Topic 3: These chapters suggest that quantifying and incorporating shadow prices into market decisions is key for ensuring a sustainable future. How feasible is this pursuit - and are there any alternatives?

The question around quantifying human well being is interesting in general. I would say that it is indeed feasible to take a quantitative approach to human well being, but it requires many assumptions. In chapter 2.1, Dasgupta includes some interesting and clever equations to describe and quantify human well being, and the social constructs and policies that affect human well being. The variables chosen - especially the "Us" all the component variables that quantitatively feed into the total happiness quotient.  As I read it (and I'm still re-reading to be sure I've got things straight - there's a lot going on in these chapters!) deciding on a common set of Us would be necessary to make policy evaluations and recommendations. This totally makes sense mathematically and to build a sound equation, but in real life I can't see any set ever being comprehensive and agreed upon. 

But maybe that doesn't matter. Maybe a partial list of components is sufficient to be useful, and to allow policy evaluation and recommendations.

The use of shadow prices seems obvious to me within this approach - each component of an individual's (or society's right?) well being will have a different importance. This is something that can be defined and compared by assigning weights to the variables, which will lead to weighted outcomes when an additive equation is run. Thus, finding the sensitivity analysis is useful, and in a well run model with come along with the quantitative results.

But Dasgupta writes on page 8 that any differences between how two different evaluators assign weights and form conclusions will be sorted out through the democratic process.

Really? That seems idealistic and not realistic to me - although this is my own assumption. Perhaps I'm not fully understanding the democratic processes referred to and so I hope others might comment on this topic with their own ideas and explanations. But in my understanding, policy decisions are not universally decided on by vote, and when they are - the voting (or voicing) parties do not always include those whom the policy affects.

I am curious to hear more discussion around the postulates at the base of the conclusions around quantifying human (and societal) well being. I think that a quantitative approach can be useful, but that one of the uses of a well thought out quantitative approach is for others to comment on the utility/accuracy of the approach - and to point out where the basic approach is sound, but the details could be improved, or why (or under what conditions) the basic approach does or does not hold as useful and accurate.

 - Christina

It seems to me that any attempt to empirically estimate these shadow prices will only actually estimate the numerical value of the shadow prices at a given point in time. However, based on all the complexities of HES that we've discussed so far, (if I've understood correctly) it's clear that these shadow prices will not be constants, but will instead be functions of the assets they are pricing themselves. As our total stock of one of these assets changes over time, the shadow price of it will change as well. The idea of decreasing returns implies this.

But what's worse -- these are not even likely to be well-behaved linear or even continuous functions, as is usually assumed when we try to estimate weights through, say, a regression model. They will exhibit discontinuous jumps. Though we can empirically estimate these weights at various points in time and use that information to guess at the shape of their change over time, we still would not be able to predict the occurrence of discontinuities. (For example, as a particular environmental stock gets depleted, the marginal benefit/loss to well-being from an additional unit may change suddenly and drastically as it crosses a threshold leading to irreversible loss.)

It seems to me that estimating these weights will be a lot more complex than the chapter implies, and I would have appreciated a brief discussion of how discontinuities can be incorporated into the forecasting methods.

Previously Tara Grillos wrote:

It seems to me that any attempt to empirically estimate these shadow prices will only actually estimate the numerical value of the shadow prices at a given point in time. However, based on all the complexities of HES that we've discussed so far, (if I've understood correctly) it's clear that these shadow prices will not be constants, but will instead be functions of the assets they are pricing themselves. As our total stock of one of these assets changes over time, the shadow price of it will change as well. The idea of decreasing returns implies this.

But what's worse -- these are not even likely to be well-behaved linear or even continuous functions, as is usually assumed when we try to estimate weights through, say, a regression model. They will exhibit discontinuous jumps. Though we can empirically estimate these weights at various points in time and use that information to guess at the shape of their change over time, we still would not be able to predict the occurrence of discontinuities. (For example, as a particular environmental stock gets depleted, the marginal benefit/loss to well-being from an additional unit may change suddenly and drastically as it crosses a threshold leading to irreversible loss.)

It seems to me that estimating these weights will be a lot more complex than the chapter implies, and I would have appreciated a brief discussion of how discontinuities can be incorporated into the forecasting methods.

Some comments from Univ of Minnesota discussion:

A lot of effort has gone into determining shadow prices (e.g., getting the right price on carbon), but it has not proved very fruitful given the political economy.  Two alternatives to getting shadow prices right are:
1.    Appeal to people’s sense of duty to make right decisions (without changing incentives)
2.    Via policy intervention and technology development, make the right choices (e.g., renewables) cheaper than the wrong decisions

The second of these two seems generally more feasible, but you need to find a policy that will work to drive decisions, and the social-political context is important (e.g., carbon reduction policies in Europe vs U.S.).  For example, with the demise of CO2 cap and trade, we are now trying to stimulate investment in research and development for renewables (e.g., stimulus package).  We seem to be somewhat stuck, however, in the “cheapness” of bad decisions, such as burning fossil fuels.  We have been living in a temporarily subsidized energy environment.

The analogy of having savings to rely upon to sustain high consumption and high standard of living: fossil fuels has been a huge savings account.  This is a large part of the paradox of why we are getting better off as natural capital declines (See discussion topic #2).

Is the idea of what technology can do overly-optimistic?  Can technology correct for such a long-term subsidized energy source of fossil fuels?