Global Retail Sustainability: A Comparative Analysis of Environmental Management Practices and Technology Adoption Across Developed and Developing Economies
Bibliographic record
Abstract
This paper focuses on the technology used to automate and support retail business functions, including environmental management across different markets. Data were obtained from Nigeria. Additionally, qualitative data from Malaysia, Sweden, and Canada provided context for the scope of the study. The study analysed ecological issues relating to international trade and consumption patterns between 2018 and 2023, highlighting how retailers responded to environmental policies in developing and developed economies. The approach adopted is a convergent parallel design, which integrates qualitative and quantitative data. In this case, primary sustainability performance data from 71 retail businesses in Nigeria were supplemented with qualitative data from other global markets. The model utilised a sustainability maturity model specifically designed for cross-cultural retail settings to assess barriers to success, performance enhancements, and change management at a particular level within a cross-cultural retail setting. The study’s outcome suggests a noteworthy difference across the regions regarding technology adoption, with the most excellent circular economy practices noted among Swedish retailers, followed by Canadian, Malaysian, and Nigerian retailers in that order. The algorithms for supply chain optimisation resulted in average waste reductions of 18-23%, while energy management systems enhanced efficiency by 14-19%. Some of the recommendations made for the study include redressing the balance of environmental performance indicators, developing self-regulatory policies on business ethics for the industry, including local communities in poverty-reduction solution design, and other mechanisms of region- and organisation-tailored implementation strategies relative to the capabilities of the organising entity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".