Tag Archives: James A. Rising

Improving IAMs: From problems to priorities

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 The calibrations of the IAMs are often decades out of date (Rising 2018) and represent empirical methods which are no longer credible (e.g. Huber et al. 2017).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.

References:

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://bit.ly/2SKT8XB.
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://bit.ly/1nY5dZX
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 http://bit.ly/2SLnQ2F
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).

The power of informal transit

The Journal of Transport Geography just published a study that I worked on with Kayleigh Campbell, Jacqueline Klopp, and Jacinta Mwikali Mbilo. The question address is “How important is informal transit in the developing world?” (Jump to the paper.)

What’s informal transit?

A lot of people get around Nairobi in works of art on wheels called “matatus”:

The matatu system is extensive, essential, efficient, and completely unplanned. In Nairobi’s hurry to accommodate the transport needs of a population that grows by 150,000 people a year, it has ignored this piece of infrastructure. Sometimes it has even undermined it.

The goal of this paper is to measure how important matatus are, in the context of the whole range of transportation options and income groups.

What does this paper bring to the table?

This is one of very few analyses on informal transportation networks anywhere, building upon the incredible work of the Digital Matatus Project, co-led by our co-author, Dr. Klopp.

It’s also fairly unique in looking at transport accessibility in the developing world at all (most work on accessibility is done in rich countries). Not surprisingly, transport needs in developing countries are different.

What do we find?

Some of the results are unsurprising: matatus boost measures of access by 5-15 times, compared to walking, with accessibility highest in the central business district. Of somewhat more interest:

  • Matatu access drops more quickly then driving or walking accessibility as you move away from Nairobi’s center. That’s an indication of the structure of the matatu network, helping people in Nairobi center the most.
  • Controlling for distance from the center, richer communities have low accessibility. Many people from those communities have cars, but it matters because their workers do not. In fact this communities tend to be quite isolated.
  • Tenement housing has quite strong accessibility, because matatu networks tend to organize around it.

What tools do we have for research in this area?

We developed quite an extensive body of tools for studying (1) accessibility in general, and (2) transit networks in particular. If you find yourself in the possession of a cool new transit network database, in “GTFS” format, we have code that can analyze it. Prompt me, and I can work with you to open-source it.

Enjoy the new paper! Accessibility across transport modes and residential developments in Nairobi

Blockchain and the dystopian present

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.

Complexity Science Methods for Sustainable Development

I recently had the pleasure of speaking last week to the Science and Policy Summer School, in Paris. This is an interdisciplinary event that I helped to start back in 2011, under the tutelage of Laurence Tubiana, bringing together students from Columbia’s Sustainable Development program, Sciences Po’s IDDRI, and various Masters’ programs, to have some big discussions on bridging the gap between science and policy.

The topic for this year was “Methods in Sustainable Development”. For my part, I gave a 10,000 ft. view of Complexity Science, and some of the methods available from it.

Here is my complexity science methods presentation, in Prezi form.

Templates for Bayesian Regressions

At the Sustainable Development Research (SusDeveR) conference this weekend, I offered some simple tools for performing Bayesian Regressions: Jump to the Github Repository.

The point of these templates is to make it possible for anyone who is familiar with OLS to run a Bayesian regression. The templates have a chunk at the top to change for your application, and a chunk at the bottom that uses Gelman et al.’s Stan to estimate the posterior parameter distributions.

In general, the area at the top is just to create an output vector and a predictor matrix. Like this:
Constructing yy and XX

The template part has all of the Stan code, which (for a Bayesian regression) always has a simple form:
Simple Stan regression model

The last line does all of the work, and just says (in OLS speak) that the error distribution follows a normal distribution. Most of the templates also have a more efficient version, which does the same thing.

I say in the README what Bayesian regressions are and what they do. But why use them? The simple answer is that we shouldn’t expect the uncertainty on our parameters to be well-behaved. It’s nice if it is, and then OLS and Bayesian regressions will give the same answer. But if the true uncertainty on your parameter of interest is skewed or long-tailed or bimodal, the OLS assumption can do some real harm.

Plus, since Bayesian regressions are just a generalization of MLE, you can setup any functional form you like, laying out multiple, nonlinear expressions, estimating intermediate variables, and imposing additional probabilistic constraints, all in one model. Of course, the templates don’t show you how to do all that, but it’s a start.

Water-Energy-Food Flows

The water-energy-food nexus has become a popular buzz-word in the sustainability field. It aims to capture the idea that water, energy, and food challenges are intertwined, and that shocks to any one can precipitate problems to all three.

I’ve often wondered how closely these three are intertwined though. Water is certainly needed for energy (for thermoelectric cooling and hydropower), but the reverse link (mostly pumping) seems a lot weaker. Water is also needed for food production, but is food needed for water availability? Energy and food have some links, with a fair amount of energy needed to produce fertilizer, and a some “food” production actually going to biofuelds, but the sizes aren’t clear.

Below is my attempt to show these flows, for the United States:

Water-Energy-Food Flows

It seems to me, based on this, that this is less a nexus than water-centered system. Every drop of water is fought over for energy, food, and urban production. It’s less a interconnected nexus than a hub-with-spokes. A way to recognize that water is at the center of it all.

Sources:
– Hydrological flows: Total water (GW+SW) extractions from USGS. Food system only has irrigation and livestock; energy only has thermoelectric. The rest make up the difference.
– Energy system flows: Food system energy from Canning, P. 2010. Energy Use in the U.S. Food System. USDA Economic Research Report Number 94; “In 2010, the U.S. water system consumed over 600 billion kWh, or approximately 12.6 percent of the nation’s energy according to a study by researchers at the University of Texas at Austin.” from http://ift.tt/UJ5XlT “Energy consumption by public drinking water and wastewater utilities, which are primarily owned and operated by local governments, can represent 30%-40% of a municipality’s energy bill.” from https://fas.org/sgp/crs/misc/R43200.pdf; remainder to 100%.
– Biofuels: 18.38e6 m^3 ethanol + 1.7e6 m^3 biodiesel, at a density of 719.7 kg/m^3 is 14.45e6 MT.
– Remainder of food: http://ift.tt/2gMffOn reports 635 billion pounds consumption, 81% of which was domestically produced.

Extrapolating the 2017 Temperature

After NASA released the 2017 global average temperature, I started getting worried. 2017 wasn’t as hot as last year, but it was well above the trend.


NASA yearly average temperatures and loess smoothed.

Three years above the trend is pretty common, but it makes you wonder: Do we know where the trend is? The convincing curve above is increasing at about 0.25°C per decade, but in the past 10 years, the temperature has increased by almost 0.5°C.

Depending on how far back you look, the more certain you are of the average trend, and the less certain of the recent trend. Back to 1900, we’ve been increasing at about 0.1°C per decade; in the past 20 years, about 0.2°C per decade; and an average of 0.4°C per decade in the past 10 years.

A little difference in the trend can make a big difference down the road. Take a look at where each of these get you, uncertainty included:

A big chunk of the fluctuations in temperature from year to year are actually predictable. They’re driven by cycles like ENSO and NAO. I used a nice data technique called “singular spectrum analysis” (SSA), which identifies the natural patterns in data by comparing a time-series to itself at all possible offsets. Then you can take extract the signal from the noise, as I do below. Black is the total timeseries, red is the main signal (the first two components of the SSA in this case), and green is the noise.

Once the noise is gone, we can look at what’s happening with the trend, on a year-by-year basis. Suddenly, the craziness of the past 5 years becomes clear:

It’s not just that the trend is higher. The trend is actually increasing, and fast! In 2010, temperatures were increasing at about 0.25°C per decade, an then that rate began to jump by almost 0.05°C per decade every year. The average from 2010 to 2017 is more like a trend that increases by 0.02°C per decade per year, but let’s look at where that takes us.

If that quadratic trend continues, we’ll blow through the “safe operating zone” of the Earth, the 2°C over pre-industrial temperatures, by 2030. Worse, by 2080, we risk a 9°C increase, with truly catastrophic consequences.

This is despite all of our recent efforts, securing an international agreement, ramping up renewable energy, and increasing energy efficiency. And therein lies the most worrying part of it all: if we are in a period of rapidly increasing temperatures, it might be because we have finally let the demon out, and the natural world is set to warm all on its own.

1 Million Years of Stream Flow Data

The 9,322 gauges in the GAGES II database are picked for having over 20 years of reliable streamflow data from the USGS archives. Combined, these gauges represent over 400,000 years of data.
They offer a detailed sketch of water availability over the past century. But they miss the opportunity to describe a even fuller portrait.

In the AWASH model, we focus on not only gauged points within the river network and other water infrastructure like reservoirs and canals, but also on the interconnections between these nodes. When we connect gauge nodes into a network, we can infer something about the streamflows between them. In total, our US river network contains 22,619 nodes, most of which are ungauged.

We can use the models and the structure of the network to infer missing years, and flows for ungauged junctions. To do so, we create empirical models of the streamflows for any guages for which we have a complete set of gauged of upstream parents. The details of that, and the alternative models that we use for reservoirs, can be details for another post. For the other nodes, we look for structures like these:

Structures for which we can infer missing month values, where hollow nodes are ungauged and solid nodes are gauged.

If all upstream values are known, we can impute the downstream; if the downstream value is known and all but one upstream values are known, we can impute the remaining one; if upstream or downstream values can be imputed according to these rules, they may allow other values to be imputed using that new knowledge. Using these methods, we can impute an average of 44 years for ungauged flows, and an average 20 additional years for gauged flows. The result is 1,064,000 years of gauged or inferred streamflow data.

We have made this data available as a Zenodo dataset for wider use.

Economic Damages from Climate Change

When I tell people I study climate change, sooner or later they usually ask me a simple question: “Is it too late?” That is, are we doomed, by our climate inaction? Or, less commonly, they ask, “But what do we really know?”

With our new paper, Estimating Economic Damage from Climate Change in the United States, I finally have an answer to both of these questions; one that is robust and nuanced and shines light on what we know and still need to understand.

The climate change that we have already committed is going to cost us trillions of dollars: at least 1% of GDP every year until we take it back out of the atmosphere. That is equivalent to three times Trump’s proposed cuts across all of the federal programs he cuts.

If we do not act quickly, that number will rise to 3 – 10% by the end of the century. That includes the cost of deaths from climate change, lost labor productivity, increased energy demands, costal property damage. The list of sectors it does not include– because the science still needs to be done– is much greater: migration, water availability, ecosystems, and the continued potential for catastrophic climate tipping points.

But many of you will be insulated from these effects, by having the financial resources to adapt or move, or just by living in cooler areas of the United States which will be impacted less. The worst impacts will fall on the poor, who in the Untied States are more likely to live in hotter regions in the South and are less able to respond.

Economic damages by income deciles

One of the most striking results from our paper is the extreme impact that climate change will have on inequality in the United States. The poorest 10% of the US live in areas that lose 7 – 17% of their income, on average by the end of the century, while the richest 10% live where in areas that will lose only 0 – 4%. Climate change is like a subsidy being paid by the poor to the rich.

That is not to say that more northern states will not feel the impacts of climate change. By the end of the century, all by 9 states will have summers that are more hot and humid than Louisiana. It just so happens that milder winters will save more lives in many states in the far north than heat waves will kill. If you want to dig in deeper, our data is all available, in a variety of forms, on the open-data portal Zenodo. I would particularly point people to the summary tables by state.

Economic damages by county

What excites me is what we can do with these results. First, with this paper we have produced the first empirically grounded damage functions that are driven by causation rather than correlation. Damage functions are the heart of an “Integrated Assessment Model”, the models that are used by the EPA to make cost-and-benefit decisions around climate change. No longer do these models need to use out-dated numbers to inform our decisions, and our numbers are 2-100 times as large as they are currently using.

Second, this is just the beginning of a new collaboration between scientists and policy-makers, as the scientific community continues to improve these estimates. We have built a system, the Distributed Meta-Analysis System, that can assimilate new results as they come out, and with each new result provide a clearer and more complete picture of our future costs.

Finally, there is a lot that we as a society can do to respond to these projected damages. Our analysis suggests that an ounce of protection is better than a pound of treatment: it is far more effective (and cheaper) to pay now to reduce emissions than to try to help people adapt. But we now know who will need that help in the United States: the poor communities, particularly in the South and Southeast.

We also know what needs to be done, because the biggest brunt of these impacts by far comes from pre-mature deaths. By the end of the century, there are likely to be about as many deaths from climate change as there are currently car crashes (about 9 deaths per 100,000 people per year). That can be stemmed by more air-conditioning, more real-time information and awareness, and ways to cool down the temperature like green spaces and white roofs.

Our results cover the United States, but some of the harshest impacts will fall on poorer countries. At the same time, we hope the economies of those countries will continue to grow and evolve, and the challenges of estimating their impacts need to take this into account. That is exactly what we are now doing, as a community of researchers at UC Berkeley, the University of Chicago, and Rutgers University called the Climate Impacts Lab. Look for more exciting news as our science evolves.