Department of Environmental Science, Radboud University Nijmegen , Toernooiveld 1, 6525 ED Nijmegen, Netherlands.
Environ Sci Technol. 2014 May 6;48(9):5282-9. doi: 10.1021/es500757p. Epub 2014 Apr 21.
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.
在生命周期评估（LCA）中，主要挑战之一是用于开发模型和提出适当建议的数据的可用性和质量。如果没有适当的数据，通常会进行近似和假设。但是，这些代理可能会给结果带来不确定性。可以采用回归模型框架来评估产品和工艺的 LCA 中缺失的数据。在这项研究中，我们开发了这样一个基于回归的框架，以在没有报告数据的情况下估算与燃煤电厂相关的 CO2 排放因子。我们的框架假设可以通过包括蒸汽压力、总容量、电厂年龄、燃料类型和电厂所在国家/地区的人均国内生产总值（GDP）在内的特定于电厂的因素（预测因素）来解释燃煤电厂的排放。使用全球 444 个电厂的报告排放数据，通过多元线性回归模型和局部线性回归模型将电厂级别的 CO2 排放因子拟合到选定的预测因素上。然后，将经过验证的模型应用于全球 764 个没有报告数据的燃煤电厂。总的来说，可用的报告数据和我们的预测共同占全球燃煤发电总容量的 74%。
