Measuring GHG Emissions Across the Agri‐Food Sector Value Chain: The Development of a Bioeconomy Input‐Output Model

Increasing food production to meet rising global demand while minimising negative environmental impacts such as agricultural greenhouse gas (GHG) emissions is one of the greatest challenges facing the agri‐food sector. Sustainable food production relates not only to primary production, but also has wider value chain implications. Aninput‐output (IO) model is a modelling framework which contains information on the flow of goods and services across a value chain at a regional or national economy level. This paper provides a detailed description of the development of a Bioeconomy IO (BIO) model which is disaggregated across the subs‐sectors of the agri‐food valuechain and environmentally extended (EE) to examine environmental outputs, including GHG emissions, We focus on Ireland, where emissions from agriculture comprise 33% of national GHG emissions and where there has been a major expansion and transformation in agriculture supported by national and EU policy. In a substantial Annex to this paper, we describe the modelling assumptions made in developing the BIO model. Breaking up the value chain into components, we find that most value is generated at the processing stage of the value chain, with greaterprocessing 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 are higher for poultry than for other value chains, given the lower animal based emissions from poultry than from cows or sheep. The level ofdisaggregation also shows that the sub‐sectors are themselves discrete value chains. The analysis highlights that emissions per unit of output are much higher for beef and sheep meat value chains than for pig and poultry. The analysis facilitated by the BIO model also allows for the mapping of emissions along the agri‐food value chain using the adapted IO EE approach. Such analysis is valuable in identifying emissions ‘hot‐spots’ along the value chains and analysing potential avenues for emission efficiencies

MEASURING GHG EMISSIONS ACROSS THE AGRI-FOOD SECTOR VALUE CHAIN: THE DEVELOPMENT OF BIO - A BIO-ECONOMY INPUT-OUTPUT MODEL

peer-reviewedSustainable 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

Challenging Practices : Experiences from community and individual living lab approaches

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Comprehensive Open Access Dataset of Sustainable Energy Consumption Initiatives (SECIs) : Deliverable 2.3

This document (ENERGISE D2.3) provides a background report on the process and result of developing and constructing a comprehensive open access dataset of sustainable energy consumption initiatives (SECIs) that have been collected and assessed as part of Work Package 2 (WP2) in ENERGISE. The dataset is designed as a map that is intended to be a user-friendly device that provides an overview of sustainable energy consumption initiatives (SECIs) in Europe. In particular, the map shows the variety in scope, content and approach in the identified SECIs

Challenging social norms to recraft practices : A Living Lab approach to reducing household energy use in eight European countries

ENERGISE is the first large-scale European effort to reduce household energy use through a change initiative that adopted a ‘living lab’ approach informed by social practice theory. Two challenges were introduced to 306 households in eight countries: to lower indoor temperatures and to reduce laundry cycles. This contribution demonstrates the usefulness of a practice-centered design that takes habits and routines as an entry point for understanding how different ‘elements of practices’ can be re-crafted. We discuss how a participatory ‘living lab’ approach that explicitly encouraged deliberation and reflexivity served to sharpen attention on practices as central to change. We discuss how ‘doing laundry’ and ‘keeping warm’, as very different types of practices, responded to the change initiative. For laundry, tangible changes in material arrangements, news skills and sensory competencies, and shifts in what is seen as ‘normal’ proved to be central to reducing wash cycles, including wearing clothes more often, airing them out, using smell to gauge cleanliness, or keeping dirty clothes out of sight. Warming people rather than spaces through added layers and activities, and related shifts in norms around thermal comfort, emerged as crucial steps towards lowering indoor temperatures. Average changes in reported temperatures and wash cycles indicate that reductions are possible, without an emphasis on individuals or technologies as central to change. We end with a discussion on the implications of our approach for energy sufficiency thinking and practice, emphasizing the merits of taking the complexity of everyday life seriously when designing change initiatives.Peer reviewe

Catalogue of existing good practice examples of programmes and interventions : Deliverable 2.1

This document (D2.1) provides an overview of the extensive data that has been collected on sustainable energy consumption initiatives as part of Work Package 2 (WP2) in ENERGISE. The deliverable provides a general introduction to the scope and objectives of WP2 specifically, as well as a short introduction as to how sustainable energy consumption initiatives are defined in ENERGISE. In addition, a full list is provided of 1000+ sustainable energy consumption initiatives that have been identified throughout Europe

Production of 30 National Summary briefs : Deliverable 2.5

This document (ENERGISE D2.5) provides an overview of national energy and supply dynamics across 30 European countries. The Deliverable encompasses reviews of the current state of the art and existing trends in national energy policies, energy systems and energy campaigns in each of the 30 countries. To enhance accessibility and engagement with the material, the information gathered is presented in 30 independent National Briefs

A Dynamic Spatial Microsimulation Model for Irish Agricultural Emissions

This thesis describes the development of a dynamic spatial microsimulation model for Irish agriculture and its use in providing a spatially disaggregated profile of resultant emissions. Following the establishment of a baseline spatial agricultural emissions inventory, a dynamic microsimulation model is developed and is used to simulate agricultural activity forward in time to provide an estimation of future emissions outcomes based on previous historical trends. Finally, in the context of potentially conflicting economic and environmental policies for Irish Agriculture a scenario analysis is undertaken in order to assess the potential emissions impact of achieving the expansionary targets outlined for the dairy sector in the Food Harvest 2020 programme. An adaptation of the SMILE (Simulated Model of the Irish Local Economy) quota sampling procedure involving the incorporation of a novel stocking rate ranking methodology was found to dramatically improve results for the preservation of spatial heterogeneity of stocking levels and associated agri-emissions. Results from a dynamic spatial microsimulation model based on the Teagasc National Farm Survey project a gradual decline in agricultural activity based on historical trends over a ten year simulation period with a concomitant marginal reduction in associated emissions. Results from a multi-scenario analysis in the post-quota era reveal the potential future spatial locations of new dairy farms required to enter in order to meet target. For three alternative dairy expansion scenarios, total emissions from agriculture are projected to fall by between 2-5% by 2020. Information on the potential future spatial disaggregation of emissions related activities provides an opportunity for the advanced planning and design of novel mitigation strategies at the sub-national level. This thesis offers a solution to this information deficit for Irish agriculture, the largest contributor to non-Emissions Trading Scheme emissions. It also provides a unique contribution to knowledge by establishing a framework under which economic and environmental policies for the agricultural sector can be assessed in tandem in terms of their future consequences for national emissions