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.

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.

Thermal power plants will function as a flexible load regulation in a low-carbon grid, which requires operation adaption for the whole system. Energy transition in the electricity sector is the core to realizing carbon neutrality. The power grid will be gradually dominated by renewable energy, such as wind power and photovoltaic solar power. However, renewable energy has problems such as insufficient power supply and output fluctuation; thermal power will be required to regulate the peak load flexibly to meet demand. Therefore, the supply of boiler make-up water prepared by electrodeionization (EDI) in thermal power plants should also be flexibly changed. This study focused on the ultrapure water preparation system by EDI with variable flow rates. For an EDI system with a maximum ultrapure water capacity of 20 m³/h, the power consumption, annual cost, and carbon footprint of different designs are compared. The operation parameters were optimized based on the optimal cost design when the temporal demand of boiler make-up water is reduced to 75%, 50%, and 25%, respectively, considering thermal power as peak load regulation technology. The results showed that the optimized system could significantly reduce power consumption and carbon footprint by up to 30.21% and 30.30%, respectively. The proposed strategy is expected to be widely applied for design and operation optimization when considering the low-carbon but unstable energy system dominated by renewable energy. The carbon footprint could be a feasible optimization object to balance the greenhouse gas emissions from the module manufacturing and operation consumption.

Rivers are not only a vital part of the Earth’s water cycle but also sources and sinks for greenhouse gases (GHGs), exerting a significant influence on the global carbon budget. Rapid urbanization and intense human activities lead to water pollution and river habitat degradation, thereby affecting riverine greenhouse gas (GHG) emissions indirectly. Artificial management and restoration measures taken for rivers further increase the uncertainty of GHG emissions from rivers. In the context of carbon neutrality goals, research on GHG emissions from rivers has gradually become a hot topic. However, there is a scarcity of collective and comparative studies on the spatiotemporal patterns and mechanisms of riverine GHG emissions, especially a lack of summaries exploring the impacts of pollution and restoration on GHG emissions from rivers. This work systematically reviews recent studies concerning the emissions of CO₂, CH₄, and N₂O from rivers, with a particular focus on the characteristics and driving factors. Results have shown that riverine GHG emissions exhibit significant spatiotemporal heterogeneity. Besides hydrological factors such as wind speed, flow velocity, rainfall, and water level, large amounts of pollutants entering rivers strongly affect the production and emission of GHGs, since nutrients, organic matter, heavy metals, microplastics, and antibiotics can alter the biogeochemical processes in river ecosystems. Remediation measures can reduce water pollution levels, but some measures may further increase the emission of GHGs from rivers. This work emphasizes the need for conducting in-depth research on the synergies between treating river pollution and reducing riverine GHG emissions. It also proposes to reinforce the monitoring of GHGs and construct emission databases of rivers for sustainable watershed management.