Rural carbon emissions from home energy usage, animal behaviour, and agricultural practices have a major impact on climate change. To precisely anticipate and control rural carbon emissions and support sustainable rural development, the project attempts to create an artificial intelligence (AI)-driven smart prediction and regulation model. An Efficient Gannet Optimization–Nested Long Short-Term Memory (EGO–Nested LSTM) model is integrated into the suggested structures. The dataset, which includes 3,000 entries from China’s rural areas between 2018 and 2024, includes important characteristics such crop kinds, livestock numbers, energy consumption, fertilizer use, and seasonal activity patterns. In data preparation, missing values were handled and Min–Max normalization was used to guarantee high-quality input. In order to minimize dimensionality while maintaining essential emission patterns, feature extraction was carried out utilizing Principal Component Analysis (PCA) and auto encoders. Complex temporal relationships are captured by the Nested LSTM network, while the EGO algorithm identified an efficient involvement strategy and optimized hyperparameters. High predictive performance was demonstrated by the experimental implementation in Python, which achieved a MAE of 0.075, RMSE of 0.123, and Mean MAPE of 0.110%. In addition to recommending energy-efficient irrigation systems, renewable energy integration, and optimized livestock organization, the EGO algorithm effectively identified the best options for reducing emissions. The suggested EGO-Nested LSTM model offers rural policymakers a precise, comprehensible, and data-driven decision-support tool. In order to reduce carbon emissions, enhance energy competence, and promote sustainable rural expansion, it makes precise forecasting and the development of workable, sustainable strategies possible.
Cuiying Luo (Thu,) studied this question.