English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The price of carbon emission rights play a crucial role in carbon tradingmarkets. Therefore, accurate prediction of the price is critical. Taking theShanghai pilot market as an example, this paper attempted to design a carbonemission purchasing strategy for enterprises, and establish a carbon emissionprice prediction model to help them reduce the purchasing cost. To makepredictions more precise, we built a hybrid deep learning model by embeddingGeneralized Autoregressive Conditional Heteroskedastic (GARCH) into the GateRecurrent Unit (GRU) model, and compared the performance with those of othermodels. Then, based on the Iceberg Order Theory and the predicted price, weproposed the purchasing strategy of carbon emission rights. As a result, theprediction errors of the GARCH-GRU model with a 5-day sliding time window werethe minimum values of all six models. And in the simulation, the purchasingstrategy based on the GARCH-GRU model was executed with the least cost as well.The carbon emission purchasing strategy constructed by the hybrid deep learningmethod can accurately send out timing signals, and help enterprises reduce thepurchasing cost of carbon emission permits.

A hybrid deep learning approach for purchasing strategy of carbon emission rights -- Based on Shanghai pilot market