James A. Rising

Entries categorized as ‘Essays’

Improving IAMs: From problems to priorities

January 9, 2019 · Leave a Comment

I wrote this up over the holidays, to feed into some discussions about the failings of integrated assessment models (IAMs). IAMs have long been the point at which climate science (in a simplistic form), economics (in a fanciful form), and policy (beyond what they deserve) meet. I’m a big believer in the potential of models to bring those three together, and the hard work of improving them will be a big part of my career (see also my EAERE newsletter piece). The point of this document is to highlight some progress that’s being made, and the next steps that are needed. Thanks to D. Anthoff and F. Moore for many of the citations.

Integrated assessment models fail to accurately represent the full risks of climate change. This document outlines the challenges (section 1), recent research and progress (section 2), and priorities to develop the next generation of IAMs.

1. Problems with the IAMs and existing challenges

The problems with IAMs have been extensively discussed elsewhere (Stern 2013, Pindyck 2017). The purpose here is to highlight those challenges that are responsive to changes in near-term research priorities. I think there are three categories: scientific deficiencies, tipping points and feedbacks, and disciplinary mismatches. The calibrations of the IAMs are often decades out of date (Rising 2018) and represent empirical methods which are no longer credible (e.g. Huber et al. 2017). The IAMs also miss the potential and consequences of catastrophic feedback in both the climate and social systems, and the corresponding long-tails of risk. Difficulties in communication between natural scientists, economists, and modelers have stalled the scientific process (see previous document, Juan-Carlos et al. WP).

2. Recent work to improve IAMs

Progress is being made on each of these three fronts. A new set of scientific standards represents the environmental economic consensus (Hsiang et al. 2017). The gap between empirical economics and IAMs has been bridged by, e.g., the works of the Climate Impact Lab, through empirically-estimated damage functions, with work on impacts on mortality, energy demand, agricultural production, labour productivity, and inter-group conflict (CIL 2018). Empirical estimates of the costs and potential of adaptation have also been developed (Carleton et al. 2018). Updated results have been integrated into IAMs for economic growth (Moore & Diaz 2015), agricultural productivity (Moore et al. 2017), and mortality (Vasquez WP), resulting in large SCC changes.

The natural science work on tipping points suggest some stylized results: multiple tipping points are already at risk of being triggered, and tipping points are interdependent, but known feedbacks are weak and may take centuries to unfold (O’Neill et al. 2017, Steffen et al. 2018, Kopp et al. 2016). Within IAMs, treatment of tipping points has been at the DICE-theory interface (Lemoine and Traeger 2016, Cai et al. 2016), and feedbacks through higher climate sensitivities (Ceronsky et al. 2005, Nordhaus 2018). Separately, there are feedbacks and tipping points in the economic systems, but only some of these have been studied: capital formation feedbacks (Houser et al. 2015), growth rate effects (Burke et al. 2015), and conflict feedbacks (Rising WP).

Interdisciplinary groups remain rare. The US National Academy of Sciences has produced suggestions on needed improvements, as part of the Social Cost of Carbon estimation process (NAS 2016). Resources For the Future is engaged in a multi-pronged project to implement these changes. This work is partly built upon the recent open-sourcing of RICE, PAGE, and FUND under a common modeling framework (Moore et al. 2018). The Climate Impact Lab is pioneering better connections between climate science and empirical economics. The ISIMIP process has improved standards for models, mainly in process models at the social-environment interface.

Since the development of the original IAMs, a wide variety of sector-specific impact, adaptation, and mitigation models have been developed (see ISIMIP), alternative IAMs (WITCH, REMIND, MERGE, GCAM, GIAM, ICAM), as well as integrated earth system models (MIT IGSM, IMAGE). The latter often include no mitigation, but mitigation is an area that I am not highlighting in this document, because of the longer research agenda needed. The IAM Consortium and Snowmass conferences are important points of contact across these models.

3. Priorities for new developments

Of the three challenges, I think that significant progress in improving the science within IAMs is occurring and the path forward is clear. The need to incorporate tipping points into IAMs is being undermined by (1) a lack of clear science, (2) difficulties in bridging the climate-economic-model cultures, and (3) methods of understanding long-term long-tail risks. Of these, (1) is being actively worked on the climate side, but clarity is not expected soon; economic tipping points need much more work. A process for (2) will require the repeated, collaboration-focused covening of researchers engaged in all aspects of the problem (see Bob Ward’s proposal). Concerning (3), the focus on cost-benefit analysis may poorly represent the relevant ethical choices, even under an accurate representation of tipping points, due to their long time horizon (under Ramsey discounting), and low probabilities. Alternatives are available (e.g., Watkiss & Downing 2008), but common norms are needed.


Burke, M., Hsiang, S. M., & Miguel, E. (2015). Global non-linear effect of temperature on economic production. Nature, 527(7577), 235.
Cai, Y., Lenton, T. M., & Lontzek, T. S. (2016). Risk of multiple interacting tipping points should encourage rapid CO 2 emission reduction. Nature Climate Change, 6(5), 520.
Ceronsky, M., Anthoff, D., Hepburn, C., & Tol, R. S. (2005). Checking the price tag on catastrophe: the social cost of carbon under non-linear climate response. Climatic Change.
CIL (2018). Climate Impact Lab website: Our approach. Accessible at http://www.impactlab.org/our-approach/.
Houser, T., Hsiang, S., Kopp, R., & Larsen, K. (2015). Economic risks of climate change: an American prospectus. Columbia University Press.
Huber, V., Ibarreta, D., & Frieler, K. (2017). Cold-and heat-related mortality: a cautionary note on current damage functions with net benefits from climate change. Climatic change, 142(3-4), 407-418.
Kopp, R. E., Shwom, R. L., Wagner, G., & Yuan, J. (2016). Tipping elements and climate–economic shocks: Pathways toward integrated assessment. Earth’s Future, 4(8), 346-372.
Lemoine, D., & Traeger, C. P. (2016). Economics of tipping the climate dominoes. Nature Climate Change, 6(5), 514.
Moore, F. C., & Diaz, D. B. (2015). Temperature impacts on economic growth warrant stringent mitigation policy. Nature Climate Change, 5(2), 127.
Moore, F. C., Baldos, U., Hertel, T., & Diaz, D. (2017). New science of climate change impacts on agriculture implies higher social cost of carbon. Nature Communications, 8(1), 1607.
NAS (2016). Assessing Approaches to Updating the Social Cost of Carbon. Accessible at http://sites.nationalacademies.org/DBASSE/BECS/CurrentProjects/DBASSE_167526
Nordhaus, W. D. (2018). Global Melting? The Economics of Disintegration of the Greenland Ice Sheet (No. w24640). National Bureau of Economic Research.
O’Neill, B. C., Oppenheimer, M., Warren, R., Hallegatte, S., Kopp, R. E., Pörtner, H. O., … & Mach, K. J. (2017). IPCC reasons for concern regarding climate change risks. Nature Climate Change, 7(1), 28.
Pindyck, R. S. (2017). The use and misuse of models for climate policy. Review of Environmental Economics and Policy, 11(1), 100-114.
Rising, J. (2018). The Future Of The Cost Of Climate Change. EAERE Newsletter. Accessible at https://www.climateforesight.eu/global-policy/the-future-of-the-cost-of-climate-change/
Steffen, W., Rockström, J., Richardson, K., Lenton, T. M., Folke, C., Liverman, D., … & Donges, J. F. (2018). Trajectories of the Earth System in the Anthropocene. Proceedings of the National Academy of Sciences, 115(33), 8252-8259.
Stern, N. (2013). The structure of economic modeling of the potential impacts of climate change: grafting gross underestimation of risk onto already narrow science models. Journal of Economic Literature, 51(3), 838-59.
Vasquez, V. (WP). Uncertainty in Climate Impact Modelling: An Empirical Exploration of the Mortality Damage Function and Value of Statistical Life in FUND. Masters Dissertation.
Watkiss, P., & Downing, T. (2008). The social cost of carbon: Valuation estimates and their use in UK policy. Integrated Assessment, 8(1).

Categories: Essays · Research

Blockchain and the dystopian present

November 3, 2018 · Leave a Comment

People often assume that since I have background in computers, I must be an enthusiast of blockchain technology. I have never seen much use for it, since anything that blockchains can do, a traditional database can do more efficiently. But I understand that blockchains have an application, a situation in which they are the right tool for the job: if you cannot build a trustworthy institution and want to keep shared records, blockchains will let you.

By institutions, I mean organizations like banks or government, which could keep these records, along with a common understanding of the rules they use. If I, as an individual, want to make a system for distributed, anonymous users to keep records, it is easy to make an interface to a database that provides that. I would define the rules, and my software would follow them. But then you have to trust me to not use my power over the rules to my advantage. Or, in the case of societal institutions, we have to believe in systems of oversight to ensure good behavior and procedures for responding to bad behavior. If you cannot trust a central authority, traditional databases will not work.

The cost to pay for this lack of trust is energy use. The blockchain mining system turns computing power into security, with bitcoin alone consuming more electricity annually than Austria (73 TWh/yr vs. 70 TWh/yr). Blockchain technology is built on plentiful, cheap energy.

I think the excitement about blockchain technology offers some insight into the world today, and the world that we are working to create. The world that blockchains are made for is a world of abundance, but abundance squandered by the lack of trusted institutions. And that is not all.

It is a world not overly concerned with inequality. If there was extreme inequality of mining power, or collusion at the top, blockchain ledgers could be forged. Instead, the fear is against petty theft. We worry about minor actors breaking the law, and no institutions to recognize it and undo the damage.

It is a world where anonymity is supreme. Letting institutions know our identity a necessary condition for allowing them to provide oversight. In a world of corrupt institutions, your identity might be used against you.

It is a world in which you pay to maintain your own security. As mining rewards dwindle, it will be those who have the most to lose who will maintain the system. But in this, it must also be a world of continual competition, because if a single user or cartel effectively paid for the whole system, it would also control the ledgers.

So, when people express such excitement about this or that application of blockchains, I mourn the loss of cooperation and common ground. Only a world of abundance could support blockchains, but only a fragmented world would need them.

Categories: Essays

Science and language

February 6, 2016 · Leave a Comment

One of the rolling banners at last year’s meeting of the American Geophysical Union had a scantly-clad woman and the words “This is what most people think of as a ‘model'”. See, scientists have a communications problem. It’s insidious, and you forget how people use words and then feel attacked when you have to change how you speak.

I have a highly-educated editor working with me on the coffee and climate change report, and she got caught up on a word I use daily: “coefficient”. For me, a coefficient is just a kind of model parameter. I replaced all the uses of “coefficient” with “parameter”, but I simultaneously felt like it dumbed out an important distinction and wondered if “parameter” was still not dumbed down enough.

AGU has a small team trying to help scientists communicate better. I think they are still trying to figure out how to help those of us who want their help. I went to their session on bridging the science-policy divide, and they spent a half hour explaining that we have two houses of congress. Nonetheless, it is a start, and they sent us home with communication toolkits on USB. One gem stood out in particular:

So I will try to reduce the ignorance and political distortions of my devious communication plots, until I can flip the zodiac on this good response loop. Wish me luck.

Categories: Essays · Policy

Top 500: Leverage Points: Places to Intervene in a System

December 9, 2015 · Leave a Comment

This is another installment of my top 500 journal articles: the papers that I keep coming back to and recommending to others.

Few papers have had a larger impact on my thinking and goals as Donella Meadows’s article Leverage Points: Places to Intervene in a System:

Folks who do systems analysis have a great belief in “leverage points.” These are places within a complex system (a corporation, an economy, a living body, a city, an ecosystem) where a small shift in one thing can produce big changes in everything.

She then explains how to understand them and where to find them, with fantastic examples from across the systems literature: global trade, ecology, urban planning, energy policy, and more. Reading it makes you feel like a kid in a candy shop, with so many leverage points to choose from. Shamelessly stealing a punch-line graphic, here are the leverage points:

leverage points

I have a small example of this, which you can try out. Go to my Thermostat Experiment and try to stabilize the temperature at 4 °C without clicking the “Show Graph” button until at least 30 “game minutes”. Then read on.

I’ve had people get very mad at me after playing this game. Some people find it impossible, get frustrated, and want to lash out. It’s a very simple system, but you are part of the system and you’re only allowed to use the weakest level of leverage point: the parameter behind the thermostat knob. What would each of the other leverage points look like?

  • 11. Buffer sizes: you can sit at a bad temperature for longer without hurting your supplies
  • 10. Material stocks and flows: you can move all the supplies out of the broken refrigerator
  • 9. Length of delays: the delay between setting the thermostat and seeing a temperature change is less
  • 8. Negative feedback: you’re better at setting the temperature
  • 7. Positive feedback: the recovery from a bad temperature is faster
  • 6. Information flows: you get to use the “Show Graph” button
  • 5. Rules of the system: you can get a new job not working at a refigerator warehouse
  • 4. Change system structure: you can modify the Thermostat experiment code
  • 3. Goals of the system: you replace the thermostat with a “fresh-o-stat” and just turn that up
  • 2. System mindset: you can close the website
  • 1. Transcending paradigms: you can close your computer

Categories: Essays · References

The role of non-empirical science

September 2, 2015 · Leave a Comment

The New York Times has an op-ed today about that argues “Psychology Is Not in Crisis, in response to the response to a paper that tried and failed to reproduce 60 of 100 psychology experiments. I have been thinking for a long time about the importance of falsifiability in science, and the role of the many kinds of research we do in light of it.

I was recently re-perusing Collins et al. 2010, which purports to address the need for an integrated approach to environmental science, with a new conceptual framework. The heart of the framework is the distinction between “pulse” and “press” dynamics. I do not want to explain the difference here though. I want to know if we learn something from it.

Knowledge comes in many forms. There’s empirical knowledge, facts about the world that we know could not have known until they were observed; analytical knowledge, resulting from the manipulation of logical constructs; and wisdom, inarticulable knowledge that comes from experience.

The Collins et al. paper uses analysis, but it proves no theorems. But of course analysis can be a powerful tool without mathematical analytics. Recognizing multiple parts of a whole can open doors in the mind, and provide substance to a question. Nonetheless, the criteria for science of the usefulness of analysis is, does it allow us to learn something we did not already know? Knowing that fire is a pulse dynamic while climate change is a press dynamic could come in handy, if these categories added additional knowledge.

I claim that papers like this do not try to teach analytical knowledge, although they focus on a piece of analysis. Their goal is to expand our wisdom, by giving it shape. The distinction is not tied to anything we did not already know about fire and climate change. Like a professor who notices two things being conflated, the paper tries to expand our vocabulary and through it our world. Alas, it is exactly the wherewithal to shape our conceptual world that constitutes the wisdom sought. Pulse and press dynamics are one nice distinction, but there are so many others that might be relevant. Having a distinction in mind of pulse and press dynamics is only useful if I can transcend it.

Knowledge builds upon itself, and naturally bleeds between empirics, analysis, and wisdom. I am not a psychologist, but I presume that they are seeking knowledge in all of its forms. The discovery that 60 empirical building blocks were not as sure as they appeared does not undermine the process of science in psychology, and indeed furthers it along, but I hope that it undermines psychology-the-field, and the structure of knowledge that it has built.

Categories: Essays

Guest Post: The trouble with anticipation (Nate Neligh)

July 2, 2015 · Leave a Comment

Hello everyone, I am here to do a little guest blogging today. Instead of some useful empirical tools or interesting analysis, I want to take you on a short tour through of the murkier aspects of economic theory: anticipation. The very idea of the ubiquitous Nash Equilibrium is rooted in anticipation. Much of behavioral economics is focused on determining how people anticipate one another’s actions. While economists have a pretty decent handle on how people will anticipate and act in repeated games (the same game played over and over) and small games with a few different decisions, not as much work has been put into studying long games with complex history dependence. To use an analogy, economists have done a lot of work on games that look like poker but much less work on games that look like chess.

One of the fundamental problems is finding a long form game that has enough mathematical coherence and deep structure to allow the game to be solved analytically. Economists like analytical solutions when they are available, but it is rare to find an interesting game that can be solved by pen and paper.

Brute force simulation can be helpful. Simply simulating all possible outcomes and using a technique called backwards induction, we can solve the game in a Nash Equilibrium sense, but this approach has drawbacks. First, the technique is limited. Even with a wonderful computer and a lot of time, there are some games that simply cannot be solved in human time due to their complexity. More importantly, any solutions that are derived are not realistic. The average person does not have the ability to perform the same computations as a super computer. On the other hand, people are not as simple as the mechanical actions of a physics inspired model.

James and I have been working on a game of strategic network formation which effectively illustrates all these problems. The model takes 2 parameters (the number of nodes and the cost of making new connections) and uses them to strategically construct a network in a decentralized way. The rules are extremely simple and almost completely linear, but the complexities of backwards induction make it impossible to solve by hand for a network of any significant size (some modifications can be added which shrink the state space to the point where the game can be solved). Backwards induction doesn’t work for large networks, since the number of possible outcomes grows at a rate of (roughly) but what we can see is intriguing. The results seem to follow a pattern, but they are not predictable.

The trouble with anticipation


Each region of a different color represents a different network (colors selected based on network properties). The y-axis is discrete number of nudes in the network. The x axis is a continuous cost parameter. Compare where the color changes as the cost parameter is varied across the different numbers of nodes. As you can see, switch points tend to be somewhat similar across network scales, but they are not completely consistent.

Currently we are exploring a number of options; I personally think that agent-based modeling is going to be the key to tackling this type of problem (and those that are even less tractable) in the future. Agent based models and genetic algorithms have the potential to be more realistic and more tractable than any more traditional solution.

Categories: Essays · Guest Post · Research

Resolving a Hurricane of Questions

August 28, 2014 · Leave a Comment

Maybe questions of social science never get truly resolved. The first year of my PhD, I remember John Mutter describing the question of creative destruction. Sometimes, the story goes, a disaster can lead to an unexpected silver lining. By destroying outdated infrastructure, or motiving people to work together, or driving a needed influx of aid, a disaster can eventually leave a community better off than beforehand. Mutter described it almost like a philosophical quandary. In the face of all the specifics of institutions, internal perceptions, and international relations, how will we ever know?

For years now, Solomon Hsiang has been producing insights from his LICRICE model, turning hurricanes into exogenous predictors. As these random shocks echo through societies, he’s been picking up everything that falls out. I got to listen to some of it when news reporters would call his office. His work with Jesse Anttila-Hughes turned up the true mortality threat of disasters, typically 10x the lives lost at the time of the event. Jesse dug further, finding how family assets changed, how meals were redistributed, and how young girls are often the most hurt, even those born after the disaster.

Last month, Sol and Amir Jina produced an NBER working paper that steps back from the individual lives affected. Their result is that a single storm produces losses that continue to accumulate for 20 years. People are not only continuing to feel the effects of a hurricane 20 years down the road, but they additional poverty they feel at 10 years is only half of the poverty that they’ll feel in another 10.


Of course, this is an average effect, and an average of 6415 different country results. But that means that for every country that experiences no long-term effect, one experiences twice the poverty.

So, is there creative destruction? It’s very, very unlikely. The most likely situation is “no recovery”: countries will never return to the trend that they were on prior to the event. Things are even more dire under climate change,

For a sense of scale, our estimates suggest that under the “Business as usual” scenario (with a 5% discount rate) the [present discounted value] of lost long-run growth is $855 billion for the United States (5.9% of current GDP), $299 billion for the Philippines (83.3% of current GDP), $1 trillion for South Korea (73% of current GDP), $1.4 trillion for China (12.6% of current GDP), and $4.5 trillion for Japan (101.5% of current GDP).

That’s what we should be willing to pay to avoid these costs. In comparison to the $9.7 trillion that just additional hurricanes are expected to cost, the $2 trillion that Nordhaus (2008) estimates for the cost of a climate policy seems trivial. That’s two big, seemingly unanswerable questions as settled as things get in social science.

Categories: Essays

Multi-Level Governance in Fisheries

February 7, 2013 · Leave a Comment

Here’s a draft of the short talk I gave at the Multi-Level Governance plenary at the Earth System Governance Tokyo Conference:

Fisheries are an ideal example of the need and potential for multi-level governance.  For decades, governments have struggled with overfishing and degradation of marine and inland waters.  Small fishing communities, fish stocks and food chains, factory ships, and policy-makers all act on different scales.  The various components of fisheries policy—gear and catch restrictions, protected areas, and monitoring—also act on idiosyncratic scales.

Despite decades of experience, fish stocks continue to collapse.  This is the consequence of multi-level complexity, and in many ways, the result of a deep tragedy of the commons, playing out across scales and across boundaries.  Fisheries are constantly confronted with multi-level issues: multiple stressors, from acidification to invasive species; environmental and human variability; cross-scale issues driven by the scale of fish ranges, environmental forcing, and foreign fleets; and failures of traditional management.  Governance of fisheries that is focused on a single scale cannot effectively manage resources that have their dynamics playing out on multiple levels and multiple scales.  Fishery contexts have many of the characteristics that make commons management difficult: ownership rights are weak, dynamics are unpredictable, stocks are mobile and widely dispersed, and outside pressures are strong.  Many effective traditional management practices fail when confronted with modern demands.  These are many of the same problems confronted in other areas of sustainable development, for example around climate change, water use, and biodiversity loss.

However, multi-level governance exposes possibilities for management that do not exist at any single scale.  The basic approach in fisheries multi-level management is called “co-management”: regional and national government acts on a large scale with policies explicitly designed to support local fishing communities acting on small scales.  The effective functions of large-scale government include monitoring of fish stocks, setting targets and allotments, identifying ecosystems for protection, enforcing boundaries, capacity building and legitimization, and facilitating communication across boundaries.  The local management level, then, is free to ensure fair fishing practices, coordinate amongst stakeholders, identify community needs, and monitor fisherman compliance and boundaries.

More generally, the fishery is an example of a multi-level commons, and I think that many of the lessons from fisheries are applicable to other commons.  The potentials for management in the multi-level commons are greater than the traditional commons in a number of ways.

First, regime shifts, and tipping points and resilience, are concrete, measurable phenomena.  We see them all the time in fisheries, and they manifest in multi-scale ways.  They can be very difficult to reverse, but sometimes they heal far better than we could expect when allowed the room to do so.  Strong management can ensure sustainability—we see it in some of the fisheries in California and elsewhere.

Second, uncertainty and unpredictability are a norm of fish ecosystems, and the multi-scale perspective will not diminish that problem.  We need robust institutions that can coexist with chaos and catastrophe.

Third, in multi-level situations, spatial organization matters.  Models that ignore spatial structures, spatial heterogeneity, spatially-mediated resilience, neighborhood effects and teleconnected regions typically miss important dynamics.  Policies that do not support spatial choices or recognize the importance of spatial arrangements can miss important opportunities.  Key dynamics play out differently in different areas, and how areas interact with each other is important for governance.

Fourth, boundaries within multi-level environments are not predetermined and where they are drawn can make a huge difference.  The divisions that seem natural at one scale can be integral components of another scale, which highlights the opportunities to make important choices.  Boundaries create institutions, and they can be formed to delineate groups with common interests and or areas with coherent dynamics.  Boundaries allow groups the space to self-organize local institutions.   Boundaries can carve out healthy areas to be maintained, which, through cross-boundary effects, can support sustainability throughout a region.

The construction of institutional boundaries and other government policies has also been at the heart of much harm in fishery commons, by undermining traditional regimes.  The process of boundary construction needs to be married to a deep political process that engages both stakeholders and scientists.

Fifth, cross-boundary effects are the norm in multi-level commons.  Whether they are in the form of the benefits beyond boundaries of protected areas, or cross-boundary pollution, or the impact of exploitation of resources on scales greater than that of a given community, when a commons is situated within a large area, the larger scale dictates the constraints of the local commons.

A wide range of empirical questions remains to be addressed, but there are two challenges that ahead that are most central.  First, we need to better understand how multi-level and multi-scale perspectives can be incorporated into our quantitative models, to form new techniques of asking multi-level research questions, and to bring those results into the policy realm.

Second, inequality is a core factor and a key challenge for multi-level governance.  The history of local fisheries management is centered around transfers of power, often across levels of exploitation—either through centralization, or to the much larger players that come with greater market forces.  Early critiques of the privatization of the commons focused on the inequality that it created.  The measurement of success in fisheries—whether by maximum yield or maximum economic benefit—is politically charged.

I recently attended a talk by E.O. Wilson, where he gave “Wilson’s Law”: “If you save the living environment, the biodiversity that we have left, you will also automatically save the physical environment too. But if you save only the physical environment, you will ultimately lose both.”

We need to find a place in our world for healthy relationships with our ecosystems and the people who rely on them.  Thank you.

Categories: Essays