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Purpose-This paper provides an Input-Output Life Cycle assessment model to estimate the carbon footprint of U.S. manufacturing sectors. To achieve this, the paper sets out the following objectives: 1) Develop a time series carbon footprint estimation model for U.S. manufacturing sectors; 2) Analyze the annual and cumulative carbon footprint; 3) Analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and 4) Analyze the supply chains of U.S. manufacturing industries to help identify the most critical carbon emitting industries. Design/Methodology/Approach-Initially, the economic input output tables of U.S. economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, Economic Input Output Life Cycle Assessment (EIO-LCA) models were developed to quantify the carbon footprint extents of the U.S. manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO2equivalent, whereas the economic outputs were measured in million dollar economic activity. Findings-The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately overshadowed the potential benefits that were obtained from lower CO2 intensities. Analysis of specific industry results indicate that the top 5 manufacturing sectors based on total carbon footprint share are "petroleum refineries", "Animal (except poultry) slaughtering, rendering, and processing", "Other basic organic chemical manufacturing", "Motor vehicle parts manufacturing", and "Iron and steel mills and ferroalloy manufacturing". Originality/value-This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the U.S. manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the U.S. manufacturing industries from a supply chain footprint perspective.

Author Bios: Gokhan Egilmez serves as an assistant professor of Industrial and Manufacturing Engineering at North Dakota State University. He also worked as postdoctoral research associate in the dept. of Civil, Environmental and Construction Engineering at University of Central Florida prior to joining department of IME at NDSU. He obtained a doctorate degree in Mechanical and Systems Engineering and two master degrees in Industrial and Systems Engineering and Civil Engineering at Ohio University between 2007 and 2012. Prior to his higher education, in the United States, he received his BS in Industrial Engineering at Istanbul Technical University, Turkey in 2007. His research interests include sustainability assessment of social, environmental, and economic aspects of engineered systems, transportation sustainability & safety, applied operations research and metaheuristic optimization, parametric & nonparametric statistical modeling and dynamic simulation modeling. Gokhan has various peer-reviewed research articles, book chapter and conference proceedings related to sustainable development, manufacturing system design & control, supply chain management, transportation safety and predictive modeling & machine learning. http://gokhanegilmez.wordpress.com/