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Model Name: payments, penalties, and environmental ethic
Citation: Dudley, R. 2007. Payments, penalties, payouts, and environmental ethics: a system dynamics examination Sustainability: Science, Practice, & Policy 3(2):24-35. http://ejournal.nbii.org/archives/vol3iss2/0706-013.dudley.html.
Source: Richard G. Dudley
Copyright: Richard G. Dudley (2007)
License: Gnu GPL
Peer reviewed: Yes (probably when submitted for publication?)
Units balance: Yes
Target audience: People interested in the concept of payments for environmental services as a means of improving land use and conservation of natural resources.
Questions answered: How might land users’ environmental ethic be influenced by, and influence, payments for environmental services.
Models in the Special Issue of the System Dynamics Review on Environmental and Resource Systems, Andrew Ford & Robert Cavana, Editors. System Dynamics Review, Volume 20, Number 2, Summer of 2004.
- Modeling the Effects of a Log Export Ban in Indonesia by Richard G. Dudley
- The Dynamics of Water Scarcity in Irrigated Landscapes: Mazarron and Aguilas in South-eastern Spain by Julia Martinez Fernandez & Angel Esteve Selma
- Misperceptions of Basic Dynamics: The Case of Renewable Resource Management by Erling Moxnes
- Models for Management of Wildlife Populations: Lessons from Spectacle Bears in Zoos and Gizzly Bears in Yellowstone by Lisa Faust, Rosemary Jackson, Andrew Ford, Joanne Earnhardt and Steven Thompson
- Modeling a Blue-Green Algae Bloom by Steven Arquitt & Ron Johnstone
See the following web site for article summaries and downloadable models described in this special issue: http://www.wsu.edu/~forda/SIOpen.html
Submitted by Richard Dudley, 23 April 2008
Pew Climate has a nice summary of attempts to add up country emissions, including Climate Interactive‘s.
Somewhere in the blogosphere I ran across this nice infographic contrasting European aviation and Icelandic volcano emissions:
This is a little experimental model that I developed to investigate stochastic allocation of rental cars, in response to a Vensim forum question.
There’s a single fleet of rental cars distributed around 50 cities, connected by a random distance matrix (probably not physically realizable on a 2D manifold, but good enough for test purposes). In each city, customers arrive at random, rent a car if available, and return it locally or in another city. Along the way, the dawdle a bit, so returns are essentially a 2nd order delay of rentals: a combination of transit time and idle time.
The two interesting features here are:
- Proper use of Poisson arrivals within each time step, so that car flows are dimensionally consistent and preserve the integer constraint (no fractional cars)
- Use of Vensim’s ALLOC_P/MARKETP functions to constrain rentals when car availability is low. The usual approach, setting actual = MIN(desired, available/TIME STEP), doesn’t work because available is subscripted by 50 cities, while desired has 50 x 50 origin-destination pairs. Therefore the constrained allocation could result in fractional cars. The alternative approach is to set up a randomized first-come, first-served queue, so that any shortfall preserves the integer constraint.
The interesting experiment with this model is to lower the fleet until it becomes a constraint (at around 10,000 cars).
Documentation is sparse, but units balance.
Requires an advanced Vensim version (for arrays) or the free Model Reader.
Update, with improved distribution choice and smaller array dimensions for convenience:
Wired covers a new article in Nature, investigating massive failures in linked networks.
The interesting thing is that feedback between the connected networks destabilizes the whole:
“When networks are interdependent, you might think they’re more stable. It might seem like we’re building in redundancy. But it can do the opposite,” said Eugene Stanley, a Boston University physicist and co-author of the study, published April 14 in Nature.
The interconnections fueled a cascading effect, with the failures coursing back and forth. A damaged node in the first network would pull down nodes in the second, which crashed nodes in the first, which brought down more in the second, and so on. And when they looked at data from a 2003 Italian power blackout, in which the electrical grid was linked to the computer network that controlled it, the patterns matched their models’ math.
Interestingly, the interconnection alters the relationship between network structure (degree distribution) and robustness:
Surprisingly, a broader degree distribution increases the vulnerability of interdependent networks to random failure, which is opposite to how a single network behaves.
Chalk one up for counter-intuitive behavior of complex systems.
What looks like last year’s version of the paper is on arXiv.
Read all about it at Climate Interactive.
This American Life had a great show on the NUMMI car plant, a remarkable joint venture between Toyota and GM. It sheds light on many of the reasons for the decline of GM and the American labor movement. More generally, it’s a story of a successful innovation that failed to spread, due to policy resistance, inability to confront worse-before-better behavior and other dynamics.
I noticed elements of a lot of system dynamics work in manufacturing. Here’s a brief reading list:
A selection of data and projections on past and future climate in Montana:
Pederson et al. (2010) A century of climate and ecosystem change in Western Montana: what do temperature trends portend? Climatic Change 98:133-154. It’s hard to read precisely off the graph, but there have been significant increases in maximum and minimum temperatures, with the greatest increases in the minimums and in winter – exactly what you’d expect from a change in radiative properties. As a result the daily temperature range has shrunk slightly and there are fewer below freezing and below zero days. That last metric is critical, because it’s the severe cold that controls many forest pests. There’s much more on this in a poster.
Not every station shows a trend – the figure above contrasts Bozeman (purple, strong trend) with West Yellowstone (orange, flat). The Bozeman trend is probably not an urban heat island effect – surfacestations.org thinks it’s a good site, and White Sulphur (a nice sleepy town up the road a piece) is about the same. The red line is an ensemble of simulations (GISS, CCSM & ECHAM5) from climexp.knmi.nl, projected into the future with A1B forcings (i.e., a fairly high emissions trajectory). I interpolated the data to latitude 47.6, longitude -110.9 (roughly my house, near Bozeman). Simulated temperature rises about 4C, while precipitation (green) is almost unmoved. If that came true, Montana’s future climate might be a lot like current central Utah.
The figure above – from John W. Williams, Stephen T. Jackson, and John E. Kutzbach. Projected distributions of novel and disappearing climates by 2100 AD. PNAS, vol. 104 no. 14 – shows global grid points that have no neighbors within 500km that now have a climate like what the future might bring. In panel C (disappearing climates with the high emissions A2 scenario), there’s a hotspot right over Montana. Presumably that’s loss of today’s high altitude ecosystems. As it warms up, climate zones move uphill, but at the top of mountains there’s nowhere to go. That’s why pikas may be in trouble.
MT Field Guide
Realclimate has Martin Vermeer’s reflections on the making of his recent sea level paper with Stefan Rahmstorf. At some point I hope to post a replication of that study, in a model with the Grinsted and Rahmstorf 2007 structures, but I haven’t managed to replicate it yet. The problem may be that I haven’t yet tackled the reservoir storage issue.
At Nature Reports, Olive Heffernan introduces several sea level articles. Rahmstorf contrasts the recent set of semi-empirical models, predicting sea level of a meter or more this century, with the AR4 finding. Lowe and Gregory wonder if the semi-empirical models are really seeing enough of the dynamic ice signal to have predictive power, and worry about overadaptation to high scenarios. Mark Schrope reports on underadaptation – vulnerable developments in Florida. Mason Inman reports on ecological engineering, a softer approach to coastal defense.
From the Asilomar geoengineering conference, via WorldChanging:
Lesson two: Nobody has any clear idea how to resolve the inequalities inherent in geoengineering. One of the most quoted remarks at the conference came from Pablo Suarez, the associate director of programs with the Red Cross/Red Crescent Climate Centre, who asked during one plenary session, “Who eats the risk?” In Suarez’s view, geoengineering is all about shifting the risk of global warming from rich nations — i.e., those who can afford the technologies to manipulate the climate — to poor nations. Suarez admitted that one way to resolve this might be for rich nations to pay poor nations for the damage caused by, say, shifting precipitation patterns. But that conjured up visions of Bangladeshi farmers suing Chinese geoengineers for ruining their rice crop — a legalistic can of worms that nobody was willing to openly explore.
If geoengineering is a for-profit operation, it presumably also involves the public bearing the risk of private acts, because investors aren’t likely to have an appetite for the essentially unlimited liability.