Sustainable development study of biomass new energy in rural buildings based on LCA-emergy-carbon footprint and machine learning methods

This study delves deeply into the impact of the application of biomass new energy in rural buildings on overall sustainability, particularly in terms of energy conservation and emission reduction. The research comprehensively considers the energy value and carbon emissions of biomass energy throughout its entire life cycle, from production to consumption, ensuring the comprehensiveness and accuracy of the assessment. The study employs a random forest model, going through steps such as data preprocessing (cleaning, feature selection, standardization), data splitting (80 % training set, 20 % test set), model training (random sampling, feature selection, decision tree construction), hyperparameter optimization (number of decision trees, maximum depth, number of features), and performance evaluation (mean squared error, coefficient of determination, etc.), to ensure the scientificity and accuracy of the model. Key parameters include the number of decision trees (100-500), maximum depth, and the number of features selected, which are optimized through grid search and random search to enhance the model's predictive ability. The main research findings include: Based on the data from the case study in this paper, at a 95 % confidence level, it is believed that farmers' heating and cooking costs could be reduced by 30 % to 50 % through the use of biomass energy. According to the technological improvements, market conditions, policy changes and other factors in this case, a rural community can reduce about 3000 tons of carbon dioxide emissions annually by replacing 1000 tons of coal with biomass energy, with an error range of 3 % to 6 %. Replacing coal with biomass energy can reduce sulfur dioxide and nitrogen oxide emissions by approximately 90 %. Future research will deepen data analysis, explore the impact of economic activities and energy prices and other factors, and focus on the issue of carbon emissions growth in the long-term operation of biomass energy systems to explore effective emission reduction pathways.