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Summary: Anthropogenic activities including the combustion of fossil fuels have led to a dramatic increase in the rate of carbon dioxide (CO2) emission in the last three decades. Since fossil‐based fuels are still the predominant energy source for this century, CO2 is a colossal problem. It is emitted as a consequence of combustion and human activities and is a major greenhouse gas (GHG) that significantly contributes to climate change and global warming, making CO2 emission a worldwide problem. The Intergovernmental Panel on Climate Change (IPCC) has proposed a 45% decrease in anthropogenic CO2 emissions by 2030, with a target of "net‐zero" CO2 emissions by 2050. Despite its harmful effects, CO2 has the potential to be used for a wide range of different industrial needs, after its capture. CO2 capture technologies are still in the early stage of development because of economic and technological issues. However, in the future, carbon capture and related application technologies and methods may become easier and more accessible due to the new developments in the materials synthesis, strategies and skills and inexpensive utilization, and functioning cost of the employed methods. Furthermore, carbon capture system (CCS) might improve the recent power plan system properties. Concerning climate change, carbon capture is deemed as a promising solution to prevent CO2 emissions. CO2 capture, storage, and utilization are garnering intensive interest from scientists worldwide. This review paper identifies and gave particular attention to the literature on the recent CO2 capture technologies, for example, adsorption, absorption, membrane, and algae‐based separation techniques for pre‐combustion, oxy‐fuel combustion, and post‐combustion periods. Aside from all of these capture technologies, the utilization and application of captured CO2 in various industrial fields such as solvents, chemicals, and fuels are evaluated. [ABSTRACT FROM AUTHOR]

Global warming has become the focus of attention of the international community, and the control of carbon dioxide emissions has become one of the necessary choices for the development strategies of countries around the world. Cities are places where carbon dioxide emissions are concentrated. The key to controlling carbon emissions is to control the carbon emissions of cities. My country is currently in the process of rapid urbanization. Quantitative studies of the carbon cycle at the city level will help to take stock of carbon dioxide emissions in cities. On the other hand, it is helpful to understand the status and role of the urban carbon cycle in the process of the regional carbon cycle. Through the analysis and prediction of the elements influencing the carbon cycle of smart cities, this paper first determines the factors affecting smart cities in the carbon cycle process as industrial carbon emission strength factors, industrial structure effects, economic development factors, and population elements. It is found that the major positive factors affecting the significant add of CO2 emissions in smart cities from 2010 to 2019 are economic development factors and demographic factors, including economic development factors GDP/per capita GDP. The per capita contribution to CO2 emissions is higher than the model established by adjusting the affecting elements of overall CO2 emissions, except that the proportion of economic development factors in total CO2 emissions from 2013 to 2015 was lower than the increase in total CO2 emissions. The comparison can better reflect the relation between CO2 emissions and influencing elements. The main determinants affecting CO2 emissions are the expansion of the financial condition, the increase in the average daily population, and the increase in construction work. The adaptation index is judged to be consistent, indicating that the model adjustment effect is good; finally, the green computing in the smart city predicts the carbon cycle process, and the actual value trend line and the predicted value trend line are not much different from the practical value, the forecast error is small, and the prediction results are credible. Global warming has become the focus of attention of the international community, and carbon emission control has become one of the necessary options in the development strategies of countries around the world. Cities are the places where carbon emissions are concentrated. The key to controlling carbon emissions is to control urban carbon emissions. At present, my country is in the process of rapid urbanization. Quantitative research on the carbon cycle at the city level will help to establish an inventory accounting of urban carbon emissions. On the other hand, it is convenient to deeply understand the status and role of the urban carbon cycle in the process of the regional carbon cycle. [ABSTRACT FROM AUTHOR]

Global warming caused by carbon emissions has become increasingly prominent. As the world's second-largest economy, China is under enormous pressure to cut down its carbon dioxide emissions. It is urgent to seek effective methods to forecast carbon emissions and put forward the pointed and effective measures to reduce emissions. In this paper, we first use the software Eviews to make an analysis of randomness on data of carbon emissions in the four representative provinces and city, Beijing, Henan, Guangdong, and Zhejiang, in terms of their carbon emissions data from 1997 to 2017. Then, according to their distinct characteristics, the methods of stationary processing of the difference, moving average, and substituting strong impact points, respectively, are adopted to perform the data preprocessing. Then, model identification, parameter estimation, and model test are carried out to establish the model of ARIMA(p , d, q) for the prediction of the carbon emissions of the four regions, respectively. Finally, the model is used to forecast the data and analyze their tendency for their carbon emissions in the next three years. The results can provide guidance for decision-makers to set reasonable carbon emission reduction targets and take appropriate energy conservation and emission reduction measures. [ABSTRACT FROM AUTHOR]

Nowadays, the transports vehicles are required improvement or alternative source for fuel requirement, because of fuel shortage and reducing the global warming. Due to this reason, the electrical vehicle is more concentrated now. The electrical vehicle reduces the carbon emission and reduces the global warming. The main drawback for electrical vehicle is charging time and battery size. To overcome this problem, we use nanomaterial using batteries (like Li-ion battery and Li-based batteries). The nanomaterial particles increase the performance and storage battery capacity and reduce the size of the batteries. [ABSTRACT FROM AUTHOR]

Nowadays, it is well known that global warming is a great hazard to the planet, and the carbon emissions are a principal source of global warming. For this reason, the customers have become more environment and quality conscious than before, and as a result, they request the firms to be ecofriendly. In this context, it is desirable that companies develop and implement inventory models which consider sustainability issues. Furthermore, the companies face problems of shortages and setting prices in order to persist in a competitive and challenging business. Besides, there exists a kind of items different than the traditional products that it is necessary to feed them until a target weight is reached in order to slaughter and sell to customers. These are named as growing items. In this sense, this research work proposes an inventory model for growing items with imperfect quality when the demand is price sensitive under carbon emissions and shortages. The shortages are fully backordered. The demand is price sensitive according to a polynomial function. The proposed inventory model determines jointly the optimal policy for the selling price of perfect-quality growing items, the order quantity, and the backordering quantity which maximize the expected total profit per unit of time. Some numerical examples are resolved in order to illustrate the use and the applicability of the inventory model. Finally, a sensitivity analysis is conducted and some managerial insights are given. [ABSTRACT FROM AUTHOR]