Figure 14 - uploaded by Erin Hayde
Content may be subject to copyright.
Source publication
This paper examines the dynamic interactions between forest biomass, atmospheric carbon, and land use change. Using a dynamic system model, these three factors are modeled as state equations while the policy maker decides the optimal timber harvesting and deforestation quantities. An extension of the model with a tax on forest products is included...
Contexts in source publication
Context 1
... the tax and recycling mechanism scenario under an increased growth rate (↵ = 0.02) and Figure 11: Fixed tax, increased forest growth and productivity Figure 11a: Choice variables, c x , c z productivity (µ = 0.7) of forests yields the results seen in Figure 11. Forest biomass increases from 1,000 to the upper limit, 5,000 Gt C, and atmospheric CO 2 increases from 3,666 to 4,327.15 Gt. Land available for forests decreases from 40 hundred million hectares to just 0.08 hundred million hectares -the most significant amount of deforestation seen in the model. The choice variables, seen in Figure 11a, show the same behavior seen in Figure 3a: both converge towards 9, then as timber harvest decreases slightly (to a low of 3.35), deforestation increases to 20 for several steps, then sharply falls to end at 1.86. This extreme level of deforestation could be explained by the increased levels of both forest growth rates and forest productivity, as lower levels of forestable land would be needed for the same amount of atmospheric CO 2 mitigation. Decreasing forest growth from ↵ = 0.02 to ↵ = 0.008, yields the dynamics seen in Figure 12. Forest biomass increases from 1,000 to the upper limit of 5,000 Gt Carbon, while atmospheric carbon dioxide increases from 3,666 to 4,406.42 Gt. Land available to a↵orestation e↵orts decreases from 40 to 0.31 hundred million hectares. The behavior of the choice variables is displayed in Figure 12a. Varying the tax on timber harvest shows some interesting dynamics as well. Decreasing the tax from ⌧ 0 = 0.1 to ⌧ 1 = 0.01, yields results seen in Figure 13 and Figure 13a. With the decreased tax rate, forest biomass increases to 1128.04, a decrease of 120.6 Gt C relative to the scenario with the tax of 0.1. Atmospheric carbon increased to 4608.66, an increase of 4.49 Gt CO 2 as compared to the higher tax scenario. Available land decreases from 40 to 29.9, a slightly smaller decrease than in the higher tax rate scenario. The control variables demonstrate the behavior observed in Figure 13a; both timber harvest, (c x ), and deforestation, (c z ), begin at 9.2 Gt C. Timber harvest decreases to 9.14, while deforestation decreases to 9.15. Both control variables then decrease to 9.09 in the third period, where they remain for the rest of the simulation. Increasing the tax rate from ⌧ 0 = 0.1 to ⌧ 1 = 0.2 yields the dynamics seen in Figure 14 and Figure 14a. Forest biomass increases to 1398.37 Gt C -an increase of 149 Gt C from the initial ⌧ 0 = 0.1 tax scenario. Atmospheric carbon increases to 4598.62 Gt CO 2 , a decrease of 5 Gt CO 2 from the initial tax scenario. In other words, doubling the tax rate on timber harvest, yields a decrease of 5 Gt CO 2 over the course of the simulation. Land available for forests decreases to 31.04, a slightly smaller decrease than under the ⌧ 0 = 0.1 tax scenario. The control variables display equal behavior, seen in Figure 14a: both begin at 9.2, decrease to 9.15 in the second period, then to 9.09 in the third period, and decrease to 9.03 Gt C, where they remain for the rest of the ...
Context 2
... the tax and recycling mechanism scenario under an increased growth rate (↵ = 0.02) and Figure 11: Fixed tax, increased forest growth and productivity Figure 11a: Choice variables, c x , c z productivity (µ = 0.7) of forests yields the results seen in Figure 11. Forest biomass increases from 1,000 to the upper limit, 5,000 Gt C, and atmospheric CO 2 increases from 3,666 to 4,327.15 Gt. Land available for forests decreases from 40 hundred million hectares to just 0.08 hundred million hectares -the most significant amount of deforestation seen in the model. The choice variables, seen in Figure 11a, show the same behavior seen in Figure 3a: both converge towards 9, then as timber harvest decreases slightly (to a low of 3.35), deforestation increases to 20 for several steps, then sharply falls to end at 1.86. This extreme level of deforestation could be explained by the increased levels of both forest growth rates and forest productivity, as lower levels of forestable land would be needed for the same amount of atmospheric CO 2 mitigation. Decreasing forest growth from ↵ = 0.02 to ↵ = 0.008, yields the dynamics seen in Figure 12. Forest biomass increases from 1,000 to the upper limit of 5,000 Gt Carbon, while atmospheric carbon dioxide increases from 3,666 to 4,406.42 Gt. Land available to a↵orestation e↵orts decreases from 40 to 0.31 hundred million hectares. The behavior of the choice variables is displayed in Figure 12a. Varying the tax on timber harvest shows some interesting dynamics as well. Decreasing the tax from ⌧ 0 = 0.1 to ⌧ 1 = 0.01, yields results seen in Figure 13 and Figure 13a. With the decreased tax rate, forest biomass increases to 1128.04, a decrease of 120.6 Gt C relative to the scenario with the tax of 0.1. Atmospheric carbon increased to 4608.66, an increase of 4.49 Gt CO 2 as compared to the higher tax scenario. Available land decreases from 40 to 29.9, a slightly smaller decrease than in the higher tax rate scenario. The control variables demonstrate the behavior observed in Figure 13a; both timber harvest, (c x ), and deforestation, (c z ), begin at 9.2 Gt C. Timber harvest decreases to 9.14, while deforestation decreases to 9.15. Both control variables then decrease to 9.09 in the third period, where they remain for the rest of the simulation. Increasing the tax rate from ⌧ 0 = 0.1 to ⌧ 1 = 0.2 yields the dynamics seen in Figure 14 and Figure 14a. Forest biomass increases to 1398.37 Gt C -an increase of 149 Gt C from the initial ⌧ 0 = 0.1 tax scenario. Atmospheric carbon increases to 4598.62 Gt CO 2 , a decrease of 5 Gt CO 2 from the initial tax scenario. In other words, doubling the tax rate on timber harvest, yields a decrease of 5 Gt CO 2 over the course of the simulation. Land available for forests decreases to 31.04, a slightly smaller decrease than under the ⌧ 0 = 0.1 tax scenario. The control variables display equal behavior, seen in Figure 14a: both begin at 9.2, decrease to 9.15 in the second period, then to 9.09 in the third period, and decrease to 9.03 Gt C, where they remain for the rest of the ...
Similar publications
In this paper, we aim to assess the impacts of the forest definitions adopted by each African country involved in the global climate change programmes of the United Nations on national carbon emission estimations. To do so, we estimate the proportion of national carbon stocks and tree cover loss that are found in areas considered to be non-forest a...
Citations
... In the meantime, a vast technical and scientific literature on carbon capture and storage has emerged (Martin et al 2017;Hayde and Semmler 2018;Minx et al. 2018;Morrow et al 2018). For our analysis, we reviewed over 200 scientific papers as well as journalistic reports. ...
From typhoons to wildfires, as the visible impacts of climate change mount, calls for mitigation through carbon drawdown are escalating. Environmentalists and many climatologists are urging steps to enhance biological methods of carbon drawdown and sequestration. Market actors seeing avenues for profit have launched ventures in mechanical–chemical carbon dioxide removal (CDR), seeking government support for their methods. Governments are responding. Given the strong, if often unremarked, momentum of demands for public subsidy of these commercial methods, on what cogent bases can elected leaders make decisions that, first and foremost, meet societal needs? To address this question, we reviewed the scientific and technical literature on CDR, focusing on two methods that have gained most legislative traction: point-source capture and direct air capture–which together we term “industrial carbon removal” (ICR), in contrast to biological methods. We anchored our review in a standard of “collective biophysical need,” which we define as a reduction of the level of atmospheric CO2. For each ICR method, we sought to determine (1) whether it sequesters more CO2 than it emits; (2) its resource usage at scale; and (3) its biophysical impacts. We found that the commercial ICR (C-ICR) methods being incentivized by governments are net CO2 additive: CO2 emissions exceed removals. Further, the literature inadequately addresses the resource usage and biophysical impacts of these methods at climate-significant scale. We concluded that dedicated storage, not sale, of captured CO2 is the only assured way to achieve a reduction of atmospheric CO2. Governments should therefore approach atmospheric carbon reduction as a public service, like water treatment or waste disposal. We offer policy recommendations along this line and call for an analysis tool that aids legislators in applying biophysical considerations to policy choices.
