ML Based Solutions for Greenhouse Gas Emission and Impacts on Leading Countries A Preliminary Work
DOI:
https://doi.org/10.31436/ijpcc.v9i1.367Keywords:
Global-warming, Greenhouse Gas, Machine-learning, Algorithm, Time-series, SDG goals, climate action, renewable energyAbstract
This literature review will serve as the basis for a preliminary work that is part of the project on the analysis of greenhouse gas emission and its impact on leading countries. The research's main tasks include taking accurate measurements, understanding how the greenhouse Effect works, identifying instances of it, and interpreting the results while taking into consideration all natural and artificial factors that have an impact on the climate and the earth's environment. It will provide an effort to address the core concern of greenhouse impacts. It also discusses SDG objectives and how it connects to this work, as well as providing a brief overview of climate action and its effects. A brief introduction describes the economic scale, economic structure, and technical level, impact categories on energy use and greenhouse gas emissions, application of machine learning approaches, contradictory results, the environmental cost of algorithms, and the impact of AI in literature reviews. The goal of the literature review is to provide an overview of the methodology and describe the important variables that list the major factors that influence how greenhouse gas emissions are reduced in the environment
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