Journal of Cleaner Production最新文献

With the new round of technology revolution and lithium-ion batteries decommissioning tide, how to efficiently recover the valuable metals in the massively spent lithium iron phosphate batteries and regenerate cathode materials has become a critical problem of solid waste reuse in the new energy industry. In this paper, we review the hazards and value of used lithium iron phosphate batteries and evaluate different recycling technologies in recent years from the perspectives of process feasibility, environment, and economy, including traditional processes such as mechanical milling, magnetic separation, and flotation, as well as pyrometallurgical and hydrometallurgical processes. This paper also introduces the highly promising salt electrolysis and direct regeneration restoration technologies, and we pay special attention to simple and efficient new methods for lithium replenishment restoration, which will provide new insights for a wide range of researchers to implement in-depth experiments.

Methanol is one of the main raw materials and a building block for many essential chemicals in the industry, and it is widely produced from the synthesis gas. It is used as a fuel, solvent, inhibitor, antifreeze agent, and in producing a variety of chemicals including olefins and paraffin. The global demand for methanol is 70 million tons per year and it is estimated to grow by a factor of 6–8% per year so it is necessary to develop a methanol production process, which is economical, and environment friendly. The multi-objective optimization (MOO) approach is used to optimize the large scale Lurgi two-step methanol synthesis process using the non-dominated sorting genetic algorithm-II (NSGA-II) to find the Pareto front solutions involving the process, economic, and environmental-based objectives. Methanol production rate, total energy consumption, total annual CO₂ emissions, and profit are considered as objectives. The five decision variables include syngas molar flow, reactor feed pressure, reactor 1 temperature, reactor 2 temperature, and by-product mass flow in the methanol distillation column. Two constraints are set on ethanol mole fraction in the methanol stream, and methanol mole flow in water to the treatment stream. Three optimization cases (two bi-objective and one tri-objective) are developed. In the Case 1 study, CO₂ emissions decreased by 65.83%, the methanol production rate increased by 10.11%, and total energy was reduced by 57.55%. The Pareto ranking is carried out using the Preference Ranking On the Basis of Ideal-average Distance (PROBID) method and the best optimal solution is reported for all cases.

Although emotions can affect the decision-making process of purchasing professionals, limited attention has been dedicated to how they shape the decision to maintain the relationship with suppliers when the latter behave in a socially irresponsible way. Considering this gap, we build on Appraisal Theory to analyze how guilt and trust affect the decision to continue working with a socially irresponsible supplier when a disruption occurs. We conducted a scenario-based experiment with 148 buyers, in which we simulated an extraordinary situation, and then analyzed the data using multiple regression analysis. The results showed that when a supplier is not socially responsible, buyers experience a feeling of guilt and trust them less, which has a negative impact on their decision to continue working with that supplier. The study also found that the effect of the guilt experienced by the purchasing professional is heightened when the purchased item is strategic. This study highlights the importance of paying attention to purchasers' emotional experiences with suppliers' practices during challenging times, as these can shape buyer-supplier relationships in socially responsible supply chains, select sustainable suppliers, and engage in socially responsible purchasing.

The expansion of aluminum smelter capacity generates high CO₂ emissions, exhibiting spatiotemporal diversity due to diverse processing routes and technologies. Despite extensive discussions on macro abatement options, facility-specific mitigation potentials and decarbonization strategies remain unclear, which cumulatively affects the progress towards global 1.5 °C and 2 °C warming control targets. This study develops a carbon footprint inventory of 163 primary aluminum smelters in 2022 and analyzes current emission structures concerning location, age, production, and input-output. Then adjust available technologies and efficiencies to match global plant-level decarbonization routes. The potential contributions of each strategy are finally quantified, accounting for significant geographic variations in emissions, thereby supplementing tailored technology transformation pathways for smelters across nine focus regions. The results show that: (1) Young smelters, predominantly in developing regions, are crucial for abatement. Rapid retrofits ahead could save CO₂ budgets by 0.6–28.56 Gt, potentially delaying excess emissions under climate targets, particularly with comprehensive retrofits. (2) The paramount technical retrofitting for carbon neutrality differs regionally and evolves. Smelters in developing countries should prioritize renewable energy investment in the immediate future, succeeded by the optimization of power generation infrastructures. To liberate more emission allowances, developed countries ought to contemplate the decommissioning of aged smelters or upgrading through emerging supply-side technologies. (3) Available retrofits can facilitate the aluminum industry's contribution to the 2 °C climate target, whereas addressing the 1.5 °C target will necessitate the adoption of more innovative technologies and materials. This paper endeavors to assist global and regional smelters in developing more targeted decarbonization pathways.

China has implemented a variety of energy efficiency policies to achieve energy saving and carbon abatement targets. While the national carbon market has been launched and China has opted to give market-oriented policies a greater role in achieving net zero, energy efficiency policies still constitute a significant portion of the policy mix. However, the substantial drawback of energy efficiency policies compared to carbon trading, the rebound effect, has made energy efficiency policies debatable. It is crucial to precisely understand the magnitude of the rebound effect to design and implement effective energy policies. Our study estimates and compares the direct energy rebound and output expansion effects in China's manufacturing and service sectors using firm-level data from 2013 to 2016 and employing panel regression analysis. We also analyze the impact of firm characteristics such as ownership status, geographical location, and industry type on enterprises' rebound effects. We found a 33.5% direct rebound effect and a 19.3% output expansion effect in the manufacturing sector. In the service sector, the direct energy rebound effect was 57.9%, a larger one than the manufacturing sector. We also estimate direct energy rebound effects for key manufacturing and service enterprises. Our findings reveal that key manufacturing enterprises exhibit a larger rebound effect compared to the overall manufacturing sector, while key service enterprises show a smaller rebound effect than the overall service sector. Public, foreign, and heavy service enterprises show a smaller energy rebound effect, while northeastern manufacturing and service enterprises exhibit a larger rebound effect. We suggest that China's carbon market should also cover key service enterprises. For those below the threshold, enhancing energy management is essential to mitigate the rebound effect. Aligning with carbon emission cap targets, rather than relying solely on energy efficiency policies, could more effectively achieve energy savings and carbon abatement.

The demand for oil and natural gas energy is increasing dramatically, and the extraction of conventional oil and gas reservoirs is in a stage of exhaustion. In order to stabilize the functionalized properties of oil-based drilling fluids (OBDF) during the extraction of oil and gas reservoirs in ultra-deep wells, the present experiments were carried out on the material (EG-NIT/CB[7]) obtained by encapsulating nitrate (NIT) in expanded graphite (EG) with porous structure through the supramolecular self-assembly property of cucurbit[7]uril (CB[7]). The structural properties were analyzed using various analytical tools and the results showed that our material synthesis was successful. And the effect of mass fraction of EG, CB[7] on the heat transfer and storage of EG-NIT and EG-NIT/CB[7] materials was investigated. The increase in mass fraction of EG can increase the thermal conductivity of EG-NIT up to 7.4 times and the introduction of CB[7] further improves the thermal conductivity of EG-NIT/CB[7] by up to 1.38 W/m·k. The latent heat value of EG-NIT/CB[7] decreased by only 4.7% after 200 cycles. Meanwhile, under the condition of 150 °C, adding EG-NIT/CB[7] to drilling fluids can reduce the temperature of drilling fluids by 2.8 °C. And it improves the stability of drilling fluids, the viscosity almost does not change, and it can also play a certain role in inhibiting the leakage of drilling fluids. Therefore, the EG-NIT/CB[7] developed by us can be practically applied in ultra-deep drilling projects.

Considerable attention has been given to the control of perfluorooctanoic acid (PFOA) environmental pollution due to its degradation resistance and potential risks. In this study, Bismuth oxychloride based nanodiamond (BiOCl-ND) composite materials were successfully synthesized by using hydrothermal method. The complete decomposition of PFOA and a high defluorination ratio of 74.6% by BiOCl-ND 10:1 was obtained under UV light irradiation within 60 min, and the composite's degradation rate constant was nearly 3.8 times than that of pure BiOCl. It was demonstrated that critical roles played in PFOA degradation were photogenerated holes (h⁺) and superoxide radicals (·O₂⁻). A synergistic effect between BiOCl and ND and the heterostructure was revealed, the differences of the energy band structure between BiOCl and ND reduced the recombination of photogenerated e⁻-h⁺ and enhanced the charge transfer. In this process, a large number of h⁺ with oxidizing properties and·O₂⁻ generated from both BiOCl and ND played a critical role, and oxygen vacancies (OVs) also participated in the reaction as active species and contributed to electron transfer. Additionally, the photocatalytic decomposition intermediates of PFOA and the release of fluorine ions were analyzed to verify the effective defluorination of PFOA, and the results confirmed a gradual decrease in the toxicity of the reaction matrix during PFOA degradation. Hence, these results might provide insights for the development of photocatalysts to improve the photodegradation effect of PFOA in water.

There is an urgent need for a transition to healthy diets that can be produced in a sustainable food system. Different modelling approaches are used to understand what constitutes such sustainable healthy diets. Diet optimization and environmental food system optimization are modelling approaches that are developed from different angles, i.e. either starting from dietary changes or from food system changes. The aim of this study was to investigate the consequences of using a diet optimization model or an environmental food system optimization model on the healthiness, the environmental impact, the cultural acceptability, and the composition of modelled diets. To illustrate these consequences we used CiFoS (Circular Food System) and SHARP (Sustainable, Healthy, Affordable, Reliable, and Preferable diets) as case models. CiFoS is a biophysical environmental food system optimization model that allows to redesign the food system to produce a (healthy) diet for the current or future population within planetary boundaries. SHARP is a diet optimization model that is based on benchmarking and aims to calculate healthier and more sustainable diets that are acceptable for consumers. We found that both optimization approaches have unique applications in time scale (long vs short term solutions), geographic scale (national vs continental scale) and an individual versus a population approach. However, due to different approaches in calculating environmental impacts, accounting for co-products and biophysical boundaries in the food system, translating commodities into realistic diets, and considering cultural acceptability, modelled diets from these two approaches are not comparable. These findings show that, when used in the right context, both models provide unique insights in how sustainable healthy diets may be achieved, but also stress the need to understand the methodology behind models. This paper is an important step in strengthening the integration of different disciplines to make well informed decisions for healthy diets within planetary boundaries.

This study examines the complex relationship between environmental taxation, green technology, green investment, real GDP, and environmental pollution by employing the Nonlinear autoregressive distributive lag model for China. The findings indicate that environmental tax policies have significant short- and long-term effects, with positive shocks leading to pollution reductions. The positive and negative shocks to green investment increase pollution levels in the long run. Moreover, the real GDP displays pollution reductions in the short run. The results provide valuable insights into China's environmental and economic situation and offer policy recommendations. These findings were confirmed using the Wald test. The findings emphasize China's need for stronger environmental taxes, green technology adoption, evaluated green investments, and sustainable economic growth to achieve cleaner production and environmental sustainability.

Commonly used numerical prediction models for PM_2.5 maps suffer from low accuracy and high computation cost, which cannot meet the requirements for fine-scale air pollution control. In this study, we propose a framework based on the spatiotemporal recurrent neural network (PredRNN) to efficiently generate accurate 3-h and 6-km PM_2.5 maps with a lead time of 5 days. In this framework, two PredRNN networks are initially utilized to forecast PM_2.5 concentration at ground monitoring sites and the spatial distribution of aerosol optical depth (AOD) by assimilating the output of numerical prediction model. Subsequently, the 3-h and 6-km PM_2.5 forecasted maps with a lead time of 5 days can be inferred by establishing the regression links between the forecasted results of PM_2.5 concentration at ground sites and AOD maps. We evaluate the proposed framework in the Beijing-Tianjin-Hebei urban agglomeration region during 2017–2020. Compared with the numerical prediction products of the Copernicus Atmosphere Monitoring Service, the proposed framework achieves higher accuracy, with R² of 0.83 at the forecast base time and 0.70 at the fifth day. The spatial information richness is also enhanced by approximately 15.67% according to the information entropy metrics. Notably, the proposed framework only requires 1 min for forecasting 5-days PM_2.5 maps. These results demonstrate that our framework can efficiently generate accurate and fine PM_2.5 maps with a lead time of 5 days.