the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Submitted as: model description paper 21 Jan 2021
Submitted as: model description paper | 21 Jan 2021
NDCmitiQ v1.0.0: a tool to quantify and analyse GHG mitigation targets
- 1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, D-14412 Potsdam, Germany
- 2NewClimate Institute, Schönhauser Allee 10–11, 10119 Berlin, Germany
- 1Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 601203, D-14412 Potsdam, Germany
- 2NewClimate Institute, Schönhauser Allee 10–11, 10119 Berlin, Germany
Abstract. Parties to the Paris Agreement (PA, 2015) outline their planned contributions towards achieving the PA temperature goal to hold [...] the increase in the global average temperature to well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C
(Article 2.1.a, PA) in their Nationally Determined Contributions (NDCs). Most NDCs include targets to mitigate national greenhouse gas (GHG) emissions, which need quantifications to assess, i.a., whether the current NDCs collectively put us on track to reach the PA temperature goals or the gap in ambition to do so. We implemented the new open-source tool NDCmitiQ
to quantify GHG mitigation targets defined in the NDCs for all countries with quantifiable targets on a disaggregated level, and to create corresponding national and global emissions pathways. In light of the five-year update cycle of NDCs and the global stocktake, the quantification of NDCs is an ongoing task for which NDCmitiQ can be used, as calculations can easily be updated upon submission of new NDCs. In this paper, we describe the methodologies behind NDCmitiQ and quantification challenges we encountered by addressing a wide range of aspects, including: target types and the input data from within NDCs; external time series of national emissions, population, and GDP; uniform approach vs. country specifics; share of national emissions covered by NDCs; how to deal with the Land Use, Land-Use Change and Forestry (LULUCF) component and the conditionality of pledges; establishing pathways from single year targets. For use in NDCmitiQ, we furthermore construct an emissions data set from the baseline emissions provided in the NDCs. Example use cases show how the tool can help to analyse targets on a national, regional, or global scale, and to quantify uncertainties caused by a lack of clarity in the NDCs. Results confirm that the conditionality of targets and assumptions on economic growth dominate uncertainty in mitigated emissions on a global scale, which are estimated as 49.2–55.7 Gt CO2 eq AR4 for 2030 (10th/90th percentiles, median: 52.4 Gt CO2 eq AR4; excl. LULUCF and bunker fuels). We estimate that 77 % of global 2017 emissions were emitted from sectors and gases covered by current NDCs (excl. the USA).
Annika Günther et al.
Status: closed
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RC1: 'Comment on gmd-2020-392', Anonymous Referee #1, 16 Feb 2021
This is an interesting analysis on an uncertainty analysis for the impact of the uncertainties related to the INDCs on the global emission levels. It includes many detailed analysis and insights, which are well described. The work is highly relevant and interesting, and also the tool looks promising. The paper itself is rather detailed and lengthy, and in my view reads more as a technical report, than a journal paper.
However, I see some short-comings, which in my view can highly influence the resulting global emissions projections. Unfortunately the results of some main emitting countries, such as China and India are not included in the paper, so I could not check the projections.
In general the NDC emissions projections differ across studies mainly due to a couple of important factors.
1/ The authors assumed in their calculations that the NDC targets of China and India is calculated in terms of carbon intensity improvement. They mention: “Similar to Benveniste et al. (2018), targets for fossil fuel shares are not included in NDCmitiQ, and the non-fossil fuel targets the large emitters China and India stated additionally to emissions intensity targets are not quantified.”. The NDC of China also includes (i) the target to peak CO2 emissions by 2030 at the latest, (ii) increase the share of non-fossil energy carriers of the total primary energy supply to around 20% by that time, and (iii) increase its forest stock volume by 4.5 billion cubic metrics, compared to 2005 levels. In fact the factor (i) and (ii) are more important for the final 2030 emissions than the intensity target, as factor (i) and (ii) are the dominant factor. See literature around this issue from climate action tracker, but also UNEP Gap rapport, etc.. This also holds for India, since the NDC target of also includes (i) to increase the share of non-fossil based power generation capacity to 40% of installed electric power capacity by 2030 (equivalent to 26–30% of generation in 2030), and (ii) to create an additional (cumulative) carbon sink of 2.5–3 GtCO2e through additional forest and tree cover by 2030.
For the calculation of the impact of the NDC for China and India the authors need to account for the factors (i) and (ii) for China and (i) for India, and this highly affects the outcomes, as these factors are more dominant that changes in the GDP. Accounting for these interactions in the calculations would significantly change the result of the analysis, and the impact of the uncertainties in the GDP projections would be much less.
This is not easy, as you need to account for energy model calculations, and since the authors have used different model projections for the SSP scenarios, and the authors may not have access to all energy calculations, I foresee a difficult issue here how to improve the analysis. However, I think this issue needs to be addressed, as the current analysis overestimates the impact of uncertainties on the projections, and it leads to rather high NDC emissions projections.
The author refer to Benveniste et al., but this study is an outlyer in the range of NDC studies, mainly due to the high emissions projection of China. Benveniste et al also do not include current policies and all NDC targets.
Unfortunately I could not find any details on the NDC emissions projection of China, so I could not check this.
2/ I would also recommend that the authors use as a starting point a current policies scenario, and not the SSP no policy scenario. Some SSP scenarios do not include impact of current policies that are adopted after 2005 or 2010, and these scenarios are rather hypothetical scenarios, and not very realistic. As mentioned above, you can better account for the current policies in the NDC calculations for India and China, but also for many other countries.
Some SSP scenario lead to very high short-term emissions, which are highly criticized in the literature, see: Hausfather, Zeke, and Glen P. Peters. "RCP8. 5 is a problematic scenario for near-term emissions." Proceedings of the National Academy of Sciences 117.45 (2020): 27791-27792.
3/ How does this study includes surplus emissions? The global NDC emissions projections from various NDC studies excludes the impact of surpluses, so if the current policies projection for a country is below the NDC target, the NDC emissions projection is equal to the current policies scenario. This has a large impact on the global emissions projections, in the order of 2-3 GtCO2e. This issue is not only relevant for India, China, Turkey, which overachieve their NDC target, but for also some countries with lower emissions projections. For me, it is not fully clear how the authors includes this impact, and in my view, it would lower the global NDC emissions projections, in particular the analysis uses as a starting point current policies scenarios.
4/ Land use emissions. I agree that it is very challenging to include LULUCF in the projections, but it is an important source of uncertainty that needs to some discussions. I noticed the -2.1 GtCO2 estimate., which seems rather low compared to the analysis of the LULUCF inventory data by Grassi et al. (2017; 2018) in Nature Climate Change, which discusses the impact of the LULUCF data in the NDC emissions projections in much detail. Can you explain why your estimate falls outside the range presented by Grassi et al.
Detailed comments:
Line 246: JRC? Why do you refer to JRC?
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AC2: 'Reply on RC1', Annika Günther, 27 Mar 2021
Dear Referee,
I wish to thank you very much for the review of this long manuscript in which we present the methods behind NDCmitiQ and the challenges we encountered while quantifying the NDCs' mitigation targets. Considering your comments, we are preparing a revised version of the manuscript in which we discuss the tool's methodological limits and their implications in more detail, and clarify why certain approaches were chosen. Along with the revised paper, we reply to your specific comments regarding the addressed target types, chosen baseline emissions, and overachieving countries.
Best regards, Annika Günther
-
AC2: 'Reply on RC1', Annika Günther, 27 Mar 2021
-
RC2: 'Comment on gmd-2020-392', Anonymous Referee #2, 17 Feb 2021
The manuscript presents in a nice and comprehensive way substantial amount of work the authors completed to develop the tool, which includes several layers and steps to ensure the quality of the tool and the results. This tool is of high scientific relevance, considering the need to monitor and quantify progression overtime of global ambitions in climate change mitigation efforts, and given its open-access nature, it could become a very important modelling resource for researchers dealign with global emissions modelling. However, in its current form, the manuscripts focuses disproportionately on the methods and could be improved with a more balanced version that increases the weight and importance of the results and discussion sections.
Regarding the aim, relevance and reach of the manusript and tool, I think the authors should limit more explicitelly the audience to research and modelling community and be more concrete about the potential applications of the tool, which mostly relate to comparing on an equal basis mitigation pledges overtime and monitoring progress towards global mitigation ambition. Also the shortcomings of the tool, in particular for interested stakeholders should be mentioned more explicitely. For instance, while many stakeholders interested in tracking and monitoring mitigation ambition can be interested in keeping specific countries accountable on their mitigation targets, the applicability of the tool for this purpose is limited considering that the multiple underlying assumptions and harmonisation steps involved in the tool and the underlying databases (e.g. population projections, data filling, etc) make it almost impossible to track with accuracy individual targets and analyse their evolution over time (e.g. changes in assumptions in the base year emissions, qualitative improvement in transparency or other elements, etc.).
Regarding the style, I agree with the other reviewer regarding the fact that the manuscript is written in a way that is more suitable for model documentation or user manual than a scientific journal, and would therefore suggest the authors move large parts of the main body to an annex, or supplementary information and instead expand the results and discussions sections and focus them on practical applications of the tool (e.g. comparison of the first and second round of NDCs, evaluation criteria or ranking for NDCs). However, considering the focus and target audience of the journal, which relate to modelling (outside my personal expertise) I consider the manuscript can be published in this journal subject to minor revisions, along the lines of my comments above.
-
AC3: 'Reply on RC2', Annika Günther, 27 Mar 2021
Dear Referee,
thank you very much for your review of our manuscript on the methods behind NDCmitiQ, and the challenges we encountered during the quantification of the NDCs' mitigation targets. Based on your comments we are preparing a revised version for which we are reconsidering which parts of the manuscript are integral for the main text and which parts can be rearranged (e.g., moved to annex) or shortened. By integrating assessments of the updated NDCs (updates mostly in December 2020), we will show potential applications of NDCmitiQ more concretely. Furthermore, along with the revised manuscript, we address your specific comments.
Best regards, Annika Günther
-
AC3: 'Reply on RC2', Annika Günther, 27 Mar 2021
-
CEC1: 'Comment on gmd-2020-392', Juan Antonio Añel, 03 Mar 2021
Dear authors,
In the next step of the review process, please be sure that you include in the Code availability section the DOI and link to the Zenodo web page containing the code of the model.
Also, the statement on your use of GitHub is irrelevant and can lead to confusion. Please remove this sentence 'We use a GitHub repository to work on the Python-based tool to quantify GHG mitigation targets and emissions pathways NDCmitiQ (https://github.com/AnnGuenther/ndc_quantifications.git).'Many thanks,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor- AC1: 'Reply on CEC1', Annika Günther, 05 Mar 2021
Status: closed
-
RC1: 'Comment on gmd-2020-392', Anonymous Referee #1, 16 Feb 2021
This is an interesting analysis on an uncertainty analysis for the impact of the uncertainties related to the INDCs on the global emission levels. It includes many detailed analysis and insights, which are well described. The work is highly relevant and interesting, and also the tool looks promising. The paper itself is rather detailed and lengthy, and in my view reads more as a technical report, than a journal paper.
However, I see some short-comings, which in my view can highly influence the resulting global emissions projections. Unfortunately the results of some main emitting countries, such as China and India are not included in the paper, so I could not check the projections.
In general the NDC emissions projections differ across studies mainly due to a couple of important factors.
1/ The authors assumed in their calculations that the NDC targets of China and India is calculated in terms of carbon intensity improvement. They mention: “Similar to Benveniste et al. (2018), targets for fossil fuel shares are not included in NDCmitiQ, and the non-fossil fuel targets the large emitters China and India stated additionally to emissions intensity targets are not quantified.”. The NDC of China also includes (i) the target to peak CO2 emissions by 2030 at the latest, (ii) increase the share of non-fossil energy carriers of the total primary energy supply to around 20% by that time, and (iii) increase its forest stock volume by 4.5 billion cubic metrics, compared to 2005 levels. In fact the factor (i) and (ii) are more important for the final 2030 emissions than the intensity target, as factor (i) and (ii) are the dominant factor. See literature around this issue from climate action tracker, but also UNEP Gap rapport, etc.. This also holds for India, since the NDC target of also includes (i) to increase the share of non-fossil based power generation capacity to 40% of installed electric power capacity by 2030 (equivalent to 26–30% of generation in 2030), and (ii) to create an additional (cumulative) carbon sink of 2.5–3 GtCO2e through additional forest and tree cover by 2030.
For the calculation of the impact of the NDC for China and India the authors need to account for the factors (i) and (ii) for China and (i) for India, and this highly affects the outcomes, as these factors are more dominant that changes in the GDP. Accounting for these interactions in the calculations would significantly change the result of the analysis, and the impact of the uncertainties in the GDP projections would be much less.
This is not easy, as you need to account for energy model calculations, and since the authors have used different model projections for the SSP scenarios, and the authors may not have access to all energy calculations, I foresee a difficult issue here how to improve the analysis. However, I think this issue needs to be addressed, as the current analysis overestimates the impact of uncertainties on the projections, and it leads to rather high NDC emissions projections.
The author refer to Benveniste et al., but this study is an outlyer in the range of NDC studies, mainly due to the high emissions projection of China. Benveniste et al also do not include current policies and all NDC targets.
Unfortunately I could not find any details on the NDC emissions projection of China, so I could not check this.
2/ I would also recommend that the authors use as a starting point a current policies scenario, and not the SSP no policy scenario. Some SSP scenarios do not include impact of current policies that are adopted after 2005 or 2010, and these scenarios are rather hypothetical scenarios, and not very realistic. As mentioned above, you can better account for the current policies in the NDC calculations for India and China, but also for many other countries.
Some SSP scenario lead to very high short-term emissions, which are highly criticized in the literature, see: Hausfather, Zeke, and Glen P. Peters. "RCP8. 5 is a problematic scenario for near-term emissions." Proceedings of the National Academy of Sciences 117.45 (2020): 27791-27792.
3/ How does this study includes surplus emissions? The global NDC emissions projections from various NDC studies excludes the impact of surpluses, so if the current policies projection for a country is below the NDC target, the NDC emissions projection is equal to the current policies scenario. This has a large impact on the global emissions projections, in the order of 2-3 GtCO2e. This issue is not only relevant for India, China, Turkey, which overachieve their NDC target, but for also some countries with lower emissions projections. For me, it is not fully clear how the authors includes this impact, and in my view, it would lower the global NDC emissions projections, in particular the analysis uses as a starting point current policies scenarios.
4/ Land use emissions. I agree that it is very challenging to include LULUCF in the projections, but it is an important source of uncertainty that needs to some discussions. I noticed the -2.1 GtCO2 estimate., which seems rather low compared to the analysis of the LULUCF inventory data by Grassi et al. (2017; 2018) in Nature Climate Change, which discusses the impact of the LULUCF data in the NDC emissions projections in much detail. Can you explain why your estimate falls outside the range presented by Grassi et al.
Detailed comments:
Line 246: JRC? Why do you refer to JRC?
-
AC2: 'Reply on RC1', Annika Günther, 27 Mar 2021
Dear Referee,
I wish to thank you very much for the review of this long manuscript in which we present the methods behind NDCmitiQ and the challenges we encountered while quantifying the NDCs' mitigation targets. Considering your comments, we are preparing a revised version of the manuscript in which we discuss the tool's methodological limits and their implications in more detail, and clarify why certain approaches were chosen. Along with the revised paper, we reply to your specific comments regarding the addressed target types, chosen baseline emissions, and overachieving countries.
Best regards, Annika Günther
-
AC2: 'Reply on RC1', Annika Günther, 27 Mar 2021
-
RC2: 'Comment on gmd-2020-392', Anonymous Referee #2, 17 Feb 2021
The manuscript presents in a nice and comprehensive way substantial amount of work the authors completed to develop the tool, which includes several layers and steps to ensure the quality of the tool and the results. This tool is of high scientific relevance, considering the need to monitor and quantify progression overtime of global ambitions in climate change mitigation efforts, and given its open-access nature, it could become a very important modelling resource for researchers dealign with global emissions modelling. However, in its current form, the manuscripts focuses disproportionately on the methods and could be improved with a more balanced version that increases the weight and importance of the results and discussion sections.
Regarding the aim, relevance and reach of the manusript and tool, I think the authors should limit more explicitelly the audience to research and modelling community and be more concrete about the potential applications of the tool, which mostly relate to comparing on an equal basis mitigation pledges overtime and monitoring progress towards global mitigation ambition. Also the shortcomings of the tool, in particular for interested stakeholders should be mentioned more explicitely. For instance, while many stakeholders interested in tracking and monitoring mitigation ambition can be interested in keeping specific countries accountable on their mitigation targets, the applicability of the tool for this purpose is limited considering that the multiple underlying assumptions and harmonisation steps involved in the tool and the underlying databases (e.g. population projections, data filling, etc) make it almost impossible to track with accuracy individual targets and analyse their evolution over time (e.g. changes in assumptions in the base year emissions, qualitative improvement in transparency or other elements, etc.).
Regarding the style, I agree with the other reviewer regarding the fact that the manuscript is written in a way that is more suitable for model documentation or user manual than a scientific journal, and would therefore suggest the authors move large parts of the main body to an annex, or supplementary information and instead expand the results and discussions sections and focus them on practical applications of the tool (e.g. comparison of the first and second round of NDCs, evaluation criteria or ranking for NDCs). However, considering the focus and target audience of the journal, which relate to modelling (outside my personal expertise) I consider the manuscript can be published in this journal subject to minor revisions, along the lines of my comments above.
-
AC3: 'Reply on RC2', Annika Günther, 27 Mar 2021
Dear Referee,
thank you very much for your review of our manuscript on the methods behind NDCmitiQ, and the challenges we encountered during the quantification of the NDCs' mitigation targets. Based on your comments we are preparing a revised version for which we are reconsidering which parts of the manuscript are integral for the main text and which parts can be rearranged (e.g., moved to annex) or shortened. By integrating assessments of the updated NDCs (updates mostly in December 2020), we will show potential applications of NDCmitiQ more concretely. Furthermore, along with the revised manuscript, we address your specific comments.
Best regards, Annika Günther
-
AC3: 'Reply on RC2', Annika Günther, 27 Mar 2021
-
CEC1: 'Comment on gmd-2020-392', Juan Antonio Añel, 03 Mar 2021
Dear authors,
In the next step of the review process, please be sure that you include in the Code availability section the DOI and link to the Zenodo web page containing the code of the model.
Also, the statement on your use of GitHub is irrelevant and can lead to confusion. Please remove this sentence 'We use a GitHub repository to work on the Python-based tool to quantify GHG mitigation targets and emissions pathways NDCmitiQ (https://github.com/AnnGuenther/ndc_quantifications.git).'Many thanks,
Juan A. Añel
Geosc. Mod. Dev. Executive Editor- AC1: 'Reply on CEC1', Annika Günther, 05 Mar 2021
Annika Günther et al.
Data sets
NDCmitiQ: a tool to quantify and analyse GHG mitigation targets (Version v1.0.0) Annika Günther, Johannes Gütschow, and M. Louise Jeffery https://doi.org/10.5281/zenodo.4286369
Model code and software
NDCmitiQ: a tool to quantify and analyse GHG mitigation targets (Version v1.0.0) Annika Günther, Johannes Gütschow, and M. Louise Jeffery https://doi.org/10.5281/zenodo.4286369
Annika Günther et al.
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