Our response to climate change at the global level clearly needs improving. While some governments are managing to set and enforce limits on the emission of greenhouse gases, an international agreement that is both enforceable and meaningful remains elusive. Measures undertaken by private individuals and organizations, though plentiful, largely fail to connect to the political process and continue to fall short in aggregate. Is there a way to combine these public and private efforts? We think there is, as we’ve explored in a recent NZZ article and ETH blog post: a new type of liability insurance.
Looking to the insurance industry for addressing climate change is not new (see, for example, Nobel Laureate Robert Shiller’s column; the Geneva Association’s statement; and the climate change and insurance links discussed at the World Bank’s recent Understanding Risk conference). What has been lacking, however, are ideas for employing insurance instruments at scale, across national boundaries, and in a way that maximizes existing capacities and market mechanisms.
Back in March 2014, I had the opportunity to be part of a World Bank team supporting the Tongan government to develop a reconstruction policy after Tropical Cyclone Ian hit earlier this year. To implement the policy, the Ministry of Infrastructure led a series of surveys to inform housing reconstruction. This post, which does not intend to be scientific or exhaustive, is to share some of the key lessons I learned from this experience.
Damage assessments are routine in the aftermath of disasters, but they differ depending on their objectives (Hallegatte, 2012 - pdf). A rapid survey in the wake of a disaster event could help to estimate grossly the direct human and economic losses and damages. This type of survey is best to capture the amplitude and the severity of the disaster. However, such survey could present some flaws, often because the survey will be conducted in a very short time frame with minimal design. On the other hand, a survey conducted a few months after the event is best to understand better the context of the disaster. It also allows a better design and better preparation. But, equally, such survey could include biases. For instance, the time lag between the event and the survey itself could create some level of challenges. Most likely, people would have started to fix their houses or have moved away from the affected area, and that will add a layer of complexity to the survey.