Expanding upon previous studies dealing with the assessment of economic impacts of the Paris Agreement (Babonneau et al. 2016, 2018), we introduce a macroeconomic framework that combines a computable general equilibrium (CGE) model, namely GEMINI-E3, with a dynamic game model. The resulting “meta-game” model—under simplifying, but reasonable assumptions—is used to provide a first insight into possible welfare losses in GCC countries, if they were to implement a long-term mitigation strategy with CDR technologies. The full mathematical description of this model is given in the Electronic Supplemental Material.
Briefly, the model describes 10 coalitions of countries (including a grouping of GCC countries) competing for the supply of emissions permits in an international cap and trade system as designed to satisfy a global safety cumulative emissions budget (evaluated at 1170 GT of CO2 over the 2020–2100 period). A net-zero emission regime is reached at the end of this period. To summarize climate negotiations on the burden sharing issue, we consider different possible allocations of a global safety cumulative emissions budget among different coalitions. Once the share of the emissions budget that goes to each coalition is decided, the coalitions are assumed to play a non-cooperative game for the supply of emissions permits in the international carbon market. Depending on their respective abatement policies, the coalitions can be net buyers or net sellers of permits. This generates payment transfers that converge toward fair burden sharing. Furthermore, our modeling approach encapsulates several key elements of the design of an international climate regime consistent with Paris Agreement goals. The payoffs for the game-theoretic model are obtained from statistical emulation of the GEMINI-E3 CGE model presented below.
Evaluation of welfare losses with the GEMINI-E3 model
GEMINI-E3 (Bernard and Vielle 2008) is a CGE model specifically designed to assess the impact of climate change mitigation policies in different regions of the world. It has recently been used to assess the COP21 pledges and a fair 2°C pathway compatible with the Paris Agreement objectives (Babonneau et al. 2018). For this study, the model has been extended to permit a more detailed representation of the GCC countries and their risk exposure to stranded assets. The model is built on the GTAP 9 database (Aguiar et al. 2016), with reference year 2011. In this version, we detail 10 groupings (regions or coalitions) of countries, the GCC countries being one of them; they are as follows: European Union (28 countries), USA, China, India, the GCC, Russia, other Asian countries, other energy-exporting countries, Latin America, and the rest of the world.Footnote 7 Extraction of fossil fuel energy is modeled by carbon content in order to evaluate the “unburnable-oil” effect of climate change mitigation policies. Three fossil fuel sectors/products are represented: coal, crude oil, and natural gas. In the model, the impact of deep decarbonization pathways on stranded fossil fuel assets occurs via two main channels: (i) fossil fuel resources localized in energy exporting countries lose their value, energy rents associated with these resource decrease (i.e., inground reserves become stranded assets), and welfare is directly negatively impacted in countries that own these resources; (ii) capital invested in energy sectors (coal mining, refineries, pipeline infrastructure) and energy-intensive industrial sectors are further depreciated, which in turn negatively impacts households that own these assets. In GEMINI-E3, like most CGE models, households own capital and other resources, e.g., land, fossil fuel resources. While we do not consider national oil companies, nevertheless where NOCs are owned by the government, our results are not affected. Indeed, our scenarios assume that the government budget is unchanged with respect to the reference scenario. In this sense, a decline in oil revenue allocated to the government budget requires an increase in household taxation (e.g., direct tax) and a decrease in household income equivalent to our current closure rule.
Since GEMINI-E3 was designed to run on the 2011–2050 period, we take a versatile approach to extend it to 2100 based on steady-state growth through the end of the century. We first, selected a demographic scenario, then used a production function approach to indicate the relationship between GDP per capita and the total factor productivity (TFP). We assume that regional TFPs converge to an exogenously defined common value at the end of our century, represented by the US figure. Finally, we also assume that CO2 emissions per unit of GDP decrease at an annual rate and converge also to a single common value for each region. This allows us to simulate a BaU scenario through 2100 by setting a value for the three parameters defined above: demographic scenario, TFP, and a carbon intensity per GDP. In this paper, we assume that the TFP and carbon intensity per GDP converge to 1% and − 1%, respectively at the end of our century.Footnote 8
This macroeconomic model reproduces historical emissions (2011 to 2018) and its medium term forecast is based on the WEO outlook 2016 (International Energy Agency 2016). The economic impact of mitigation policies is measured by the gains (or losses) in terms of trade (GTT) and the domestic abatement costs.Footnote 9 For energy-exporting countries, like the GCC countries, the GTT component represents decreases in energy exporting revenues. CDR technologies are not modelled in GEMINI-E3 since they are new technologies with strategic importance in their development for oil- and gas-exporting countries. In addressing this, our game model includes explicit decision variables for the investment and use of these technologies.
Meta-Game model and linkage with GEMINI-E3
First proposed in Haurie et al. (2014), the meta-game model presents coalition payoffs as a function of the macroeconomic costs of abatement policies, the cost of developing CDR technologies, the gains in the terms of trade (GTT) due to global impacts on world energy prices, and the financial gains or losses from trading permits. Statistical emulation of the macroeconomic model are used to calibrate marginal abatement costs and GTT functions.
Regression analysis is used to estimate the payoff functions of the game, where strategic variables are the quotas supplied by the different coalitions, at different times, under an emissions trading scheme. Statistical analysis is based on a sample of 100 numerical simulations of different possible climate policy scenarios performed with GEMINI-E3, as detailed in the Electronic Supplemental Material.
Introduction of CDR alternatives
Brief review of CDR technologies
CDR aims to remove carbon dioxide directly from the atmosphere through different processes that either increase natural carbon sinks—such as oceans and lands—or use chemical engineering to suppress carbon dioxide. Of potential geo-engineering approach (Heyward 2013), CDR technologies are considered less environmentally impactful than stratospheric aerosol injection (SAI), marine cloud brightening, or space reflectors. Within CDR, several approaches have already been tested and implemented, including ocean iron fertilizationFootnote 10, biocharFootnote 11, enhanced weatheringFootnote 12, large-scale afforestationFootnote 13, BECCS, and DAC. Our analysis focuses on only the last two technologies, which are the most likely backstop technology candidates (Shell-Corp 2018; Chen and Tavoni 2013).
Techno-economic analysis of CDR technologies
Assessments of DAC technologies are discussed in Keith et al. (2006) and Keith (2009), and more recently, in House et al. (2011) and Keith et al. (2018). Their potential role in climate stabilization has been explored in Nemet and Brandt (2012), and then in Chen and Tavoni (2013), under the WITCH model (Bosetti et al. 2006), which predicts comparative advantages in deploying DAC for the Middle East and energy-exporting countries. This same comparative advantage was also observed in Marcucci et al. (2017), which used the MERGE-ETL model (Kypreos 2007) to explore the potential of the DAC technology. Under these models, the total quantity of CO2 captured by the DAC and other carbon capture technologies is constrained by the potential for CO2 storage across regions. As derived in Marcucci et al. (2017), estimates of storage potentials—including deep saline aquifers, hydrocarbon fields, and coal beds—are given in Table 1. Due to potential technical, accessibility, and social acceptance issues—among others—we assume that only a fraction (between 25 and 50%) of these potentials can be used for the DAC and BECCS operations by 2100. We also assume that the DAC technologies will be mature enough for massive deployment by 2040 with a linear deployment trend afterwards.
Cost of DAC has been discussed in recent publications. For instance, Keith et al. (2018), describes and economically assesses a process fully powered by natural gas, computing a levelized cost of 232 $/t-CO2 captured; an American Physical Society study (The American Physical Society 2011) proposed a levelized cost of 550 $/t-CO2; House et al. (2011) determined the cost for powering a DAC plant using a natural gas-fired plant with CCS at 396 $/t-CO2 avoided; and the extra energy cost of DAC was estimated around 232 $/t-CO2 captured by Lackner (2009) and Gardarsdottir et al. (2014) and Gardarsdottir et al. (2018). Storage costs were evaluated in Rubin et al. (2015) to be in the range of 6 to 13 $/t-CO2 stored. The total levelized cost is thus here set at $300/t-CO2 captured and stored, for all regions except the USA and EUR. These latter costs are priced at $350/t-CO2 captured and stored, assuming higher logistic costs.
As for BECCS, the technology standard consists of producing electricity from biomass while capturing and injecting CO2 into geological formations. We use a unique levelized cost of 60$/t-CO2 for the whole world, consistent with the IEA estimates (Koornneef et al. 2011). BECCS potentials are estimated from the global and regional assessments (Koornneef et al. 2011), which take biomass supply chains and processing into account, and also include deployment issues in terms of policy and regulatory barriers. Using the IEA estimates, we have derived a global bound on GHG captured through BECCS equal to 10.2 Gt CO2, based on technical potentials by 2050. Finally, BECCS penetration is related to electricity generation levels and composition by year 2050; we adopt what could be considered rather conservative potential estimates for the end of the century.
Evaluation of fair compensations among GCC countries
To assess the economic consequences of a proposed climate agreement, we assume “optimal” use—or at least, a second best solution—of the global emissions budget, which will correspond to a Nash equilibrium among the parties. In this sense, we assume a global safety cumulative emissions budget (SCEB) of 1170 Gt of CO2 over the time horizon 2018–2100. Climate negotiations, in one form or another, will bear how this global safety cumulative emission budget is shared among coalitions, regrouping countries with similar macroeconomic structure. We also assume an international emissions trading system. Here, the coalitions supply permits to the market, strategically crafting abatement policies for their share of the safety cumulative emissions budget. In this sense, the development of CDR activities like BECCS and DAC will allow coalitions to replenish or increase their own emission budget. We compute a Nash equilibrium around this dynamic game. Briefly, when a coalition reaches capability for a levelized cost of a CDR technology—be it BECCS or DAC—lower than the price of permit, it can then invest to increase the permit allowances and gain advantages in the equilibrium solution. We consider a fair burden sharing is obtained when the share of the remaining safety cumulative emissions budget that is given to each coalition is such that the relative losses of welfare are equal among all coalitions. For the GCC countries, the financial transfers from selling permits through the market will generate compensations for unburnable oil.