Using an emulator to apply a Carbon Takeback Obligation alongside demand-side carbon pricing in Integrated Assessment Models

Author(s):  
Stuart Jenkins ◽  
Eli Mitchell-Larson ◽  
Matthew Ives ◽  
Stuart Haszeldine ◽  
Myles Allen

<p>Integrated Assessment Model (IAM) design philosophy currently focuses on demand-side global carbon pricing as the principal policy tool to drive mitigation. However, ambitious mitigation scenarios produced with these IAMs rely heavily on the availability of carbon capture and storage (CCS) technologies in the mid-century, at the scale of billions of tonnes. If integrated assessment continues to employ demand-side policies exclusively we risk a gap forming between the requirements of economically-optimal mitigation trajectories in these IAMs and the reality of developed CCS capacity.</p><p>If CCS capacity fails to keep up with the ambition of mitigation policy, carbon prices could rise well above the cost of direct air capture as markets aim to drive residual emissions down. To avoid this, scenarios could include both supply and demand-side policies in tandem, where supply-side policies are targeted to increase CCS capacity to appropriate levels.</p><p>One such supply-side policy option is a Carbon Takeback Obligation (CTBO), where suppliers of fossil carbon are required to recapture and store an increasing fraction of the carbon in their products. This ‘stored fraction’ would be increased from near zero at present, up to 100% at the time of net-zero. By applying such a policy suppliers of fossil carbon products are forced to take responsibility for decarbonising their own products and provide the drive to develop the CCS capacity necessary to achieve net-zero emissions in the mid-century. In theory, if a CTBO was enforced globally the costs associated with the production of one tonne of CO<sub>2</sub> would be capped around the price for the capture, transport and storage of diffuse, mobile, or otherwise hard-to-abate CO<sub>2</sub> emission sources (i.e. the cost of direct air capture).</p><p>Here, we discuss the implementation of a global CTBO. Using an Integrated Assessment Model emulator, tuned to existing IAM carbon price/abatement rate relationships, we explore the total policy cost of applying a CTBO globally to achieve net-zero by 2050. Using the emulator we harmonise the combined CTBO and demand-side carbon price policies, and show how a SSP2-26 level of ambition can be achieved using these policies with a similar total policy cost. Further, we explore what additional near-term carbon prices can be included to achieve SSP2-19 level policy ambition. These results suggest there are significant benefits to defining climate policy around measures targeting suppliers of fossil carbon, including for long-term planning, implementation and governance of the policy, and overall cost. For further insight, and to provide a greater variety of policy options feeding into IPCC’s WG3, we argue IAMs should look to include CTBO-like policies in future scenario design.</p>

2018 ◽  
Vol 09 (03) ◽  
pp. 1850008 ◽  
Author(s):  
INGMAR SCHUMACHER

We show that a policy maker who ignores regional data and instead relies on aggregated integrated assessment models is likely underestimating the carbon price and thus the required climate policy. Based on a simple theoretical model, we give conditions under which the Aggregation Dilemma is expected to play a role in climate change cost-benefit analysis. We then study the importance of the Aggregation Dilemma with the integrated assessment model RICE [Nordhaus and Boyer, (2000) Warning the World: Economic Models of Global Warming. MA: MIT Press]. Aggregating all regions of the RICE-99 model into one region yields a 40% lower social cost of carbon than the RICE model itself predicts. Based on extrapolating the results, a country-level integrated assessment model would give a more than eight times higher social cost of carbon compared to a fully aggregated model. We suggest that these tentative results require researchers to rethink the aggregation level used in integrated assessment models and to develop models at much lower levels of aggregation than currently available.


2021 ◽  
Vol 167 (3-4) ◽  
Author(s):  
Camilla C. N. de Oliveira ◽  
Gerd Angelkorte ◽  
Pedro R. R. Rochedo ◽  
Alexandre Szklo

2017 ◽  
Author(s):  
Abigail C. Snyder ◽  
Robert P. Link ◽  
Katherine V. Calvin

Abstract. Hindcasting experiments (conducting a model forecast for a time period in which observational data is available) are rarely undertaken in the Integrated Assessment Model (IAM) community. When they are undertaken, the results are often evaluated using global aggregates or otherwise highly aggregated skill scores that mask deficiencies. We select a set of deviation based measures that can be applied at different spatial scales (regional versus global) to make evaluating the large number of variable-region combinations in IAMs more tractable. We also identify performance benchmarks for these measures, based on the statistics of the observational dataset, that allow a model to be evaluated in absolute terms rather than relative to the performance of other models at similar tasks. This is key in the integrated assessment community, where there often are not multiple models conducting hindcast experiments to allow for model intercomparison. The performance benchmarks serve a second purpose, providing information about the reasons a model may perform poorly on a given measure and therefore identifying opportunities for improvement. As a case study, the measures are applied to the results of a past hindcast experiment focusing on land allocation in the Global Change Assessment Model (GCAM) version 3.0. We find quantitative evidence that global aggregates alone are not sufficient for evaluating IAMs, such as GCAM, that require global supply to equal global demand at each time period. Additionally, the deviation measures examined in this work successfully identity parametric and structural changes that may improve land allocation decisions in GCAM. Future work will involve implementing the suggested improvements to the GCAM land allocation system identified by the measures in this work, using the measures to quantify performance improvement due to these changes, and, ideally, applying these measures to other sectors of GCAM and other land allocation models.


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