How to address data gaps in life cycle inventories: a case study on estimating CO2 emissions from coal-fired electricity plants on a global scale.
Environ Sci Technol
; 48(9): 5282-9, 2014 May 06.
Article
de En
| MEDLINE
| ID: mdl-24749645
ABSTRACT
One of the major challenges in life cycle assessment (LCA) is the availability and quality of data used to develop models and to make appropriate recommendations. Approximations and assumptions are often made if appropriate data are not readily available. However, these proxies may introduce uncertainty into the results. A regression model framework may be employed to assess missing data in LCAs of products and processes. In this study, we develop such a regression-based framework to estimate CO2 emission factors associated with coal power plants in the absence of reported data. Our framework hypothesizes that emissions from coal power plants can be explained by plant-specific factors (predictors) that include steam pressure, total capacity, plant age, fuel type, and gross domestic product (GDP) per capita of the resident nations of those plants. Using reported emission data for 444 plants worldwide, plant level CO2 emission factors were fitted to the selected predictors by a multiple linear regression model and a local linear regression model. The validated models were then applied to 764 coal power plants worldwide, for which no reported data were available. Cumulatively, available reported data and our predictions together account for 74% of the total world's coal-fired power generation capacity.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Centrales énergétiques
/
Dioxyde de carbone
/
Charbon
/
Polluants atmosphériques
/
Électricité
Type d'étude:
Prognostic_studies
Aspects:
Patient_preference
Langue:
En
Journal:
Environ Sci Technol
Année:
2014
Type de document:
Article
Pays d'affiliation:
Pays-Bas