Facing green trade barriers from developed nations, particularly the EU, based on product carbon footprints, China’s renewable energy industries confront significant challenges in transitioning towards sustainability and low carbon emissions. This study delves into the carbon footprint of China’s renewable infrastructure, evaluating wind turbines, photovoltaic (PV) panels, and lithium batteries across varied decarbonization scenarios, emphasizing both production and international trade transportation. The initial findings for 2022 indicate baseline carbon footprints of 990,701 kg CO₂-eq/MW for wind turbines, 2994.97 kg CO₂-eq/kWp for PV panels, and 67.53 kg CO₂-eq/kWh for batteries. Projections for 2050 suggest that decarbonization advancements could slash these footprints by up to 36.1% for wind turbines, 76.7% for PV panels, and 72.5% for batteries, closely mirroring the EU’s 2050 low-carbon benchmarks. Considerable carbon footprints from both domestic and international transportation have been quantified, underscoring the importance of logistic decarbonization. Based on these results, it is concluded that China’s steadfast commitment to a sustainable and climate-ambitious development path can provide globally competitive, low-carbon renewable infrastructure after 2030. The study advocates for a collaborative approach to product decarbonization across international trade, as opposed to erecting barriers, to effectively contribute to global climate objectives.

Polyvinyl chloride (PVC) resins are widely used in modern society due to their acid and alkali resistance, low cost, and strong insulation properties. However, the high chlorine (Cl) content in PVC poses significant challenges for its recycling. This study reviews the treatment processes, model construction, and economic and environmental assessments to construct a methodological framework for the sustainable development of emerging dechlorination technologies. In terms of treatment processes, this study summarizes three types of processes, pretreatment, simultaneous dechlorination during chemical recycling, product purification, and emphasizes the necessity of dechlorination treatment from a systematic perspective. Additionally, the construction of models for dechlorination processes is investigated from the laboratory to the industrial production system to macro-scale material, in order to evaluate the potential inventory data and material metabolism behaviors. This review also summarized the methodology framework of Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), which can be applied for evaluation of the economic and environmental performance of the dechlorination processes. Overall, this review provides readers with a comprehensive perspective on the state-of-the-art for PVC dechlorination technologies, meanwhile offering sustainable guidance for future research and industrial applications of chemical recycling of PVC waste.

The design of distillation columns significantly impacts the economy, energy consumption, and environment of chemical processes. However, optimizing the design of distillation columns is a very challenging problem. In order to develop an intelligent technique to obtain the best design solution, improve design efficiency, and minimize reliance on experience in the design process, a design methodology based on the GA-BP model is proposed in this paper. Firstly, a distillation column surrogate model is established using the back propagation neural network technique based on the training data from the rigorous simulation, which covers all possible changes in feed conditions, operating conditions, and design parameters. The essence of this step is to turn the distillation design process from model-driven to data-driven. Secondly, the model takes the minimum TAC as the objective function and performs the optimization search using a Genetic Algorithm to obtain the design solution with the minimum TAC, in which a life-cycle assessment (LCA) model is incorporated to evaluate the obtained optimized design solution from both economic and environmental aspects. Finally, the feasibility of the proposed method is verified with a propylene distillation column as an example. The results show that the method has advantages in convergence speed without sacrificing accuracy and can obtain an improved design solution with reduced cost and environmental impact. Compared with the original design using rigorous simulation, the TAC is reduced by 6.1% and carbon emission by 27.13 kgCO₂/t.

Silicon carbide (SiC) membrane has emerged as a promising class of inorganic ceramic membranes with many advantageous attributes and has been used for a variety of industrial microfiltration (MF) processes. The state-of-the-art industrial manufacturing of SiC membranes based on the particle sintering method can only achieve an average pore size that ranges from 40 nm to a few micrometers, which is still unsatisfactory for ultrafiltration (UF) applications. Thus, the pore size control of SiC membranes remains a focus of continuing study. Herein, we provide an in situ sol–gel modification strategy to tailor the pore size of SiC membranes by a superficial deposition of SiO₂ onto the membrane surface and membrane pore channels. Our in situ sol–gel modification method is simple and effective. Furthermore, the physical characteristics and the filtration performance of the membrane can easily be controlled by the in situ reaction time. With an optimal in situ reaction time of 30 min, the average pore size of the membrane can be reduced from macropores (400 nm) to mesopores (below 20 nm), and the retention ability for 20 nm fluorescent PS microspheres can be improved from 5% to 93%; the resultant SiC/SiO₂ composite membranes are imparted with water permeance of 77 L·m⁻²·h⁻¹·bar⁻¹, improved anti-protein-fouling properties, excellent performance, and anti-acid stabilities. Therefore, modified SiC/SiO₂ membranes based on the in situ sol–gel process have great potential as UF membranes for a variety of industrial processes.

High operating cost caused by electric energy consumption is a common problem challenging many municipal wastewater treatment plants (WWTPs). Due to the characteristics of intermittent inflow and aeration, WWTPs using sequencing batch reactor technology and its variants can be managed to relieve operating cost through taking advantage of time-of-use electricity pricing. However, little attention has been paid to the scheduling of treatment processes in the context of WWTPs. In this paper, a novel mixed-integer linear programming model is established for scheduling the batch operation of a WWTP under time-of use electricity pricing, which considers constraints arising from task allocation, processing sequence, and processing duration. The modeling method is developed from the event-based continuous-time approach. The start time and end time of each treatment task are optimized to shift electricity consumption from peak hours to off-peak hours to the greatest extent, thus minimizing electricity cost. A case study demonstrates that the proposed model can quickly generate precise operational plans for the investigated WWTP. By implementing the optimum schedules, the WWTP can save on its electricity bill without changing the treatment capacity or the treatment process. The widening of peak and off-peak electricity pricing gap is favorable for the proposed model to display a more significant effect in reducing electricity cost.