A MERGE Model with Endogenous Technological Change and the Cost of Carbon Stabilization

Two stylized backstop systems with endogenous technological learning formulations (ETL) are introduced in MERGE: one for the electric and the other for the non-electric markets. Then the model is applied to analyze the impacts of ETL on carbon-mitigation policy, contrasting the resulting impacts with the situation without learning. As the model considers endogenous technological change in the energy sector only some exogenous key parameters defining the production function are varied together with the assumed learning rates to check the robustness of our results. Based on model estimations and the sensitivity analyses we conclude that increased commitments for the development of new technologies to advance along their learning curves has a potential for substantial reductions in the cost of climate mitigation helping to reach safe concentrations of carbon in the atmosphere.Climate change stabilization policies, Non-linear optimization, Induced technological change, Energy and macroeconomy

Stabilizing global temperature change below thresholds: Monte Carlo analyses with MERGE

Policies may help to keep the anthropogenic temperature change below some critical temperature thresholds. We apply MERGE in a probabilistic risk assessment framework to assess the risk of action versus inaction on climate change. The method applied gives a probabilistic assessment of the associated economic costs and levels of carbon-values and emissions reduction, as well as the needed technological change to restructure the energy system. The study suggests that a set of low-carbon and carbon-free technologies has to be developed and diffused around the world in order to reduce the risk of serious, adverse climate change. Eventually, a mass deployment of biomass farming technologies for bio-fuels and/or hydrogen production, in conjunction with carbon capture and sequestration options, are needed to satisfy the EU threshold of 2°C average atmospheric temperature rise above the pre-industrial temperature levels by the year 2100. However, because this temperature threshold represents a severe target, the global "willingness-to-pay” (WTP) must be significantly improved in relation with present attitude

Combining policy instruments for sustainable energy systems: An assessment with the GMM model

An assessment of the impact of an illustrative portfolio of policy instruments that address different sustainability concerns in the global energy system in areas of climate change, air pollution and introduction of renewable-energy resources is conducted. The effects of a policy set containing three instruments, implemented either individually or in combination, were examined. The policy instruments under examination in this work include: Cap-and-Trade policies imposing a CO2 emission reduction target on the global energy system, a renewable portfolio standard that forces a minimum share of renewable electricity generation, and the internalisation of external costs of power generation associated with local pollution. Implementation of these policy instruments significantly changes the structure and environmental performance of the energy sector, and particularly the structure of the electric-generation sector. The positive effects are amplified when the policy instruments are simultaneously applied, illustrating the potential for synergies between these energy-policy domains. The analysis has been conducted with the multi-regional, energy-system Global MARKAL Model (GMM), a "bottom-up” partial-equilibrium model that provides a detailed representation of energy technologies and endogenizes technology learnin

Linking energy system and macroeconomic growth models

We compare two alternative approaches for coupling macroeconomic growth models (MGM) and energy system models (ESM). The hard-link approach integrates the techno-economics of the ESM completely into the MGM and solves one highly complex optimisation problem. The soft-link leaves the two models separate and energy supply functions are integrated into the MGM that are derived from the optimal solution of the ESM. The energy supply functions relate the price of energy computed with the ESM to the quantity of energy computed with the MGM. An iterative process exchanges price-quantity information between the models. Hence, the soft-link leads to an energy market equilibrium. But energy supply functions do not consider variable interest rates that influence the energy supply functions. This is due to the fact that ESMs are partial models that assume an exogenous interest rate; however the interest rate is computed endogenously in MGMs. This missing interaction leads to a capital market dis-equilibrium in the soft-link compared to the hard-link approach inducing a mis-allocation of investments. Extending the soft-link approach by also considering the time variable interest rate of the MGM does not improve the results. Though the computational complexity is greater the hard-link approach assures simultaneous energy and capital market equilibriu

The Role of Non-CO2 Gases in Flexible Climate Policy: An Analysis with the Energy-Systems GMM Model

This paper examines the effects of incorporating two main non-CO2 greenhouse gases, namely methane (CH4) and nitrous oxide (N2O) into the "bottom-up", partial equilibrium, energy-systems Global Multi-regional MARKAL model (GMM). Abatement possibilities for these two greenhouse gases have been included using marginal abatement curves from the U.S. EPA study (2003). Our results illustrate the effect of these greenhouse gases on the composition of emissions mitigation strategies and associated costs, highlighting the importance of the "what" flexibility in climate-change policies. In addition, we emphasize the influence of assumptions regarding rate of deployment and technological change in non-CO2 abatement potentials on the model's outcome

Supporting hydrogen based transportation: case studies with Global MARKAL Model

Steadily growing prices of oil and emissions coming from conventional vehicles, might force a switch to an alternative and less polluting fuel in the coming future. In this article we analyze the potential influence of selected factors for successful market penetration of hydrogen fuel cell vehicles in hydrogen based private transportation economy. Using a world scale, full energy system, bottom-up, optimization model (Global MARKAL Model—GMM) we address the possibility of supporting the fuel cell vehicle technology to become competitive in the markets. In a series of optimizations we evaluate the potential influence of governmental supports and the internalization of externalities related to CO2 and local pollution emissions originating from the transportation sector, as well as preferential crediting options and demonstration projects promoting fuel cell vehicles. The results suggest that the crucial element is the price of fuel cells and their further potential to reduce costs. This reduction of costs may be triggered by governmental support such as direct subsidies to fuel cells, preferential crediting options for the buildup of hydrogen infrastructure as well as penalization of emitters of CO2 and/or local pollutant

Modeling endogenous learning and imperfect competition effects in climate change economics

In this two-part paper we evaluate the effect of "endogenizing” technological learning and strategic behavior of agents in economic models used to assess climate change policies. In the first part we show the potential impact of R&D policies or demonstration and deployment (D&D) programs in the context of stringent stabilization scenarios. In the second part we show how game-theoretic methods can be implemented in climate change economic models to take into account three types of strategic interactions: (i) the market power of the countries benefiting from very low abatement costs on international markets for CO2 emissions, (ii) the strategic behavior of governments in the domestic allocation of CO2 emissions quotas, and (iii) the non-cooperative behavior of countries and regions in the burden sharing of CO2 concentration stabilization. The two topics of endogenous learning and game-theoretic approach to economic modeling are two manifestations of the need to take into account the strategic behavior of agents in the evaluation of climate change policies. In the first case an R&D policy or a demonstration and deployment (D&D) program are put in place in order to attain a cost reduction through the learning effect; in the second case the agents (countries) reply optimally to the actions decided by the other agents by exploiting their strategic advantages. Simulations based on integrated assessment models illustrate the approaches. These studies have been conducted under the Swiss NCCR-Climate progra

Emissions Trading and Technology Deployment in an Energy-Systems "Bottom-Up" Model with Technology Learning

An important criterion in the analysis of climate policy instruments is their ability to stimulate the technological change necessary to enable the long-term shift towards a low-carbon global energy system. In this paper, some effects of emissions trading on technology deployment when technology learning is endogenized are examined with a multi-regional "bottom-up" energy-systems optimization MARKAL model of the global energy system. In this framework, due to the action of spillovers of learning, imposing emission constraints on a given region may affect the technology choice and emissions profiles of other (unconstrained) regions. The effects depend on the geographical scale of the learning process but also on the presence of emissions trading, the regions that join the trade system and their timing for doing so. Incorporating endogenous technology learning and allowing for spillovers across regions appears as an important mechanism for capturing the possibility of induced technological change due to environmental constraints in "bottom-up" models

Endogenous Technological Change in Energy Systems Models: Synthesis of Experience with ERIS, MARKAL, and MESSAGE

Technological change is widely recognised as a key factor in economic progress, as it enhances the productivity of factor inputs. In recent years also the notion has developed that targeted technological development is a main means to reconcile economic ambitions with ecological considerations. This raises the issue that assessments of future trajectories of for example en-ergy systems should take into account context-specific technological progress. Rather than tak-ing characteristics of existing and emerging technologies as a given, their development should be a function of dedicated Research, Development and Demonstration (RD&D) and market de-ployment under varying external conditions. Endogenous technological learning has recently shown to be a very promising new feature in energy system models. A learning, or experience curve, describes the specific (investment) cost as a function of the cumulative capacity for a given technology. It reflects the fact that tech-nologies may experience declining costs as a result of its increasing adoption into the society due to the accumulation of knowledge through, among others, processes of learning-by-doing and learning-by-using. This report synthesises the results and findings from experiments with endogenous technologi-cal learning, as reported separately within the EU TEEM project. These experiments have been carried out by three TEEM partners using three models: ERIS (PSI), MARKAL (ECN and PSI), and MESSAGE (IIASA). The main objectives of this synthesis are: to derive common methodo-logical insights; to indicate and assess benefits of the new feature, but also its limitations and issues to solve; and to recommend further research to solve the main issues. This synthesis shows that all model applications are examples of successful first experiments to incorporate the learning-by-doing concept in energy system models. Incorporating the learning-by-doing concept makes an important difference. The experiments demonstrate and quantify the benefits of investing early in emerging technologies that are not competitive at the moment of their deployment. They also show that the long-term impact of policy instruments, such as CO2 taxes or emission limits and RD&D instruments, on technological development can be assessed adequately with models including technology learning. Adopting the concept of endogenous learning, several types of RD&D interventions can be addressed that aim at accelerating the market penetration of new technologies. The directions into which such interventions might lead have been illustrated in some of the experiments. However, quantitative relationships between R&D policy and learning data parameters are still unknow