Prediction of Planting Carbon Emission based on LSTM Model with Improved Whale Optimization Algorithm

Planting carbon emissions are a significant source of greenhouse gas emissions. Accurate prediction and effective management of these emissions are crucial for mitigating climate change and promoting sustainable agricultural development. To optimize existing methods for predicting planting carbon emissions, this study focuses on Heilongjiang Province. First, the IPCC method is applied to comprehensively account for three major sources of carbon emissions: agricultural land use, CH₄ emissions from rice fields, and N₂O emissions from agricultural land. The carbon emissions from planting activities in Heilongjiang Province from 2001 to 2022 were systematically calculated. Based on this, an LSTM model was developed, incorporating three key dimensions: social and economic drivers, production scale effects, and technical energy consumption intensity. To enhance the model’s predictive performance, the IWOA (Improved Whale Optimization Algorithm) was introduced to optimize four hyperparameters of the LSTM model: the number of hidden units, learning rate, batch size, and training epochs. Finally, the IWOA-LSTM model was used to predict future planting carbon emissions in Heilongjiang Province under both baseline and low-carbon scenarios. The results indicate several findings: 1) The planting carbon emissions in Heilongjiang Province show a trend of "rapid growth followed by fluctuating decline." The main sources of carbon emissions include N₂O emissions from crop cultivation, CH₄ emissions from rice growth, and carbon emissions resulting from fertilizer use. 2) The IWOA-LSTM model exhibits excellent predictive performance, with an average absolute error of 686, 800 tons, a root mean square error of 620, 200 tons, and an average absolute percentage error of 3.15%. Compared to the LSTM model, the IWOA-LSTM model shows significant improvements in both prediction accuracy and stability. The study demonstrates that the IWOA-LSTM model can effectively predict planting carbon emissions, providing a scientific basis for the formulation of carbon reduction policies in Heilongjiang Province's planting sector. 3) The IWOA-LSTM model was used to predict the carbon emissions of the planting industry in Heilongjiang Province for the next five years under both the baseline and low-carbon scenarios. The results showed that the planting carbon emissions in Heilongjiang Province could be effectively suppressed by controlling crop planting area, improving fertilizer application efficiency and reducing agricultural machinery diesel consumption. Based on the conclusions drawn above, the following recommendations for emission reduction are proposed: First, optimize land use structure and control crop planting area. Second, increase the application and innovation of green agricultural technologies. Third, promote rural economic development and increase farmers' income. Forth, strengthen policy support and incentive mechanisms. Through the above measures, sustainable development of Heilongjiang Province's agriculture could be further achieved.