AbstractThe risk of an oil spill in Asia Pacific region has escalated in recent years due to increases in maritime traffic and oil and gas activities. It is possible that oil spills may result in serious environmental and socio-economic consequences with long term detrimental effects to the wider community. Implementing effective response to an oil spill incident is a complex decision-making process involving the consideration of multiple variables such as available response technologies, operational factors and logistical constraints, and is often carried out in the face of incomplete information. A rapid decision making process is also needed as the effective implementation of the various options to mitigate the oil spill impacts may be reduced with time.Comparative Ecological Risk Assessment is a widely accepted tool for evaluating ecological concerns that allows for the objective comparison of the relative risk contributed by each specific ‘stressor’ or ‘option’ being considered. The process facilitates optimum decision making through a more complete use of available information and participative consultation with key stakeholders.The focus of this paper is the adaptation of the widely accepted comparative ecological risk assessment process towards a uniquely complex trans-boundary oil spill scenario for the Asia Pacific Region. The application of the comparative ecological risk assessment to region-specific scenarios will drew upon the authors' operational knowledge of responding to oil spills in the region and similar project studies conducted in other regions globally. While presenting oil spill response issues that are unique to Asia Pacific region, the paper will detail a fit for purpose comparative ecological risk assessment framework that can be implemented during a trans-boundary oil spill scenario with limited information and few resources. Emphasis will be given to problem formulation that takes temporal considerations and long term net benefits while combining expert judgment, stakeholder values and local knowledge to reach optimum decisions without undue stress on the resources available to do so.