The Effects of Climate Change to Weather-Related Environmental Hazards: Interlinkages of Economic Factors and Climate Risk
Bibliographic record
Abstract
Climate change has become an increasingly intense global phenomenon in recent years. A great number of researchers support the idea that climate change is strongly connected to some environmental hazards, and specifically, those correlated to extreme weather events. Following the Paris Agreement, and due to the increased concern regarding climate change impacts, several indices have been established. The Climate Change Performance Index (CCPI) includes 59 countries and the EU, which cumulatively emit 92% of global greenhouse gases (GHGs), while the Global Climate Risk Index (CRI) analyzes to what extend countries have been affected by impacts of weather-related loss events. Both indices provide annual scores to each country and rank them based on those scores indicating the existing environmental situation. Our main purpose is to examine whether there is an interconnection between those two indices as well as testify whether economic growth is a great contributor to country’s environmental performance and as a result to climate risk. Using a sample of the reported countries for the year 2019, the latest reported year for both indices, and following a cross-sectional econometric analysis, we provide evidence regarding the connection of CCPI and CRI by using graphs, mapping visualization and econometric estimations in order to draw lines between indices. Moreover, we examine the interlinkages, and we estimate the influence caused by socio-economic factors and emissions levels per country. We provide evidence regarding the high-ranked and low-ranked countries and how they perform not only to an environmental base, but also to an economic base. Regarding the major finding, based on our analysis, no proven causality between CRI and CCPI was observed. Economic growth appears to have a significant impact on CRI but not on the CCPI, for the year 2019, while population density has an impact on both indices. Regarding greenhouse gas emissions, the econometric estimations provide evidence of significance for CRI but not for CCPI. An in-depth understanding of the current situation as well as of the factors affecting the climate conditions will give us the needed elements in order to minimize the adverse impact, if not improve the current situation. It is well known and stated that climate action should be taken so that we bequeath a safer and more sustainable planet to the next generations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".