Climate Change Vulnerability, Adaptation, and Feedback Hypothesis: A Comparison of Lower-Middle, Upper-Middle, and High-Income Countries
Bibliographic record
Abstract
Governments and policymakers are increasingly concerned about climate change. To cope with this inevitable issue, the SDGs-13 target underscores the importance of developing adaptation measures that reduce its adverse effects and ultimately safeguard both society and the environment. This issue is critical in developing countries, which are unable to counter climate-related risks because they lack adaptive capacity, suitable infrastructure, technology and, most importantly, human and physical capital. By contrast, resource-endowed developed countries have succeeded in integrating adaptative and protective policies into their developmental agenda using human power, technology, and especially investment. Keeping these facts in mind, this study is framed to examine the nexus between climate change, adaptation measures, and economic development across different income groups (lower-middle, upper-middle, and high income), using the Driscoll–Kraay (D/K) standard errors method for panel data from the period of 1995 to 2020. This study incorporates two indices (i.e., adaptive capacity and adaptation readiness) in the adaptation framework. The results demonstrate that developed countries such as Australia, Austria, Belgium, Canada, Denmark, France, Germany, Ireland, New Zealand, Sweden, Switzerland, the USA, and the UK are highly adaptive countries due to their readiness for adaptation. Developing countries with very low levels of readiness have a lower adaptive capacity and are, therefore, more vulnerable to climate change. Additionally, a non-causality test demonstrates that a one-way causality runs from readiness, ecological footprint, GDP, renewable energy, FDI, and natural resource investment to the adaptive capacity in all panels. The developed countries are less vulnerable to climate change because of their well-established economies, rich capital resources, good governance, and timely and effective readiness strategies. Adaptation readiness is a vital tool in capacity building for societal adaptation to minimize the effects of disasters on the living standard of communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".