A novel token-distance-based triple approach is proposed for identifying EPU mentions in textual documents. The method is applied to a corpus of French-language news to construct a century-long historical EPU index for the Canadian province of Quebec. The relevance of the index is shown in a macroeconomic nowcasting experiment.

Solar Renewable Energy Certificate (SREC) markets are a market-based system that incentivizes solar energy generation. A regulatory body imposes a lower bound on the amount of energy each regulated firm must generate via solar means, providing them with a tradeable certificate for each MWh generated. Firms seek to navigate the market optimally by modulating their SREC generation and trading rates. As such, the SREC market can be viewed as a stochastic game, where agents interact through the SREC price. We study this stochastic game by solving the mean-field game (MFG) limit with sub-populations of heterogeneous agents. Market participants optimize costs accounting for trading frictions, cost of generation, non-linear non-compliance costs, and generation uncertainty. Moreover, we endogenize SREC price through market clearing. We characterize firms' optimal controls as the solution of McKean-Vlasov (MV) FBSDEs and determine the equilibrium SREC price. We establish the existence and uniqueness of a solution to this MV-FBSDE, and prove that the MFG strategies form an $\epsilon$-Nash equilibrium for the finite player game. Finally, we develop a numerical scheme for solving the MV-FBSDEs and conduct a simulation study.

Developing ways to affordably deliver broadband connectivity is one of the major issues of our time. In challenging deployment locations with irregular terrain, fiber optic or traditional Clear-Line-Of-Sight (CLOS) wireless links can be uneconomical to deploy, resulting from the number of required towers making infrastructure deployment unviable. With the emergence of new research focusing on developing wireless diffractive backhaul technologies to provide diffractive Non-Line-Of-Sight (NLOS) links, this paper evaluates the engineering-economic implications of such approaches. To quantify different technology strategies, a Three-Dimensional (3D) techno-economic assessment framework is presented to help prioritize regions for future investment in broadband connectivity, utilizing a combination of remote sensing and viewshed geospatial techniques. Such a method is an essential evaluation step prior to beginning detailed Radio Frequency (RF) Quality of Service engineering but has hitherto received less research attention in the literature. This framework is applied to assess both Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight strategies for deployment in Peru, as well as the islands of Kalimantan and Papua, in Indonesia. The results find that a hybrid strategy combining the use of Clear-Line-Of-Sight and diffractive Non-Line-Of-Sight links produces a 15-43 percent cost-efficiency saving, relative to only using traditional Clear-Line-Of-Sight wireless backhaul links. The codebase is released opensource via the Engineering-Economic Evaluation of Non-Line-of-Sight Backhaul (e3nb) repository.

Approximately half of the global population does not have access to the internet, even though digital connectivity can reduce poverty by revolutionizing economic development opportunities. Due to a lack of data, Mobile Network Operators and governments struggle to effectively determine if infrastructure investments are viable, especially in greenfield areas where demand is unknown. This leads to a lack of investment in network infrastructure, resulting in a phenomenon commonly referred to as the `digital divide`. In this paper we present a machine learning method that uses publicly available satellite imagery to predict telecoms demand metrics, including cell phone adoption and spending on mobile services, and apply the method to Malawi and Ethiopia. Our predictive machine learning approach consistently outperforms baseline models which use population density or nightlight luminosity, with an improvement in data variance prediction of at least 40%. The method is a starting point for developing more sophisticated predictive models of infrastructure demand using machine learning and publicly available satellite imagery. The evidence produced can help to better inform infrastructure investment and policy decisions.