Integrating waste heat and biomass energy in a three-entropy state framework optimized by emergy-carbon-neural network for sustainable buildings

This study addresses the urgent need for low-carbon transformation in the construction sector by proposing a novel building energy supply system that integrates industrial waste heat and biomass energy to tackle the issues of low energy efficiency and high carbon emissions in traditional energy systems. By establishing an emergy-carbon footprint coupling analysis model and conducting empirical project tests and sensitivity analyses, the comprehensive performance of the system was systematically evaluated. The research findings indicate that the overall energy efficiency of the system reaches 72%, an increase of over 40% compared to traditional systems; the annual operational-stage carbon offset is 45,786 tons of CO₂, with a 63% reduction in carbon emissions during the operation phase; the unit energy cost drops by 44%, and the annual energy-saving benefit is 21.5 million yuan; 92% of the operation is automated through an intelligent cloud platform, significantly enhancing the stability of energy supply. It should be particularly noted that although the system can achieve carbon-negative performance within the operational boundary, with an annual net offset of 45,786 tons of CO₂, a full life-cycle assessment incorporating upstream agricultural emissions reveals a net carbon footprint of 80,132 tons of CO₂ per year, including carbon emissions from fertilizer application during straw cultivation, field collection, transportation, and pretreatment. This result indicates that achieving genuine full life-cycle carbon negativity under current agricultural practices requires concurrent deep decarbonization of upstream agricultural and logistical processes. This research provides an economic, efficient, and low-carbon energy solution for the construction sector, which holds significant practical significance for promoting the realization of carbon neutrality goals in the construction industry.