Exploring the Factors Associated with Climate-Related Issues in a Special Economic Development Zone: Application of a DPSIR Framework
Bibliographic record
Abstract
The rapid global increase in Special Economic Zones (SEZs) raises concerns regarding potential impacts on the environment, especially water use intensity, an increased risk of natural disasters, and an elevated greenhouse gas (GHG) emissions. However, studies examining these impacts are limited. Therefore, the aim of this paper is to examine the influence of SEZ development factors on flooding, water scarcity, and GHG emissions using Tak SEZ in Thailand as a case study. A Driver-Pressure-State-Impact-Response (DPSIR) framework, together with structural equation modeling (SEM) through the partial least squares (PLS) approach, has been used to examine the interrelationships between these factors. The results revealed that economic, industrial, and urban development are key drivers associated with flooding, water scarcity, and GHG emissions in the zone. The increased population density, water consumption, waste generation, and vehicular traffic are all significantly put pressure on climate change impacts. The integration of DPSIR framework together with PLS-SEM technique to explore the relationship among multiple sustainability indicators contributes to the existing sustainability assessment methodology. Future research can utilize the presented indicators to identify potential factors for the evaluation of other types of development zones that have a variety of socio-economic activities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".