Introduction

Most research on the value of changes in environmental quality focuses on values from the standpoint of individual consumers. Three valuation methods dominate this research – contingent valuation, hedonic pricing, and travel cost models. These are sometimes the only methods considered in references on valuation methods. One example of this is the excellent primer by Champ et al. (2003). Yet, environmental quality can also affect production. For example, infiltration of saline water from shrimp farms can damage harvests on neighbouring rice farms, the loss of spawning grounds when mangroves are cut down can reduce fish catch, and damage from acid rain and other forms of air pollution can reduce timber harvests. This chapter focuses on the valuation of these sorts of effects.

In these cases, environmental quality is acting as a non-market, or unpriced, production input. Damage to the environment reduces the supply of this input, and as a result production falls. Conversely, programmes to improve environmental quality can benefit environmentally sensitive forms of production by raising the supply of such inputs. These production-related benefits can be among the most important benefits generated by environmental improvements. This is especially likely to be the case in developing regions of the world such as South Asia, where agriculture accounts for a larger share of GDP than in higher-income regions and renewable resources such as forests and fisheries underpin local economies.

Introduction

Most of what we know about protein folding comes from experiments on polypeptides in dilute solutions [1–4] or from theoretical models of isolated proteins in either explicit or implicit solvent [5–12]. However, neither biological cells nor protein solutions encountered in biopharmaceutical development generally classify as dilute. Instead, they are concentrated or “crowded” with solutes such as proteins, sugars, salts, DNA, and fatty acids [13–15]. How does this crowding affect native-state protein stability? Are all crowding agents created equal? If not, can generic structural or chemical features forecast their effects?

To investigate these and other related questions with computer simulations requires models rich enough to capture three parts of the folding problem: the intrinsic free energy of folding of a protein in solvent, the main structural features of the native and denatured states, and the connection between protein structure and effective protein–protein interactions. The model must also be simple enough to allow for the efficient simulation of hundreds to thousands of foldable protein molecules in solution, which precludes the use of atomistically detailed descriptions of either the proteins or the solvent.

We recently developed a coarse-grained modeling strategy that satisfies these criteria. It is not optimized to describe any specific protein solution. Rather, it is a general tool for understanding experimental trends regarding how concentration or crowding impact the thermodynamic stability of globular proteins.