John E. Bistline

The effect of natural gas supply on US renewable energy and CO 2 emissions

Increased use of natural gas has been promoted as a means of decarbonizing the US power sector, because of superior generator efficiency and lower CO2 emissions per unit of electricity than coal. We model the effect of different gas supplies on the US power sector and greenhouse gas (GHG) emissions. Across a range of climate policies, we find that abundant natural gas decreases use of both coal and renewable energy technologies in the future. Without a climate policy, overall electricity use also increases as the gas supply increases. With reduced deployment of lower-carbon renewable energies and increased electricity consumption, the effect of higher gas supplies on GHG emissions is small: cumulative emissions 2013–55 in our high gas supply scenario are 2% less than in our low gas supply scenario, when there are no new climate policies and a methane leakage rate of 1.5% is assumed. Assuming leakage rates of 0 or 3% does not substantially alter this finding. In our results, only climate policies bring about a significant reduction in future CO2 emissions within the US electricity sector. Our results suggest that without strong limits on GHG emissions or policies that explicitly encourage renewable electricity, abundant natural gas may actually slow the process of decarbonization, primarily by delaying deployment of renewable energy technologies. S Online supplementary data available from stacks.iop.org/ERL/9/094008/mmedia

Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach

Although emerging technologies like carbon capture and storage and advanced nuclear are expected to play leading roles in greenhouse gas mitigation efforts, many engineering and policy-related uncertainties will influence their deployment. Capital-intensive infrastructure decisions depend on understanding the likelihoods and impacts of uncertainties such as the timing and stringency of climate policy as well as the technological availability of carbon capture systems. This paper demonstrates the utility of stochastic programming approaches to uncertainty analysis within a practical policy setting, using uncertainties in the US electric sector as motivating examples. We describe the potential utility of this framework for energy-environmental decision making and use a modeling example to reinforce these points and to stress the need for new tools to better exploit the full range of benefits the stochastic programming approach can provide. Model results illustrate how this framework can give important insights about hedging strategies to reduce risks associated with high compliance costs for tight CO2 caps and low CCS availability. Metrics for evaluating uncertainties like the expected value of perfect information and the value of the stochastic solution quantify the importance of including uncertainties in capacity planning, of making precautionary low-carbon investments, and of conducting research and gathering information to reduce risk.

The role of carbon capture technologies in greenhouse gas emissions-reduction models: A parametric study for the U.S. power sector

This paper analyzes the potential contribution of carbon capture and storage (CCS) technologies to greenhouse gas emissions reductions in the U.S. electricity sector. Focusing on capture systems for coal-fired power plants until 2030, a sensitivity analysis of key CCS parameters is performed to gain insight into the role that CCS can play in future mitigation scenarios and to explore implications of large-scale CCS deployment. By integrating important parameters for CCS technologies into a carbon-abatement model similar to the EPRI Prism analysis (EPRI, 2007), this study concludes that the start time and rate of technology diffusion are important in determining emissions reductions and fuel consumption for CCS technologies. Comparisons with legislative emissions targets illustrate that CCS alone is very unlikely to meet reduction targets for the electric-power sector, even under aggressive deployment scenarios. A portfolio of supply and demand-side strategies is needed to reach emissions objectives, especially in the near term. Furthermore, model results show that the breakdown of capture technologies does not have a significant influence on potential emissions reductions. However, the level of CCS retrofits at existing plants and the eligibility of CCS for new subcritical plants have large effects on the extent of greenhouse gas emissions reductions.

Electric sector policy, technological change, and U.S. emissions reductions goals: Results from the EMF 32 model intercomparison project

The Energy Modeling Forum (EMF) 32 study compares a range of coordinated scenarios to explore implications of U.S. climate policy options and technological change on the electric power sector. Harmonized policy scenarios (including mass-based emissions limits and various power-sector-only carbon tax trajectories) across 16 models provide comparative assessments of potential impacts on electric sector investment and generation outcomes, emissions reductions, and economic implications. This paper compares results across these policy alternatives, including a variety of technological and natural gas price assumptions, and summarizes robust findings and areas of disagreement across participating models. Under a wide range of policy, technology, and market assumptions, model results suggest that future coal generation will decline relative to current levels while generation from natural gas, wind, and solar will increase, though the pace and extent of these changes vary by policy scenario, technological assumptions, region, and model. Climate policies can amplify trends already under way and make them less susceptible to future market changes. The model results provide useful insights to a range of stakeholders, but future research focused on intersectoral linkages in emission reductions (e.g., the role of electrification), effects of energy storage, and better coverage of bioenergy with carbon capture and storage (BECCS) can improve insights even further.

Effects of technology assumptions on US power sector capacity, generation and emissions projections: Results from the EMF 32 Model Intercomparison Project

This paper is one of two syntheses in this special issue of the results of the EMF 32 power sector study. This paper focuses on the effects of technology and market assumptions with projections out to 2050. A total of 15 models contributed projections based on a set of standardized scenarios. The scenarios include a range of assumptions about the price of natural gas, costs of end-use energy efficiency, retirements of nuclear power, the cost of renewable electricity, and overall electricity demand. The range of models and scenarios represent similarities and differences across a broad spectrum of analytical methods. One similarity across almost all results from all models and scenarios is that the share of electricity generation and capacity fueled by coal shrinks over time, although the rate at which coal capacity is retired depends on the price of natural gas and the amount of electricity that is demanded. Another similarity is that the models project average increases in natural gas power generating capacity in every scenario over the 2020-2050 period, but at lower average annual rates than those that prevailed during the 2000-2015 period. The projections also include higher gas capacity utilization rates in the 2035-2050 period compared to the 2020-2050 period in every scenario, except the high gas price sensitivity. Renewables capacity is also projected to increase in every scenario, although the annual new capacity varies from modest rates below the observed 2000-2015 wind and solar average to rates more than 3 times that high. Model estimates of CO2 emissions largely follow from the trends in generation. Low renewables cost and low gas prices both result in lower overall CO2 emission rates relative to the 2020-2035 and 2035-2050 reference. Both limited nuclear lifetimes and higher demand result in increased CO2 emissions.

Energy technology R&D portfolio management: Modeling uncertain returns and market diffusion

The allocation of research and development (R&D) funds across a portfolio of programs must simultaneously consider uncertainty from research outcomes and from market acceptance of the resulting technologies. We introduce a stochastic R&D portfolio management framework for addressing both sources of uncertainty and present numerical results for energy technology R&D strategy under uncertainties in climate policy and natural gas prices. Numerical experiments indicate that R&D may be more valuable in second-best planning environments where decision-makers use expected-value approaches, and recourse investments occur after R&D has reduced costs. We also find that deterministic R&D valuation approaches likely overestimate the expected value of R&D success but undervalue the optionality and hedging potential of technologies relative to sequential decision-making approaches under uncertainty. The results also highlight the role of R&D in second-best policy environments.

More than one arrow in the quiver: Why “100% renewables” misses the mark

Jacobson et al. (1) aim to demonstrate that an all-renewable energy system is technically feasible. Not only are the study’s conclusions based on strong assumptions and key methodological oversights, but its framing also omits the essential notion of trade-offs. A far more relevant question is how renewable energy technologies relate to the broader set of options for meeting long-term societal goals like managing climate change. Even if the goal were to maximize the deployment of renewable energy (and not decarbonization more generally), Jacobson et al. still fail to provide a satisfactory analysis by glossing over fundamental implications of the technical and economic dimensions of intermittency. We briefly highlight two prominent examples, and then …

Banking on banking: does “when” flexibility mask the costs of stringent climate policy?

Banking and borrowing emission allowances provide temporal flexibility in cap-and-trade systems, which can enhance the economic efficiency of environmental policy while adhering to the same cumulative emission budget. This paper investigates the role of temporal (“when”) flexibility from emission banking provisions under an economy-wide cap-and-trade policy in the USA. The current literature on meeting deep decarbonization targets almost exclusively assumes unlimited banking, which may bias policy recommendations and have important consequences for R&D prioritization and model development. Numerical experiments using the energy-economic model US Regional Energy, GHG, and Economy (US-REGEN) indicate that assumptions about banking materially impact cost and emission pathways in meeting long-term targets like 80% reductions by 2050 relative to 2005 levels. Given the stringency of long-run targets and convexity of marginal abatement costs, the cost-minimizing time path for mitigation with banking suggests that 2025 abatement should exceed the pledged level under the Paris Agreement (42% instead of 26–28%) to reduce future costs. Total policy costs are approximately 30% higher when banking is excluded; however, political economy barriers and uncertainty may limit the use of banking provisions despite their appeal on economic efficiency grounds. Banking on policy implementation with unlimited temporal flexibility may distort insights about the pace, extent, and economic impacts of future energy transitions associated with long-term abatement targets, especially for more stringent climate policies.

A Bayesian Model to Assess the Size of North Korea's Uranium Enrichment Program

This article presents a model to estimate North Koreas uranium enrichment capacity and to identify probable bottlenecks for scaling up that capacity. Expert assessment is used to identify and estimate the size of key centrifuge materials and component stockpiles. Bayesian probability networks are used to characterize uncertainties in these stockpiles and a deterministic optimization model to estimate the capacity of North Koreas uranium enrichment program given the assumed components and materials constraints. A Monte Carlo simulation model is used to propagate uncertainties through the optimization model. An illustration of this approach, based on the opinions of three experts, suggests that North Korea was likely (about 80 percent chance) to have a larger uranium enrichment capacity than what was displayed to visitors to the Yongbyon nuclear complex in 2010. The three most important bottlenecks to increases in enrichment capacity are the availability of pivot bearings, maraging steel, and high-strength aluminum. The nature of the model allows it to be easily updated as new information becomes available about centrifuge materials and component stockpiles.