This study gives a synthesis of a model comparison assessing the technological feasibility and economic consequences of achieving greenhouse gas concentration targets that are sufficiently low to keep the increase in global mean temperature below 2 degrees Celsius above pre-industrial levels. All five global energy-environment-economy models show that achieving low greenhouse gas concentration targets is technically feasible and economically viable. The ranking of the importance of individual technology options is robust across models. For the lowest stabilization target (400 ppm CO 2 eq), the use of bio-energy in combination with CCS plays a crucial role, and biomass potential dominates the cost of reaching this target. Without CCS or the considerable extension of renewables the 400 ppm CO 2 eq target is not achievable. Across the models, estimated aggregate costs up to 2100 are below 0.8% global GDP for 550 ppm CO 2 eq stabilization and below 2.5% for the 400 ppm CO 2 eq pathway.

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In this paper, we quantify the energy transition and economic consequences of the long-term targets from the Paris agreement, with a particular focus on the targets of limiting global warming by the end of the century to 2 and 1.5 °C. The study assumes early actions and quantifies the market penetration of low carbon technologies, the emission pathways and the economic costs for an efficient reduction of greenhouse gas (GHG) emissions such that the temperature limit is not exceeded. We evaluate the potential role of direct air capture (DAC) and its impact on policy costs and energy consumption. DAC is a technology that removes emissions directly from the atmosphere contributing to negative carbon emissions. We find that, with our modelling assumptions, limiting global temperature to 1.5 °C is only possible when using DAC. Our results show that the DAC technology can play an important role in realising deep decarbonisation goals and in the reduction of regional and global mitigation costs with stringent targets. DAC acts a substitute to Bio-Energy with Carbon Capture and Storage (BECCS) in the stringent scenarios. For this analysis, we use the model MERGE-ETL, a technology-rich integrated assessment model with endogenous learning.

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ERIS, an energy-systems optimization model that endogenizes learning curves, is modified in order to incorporate the effects of R&D investments, an important contributing factor to the technological progress of a given technology. For such purpose a modified version of the standard learning curve formulation is applied, where the investment costs of the technologies depend both on cumulative capacity and the so-called knowledge stock. The knowledge stock is a function of R&D expenditures that takes into account depreciation and lags in the knowledge accumulated through R&D. An endogenous specification of the R&D expenditures per technology allows the model to perform an optimal allocation of R&D funds among competing technologies. The formulation is described, illustrative results presented, some insights are derived, and further research needs are identified.