Harnessing the power of input-output analysis for sustainability : a simulation study based on US data

Measuring carbon contents reliably, for products, firms and industries, is key for identifying climate change related transition risks. Phase 3 of the G20 Data Gaps Initiative requests the collection of emission data and multiregional Input-Output (IO) tables to enable the calculation of aggregate carbon contents. What sectoral distinctions do we need – and at what level of granularity? Do we need information on technology? How can statistical data be used in carbon accounting? Based on IO tables and company-level data from the United States (US), I construct a micro simulation environment that can act as a laboratory for answering these questions. The database consists of almost 5000 units located (with few exceptions) in the US and Canada. The analysis focuses on indirect emissions and carbon contents. For levels of aggregation typical of IO tables, the within-sector heterogeneity of carbon contents is very high in some industries. Still, averages can be very useful for company-level carbon accounting. Statistical data can provide consistent starting values for inputs in cases where direct information from providers is missing. Specifically, they may be used to approximate indirect emissions of suppliers, when company-level information on their direct emissions is available. This will be the standard case in the European Union (EU), once upcoming reporting requirements are in place.