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In the global context, countries must reduce carbon dioxide emissions to "net zero" by 2050 to limit global warming to 1.5 °C above pre-industrial levels. China's Nationally Determined Contributions (NDCs) are to peak carbon dioxide emissions by 2030 and achieve the carbon neutrality target by 2060. To monitor the achievements of the NDCs, establishing an annual-based accounting mechanism is necessary to record the carbon stocks in China, especially for Blue Carbon. The paper aims to present China's first national-scale systematic Blue Carbon accounting for mangroves, salt marshes, and seagrasses, covering the accounting subjects of physical and monetary assets, as well as relevant indicators for investors. It focuses on the design of the accounting method and system, the selection of data categories applied to this system, and the application of China's Blue Carbon accounting based on this accounting system. Taking Blue Carbon accounting as a starting point, this paper analyses the restoration prospects of these ecosystems and their application potential for NDCs in China compared with the terrestrial ecosystems. The results indicate that mangroves are the most cost-effective type of ecosystem, even compared to terrestrial forests. Thus, the paper provides policymakers with a new perspective on the decision-making of carbon sequestration relevant decisions, aiming to promote the monitoring, restoration, and expansion of China's coastal blue carbon ecosystem through the establishment of a sound Blue Carbon accounting system, and to help achieve the carbon neutrality goal in China's NDCs through regular and systematic monitoring of its national Blue Carbon inventories. • China's coastal blue carbon ecosystems are carbon sources rather than carbon sinks. • Mangroves are the most cost-effective coastal blue carbon ecosystem compared to salt marshes and seagrass beds. • The Blue Carbon of mangroves is more cost-effective than the Green Carbon of terrestrial forests when meeting China's Nationally Determined Contributions target of increasing forest stocks. • Establishing guidelines for Blue Carbon data requirements will lay a scientific foundation for formulating strategies and policies to cope with global climate change. [ABSTRACT FROM AUTHOR]

In recent years, the importance of capturing CO 2 has increased due to the necessity of minimizing climate change and the detrimental effects of CO 2 emissions from industrial processes. CO 2 absorption, as one of the most mature carbon capture technologies, has been improved by introducing nanosized particles into liquid absorbents. Nanofluids have been the subject of interest in many studies recently due to their tremendous impact on absorption. This review comprehensively examines the CO 2 absorption behavior for nanofluids through the investigation of different absorption systems. Potential mechanisms for improving the absorption/regeneration performance of nanoabsorbents as well as the synergistic effects of physicochemical properties of nanofluids, such as viscosity and density on CO 2 capture behavior, are reviewed. Nanofluid enhancement factors in terms of absorption rate and capacity towards CO 2 are also compiled. Mathematical models, which have been proposed for calculating mass transfer coefficient and mass diffusivity, are comprehensively outlined. The paper discusses conventional methods for nanofluid preparation affecting the physicochemical properties of nanofluids. Strategies for enhancing nanofluid stability, as well as approaches to examine their stability are discussed. Finally, nanoparticle concentration, types and size of them, and selection of the base liquid absorbent as the key factors influencing the CO 2 removal process by nanofluids, are considered in this paper, as well. [Display omitted] • Synthesis of nanofluids (NFs) and techniques to promote and inspect their stability. • Experimental apparatus and calculation methods for CO 2 absorption by NFs. • Mathematical models for estimating mass transfer coefficient and diffusivity in NFs. • Effective factors in enhancement of nanofluids' CO 2 absorption capacity and rate. [ABSTRACT FROM AUTHOR]

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. • Increase of the use of AI models for optimizing solutions to reduce CO2 emissions. • Training and using AI models also generates CO2 emissions. • Method to quantify positive and negative environmental impacts of AI models. • Evaluations based on three state-of-the-art AI models. • Seven global recommendations for reducing CO2 emissions of AI models. [ABSTRACT FROM AUTHOR]

Although there are some review papers on carbon capture, utilization and storage (CCUS), hardly any of these reviews are focused on the role of CO 2 enhanced oil recovery (EOR) in accelerating carbon neutrality in China. In this review, strategies to achieve carbon neutrality is briefly but critically discussed, followed by a review of CO 2 -EOR as a promising technology. Especially, data analysis, including the number of publications on China's carbon neutrality, per capita CO 2 emissions, China's power generation, and the crude oil production of China's large oilfields, is carried out to make the discussion more comprehensive. Given the large amount of coal consumed in China, the high percent of electricity generated with coal, and the slow penetration of renewables already observed, it seems unlikely that 2060 targets will be met without CCUS. In order to achieve carbon neutrality, both reduction in carbon emissions and increase in carbon sequestration are inevitable. Furthermore, it is concluded that CO 2 storage through EOR is likely to have a bright future. However, there are some critical issues to be solved, including the technical issues, leakage and safety issues, cost issues, policy issues, etc. In order to turn CO 2 -EOR into a reliable and more favorable technology, more research and efforts are needed to solve these issues, including advancing carbon capture technologies, improving storage technologies, developing effective monitoring technologies, deploying government support and incentive policies, etc. • CO 2 emission reduction and sequestration are needed to achieve carbon neutrality. • Carbon capture and storage without utilization is not suitable for China. • CO 2 -enhanced oil recovery (EOR) is more viable as a cost-effective CCUS technology. • CO 2 -EOR is likely to make great contributions to China's carbon neutrality target. • CO 2 leakage risk exists and more research is needed to ensure CO 2 is safely stored. [ABSTRACT FROM AUTHOR]

Globally, all countries have producing different levels of carbon emissions and also facing both the problems of climate change and global warming due high carbon emissions in the atmosphere. Therefore, it is important to cutting carbon emissions in the atmosphere. This is only possible by switching to cleaner fuels, use of innovation technologies and development of carbon capture storages. These can substantially help the nations to reaching carbon neutrality. Given this background, t his paper examines the effect of disaggregated energy consumption, technological innovations, capital on economic output and CO 2 emissions in India for the period of 1990–2018. Based on empirical analysis, our long-run elasticities indicate that disaggregated energy consumption and technological innovations have a positive impact on economic growth, while renewable energy consumption and technological innovations have a positive impact on CO 2 emissions. It implies that more use of energy consumption producing significant amount of CO 2 emissions and by using renewable energy consumption and technological innovations (i.e. carbon capture storages) can significantly lowering CO 2 emissions, which is clearly indicating that India has moving towards carbon neutrality. The causality analysis further indicates a unidirectional causal relationship running from disaggregated energy usage to economic growth and carbon emissions. These empirical findings suggest that the increased consumption of renewable power does not lead to rise carbon emissions, which, in turn, ensures sustainable economic growth. • Examines the disaggregated energy consumption, technological innovations, capital on economic output and CO 2 emissions. • Time series data from 1990 to 2018 was used to analyze the empirical results. • Disaggregated energy consumption and technological innovations have a positive impact on economic growth. • Renewable energy consumption and technological innovations have a positive impact on CO 2 emissions. • Suggest that increased in consumption of renewable power does not lead to rise carbon emissions. [ABSTRACT FROM AUTHOR]