Externalities
Lightning Round SessionSunday, Jan. 4, 2026 8:00 AM - 10:00 AM (EST)
- Chair: Sarah Elven, London School of Economics
Directed Technological Change and General Purpose Technologies: Can AI Accelerate Clean Energy Innovation?
Abstract
Accelerating the rate of clean innovation relative to dirty innovation is critical to decarbonization. This race between clean and dirty technologies is taking place against the backdrop of improvements in general-purpose technologies (GPT) such as information and communication technologies (ICT) and artificial intelligence (AI). Using patent data, we find that clean energy technologies enjoy more spillovers from AI and ICT than dirty energy technologies, and that these increase the value of patents. Theoretically, we show that this differential rate of spillovers from a GPT should induce firms to redirect their efforts towards clean technologies. This would suggest that exposure to AI/ICT may be a new channel encouraging clean innovation. We then explore this causal mechanism using a shift-share instrument exploiting changes in firms’ exposure to AI R&D based on the prior geographical location of their energy inventors. We discuss policy implications using a directed technical change model: we show that this induced clean innovation channel can reduce the carbon tax needed to achieve emission targets, but that AI R&D subsidies could crowd out clean R&D efforts without commensurate increases in clean R&D subsidies.Selection and Over-Crediting in Forest-Based Carbon Offset Projects: A Comparison of Regulated and Voluntary Carbon Markets
Abstract
We undertake the first systematic analysis comparing and contrasting U.S. improved forest management (IFM) projects participating in the California regulated and global voluntary carbon offset markets. We link a novel geospatial dataset of IFM offset projects located in the U.S. Forest Northern Region to measurements of above-ground live carbon collected through U.S. Forest Service Forest and Inventory Analysis (FIA). Statistical and calibrated simulation analyses both provide evidence for market entrance selections and excessive offset credit issuance. As revealed by event study analysis and two-stage logistic regression with carbon trends, IFM projects on regulated and voluntary markets differ significantly in pre-market forest management relative to their statistical counterfactual forestlands with similar carbon storage capacities. Payoff estimations based on forest simulation modeling replicate this entrance outcome, illustrating that regulated market projects realize greater revenues under the regulated market rules than they would under the voluntary market rules, and likewise voluntary market projects realize greater revenues under the voluntary market rules. Using the simulation model, we also find that real-world market entrance outcomes match with the generous lower baselines implied in the IFM projects' registry documentation, rather than the more appropriate business-as-usual baselines. Because of that, regulated market projects are non-additional and voluntary market projects also issue about 1.8 times the offset credits of their justified emission reduction. While current offset markets promote forest-based projects as nature-based climate solutions, they also raise serious integrity concerns that may weaken overall incentives for carbon reduction by corporate buyers. Based on the results from a nested logistic model with simulation approximated project payoffs, switching to stricter business-as-usual baselines would solve the overcrediting problem, but also lower the market participation of forest-based projects, and reduce the potential of carbon offset markets to mitigate emissions through motivating forest conservations.Persuade or Price? Bayesian Persuasion vs. Pigouvian Taxation Under Congestion
Abstract
Shared resources suffer from overuse when users ignore costs imposed on others. I ex-perimentally compare taxes versus strategic information provision for managing congestion.
Groups of five choose between a shared resource with congestion and an uncertain alter-
native. Without intervention, two of five participants use the shared resource, matching
theoretical predictions. A tax reduces usage to 1.4 participants, improving welfare. Informa-
tion provision achieves 75 percent compliance with tailored recommendations yet increases
congestion to 2.5 participants and reduces welfare. This failure occurs because partial infor-
mation creates strategic gambling: participants told to avoid the resource sometimes enter,
betting others’ compliance leaves it underutilized. Results show information policies can
backfire when individual sophistication undermines coordination, while simple taxes prove
more reliable. This provides first experimental evidence comparing price and information
mechanisms for managing shared resources.
Air Pollution and Economic Activity: Evidence from Foot Traffic Patterns
Abstract
I investigate how air pollution affects economic activity. Using over 600 million phone-location-based foot traffic data points from SafeGraph, I conduct a large-scale analysis to examine the causal effect of air pollution on activity patterns across the US. Using changes in local wind direction as an instrumental variable (IV) for air pollution, I find that a 1 µg/m³ increase in PM2.5 concentration leads to a 0.50% decrease in economic activity, resulting in a nationwide reduction of 43 million trips annually. The reductions are widespread across different economic sectors, with recreational activities experiencing the largest decline. The effect is more pronounced in higher-income counties and areas with a larger share of children, suggesting greater awareness among wealthier or more vulnerable populations.Spatial Dynamics with Environmental Regulation
Abstract
Environmental regulation protects public health but may disrupt production and employment. I study how innovation shapes long-run local labor market responses to environmental regulation and the resulting welfare effects. I develop a novel spatial model with directed innovation, forward-looking migration, and cross-regional trade, in which policy-induced shifts in innovation reshape local labor market dynamics. The model features R\&D investment directed across space and sectors, endogenously altering productivity and pollution patterns. Calibrating the model to the U.S. Clean Air Act, I find that the regulation reduced employment in polluting manufacturing industries by about 0.36 million jobs. The general equilibrium model-implied decline is substantially smaller than reduced-form difference-in-differences estimates exploiting spatial variation in regulatory stringency. The Act increased aggregate U.S. welfare by lowering emissions. However, welfare effects are highly uneven across space and sectors, and aggregate gains would have been larger under a uniform national policy than the existing place-based policy. Transition dynamics highlight the importance of accounting for endogenous shifts in comparative advantage and labor dynamic adjustment.JEL Classifications
- Q5 - Environmental Economics