Governments and companies constantly face decisions about how to allocate finite amounts of money to clean energy technologies that can make a difference to the world’s climate, its economies, and to society as a whole. The process is inherently uncertain, but research has been shown to help predict which technologies will be most successful. Using data-driven bases for such decisions can have a significant impact on allowing more informed decisions that produce the desired results.

The role of these predictive tools, and the areas where further research is needed, are addressed in a perspective article published Nov. 24 in Nature Energy, by professor Jessika Trancik of MIT’s Sociotechnical Systems Research Center and Institute of Data, Systems, and Society and 13 co-authors from institutions around the world.

She and her co-authors span engineering and social science and share “a common interest in understanding how to best use data and models to inform decisions that influence how technology evolves,” Trancik says. They are interested in “analyzing many evolving technologies — rather than focusing on developing only one particular technology — to understand which ones can deliver.” Their paper is aimed at companies and governments, as well as researchers. “Increasingly, companies have as much agency as governments over these technology portfolio decisions,” she says, “although government policy can still do a lot because it can provide a sort of signal across the market.”

The study looked at three stages of the process, starting with forecasting the actual technological changes that are likely to play important roles in coming years, then looking at how those changes could affect economic, social, and environmental conditions, and finally, how to apply these insights into the actual decision-making processes as they occur.

Forecasting usually falls into two categories, either data-driven or expert-driven, or a combination of those. That provides an estimate of how technologies may be improving, as well as an estimate of the uncertainties in those predictions. Then in the next step, a variety of models are applied that are “very wide ranging,” Trancik says, “different models that cover energy systems, transportation systems, electricity, and also integrated assessment models that look at the impact of technology on the environment and on the economy.”

And then, the third step is “finding structured ways to use the information from predictive models to interact with people that may be using that information to inform their decision-making process,” she says. “In all three of these steps, how you need to recognize the vast uncertainty and tease out the predictive aspects. How you deal with uncertainty is really important.”

In the implementation of these decisions, “people may have different objectives, or they may have the same objective but different beliefs about how to get there. And so, part of the research is bringing in this quantitative analysis, these research results, into that process,” Trancik says. And a very important aspect of that third step, she adds, is “recognizing that it’s not just about presenting the model results and saying, ‘here you go, this is the right answer.’ Rather, you have to bring people into the process of designing the studies and interacting with the modeling results.”

She adds that “the role of research is to provide information to, in this case, the decision-making processes. It’s not the role of the researchers to push for one outcome or another, in terms of balancing the trade-offs,” such as between economic, environmental, and social equity concerns. It’s about providing information, not just for the decision-makers themselves, but also for the public who may influence those decisions. “I do think it’s relevant for the public to think about this, and to think about the agency that actually they could have over how technology is evolving.”

In the study, the team highlighted priorities for further research that needs to be done. Those priorities, Trancik says, include “streamlining and validating models, and also streamlining data collection,” because these days “we often have more data than we need, just tons of data,” and yet “there’s often a scarcity of data in certain key areas like technology performance and evolution. How technologies evolve is just so important in influencing our daily lives, yet it’s hard sometimes to access good representative data on what’s actually happening with this technology.” But she sees opportunities for concerted efforts to assemble large, comprehensive data on technology from publicly available sources.

Trancik points out that many models are developed to represent some real-world process, and “it’s very important to test how well that model does against reality,” for example by using the model to “predict” some event whose outcome is already known and then “seeing how far off you are.” That’s easier to do with a more streamlined model, she says.

“It’s tempting to develop a model that includes many, many parameters and lots of different detail. But often what you need to do is only include detail that’s relevant for the particular question you’re asking, and that allows you to make your model simpler.” Sometimes that means you can simplify the decision down to just solving an equation, and other times, “you need to simulate things, but you can still validate the model against real-world data that you have.”

“The scale of energy and climate problems mean there is much more to do,” says Gregory Nemet, faculty chair in business and regulation at the University of Wisconsin at Madison, who was a co-author of the paper. He adds, “while we can’t accurately forecast individual technologies on their own, a variety of methods have been developed that in conjunction can enable decision-makers to make public dollars go much further, and enhance the likelihood that future investments create strong public benefits.”

This work is perhaps particularly relevant now, Trancik says, in helping to address global challenges including climate change and meeting energy demand, which were in focus at the global climate conference COP 30 that just took place in Brazil. “I think with big societal challenges like climate change, always a key question is, ‘how do you make progress with limited time and limited financial resources?’” This research, she stresses, “is all about that. It’s about using data, using knowledge that’s out there, expertise that’s out there, drawing out the relevant parts of all of that, to allow people and society to be more deliberate and successful about how they’re making decisions about investing in technology.”

As with other areas such as epidemiology, where the power of analytical forecasting may be more widely appreciated, she says, “in other areas of technology as well, there’s a lot we can do to anticipate where things are going, how technology is evolving at the global or at the national scale … There are these macro-level trends that you can steer in certain directions, that we actually have more agency over as a society than we might recognize.”

The study included researchers in Massachusetts, Wisconsin, Colorado, Maryland, Maine, California, Austria, Norway, Mexico, Finland, Italy, the U.K., and the Netherlands.