碳足迹
下游(制造业)
供应链
温室气体
持续性
产品(数学)
术语
生命周期评估
相关性(法律)
环境经济学
业务
路径分析(统计学)
路径(计算)
企业社会责任
环境资源管理
计算机科学
营销
生产(经济)
环境科学
经济
微观经济学
生物
哲学
程序设计语言
法学
几何学
数学
机器学习
语言学
生态学
政治学
作者
Manfred Lenzen,Joy Murray
标识
DOI:10.1016/j.ecolecon.2010.04.005
摘要
Downstream responsibility is rarely addressed in the academic literature and in corporate sustainability reporting. We conceptualise downstream responsibility for the example of carbon emissions, by establishing a terminology as well as a framework for quantifying downstream carbon footprints. By extracting emissions-intensive sales chains for a number of Australian industry sectors, and comparing these to emissions-intensive supply chains, we demonstrated the ability of input–output analysis to quantify emissions responsibility in both directions. We extend the definition of downstream responsibility beyond the product use and disposal phases, to include what we call “enabled” emissions. This term implies that whatever is sold downstream enables our customers to operate and emit, irrespective of whether it is our product that is combusted, or that directly combusts fuels, or not. Our structural path analyses and threshold-capture relationships reveal stark differences between industries with regard to the data collection efforts necessary to achieve a reasonably complete footprint assessment. Industries appear to have their own specific carbon footprint profiles, and one cannot design generic relevance tests that tell which data to collect. Moreover we conclude that current completeness standards in carbon reporting cannot be satisfied using relevance thresholds. Input–output analysis and structural path analysis are excellent tools that can help companies undertake screening exercises, which in turn help prioritising and streamlining the collection of data needed to establish a corporate downstream carbon footprint. Compared to conventional manual approaches, hybrid life-cycle assessments assisted by input–output analysis and structural path analysis achieve more complete results, with substantially less staff, money and time.
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