ABSTRACT

Diffusion of low-carbon technologies is critical to global decarbonization efforts aimed at achieving the Paris Agreement climate goals. In this paper, we represent a social system as a network of agents and model the process of technology diffusion as a contagion propagating in such a network. By setting the necessary conditions for an agent to switch (i.e. to adopt the technology), we address the question of how to maximize the contagion of a technology subject to learning effects (e.g. solar PV) in a network of agents. We focus the analysis on the influence of the network structure and technological learning on diffusion. Our numerical results show that clusters of agents are critical in the process of technology contagion although they generate high levels of variance in aggregate diffusion. Whatever the network structure, learning effects ease the technology contagion in social networks.

Key policy insights:

• Policy makers should take advantage of, or favour, clustered organizations to deploy renewables (e.g. cooperatives of farmers, online social networks)

• Social dimensions are critical in the design of environmental policies (e.g. peer effects)

• Technologies subject to learning effects spread further in the population

• When considering social connections, policy makers should design policies to cope with uncertainty in diffusion (clustered organizations exhibit high levels of uncertainty in adoption).

摘要

低碳技术的传播对于旨在实现《巴黎协定》气候目标的全球脱碳努力至关重要。在本文中，我们将社会系统表示为一个代理网络，并将技术传播过程建模为在这种网络中传播的传染病。通过设置代理切换（即采用该技术）的必要条件，我们解决了如何在代理网络中最大化受学习效果（例如太阳能光伏）影响的技术的传染性问题。我们重点分析网络结构和技术学习对扩散的影响。我们的数值结果表明，代理集群在技术传播过程中至关重要，尽管它们在聚合扩散中产生了高水平的差异。无论网络结构如何，

关键政策见解：

• 政策制定者应利用或支持集群组织来部署可再生能源（例如农民合作社、在线社交网络）

• 社会维度在环境政策的设计中至关重要（例如同伴效应）

• 受学习效果影响的技术在人群中进一步传播

• 在考虑社会联系时，政策制定者应设计应对传播不确定性的政策（集群组织在采用方面表现出高度的不确定性）。