With COP21 wrapping up, many have been calling to scale-up energy technology investment, to shepherd in a new wave of low-carbon technology innovation. Bill Gates and Mark Zuckerberg and devoting some of their wealth to directly address this challenge, and even Peter Theil has recently called for action to enable Nuclear Energy innovation.

The next step in this sequence is to have government form policies that better foster and encourage energy innovation, enabling and facilitating the entry of new, low-carbon technologies directly into the market. In this utopian ideal of innovation, it seems all too easy to use money as a carrot and to expect the fruits of innovation to manifest.

But what exactly does “technology innovation” mean, and further more, can the innovation process be efficiently directed? Surely there’s more to the story than having philanthropic organizations (or governments) allocate billions of dollars to their preferred companies, technologies, and organizations.

We’re all too familiar with the story of Solyndra, the failed solar startup that received significant federal support, and how many point to Solyndra as the reason why government shouldn’t pick winners and losers. Yet the Solyndra story provides few answers to the question, “what kinds of investments should be made?”

A price signal alone may not guarantee technology innovation – have we seen more innovative automobiles out of Europe due to their substantially higher gas prices? There is uncertainty in how big of a price signal we need, or how much investment and where we need to put it to truly bring about major energy technology innovations and societal transitions.

The government already has a number of energy hubs and innovation centers working to experiment with and bring new technologies to market. These institutions should likely be better funded. Given that we do not have an unlimited budget to fund everything, though, how can we best support innovation goals – do we support all technologies equally? Should we support transition of all technologies from the R&D to early stage commercialization? Should we focus solely on deployment? Should we evenly divide resources across every stage of the innovation process? Should we provide more small-business loans for businesses interested in clean tech?

There simply aren’t good answers to these questions. For those that really don’t trust economics, technology innovation is a realm where economics fails to even provide insight, typically treating innovation as a “black box,” where magic things happen if the price signal is right. So what are we to do?

Understanding Technology Innovation

Before we can even start to answer the question about what to do, we need to better understand the innovation process. Building on decades of research, many innovation scholars have started to view innovation as a systems-based phenomenon. For instance, the Technology Innovation System framework takes the view that the generation, dissemination, and use of knowledge and information is what ultimately drives innovation. With a strong enough knowledge sharing network, the ultimate physical manifestations of innovation become trivial.

When innovation is viewed from a systems perspective, it becomes obvious that had Thomas Edison not fostered light bulb development, surely someone else would have around the same time (indeed, history shows that this is a certainty). The system of knowledge and information coalesced, and Thomas Edison just happened to be in the right place at the right time.

New technologies rely on an extensive network of actors, agents, laws, and institutions. The role of government in supporting innovation, in this context, is to facilitate the growth of this network, providing support until a critical stage is reached, at which point positive feedback loops will continue to promote network development.

Certain actions may cause the network to develop in different and unique ways. Increasing NSF funding, for instance, may promote more lab-based knowledge, while encouraging technology deployment may better promote knowledge and information about manufacturing and scaling technologies.

I provide the diagram below to better illustrate knowledge and information networks. Each knowledge link is supported by various structural components, such as non-profit organizations, companies, or individuals. Different government actions cause different structural components to fall into place, ultimately influencing the way knowledge and information is shared and developed.

If we want the network to develop in a specific way, actions can be taken to achieve that development trajectory. For instance, if we want to support graduate students researching energy technology, we can provide more NSF-supported fellowships. Unfortunately, we don’t have a good idea about what a network should look like, or how different components should develop at different times. We really need a new set of tools and modeling approaches for measuring and discussing innovation trajectories.

Which brings us back to climate change – if we are going to tackle climate change from a technology innovation perspective, we have a dearth of usable innovation data. We need to improve our data, and better map out the trajectories we’re on, so we can be more effective at allocating the resources that we have – do we support nuclear or electric vehicles and energy storage? How much should we support one compared to the other? How much does price, and price alone, drive adoption (your smartphone is definitely not cheaper than a rotary phone)?

No current integrated assessment models (the models that show us what we might do about climate change), take into account the network effects of technology innovation. If we are serious about addressing climate change and framing it as innovation, we should probably get more serious about developing a set of tools that can actually help us do that. Otherwise, it just comes down to value-based political arguments, and maybe not a whole lot of action.