Juan Moreno-Cruz

A simple model to account for regional inequalities in the effectiveness of solar radiation management

We present a simple model to account for the potential effectiveness of solar radiation management (SRM) in compensating for anthropogenic climate change. This method provides a parsimonious way to account for regional inequality in the assessment of SRM effectiveness and allows policy and decision makers to examine the linear climate response to different SRM configurations. To illustrate how the model works, we use data from an ensemble of modeling experiments conducted with a general circulation model (GCM). We find that an SRM scheme optimized to restore population-weighted temperature changes to their baseline compensates for 99% of these changes while an SRM scheme optimized for population-weighted precipitation changes compensates for 97% of these changes. Hence, while inequalities in the effectiveness of SRM are important, they may not be as severe as it is often assumed.

Climate Policy Under Uncertainty: A Case for Solar Geoengineering

Solar Radiation Management (SRM) has two characteristics that make it useful for managing climate risk: it is quick and it is cheap. SRM cannot, however, perfectly offset CO2-driven climate change, and its use introduces novel climate and environmental risks. We introduce SRM in a simple economic model of climate change that is designed to explore the interaction between uncertainty in the climate’s response to CO2 and the risks of SRM in the face of carbon-cycle inertia. The fact that SRM can be implemented quickly, reducing the effects of inertia, makes it a valuable tool to manage climate risks even if it is relatively ineffective at compensating for CO2-driven climate change or if its costs are large compared to traditional abatement strategies. Uncertainty about SRM is high, and decision makers must decide whether or not to commit to research that might reduce this uncertainty. We find that even modest reductions in uncertainty about the side-effects of SRM can reduce the overall costs of climate change in the order of 10%.

Strategic incentives for climate geoengineering coalitions to exclude broad participation

Solar geoengineering is the deliberate reduction in the absorption of incoming solar radiation by the Earth’s climate system with the aim of reducing impacts of anthropogenic climate change. Climate model simulations project a diversity of regional outcomes that vary with the amount of solar geoengineering deployed. It is unlikely that a single small actor could implement and sustain global-scale geoengineering that harms much of the world without intervention from harmed world powers. However, a sufficiently powerful international coalition might be able to deploy solar geoengineering. Here, we show that regional differences in climate outcomes create strategic incentives to form coalitions that are as small as possible, while still powerful enough to deploy solar geoengineering. The characteristics of coalitions to geoengineer climate are modeled using a ‘global thermostat setting game’ based on climate model results. Coalition members have incentives to exclude non-members that would prevent implementation of solar geoengineering at a level that is optimal for the existing coalition. These incentives differ markedly from those that dominate international politics of greenhouse-gas emissions reduction, where the central challenge is to compel free riders to participate.

Opportunities for advances in climate change economics

Target carbons costs, policy designs, and developing countries There have been dramatic advances in understanding the physical science of climate change, facilitated by substantial and reliable research support. The social value of these advances depends on understanding their implications for society, an arena where research support has been more modest and research progress slower. Some advances have been made in understanding and formalizing climate-economy linkages, but knowledge gaps remain [e.g., as discussed in (1, 2)]. We outline three areas where we believe research progress on climate economics is both sorely needed, in light of policy relevance, and possible within the next few years given appropriate funding: (i) refining the social cost of carbon (SCC), (ii) improving understanding of the consequences of particular policies, and (iii) better understanding of the economic impacts and policy choices in developing economies.

The Intergenerational Transfer of Solar Radiation Management Capabilities and Atmospheric Carbon Stocks

Solar radiation management (SRM) technologies are considered one of the likeliest forms of geoengineering. If developed, a future generation could deploy them to limit the damages caused by the atmospheric carbon stock inherited from the current generation, despite their negative side effects. Should the current generation develop these geoengineering capabilities for a future generation? And how would a decision to develop SRM impact on the current generation’s abatement efforts? Natural scientists, ethicists, and other scholars argue that future generations could be more sanguine about the side effects of SRM deployment than the current generation. In this paper, we add economic rigor to this important debate on the intergenerational transfer of technological capabilities and pollution stocks. We identify three conjectures that constitute potentially rational courses of action for current society, including a ban on the development of SRM. However, the same premises that underpin these conjectures also allow for a novel possibility: If the development of SRM capabilities is sufficiently cheap, the current generation may for reasons of intergenerational strategy decide not just to develop SRM technologies, but also to abate more than in the absence of SRM.

New approach for optimal electricity planning and dispatching with hourly time-scale air quality and health considerations.

Significance The production of electricity from coal, natural gas, petroleum, and biomass releases air pollutants with significant impacts on ecosystems and human health. Pollutant exposure depends not only on the pollutant source emissions rate and the relative location of the power plant to population centers but also on temperature, wind velocity, and other atmospheric conditions, all of which vary by hour, day, and season. We have developed a method to evaluate fluctuating pollutant formation from source emissions, which we integrate within an electricity production model. In a case study of the state of Georgia from 2004 to 2011, we show how to reduce air pollutants and health impacts by shifting production among plants during a select number of hourly periods. Integrating accurate air quality modeling with decision making is hampered by complex atmospheric physics and chemistry and its coupling with atmospheric transport. Existing approaches to model the physics and chemistry accurately lead to significant computational burdens in computing the response of atmospheric concentrations to changes in emissions profiles. By integrating a reduced form of a fully coupled atmospheric model within a unit commitment optimization model, we allow, for the first time to our knowledge, a fully dynamical approach toward electricity planning that accurately and rapidly minimizes both cost and health impacts. The reduced-form model captures the response of spatially resolved air pollutant concentrations to changes in electricity-generating plant emissions on an hourly basis with accuracy comparable to a comprehensive air quality model. The integrated model allows for the inclusion of human health impacts into cost-based decisions for power plant operation. We use the new capability in a case study of the state of Georgia over the years of 2004–2011, and show that a shift in utilization among existing power plants during selected hourly periods could have provided a health cost savings of

Pitfalls of coal peak prediction

175.9 million dollars for an additional electricity generation cost of

Adapting to rates versus amounts of climate change: a case of adaptation to sea-level rise

83.6 million in 2007 US dollars (USD2007). The case study illustrates how air pollutant health impacts can be cost-effectively minimized by intelligently modulating power plant operations over multihour periods, without implementing additional emissions control technologies.

An Economic Anatomy of Optimal Climate Policy

Richard Heinberg and David Fridley argue that coal reserves may be exhausted within decades (Nature 468, 367–369; 2010), basing much of their analysis on fits of cumulative coal production to logistic functions in the style of M. King Hubbert, who famously predicted peak oil supply. But this method is problematic — for example, fitting the decline in production of LP records to a logistic curve would incorrectly indicate that vinyl is a limited resource. If scarcity were an important determinant of US coalproduction history, prices should have increased. Yet they have stayed around US

CLIMATE ECONOMICS. Opportunities for advances in climate change economics.

34 per tonne for the past 50 years, irrespective of production trends. Alternative explanations could include changes in electricity demand and market structure. There would then be no justification for calculating the limit of coal resources from a logistic graph of production history. The logistic fits that drive forecasts of coal exhaustion depend on which years are included in the analysis. Logistic fits using data up to 1989, 1999 or 2009 forecast an ultimate coal reserve of 52, 71 or 96 gigatonnes, respectively, and predict that production should have peaked in 1951, 1967 or 1986. In fact, coal production has increased since 1986 — highlighting the weakness of the scarcity-driven Hubbert model in explaining production. An exponential fit explains as much of the variation in US production data as does a logistic fit. Yet the interpretation of the two models is different: the logistic model predicts the end of coal; the exponential fit predicts an infinite supply. Supply is obviously not infinite, but without a theoretical framework to support the choice of a logistic fit, its prediction may be just as wrong. The end of easy oil is driving a shift towards carbon-intensive options, such as oil-sands mining or converting coal to liquid fuel. We must rely on policy changes to ensure a less carbon-intensive future, not the end of cheap coal. David Keith, Juan MorenoCruz University of Calgary, Alberta, Canada. keith@ucalgary.ca