Learning and climate change

Learning – i.e. the acquisition of new information that leads to changes in our assessment of uncertainty – plays a prominent role in the international climate policy debate. For example, the view that we should postpone actions until we know more continues to be influential. The latest work on learning and climate change includes new theoretical models, better informed simulations of how learning affects the optimal timing of emissions reductions, analyses of how new information could affect the prospects for reaching and maintaining political agreements and for adapting to climate change, and explorations of how learning could lead us astray rather than closer to the truth. Despite the diversity of this new work, a clear consensus on a central point is that the prospect of learning does not support the postponement of emissions reductions today.Learning; Uncertainty; Climate change; Decision analysis

Work at night and breast cancer - report on evidence-based options for preventive actions

In 2007, the International Agency for Research on Cancer classified shift work involving circadian disruption as probably carcinogenic to humans (group 2A), primarily based on experimental and epidemiologic evidence for breast cancer. In order to examine options for evidence-based preventive actions, 16 researchers in basic, epidemiological and applied sciences convened at a workshop in Copenhagen 26-27 October 2011. This paper summarizes the evidence from epidemiological and experimental studies and presents possible recommendations for prevention of the effects of night work on breast cancer. Among those studies that quantified duration of shift work, there were statistically significant elevations in risk only after about 20 years working night shift. It is unclear from these studies whether or not there is a modest but real elevated risk for shorter durations. Hence, restriction of the total number of years working night shift could be one future preventive recommendation for shift workers. The diurnal secretion of melatonin by the pineal gland with peak in secretory activity durin Work during the night is widespread worldwide. To provide additional evidence-based recommendations on prevention of diseases related to night shift work, large studies on the impact of various shift schedules and type of light on circadian rhythms need to be conducted in real work environments

Optimal learning on climate change: Why climate skeptics should reduce emissions

Climate skeptics typically argue that the possibility that global warming is exogenous, implies that we should not take additional action towards reducing emissions until we know what drives warming. This paper however shows that even climate skeptics have an incentive to reduce emissions: such a directional change generates information on the causes of global warming. Since the optimal policy depends upon these causes, they are valuable to know. Although increasing emissions would also generate information, that option is inferior due its irreversibility. We show that optimality can even imply that climate skeptics should actually argue for lower emissions than believers

Endogenous Risks and Learning in Climate Change Decision Analysis

We analyze the effects of risks and learning on climate change decisions. A two-stage, dynamic, climate change stabilization problem is formulated. The explicit incorporation of ex-post learning induces risk aversion among ex-ante decisions, which is characterized in linear models by VaR- and CVaR-type risk measures. Combined with explicit introduction of "safety" constraints, it creates a "hit-or-miss" type decision-making situation and shows that, even in linear models, learning may lead to either less-or more restrictive ex-ante emission reductions. We analyze stylized elements of the model in order to identify the key factors driving outcomes, in particular, the critical role of quantiles of probability distributions characterizing key uncertainties

Sequential climate change policy

Successfully managing global climate change will require a process of sequential, or iterative, decision‐making, whereby policies and other decisions are revised repeatedly over multiple decades in response to changes in scientific knowledge, technological capabilities, or other conditions. Sequential decisions are required by the combined presence of long lags and uncertainty in climate and energy systems. Climate decision studies have most often examined simple cases of sequential decisions, with two decision points at fixed times and initial uncertainties that are resolved at the second decision point. Studies using this formulation initially suggested that increasing uncertainty favors stronger immediate action, while the prospect of future learning favors weaker immediate action, but subsequent work with more general formulations showed that the direction of either effect is indeterminate, depending on multiple elements of model structure and parameter values. Current issues in sequential climate decision‐making include assessing responses to potential slow learning or negative learning, and examining the implications of various mechanisms by which current decision‐makers may seek to influence future decisions by altering the choice sets, knowledge states, marginal costs and benefits, or default procedural requirements faced by future decision‐makers. WIREs Clim Change 2011 2 744–756 DOI: 10.1002/wcc.128 For further resources related to this article, please visit the WIREs websitePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86798/1/128_ftp.pd