Rob Wilby and Conor Murphy
Some of the most profound impacts of climate variability and change are expected in the water sector. These include more frequent, severe, and persistent droughts; more frequent, widespread, and extreme floods; more episodic and harmful water pollution episodes. Coping with more variable water supplies alongside rising demand will involve institutional reform, new infrastructure, adjustments to operations, and water demand management. A smarter, decision-led approach to deploying climate information in water management will also be required. This chapter begins with an overview of analytical frameworks for assessing and adapting water resource systems to uncertain climate threats and opportunities. It then gives examples of the diverse sources of information that are being accessed by some water managers to establish plausible ranges of climate change as a basis for decision-making. Examples from Denver, Colorado, and Dublin, Republic of Ireland show how narratives of future system changes and historical data can help test the efficacy of decisions under uncertainty. These two case studies demonstrate how early dialogue and information exchange among practitioners and scientists are fundamental to adaptation planning. In both places, unconventional sources of climate risk information were used to more rigorously stress test water management and planning assumptions. The preferred adaptation decision frameworks were dynamic, iterative, and open-ended. The chapter closes by acknowledging that further development of the decision-making approaches described herein may be needed to evaluate mixtures of adaptation options across multiple sectors.
Julie Rozenberg, Laura Bonzanigo, and Claire Nicolas
Increasing the amount of resilient infrastructure investments in developing countries is key to achieving development goals. Two issues need to be addressed to better support investment decisions. First, analysts need to better integrate the social, economic, and environmental dimensions of investment decisions in their quantitative analyses, given the intertwined objectives of climate change adaptation and poverty reduction. Second, analysts and practitioners need to recognize that the future state of those three dimensions is deeply uncertain and that new techniques need to be used that look for robust investments—performing well under multiple future conditions—rather than an optimal solution under a single prediction of the future. Doing so can be achieved by beginning important decision processes with an integrated model representing technical and socioeconomic factors, and exploring various interventions under many possible futures.
Peter Challenor, Doug McNeall, and James Gattiker
This article examines the dynamics of the US economy over the last five decades using Bayesian analysis of dynamic stochastic general equilibrium (DSGE) models. It highlights an example application in what is commonly referred to as the new macroeconometrics, which combines macroeconomics with econometrics. The article describes a benchmark New Keynesian DSGE model that incorporates four types of agents: households that consume, save, and supply labour to a labour ‘packer’; a labour ‘packer’ that puts together the labour supplied by different households into an homogeneous labour unit; intermediate good producers, who produce goods using capital and aggregated labour; and a final good producer that mixes all the intermediate goods. It also considers the application of the model in policy analysis for public institutions such as central banks, along with private organizations and businesses. Finally, it discusses three avenues for further research in the estimation of DSGE models.