the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Temporal and spatial mapping of theoretical biomass potential across the European Union
Tom Karras
Friederike Naegeli de Torres
Sebastian Semella
Daniela Thrän
Abstract. With the increasing challenge to shift our economic system from carbon to renewable energy carriers the demand for biogenic resources is growing. Biogenic municipal waste, agricultural by-products and industrial residues are under-utilised but are increasingly gaining in value. To date, there is no continuous database for these resources in the EU-27 countries. Existing datasets that estimate resource potentials in a single time step often lack validation. A reliable and continuous database is thus needed to support the growing bioeconomy.
Spatial and temporal high-resolution data of biogenic residues serve as an invaluable resource for identifying areas with significant theoretical biomass potential and allows an in-depth understanding of dynamic patterns over time. This study elucidates the theoretical biomass potentials of 13 distinct biomasses from municipal waste, agricultural by-products and industrial residues quantified annually from 2010–2020. The spatial scope of the research covers the EU-27 Member States incorporating all entities represented at various levels within the Nomenclature of Territorial Units for Statistics (NUTS) as delineated by Eurostat, where possible. The regionalised data are subsequently validated against national statistics. The findings demonstrate the feasibility of creating a time series of theoretical biomass potentials for the 13 selected waste types, by-products, and residues, and underscore the critical role of data validation when regionalising national or sub-national data to smaller NUTS entities. It could be shown that the values of small regions (NUTS 3) correlated well on average. When looking at individual regions in detail, regional characteristics such as the location of cultivation, waste management or reporting methods could lead to over- or underestimates of up to 100 %. Therefore, data at the regional level provide only limited information. In the case of industrial residues regionalisation gave good results localising preference regions of high theoretical biomass potential but more data on industrial production are needed to estimate also residual quantities at subnational and local levels.
Biomass potentials modelled in this study are published in an open access database, which is designed as an extensible tool, enabling the understanding of national and regional trends of theoretical biomass potentials in the European Union and of the reliability of the regionalised data.
The estimated theoretical potential dataset can be downloaded free of charge from: https://doi.org/10.48480/g53t-ks72 (Günther et al., 2023).
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Susann Günther et al.
Status: final response (author comments only)
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RC1: 'Comment on essd-2023-179', Anonymous Referee #1, 23 Jul 2023
The study, “Temporal and spatial mapping of theoretical biomass potential across the European Union,” extends the existing literature to construct a time-series dataset of potential biomass from major sources in the EU, and downscaling from country-level data to finer scales was made where possible. Overall, I (as an agricultural economist and integrated assessment modeler) find the data could be of important use. The paper is well-structured, but the writing and analysis can be significantly improved. In many places, additional details and clarification are needed. The contribution of the data/study, the usefulness of the data, and the impact of the analysis are not well-discussed. Authors may consider expanding the introduction and/or discussion sections to communicate why/how the data could be needed and used. Here are my detailed comments/questions.
- Importance & contribution of the study
- Based on the abstract, intro, or conclusion, I am not convinced that the data is important and useful. E.g., why expanding the biomass potential data temporally and improving the quality of the data important, besides understanding the trend and regional heterogeneity?
- In the recent literature of climate change mitigation scenarios (e.g., those projecting future bioenergy production using integrated assessment models such as GCAM, GLOBIOM, IMAGE, etc., see the recent IPCC WG3 report or the scenario database), almost all scenarios require large scale residual biomass to be used in near future. So those models indeed included methods of calculating the biomass potential (and more importantly, a supply curve) for all regions. I believe there is an important need in that literature to utilize your data. Some discussion could be useful, e.g., comparing approaches.
- A broader literature review/discussion could be useful.
- Big picture & definition of biomass and connection across sources
- I understand the study tries to construct and connect the biomass data from three sources, Ag, MSW, & industry. However, what is the big picture? E.g., how important are the sources you didn’t consider? E.g., how important are other sources like forestry/milling residues, other crops & industries.
- You discussed the total potential in section 3.4, which I find useful. However, in aggregation, unit matters. Were you trying to add up biomass across sources with different water content? I suggest using dry matter tons and additional conversion to energy units, e.g., EJ/MJ could be extremely useful.
- Some clarifications on agricultural “by-product” and residues could be useful, and maybe also primary vs. secondary (industrial?). For example, it seems for maize, only strew in included, but not cob or other residues?
- Also, some discussion of the gap between potential biomass and the harvestable fraction could be useful.
- Downscaling and validation
- Line 125, by model, do you refer to the downscaling model shown in Fig. 1?
- Line 145, the equation is not very clear to me. Should it be RCR?
- I think residue to crop ratio (RCR) should be discussed. Is it the same across places and years?
- RPR was discussed, but Table 2 was not clear. Please make it independent.
- I agree the validation is useful. However, please note that it is only partial identification/validation. The external validity is not guaranteed. Source data quality is always the most important.
- Similarly, I guess your approach may also be applied to downscaling the biomass data produced by IAM in future periods, subject to some additional assumptions.
- I appreciate the discussion of the limitations of the downscaling approach.
- I guess it is also related to the use of the data. In many cases, NUTS1 or NUTS2 are enough, where you have better data.
- More importantly, I think it would be extremely useful to discuss future work or the potential for the continuation of the work. For example, I wonder if the code was written in a flexible way for one to update the data, e.g., when new data (after 2020) become available. In my opinion, it is important to make the data/processing/update “live” since the data may become increasingly useful.
- FAOSTAT has some supply-utilization data that
- Final data
- In the final data you produced, did you use the data you produced, or you used the source data, where applicable, you used in the validation?
- Results
- Was agricultural production increasing over time? Why ag residues are not changing much over time. I would appreciate discussion of the potential drivers of the trend.
- Any thoughts on the residues of other crops/livestock products?
- Line 404, what happened in 2017?
Minor comments:
- Line 40, MSW stands for municipal solid waste?
- Fig1, adding time dimensions might be useful? And some explanations (e.g., datasets) in the caption to make the figure self-explanatory would be great.
- Lines around 110, linear interpolation for in-between years, why not use population trend or the approach you used for downscaling?
- Line 200, causes for the changes in Luxembourg?
Citation: https://doi.org/10.5194/essd-2023-179-RC1 -
RC2: 'Comment on essd-2023-179', Matthew Langholtz, 13 Aug 2023
Valuable assessment of select waste and agricultural processing residues at various scales in the EU.
Good job assessing scale-specific data uncertainty.
Some questions and suggestions follow. Other copy-edit suggestions for your consideration tracked in the PDF.
Line 41: “Data on MSW streams indicate that landfill declined from over 60 % to 24 % over the last three decades.” Percent of what? I think this is percent of MSW landfilled but this is not clear.
Line 43: “with each an increase of over 10 %” percent of what?
Line 174: “For almost none industrial food production sites individual production values can be found.” Not clear, please rephrase.
Line 185: Some more explanation is suggested to help the reader understand why a validation is not needed.
Line 210: Define mio. t [FM] at first instance in caption and first instance in text. Acknowledging the EU perspective, I don’t think this is universally standard, and I don’t recognize “[FM]”.
Line 298: “The production site mapping of the 50 biggest companies show accordingly a high density in Germany and the Netherlands although very spread.” Suggest clarify wording.
Figure 7: change “Amount…” to “Number…”
Line 313: “Agricultural by-products vary more in between each year.” Clarify wording.
Line 316: “Decreasing in 2014 to 2015 by 12 % in one year and increasing in 2016 to 2017 by 17 % in the other year.” Incomplete sentence.
Line 320: “2017 is the year in contrast has with 469 mio. t [FM] the highest available biomass amount and2020 is included as the last year of the time series with 427 mio. t [FM].” Clarify wording.
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AC1: 'Comment on essd-2023-179', Susann Günther, 11 Sep 2023
We would like to thank the reviewers and the editorial team for their valuable time and constructive feedback. We sincerely appreciate their expertise, which has undoubtedly improved the overall quality of our work.
We have carefully addressed each of the reviewers' comments in our revision, which you can see in more detail in the attached table "AuthorsResponse.pdf". In the revised manuscript, we hope to have achieved improvements in clarity, methodology and overall presentation of the research. The most important changes can also be found in the attached table.
Yours sincerely.
Susann Günther et al.
Data sets
Theoretical biomass potentials for EU 27 Susann Günther, Tom Karras, and Sebastian Semella https://doi.org/10.48480/g53t-ks72
Susann Günther et al.
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