A Review: Keystone Environmental Problems are the Labyrinth Root causes that Resonate together with Socioeconomic Factors on Igniting Global Conflicts and Warfare
Bibliographic record
Abstract

 When mitigating a man-made environmental problem, if it results in the permanent disappearance of one or more other environmental problems, then that mitigated problem can be considered a keystone environmental problem. Based on the aforesaid definition, eight environmental problems have been identified, such as population explosion, air pollution, deforestation, water pollution, scarcity and salination, overexploitation of natural resources, urbanisation (including industrialization, urban sprawl and settlements), intensive farming, and the global energy crisis. It has also been found that there is a significant relationship between these keystone environmental problems and global conflicts. For instance, according to the United Nations Environmental Programme (UNEP), over the last six decades, globally, more than 40% of the internal conflicts have been caused by overexploitation of natural resources, which is a known keystone environmental issue. Thus, the importance of identifying the role of keystone environmental problems in igniting global environmental conflicts and warfare has been widely realised. This study was conducted using qualitative content analysis methodology reveals that labyrinth established by root keystone environmental problems resonates together with socio-economic factors conflagrate global environmental conflicts and warfare, such as Cauvery River conflict between Tamil Nadu and Kerala States in South India, conflict from trespassing fishermen poaching in waters of neighbouring country from both India and Sri Lanka, Environmental Conflict in Northeast India and Bangladesh due to flooding (natural disaster) caused by climate change led to migration, West bank water crisis between Israel and Palestine, Russia’s invasion into Ukraine in 2022, Alto Cenepa war, Grand Ethiopian Renaissance Dam (GERD) crisis between Ethiopia and Egypt, environmental conflicts in Philippines, conflict for land resources in Ethiopia, environmental conflict in Mexico, environmental conflict in Peru, Northeast India, Pakistan, Israel (Gaza), conflict of Mauritania and Senegal, Israel-Palestine (West bank), Somalia- Ethiopia, El Salvador-Honduras, Conflict between Kenyan tribes, dispute between North and South Sudan, and transboundary air pollution (causing acid rain) issue between the USA and Canada.
 Keywords: environment, environmental problems, environmental conflicts, keystone environmental problems, primary environmental problems, wars
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".