资源环境科技发展态势分析平台

Deployment policies based on economic incentives are among the most effective tools for speeding up the diffusion of clean energy technologies. Policy instruments such as feed-in tariffs have played a critical role in driving the growth of solar photovoltaics, and could accelerate the uptake of other technologies that are key to the decarbonization of energy systems. Historical experiences, however, show that failing to adjust economic incentives to falling technology prices can fundamentally undermine these policies' effectiveness and cost-efficiency. This paper addresses this challenge by assessing three novel policy designs. Based on control-theory principles, the proposed mechanisms modify incentives in response to changes in deployment, policy costs, or profitability for adopters. We assess the outcomes that each policy design would have achieved when applied to Germany's feed-in tariff for solar photovoltaics between 2000 and 2016. For this purpose, we developed an agent-based model that allows us to simulate the adoption decisions of individual households and medium-sized and large firms, as well as the evolution of technology prices. Our results show that responsive designs inspired by control theory might produce policies that follow their targets more closely, and at a lower cost. In addition, our analysis suggests that the studied designs could greatly reduce uncertainty over policy outcomes and windfall profits. This research also highlights the role of the temporal distribution of policy targets, and identifies policy design tradeoffs, drawing relevant implications for the design of future deployment policies.