Manufacturing Industry: LMDI Decomposition Analysis

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 8006
Author(s):  
Kristiāna Dolge ◽  
Dagnija Blumberga
Keyword(s):  
Energy Efficiency ◽  
Co2 Emissions ◽  
Manufacturing Industry ◽  
Decomposition Analysis ◽  
Clean Energy ◽  
Industrial Activity ◽  
Intensity Effect ◽  
Wood Processing ◽  
Activity Effect ◽  
The Impact

The manufacturing industry is often caught in the sustainability dilemma between economic growth targets and climate action plans. In this study, a Log-Mean Divisia Index (LMDI) decomposition analysis is applied to investigate how the amount of industrial energy-related CO2 emissions in Latvia has changed in the period from 1995 to 2019. The change in aggregate energy-related CO2 emissions in manufacturing industries is measured by five different factors: the industrial activity effect, structural change effect, energy intensity effect, fuel mix effect, and emission intensity effect. The decomposition analysis results showed that while there has been significant improvement in energy efficiency and decarbonization measures in industry, in recent years, the impact of the improvements has been largely offset by increased industrial activity in energy-intensive sectors such as wood processing and non-metallic mineral production. The results show that energy efficiency measures in industry contribute most to reducing carbon emissions. In the future, additional policies are needed to accelerate the deployment of clean energy and energy efficiency technologies.

scholarly journals Evaluation of anthropogenic CH<sub>4</sub> emissions over China using bottom-up inventories

2020 ◽  
Author(s):  
Xiaohui Lin ◽  
Wen Zhang ◽  
Monica Crippa ◽  
Shushi Peng ◽  
Pengfei Han ◽  
...  
Keyword(s):  
Waste Treatment ◽  
Comprehensive Evaluation ◽  
Energy Sector ◽  
Atmospheric Methane ◽  
Activity Data ◽  
Bottom Up ◽  
Average Growth ◽  
Ch4 Emissions ◽  
Source Contributions ◽  
Change Mitigation

Abstract. Atmospheric methane (CH4) is a potent greenhouse gas that is strongly influenced by several human activities. China, as one of the major agricultural and energy production countries, e.g., rice cultivation, ruminant feeding and coal production, contributes considerably to the global anthropogenic CH4 emissions. Understanding the characteristics of China's CH4 emissions is necessary for interpreting source contributions and for further climate change mitigation. However, the scarcity of data from some sources or years and spatially explicit information pose great challenges to completing an analysis of CH4 emissions. This study provides a comprehensive evaluation of China's anthropogenic CH4 emissions by synthesizing most of the currently available data (12 inventories). The results show that anthropogenic CH4 emissions differ widely among inventories, with values ranging from 41.9–57.5 Tg CH4 yr−1 in 2010. The discrepancy primarily resulted from the energy sector (27.3–60.0 % of total emissions), followed by the agricultural (26.9–50.8 %), and waste treatment (8.1–21.2 %) sectors. Temporally, emissions among inventories stabilized in the 1990s, but increased significantly thereafter, with annual average growth rates (AAGRs) of 1.8–3.9 % during 2000–2010, but slower AAGRs of 0.5–2.2 % during 2011–2015. Spatially, the growth of CH4 emissions could be attributed mostly to an increase in emissions from the energy sector (mainly from coal mining) in the northern and central inland regions, followed by waste treatment in the southern and eastern regions. The availability of detailed activity data for sectors or subsectors and the use of region-specific emission factors play important roles in understanding source contributions, and reducing the uncertainty of bottom-up inventories.

scholarly journals Updated SO<sub>2</sub> emission estimates over China using OMI/Aura observations

2018 ◽  
Vol 11 (3) ◽  
pp. 1817-1832 ◽  
Author(s):  
Maria Elissavet Koukouli ◽  
Nicolas Theys ◽  
Jieying Ding ◽  
Irene Zyrichidou ◽  
Bas Mijling ◽  
...  
Keyword(s):  
Emission Inventory ◽  
Satellite Observations ◽  
Ozone Monitoring Instrument ◽  
So2 Emissions ◽  
Transport Modelling ◽  
So2 Emission ◽  
Bottom Up ◽  
Beijing Area ◽  
Entire Domain ◽  
Particulate Matter Emissions

Abstract. The main aim of this paper is to update existing sulfur dioxide (SO2) emission inventories over China using modern inversion techniques, state-of-the-art chemistry transport modelling (CTM) and satellite observations of SO2. Within the framework of the EU Seventh Framework Programme (FP7) MarcoPolo (Monitoring and Assessment of Regional air quality in China using space Observations) project, a new SO2 emission inventory over China was calculated using the CHIMERE v2013b CTM simulations, 10 years of Ozone Monitoring Instrument (OMI)/Aura total SO2 columns and the pre-existing Multi-resolution Emission Inventory for China (MEIC v1.2). It is shown that including satellite observations in the calculations increases the current bottom-up MEIC inventory emissions for the entire domain studied (15–55° N, 102–132° E) from 26.30 to 32.60 Tg annum−1, with positive updates which are stronger in winter ( ∼  36 % increase). New source areas were identified in the southwest (25–35° N, 100–110° E) as well as in the northeast (40–50° N, 120–130° E) of the domain studied as high SO2 levels were observed by OMI, resulting in increased emissions in the a posteriori inventory that do not appear in the original MEIC v1.2 dataset. Comparisons with the independent Emissions Database for Global Atmospheric Research, EDGAR v4.3.1, show a satisfying agreement since the EDGAR 2010 bottom-up database provides 33.30 Tg annum−1 of SO2 emissions. When studying the entire OMI/Aura time period (2005 to 2015), it was shown that the SO2 emissions remain nearly constant before the year 2010, with a drift of −0.51 ± 0.38 Tg annum−1, and show a statistically significant decline after the year 2010 of −1.64 ± 0.37 Tg annum−1 for the entire domain. Similar findings were obtained when focusing on the greater Beijing area (30–40° N, 110–120° E) with pre-2010 drifts of −0.17 ± 0.14 and post-2010 drifts of −0.47 ± 0.12 Tg annum−1. The new SO2 emission inventory is publicly available and forms part of the official EU MarcoPolo emission inventory over China, which also includes updated NOx, volatile organic compounds and particulate matter emissions.

scholarly journals A bottom-up spatially explicit methodology to estimate the space heating demand of the building stock at regional scale

2020 ◽  
Vol 206 ◽  
pp. 109581 ◽  
Author(s):  
Valentina D'Alonzo ◽  
Antonio Novelli ◽  
Roberto Vaccaro ◽  
Daniele Vettorato ◽  
Rossano Albatici ◽  
...  
Keyword(s):  
Regional Scale ◽  
Space Heating ◽  
Spatially Explicit ◽  
Building Stock ◽  
Bottom Up
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