Using text mining to retrieve information about circular economy

Synergistic influence of iodine and hydrogen peroxide towards the degradation of harmful algal bloom of Microcystis aeruginosa

Cyanobacterial blooming due to the influence of temperature and increased nutrients in ponds/lakes aided by the runoff from agricultural lands, is a serious environmental issue. The presence of cyanotoxins in water may poison the health of aquatic organisms, animals, and humans. In this study, we focus on chemical assisted degradation of Microcystis aeruginosa- an alga that is of special relevance owing to its consistent blooming, especially in tropical regions. The study aims to ascertain the individual iodine (I) and hydrogen peroxide (H2O2) and their combination (hereinafter referred to as IH) effects on the degradation of Microcystis aeruginosa. As expected, the collected pond water revealed the presence of metal ions viz., Ni, Zn, Pb, Cu and Mn, which enriched the blooming of M. aeruginosa. Interestingly, a complete rupture of the cells - pigment loss, biochemical degradation and oxidative damage-was observed by the IH solution after exposure for ∼9 h under ambient conditions. In comparison to control (original water without chemicals), the addition IH completely eliminated the pigments phycocyanin (99.5%) and allophycocyanin (98%), and degraded ∼81% and 91% of carbohydrates and proteins, respectively due to the synergistic action of I and H. Superior degradation of algae through a simple and eco-friendly approach presented in this study could be explored more effectively towards its large-scale applicability.

Decarbonization in waste recycling industry using digitalization to promote net-zero emissions and its implications on sustainability

Digitalization and sustainability have been considered as critical elements in tackling a growing problem of solid waste in the framework of circular economy (CE). Although digitalization can enhance time-efficiency and/or cost-efficiency, their end-results do not always lead to sustainability. So far, the literatures still lack of a holistic view in understanding the development trends and key roles of digitalization in waste recycling industry to benefit stakeholders and to protect the environment. To bridge this knowledge gap, this work systematically investigates how leveraging digitalization in waste recycling industry could address these research questions: (1) What are the key problems of solid waste recycling? (2) How the trends of digitalization in waste management could benefit a CE? (3) How digitalization could strengthen waste recycling industry in a post-pandemic era? While digitalization boosts material flows in a CE, it is evident that utilizing digital solutions to strengthen waste recycling business could reinforce a resource-efficient, low-carbon, and a CE. In the Industry 4.0 era, digitalization can add 15% (about USD 15.7 trillion) to global economy by 2030. As digitalization grows, making the waste sector shift to a CE could save between 30% and 35% of municipalities' waste management budget. With digitalization, a cost reduction of 3.6% and a revenue increase of 4.1% are projected annually. This would contribute to USD 493 billion in an increasing revenue yearly in the next decade. As digitalization enables tasks to be completed shortly with less manpower, this could save USD 421 billion annually for the next decade. With respect to environmental impacts, digitalization in the waste sector could reduce global CO₂ emissions by 15% by 2030 through technological solutions. Overall, this work suggests that digitalization in the waste sector contributes net-zero emission to a digital economy, while transitioning to a sustainable world as its social impacts.

Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions

Nowadays, there is an increasing use of digital technologies and Artificial Intelligence (AI) such as Machine Learning (ML) models that leverage data to optimize the performances of systems in almost all activity sectors, including ML models for optimizing solutions related to CO2 capture from the atmosphere or CO2 emissions reduction from human activities. However, on the other hand, the use of AI models is leading to an increasing energy consumption that also raises environmental issues (in terms of CO2 emissions) which are less studied in the literature. This then raises the new question of a more realistic estimate of the carbon footprint (CO2 emissions in particular) of AI models in general, and particularly AI models aimed at reducing CO2 emissions. Thus, in this paper, for an AI model in this latter context, we propose a method to quantify both his negative impacts (quantity of CO2 emissions emitted by the training and use of the model) and his positive impacts (quantity of CO2 emissions saved when the model is used). The method is evaluated with three state-of-the-art AI models: (i) an artificial neural network model for managing the energy demand of Brazilian households, (ii) an adaptive neuro-fuzzy inference system for photovoltaic power forecast in Tunisia, (iii) and a Bayesian regression model for the electric vehicle routing problem in Sweden and Luxembourg. Results show that, if only the positive impacts are considered, the reduction of CO2 emitted due to the usage of the models is significant, but depends on each context (34%, 73%, and 9%, respectively). However, when both positive and negative impacts are considered, the negative impacts are sometimes higher than the positive impacts (the first and the third model) for a nominal use (1 user). Nevertheless, the balance becomes highly positive again, when these two projects are scaled up (realistic projections with many users). The second model cannot be scaled up, but the balance remains positive, even if the gains are much smaller. More generally, the CO2 emissions gain metrics provided by our method can be used as new metrics for comparing the efficiency of AI models (for reducing CO2 emissions) beyond predictive capacity-based traditional ML evaluation metrics. Based on the lessons learned from our study, we also provide seven global recommendations that can contribute to the reduction of the carbon footprint of ML models in general.