11:30 〜 11:45
[ACG46-04] Extending emission pathways to 2300 using an Integrated Assessment Model emulator
キーワード：IAM emulator, Marginal abatement cost curve, Extension, CDR
Integrated Assessment Models (IAMs) combine economy, energy, and sometimes land-use modeling approaches and are commonly used to evaluate climate policies under least-cost scenarios. The marginal abatement cost (MAC) curve approach has been commonly used in climate policy analyses to show the carbon price level for a given abatement level, which has also been applied as a way to parameterize the complex behavior of IAMs. Here, we propose a new methodological framework to i) emulate the IAM’s emission reductions in response to carbon price pathways through MAC curves (i.e., IAM emulator, emIAM) and then ii) extend IAM’s emission pathways (usually given until 2100) to 2300 by using the emulator.
Our approach will be used to extend the emission scenarios generated by the REMIND-MAgPIE model as part of the Horizon Europe RESCUE and OptimESM projects. A key feature of our approach is that we individually capture the emission reductions associated with CDRs (i.e., afforestation, bioenergy and carbon capture and storage (BECCS), direct air capture with carbon storage (DACCS), and industry CCS) through MAC curves. Our approach relies on the following simplifying assumptions: i) MAC curves are assumed constant over time, and ii) abatement levels are assumed independent across greenhouse gases (GHG; CO2, CH4, and N2O) and sectors (energy- and non-energy-related emissions and different CDR types). While MAC curves are typically time-dependent or given for a specific point in time, time-independent MAC curves have also been used earlier.
We proxied the IAM dynamics with MAC curves for different GHGs and sectors using the equation f(x)=a*xb+c*xd. fx represents the corresponding carbon price level at x, while the variable x represents the abatement level in percent relative to the assumed baseline level (for energy- and non-energy-related emissions) or total removed CO2 (for CDR options). a, b, c, and d are the parameters that are optimized for each case. Additionally, we derived the maximum abatement levels of REMIND-MAgPIE from its simulation results under all carbon budgets, which reflect the limit of, for example, CCS capacity and sectoral mitigation potential. We also calculated for each gas and each sector the maximum first and second derivatives of temporal changes in abatement levels to capture the limits of the technological change rate and the socio-economic inertia.
Figure 1 illustrates the process to develop the IAM emulator described above. Panels a and b are the results of carbon prices and CO2 emissions obtained from REMIND-MAgPIE with the range of carbon budgets, respectively. Panel c shows the relationship between the carbon price and the abatement level at each point in time (every ten years). Merging carbon price and abatement level combinations for all years, Panel d shows a long-term relationship, which allows us to approximate with a time-independent MAC curve.
By combining the IAM emulator with a reduced-complexity climate model ACC2, we further derived extended scenarios until 2300 using the least-cost approach for given temperature trajectories. Energy- and non-energy-related emissions are assumed not to increase after 2100. Figure 2 shows the CO2 emission pathways by sector that are cost-effective in meeting the targets, including negative emissions from CDR deployment. The preliminary results show that BECCS plays a key role in mitigating future global warming, even outperforming DACCS in low-carbon budget cases. Our approach can be used to generate a set of plausible scenarios for different gases and sources based on long-term expectations, which may help enhance the applicabilities of IAMs. This is in particular interesting for exploring scenarios with a longer temperature overshoot duration. Note that the development of extended scenarios requires an extended baseline emission scenario, which is currently in progress.
Our approach will be used to extend the emission scenarios generated by the REMIND-MAgPIE model as part of the Horizon Europe RESCUE and OptimESM projects. A key feature of our approach is that we individually capture the emission reductions associated with CDRs (i.e., afforestation, bioenergy and carbon capture and storage (BECCS), direct air capture with carbon storage (DACCS), and industry CCS) through MAC curves. Our approach relies on the following simplifying assumptions: i) MAC curves are assumed constant over time, and ii) abatement levels are assumed independent across greenhouse gases (GHG; CO2, CH4, and N2O) and sectors (energy- and non-energy-related emissions and different CDR types). While MAC curves are typically time-dependent or given for a specific point in time, time-independent MAC curves have also been used earlier.
We proxied the IAM dynamics with MAC curves for different GHGs and sectors using the equation f(x)=a*xb+c*xd. fx represents the corresponding carbon price level at x, while the variable x represents the abatement level in percent relative to the assumed baseline level (for energy- and non-energy-related emissions) or total removed CO2 (for CDR options). a, b, c, and d are the parameters that are optimized for each case. Additionally, we derived the maximum abatement levels of REMIND-MAgPIE from its simulation results under all carbon budgets, which reflect the limit of, for example, CCS capacity and sectoral mitigation potential. We also calculated for each gas and each sector the maximum first and second derivatives of temporal changes in abatement levels to capture the limits of the technological change rate and the socio-economic inertia.
Figure 1 illustrates the process to develop the IAM emulator described above. Panels a and b are the results of carbon prices and CO2 emissions obtained from REMIND-MAgPIE with the range of carbon budgets, respectively. Panel c shows the relationship between the carbon price and the abatement level at each point in time (every ten years). Merging carbon price and abatement level combinations for all years, Panel d shows a long-term relationship, which allows us to approximate with a time-independent MAC curve.
By combining the IAM emulator with a reduced-complexity climate model ACC2, we further derived extended scenarios until 2300 using the least-cost approach for given temperature trajectories. Energy- and non-energy-related emissions are assumed not to increase after 2100. Figure 2 shows the CO2 emission pathways by sector that are cost-effective in meeting the targets, including negative emissions from CDR deployment. The preliminary results show that BECCS plays a key role in mitigating future global warming, even outperforming DACCS in low-carbon budget cases. Our approach can be used to generate a set of plausible scenarios for different gases and sources based on long-term expectations, which may help enhance the applicabilities of IAMs. This is in particular interesting for exploring scenarios with a longer temperature overshoot duration. Note that the development of extended scenarios requires an extended baseline emission scenario, which is currently in progress.