ارزیابی پایداری بخش تولیدی ایالات متحده: رویکرد مرزی مبتنی بر خروجی ورودی اقتصادی

Due to increasing concerns related to emerging environmental problems as a result of industrial activities, sustainable manufacturing has become a topic of considerable interest worldwide. In this study, Economic Input-Output Life Cycle Assessment (EIO-LCA) and Data Envelopment Analysis (DEA), a linear programming-based mathematical optimization model, were integrated to analyze the eco-efficiency of manufacturing sectors in the United States. This integration was achieved by aggregating different environmental pressures into a single eco-efficiency score. First, greenhouse gas emissions, energy use, water withdrawals, hazardous waste generation, and toxic releases of each manufacturing sector were quantified using the EIO-LCA model. Second, an input-oriented DEA multiplier model was developed. Third, eco-efficiency scores and rankings, target and performance improvement values of each environmental category were determined. Finally, the sensitivity of each environmental impact category was analyzed. Analysis results showed that five industrial sectors, such as “Petroleum and Coal Products Manufacturing”, “Food Manufacturing”, “Printing and Related Support Activities”, “Ordinance and Accessories Manufacturing”, and “Motor Vehicle Manufacturing” were 100% eco-efficient compared to other manufacturing sectors. On the other hand, approximately 90% of U.S. manufacturing sectors were found to be inefficient and require significant improvements in their life cycle performance. Among the environmental impact categories, energy use had the highest sensitivity on the eco-efficiency of U.S. manufacturing sectors, and therefore improved energy efficiency in industrial processes and successful policy making toward increasing the share of renewable energy utilization were highly recommended.