GOBLIN Model

The General Overview for a Back-casting approach of the Livestock Intensification (GOBLIN) model is a first version of a python-code-based bio-physical national model of the Agriculture, Forest and Other Land-Use (AFOLU) sector in Ireland (Figures 4 and 5). It has been developed by the NUI Galway team4 and represents the integration of a number of livestock, arable and bioenergy models5. The model has just been completed in its first iteration for the SeQUEsTER project6.

Figure 4 Schematic representation of the GOBLIN model.

Figure 5. Data flow in core land use model

How does it work?

GOBLIN integrates key parameters that influence major greenhouse gas (GHG) fluxes in the AFOLU sector in Ireland. It has the objective to identify broad pathways towards carbon neutrality in Ireland.

The model run repeatedly with varying (biophysically compatible) combinations of parameter inputs in order to identify specific combinations that achieve climate neutrality in 2050 and beyond or “net zero emissions”7. Scenario pathways respecting the climate neutrality objective can then be put forward for further elaboration with stakeholders, removing bias in the initial selection of potential pathways towards climate neutrality.

The different steps in GOBLIN to compute emissions from the Irish AFOLU sector are:

  1. Computation of future ruminant production (activity)
  2. Computation of future ruminant emissions
  3. Computation of the area required for future ruminant production
  4. Computation of emission offset potential via forestry on spared land (including downstream wood use and substitution effects)
  5. Computation of emission fluxes (including offsets) from anaerobic digestion (AD), including substitution effects
  6. Computation of emission fluxes on wetland
  7. Computation of emission fluxes from alternative (to be defined) bio based value chains

During INFORMBIO, it is planned to adapt the GOBLIN model to fully represent promising biobased value chains as explained in Figure 6, where is displayed the multi-sectoral and transboundary nature of biobased value chains, and implications for GHG emissions.

A cross-cutting academic endeavour within INFORMBIO will be to identify complementarities and trade-offs across different scales and approaches of bioeconomy monitoring, in terms of capturing wider sustainability consequences.

Can sectoral indicators of activity be used to reliably indicate net climate mitigation effects that may rely on avoidance of production emissions that arise in other sectors, and possibly other countries? This question is what it is going to be approached by INFORMBIO using the adapted version of the GOBLIN model.

Figure 6. Overview activities associated with biobased value chains that transect the AFOLU sector and downstream sectors of the economy, generating and avoiding (via substitution) GHG emissions across sectors and national boundaries. Source: Duffy et al. (in prep.).

4. Duffy et al. (2022). https://gmd.copernicus.org/articles/15/2239/2022/gmd-15-2239-2022-discussion.html

5. Soteriades, A. D., Gonzalez-Mejia, A. M., Styles, D., Foskolos, A., Moorby, J. M., & Gibbons, J. M. (2018). Effects of high-sugar grasses and improved manure management on the environmental footprint of milk production at the farm level. Journal of Cleaner Production, 202, 1241–1252. https://doi.org/10.1016/J.JCLEPRO.2018.08.206
Styles, D., Dominguez, E. M., & Chadwick, D. (2016). Environmental balance of the of the UK biogas sector: An evaluation by consequential life cycle assessment. Science of the Total Environment, 560–561, 241–253. https://doi.org/10.1016/j.scitotenv.2016.03.236
Styles, D., Gonzalez-Mejia, A., Moorby, J., Foskolos, A., & Gibbons, J. (2018). Climate mitigation by dairy intensification depends on intensive use of spared grassland. Global Change Biology, 24(2), 681–693. https://doi.org/10.1111/gcb.13868
Styles, David, Gibbons, J., Williams, A. P., Dauber, J., Stichnothe, H., Urban, B., Chadwick, D. R., & Jones, D. L. (2015). Consequential life cycle assessment of biogas, biofuel and biomass energy options within an arable crop rotation. GCB Bioenergy, 7(6), 1305–1320. https://doi.org/10.1111/gcbb.12246
Styles, David, Gibbons, J., Williams, A. P., Stichnothe, H., Chadwick, D. R., & Healey, J. R. (2015). Cattle feed or bioenergy? Consequential life cycle assessment of biogas feedstock options on dairy farms. GCB Bioenergy, 7(5), 1034–1049. https://doi.org/10.1111/gcbb.12189

6. https://www.plantagbiosciences.org/project/sequester/

7. Allen, M. R., Shine, K. P., Fuglestvedt, J. S., Millar, R. J., Cain, M., Frame, D. J., & Macey, A. H. (2018). A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation. Npj Climate and Atmospheric Science, 1(1), 16. https://doi.org/10.1038/s41612-018-0026-8
Cain, M., Lynch, J., Allen, M. R., Fuglestvedt, J. S., Frame, D. J., & Macey, A. H. (2019). Improved calculation of warming-equivalent emissions for short-lived climate pollutants. Climate and Atmospheric Science, 2(1), 1–7. https://doi.org/10.1038/s41612-019-0086-4
Rogelj, J., & Schleussner, C.-F. (2019). Unintentional unfairness when applying new greenhouse gas emissions metrics at country level. Environmental Research Letters, 14(11), 114039. https://doi.org/10.1088/1748-9326/ab4928

GOBLIN Model

The General Overview for a Back-casting approach of the Livestock Intensification (GOBLIN) model is a first version of a python-code-based bio-physical national model of the Agriculture, Forest and Other Land-Use (AFOLU) sector in Ireland (Figures 4 and 5). It has been developed by the NUI Galway team4 and represents the integration of a number of livestock, arable and bioenergy models5. The model has just been completed in its first iteration for the SeQUEsTER project6.

Figure 4. Schematic representation of the GOBLIN model.

Figure 5. Data flow in core land use model

How does it work?

GOBLIN integrates key parameters that influence major greenhouse gas (GHG) fluxes in the AFOLU sector in Ireland. It has the objective to identify broad pathways towards carbon neutrality in Ireland.

The model run repeatedly with varying (biophysically compatible) combinations of parameter inputs in order to identify specific combinations that achieve climate neutrality in 2050 and beyond or “net zero emissions”7. Scenario pathways respecting the climate neutrality objective can then be put forward for further elaboration with stakeholders, removing bias in the initial selection of potential pathways towards climate neutrality.

The different steps in GOBLIN to compute emissions from the Irish AFOLU sector are:

  1. Computation of future ruminant production (activity)
  2. Computation of future ruminant emissions
  3. Computation of the area required for future ruminant production
  4. Computation of emission offset potential via forestry on spared land (including downstream wood use and substitution effects)
  5. Computation of emission fluxes (including offsets) from anaerobic digestion (AD), including substitution effects
  6. Computation of emission fluxes on wetland
  7. Computation of emission fluxes from alternative (to be defined) bio based value chains

During INFORMBIO, it is planned to adapt the GOBLIN model to fully represent promising biobased value chains as explained in Figure 6, where is displayed the multi-sectoral and transboundary nature of biobased value chains, and implications for GHG emissions.

A cross-cutting academic endeavour within INFORMBIO will be to identify complementarities and trade-offs across different scales and approaches of bioeconomy monitoring, in terms of capturing wider sustainability consequences.

Can sectoral indicators of activity be used to reliably indicate net climate mitigation effects that may rely on avoidance of production emissions that arise in other sectors, and possibly other countries? This question is what it is going to be approached by INFORMBIO using the adapted version of the GOBLIN model.

Figure 6. Overview activities associated with biobased value chains that transect the AFOLU sector and downstream sectors of the economy, generating and avoiding (via substitution) GHG emissions across sectors and national boundaries. Source: Duffy et al. (in prep.).

4. Duffy et al. (2022). https://gmd.copernicus.org/articles/15/2239/2022/gmd-15-2239-2022-discussion.html

5. Soteriades, A. D., Gonzalez-Mejia, A. M., Styles, D., Foskolos, A., Moorby, J. M., & Gibbons, J. M. (2018). Effects of high-sugar grasses and improved manure management on the environmental footprint of milk production at the farm level. Journal of Cleaner Production, 202, 1241–1252. https://doi.org/10.1016/J.JCLEPRO.2018.08.206
Styles, D., Dominguez, E. M., & Chadwick, D. (2016). Environmental balance of the of the UK biogas sector: An evaluation by consequential life cycle assessment. Science of the Total Environment, 560–561, 241–253. https://doi.org/10.1016/j.scitotenv.2016.03.236
Styles, D., Gonzalez-Mejia, A., Moorby, J., Foskolos, A., & Gibbons, J. (2018). Climate mitigation by dairy intensification depends on intensive use of spared grassland. Global Change Biology, 24(2), 681–693. https://doi.org/10.1111/gcb.13868
Styles, David, Gibbons, J., Williams, A. P., Dauber, J., Stichnothe, H., Urban, B., Chadwick, D. R., & Jones, D. L. (2015). Consequential life cycle assessment of biogas, biofuel and biomass energy options within an arable crop rotation. GCB Bioenergy, 7(6), 1305–1320. https://doi.org/10.1111/gcbb.12246
Styles, David, Gibbons, J., Williams, A. P., Stichnothe, H., Chadwick, D. R., & Healey, J. R. (2015). Cattle feed or bioenergy? Consequential life cycle assessment of biogas feedstock options on dairy farms. GCB Bioenergy, 7(5), 1034–1049. https://doi.org/10.1111/gcbb.12189

6. https://www.plantagbiosciences.org/project/sequester/

7. Allen, M. R., Shine, K. P., Fuglestvedt, J. S., Millar, R. J., Cain, M., Frame, D. J., & Macey, A. H. (2018). A solution to the misrepresentations of CO2-equivalent emissions of short-lived climate pollutants under ambitious mitigation. Npj Climate and Atmospheric Science, 1(1), 16. https://doi.org/10.1038/s41612-018-0026-8
Cain, M., Lynch, J., Allen, M. R., Fuglestvedt, J. S., Frame, D. J., & Macey, A. H. (2019). Improved calculation of warming-equivalent emissions for short-lived climate pollutants. Climate and Atmospheric Science, 2(1), 1–7. https://doi.org/10.1038/s41612-019-0086-4
Rogelj, J., & Schleussner, C.-F. (2019). Unintentional unfairness when applying new greenhouse gas emissions metrics at country level. Environmental Research Letters, 14(11), 114039. https://doi.org/10.1088/1748-9326/ab4928