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Research on relationship between carbon dioxide emissions, imports, exports, and population in China

100%

Jin Q.-Y. , Zhang J. , Gao Y.-Q. , Su X. , Fu Y.-S.

tom Vol. 40, nr 2

75--85

Empirical relationships between carbon dioxide emission, imports, exports, and population have been investigated. An empirical model with carbon dioxide emissions, structure and scale of import and exports, populations was built Using ridge regression analysis and observed data from 1985 to 2006 in China, we examined the relationship between each part of carbon dioxide emission and corresponding coefficients, including GIV (gross imports value), GXV (gross exports value), and P (populations). The results have shown that the increasing trend in TCOE (total carbon dioxide emissions) was determined by the exports, while its standard level is determined by population. Increasing the imports may reduce TCOE. Considering working to expand economy, the best ways for China to reduce TCOE are to introduce advanced technology and take actions to guarantee strict execution of cut-emission policy. Although the increasing imports also can reduce TCOE, it is not reasonable for the global cut-emission policy. To control population is not applicable as the immense population base, so government's publicity for low-carbon live is a necessary and feasible way to reduce carbon dioxide emission.

DCSNN optimized with hybrid Border Collie optimization and Archimedes optimization algorithms for solid waste prediction in Chennai

80%

Vidya S. , Janani E. S. V. , Sreedevi B.

tom Vol. 50, nr 1

5--25

The rapid growth of smart cities and industry causes an increase in waste production. The amount of municipal solid waste (MSW) increases by several factors, including population growth, economic status, and consumption trends. The inadequacy of basic trash data is a major issue for managing MSW. Numerous existing models based on solid waste prediction have been presented so far, but none of them predict solid waste accurately and also it consumes more time. To address these concerns, a deep convolutional spiking neural network for solid waste prediction (DCSNN-SWP) is proposed in this paper. Here, the real-time solid waste prediction data are gathered from the quantity of municipal corporation of Chennai (MCC), landfill, garden garbage, and coconut shell reports in Tamil Nadu (Chennai), such as Zone 9 (Nungambakkam), Zone 10 (Kodambakkam) and Zone 13 (Adyar). Then the collected solid waste data are pre-processed using the kernel correlation model. Then the pre-processing data is given to DCSNN-hybrid BCMO and Archimedes optimization algorithm which accurately predicts the solid waste as wet waste, dry waste, horticulture waste, and dumping yard for 2022-2032 years. The proposed DCSNN-SWP method has been implemented in Python.