Brain Fog: Does Air Pollution Make Us Less Productive?

Schmidt, Silke

doi:10.1289/ehp4869

Cited by 8 publications

(3 citation statements)

References 46 publications

(56 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 Note that the increase in standard errors for the analysis on this subset of workers is likely due to a combination of the much smaller sample size and an increase in classical measurement error. Given the long commutes of many of the workers, their amount of pollution exposure may be measured with more error (see Bloom et al 2015 for details regarding the characteristics of the workers in the Working From Home experiment) 17 Including worker fixed effects does not meaningfully affect the results (Appendix Table A1). 11,897 14,886 11,892 14,800 Notes: The logarithm of the indicated dependent variable is the outcome of interest.…”

Section: Robustness Checksmentioning

confidence: 99%

The Effect of Pollution on Worker Productivity: Evidence from Call Center Workers in China

Chang¹,

Zivin²,

Gross

et al. 2019

American Economic Journal: Applied Economics

285

162

View full text Add to dashboard Cite

show abstract

Section: Robustness Checksmentioning

confidence: 99%

The Effect of Pollution on Worker Productivity: Evidence from Call Center Workers in China

Chang¹,

Zivin²,

Gross

et al. 2019

American Economic Journal: Applied Economics

285

162

View full text Add to dashboard Cite

show abstract

“…Thus, from a micro‐perspective, organizations with a colossal bundle of HC may possess higher prospects for considerably diminishing the environmental costs (Sharif et al, 2019). Conversely, environmental pollution is also identified as having possibly the largest influence on HC depreciation via loss of health, labor value, population, and market size (Schmidt, 2019). Again, although HC is found to constructively lower environmental degradation (Bano et al, 2018; Hao et al, 2021; Huang et al, 2021; Rahman et al, 2021), there is also evidence of its positive impact (Haini, 2021; Khan et al, 2021; Li et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

New insight into decoupling carbon emissions from economic growth: Do financialization, human capital, and energy security risk matter?

Iorember,

Gbaka,

Işık

et al. 2023

Review Development Economics

View full text Add to dashboard Cite

Against the backdrop of persistent climate change and deteriorating environmental pressure, this study integrates financialization, human capital, and energy security risks to provide new insight into decoupling carbon emissions from economic growth. The study employs annual panel data on the BRICS (Brazil, Russia, India, China, and South Africa) countries for the period of 1990–2019. The research employs the C‐S ARDL approach and the Tapio decoupling index to assess the decoupling status of the BRICS countries. In addition, this study applies the recently developed Juodis, Karavias, and Sarafidis Granger noncausality test for robustness. The findings offer compelling evidence of an inverted U‐shaped curve, aligning with the environmental Kuznets curve hypothesis. Consequently, the results confirm the decoupling of carbon emissions in the BRICS nations. Furthermore, the Tapio decoupling elasticity index confirms different carbon decoupling statuses among the BRICS. The results show expansive negative decoupling for Brazil, weak decoupling for India and China, and strong decoupling for Russia and South Africa. In terms of policy, achieving strong decoupling status in the BRICS requires that financial institutions' lending and investing strategies align with environmental objectives. In addition, human capital development policies such as increased spending on education should be vigorously pursued to empower people to lead sustainable development projects.

show abstract

“…Exposure to aerosol particles for a long time also have an adverse impact on human health, especially fine aerosol particles from industries/urban emission. Moreover, ambient fine aerosol particles less than 2.5 micrometers have attracted public attention Because they can lead to several potential disease risks, such as respiratory, lung, cardiovascular diseases, even the neurological disorders (Schmidt 2019). However, ambient aerosols are difficult to quantify accurately due to the high spatial and temporal variability.…”

Section: Introductionmentioning

confidence: 99%

Performance of Ahi Hourly Aerosol Optical Property During Frequent Haze-Fog Events: A Case Study of Beijing

Chen

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. The Advanced Himawari Imager (AHI) onboard Himawari-8, a next-generation geostationary meteorological satellite, provided firstly the full-disk aerosol observations every 10 min at sub-kilometer spatial resolution(5 km). This is responsible for retrieving the ground-level particulate matter of fewer than 2.5 micrometers and improving assimilation model. However, the representativeness of AHI L3 hourly Aerosol Optical Thickness (AOT) products remains unclear under different air quality conditions, Especially, over frequently polluted urban areas that feature complex surface characteristics and aerosol models. In this study, One-to-one comprehensive comparisons were conducted to evaluate the performance of three types of AHI L3 AOT products (version 3.0) based on the Aerosol Robotic NETwork (AERONET) aerosol measurements over Beijing. The overall comparisons of AHI and ground AOTs show the AHI merged AOT perform best, which the R is 0.87, RMSE is 0.25 and 52.5% of retrievals fall within the envelope of Expect Error (EE, ±(0.05 + 0.2 * AOTground)). For the different primary pollutants, the results suggested the three types AHI hourly AOT products are more suitable for the fine particulate matters (PM2.5) retrievals, especially the merged AOT with 0.87 of R, 0.29 of RMSE and 58.8% of within EE. Furthermore, when the slight and moderate pollution happened over Beijing, the AHI hourly AOT products perform well. And when the heavy pollution happened, the performance of the AHI merge AOT and L2 mean AOT is better. a case during low to high pollution suggested that AHI merged AOT can capture the similar spatial pattern to the MODIS (Deep Blue) DB or (Dark Target) DTDB merged AOT and has good consistency with ground-based air quality monitoring. These results demonstrate the AHI hourly merged AOT is a promising aerosol retrieval for air quality.

show abstract

Brain Fog: Does Air Pollution Make Us Less Productive?

Cited by 8 publications

References 46 publications

The Effect of Pollution on Worker Productivity: Evidence from Call Center Workers in China

The Effect of Pollution on Worker Productivity: Evidence from Call Center Workers in China

New insight into decoupling carbon emissions from economic growth: Do financialization, human capital, and energy security risk matter?

Performance of Ahi Hourly Aerosol Optical Property During Frequent Haze-Fog Events: A Case Study of Beijing

Contact Info

Product

Resources

About