Innovative energy technologies in energy-economy models

Assessing economic, energy and environmental impacts of climate policy and technological change in Germany

Dissertation

zur Erlangung des akademischen Grades
doctor rerum politicarum
(Doktor der Wirtschaftswissenschaft)

eingereicht an der
Wirtschaftswissenschaftlichen Fakultät
der Humboldt-Universität zu Berlin

Von

Diplom Volkswirtin Katja Schumacher
(geb. am 26. Nov. 1968 in Düsseldorf)

Präsident der Humboldt-Universität zu Berlin:

Prof. Dr. Christoph Markschies

Dekan der wirtschaftswissenschaftlichen Fakultät
Prof. Oliver Günther, Ph.D.

Gutachter:
1. Prof. Dr. Claudia Kemfert
2. Prof. Dr. Heinz Welsch

eingereicht: 18. April 2007

Tag des Kolloquiums: 18. Juli 2007

Abstract

Energy technologies and innovation are considered to play a crucial role in climate change mitigation. Yet, the representation of technologies in energy-economy models, which are used extensively to analyze the economic, energy and environmental impacts of alternative energy and climate policies, is rather limited. This dissertation presents advanced techniques of including technological innovations in energy-economy computable general equilibrium (CGE) models. New methods are explored and applied for improving the realism of energy production and consumption in such top-down models. The dissertation addresses some of the main criticism of general equilibrium models in the field of energy and climate policy analysis: The lack of detailed sectoral and technical disaggregation, the restricted view on innovation and technological change, and the lack of extended greenhouse gas mitigation options. The dissertation reflects on the questions of (1) how to introduce innovation and technological change in a computable general equilibrium model as well as (2) what additional and policy relevant information is gained from using these methodologies. Employing a new hybrid approach of incorporating technology-specific information for electricity generation and iron and steel production in a dynamic multi-sector computable equilibrium model it can be concluded that technology-specific effects are crucial for the economic assessment of climate policy, in particular the effects relating to process shifts and fuel input structure. Additionally, the dissertation shows that learning-by-doing in renewable energy takes place in the renewable electricity sector but is equally important in upstream sectors that produce technologies, i.e. machinery and equipment, for renewable electricity generation. The differentiation of learning effects in export sectors, such as renewable energy technologies, matters for the economic assessment of climate policies because of effects on international competitiveness and economic output.

Keywords: Energy technologies, electricity generation, iron and steel production, technological change, learning-by-doing, general equilibrium modelling, hybrid modelling, climate policy, international trade.

Zusammenfassung

Die Einführung neuartiger Energietechnologien wird allgemein als der Schlüssel zur Senkung klimaschädlicher Treibhausgase angesehen. Allerdings ist die Abbildung derartiger Technologien in numerischen Modellen zur Simulation und ökonomischen Analyse von energie- und klimaschutzpolitischen Maßnahmen vielfach noch rudimentär. Die Dissertation entwickelt neue Ansätze zur Einbindung von technologischen Innovationen in energie-ökonomische allgemeine Gleichgewichtsmodelle, mit dem Ziel den Energiesektor realitätsnäher abzubilden. Die Dissertation adressiert einige der Hauptkritikpunkte an allgemeinen Gleichgewichtsmodellen zur Analyse von Energie- und Klimapolitik: Die fehlende sektorale und technologische Disaggregation, die beschränkte Darstellung von technologischem Fortschritt, und das Fehlen von einem weiten Spektrum an Treibhausgasminderungsoptionen. Die Dissertation widmet sich zwei Hauptfragen: (1) Wie können technologische Innovationen in allgemeine Gleichgewichtsmodelle eingebettet werden? (2) Welche zusätzlichen und politikrelevanten Informationen lassen sich durch diese methodischen Erweiterungen gewinnen? Die Verwendung eines sogenannten Hybrid-Ansatzes, in dem neuartige Technologien für Stromerzeugung und Eisen- und Stahlherstellung in ein dynamisch multi-sektorales CGE Modell eingebettet werden, zeigt, dass technologiespezifische Effekte von großer Bedeutung sind für die ökonomische Analyse von Klimaschutzmaßnahmen, insbesondere die Effekte hinsichtlich von Technologiewechsel und dadurch bedingten Änderungen der Input- und Emissionsstrukturen. Darüber hinaus zeigt die Dissertation, dass Lerneffekte auf verschiedenen Stufen der Produktionskette abgebildet werden müssen: Für regenerative Energien, zum Beispiel, nicht nur bei der Anwendung von Stromerzeugungsanlagen, sondern ebenso auf der vorgelagerten Produktionsstufe bei der Herstellung dieser Anlagen. Die differenzierte Abbildung von Lerneffekten in Exportsektoren, wie zum Beispiel Windanlagen, verändert die Wirtschaftlichkeit und die Wettbewerbsfähigkeit und hat wichtige Implikationen für die ökonomische Analyse von Klimapolitik.

Eigene Schlagworte: Energietechnologien, Stromerzeugung, Eisen- und Stahlproduktion, technologischer Wandel, Lerneffekte, allgemeine Gleichgewichtsmodelle, Hybridmodellierung, Klimapolitik, internationaler Handel.

Table of contents

• 1 Introduction
  • 1.1  Energy-economy-environment modeling
  • 1.2 Innovation and technological change in energy-economy-environment models
  • 1.3 Goal and structure of dissertation
  • 1.4 Acknowledgements
  • 1.5 References
• 2  Electricity sector innovations in climate policy modeling for Germany
  • 2.1  Introduction
  • 2.2 German electricity sector
  • 2.3 SGM-Germany
  • 2.4 Analysis and results
    • 2.4.1  Technology choice
    • 2.4.2 Electricity sector results
    • 2.4.3 Economic and emissions results
  • 2.5 Summary and conclusions
  • 2.6 References
• 3  An innovative CGE approach for the inclusion of industrial technologies in energy-economy models
  • 3.1  Introduction
  • 3.2 Methods
    • 3.2.1  Benchmark data
    • 3.2.2 Technology-based approach
    • 3.2.3 Aggregate production function approach
    • 3.2.4 CGE framework
    • 3.2.5 Technical change
  • 3.3 Iron and steel technologies
    • 3.3.1  Background on production routes
    • 3.3.2 Production costs and energy use for iron and steel
  • 3.4 Analysis and results
    • 3.4.1  Technology-based analysis
      • 3.4.1.1 Sensitivity analysis
    • 3.4.2 Aggregate CES versus technology-based approach
    • 3.4.3 Economic and emissions results
  • 3.5 Conclusions
  • 3.6 References
• 4  Economic comparison of GHG mitigation strategies
  • 4.1  Introduction
  • 4.2 Background
  • 4.3 Methods
    • 4.3.1  SGM-Germany
    • 4.3.2 Greenhouse gas emissions
  • 4.4 Results
    • 4.4.1  Electricity sector results
    • 4.4.2 Results for greenhouse gas emissions
    • 4.4.3 Economic comparison
    • 4.4.4 From partial to full coverage of economy
  • 4.5 Conclusions
  • 4.6 References
• 5  Learning-by-doing in renewable energy technologies
  • 5.1  Introduction
  • 5.2 Renewable energy in Germany
  • 5.3 Learning-by-doing and renewable energy
    • 5.3.1  Learning-by-doing in renewable energy machinery and equipment
    • 5.3.2 Learning-by-doing in renewable electricity production
  • 5.4 LEAN_2000
    • 5.4.1  The model
    • 5.4.2 Implementation of learning-by-doing in LEAN_2000
    • 5.4.3 Renewable energy equipment in LEAN_2000
  • 5.5 Analysis and results
    • 5.5.1  Output, investment and price effects
    • 5.5.2 Macro-economic and international trade effects
    • 5.5.3 Energy and environmental effects
    • 5.5.4 Relative speed of learning and spillovers
  • 5.6 Summary and conclusions
  • 5.7 References
  • 5.8  Appendix
• 6  Summary and conclusions
  • 6.1  References
• All references
• Erklärung

Tables

• Table 2.1 Cost and performance measures of new electricity technologies with and without CO₂ capture and storage
• Table 2.2 Overview of cost and performance of new fossil technologies with and without carbon dioxide capture and storage
• Table 3.1 Cost structure of iron and steel technologies
• Table 4.1 Greenhouse gas emission sources
• Table 4.2 Greenhouse gas price scenarios. All scenarios reach a maximum CO₂-equivalent price in 2025 and the price remains constant thereafter. These prices can be applied to either the entire economy (full coverage) or sectors covered by the EU emissions trading program (partial coverage).
• Table 5.1 Production sectors in LEAN_2000
• Table 5.2 Assumptions on learning rates in scenario analysis
• Table 5.3 Assumptions for spillover analysis

Images

• Figure 1.1 Energy-economy-environment models
• Figure 2.1 Gross electricity production by fuel (in TWh)
• Figure 2.2 CO₂ emissions by sector (% share); Germany 2003
• Figure 2.3 Production in SGM-Germany 1995 (billion Euro)
• Figure 2.4 Nested logit structure of electric generating technologies in SGM-Germany
• Figure 2.5 Levelized cost as a function of carbon price
• Figure 2.6 Sensitivity of crossover price with respect to interest rate
• Figure 2.7 Sensitivity of crossover price with respect to fuel price increase (at fixed 7% interest rate and starting with 2010 fuel prices)
• Figure 2.8 Baseline electricity generation in TWh
• Figure 2.9 Electricity generation mix with carbon price 50 € per t CO₂
• Figure 2.10 Projections of carbon dioxide emissions in Germany (Mt CO₂)
• Figure 2.11 Marginal abatement cost curves with and without CO₂ capture and storage (CCS)
• Figure 2.12 Decomposition of emissions reductions with a carbon price of 50 € per t CO₂
• Figure 2.13 Change in sectoral output, 50 € per t CO₂ case compared to baseline
• Figure 3.1 Organization of benchmark use table for Germany in 1995. Each row is a distinct commodity and each column represents production activities. Three distinct activities (technologies) are available for crude steel production: basic oxygen furnace (BOF), electric arc furnace (EAF), and a direct reduction process (DRP).
• Figure 3.2 Nesting structure of steel technologies. Each leaf of the nesting structure is a fixed-coefficient technology: basic oxygen furnace (BOF), advanced BOF (BOFA), electric arc furnace (EAF), advanced EAF (EAFA), and a direct reduction process (DRP). The model has space for another advanced technology, smelt reduction process (SRP), but it is not presently populated with data.
• Figure 3.3 Crude steel production by process type, Germany 1985-2004
• Figure 3.4 Iron and steel production routes. The dotted rectangle indicates the system boundary for the crude steel production process in SGM-Germany.
• Figure 3.5 Production of crude steel through 2050 in a base case for Germany. Steel production occurs with basic oxygen furnace (BOF) and electric arc furnace (EAF) technologies before 2010. Advanced technologies are introduced after 2010, including advanced versions of BOF and EAF, and a direct reduction process (DRP).
• Figure 3.6 Development of levelized costs for five technologies (BOF, BOFA, EAF, EAFA, DRP) over time and with a stepwise increase of the CO₂ price (2005=10€/t CO₂, 2010=20€/t CO₂, 2015=30€/t CO₂, 2020=40€/t CO₂, 2025=50€/t CO₂). Levelized costs are indexed to the average cost of crude steel production in the base year (cost index equals 1 in 1995).
• Figure 3.7 Simulated production of crude steel in 2020 and 2030 at three CO₂ prices: zero €/t CO₂ or business as usual (BAU), 10€/t CO₂, and 50€/t CO₂
• Figure 3.8 Percentage change in crude steel output from direct reduction process/electric arc furnace production (DRP/EAF) in three sensitivity cases relative to the base case. Sensitivity runs i) with respect to increased natural gas prices (assumed to be 30% higher than in base case in 2005, 50% in 2010, 70% in 2015, 90% in 2020 and 100% 2025 and thereafter; all relative to the base case); ii) with respect to increased scrap prices (assumed to more than double to 250 US$/t scrap from 2005 on); and iii) with respect to both increased natural gas and scrap prices (combined effect of i and ii). In all cases no CO₂ price is applied.
• Figure 3.9 Percentage change in crude steel output from advanced electric arc furnaces (EAFA) in three sensitivity cases relative to the base case. Sensitivity runs as above (Figure 3.8).
• Figure 3.10 Specific fuel input to iron and steel production, base year and 2010. Units are gigajoules (GJ) per ton of crude steel. LOGIT refers to the technology-based approach, CES to the aggregate CES approach.
• Figure 3.11 Specific fuel input to iron and steel production, year 2030. Units are gigajoules (GJ) per ton of crude steel. LOGIT refers to the technology-based approach, CES to the aggregate CES approach.
• Figure 3.12 CO₂ emissions in Germany: historical and future projections from various sources
• Figure 3.13 Decomposition of emissions reductions at 50€/t CO₂ across households and major types of industries.
• Figure 4.1 Non-CO₂ greenhouse gas emissions in Germany, 1995-2004
• Figure 4.2 Nested logit structure of electric generating technologies in SGM-Germany
• Figure 4.3 Baseline electricity generation in TWh
• Figure 4.4 Electricity generation mix with a stepwise CO₂ price increase
• Figure 4.5 Greenhouse gas emissions pathway, baseline
• Figure 4.6 Greenhouse gas emissions pathway, 20 € per ton CO₂-eq
• Figure 4.7 Greenhouse gas emissions pathway, 50 € per ton CO₂-eq
• Figure 4.8 Simulated emissions reductions over a range of CO₂ prices, Germany 2020
• Figure 4.9 Simulated emissions reductions over a range of CO₂ prices, Germany 2040
• Figure 4.10 Decomposition of emissions reduction with a stepwise increasing CO₂ price fully and partially covering the economy
• Figure 4.11 Change in sectoral output, stepwise CO₂ price applied to all sectors of the economy (full coverage), compared to baseline
• Figure 4.12 Change in sectoral output, stepwise CO₂ price increase applied to parts of the economy (partial coverage) compared to baseline
• Figure 5.1 Installed renewable electricity capacity in Germany
• Figure 5.2 Total installed capacity of wind power, 1997-2004
• Figure 5.3 Learning effects for electricity technologies in the European Union, 1985-1990
• Figure 5.4 Cumulated output in the renewable energy equipment sector: base case and two counterfactual scenarios, indexed to 1995 thus reflecting quantity changes over time
• Figure 5.5 Cumulated investmentin the renewable energy equipment sector: base case and two counterfactual scenarios, indexed to 1995 thus reflecting quantity changes over time
• Figure 5.6 Output price renewable equipment sector, percentage change over base case
• Figure 5.7 Cumulated output (line) and cumulated investment (bars) in the renewable electricity sector: base case and two counterfactual scenarios, indexed to 1995 thus reflecting quantity changes over time
• Figure 5.8 Output price renewable electricity, change over base case
• Figure 5.9 Domestic production and exports of renewable energy equipment: scenario learning-by-doing in renewable equipment, lbd_eqip (percentage change over base case)
• Figure 5.10 GDP real (percentage change compared to base case)
• Figure 5.11 Electricity production (TWh) in Germany, 2000 to 2030
• Figure 5.12 CO₂ emissions (Mt CO₂) in Germany, 2000 to 2030
• Figure 5.13 Domestic production and exports of Germany’s renewable energy equipment industry with and without knowledge spillover of learning in renewable energy equipment. In the spillover case, both Germany and the rest of the European Union experience learning in response to increased cumulated total output of both regions (Spillover case 2). In the 'no spillover' case, only Germany experiences learning in response to increased cumulative output within its own borders.
• Figure 5.14 Output price renewable electricity with a learning rate of 10% for renewable electricity production and 40% for the production of renewable energy equipment, change over base case
• Figure 5.15 Output prices of renewable energy equipment at two different assumptions about learning rates in the production of renewable energy equipment: 10% (lbd_eqip10) and 40% (lbd_eqip40)
• Figure 5.16 Domestic production and exports of the renewable energy equipment sector with a learning rate of 10% and of 40% for renewable energy equipment and no spillover effect
• Figure 5.17 Domestic production and exports of the renewable energy equipment sector in Germany at a learning rate of 40% with and without knowledge spillover to and from the rest of the European Union