MétaCan
Menu
Back to cohort
Record W2166890592 · doi:10.3386/w18754

Incentives and Outcomes: China's Environmental Policy

2013· report· en· W2166890592 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNational Bureau of Economic Research · 2013
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Alberta
FundersNational Natural Science Foundation of ChinaNational University of Singapore
KeywordsIncentiveEnvironmental policyChinaBusinessPublic economicsNatural resource economicsEconomicsPolitical scienceMicroeconomicsLaw

Abstract

fetched live from OpenAlex

In generating fast economic growth, China is also generating growing concern about its environmental record. Using 2000-2009 data, we find that, while spending on environmental infrastructure has visible positive environmental impact, city spending is strongly tilted towards transportation infrastructure. Investment in transportation infrastructure correlates strongly with both real GDP growth, a measure of tangible economic growth relevant to city-level Party and government cadres' promotion odds, and with land prices, which affect city governments' revenues from land lease sales. In contrast, city governments' spending on environmental improvements is at best uncorrelated with cadres' promotion odds, and is uncorrelated with local GDP growth and land prices. These findings suggest that, were environmental quality explicitly linked to a cadre's chance of promotion, or were environmental quality to affect land prices substantially, city-level public investment in environmental improvement would rise.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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.

Opus teacher head0.252
GPT teacher head0.443
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it