Global consumption-based deforestation carbon emissions and regional hotspots: 2001&ndash;2020

Tang, Dongmei; Yao, Yuanzhi; Li, Rui; Li, Xia

doi:10.5194/essd-2026-243

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https://doi.org/10.5194/essd-2026-243

Preprints

08 May 2026

| 08 May 2026

Status: this preprint is currently under review for the journal ESSD.

Global consumption-based deforestation carbon emissions and regional hotspots: 2001–2020

Dongmei Tang, Yuanzhi Yao, Rui Li, and Xia Li

Abstract. The consumption-based carbon emissions dataset for deforestation provides an important perspective for developing effective emission mitigation policies. However, existing datasets are largely limited to national-scale and lack spatially explicit, high-resolution data at the global level. Here, we present a global, gridded dataset of consumption-based deforestation carbon emissions at 1 km resolution for 2001–2020. The dataset integrates spatial information on road networks, deforestation, and forest carbon fluxes with country-level global trade statistics. Our dataset shows that trade-related deforestation emissions amount to 39.6 Gt CO₂e, constituting 31.8 % of global deforestation emissions. This new dataset fills a critical gap in spatially explicit consumption-based deforestation emissions data, and supports the development of targeted mitigation strategies from the consumer perspective. It also provides a valuable input for climate and carbon cycle models to assess the contribution of consumption-driven deforestation to global warming. Datasets are available at https://doi.org/10.6084/m9.figshare.28091879 (Tang et al., 2024).

Received: 31 Mar 2026 – Discussion started: 08 May 2026

Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth System Science Data.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Dongmei Tang, Yuanzhi Yao, Rui Li, and Xia Li

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RC1:
'Comment on essd-2026-243', Anonymous Referee #1, 28 May 2026 reply
Tang et al. solve an important consumption attribution gap by creating the first annual global 1 km gridded dataset attributing intra-country deforestation carbon emissions to countries consuming trade-based products from 2001 to 2020. The dataset was created using a spatial multi-regional input-output framework, which disaggregates national emissions to grid cells based on data for deforestation, road characteristics, and export priority zones. Export priority zones were chosen based on results from a sensitivity analysis of deforestation patch sizes and road densities, with zones subsequently chosen based on spatial plausibility criteria. Overall, this paper is structured well and addresses a critical gap in the literature. With minor revisions, this paper will be ready for publication in Earth System Science Data. The authors must address minor concerns by adding methodological details, correcting minor typographical errors, and adding more figures to the results. Specifically, ranking the global net importers of deforestation and providing additional visual representation (e.g. bar charts) for consumption-based deforestation emissions footprints in specific regions for the United States and China would support the study’s data.
Major Comments
It’s unclear what exact data are available in MRIO tables and how they help with estimating deforestation emissions. Considering this is critical for the analysis, it would benefit to further clarify what the MRIO tables contain and how they can be interpreted.

The resulting maps for the United States (Figure 4) and China (Figure 5) are clear and nicely made. Some suggestions for this part of the analysis:

An additional figure or bar chart showing the rankings for net importers of deforestation would complement the results. Initially, I thought the United States and China were selected as examples from Figure 3a, but that is not the case as the bar charts show consumption-based deforestation emissions both domestically and internationally.

Considering the maps for the United States and China are representative of the 1 km gridded dataset, please use the ranking diagram to include 1-2 more maps from the next two largest importers of deforestation emissions. This would further support the usefulness of and need for the dataset.

For each map showing consumption-based deforestation emissions linked to the United States and China, it might also be helpful to include additional bar charts showing the deforestation emissions footprint for each country in other countries. This would help to further visualize the data generated in this study and enhance interpretability.

Minor Comments
L25: It seems that the introduction section is split into four paragraphs, but paragraph two is not indented (starting with “International trade remains the primary…”).

L82-84: Please briefly expand on how deforestation was quantified using Landsat data in one to two sentences. Was NDVI used or another metric?

L90-91: It might be useful to provide a 2-3 word example for each deforestation driver.

L82-102: Since this study presents a gridded dataset at a 1 km resolution, please expand on how the other datasets outlined in the methods were re-gridded to the 1 km resolution, if applicable (i.e., tables do not need re-gridding, but rasters do). If re-gridding was not performed, please explain why.

Figure 2: It’s interesting that Latin America exhibits a pronounced increase in their deforestation emission footprint following 2014. It might be interesting to briefly expand on why this happened.

Make sure all maps have latitude and longitude coordinates.

Grammatical suggestions:

L179-180: There is a typographical error. “Resulting” should be “resulted.”

L186: The “R” in “Rest” should not be capitalized.

Figure 8: I recommend changing the title “My study” for Figures 8a and 8c to “This study,” for consistency with the caption that says “this study.” Additionally, in Figures 8c and 8d, make sure to put units in the legend.

Figure 9: Same suggestion as for Figure 8, change the title for Figures 9b, 9c, and 9d to “This study” instead of “My study.”

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Citation: https://doi.org/10.5194/essd-2026-243-RC1
RC2: 'Comment on essd-2026-243', Anonymous Referee #2, 27 Jul 2026 reply

This is an important topic, and the dataset could be useful both for scientific research and for initiatives to reduce commodity-driven deforestation.
Either more detail or examples are needed to effectively explain how deforestation emissions are allocated to exports vs. domestic production. It is clear that exports are assumed to be the more likely driver where deforestation patch sizes are larger and road density is higher. But it is not clear what fraction of deforestation emissions are assumed to be due to exports rather than production for domestic markets. Alternatively, what fraction of exports are assumed to be immediately associated with deforestation? For example, in Brazil, beef, palm oil, and sugar can be produced in areas that were deforested in colonial times, that were just deforested last year, or any intermediate timing. So what fraction of these exports are assumed to be associated with deforestation emissions?
As a minor technical point, Paragraph 25 reads “International trade remains the primary driver of deforestation, particularly through the expansion of commercial agriculture (Pendrill et al., 2019b; Pendrill et al., 2019a).” The following sentences suggest that international trade is better described as “a primary driver” rather than “the primary driver.”

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Citation: https://doi.org/10.5194/essd-2026-243-RC2

Dongmei Tang, Yuanzhi Yao, Rui Li, and Xia Li

Data sets

Consumption-based Carbon Emissions for Deforestation (2001–2020) Dongmei Tang, Yuanzhi Yao, Rui LI, and Xia Li https://doi.org/10.6084/m9.figshare.28091879

Dongmei Tang, Yuanzhi Yao, Rui Li, and Xia Li

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Short summary

We present a global, gridded dataset (1 km resolution) of consumption-based deforestation carbon emissions for the period 2001 to 2020. Our dataset shows that deforestation emissions embodied in international trade account for 31.8 % of global deforestation emissions. It provides spatially explicit information to support analyses of consumption-driven deforestation emissions and inform climate mitigation and policy assessments.


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