Prospect theory, mitigation and adaptation to climate change

Climate change is one of the most pressing challenges in current environmental policy. Appropriate policies intended to stimulate efficient adaptation and mitigation should not exclusively rely on the assumption of the homo oeconomicus, but take advantage of well-researched alternative behavioural patterns. Prospect theory provides a number of climate-relevant insights, such as the notion that evaluations of outcomes are reference dependent, and the relevance of perceived certainty of outcomes. This paper systematically reviews what prospect theory can offer to analyse mitigation and adaptation. It is shown that accounting for reference dependence and certainty effects contributes to a better understanding of some well-known puzzles in the climate debate, including (but not limited to) the different uptake of mitigation and adaptation amongst individuals and nations, the role of technical vs. financial adaptation, and the apparent preference for hard protection measures in coastal adaptation. Finally, concrete possibilities for empirical research on these effects are proposed

Optimizing a chromatographic three component separation: a comparison of mechanistic and empiric modeling approaches

The search for a favorable and robust operating point of a separation process represents a complex multi-factor optimization problem. This problem is typically tackled by design of experiments (DoE) in the factor space and empiric response surface modeling (RSM); however, separation optimizations based on mechanistic modeling are on the rise. In this paper, a DoE-RSM-approach and a mechanistic modeling approach are compared with respect to their performance and predictive power by means of a case study - the optimization of a multicomponent separation of proteins in an ion exchange chromatography step with a nonlinear gradient (ribonuclease A, cytochrome c and lysozyme on SP Sepharose FF). The results revealed that at least for complex problems with low robustness, the performance of the DoE-approach is significantly inferior to the performance of the mechanistic model. While some influential factors of the system could be detected with the DoE-RSM-approach, predictions concerning the peak resolutions were mostly inaccurate and the optimization failed. The predictions of the mechanistic model for separation results were very accurate. Influences of the experimental factors could be quantified and the separation was optimized with respect to several objectives. However, the discussion of advantages and disadvantages of empiric and mechanistic modeling generates synergies of both methods and leads to a new optimization concept, which is promising with respect to an efficient employment of high throughput screening data

Determination of parameters for the steric mass action model - A comparison between two approaches

The application of mechanistic modeling for the optimization of chromatographic steps increased recently due to time efficiency of algorithms and rising calculation power. In the modeling of ion exchange chromatography steps, the sorption processes occurring on adsorbent particle surfaces can be simulated with the steric mass action (SMA) model introduced by Brooks and Cramer (1992) [14]. In this paper, two approaches for the determination of SMA parameters will be carried out and discussed concerning their specific experimental effort, quality of results, method differences, reasons for uncertainties and consequences for SMA parameter determination: Approach I: estimation of SMA parameters based on gradient and frontal experiments according to instructions in Brooks and Cramer (1992) [14] and Shukla et al. (1998) [16]. Approach II: application of an inverse method for parameter estimation, resulting in SMA parameters that induce a best fit of chromatographic data to a mechanistic model for column chromatography. These approaches for SMA parameter determination were carried out for three proteins (ribonuclease A, cytochrome c and lysozyme) at pH 5 and pH 7. The results were comparable and the order of parameter values and their relations to the chromatographic data similar. Nevertheless, differences in the complexity and effort of methods as well as the parameter values themselves were observed. The comparison of methods demonstrated that discrepancies depend mainly on model sensitivities and additional parameters influencing the calculations. However, the discrepancies do not affect predictivity; predictivity is high in both approaches. The approach based on an inverse method and the mechanistic model has the advantage that not only retention times but also complete elution profiles can be predicted. Thus, the inverse method based on a mechanistic model for column chromatography is the most comfortable way to establish highly predictive SMA parameters lending themselves for the optimization of chromatography steps and process control

Model-integrated process development demonstrated on the optimization of a robotic cation exchange step

A new concept for chromatography process development based on high-through put data and mechanistic modeling will be presented in this paper. The concept is established in close cooperation between experimentation, modeling and model-based experimental design and allows for robustness analyses and upscale predictions. It will be demonstrated based on a case study: the optimization of a multicomponent separation (lysozyme, ribonuclease A and cytochrome c on SP Sepharose FF (TM)), subject to pH conditions and optimal settings for the shape of the elution gradient. Peak resolution and a precise prediction of retention times were chosen as performance variables in the case study to demonstrate the flexibility of the concept. It was shown that the concept of model-integrated process development is simple to perform from miniaturized scale on. The data, derived from model-based optimally designed experiments, provided sufficient information for process development, the model was calibrated and predictions for optimal separation setups as well as for the upscale showed a high precision. Consequently, the accumulation of data from high-throughput screenings can be used profitably for model-based process optimization and upscale predictions. (c) 2012 Elsevier Ltd. All rights reserved