气候变化领域集成服务门户(CCPortal): Quantifying uncertainty in carbon emission estimation: Metrics and methodologies

CCPortal

DOI	10.1016/j.jclepro.2024.142141
	Quantifying uncertainty in carbon emission estimation: Metrics and methodologies
	Lee, Kunmo; Ko, Jeonghan; Jung, Seungho
发表日期	2024
ISSN	0959-6526
EISSN	1879-1786
起始页码	452
卷号	452
英文摘要	Carbon emissions are a significant driver of global climate change in today ' s world. A central concern in discussing carbon emissions is the level of uncertainty associated with them. This study aims to assess the feasibility of using the Mean Squared Error (MSE) as a point estimation measure and confidence interval (CI) as an interval estimation measure to quantify the uncertainty surrounding carbon emissions. To achieve this goal, the bootstrap and Markov Chain Monte Carlo (MCMC) sampling methods were used, utilizing both classical and Bayesian inference methods to uncover the true parameter value. In the context of Bayesian inference, a 2-chain MCMC method proved to be the optimal choice for generating posterior distributions and accurately estimating the true parameter of the distribution theta . The analysis also shows that, while a CI is valuable as an evaluative measure, it does not inherently provide a quantified form of uncertainty and should not be used for quantitative uncertainty assessment. Instead, the Relative Standard Error (RSE) emerges as a promising quantitative measure for capturing the uncertainty of theta , while the percentage uncertainty offers a qualitative perspective. These combined measures provide a comprehensive toolkit for computing and communicating the uncertainty in carbon emission and emission factor values. This reinforces a move towards incorporating transparent uncertainty metrics, ensuring that stakeholders have access to reliable and accurate carbon emission values.
英文关键词	Carbon emission; Inference; Bootstrap; Markov chain Monte Carlo; Mean squared error; Relative standard error; Percentage uncertainty
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Engineering ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Engineering, Environmental ; Environmental Sciences
WOS记录号	WOS:001229047500001
来源期刊	JOURNAL OF CLEANER PRODUCTION
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/297013
作者单位	Ajou University; Ajou University
推荐引用方式 GB/T 7714	Lee, Kunmo,Ko, Jeonghan,Jung, Seungho. Quantifying uncertainty in carbon emission estimation: Metrics and methodologies[J],2024,452.
APA	Lee, Kunmo,Ko, Jeonghan,&Jung, Seungho.(2024).Quantifying uncertainty in carbon emission estimation: Metrics and methodologies.JOURNAL OF CLEANER PRODUCTION,452.
MLA	Lee, Kunmo,et al."Quantifying uncertainty in carbon emission estimation: Metrics and methodologies".JOURNAL OF CLEANER PRODUCTION 452(2024).