Credit:
Pixabay/CC0 Public Domain
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.
How predictive tools inform decisions
"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 to 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, you
need to recognize the vast uncertainty and tease out the predictive aspects.
How you deal with uncertainty is really important."
Challenges in implementation and research
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 they could actually 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.
Validating models and future opportunities
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 broader impact and global relevance
"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.
Provided by Massachusetts Institute of Technology
Source: Making clean energy investments more successful with forecasting tools
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