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Article analysis:

The article "Protecting climate with forests" published in IOPscience discusses the importance of considering biophysical factors in policies for climate mitigation on land. The authors argue that such factors can have a significant impact on temperatures and should not be ignored. They provide a framework for examining biophysical factors and offer some best-practice recommendations based on it.

One potential bias in the article is its focus on tropical projects, such as avoided deforestation, forest restoration, and afforestation, as providing the greatest climate value. While these projects may align carbon storage and biophysics to cool the Earth, they may not be feasible or effective in all regions. The article does acknowledge that boreal and other snow-covered regions may counteract the climate benefits of carbon storage due to darker trees trapping more heat than snow does. However, it does not explore alternative solutions for these regions.

Another potential bias is the promotion of using more reflective and deciduous species in forest projects to increase their climate benefit. While this may be effective in some cases, it may not always be practical or desirable depending on local conditions and ecological considerations.

The article also makes unsupported claims about ignoring biophysical interactions resulting in millions of dollars being invested in some mitigation projects that provide little climate benefit or are counter-productive. While this may be true in some cases, there is no evidence provided to support this claim.

Additionally, the article does not explore counterarguments or alternative perspectives on the role of biophysical factors in climate mitigation policies. It presents a one-sided view that prioritizes certain types of forest projects over others without fully considering their limitations or potential drawbacks.

Overall, while the article provides useful insights into the importance of considering biophysical factors in climate mitigation policies, it has potential biases and limitations that should be taken into account when interpreting its findings.