Global Warming and Climate Change (GWCC) Realities is a research paper published in The Nature, Causes, Effects and Mitigation of Climate Change on the Environment (2022). On theSindex it has a DataRank of 0.310. It has been cited 6 times, with 6 citing works in its 1-hop citation network.
The study attempted to investigate the urgency of the global warming and climate change by analyzing the available data from the secondary sources. The document analysis technique was used to examine the available literature. When it comes to the urgency of global warming and climate change, the study showed that there are two schools of thought. One is in support of the motion, claiming that global warming is a real phenomenon triggered by anthropogenic behavior, while the other is opposed to the motion, claiming that global warming and climate change are complicated phenomena, and that forecasting future climates is difficult due to the various players involved, about which climate specialists know little or nothing. Based on document analysis, study infers that there is certain uncertainty about the future of the climate, because climate always changes, and it cannot be certainly affirmed that the climate change is man- made (anthropogenic activities) or is due to natural occurrence. However, it is evident that the global surface temperature, borehole temperature, sea surface temperature, and the sea level is increasing over the years. The study suggests that for the humanity to be certain about their future, treating the global warming and climate change as an act of urgency and working towards prevention and mitigation by limiting the production of greenhouse gases and mindfully consuming the natural resources would be the plausible solution for the larger problem of Global Warming and Climate change.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.292
From this paper's citation signal
Citation Network Contribution
0.0180
From 1 citing papers with measurable signal
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 94% comes from its base citations and 6% from the citation network (1 citing paper contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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