Creating the Global Greenscape: Developing a Global Market-Entry Framework for the Green and Renewable Technologies
Bibliographic record
Abstract
Purpose – This chapter develops the case for a global Greenscape. It introduces the green global marketplace (Greenscape) to better understand the global green market.Design/methodology/approach – The chapter introduces current green market practices and adopts case study methodology to present three distinct green cases related to renewable energy, process technology and wastewater recycling and their international market activities. The chapter offers discussion on findings and incorporates the novel technique of discourse analysis using Leximancer 3.0.Findings – The case shows how the Greendex Report (2012) positions Brazil, India, China and Russia at the top of the markets for green product penetration. The developed nations of USA, France and Canada make up the bottom rankings. The chapter finds essential elements for creating the global Greenscape and marketing of green technologies.Research limitations/implications (if applicable) – Empirical research testing success pathways and destination opportunities is desirable.Practical implications (if applicable) – The ‘success and failure criteria’ identify how planning, patent and partnerships are essential for successful entry. Specific market research on G(reen) markets, market information, marketing functions for market entry and market diffusion for renewable products and process technologies such as supply chain elements, and how these interrelate with achieving sustainability goals is essential for successful entry.Originality/value of chapter – The chapter offers a novel and original approach to international green market penetration and offers analysis related to the new world BRIC countries that have been little explored.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.027 |
| Scholarly communication | 0.020 | 0.025 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".