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

The Extended Energy-Systems ERIS Model: An Overview

This report describes the extensions to the "bottom-up" energy-systems ERIS (Energy Research and Investment Strategies) model carried out by the authors at IIASA-ECS for, among others, the EC-Sponsored SAPIENTIA and MINIMA-SUD projects. The original version of the ERIS model was developed as a joint effort between the Environmentally Compatible Energy Strategies (ECS) project at IIASA and the Energy Economics Group of the Paul Scherrer Institute (PSI) in Switzerland during the EC-sponsored TEEM and SAPIENT projects, in which it was mainly used to examine issues related to the endogenization of mechanisms of technological change. The extension of the ERIS model developed at IIASA-ECS include: the implementation of a clusters approach to technology learning, the inclusion of emissions and marginal abatement curves for two main non-CO2 greenhouse gases (methane (CH4) and nitrous oxide (N2O)), the inclusion of sulfur dioxide (SO2) emissions, the incorporation of a transportation sector with emphasis on the passenger car sub-sector, the inclusion of fuel production technologies (i.e. hydrogen, alcohol, Fischer-Tropsch liquids, etc.) as well as geological and terrestrial CO2 storage and a calibration to the year 2000 energy statistics

The WITCH Model. Structure, Baseline, Solutions

WITCH – World Induced Technical Change Hybrid – is a regionally disaggregated hard-link hybrid global model with a neoclassical optimal growth structure (top-down) and a detailed energy input component (bottom-up). The model endogenously accounts for technological change, both through learning curves that affect the prices of new vintages of capital and through R&D investments. The model features the main economic and environmental policies in each world region as the outcome of a dynamic game. WITCH belongs to the class of Integrated Assessment Models as it possesses a climate module that feeds climate changes back into the economy. Although the model’s main features are discussed elsewhere (Bosetti et al., 2006), here we provide a more thorough discussion of the model’s structure and baseline projections, to describe the model in greater detail. We report detailed information on the evolution of energy demand, technology and CO2 emissions. We also explain the procedure used to calibrate the model parameters. This report is therefore meant to provide effective support to those who intending to use the WITCH model or interpret its results.Climate Policy, Hybrid Modelling, Integrated Assessment, Technological Change

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

WITCH. A World Induced Technical Change Hybrid Model

The need for a better understanding of future energy scenarios, of their compatibility with the objective of stabilizing greenhouse gas concentrations, and of their links with climate policy, calls for the development of hybrid models. Hybrid because both the technological detail typical of Bottom Up (BU) models and the long run dynamics typical of Top Down (TD) models are crucially necessary. We present WITCH – World Induced Technical Change Hybrid model – a neoclassical optimal growth model (TD) with energy input detail (BU). The model endogenously accounts for technological progress, both through learning curves affecting prices of new vintages of capital and through R&D investments. In addition, the model captures the main economic interrelationships between world regions and is designed to analyze the optimal economic and environment policies in each world region as the outcome of a dynamic game. This paper provides a detailed description of the WITCH model, of its baseline, and of the model calibration procedure.Climate Policy, Hybrid Modelling, Integrated Assessment, Technological Change, Energy Mix.

Emission Trading and the Role of Learning-By-Doing Spillovers in the Bottom-Up Energy-System ERIS Model

In this paper, using the "bottom-up" energy-system optimisation ERIS model, we examine the effects of emission trading on technology deployment, emphasising the role of technology learning spillovers. That is, the possibility that the learning accumulated in a particular technology in a given region may spill to other regions as well, leading to cost reductions there also. The effects of different configurations of interregional spillovers of learning in ERIS and the impact of the emission trading mechanism under those different circumstances are analysed. Including spatial spillovers of learning allows capturing the possibility that the imposition of greenhouse gas emission constraints in a given region may induce technological change in other regions, such as developing countries, even if the latter regions do not face emission constraints. Our stylised results point out the potential benefits of sound international cooperation between industrialised and developing regions on research, development, demonstration and deployment (RD3) of clean energy technologies and on the implementation of emission trading schemes

Representing energy technologies in top-down economic models using bottom-up information

Abstract in HTML and technical report in PDF available on the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global Change Website. (http://mit.edu/globalchange/www/)Includes bibliographical references (p. 22).This paper uses bottom-up engineering information as a basis for modeling new technologies within the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the world economy. Natural gas combined cycle (NGCC) without carbon capture and sequestration (CCS), natural gas combined cycle with CCS, and integrated coal gasification with CCS power generation technologies are introduced into the EPPA model. These compete in the electricity sector with conventional fos sil generation, nuclear, hydro, wind, and biomass power generation. Engineering cost data are used together with EPPA data, including the underlying Social Accounting Matrix (SAM) and supplementary physical energy accounts, to assure that technologies, w h en simulated within the model, meet thermodynamic efficiency limits, and that they reflect regional differences in the cost structure of the electric sector. Alternative capital vintaging approaches are investigated and an explicit treatment of market p enetration of new technologies is developed. Simulations through 2100 show the introduction of the new technologies and their decline as fuel and input prices, and carbon policies, change. A general result is that NGCC plants with or without capture, wh il e currently less costly methods of abating carbon emissions from the electric sector based on engineering data, play only a limited and short-term role in meeting carbon limits. By 2050 the coal CCS plants, currently the most costly of the three techno logies, dominate in the simulated policy scenarios because rising gas prices raise the cost of the gas-based technologies

Large Scale Deployment of Renewables for Electricity Generation

Comparisons of resource assessments suggest resource constraints are not an obstacle to the large-scale deployment of renewable energy technologies. Economic analysis identifies barriers to the adoption of renewable energy sources resulting from market structure, competition in an uneven playing field and various non-market place barriers. However, even if these barriers are removed, the problem of ‘technology lock-out’ remains. The key policy response is strategic deployment coupled with increased R&D support to accelerate the pace of improvement through market experience. The paper suggests significant contributions from various technologies, but does not assess their optimal or maximal market share

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

REMIND-D: A Hybrid Energy-Economy Model of Germany

This paper presents a detailed documentation of the hybrid energy-economy model REMIND-D. REMIND-D is a Ramsey-type growth model for Germany that integrates a detailed bottom-up energy system module, coupled by a hard link. The model provides a quantitative framework for analyzing long-term domestic CO2 emission reduction scenarios. Due to its hybrid nature, REMIND-D facilitates an integrated analysis of the interplay between technological mitigation options in the different sectors of the energy system as well as overall macroeconomic dynamics. REMIND-D is an intertemporal optimization model, featuring optimal annual mitigation effort and technology deployment as a model output. In order to provide transparency on model assumptions, this paper gives an overview of the model structure, the input data used to calibrate REMIND-D to the Federal Republic of Germany, as well as the techno-economic parameters of the technologies considered in the energy system module.Hybrid Model, Germany, Energy System, Domestic Mitigation