ArticleEnvironmental science and pollution research international2021
Has ridesourcing reduced haze? An analysis using the Didi app.
Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Can transportation infrastructure reduce haze pollution in China?Environmental science and pollution research international · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Environmental degradation, often evidenced by haze, has not only increased the cost of China's economic development, but its unfair distribution among regional development, social hierarchy, and generations has also reduced quality of life. Hence, this study uses cross-sectional data from Didi, a Chinese ridesourcing service company, from 2017 to study the impact mechanism of shared mobility on haze. First, it applies the generalized spatial two-stage least squares model after controlling for urban economic, geographic, transportation, meteorological, and policy variables. We then use urbanization depth (urban agglomeration) and breadth (urban sprawl) variables, which represent the intensity and scope of human activities, respectively, as mediators to test the transmission mechanism from shared mobility to haze. Finally, differences based on urban heterogeneity were tested in the end. The study finds that the scale of ridesourcing and haze show a significant U-shaped relationship: the initial development of ridesourcing has a significant effect on reducing haze, but will then intensify haze as it develops to scales such as those of Beijing and Shenzhen. Moreover, the depth of urbanization is the main transmission mechanism of haze. This conclusion is verified in Tier 3 cities and above; however, the transmission mechanism in Tier 4 and Tier 5 cities is not obvious, indicating that the scale of ridesourcing should be compatible with the scale of urban development. Therefore, the design of emission reduction policies should be considered from the perspective of urban agglomeration.
Indexed as
Identifiers
33876364What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.