Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China

Bottom-up CH<sub<4</sub< emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed t...
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Autor*in:	Penwadee Cheewaphongphan [verfasserIn] Satoru Chatani [verfasserIn] Nobuko Saigusa [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	CH anthropogenic sources bottom up estimation uncertainty analysis

Übergeordnetes Werk:	In: Sustainability - MDPI AG, 2009, 11(2019), 7, p 2054
Übergeordnetes Werk:	volume:11 ; year:2019 ; number:7, p 2054

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/su11072054

Katalog-ID:	DOAJ017730287

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520			\|a Bottom-up CH<sub<4</sub< emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH<sub<4</sub< bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH<sub<4</sub< emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH<sub<4</sub< emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.
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callnumber-first	T - Technology
author	Penwadee Cheewaphongphan
spellingShingle	Penwadee Cheewaphongphan misc TD194-195 misc TJ807-830 misc GE1-350 misc CH<sub<4</sub< emission inventory misc anthropogenic sources misc bottom up estimation misc uncertainty analysis misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China
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topic_title	TD194-195 TJ807-830 GE1-350 Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China CH<sub<4</sub< emission inventory anthropogenic sources bottom up estimation uncertainty analysis
topic	misc TD194-195 misc TJ807-830 misc GE1-350 misc CH<sub<4</sub< emission inventory misc anthropogenic sources misc bottom up estimation misc uncertainty analysis misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
topic_unstemmed	misc TD194-195 misc TJ807-830 misc GE1-350 misc CH<sub<4</sub< emission inventory misc anthropogenic sources misc bottom up estimation misc uncertainty analysis misc Environmental effects of industries and plants misc Renewable energy sources misc Environmental sciences
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title	Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China
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title_full	Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China
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author_browse	Penwadee Cheewaphongphan Satoru Chatani Nobuko Saigusa
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title_sort	exploring gaps between bottom-up and top-down emission estimates based on uncertainties in multiple emission inventories: a case study on ch<sub<4</sub< emissions in china
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title_auth	Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China
abstract	Bottom-up CH<sub<4</sub< emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH<sub<4</sub< bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH<sub<4</sub< emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH<sub<4</sub< emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.
abstractGer	Bottom-up CH<sub<4</sub< emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH<sub<4</sub< bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH<sub<4</sub< emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH<sub<4</sub< emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.
abstract_unstemmed	Bottom-up CH<sub<4</sub< emission inventories, which have been developed from statistical analyses of activity data and country specific emission factors (EFs), have high uncertainty in terms of the estimations, according to results from top-down inverse model studies. This study aimed to determine the causes of overestimation in CH<sub<4</sub< bottom-up emission inventories across China by applying parameter variability uncertainty analysis to three sets of CH<sub<4</sub< emission inventories titled PENG, GAINS, and EDGAR. The top three major sources of CH<sub<4</sub< emissions in China during the years 1990–2010, namely, coal mining, livestock, and rice cultivation, were selected for the investigation. The results of this study confirm the concerns raised by inverse modeling results in which we found significantly higher bottom-up emissions for the rice cultivation and coal mining sectors. The largest uncertainties were detected in the rice cultivation estimates and were caused by variations in the proportions of rice cultivation ecosystems and EFs; specifically, higher rates for both parameters were used in EDGAR. The coal mining sector was associated with the second highest level of uncertainty, and this was caused by variations in mining types and EFs, for which rather consistent parameters were used in EDGAR and GAINS, but values were slightly higher than those used in PENG. Insignificant differences were detected among the three sets of inventories for the livestock sector.
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title_short	Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China
url	https://doi.org/10.3390/su11072054 https://doaj.org/article/2007a5454d454b5da09672f6cd05929b https://www.mdpi.com/2071-1050/11/7/2054 https://doaj.org/toc/2071-1050
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Exploring Gaps between Bottom-Up and Top-Down Emission Estimates Based on Uncertainties in Multiple Emission Inventories: A Case Study on CH<sub<4</sub< Emissions in China

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