On the Change of China’s Identity, Interests and Behavior in Its Climate Diplomacy From 1999 to 2014
Bibliographic record
Abstract
According to the constructivist international relations theory, the social structure of international politics not only influences an agent’s behavior, but more importantly constructs its identities and interests. Are there any change happened to China’s national identity, interests and behavior after its participation in the global course on tackling climate change? If so, what are these changes? This article uses content analysis to examine the identity and interests change in China’s climate diplomatic discourse from 1999 to 2014 and analyses the data related to China’s action on curbing climate change in the same period. After analyzing, this article finds out that there is a gradual change of China’s identity: From a developing country sharing “common but differentiated responsibilities” to a major developing country that is willing to take responsibilities proactively under the principle of “common but differentiated responsibilities”. As for its national interests, it finds that China’s national interests change in three aspects, namely, level of importance, nature and identities of developed and developing countries in its national interests network. It also finds the change in China’s behavior: China’s carbon intensity per unit of GDP rises slightly during the 1999-2005 period, but it decreases year by year after 2005, which demonstrates China’s fulfillment of its commitment to the world. It is argued that there is a process of inter-constructive practices among China’s identity, interests and behavior. This article analyzes the change of national identity in the speeches of China’s leaders and relevant ministries in international conferences on climate change and China’s Action Reports and White Papers on tackling climate change (1999-2014). Then it analyzes China’s national interest and behavior concerning climate change in the same period and the relations among the three, namely identity, interest and behavior of China. This article adopts a content analyzes of these speeches and reports (1999-2014) to depict China’s national identity change. Then it analyzes the discourse related to China’s national interests in these materials. Next, it analyzes China’s behavior in tackling climate change by looking at the data released by the U.S. Energy Information Administration and PBL in the Netherlands Environmental Assessment Agency. After that, it analyzes the relation among China’s identity, interests and behavior through a comparative study of the three.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".