Improved Environmental Inspections in China and Their Effects

Last registered on July 21, 2022

Pre-Trial

Trial Information

General Information

Title
Improved Environmental Inspections in China and Their Effects
RCT ID
AEARCTR-0009786
Initial registration date
July 21, 2022

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
July 21, 2022, 12:29 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Region

Primary Investigator

Affiliation
Imperial College London

Other Primary Investigator(s)

PI Affiliation
Columbia University

Additional Trial Information

Status
On going
Start date
2019-07-01
End date
2023-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Despite strict environment standards, poor monitoring and targeting are persistent challenges to reducing firm emissions in developing countries. Our proposed project would “piggyback” on China’s national environmental inspection program and add randomized interventions to provide additional emissions information to environmental inspectors. From a pilot study in two Chinese provinces, we have demonstrated that our randomized interventions are both feasible and do indeed affect whether environmental inspections occurred. We propose to roll out the interventions throughout China. We will evaluate the effects of our interventions on a set of outcomes: firm emissions responses using hourly plant-level emission data in the Continuous Emissions Monitoring Systems (CEMS), local air pollution, as well as individual activities, health outcomes, and local economic output.
External Link(s)

Registration Citation

Citation
Almond, Douglas and Shuang Zhang. 2022. "Improved Environmental Inspections in China and Their Effects." AEA RCT Registry. July 21. https://doi.org/10.1257/rct.9786-1.0
Experimental Details

Interventions

Intervention(s)
We analyze the pollution reported by firms using continuous emissions monitoring systems, which are instruments that measure concentrations of pollutant emissions in firm waste streams at high frequency. We use statistical and machine learning techniques to detect anomalies in reported data and report these cases, targeting a randomly-selected subset of the firms for which data reporting problems were detected. We will further examine firms’ polluting behavior following the central environmental inspections. We will further note our intention to monitor firms CEMS data into the future, and will test whether this provides a credible threat that induces them to lower pollution concentrations, relative to statistically-similar control firms.

Intervention Start Date
2021-04-01
Intervention End Date
2023-12-31

Primary Outcomes

Primary Outcomes (end points)
To evaluate firm emission responses to inspections, we follow firm emission data in the CEMS. With the real-time emission database, we will be able to examine medium-term responses and relatively long-term responses.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We analyze the pollution reported by firms using continuous emissions monitoring systems, which are instruments that measure concentrations of pollutant emissions in firm waste streams at high frequency. We use statistical and machine learning techniques to detect anomalies in reported data and report these cases, targeting a randomly-selected subset of the firms for which data reporting problems were detected. We will further examine firms’ polluting behavior following the central environmental inspections. We will further note our intention to monitor firms CEMS data into the future, and will test whether this provides a credible threat that induces them to lower pollution concentrations, relative to statistically-similar control firms.
Experimental Design Details
Randomization Method
Randomization done in office by a computer
Randomization Unit
Firm
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
500 firms
Sample size: planned number of observations
500 firms
Sample size (or number of clusters) by treatment arms
250 treated and 250 control firms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials