Analyzing the Impacts of Inter-Provincial Trade on the Quantitative and Spatial Characteristics of Six Embodied Air Pollutants in China Through Multi-Scenario Simulation
Bibliographic record
Abstract
Inter-provincial trade is accompanied by the transfer of embodied pollution emissions, leading to emissions leakage, thereby hindering the sustainable development of society. Therefore, it is imperative to analyze the characteristics of embodied pollutant emission and spatial transfer driven by inter-provincial trade. In this study, the quantitative and spatial characteristics of the six main embodied pollutants (i.e., SO2, NOX, CO, VOC, PM2.5, and PM10) were analyzed by a hypothetical extraction method (HEM) and complex network analysis (CNA) under an input–output analysis (IOA) framework. Then, the row arrange series (RAS) method was employed to simulate the impacts of varying levels of trade intensity, economic growth rate, and technological progress on embodied pollutants and spatial-transfer characteristics. The major findings are as follows: (i) the increase in inter-provincial trade led to a corresponding rise in embodied pollutant emissions due to the relocation of production activities towards provinces with higher emission intensity. Excessive responsibility was assumed by provinces such as Shanxi and Hebei, engaging in production outsourcing for reducing pollutants. (ii) The macro direction of pollutant transfer paths was from the resource-rich northern and central provinces towards the trade-developed southern provinces. Sectors in the transfer path, such as the industry sectors of Shanxi, Guangdong, Henan, and the transport sector of Henan, exhibited high centrality and dominated pollutant transfer activities in the network. (iii) The industry sector, characterized by substantial energy consumption, was the predominant emitter of all pollutant production-based emissions, accounting for more than 40% of total emissions. This study is conducive to analyzing the impacts of inter-provincial trade on embodied pollutant emissions and developing emissions reduction policies considering equitable allocation of emissions responsibilities from both production and consumption perspectives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".