The ‘ethical turn’ in anthropology has charted alternatives to some prevailing philosophical, psychological, and anthropological approaches. This engagement forces anthropologists to reconsider old antinomies such as constraint and agency, relativism and universalism, individualism and collectivism. Like other anthropologists of ethics, we resist forms of determinism that would reduce ethical life to the putatively universal constraints of biology and psychology, the logic of rationality, or the workings of power and ideology. Instead, we consider the processes through which what seem to be constraints or universals serve as affordances that are ethicalized through historically contingent reflexive practices. Rather than being forced to choose between the individual and the collective in terms of who (or what) the ethical subject is, we demonstrate how social interaction is a critical site in which and through which such ethicalization occurs.

Executive Summary

Disaster signifies extreme impacts suffered when hazardous physical events interact with vulnerable social conditions to severely alter the normal functioning of a community or a society (high confidence). Social vulnerability and exposure are key determinants of disaster risk and help explain why non-extreme physical events and chronic hazards can also lead to extreme impacts and disasters, while some extreme events do not. Extreme impacts on human, ecological, or physical systems derive from individual extreme or non-extreme events, or a compounding of events or their impacts (for example, drought creating the conditions for wildfire, followed by heavy rain leading to landslides and soil erosion). [1.1.2.1, 1.1.2.3, 1.2.3.1, 1.3]

Management strategies based on the reduction of everyday or chronic risk factors and on the reduction of risk associated with non-extreme events, as opposed to strategies based solely on the exceptional or extreme, provide a mechanism that facilitates the reduction of disaster risk and the preparation for and response to extremes and disasters (high confidence). Effective adaptation to climate change requires an understanding of the diverse ways in which social processes and development pathways shape disaster risk. Disaster risk is often causally related to ongoing, chronic, or persistent environmental, economic, or social risk factors. [1.1.2.2, 1.1.3, 1.1.4.1, 1.3.2]

Development practice, policy, and outcomes are critical to shaping disaster risk (high confidence). Disaster risk may be increased by shortcomings in development. Reductions in the rate of depletion of ecosystem services, improvements in urban land use and territorial organization processes, the strengthening of rural livelihoods, and general and specific advances in urban and rural governance advance the composite agenda of poverty reduction, disaster risk reduction, and adaptation to climate change. [1.1.2.1, 1.1.2.2, 1.1.3, 1.3.2, 1.3.3]

Introduction

This book presents forecasts for a range of innovative energy technologies that might help stabilize atmospheric concentrations of greenhouse gases during the course of the 21st century. These forecasts provide a wealth of important information for those who wish to inform their view of the climate-change problem and the actions governments or the private sector might take to address it. But these chapters nonetheless present a fundamental dilemma – for the one thing we know for sure about forecasts is that most of them are wrong. How then should we use the information in this book to shape policy?

The difficulty resides not so much in the forecasts themselves as in the methods that we commonly employ to bring the information they contain to bear on adjudicating among alternative policy choices. Generally, we argue about policy by first settling on our view of what will happen in the future and then by using this understanding to decide what actions we should take in response. For instance, if we came to believe, through arguments such as those in this book, that there were cost-effective technological means to stabilize greenhouse-gas emissions, we might be more likely to support policies that sought to achieve such a stabilization. We often make these arguments nonquantitatively, even if systematically. There are also a host of powerful mathematical tools, based on the mathematical techniques of optimization, that help us systematize and elaborate on this style of thinking about the future.