Deep Dive Into Sustainable Development Goals Through the Lens of Triple Bottom Line: Past, Present, and Future
Bibliographic record
Abstract
ABSTRACT Recent wars, pandemics, global warming, healthcare crises, and economic strain have profoundly affected the socio‐economic‐environmental conditions of many developed & developing countries, impacting the triple bottom line. In response, governments are striving to address these challenges through the 17 sustainable development goals (SDGs). However, the United Nations (UN) SDGs mid‐term review report has recently attracted attention in response to the urgent call for action against countries' poor performance. Despite ongoing research on SDGs, the existing body of work is scattered. There is a crucial deficit in comprehension regarding the evolution of this research domain, as well as the prevailing research's fundamental focus and implicit assumptions. This study employs bibliometric and content analysis to comprehensively examine the contemporary status of research. The study examines influential works, intellectual structures, and research gaps using citation networks, bibliographic coupling, and thematic mapping. Analyzing 779 documents from January 2001 to February 2024, this study found that research interest surged significantly after 2014 and again in 2023. The United States and China are the leading contributors, whereas African and North Asian countries require greater focus. Furthermore, South Asian institutions and authors are at the forefront of contributions to sustainable development research. Developing countries increasingly emphasize rapid growth coupled with sustainable practices to strengthen the three pillars of the triple bottom line. Keyword analysis indicates that the primary focus areas are life cycle assessment, reverse logistics, social sustainability, circular economy, Industry 4.0, and sustainable business models. Further, within the content analysis, the comparative study of the top 50 cited documents and UN mid‐term SDGs review report advocates for future research on sustainability practices such as lean manufacturing, reverse logistics, life cycle assessment, supply chain management, integration of advanced technologies, and reporting quality to enhance business responsiveness toward society. The results of this study may aid scholarly research, managers, and policymakers in adopting sustainable methodologies, promoting a more enduring future.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".