Anaerobic digestion (AD) is a promising technology for converting organic waste into renewable energy, but its industrial implementation is often constrained by ammonia inhibition in nitrogen-rich feedstocks, which undermines both process stability and economic viability. Addressing this challenge is crucial for ensuring sustainable, financially resilient waste-to-energy systems. We hypothesized that the strategic addition of bamboo biochar (BBC) could mitigate ammonia stress while promoting a more robust microbial community, thereby enhancing both environmental and economic performance. To test this, batch experiments were conducted to determine optimum BBC dosages, followed by semi-continuous trials using 6.25 g/L BBC over four operational phases (Runs1–4), during which NH₄⁺-N was gradually increased from 2000 to 5000 mg/L. The biochar-amended system maintained stable performance under conditions that caused control reactors to fail, with a maximum 1447 % increase in methane production observed during the 4000 mg/L NH₄⁺-N phase. Mechanistic analysis revealed that BBC acted primarily by enriching syntrophic bacteria and hydrogenotrophic methanogens, enabling a stable syntrophic acetate oxidation pathway. Enhancing microbial resilience through biochar addition directly improves financial stability, a critical factor for industrial adoption. The biochar-added system achieved consistent profits of USD 8.08–16.27/m³ reactor/month, underscoring strong business potential in scalable waste-to-energy systems. Optimizing biochar dosing and evaluating full-scale implementation could further advance globally relevant, economically viable circular bioeconomy solutions.

This study explores the interactions between microbial communities, antibiotic resistance, and biogas production in anaerobic digestion systems, focusing on the acidogenic (AP) and methanogenic (MP) phases under varying organic loads, cefazolin (CEZ) exposure, and biochar supplementation. High organic loading (10 g/L glucose) significantly suppressed CEZ-resistant bacteria (CEZ-r) during the AP phase. However, their abundance markedly rebounded in MP, rising from 0.30 % to 36.28 % in control, indicating phase-specific dynamics. CEZ residues increased CEZ-r by 2.49 % and 9.30 % at 0 and 5 g/L glucose during AP. Although AP suppressed CEZ-r to 0.23 % in the CEZ-added reactor at 10 g/L glucose, MP rebounded CEZ-r to 8.30 %. In addition, CEZ exposure reduced methane yields by up to 28.14 %, likely due to the suppression of Methanosaetaceae and impaired acetic acid conversion. In contrast, biochar addition effectively reduced CEZ-r abundance to below 1.00 % at moderate to high organic loads and alleviated CEZ-induced inhibition on methane production. Biochar also enhanced Methanosaetaceae abundance (up to +6.55 %) compared to the control and promoted more efficient substrate utilization, possibly by facilitating direct interspecies electron transfer. These findings emphasize the role of organic load and digestion phase in shaping antibiotic resistance and system performance. Furthermore, biochar addition effectively mitigates the negative impacts of antibiotic residues, stabilizes microbial communities, and enhances biogas production.

The construction industry, being responsible for a large share of global carbon emissions, needs to reduce its high carbon output to meet carbon reduction goals. Artificial intelligence can provide efficient support for carbon emission calculation and prediction. Here, we review the use of artificial intelligence techniques in forecasting, management and real-time monitoring of carbon emissions, focusing on how they are applied, their impacts, and challenges. Compared to traditional methods, the prediction accuracy of artificial intelligence models has increased by 20%. Artificial intelligence-driven systems could reduce carbon emissions by up to 15% through real-time monitoring and adaptive management strategies. Artificial intelligence applications improve energy efficiency in buildings by up to 25%, while reducing operational costs by up to 10%. Artificial intelligence supports the establishment of a digital carbon management system and contributes to the development of the carbon trading market.