2000/10/01 by Manfred Lenzen · 3 citations
Business, Management and Accounting · Energy · Engineering · Environmental Science · Mathematics · #Applied mathematics #Computer science #Economics #Energy Efficiency and Management #Engineering #Environmental Impact and Sustainability #Life Cycle Costing Analysis #Life-cycle assessment #Mathematics #Monte Carlo method #Process (computing) #Product (mathematics) #Production (economics) #Reliability engineering #Resource (disambiguation) #Statistics #Truncation (statistics) #Truncation error
paper · doi:10.1162/10881980052541981
crossref issued 2000/10/01 · crossref published 2000/10/01 · crossref published-print 2000/10/01 · openalex publication_date 2000/10/01 · crossref created 2002/07/27 · crossref published-online 2008/02/08 · openalex created_date 2025/10/10 · crossref deposited 2026/01/15 · openalex updated_date 2026/07/04 · crossref indexed 2026/07/04
Summary Conventional process‐analysis‐type techniques for compiling life‐cycle inventories suffer from a truncation error, which is caused by the omission of resource requirements or pollutant releases of higher‐order upstream stages of the production process. The magnitude of this truncation error varies with the type of product or process considered, but can be on the order of 50%. One way to avoid such significant errors is to incorporate input‐output analysis into the assessment framework, resulting in a hybrid life‐cycle inventory method. Using Monte‐Carlo simulations, it can be shown that uncertainties of input‐output– based life‐cycle assessments are often lower than truncation errors in even extensive, third‐order process analyses.