Renewable Energy, AI, Noise, and Policy: Transformative Pressures in Transport
Paper Session
Sunday, Jan. 4, 2026 12:30 PM - 2:15 PM (EST)
- Chair: Felix Friedt, Macalester College
Impact of Generative AI Models on Labor Utilization and TFP Growth in the U.S. Airline Industry
Abstract
The airline industry is at a transformative crossroads as generative AI models redefine labor utilization and operational efficiency. While AI technologies offer promising gains in workforce productivity through automation, optimization, and improved decision-making, their impact may vary significantly across airline business models due to differences in operating strategies, staffing structures, and service complexity. This study provides a quantitative assessment of AI’s influence on Total Factor Productivity (TFP) growth in the airline industry by incorporating AI exposure into workforce dynamics. Using data from U.S. airlines between 2000 and 2019, we simulate AI’s impact on labor utilization. Our findings indicate that AI adoption has the potential to improve labor utilization in the airline industry by 30.2%, contributing to a modest yet meaningful industry-wide an average TFP increase of 0.2%. Particularly, ULCCs are expected to experience the most significant TFP growth, averaging 0.5%, driven by their streamlined workforce structures, simplified operations, homogenous passengers, and strong reliance on labor efficiency to sustain their low-cost operating models. LCCs may experience modest gains, averaging 0.1%, reflecting their balance between cost-efficiency practices and operational complexity. Finally, FSAs exhibit negligible TFP improvements, which can be attributed to the complexity of their operational requirements and the diverse nature of their tasks. These findings are consistent with existing literature, which suggests that AI’s productivity gains are more pronounced in service sectors with streamlined operations and labor-intensive models, while complex sectors face greater challenges in achieving immediate efficiency improvements. This study highlights that AI-driven efficiency gains for ULCCs have the potential to translate into lower fares for price-sensitive consumers, improving social welfare by expanding access to affordable air travel. Moreover, these efficiency gains may intensify competition within the airline industry, encouraging LCCs and FSAs to adopt tailored AI-driven strategies to maintain their market position.Capitalization of Environmental Change: Noise Trajectories and Housing Prices
Abstract
Transportation noise is considered a disamenity that is capitalized into home prices. Standard hedonic models condition on disamenity levels at a particular point in time, but housing markets may also be absorbing information about the environmental trajectory that led to those levels. Spatial heterogeneity is also often a concern in noise studies. We propose an innovative two-dimensional classification framework that separately distinguishes net outcome (improvement vs. deterioration) and temporal pattern (permanent, late, non-permanent), as well as controls for spatial effects. We use this framework to analyze three versions of Warsaw’s Noise Maps (2012, 2017, 2022) linked to more than 147,000 apartment sales. Noise improvements are capitalized into sizable price premia that are systematically moderated by trajectory type: Permanent Improvements are associated with 2.6–3.9% premia, and Non-permanent Improvements (net reductions achieved by nonmonotonic paths) with intermediate effects of 1.5–1.7%. The resulting per decibel capitalization rate is nearly twice as large for permanent vs. late arriving improvements (0.49%/dB vs. 0.26%/dB), suggesting that markets value demonstrated persistence above and beyond current conditions. Deterioration effects are statistically significant but less robust than improvement effects, with severe sample imbalance (14:1 improvement-to-deterioration ratio) reducing statistical power. The results show that trajectory characteristics hold information that can be capitalized above and beyond disamenity levels, and that standard hedonic estimates may substantially overstate deterioration effects when spatial dependence is ignored.Electric Vehicle Policies in the Inflation Reduction Act: When Do Climate Provisions and Industrial Policy Goals Align?
Abstract
It is becoming increasingly common to package environmental policies with industrial policies that promote domestic manufacturing, businesses, and jobs. Recent examples include the Inflation Reduction Act (IRA) EV Tax Credits in the US and the Green Deal Industrial Policy in the EU. These policies intend to "kill two birds with one stone," yet these two goals may not necessarily align. This study examines whether the dual objectives of EV subsidies in the IRA - reducing greenhouse gas emissions and boosting domestic vehicle production - exhibit synergies or create conflicts. Whether these goals are complementary or create tradeoffs, and whether these subsidies are efficient, crucially depend on what eligible EVs replace, particularly how consumers substitute between eligible EVs and other vehicles (e.g., other energy/fuel-efficient EVs, domestically produced gasoline vehicles). To provide useful policy implications, it is crucial to obtain accurate estimates of the substitution patterns between products for the consumer. Therefore, we estimate a structural model of new vehicle demand and supply in the US while incorporating information on production location, which we then use to simulate policy counterfactuals.JEL Classifications
- R4 - Transportation Economics
- L9 - Industry Studies: Transportation and Utilities