The Intergovernmental Panel on Climate Change in their 5th Assessment Report stated: “Human influence on the climate system is clear…” and “Warming of the climate system is unequivocal…”. In response to these facts, there has been considerable research into alternative energy sources as a way to reduce our emissions of greenhouse gases from fossil fuels. We already use plant material (biomass) as a source to generate everything from liquid fuels (e.g. ethanol from corn or sugar cane) to feedstocks that we can burn for heat or electricity. Humans have been burning wood for millennia to produce heat for cooking and warmth. Because of the rising level of greenhouse gases in the atmosphere from burning fossil fuels, there has been considerable research on using forests to provide a biomass feedstock for producing energy on a large scale. Burning biomass to produce energy still emits greenhouse gases to the atmosphere, but the thinking goes that since plants sequester carbon from the atmosphere, burning them is carbon neutral because plants growing somewhere else on a given day will make up for the carbon emitted on the same day. This is certainly a reasonable hypothesis, but when it comes to forest biomass energy, there has been a lot of scientific debate because trees take a long time to grow (see work by Gunn and colleagues and Galik and colleagues). Some argue that harvesting a forest for biomass energy creates a carbon debt because if the harvested forest is say 100 years old, it will take 100 years for a regenerating forest to recover the carbon emitted when we produce energy from burning the trees we harvested.
Quantifying the carbon balance of using forests for a biomass feedstock is not a simple task. There are many factors that have to be considered. As an example, if the forest is likely to be harvested for wood products, the demand for wood products doesn’t go away if the trees are instead harvested for biomass energy. This could cause forests elsewhere to be harvested for wood products, a concept known as leakage. However, emerging biomass markets could also increase forest carbon storage if they encourage additional forest planting or avoiding land use change from forest to some other land use. Another issue to consider is that forests and the carbon they contain are not static in time. Trees grow and die. Year-to-year variability in temperature and precipitation can affect both growth and mortality rates. Disturbances such as wildfire and insect outbreaks can kill trees. These and many other factors affect the amount of carbon stored in and removed from the atmosphere by forests and are all part of the idea called baseline. The baseline is the amount of carbon the forest would store in the absence of the biomass project, and how much carbon would be emitted to generate energy using fossil fuels (without biomass). There has been considerable debate about the type of baseline to use. Some have argued for the use of a static baseline and others for a dynamic baseline. A static baseline uses the amount of carbon stored in the forest before the project and assumes it remains unchanged over time, whereas a dynamic baseline assumes that the carbon stock will vary as the forest changes.
In a recent paper led by Thomas Buchholz of the Spatial Informatics Group, we analyzed the results of 38 previously published studies on this topic that included a measure of carbon payback period (how long until the carbon debt is gone and the forest biomass project is carbon neutral). The carbon payback period for the studies we included in our analysis ranged from 0 to 8000 years. We identified 20 different attributes to classify these studies. They included things like type of forest (plantation or natural), fossil fuel energy source displaced (coal, natural gas, etc), and whether or not the study included natural disturbance when modeling forests. Once we classified all of the studies based on these attributes, we ran an analysis to determine which attributes were most influential for determining the payback period. Given all the debate around baseline, our results were surprising. The most influential factor was whether or not the study included the effects of wildfire in the quantification of the carbon payback period for a project. Those projects that did include wildfire had a longer payback period. Other attributes like leakage and if the study included a life cycle assessment of wood products were also influential factors for determining the length of the payback period. The type of baseline (reference point or dynamic) was only influential after many other attributes were accounted for and only for a handful of the studies we evaluated.
This figure shows the outcome of our classification and regression tree analysis. The importance of different attributes decreases as you move from the top to the bottom.
Given the importance of disturbances, such as wildfire, in forests, our results make clear that we cannot neglect disturbance when quantifying the carbon payback period of a forest biomass project. Thinning forests to reduce the amount of trees and reduce the risk of wildfire is a common management strategy (see previous posts). Cutting trees to reduce wildfire risk removes carbon from the forest and we note in the paper that the payback period can vary considerably in length depending on the driver of forest thinning. If the production of biomass energy is the reason for thinning and reduced wildfire risk is the by-product, carbon payback periods can be longer. However, when thinning to reduce fire risk generates biomass and this biomass would be generated even in the absence of a biomass market, carbon payback periods can be considerably shorter. This is in large part because when small trees are cut to reduce the risk of wildfires, they are often piled and burned. Our finding about the influence of the cause of thinning is similar to that noted in a recent WRI report, that “waste” products from plant production, including timber processing, are a potential source of energy that will reduce greenhouse gas emissions. We also concluded from our study that there are a number of attributes that can be standardized in their evaluation to make cross-study comparisons more of an apples-to-apples comparison. While our study did not provide a definitive conclusion about the carbon neutrality of forest biomass energy, it does provide a framework such that future studies can be analyzed in aggregate to determine if a conclusion can be drawn on this topic for all forest biomass projects.
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