The thermal power generation sector, recognized as the leading contributor to carbon emissions, is now integral to the carbon emission rights trading system. Utilizing carbon emissions trading to effectively reduce CO2 emissions from thermal power production is crucial for achieving China's objectives in energy conservation and emissions reduction, as well as mitigating the greenhouse effect. The cost of carbon emissions significantly influences the feed-in tariffs for thermal power, prompting an urgent need among power companies for a new pricing model that incorporates carbon emission trading. This study presents a revised Shanghai thermal power feed-in tariff model, considering carbon emission rights trading (specifically Shanghai's SHEA trading). Employing the Putty-Clay Vintage model and forecasting the SHEA price using a BP neural network, we developed and validated the revised tariff model. Our computational analysis confirms that the modified feed-in tariffs align closely with actual values, exhibiting an average deviation of 9.7042% and 9.7043%, respectively.