Hydrogen is a nearly emission-free energy carrier with many enticing qualities, including wide availability, environmental friendliness, and a high calorific value. There have constantly been a lot of challenges to establish an entire fledge low-carbon hydrogen (LCH) economy in the past century. This study aims to critically analyze the economic, environmental, technological, and policy implementation and division of LCH to find novel solutions, bridging the gaps and giving a perspective approach to the study. Differentiation of various LCH components, including green and blue hydrogen, was also proposed based on the life cycle assessment emissions. Current policy perspectives and promised pledged perspectives are considered to project hydrogen demand in 2030. A thorough economic analysis of LCH system technologies is also conducted from both hydrogen production and storage perspectives by comparing various production and storage systems. Current policies toward LCH were critically viewed from policymakers, consumers, and R & D perspectives, through which several challenges, gaps, and keynote necessities were also stated. Received: 8 October 2023 | Revised: 19 February 2024 | Accepted: 8 March 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are provided as a supplementary file. Author Contribution Statement Harshit Mittal: Conceptualization, Methodology, Software, Validation, Investigation, Resources, Data curation, Writing - original draft, Writing - review & editing, Visualization. Omkar Singh Kushwaha: Conceptualization, Validation, Formal analysis, Writing - review & editing, Visualization, Supervision, Project administration. Supplementary Information Supplementary file

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Geospatial analysis plays a very crucial role in predicting environmental changes, especially in the context of CO2 emissions and climate forecasting. This study uses continent-wise geospatial analysis to assess the potential patterns and implications of CO2 emissions by 2100, using ML techniques. The objective of this research is to spatially integrate the data with ML models to predict the geographical distribution of CO2 emissions at regional levels, thereby ascertaining which factors are predominant in influencing rates of emissions. For this purpose, the world is stratified into continents so that fine-grained regional differences in trends of emissions may be understood, keeping in mind aspects such as population density, industrialization, energy consumption, and climate policies. These are regression, classification, and neural networks that the machine learning algorithms use on satellite data, demographic information, and economic indicators to predict future emissions. This helps explain those hotspots in the first place, regions of high vulnerability to impacts from emissions, and opportunities for targeted mitigation strategies. Ultimately, this study provides a basis for understanding the geographical dynamics of CO2 emissions and more effective strategies by policymakers and environmental researchers to achieve emission reduction and climate change mitigation in the following century.

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