ArticleCarbon balance and management2024
Land-use change, no-net-loss policies, and effects on carbon dioxide removals.
Article in Carbon balance and management, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- The contribution of planted forests to regional carbon storage: evidence from western Hunan, China (1990-2020).Carbon balance and management · 2026Article
- The contribution of antimicrobials and antimicrobial resistance to climate change and a possible way to reverse it whilst still offering high quality healthcare-a conceptual analysis.Frontiers in public health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundCarbon dioxide removal from the atmosphere (CDR) is a critical component of strategies for restricting global warming to 1.5°C and is expected to come largely from the sequestration of carbon in vegetation. Because CDR rates have been declining in the United States, in part due to land use changes, policy proposals are focused on altering land uses, through afforestation, avoided deforestation, and no-net-loss strategies. Estimating policy effects requires a careful assessment of how land uses interact with forest conditions to determine future CDR.
resultsWe evaluate how alternative specifications of land use-forest condition interactions in the United States affect projections of CDR using a model that mirrors land sector net emission inventories generated by the US government (EPA). Without land use change, CDR declines from 0.826 GT/yr in 2017 to 0.596 GT/yr in 2062 (28%) due to forest aging and disturbances. For a land use scenario that extends recent rates of change, we compare CDR estimated based on net changes in land use (Net Change model) and estimates that separately account for the distinct CDR implications of forest losses and forest gains (Component Change model). The Net Change model, a common specification, underestimates the CDR losses of land use by about 56% when compared with the Component Change models. We also estimate per hectare CDR losses from deforestation and gains from afforestation and find that afforestation gains lag deforestation losses in every ecological province in the US.
conclusionsNet Change approaches substantially underestimate the impact of land use change on CDR and should be avoided. Component Change models highlight that avoided deforestation may provide up to twice the CDR benefits as increased afforestation-though preference for one policy over the other would require a cost assessment. The disparities in the CDR impacts of afforestation and deforestation indicate that no-net-loss policies could mitigate some CDR losses but would lead to overall declines in CDR for our 45-year time horizon. Over a much longer period afforestation could capture more of the losses from deforestation but at a timeframe inconsistent with most climate change policy efforts.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.