Legal and Environmental Risks in Cross-Border Procurement: Implications for Ontario Businesses Importing from China with a Focus on Supply Chain and Sustainability
Bibliographic record
Abstract
Purpose: The study not only focuses on the legal risks faced by Ontario businesses in cross-border procurement from China but also considers how sustainability practices and environmental regulations impact these risks. As businesses increasingly prioritize green supply chain management and sustainability, understanding how to mitigate legal risks related to environmental laws, ethical sourcing, and the global push for sustainable practices is critical. The paper explores how these factors intertwine with legal considerations, providing a comprehensive view of the risks businesses face when importing goods and meeting consumer expectations for environmentally responsible products. Methodology: The research utilizes methods, including a mix of statutory analysis, case study, and literature review, to analysis the legal risks associated with cross-border procurement from China. This paper reviewed sources from a mix of academic and legal databases, while it gives priority to studies from the last 10-15 years. Findings: This paper identifies three significant risks facing businesses intellectual property infringement, product liability, and lack of jurisdiction and introduces a fourth: sustainability and environmental compliance. This includes navigating green procurement challenges, the environmental impact of goods being imported, and the potential legal consequences for violating international environmental laws or failing to meet sustainability standards. As sustainability becomes a driving factor in procurement, businesses must adapt to evolving green regulations and ethical sourcing practices to avoid legal pitfalls. Unique Contribution to Theory, Practice and Policy: This study extends Siedel’s Six Forces Model to include environmental and sustainability considerations in business legal risks and international procurement. It adds a dimension to Siedel’s theory, validating that understanding not only the legal but also the environmental and ethical challenges facing businesses is crucial for competitive advantage in cross-border procurement. The paper encourages future research in integrating sustainability with legal risk mitigation strategies in international procurement, offering a new framework that balances both sustainability and legal compliance.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".