The Regional Greenhouse Gas Initiative (RGGI), as the largest cap-and-trade system in the United States, employs quarterly auctions to distribute emissions permits to firms. This study examines firm behavior and auction performance from both theoretical and empirical perspectives. We utilize auction theory to offer theoretical insights regarding the optimal bidding behavior of firms participating in these auctions. Subsequently, we analyze data from the past 58 RGGI auctions to assess the relevant parameters, employing panel random effects and machine learning models. Our findings indicate that most significant policy changes within RGGI, such as the Cost Containment Reserve, positively impacted the auction clearing price. Furthermore, we identify critical parameters, including the number of bidders and the extent of their demand in the auction, demonstrating their influence on the auction clearing price. This paper presents valuable policy insights for all cap-and-trade systems that allocate permits through auctions, as we employ data from an established market to substantiate the efficacy of policies and the importance of specific parameters
