Measuring GHG Emissions Across the Agri-Food Sector Value Chain: The Development of BIO - a Bio-economy Input-Output Model
Abstract
Sustainable intensification is one of the greatest challenges facing the agri-food sector which needs to produce more food to meet increasing global demand, while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions. Sustainable intensification relates not just to primary production, but also has wider value chain implications. An input-output model is a modelling framework which contains the flows across a value chain within a country. Input-output (IO) models have been disaggregated to have finer granular detail in relation to agricultural sub-sectoral value chains. National IO models with limited agricultural disaggregation have been developed to look at carbon footprints and within agriculture to look at the carbon footprint of specific value chains. In this paper we adapt an agriculturally disaggregated IO model to analyse the source of emissions in different components of agri-food value chains. We focus on Ireland, where emissions from agriculture comprise nearly 30% of national emissions and where there has been a major expansion and transformation in agriculture since the abolition of milk quota restrictions. In a substantial Annex to this paper, we describe the modelling assumptions made in developing this model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greater processing value in more sophisticated value chains such as dairy processing. On the other hand, emissions are in general highest in primary production, albeit emissions from purchased animal feed being higher for poultry than for other value chains, given the lower direct emissions from poultry than from ruminants or sheep. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry meat value chains.
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PDFDOI: https://doi.org/10.18461/pfsd.2018.1803
ISSN 2194-511X
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