The national security implications of solar geoengineering: an Australian perspective

ABSTRACT

Given current emissions trends an increase in global temperatures in excess of 2°C is highly likely in the coming century. In this context, it seems increasingly probable that states may consider solar geoengineering as a stop-gap climate response. Solar geoengineering refers to measures that aim to alleviate some measure of global warming by intentionally increasing the amount of the sun’s energy that is reflected into space. Currently the two most discussed solar geoengineering techniques involve either marine cloud brightening or dispersing aerosols in the stratosphere. These techniques could be relatively inexpensive, are within the technological capacities (after a brief period of development) of technologically-advanced countries, and could have an almost immediate impact on temperatures. Yet, while solar geoengineering might potentially be utilised to manage some climate-linked security threats, it itself would create new security challenges. Consequently, this paper explores potential international security implications for Australia if a regional state, or group of states, initiates a solar geoengineering program. We conclude that since solar geoengineering is unlikely to become a first-order international issue, disputation over solar geoengineering will likely reflect, or act as a proxy for, wider patterns of state interaction. However, scenarios in which China and the United States take different positions, or in which there are divisions among regional powers, such as Indonesia, Malaysia, India and Singapore would pose the greatest threat to Australia’s national security.     

Development and Evaluation of an Optimal Composite Estimator in Spatial Microsimulation Small Area Estimation

A range of data is of geographic interest but is not available at a small area level from existing data sources. Small area estimation (SAE) offers techniques to estimate population parameters of target variables to detailed scales based on relationships between those target variables and relevant auxiliary variables. The resulting indirect small area estimate can deliver a lower mean squared error compared to its direct survey estimate, given that variance can be reduced markedly even if bias increases. Spatial microsimulation SAE approaches are widely utilized but only beginning to engage with the potential of composite estimators that use a weighted combination of indirect and direct estimators to reduce further the mean squared error of the small area estimate compared to an indirect SAE estimator alone. This article advances these approaches by constructing for the first time in the microsimulation literature an optimal composite estimator for such SAE approaches in which the combining weight is calculated from the mean squared errors of the two estimators; thus, optimizing the reduction in MSE of the resulting small area estimates. This optimal composite estimator is demonstrated and evaluated in a model-based simulation study and application based on the real data.