The Clean Transition in a Network Economy

预计清洁技术将在向低碳经济过渡的过程中发挥重要作用（国际能源署，2021年）。在本文中，笔者建立了一个理论模型，研究这些技术的发展和采用，以及考虑到碳排放的损害，管理向低碳经济过渡的最佳干预。首先，笔者通过建立内生生产网络模型，研究了清洁技术的采用及其对排放的影响。扇区可以被描述为“脏”（例如，油），如果它们的使用产生排放物，或者“清洁”，否则。每个部门都有一种使用“肮脏”部门投入的“肮脏”技术和一种不使用“肮脏”部门投入的“清洁”技术。这些技术之间的竞争决定了它们的采用和网络的结构。这一模式反映了各部门清洁技术的相互依存关系，因为采用清洁技术取决于中间投入的价格，而对排放的影响则取决于在直接和间接投入中使用清洁技术。本文首先分析了模型的均衡点，揭示了总排放量与污染行业中心性的关系，并发现在一个特定行业中，转向清洁技术的总收益取决于其与污染行业的间接联系，然后引入内生技术变革，研究清洁技术的发展和采用、排放的时间路径及其福利影响。这种方法捕捉了采用和开发之间的反馈，因为更高的采用会增加市场规模和创新激励，而更具生产力的技术会增加采用。在以后的工作中，我将使用计算方法来研究钢铁行业的最优政策。

Clean technologies are expected to play an important role in the transition to a low carbon economy (International Energy Agency, 2021). In this paper, I develop a theoretical model to study the development and adoption of these technologies, and the optimal intervention to manage the transition to a low carbon economy taking into account the damages from carbon emissions.I first investigate the adoption of clean technologies and their effect on emissions by developing an endogenous production network model. Sectors can be described as "dirty" (e.g., oil) if their usage generates emissions or "clean" if otherwise. Each sector features a "dirty" technology that uses inputs from "dirty" sectors and a "clean" technology that does not. Competition between these technologies determines their adoption and the structure of the network. This model captures the interdependence of clean technologies across sectors as their adoption depends on the price of intermediate inputs, while their effect on emissions depends on the use of clean technologies in their direct and indirect inputs. I characterise the equilibrium of the model, show how total emissions relates to the centrality of the dirty sectors, and find that the total benefit from switching to a clean technology in a given sector depends on its indirect links to the dirty sector.I then introduce endogenous technical change into this framework to study the development and adoption of clean technologies, the time path of emissions, and their welfare implications. This approach captures the feedback between adoption and development as higher adoption increases market size and innovation incentives, while more productive technologies increases adoption. In later work, I will use computational methods to study optimal policy in a stylised version of the steel industry.