A global policy framework for the circular use of forest biomass as building materials
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Biomass is a versatile renewable energy source with applications across the energy system, but it is a limited resource and its usage needs prioritization. We use a sector-coupled European energy system model to explore near-optimal solutions for achieving emissions targets. We find that provision of biogenic carbon has higher value than bioenergy provision. Energy system costs increase by 20% if biomass is excluded at a net-negative (−110%) emissions target and by 14% at a net-zero target. Dispatchable bioelectricity covering ~1% of total electricity generation strengthens supply reliability. Otherwise, it is not crucial in which sector biomass is used, if combined with carbon capture to enable negative emissions and feedstock for e-fuel production. A shortage of renewable electricity or hydrogen supply primarily increases the value of using biomass for fuel production. Results are sensitive to upstream emissions of biomass, carbon sequestration capacity and costs of direct air capture.
Rising greenhouse gas emissions embodied in the global bioeconomy supply chain
The bioeconomy is key to meeting climate targets. Here, we examine greenhouse gas emissions in the global bioeconomy supply chain (1995–2022) using advanced multi-regional input-output analysis and a global land-use change model. Considering agriculture, forestry, land use, and energy, we assess the carbon footprint of biomass production and examine its end-use by provisioning systems. The footprint increased by 3.3 Gt CO2-eq, with 80% driven by international trade, mainly beef and biochemicals (biofuels, bioplastics, rubber). Biochemicals showed the largest relative increase, doubling due to tropical land-use change (feedstock cultivation) and China’s energy-intensive processing. Food from retail contributes most to the total biomass carbon footprint, while food from restaurants and canteens account for >50% of carbon-footprint growth, with three times higher carbon intensity than retail. Our findings emphasize the need for sustainable sourcing strategies and that adopting renewables and halting land-use change could reduce the bioeconomy carbon footprint by almost 60%.
Co-benefit of forestation on ozone air quality and carbon storage in South China
Substantial forestation-induced greening has occurred over South China, affecting the terrestrial carbon storage and atmospheric chemistry. However, these effects have not been systematically quantified due to complex biosphere-atmosphere interactions. Here we integrate satellite observations, forestry statistics, and an improved atmospheric chemistry model to investigate the impacts of forestation on both carbon storage and ozone air quality. We find that forestation alleviates surface ozone via enhanced dry deposition and suppressed turbulence mixing, outweighing the effect of enhanced biogenic emissions. The 2005-2019 greening mitigated the growing season mean surface ozone by 1.4 ± 2.3 ppbv, alleviated vegetation exposure by 15%-41% (depending on ozone metrics) in forests over South China, and increased Chinese forest carbon storage by 1.8 (1.6-2.1) Pg C. Future forestation may enhance carbon storage by 4.3 (3.8-4.8) Pg C and mitigate surface ozone over South China by 1.4 ± 1.2 ppbv in 2050. Air quality management should consider such co-benefits as forestation becomes necessary for carbon neutrality.
Closed-loop enhancement of plant photosynthesis via biomass-derived carbon dots in biohybrids
Improving photosynthetic efficiency is pivotal for biomanufacturing and agriculture. Despite the progress on photosynthetic biohybrids integrating biocatalysts with materials, nanomaterials with increasing energy-efficiency as well as great biocompatibility and cost-effectiveness are needed. Here, we present a closed-loop strategy using biomass-derived carbon dots (CDs) for improving photosynthesis. We demonstrate that the CDs act as both light converters and photosensitizers by converting solar irradiation to red light and supplying light-excited electrons into the photosynthetic electron transfer chain. Biohybrids incorporating CDs and cyanobacteria or plant exhibited increased photosynthetic efficiency, when compared with the photosynthetic organism only. The cyanobacterial CO2-fixation rate and CO2-to-glycerol production were increased 2.4-fold and 2.2-fold, respectively, while Arabidopsis thaliana displayed a 1.8-fold increase in the fresh weight of the plant. Techno-economic analysis showed the competitive advantage of biomass-derived CDs over other nanomaterials. These CDs hold potential applications in future sustainable agriculture and solar-powered biomanufacturing.
Dynamic optimizers for complex industrial systems via direct data-driven synthesis
The chemical process industry (CPI) faces significant challenges in improving sustainability and efficiency while maintaining conservative principles for managing cost, complexity, and uncertainty. This work introduces a data-driven approach to dynamic real-time optimization (D-RTO) that addresses the aforementioned concerns by directly extracting process optimization policies from historical plant data. Our method constructs a value function to evaluate trajectory quality and employs weighted regression to derive improved policies. When applied to a plant-wide industrial process control problem, the proposed optimizer demonstrates superior performance in adapting to disturbances while maintaining stability and product quality. These results challenge conventional assumptions regarding the potential of data-driven optimization in the CPI. Although limitations exist due to the black-box nature of neural networks, this study presents a promising avenue for enhancing operational efficiency in industrial settings. The proposed approach offers a practical solution for process optimization, as it leverages readily available historical data and does not require extensive modeling efforts. By demonstrating significant efficiency improvement on a realistic industrial benchmark problem, this work paves the way for the adoption of data-driven optimization techniques in real-world CPI applications.
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