About the book: With increasing greenhouse gas emissions, we are embarked on an unprecedented experiment with an uncertain outcome for the future of the planet. The Kyoto Protocol serves as an initial step through 2012 to mitigate the threats posed by global climate change. A second step is needed, and policy-makers, scholars, business people, and environmentalists have begun debating the structure of the successor to the Kyoto agreement. Written by a team of leading scholars in economics, law, and international relations, this book contributes to this debate by examining the merits of six alternative international architectures for global climate policy. Architectures for Agreement offers the reader a uniquely wide-ranging menu of options for post-Kyoto climate policy, with a concern throughout to learn from past experience in order to maximize opportunities for future success in the real, ‘second-best’ world.

Limiting anthropogenic climate change over the next century will require controlling multiple substances. The Kyoto Protocol structure constrains the major greenhouse gases and allows trading among them, but there exist other possible regime architectures which may be more efficient. Tradeoffs between the market efficiency of all-inclusive policies and the benefits of policies targeted to the unique characteristics of each substance are investigated using an integrated assessment approach, using the MIT Emissions Prediction and Policy Analysis model, the Integrated Global Systems Model, and political analysis methods.

The thesis explores three cases. The first case addresses stabilization, the ultimate objective of Article 2 of the UN Framework Convention on Climate Change. We highlight the implications of imprecision in the definition of stabilization, the importance of non-CO2 substances, and the problems of excessive focus on long-term targets. The results of the stabilization analysis suggest that methane reduction will be especially valuable because of its importance in low-cost mitigation policies that are effective on timescales up to three centuries. Therefore in the second case we examine methane, demonstrating that methane constraints alone can account for a 15% reduction in temperature rise over the 21st century. In contrast to conventional wisdom, we show that Global Warming Potential based trading between methane reductions and fossil CO2 reductions is flawed because of the differences in their atmospheric characteristics, the uncertainty in methane inventories, the negative interactions of CO2 constraints with underlying taxes, and higher political barriers to constraining CO2. The third case examines the benefits of increased policy coordination between air pollution constraints and climate policies. We calculate the direct effects of air pollution constraints to be less than 8% of temperature rise over the century, but ancillary reductions of GHGs lead to an additional 17% decrease. Furthermore, current policies have not had success coordinating air pollution constraints and CO2 constraints, potentially leading to a 20% welfare cost penalty resulting from separate implementation. Our results lead us to recommend enacting near term multinational CH4 constraints independently from CO2 policies as well as supporting air pollution policies in developing nations that include an emphasis on climate friendly projects.

Policies under consideration within the Climate Convention would impose CO2 controls on only a subset of nations. A model of economic growth and emissions, coupled to an analysis of the climate system, is used to explore the consequences of a sample proposal of this type. The results show how economic burdens are likely to be distributed among nations, how carbon "leakage" may counteract the reductions attained, and how policy costs may be influenced by emissions trading. We explore the sensitivity of results to uncertainty in key underlying assumptions, including the influence on economic impacts and on the policy contribution to long-term climate goals.

To respond to the climate change issue, governments at various levels must make a range of decisions about the appropriate level and design of greenhouse gas mitigation, preparation for adaptation, and the funding level of research across many related disciplines. Because of the complex and global nature of climate change, these decision makers need support from scientific researchers in order to know the costs, benefits, options, and impacts for their decisions. But, of course, our current understanding of future climate change and the processes that contribute to them are incomplete and fraught with uncertainty. Thus, part of the information needed by decision makers is descriptions of the uncertainty in future costs, benefits, and impacts of potential choices.

Uncertainty is not important merely for computing an expected value or ‘best guess’. In fact, information on variability and on low-probability high-consequence events allows decision makers to account for society’s risk-aversion in their choices. Furthermore, today’s decision is not made once now, but will be continually revised in the future as our understanding evolves. The optimal decision today depends not only on current uncertainty, but our expectation of how it will change and how we will respond in the future. This adaptive decision process will be aided by carefully tracking how uncertainties change with new knowledge. Thus, carefully assessing the risks of future climate change impacts is a critical task as a component of scientific support for decision makers.

The task of providing information about uncertainty can be broadly divided into two steps: (1) quantify the uncertainty in future outcomes, and (2) communicate the quantified uncertainties. Each of these steps entails overcoming significant challenges. The paper by Patt and Schrag (2003) is a contribution to the latter step of communicating uncertainty once quantified. They raise important questions about how people translate between linguistic and numerical descriptions of uncertainty and risk that may have implications for how we communicate future assessments.

In this editorial, however, I would like to comment briefly on the former of the two steps previously mentioned, that of quantifying uncertainty. Regardless of how probabilities are communicated (i.e., whether we reflect risk or probability in our language choice), the question of how we estimate these probabilities/risks remains to be adequately addressed. In the most abstract theoretical sense, the process of uncertainty analysis is straightforward. But the operational realities present empirical, methodological, institutional, and philosophical challenges. Here, I will briefly describe these challenges and suggest some activities that the research community can focus on to improve our ability to measure uncertainty as part of the scientific assessment process.

© Springer Netherlands