Strategic development and growth of emerging renewable energy ventures from China
Bibliographic record
Abstract
Purpose Entrepreneurial ventures in the emerging renewable energy sector in China represent a variety of dynamic growth strategies that are stimulated by the domestic energy policy and the evolution of the value chain driven by technological advancements. This paper aims to examine the underlying real options among these first movers to proceed with their further strategic development and finance means. Design/methodology/approach A literature review is launched to look into the emerging renewable energy sector unveiling challenges in the growth and development of renewable energy ventures (REVs) in China. Three main types of REVs are differentiated based on their technology intensity. Findings The prospect of international technology transfer through mergers and acquisitions (M&As) in the next phase of evolution within the sector is articulated. A theoretical framework on complementary developments in the value chain is revealed with four propositions. Practical implications This paper enables the stakeholders in the renewable energy sector to critically reexamine the pathways of strategic development and finance of REVs over an evolving technological landscape. Originality/value This study integrates the theoretical real options into technology management issues pertinent to REVs under the contemporary tactics via cross‐border M&As.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".