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Accounting for risk in valuing forest carbon offsets.

Hurteau MD, Hungate BA, Koch GW - Carbon Balance Manag (2009)

Bottom Line: Forests can sequester carbon dioxide, thereby reducing atmospheric concentrations and slowing global warming.Here we show that incorporating wildfire risk reduces the value of forest carbon depending on the location and condition of the forest.There is a general trend of decreasing risk-scaled forest carbon value moving from the northern toward the southern continental U.S. Because disturbance is a major ecological factor influencing long-term carbon storage and is often sensitive to human management, carbon trading mechanisms should account for the reduction in value associated with disturbance risk.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences and Merriam-Powell Center for Environmental Research, PO Box 6077, Flagstaff, AZ 86011, USA. Matthew.Hurteau@nau.edu.

ABSTRACT

Background: Forests can sequester carbon dioxide, thereby reducing atmospheric concentrations and slowing global warming. In the U.S., forest carbon stocks have increased as a result of regrowth following land abandonment and in-growth due to fire suppression, and they currently sequester approximately 10% of annual US emissions. This ecosystem service is recognized in greenhouse gas protocols and cap-and-trade mechanisms, yet forest carbon is valued equally regardless of forest type, an approach that fails to account for risk of carbon loss from disturbance.

Results: Here we show that incorporating wildfire risk reduces the value of forest carbon depending on the location and condition of the forest. There is a general trend of decreasing risk-scaled forest carbon value moving from the northern toward the southern continental U.S.

Conclusion: Because disturbance is a major ecological factor influencing long-term carbon storage and is often sensitive to human management, carbon trading mechanisms should account for the reduction in value associated with disturbance risk.

No MeSH data available.


Related in: MedlinePlus

Data layer processing. An example of data layer processing for a tract of Redwood forest. Each panel is representative of the same land area and is 2.2 km by 1.4 km in size. Panel A is the image classified to represent either redwood forest (RW) or other forest type (OT). Panel B is the mean fire return interval in years for each 30 meter pixel from the LANDFIRE data product. The mean fire return intervals in the panel range from 23 to 137 years. Panel C is the fire regime condition class departure index value from the LANDFIRE data product. Panel D is the relative carbon value, , for each pixel of redwood forest, incorporating the mean fire return interval and fire regime condition class departure data products. Multiplying the relative carbon value by the market value of carbon determines the risk scaled value of carbon for each pixel.
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Figure 2: Data layer processing. An example of data layer processing for a tract of Redwood forest. Each panel is representative of the same land area and is 2.2 km by 1.4 km in size. Panel A is the image classified to represent either redwood forest (RW) or other forest type (OT). Panel B is the mean fire return interval in years for each 30 meter pixel from the LANDFIRE data product. The mean fire return intervals in the panel range from 23 to 137 years. Panel C is the fire regime condition class departure index value from the LANDFIRE data product. Panel D is the relative carbon value, , for each pixel of redwood forest, incorporating the mean fire return interval and fire regime condition class departure data products. Multiplying the relative carbon value by the market value of carbon determines the risk scaled value of carbon for each pixel.

Mentions: We extracted the data inputs necessary for our equation from the FRCC departure index, mean fire return interval, and existing vegetation type data layers (Figure 2). Using the raster math tool in ArcGIS (ESRI), we implemented the equation to generate Figure 1, showing the average relative carbon value by forest type for continental U.S.


Accounting for risk in valuing forest carbon offsets.

Hurteau MD, Hungate BA, Koch GW - Carbon Balance Manag (2009)

Data layer processing. An example of data layer processing for a tract of Redwood forest. Each panel is representative of the same land area and is 2.2 km by 1.4 km in size. Panel A is the image classified to represent either redwood forest (RW) or other forest type (OT). Panel B is the mean fire return interval in years for each 30 meter pixel from the LANDFIRE data product. The mean fire return intervals in the panel range from 23 to 137 years. Panel C is the fire regime condition class departure index value from the LANDFIRE data product. Panel D is the relative carbon value, , for each pixel of redwood forest, incorporating the mean fire return interval and fire regime condition class departure data products. Multiplying the relative carbon value by the market value of carbon determines the risk scaled value of carbon for each pixel.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651132&req=5

Figure 2: Data layer processing. An example of data layer processing for a tract of Redwood forest. Each panel is representative of the same land area and is 2.2 km by 1.4 km in size. Panel A is the image classified to represent either redwood forest (RW) or other forest type (OT). Panel B is the mean fire return interval in years for each 30 meter pixel from the LANDFIRE data product. The mean fire return intervals in the panel range from 23 to 137 years. Panel C is the fire regime condition class departure index value from the LANDFIRE data product. Panel D is the relative carbon value, , for each pixel of redwood forest, incorporating the mean fire return interval and fire regime condition class departure data products. Multiplying the relative carbon value by the market value of carbon determines the risk scaled value of carbon for each pixel.
Mentions: We extracted the data inputs necessary for our equation from the FRCC departure index, mean fire return interval, and existing vegetation type data layers (Figure 2). Using the raster math tool in ArcGIS (ESRI), we implemented the equation to generate Figure 1, showing the average relative carbon value by forest type for continental U.S.

Bottom Line: Forests can sequester carbon dioxide, thereby reducing atmospheric concentrations and slowing global warming.Here we show that incorporating wildfire risk reduces the value of forest carbon depending on the location and condition of the forest.There is a general trend of decreasing risk-scaled forest carbon value moving from the northern toward the southern continental U.S. Because disturbance is a major ecological factor influencing long-term carbon storage and is often sensitive to human management, carbon trading mechanisms should account for the reduction in value associated with disturbance risk.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biological Sciences and Merriam-Powell Center for Environmental Research, PO Box 6077, Flagstaff, AZ 86011, USA. Matthew.Hurteau@nau.edu.

ABSTRACT

Background: Forests can sequester carbon dioxide, thereby reducing atmospheric concentrations and slowing global warming. In the U.S., forest carbon stocks have increased as a result of regrowth following land abandonment and in-growth due to fire suppression, and they currently sequester approximately 10% of annual US emissions. This ecosystem service is recognized in greenhouse gas protocols and cap-and-trade mechanisms, yet forest carbon is valued equally regardless of forest type, an approach that fails to account for risk of carbon loss from disturbance.

Results: Here we show that incorporating wildfire risk reduces the value of forest carbon depending on the location and condition of the forest. There is a general trend of decreasing risk-scaled forest carbon value moving from the northern toward the southern continental U.S.

Conclusion: Because disturbance is a major ecological factor influencing long-term carbon storage and is often sensitive to human management, carbon trading mechanisms should account for the reduction in value associated with disturbance risk.

No MeSH data available.


Related in: MedlinePlus