Bibliographic record
Abstract
Population growth increases pressure on the environment. Immigration may be harmful to the environment because it is the major force of population growth in the United States. However, this argument has not been supported by research findings. A few studies on this topic show that locations with higher numbers of immigrants experience better air quality than locations with greater proportions of U.S.-born residents. This research investigated the environmental impact of immigration through three independent studies. First, I tested the relationship between U.S.-born population, foreign-born population, and air quality across all the U.S. continental counties. This study analyzed the air quality data extracted from the Environmental Quality Index (EQI) provided by the Environmental Protection Agency's (EPA). The results showed that U.S.-born population was associated with worse air quality, while foreign-born population was associated with better air quality. These associations varied by immigrants’ origin and year of entry. Second, I examined the association between populations and air quality across some contiguous U.S. counties over eight years from 2007 to 2014, using the EPA’s Air Quality Index (AQI). I found that total population, U.S.-born population, and foreign-born population were not associated with worse but better air quality. The results indicated that population may not be the root cause of environmental harm. Third, I explored the differential associations between populations and the environment through interviews with Chinese immigrants, Mexican immigrants, and U.S.-born Whites regarding their household environmental behaviors. The research found different environmental behaviors among the three groups. The immigrants tended to use less energy, drive less, and produce less waste. The study suggests that culture has an influence on environmental sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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".