By Shorouk Elkobros, IIASA Science Communication Fellow
Assessing energy-related choices and the behaviors of households can help us transition to a low-carbon economy. How can research provide more effective decision-making tools to policymakers for better climate change mitigation policies?
We live at a defining moment for climate change, where today’s actions affect tomorrow’s reality. Every little climate-friendly decision counts. Whether we decide to insulate our houses, put solar panels on our rooftops, or invest in energy-efficient appliances. However, our personal and energy-related decisions vary based on our awareness, age, education, income, energy provider services, social norms, culture, and many other factors. Researchers are starting to pay attention to how this diversity is not well represented in the economic models that politicians use to plan climate change policies.
@ VectorMine | Dreamstime.com
Designing policies inspired by people
Households contribute an average of 70% of global greenhouse gas emissions. Limiting global emissions requires holistic policy approaches that take households’ behaviors and lifestyle decisions into account. Adding such a dimension can potentially upscale low carbon behavioral and social changes to national and global levels, which is fundamental to tackling climate change.
Worried about the future of the planet and motivated to support policymakers in designing better climate change mitigation policies, the authors of a recent study published in the journal Environmental Modeling & Software aspired to build bridges through interdisciplinary research. The study presented a novel interdisciplinary method that aims to integrate households’ energy behavior and social dynamics in climate-energy-economy models and thus help politicians design policies inspired by people.
“I have always been interested in the science-policy-society aspect of mitigating climate change. Climate change is a collective challenge that we need to address together to come up with better solutions for future generations,” notes study lead author Leila Niamir, a researcher jointly associated with the Mercator Research Institute on Global Commons and Climate Change, Berlin and the IIASA Transitions to New Technologies Program.
Better models for a better future
Climate change mitigation policies play a pivotal role in achieving ambitious environmental targets like the Paris Agreement or the Sustainable Development Goals (SDGs). To be able to formulate appropriate mitigation policies, decision makers need assessment tools to measure complex systems quantitatively. In the past decade, a variety of assessment tools have emerged, which have since been predominantly used to support climate change policy debates. In the study, Niamir argues that current assessment models are missing bottom-up and grassroots dynamics, they cannot project realistic variables of what households’ lifestyles and social movement are, and they therefore may not be sufficient to provide reliable information for policymakers.
There is a gap between what policymakers’ current assessment tools can offer and what social scientists and behavioral economists highlight as pro-environmental behavior and climate change mitigation movements. By adding this complex behavior and social perspective to the models, the researchers make it easier for policymakers to design future policies to accommodate different societal behaviors and lifestyles.
Niamir and her team presented a novel method for systematically upscaling grassroots dynamics by linking the best of both “top-down” macroeconomic computable general equilibrium (CGE) models and “bottom-up” empirical agent-based models (ABM). Their approach demonstrates that with computational ABM directly linked to survey data and macroeconomic CGE models, individual behavioral diversity and social influences can be considered when designing implementable and politically feasible policy options.
“We need better assessment tools to quantitatively explore the complex climate-energy-economy system, and reveal the potential of demand-side mitigation strategies. To see substantial changes, we need a mix of external interventions, from soft information policies aimed at raising awareness bottom-up, to financial incentives altering the macro landscape of energy markets and technological transitions. Only modular and integrated models can help policymakers quantitatively explore this complex system and plan for changes in the coming decades,” says Niamir.
Towards a low-carbon economy
We cannot tackle what we do not know. Pathways to a low-carbon economy future entail diminishing the growing discrepancy between mitigation policies and individual and collective behaviors. When redesigning our socio-environmental systems to mitigate climate change, we need to start looking at people as case studies rather than numbers. To transition to a low-carbon economy and accelerate decarbonization, policymakers must adopt novel models that integrate energy consumption, individual behavior, heterogeneity, and social influence into current assessment tools.
“Mitigating climate change indeed requires a massive effort from individual and social movements to advance national and international collaboration. Each individual small step towards shrinking our carbon footprint creates cascading changes in social behavior and consequently mitigates climate change,” Niamir concludes.
Reference:
Niamir L, Ivanova O, & Filatova T (2020). Economy-wide impacts of behavioral climate change mitigation: linking agent-based and computable general equilibrium models. Environmental Modelling & Software 134: e104839. [pure.iiasa.ac.at/16671]
Note: This article gives the views of the authors, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Sibel Eker, researcher in the IIASA Energy Program
IIASA researcher Sibel Eker explores the usefulness and reliability of COVID-19 models for informing decision making about the extent of the epidemic and the healthcare problem.
In the early days of the COVID-19 pandemic, when facts were uncertain, decisions were urgent, and stakes were very high, both the public and policymakers turned not to oracles, but to mathematical modelers to ask how many people could be infected and how the pandemic would evolve. The response was a plethora of hypothetical models shared on online platforms and numerous better calibrated scientific models published in online repositories. A few such models were announced to support governments’ decision-making processes in countries like Austria, the UK, and the US.
With this announcement, a heated debate began about the accuracy of model projections and their reliability. In the UK, for instance, the model developed by the MRC Centre for Global Infectious Disease Analysis at Imperial College London projected around 500,000 and 20,000 deaths without and with strict measures, respectively. These different policy scenarios were misinterpreted by the media as a drastic variation in the model assumptions, and hence a lack of reliability. In the US, projections of the model developed by the University of Washington’s Institute for Health Metrics and Evaluation (IHME) changed as new data were fed into the model, sparking further debate about the accuracy thereof.
This discussion about the accuracy and reliability of COVID-19 models led me to rethink model validity and validation. In a previous study, my colleagues and I showed that, based on a vast scientific literature on model validation and practitioners’ views, validity often equates with how good a model represents the reality, which is often measured by how accurately the model replicates the observed data. However, representativeness does not always imply the usefulness of a model. A commentary following that study emphasized the tradeoff between representativeness and the propagation error caused by it, thereby cautioning against an exaggerated focus on extending model boundaries and creating a modeling hubris.
Following these previous studies, in my latest commentary in Humanities and Social Sciences Communications, I briefly reviewed the COVID-19 models used in public policymaking in Austria, the UK, and the US in terms of how they capture the complexity of reality, how they report their validation, and how they communicate their assumptions and uncertainties. I concluded that the three models are undeniably useful for informing the public and policy debate about the extent of the epidemic and the healthcare problem. They serve the purpose of synthesizing the best available knowledge and data, and they provide a testbed for altering our assumptions and creating a variety of “what-if” scenarios. However, they cannot be seen as accurate prediction tools, not only because no model is able to do this, but also because these models lacked thorough formal validation according to their reports in late March. While it may be true that media misinterpretation triggered the debate about accuracy, there are expressions of overconfidence in the reporting of these models, even though the communication of uncertainties and assumptions are not fully clear.
The uncertainty and urgency associated with pandemic decision-making is familiar to many policymaking situations from climate change mitigation to sustainable resource management. Therefore, the lessons learned from the use of COVID models can resonate in other disciplines. Post-crisis research can analyze the usefulness of these models in the discourse and decision making so that we can better prepare for the next outbreak and we can better utilize policy models in any situation. Until then, we should take the prediction claims of any model with caution, focus on the scenario analysis capability of models, and remind ourselves one more time that a model is a representation of reality, not the reality itself, like René Magritte notes that his perfectly curved and brightly polished pipe is not a pipe.
References
Eker S (2020). Validity and usefulness of COVID-19 models. Humanities and Social Sciences Communications 7 (1) [pure.iiasa.ac.at/16614]
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
By Leena Ilmola-Sheppard, IIASA Advanced Systems Analysis (ASA) Program
Crisis management problems are getting more complex and complicated, but at the same time, governments have less and less resources for their management. How can research help decision makers plan for the unplannable?
Last week in Geneva, I took part in a crisis management workshop for national decision makers organized by the OECD High Level Risk Forum and the Swiss Federation Chancellor While the meeting was very specific to national security and crisis management, I found some takeaway messages that are relevant to us researchers as well, especially for those of us that hope that to help decision makers make better decisions through modeling.
Mads Ecklon, Head of the Centre for Preparedness Planning and Crisis Management of the Danish Emergency Management Agency, used the figure above as a framework to explain crisis management. His message can also be applied to the development of any social system. Picture 1 describes the standard starting point of the modeling exercise. We are modeling one behavior and then analyze how the system performance develops in a controlled situation. Ecklon explained that potential futures are not so predictable: the crisis in hand can either be solved, solved only partially, not solved at all, or in the worst case the problem may escalate (you never know how a social system will react in the crisis situation—a small incident can turn into a massive riot). The challenge for both national level crisis managers and modelers is same; you have to take all of these potential developments into a consideration.
But what happens if a new, unexpected crisis pops up while all attention is focused on the initial problem? Such hard-to-predict events are often referred to as “black swan events.” Eclon said that their team has more frequently been seeing situations where, when attention is focused on the current crisis, a new, different or related, crisis develops and no one notices it. For example, in the UK in 2007, just when all the crisis management resources were invested in flooding crisis, foot and mouth disease broke out among cattle. The new phenomenon, Ecklon claimed, is that these crises are piling up and even if they are independent from each other, the joint impact can be disastrous.
Modeling black swan events I think that this message is important for modelers as well. We may be very happy to model all the four windows of our comic strip. But how can we include new surprises and crises into an ongoing model? We should develop models that include different development trajectories triggered by a change in one of our variables, but simultaneously we should be able to account for several overlapping surprises.
In the meeting, national risk managers spoke about ”unknown unknowns,” low probability high impact risks–strange unforeseen animals like a black swan that jump on the plate just when we think that the situation is in some kind of control.
This kind of modeling challenge is fascinating from an academic perspective, but researchers’ intellectual hunger should not be the only reason to develop methods for these kinds of situations. From decision makers’ perspective, this is exactly the case where useful models are needed. The multiple simultaneous developments of the complex systems are difficult to capture even for the brightest of the crisis teams, but a model could manage a job very well.
Most of the IIASA models are large, integrated models that cover global systems. These models are not designed for digesting black swan sandwiches. The Danish crisis management team has a solution worth for benchmarking for this problem as well. They have a specific small team that is called a Pandora’s Cell. Pandora’s Cell is dedicated to anticipating, imagining, and scanning for potential not-so-obvious developments that should be taken into consideration in decision making. This dedicated team is needed because all the other resources available have been focused on the obvious events, as described in the square one of our comic strip.
By Dan Jessie, IIASA Research Scholar, Advanced Systems Analysis Program
As policymakers turn to the scientific community to inform their decisions on topics such as climate change, public health, and energy policy, scientists and mathematicians face the challenge of providing reliable information regarding trade-offs and outcomes for various courses of action. To generate this information, scientists use a variety of complex models and methods. However, how can we know whether the output of these models is valid?
This question was the focus of a recent conference I attended, arranged by IIASA Council Chair Donald G. Saari and the Institute for Mathematical Behavioral Sciences at the University of California, Irvine. The conference featured a number of talks by leading mathematicians and scientists who research complex systems, including Carl Simon, the founding Director of the University of Michigan’s Center for the Study of Complex Systems, and Simon Levin, Director of the Center for BioComplexity at Princeton University. All talks focused on answering the question, “Validation. What is it?”
To get a feel for how difficult this topic is, consider that during the lunch discussions, each speaker professed to know less than everybody else! In spite of this self-claimed ignorance, each talk presented challenging new ideas regarding both specifics of how validation can be carried out for a given model, as well as formulations of general guidelines for what is necessary for validation.
For example, one talk discussed the necessity of understanding the connecting information between the pieces of a system. While it may seem obvious that, to understand a system built from many different components, one needs to understand both the pieces and how the pieces fit together, this talk contained a surprising twist: oftentimes, the methodology we use to model a problem unknowingly ignores this connecting information. By using examples from a variety of fields, such as social choice, nanotechnology, and astrophysics, the speaker showed how many current research problems can be understood in this light. This talk presented a big challenge to the research community to develop the appropriate tools for building valid models of complex systems.
Overall, the atmosphere of the conference was one of debate, and it seemed that no two speakers agreed completely on what validation required, or even meant. Some recurring questions in the arguments were how closely does a model need to mirror reality, and how do we assess predictions given that every model fails in some predictions? What role do funding agencies and peer review play in validation? The arguments generated by the talks weren’t limited to the conference schedule, either, and carried into the dinners and beyond.
I left the conference with a sense of excitement at seeing so many new ideas that challenge the current methods and models. This is still a new and growing topic, but one where advances will have wide-ranging impacts in terms of how we approach and answer scientific questions.
IIASA Council Chair Don Saari: Validation: What is it?
Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.
In a new commentary (subscription required) in Nature Geoscience, IIASA researchers Michael Obersteiner, Marijn van der Velde, and colleagues write about the problems facing the world’s food supply as we exhaust our supplies of phosphorus. Projections show that phosphorus supplies could run out in the next 40 to 400 years. In this interview, Obersteiner and van der Velde give more background on the “phosphorus trilemma.”
Fertilizers containing phosphorus are vital for crop production – but phosphorus is limited in availability and growing scarcer.
Why is phosphorus so important?
MV: Phosphorus is essential for life on Earth. It is a key component of DNA and cell membranes, and vital for cellular energy processes. Crops need phosphorus to grow. And to maintain crop production, and to make sure that soils remain productive, we have to add extra nitrogen and phosphorus as fertilizer. This is one of the food security issues in Africa where soils are suffering from nutrient depletion without replenishment.
Where do we get phosphorus and why is that supply in danger?
MO: Phosphorus is ubiquitous in the Earth’s crust. However, most of it is strongly bound in the soil , where plants cannot access it. Modern agriculture (which made human population explode) essentially began when we found ways to extract nitrogen from the air and phosphorus from minerals to make fertilizers for agricultural purposes.
The problem is that minable phosphorus is geographically concentrated in very few places. For example 75% of known reserves are located in Morocco and these reserves are limited. If, for example, political turmoil restricted access to the mines of Morocco, we would be in danger of short-term shortages that could lead to rising food prices or food insecurity in poor countries.
What problems do you expect as phosphorus becomes even more limited?
MO: The biggest problem we face is limited or no access to phosphorus fertilizers by the poor and food insecure.
MV: At the same time, rich countries apply excess fertilizers causing eutrophication to their lakes and rivers, while the poor cannot afford fertilizers.
What can be done about these problems?
MV: More efficient fertilizer application would make fertilizers cheaper to poor farmers, and at the same time help address the environmental problems. But in the long run we need to figure out how to produce food in a way that recycles nutrients at minimum loss rates. (This also includes losses from human excrement!)
To better solve the issues around long-term phosphorus availability and equitable use we also need better data on how much phosphate rock is remaining in the world and where it is located. Countries will need to be persuaded to collaborate on both these issues to ensure equity.
How does IIASA research inform this debate?
MV: In a paper we published earlier this year in PLOS ONE we showed the importance of soil phosphorus and the significant increases in yields that could be achieved in Africa with balanced micro-dosed applications of nitrogen and phosphorus. Available phosphorus in soils is generally low, especially in older weathered soils in the tropics where a lot of the phosphorus can be locked up in iron and aluminum complexes. We are currently investigating what application rates of nitrogen and phosphorus would be optimal for a range of soils and climates. This can then lead to better soil and nutrient management.
MO: Many things in nature that we like or depend on for our livelihood are substitutable. But phosphorus is in everything we eat and cannot be substituted by any element. If we continue business as usual we will squander this resource and thereby potentially compromising the wellbeing of our daughters and sons.
Further Reading
M. Obersteiner, J. Peñuelas, P. Ciais, M. van der Velde, and I.A. Janssens, 2013. The phosphorus trilemma.Nature Geoscience, 6, 897-898, doi:10.1038/ngeo1990 [COMMENTARY].
You must be logged in to post a comment.