ENERGY TECHNOLOGY DEVELOPMENT AND CLIMATE CHANGE MITIGATION

This dissertation examines the role that technology plays in climate change mitigation. It contains three essays each focusing on different aspects of the process in which advancements in low-carbon energy technologies impact the cost of carbon dioxide (CO2) abatement. The first essay develops the analytical foundation for understanding how heterogeneous low-carbon energy technologies induce differential impacts on the abatement cost. The analysis derives sets of conditions under which different types of advanced technologies can be evaluated for their respective strengths in reducing abatement costs at different levels of abatement. It emphasizes the weakness of a single point estimation of the impact of a technology and the importance of understanding the pattern of abatement cost reductions throughout the potential levels of abatement. The second essay focuses on the interactions of the energy technologies in the market. The analysis uses a combinatorial approach in which 768 scenarios are created for all combinations of considered technology groups. Using the dataset, the analysis shows how the reduction in the abatement cost may change significantly depending on the existence of other advanced technologies. The essay shows that many of the fundamental insights from traditional representative scenario analyses are in line with the findings from this comprehensive combinatorial analysis. However, it also provides more clarity regarding insights not easily demonstrated through representative scenario analyses. The analysis emphasizes how understanding the interactions between these technologies and their impacts on the cost of abatement can help better inform energy policy decisions. The third essay focuses on the impact technological change has on the cost of abatement, but with special attention paid to the issue of delayed technology development. By combining the probability of advanced technology success estimates from expert elicitations with the abatement cost data estimated with an integrated assessment model, a stochastic dynamic programming model is developed. A multi-period extension of the model allows intertemporal dynamic optimization where the policy-maker can select the technologies to be invested in immediately and the technologies to be invested in later. The analysis emphasizes the benefit of having a wait-and-see option that lets the policy-maker further optimize upon the observation of successes and failures of prior investments. The three essays collectively serve to demonstrate the importance of clearly understanding the differences among low-carbon technologies. They also provide methodological foundations upon which such technologies can be assessed and compared. Combining these methods with an enhanced understanding of the technologies will contribute to the body of research aimed at minimizing the cost of mitigating climate change

Modeling Uncertainty in Climate Change: A Multi‐Model Comparison

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO 2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events

Global Energy System Transitions

Energy systems power the world’s economies. They are pivotal to providing sustained economic prosperity that provides the goods and services that humans desire. Climate change is intimately linked with energy systems because CO2 from fossil fuel use is the most important anthropogenic greenhouse gas (GHG) emitted to the atmosphere, and cumulative anthropogenic emissions determine Earth’s concentration of CO2. Limiting climate change therefore means that global energy systems must reduce net CO2 emissions to zero and stabilize emissions of other GHGs. We compare energy system pathways as they are currently evolving with alternatives that have the potential to limit climate change over the twenty-first century. The differences are profound. We also discuss some frontier research issues that can provide a better understanding of potential pathways and their implications for decision makers

Stranded asset implications of the Paris Agreement in Latin America and the Caribbean

Achieving the Paris Agreement's near-term goals (nationally determined contributions, or NDCs) and long-term temperature targets could result in pre-mature retirement, or stranding, of carbon-intensive assets before the end of their useful lifetime. We use an integrated assessment model to quantify the implications of the Paris Agreement for stranded assets in Latin America and the Caribbean (LAC), a developing region with the least carbon-intensive power sector in the world. We find that meeting the Paris goals results in stranding of $37-90 billion and investment of$1.9-2.6 trillion worth of power sector capital (2021-2050) across a range of future scenarios. Strengthening the NDCs could reduce stranding costs by 27%-40%. Additionally, while politically shielding power plants from pre-mature retirement or increasing the role of other sectors (e.g. land-use) could also reduce power sector stranding, such actions could make mitigation more expensive and negatively impact society. For example, we find that avoiding stranded assets in the power sector increases food prices 13%, suggesting implications for food security in LAC. Our analysis demonstrates that climate goals are relevant for investment decisions even in developing countries with low emissions. © 2020 The Author(s). Published by IOP Publishing Ltd

Near-term transition and longer-term physical climate risks of greenhouse gas emissions pathways

Policy, business, finance and civil society stakeholders are increasingly looking to compare future emissions pathways across both their associated physical climate risks stemming from increasing temperatures, and their transition climate risks stemming from the shift to a low-carbon economy. Here we present an integrated framework to explore near term (to 2030) transition risks and longer term (to 2050) physical risks, globally and in specific regions, for a range of plausible greenhouse gas emissions and associated temperature pathways, spanning 1.5-4oC levels of long-term warming. By 2050, physical risks deriving from major heatwaves, agricultural drought, heat stress and crop duration reductions depend greatly on the temperature pathway. By 2030, transition risks most sensitive to temperature pathways stem from economy-wide mitigation costs, carbon price increases, fossil fuel demand reductions and coal plant capacity reductions. Considering several pathways with a 2oC target demonstrates that transition risks also depend on technological, policy and socio-economic factors

Emissions and Energy Impacts of the Inflation Reduction Act

If goals set under the Paris Agreement are met, the world may hold warming well below 2 C; however, parties are not on track to deliver these commitments, increasing focus on policy implementation to close the gap between ambition and action. Recently, the US government passed its most prominent piece of climate legislation to date, the Inflation Reduction Act of 2022 (IRA), designed to invest in a wide range of programs that, among other provisions, incentivize clean energy and carbon management, encourage electrification and efficiency measures, reduce methane emissions, promote domestic supply chains, and address environmental justice concerns. IRA's scope and complexity make modeling important to understand impacts on emissions and energy systems. We leverage results from nine independent, state-of-the-art models to examine potential implications of key IRA provisions, showing economy wide emissions reductions between 43-48% below 2005 by 2035

'gcamdata': An R Package for Preparation, Synthesis, and Tracking of Input Data for the GCAM Integrated Human-Earth Systems Model

The increasing data requirements of complex models demand robust, reproducible, and transparent systems to track and prepare models’ inputs. Here we describe version 1.0 of the gcamdata R package that processes raw inputs to produce the hundreds of XML files needed by the GCAM integrated human-earth systems model. It features extensive functional and unit testing, data tracing and visualization, and enforces metadata, documentation, and flexibility in its component data-processing subunits. Although this package is specific to GCAM, many of its structural pieces and approaches should be broadly applicable to, and reusable by, other complex model/data systems aiming to improve transparency, reproducibility, and flexibility.   Funding statement: Primary support for this work was provided by the U.S. Department of Energy, Office of Science, as part of research in Multi-Sector Dynamics, Earth and Environmental System Modeling Program. Additional support was provided by the U.S. Department of Energy Offices of Fossil Energy, Nuclear Energy, and Energy Efficiency and Renewable Energy and the U.S. Environmental Protection Agency

Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper

<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p><p><strong>Model version and input files:</strong> <i>gcam-trn-paper.zip</i> includes the GCAM version and add-on files used to run the scenarios. The configuration file used is <i>config.xml</i>;<i> </i>the batch files are <i>batch_cwf_trn.xml</i> and <i>batch_cwf_trn_sensitivities.xml</i> (for the primary and sensitivity runs, respectively) found in the <i>exe</i> subfolder of <i>gcam-trn-paper.zip</i>.</p><p><strong>Output data:</strong> <i>runs_primary_output.zip</i> contains the resulting GCAM output data for the primary scenarios; <i>runs_sensitivity_output.zip</i> contains the resulting GCAM output data for the sensitivity scenarios.</p><p><strong>Processing scripts:</strong> <i>processing_scripts.zip</i> contains the processing scripts needed to process and analyze the output datasets and generate the figures for the paper. <i>cwf_trn_analysis_plotting_paper.R</i> is the primary plotting script, but makes use of other scripts in the folder. The <i>add_on_xml_generation</i> folder contains the R scripts and other data needed to generate the add-on XMLs for the alternative fuel mandates for shipping and aviation for the medium and high scenarios (<i>alt_fuel_frac_xml_generation</i> subfolder), as well as to generate the add-on XMLs for the sensitivity scenarios (<i>sensitivities_xml_generation</i> subfolder).</p&gt

The damages from 2.5 °C of warming (as a percentage of global output) that would equalize the additional abatement cost <em>C</em>_{<em>x</em><em>z</em>} and expected climate benefits <em>E</em>[<em>B</em>_{<em>x</em><em>z</em>d}|<em>a</em>] from adopting CO₂ target <em>x</em> instead of one 50 ppm higher, all calculated with a 5% consumption discount rate

<p><strong>Figure 3.</strong> The damages from 2.5 °C of warming (as a percentage of global output) that would equalize the additional abatement cost <em>C</em>_{<em>x</em><em>z</em>} and expected climate benefits <em>E</em>[<em>B</em>_{<em>x</em><em>z</em>d}|<em>a</em>] from adopting CO₂ target <em>x</em> instead of one 50 ppm higher, all calculated with a 5% consumption discount rate. The range of technology scenarios (<em>n</em> = 384) is represented by each box (median and interquartile range) and its whiskers (minimum and maximum). Comparing across columns within a group reveals the effect of changing the distribution for climate sensitivity, comparing across groups reveals the effect of changing the damage function, and comparing across plots reveals the effect of changing the CO₂ target. The darker shaded region indicates output losses within one standard deviation (σ) of the average best estimate (μ) summarized in [<a href="http://iopscience.iop.org/1748-9326/8/3/034019/article#erl475796bib8" target="_blank">8</a>], and the lighter shaded region indicates those losses within two standard deviations. (a) 450 ppm CO₂ target (versus 500 ppm). (b) 500 ppm CO₂ target (versus 550 ppm).</p> <p><strong>Abstract</strong></p> <p>Climate change policies must trade off uncertainties about future warming, about the social and ecological impacts of warming, and about the cost of reducing greenhouse gas emissions. We show that laxer carbon targets produce broader distributions for climate damages, skewed towards severe outcomes. However, if potential low-carbon technologies fill overlapping niches, then more stringent carbon targets produce broader distributions for the cost of reducing emissions, skewed towards high-cost outcomes. We use the technology-rich GCAM integrated assessment model to assess the robustness of 450 and 500 ppm carbon targets to each uncertain factor. The 500 ppm target provides net benefits across a broad range of futures. The 450 ppm target provides net benefits only when impacts are greater than conventionally assumed, when multiple technological breakthroughs lower the cost of abatement, or when evaluated with a low discount rate. Policy evaluations are more sensitive to uncertainty about abatement technology and impacts than to uncertainty about warming.</p

(a) Warming

<p><strong>Figure 1.</strong> (a) Warming. (b) Damages 100 years after step forcing. (c) Abatement cost. Normally distributed uncertainty about equilibrium feedbacks and about an aggregate technology index translate into distributions for damages and abatement cost that are skewed towards undesirable outcomes. The feedback distribution follows [<a href="http://iopscience.iop.org/1748-9326/8/3/034019/article#erl475796bib13" target="_blank">13</a>] in using normally distributed equilibrium feedbacks with a mean of 0.65 and a standard deviation of 0.13. The economic loss coefficient <em>a</em> in the damage calculations has 2.5 °C of warming reducing output by 1.7% under the quadratic specification [<a href="http://iopscience.iop.org/1748-9326/8/3/034019/article#erl475796bib19" target="_blank">19</a>]. The abatement cost function assumes constant elasticity of −0.5 with respect to technology (i.e., achieving 1% more breakthroughs lowers abatement cost by 0.5%) and is normalized to the 650 ppm target's no-breakthrough scenario. The cost of each CO₂ target under the no-breakthrough scenario comes from the Global Change Assessment Model 3.0.</p> <p><strong>Abstract</strong></p> <p>Climate change policies must trade off uncertainties about future warming, about the social and ecological impacts of warming, and about the cost of reducing greenhouse gas emissions. We show that laxer carbon targets produce broader distributions for climate damages, skewed towards severe outcomes. However, if potential low-carbon technologies fill overlapping niches, then more stringent carbon targets produce broader distributions for the cost of reducing emissions, skewed towards high-cost outcomes. We use the technology-rich GCAM integrated assessment model to assess the robustness of 450 and 500 ppm carbon targets to each uncertain factor. The 500 ppm target provides net benefits across a broad range of futures. The 450 ppm target provides net benefits only when impacts are greater than conventionally assumed, when multiple technological breakthroughs lower the cost of abatement, or when evaluated with a low discount rate. Policy evaluations are more sensitive to uncertainty about abatement technology and impacts than to uncertainty about warming.</p