Population and Learning in Climate Change Decision Analysis
Brian O'Neill¹ and Warren Sanderson²

¹Population and Climate Change Program, IIASA

² Department of Economics, SUNY Stony Brooke

The question of whether to act now or wait to learn more is central to the climate change issue. Previous work indicates that the magnitude and direction of the effect of learning on optimal near-term emissions reductions is ambiguous. It depends on, among other things, how much, and how fast, learning is anticipated to take place about specific elements of the climate change issue. Here we use existing probabilistic projections of global population to investigate how fast we might expect to learn about the outlook for long-term population growth, as one determinant of future emissions. We draw on recent work showing that, because population growth is path dependent, we can learn about the long term outlook by waiting to observe how population changes in the short term. We then explore the implications of this learning potential for optimal emissions using a simple model of future emissions and costs of reductions. We find that while there is substantial scope for learning about future population growth over the next few decades, this potential does not imply large changes in near-term optimal emissions reductions. It does, however, indicate how optimal strategies might change in the future in response to realistic rates of learning about population growth.

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Last updated: 13 Feb 2007