Macroeconomic Environment for SDG – FDI Drivers in ASEAN
Bibliographic record
Abstract
Foreign direct investment (FDI) has long been regarded as a key catalyst for economic growth, productivity improvement, and sustainable development in host economies. For developing nations, particularly those in the ASEAN region, FDI not only provides essential financial capital but also facilitates technology transfer, job creation, and integration into global value chains. In the context of ASEAN’s transition toward the ASEAN Economic Community (AEC) and the global pursuit of the Sustainable Development Goals (SDGs), strengthening the macroeconomic environment to attract quality, sustainable FDI inflows has become an important policy priority. However, a lack of comprehensive and comparable macro-level data has constrained efforts to evaluate the FDI environment across ASEAN countries. To address this gap, this study employs the PESTLE framework (Political – Economic – Social – Technological – Legal – Environmental) to examine macroeconomic drivers of FDI inflows in ASEAN and assess how regional integration contributes to achieving the SDGs. By analyzing selected macroeconomic indicators and FDI data from 2005 onward, the study finds that: (i) all six PESTLE dimensions significantly influence FDI inflows across ASEAN countries; and (ii) commitments under the AEC framework have expanded the regional macroeconomic landscape, particularly in economic and legal dimensions, enhancing ASEAN’s capacity to attract sustainable and inclusive investment aligned with SDG objectives.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".