Governing Chinese Engagement with the Hydrocarbon-Rich Countries; Examining Chinese Investment in the Hydrocarbon Sector of Canada and Russia
Bibliographic record
Abstract
China’s global quest for energy has been one of the most fascinating developments of the past twenty years. As Chinese state-owned enterprises (SOEs) have increasingly gone ‘global’ in search of energy resources, scholars have explored the rationale and implications of China’s investment abroad. However, existing studies have yet to examine the ability of Chinese SOEs to complete the intended investment projects. Several studies that have noted this gap suggest that researchers should examine the ability of Chinese SOEs to adapt to different institutional environments (Smith and D’Arcy 2013) and to analyze the responses of local stakeholders to Chinese SOEs’ engagement (Abdenur 2017). Responding to their call, my study aims to explain how domestic political economy (more specifically, institutional arrangements and stakeholder relations) shapes the ability of Chinese SOEs to successfully participate in hydrocarbon projects in a host country.\nTo answer this question, I conducted a qualitative comparative study of Chinese engagement in the Canadian and Russian hydrocarbon sector. My research consisted of fieldwork, interviews, and library research in Canada and Russia. I utilized within-case studies – by looking at specific hydrocarbon projects where Chinese SOEs indicated interest to participate - to examine the reception of Chinese SOEs’ investment and loans (or other finance) along the hydrocarbon chain in both countries. My analytical framework combined historical institutionalism with stakeholder theories to analyze the ability of Chinese SOEs to participate in hydrocarbon projects in host societies. My framework proposes that stakeholder politics are shaped by an intervening variable, inter-state relations, which influences the receptiveness of stakeholders toward Chinese SOEs.\nMy research finds that Chinese SOEs’ participation – which includes direct investment, loans, and other finance – in the hydrocarbon industry is determined by host-country institutions and stakeholder politics. Relatedly, Chinese engagement/participation in the hydrocarbon sector varies on the basis of the local needs. I propose that inter-state relations influence the timing of Chinese engagement as they shape stakeholder strategies in recipient countries, while formal and informal institutions interact with stakeholder politics in shaping the ability of Chinese SOEs to participate in hydrocarbon projects. Ultimately, this study explains the responses of investment-recipient countries to foreign direct investment and loans from Chinese SOEs in the hydrocarbon sector. In doing so, it makes theoretical and empirical contributions to the existing scholarship on international business, comparative political economy, and China studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".