Considering Different Types of Learning in Low-Carbon Innovation Policy

Technological change, particularly in the energy sector, is arguably the most important lever to address climate change. In order to accelerate low-carbon innovation, policy intervention is needed. In many policy analyses, energy technologies are differentiated simply based on their carbon emission mitigation potential and cost. However, in order to effectively induce innovation, one needs to understand technologies’ differences with regards to their long-term patterns of innovation and the underlying learning mechanisms among the innovating firms.

To improve our understanding of these technologies we take an innovation system perspective, that recognizes the importance of innovator networks, interactions in these networks, and knowledge feedbacks. In several past and ongoing studies, we combine patent data with in-depth interview-based case studies of three important low-carbon energy technologies: photovoltaics (PV), wind, and lithium-ion battery storage. Based on this data, we analyze how technological innovation differs across these technologies with regards to (i) where innovation occurs throughout the industry value chain (from material and components via manufacturing to installation and use); (ii) how learning feedbacks from other stages in the industry value chain contribute to innovation; and (iii) which type of innovation (product vs process) is most relevant over the life cycle of the technology.

We find that most innovation in PV is process-related, occurs in the manufacturing phase and requires strong interaction of manufacturing equipment producers with cell manufacturers. In Wind, most innovation is product-related, related to the product integration, and supported by feedback form the use-phase. In the battery industry, both process and product-related innovation occurs related to battery cell design and manufacturing. Often product and process innovation occurs in combination. Feedbacks from the manufacturing and use phase are important. We relate these observations to the complexity of technologies’ architectures and of their production processes and thereby can derive implications for other energy technologies. Based on our analyses, we make recommendations for the design of policy portfolios to accelerate innovation in clean energy. For instance, the role of home markets seems to be particularly important for technologies with a strong role of feedbacks from the use phase. For energy technologies where innovation in the manufacturing process is key, market growth – requiring new manufacturing equipment – can accelerate innovation.