An investigation of auctions in the Regional Greenhouse Gas Initiative

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

An investigation of auctions in the Regional Greenhouse Gas Initiative

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

An investigation of auctions in the Regional Greenhouse Gas Initiative

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

Navigating the crisis: Fuel price caps in the Australian national wholesale electricity market

Fuel price caps are one of the potential regulatory tools for controlling wholesale electricity prices when fuel prices are volatile. In this paper, we introduce a theoretical model to study the effects of such caps on firms’ bidding behaviour and clearing prices in spot market auctions. We then use data from the Australian National Electricity Market (NEM), which recently implemented such caps, to empirically test and compare their effectiveness in three different states. Our theoretical findings suggest that fuel price caps can be binding, especially when electricity demand is lower and competition among generators is higher. When demand is high, alternative policy tools, such as market price caps, may be more effective in controlling auction prices. Our empirical analysis employs various techniques, such as Generalized Additive Models (GAM) and machine learning algorithms, to test the effectiveness of price caps in the NEM. We find mixed results regarding the effectiveness of fuel price caps in different states. Specifically, fuel price caps reduced wholesale electricity prices in Queensland and New South Wales, while they were not effective in controlling wholesale prices in Victoria