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Industrial Deep Decarbonization Modeling Approaches and Data Challenges A Industrial Deep Decarbonization Modeling Approaches and Data Challenges Elena Verdolini, Lorenzo Torreggiani, Sara Giarola, Massimo Tavoni, Marc Hafstead, and Lillian Anderson Report 23-10 August 2023 Resources for the Future i About the Authors Elena Verdolini is a climate economist. She is Professor of Political Economy at the Law Department, University of Brescia, and Senior Scientist at the RFF- CMCC European Institute on Economics and the Environment EIEE of the Euro- Mediterranean Center on Climate Change, where she leads the research group on “Sustainable Innovation and Digitalization”. She is the principal investigator of the 2D4D “Disruptive Digitalization for Decarbonization” project, funded by the European Research Council through a Starting Grant, and the coordinator of the AdJUST “Advancing the understanding of challenges, policy options and measures to achieve a JUST EU energy transition” funded by Horizon Europe. She was a Lead Author of the of the IPCC 6th Assessment Report, Working Group III. Elena holds a degree in Political Science from the University of Pavia, a Master of Public Administration and a Master of Arts in International Studies from the University of Washington, Seattle and a PhD in Economics and Finance of the Public Administration from the Catholic University of Milan. Her research interest includes the dynamics and drivers of innovation, adoption and transfer of energy-efficient and climate-friendly technologies; the role of digital technologies in the energy transition; and the economic and distributional implications of climate and energy policy. Lorenzo Torreggiani is a Post Degree researcher at EIEE working on renewables’ raw data analysis. He is a student in Green Economy and Sustainability with a degree in Banking and Finance at the University of Brescia. He is interested in climate change, renewable energy, environmental economics, and statistics. Sarah Giarola is the Marie Curie Research Fellow at the Polytechnic University of Milan. She works on modelling the technological, epistemic, and socio-economic uncertainty in climate economic models using machine learning techniques. She received her PhD in Chemical Engineering from the University of Padova in 2012. She joined Imperial College London in 2012, became a BG Research Fellow in 2014, at the Sustainable Gas Institute, and in 2020, she joined the Chemical Engineering Department. Her research interests lie in macro-energy systems with a focus on the multiscale modelling to capture the nexus between technology diffusion and societal behaviour. In the area of energy systems modelling and optimisation, she published 36 journal articles and 3 book chapters. Industrial Deep Decarbonization Modeling Approaches and Data Challenges ii Massimo Tavoni is director of EIEE. He is also a full professor at the School of Management of Politecnico di Milano. He coordinated the Climate Change Mitigation programme at Fondazione Eni Enrico Mattei FEEM between 2015 and 2018. He has been a fellow at the Center for Advanced Studies in Behavioural Sciences at Stanford University and a postdoctoral fellow at Princeton University His research is about climate change mitigation policies, and has appeared in major scientific journals. He is a lead author of the IPCC 5th and 6th assessment reports, co-directs of the International Energy Workshop, and was deputy editor for the journal Climatic Change. He is a recipient of a grant from the European Research Council ERC. He has advised several international institutions on climate change matters, including the OECD, the Asian Development Bank, the World Bank. Marc Hafstead joined RFF in 2013 from Stanford University. He is an RFF fellow and director of the Carbon Pricing Initiative and the Climate Finance and Financial Risk Initiative. His research has primarily focused on the evaluation and design of federal and state-level climate and energy policies using sophisticated multi-sector models of the US economy. With Stanford Professor and RFF University Fellow Lawrence H. Goulder, he wrote Confronting the Climate Challenge US Policy Options Columbia University Press to evaluate the environmental and economic impacts of federal carbon taxes, cap-and-trade programs, clean energy standards, and gasoline. His research has also analyzed the distributional and employment impacts of carbon pricing and the design of tax adjustment mechanisms to reduce the emissions uncertainty of carbon tax policies. His paper “Impacts of a Carbon Tax across U.S. Household Income Groups What Are the Equity-Efficiency Trade-Offs” with Larry Goulder, GyuRim Kim, and Xianling Long won the Journal of Public Economics 2021 Atkinson Award for best paper published in the journal between 2018 and 2020. Hafstead has been cited in the popular press, including the Wall Street Journal, the Washington Post, Axios, and CNNMoney. Lillian Anderson is a research associate at RFF. She completed an undergraduate degree in Economics and Mathematics and a master’s degree in Economic Development Programming at the University of Southern California. Previously, she worked with the Foresight and Policy Modelling team at the International Food Policy Research Institution. Her focus is on working with, developing, and adapting economic models. Industrial Deep Decarbonization Modeling Approaches and Data Challenges iii Acknowledgements This research was conducted with support from Breakthrough Energy. The results presented in this report reflect the views of the authors and not necessarily those of the supporting organization. Elena Verdolini gratefully acknowledges support from the European Research Council ERC under the European Union’s Horizon 2020 research and innovation programme Project “2D4D – Disruptive Digitalization for Decarbonization,” grant agreement No 853487. Massimo Tavoni gratefully acknowledges support from the Energy Demand changes Induced by Technological and Social innovations EDITS project funded by the Ministry of Economy, Trade, and Industry METI, Japan. About RFF Resources for the Future RFF is an independent, nonprofit research institution in Washington, DC. Its mission is to improve environmental, energy, and natural resource decisions through impartial economic research and policy engagement. RFF is committed to being the most widely trusted source of research insights and policy solutions leading to a healthy environment and a thriving economy. The views expressed here are those of the individual authors and may differ from those of other RFF experts, its officers, or its directors. Sharing Our Work Our work is available for sharing and adaptation under an Attribution- NonCommercial-NoDerivatives 4.0 International CC BY-NC-ND 4.0 license. You can copy and redistribute our material in any medium or format; you must give appropriate credit, provide a link to the license, and indicate if changes were made, and you may not apply additional restrictions. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you may not distribute the modified material. For more information, visit https//creativecommons.org/licenses/by-nc-nd/4.0/. Resources for the Future iv Abstract Industrial energy consumption represents almost 40 percent of current global total final consumption and is still dominated by fossil fuels. In this paper, we present key decarbonization optionsnamely fuel switching and electrification, carbon efficiency, material efficiency, carbon capture and storage, and circular economy practicesand analyze their potential for decarbonization in six main energy-intensive industrial sectors steel, cement, chemicals, light manufacturing, aluminum, and pulp and paper. We then develop a framework to distinguish among the different modelling approaches to industrial energy demand and emissions, with specific focus on the data challenges that constrain modelling and the difficulties of modelling innovation and technology diffusion. We present the most widely used models of industrial energy demand and emissions and classify them along three key dimensions the analytical approach underlying each model, the methodology used to generate decarbonization pathways, and the granularity with which different industrial sectors be represented. By highlighting the strengths and weaknesses of available tools for industrial emission modelling, we point to necessary future model development efforts that would greatly improve the ability to develop deep decarbonization pathways for industry. Industrial Deep Decarbonization Modeling Approaches and Data Challenges v Contents 1. Introduction 1 2. Energy-Intensive Industry Sectors 2 2.1. Steel 2 2.2. Cement 3 2.3. Chemicals 3 2.4. Light Manufacturing 3 2.5. Aluminum 3 2.6. Pulp and Paper 4 3. Available Approaches for Industry Decarbonization 4 3.1. Fuel Switching and Electrification 4 3.2. Energy Efficiency Improvements in Production Process 6 3.3. Material Efficiency 7 3.4. Carbon Capture and Storage Technologies 7 3.5. Circular Economy Approaches 8 4. Demand Reduction and Energy Efficiency Potentials of Energy-Intensive Sectors 10 4.1. Steel 10 4.2. Cement and Concrete 12 4.3. Chemicals 13 4.4. Light Manufacturing 15 4.5. Aluminum and Nonferrous Metals 15 4.6. Pulp and Paper 16 5. Data on Industrial Energy Demand and Emissions 17 5.1. Lack of A Common Detailed Statistics Classification 17 5.2. Lack of Comprehensive Data on Energy Demand of Different Energy Carriers 17 5.3. Lack of Detailed Sectorial Information on Energy Demand at the Level of Different Products 18 5.4. Limited Geographic and Time Coverage of Detailed Databases 18 5.5. Difficulty in Linking Data on Emissions and Fuel Inputs 18 5.6. Difficulty in Predicting Costs and Performance of Radically Novel Technologies 19 5.7. Lack of Comprehensive Data on Material and Energy Flows 19 Industrial Deep Decarbonization Modeling Approaches and Data Challenges vi 6. Approaches for Industrial Energy Demand Modeling 20 6.1. Analytical Approach Bottom-up and Top-down Models 20 6.2. Methodology Simulation and Optimization Approaches 21 6.3. Granularity of Sectoral Modeling 22 7. The Modeling of Innovation and Technological Change and Relevant Policies 24 8. Specific Models for Industry Energy Demand and Emissions 26 9. Conclusions 27 References 29 Appendix A. Available Datasets to Track Industrial Energy Demand and Emissions, and Their Limitations 39 Appendix B. Classification of Industrial Sectors 49 B.1. The International Standard Industrial Classification of All Economic Activities ISIC 50 B.2. The North American Industry Classification System NAICS 50 B.3. The Statistical Classification of Economic Activities in the European Community NACE 51 Appendix C. Model Summaries 52 Appendix D. Examples of Applications Using Different Models 72 D.1. World Energy Models 72 D.2. NEMS 72 D.3. GCAM 72 D.4. REMIND 72 D.5. MUSE 72 D.6. TIMES 73 D.7. IMAGE 73 D.8. Material Economics Modeling Framework 73 D.9. E3ME 73 D.10. ISEEM-IS 73 D.11. U-ISIS 74 D.12. HYBTEP 74 D.13. FORECAST 74 Resources for the Future Industrial Deep Decarbonization Modeling Approaches and Data Challenges 1 1. Introduction Increasing carbon efficiency and switching to carbon-neutral technologies for industrial production are imperative to achieve deep greenhouse gas GHG emissions reductions and to address climate change, as well as to ease concerns regarding energy security and higher energy prices. Energy consumption by the industrial sector represents almost 40 percent of current global total final consumption and is still dominated by fossil fuels, in particular coal. In 2021, industry was the second-largest emitting sector, after power generation, and was directly responsible for emitting 9.4 gigatonnes Gt of CO 2 . This estimate, which is equivalent to a quarter of global emissions, does not include indirect emissions from electricity used for industrial processes IEA 2022c. Industrial energy and carbon intensities vary significantly across sectors as well as within sectors across different countries, with six sectors emerging as particularly energy- and emissions-intensive see Section 2. The aim of this paper is to describe the most common approaches to the modeling of industrial emissions, with a particular focus on the ability of available models to depict the different mitigation options relevant for energy-intensive industries. These options range from increasing energy efficiency to developing and deploying novel negative-, zero-, or low-emissions technologies. Importantly, producing quantitative forecasts of industrial energy demand and emissions is strongly dependent on the availability of past data for model calibration. Furthermore, different modeling approaches and methods are characterized by the capacity to provide more or less detailed scenarios in terms of geographic, sectoral, and technological detail. Understanding the strengths and weaknesses of available approaches and tools for industrial energy and emissions modeling provides the basis for developing and interpreting results that can be used to inform energy- and climate-related policymaking. We also describe the most promising deep decarbonization options in each sector and discuss whether and how these options are represented in industrial energy models. It is important to recognize that deep emissions reductions cannot be achieved by pursuing a single decarbonization strategy alone; rather, these reductions are more likely to be achieved through a combination of many mitigation options, as well as investment in and support for different technologies and subtechnologies. Conversely, ignoring some of these options and promoting only select ones reduces the likelihood of achieving deep decarbonization targets. Therefore, our assessment of the relevance of various mitigation options for industrial deep decarbonization should not be interpreted as suggesting that one option should be chosen over the others. Resources for the Future 2 The paper is organized as follows. Section 2 identifies and describes the energy-intensive sectors that account for the majority of industrial energy demand and emissions, the focus of this paper. Section 3 reviews the available strategies through which industrial energy emissions can be reduced. Section 4 illustrates more detailed and specific technological options in each of the key energy-intensive sectors. Section 5 provides an overview of the data available to measure industrial energy demand and emissions, which are critical inputs for model development and calibration. It also discusses the difficulty of obtaining data to model several of the decarbonization approaches that are relevan