The Impacts of FDI Inflows on Carbon Emissions: Economic Development and Regulatory Quality as Moderators

With the accelerated development of the global economy, environmental issues have gradually become prominent, which in turn hinders further high-quality economic development. As one of the important driving factors, cross-border flowing foreign direct investment (FDI) has played a vital role in promoting economic development, but has also caused environmental degradation in most host countries. Utilizing panel data for the G20 economies from 1996 to 2018, the purpose of this study is to investigate the impacts of FDI inflows on carbon emissions, and further explore the influence channels through the moderating effects of economic development and regulatory quality. To produce more robust and accurate results in this study, the approach of the feasible generalized least squares (FGLS) is utilized. Meanwhile, this study also specifies the heteroscedasticity and correlated errors due to the large differences and serial correlations among the G20 economies. The results indicate that FDI inflows are positively associated with carbon emissions, as well as both economic development and regulatory quality negatively contribute to the impacts of FDI inflows on carbon emissions. It implies that although FDI inflows tend to increase the emissions of carbon dioxide, they are more likely to mitigate carbon emissions in countries with higher levels of economic development and regulatory quality. Therefore, the findings are informative for policymakers to formulate effective policies to help mitigate carbon emissions and eliminate environmental degradation.

EXPRESS: Handling Endogenous Regressors using Copulas: A Generalization to Linear Panel Models with Fixed Effects and Correlated Regressors

This paper proposes a panel data generalization for a recently suggested IVfree estimation method that builds on joint estimation. The author shows how the method can be extended to linear panel models by combining fixed-effects transformations with the common GLS transformation to allow for heterogeneous intercepts. To account for between-regressor dependence, the author proposes determining the joint distribution of the error term and all explanatory variables using a Gaussian copula function, with the distinction that some variables are endogenous and the others are exogenous. The identification does not require any instrumental variables if the regressor-error relation is nonlinear. With a normally distributed error, nonnormally distributed endogenous regressors are therefore required. Monte Carlo simulations assess the finite sample performance of the proposed estimator and demonstrate its superiority to conventional instrumental variable estimation. A specific advantage of the proposed method is that the estimator is unbiased in dynamic panel models with small time dimensions and serially correlated errors; therefore, it is a useful alternative to GMM-style instrumentation. The practical applicability of the proposed method is demonstrated via an empirical example.

Assimilating realistically simulated wide-swath altimeter observations in a high-resolution shelf-seas forecasting system

The impact of assimilating simulated wide-swath altimetry observations from the upcoming Surface Water and Ocean Topography (SWOT) mission is assessed using observing system simulation experiments (OSSEs). These experiments use the Met Office 1.5 km resolution North West European Shelf analysis and forecasting system. In an effort to understand the importance of future work to account for correlated errors in the data assimilation scheme, we simulate SWOT observations with and without realistic correlated errors. These are assimilated in OSSEs along with simulated observations of the standard observing network, also with realistic errors added. It was found that while the assimilation of SWOT observations without correlated errors reduced the RMSE (root mean squared error) in sea surface height (SSH) and surface current speeds by up to 20 %, the inclusion of correlated errors in the observations degraded both the SSH and surface currents, introduced an erroneous increase in the mean surface currents and degraded the subsurface temperature and salinity. While restricting the SWOT data to the inner half of the swath and applying observation averaging with a 5 km radius negated most of the negative impacts, it also severely limited the positive impacts. To realise the full benefits in the prediction of the ocean mesoscale offered by wide-swath altimetry missions, it is crucial that methods to ameliorate the effects of correlated errors in the processing of the SWOT observations and account for the correlated errors in the assimilation are implemented.

TROPOMI tropospheric ozone column data: geophysical assessment and comparison to ozonesondes, GOME-2B and OMI

Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. Aboard the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid-2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column (TrOC) data. Derived with the convective cloud differential (CCD) method, TROPOMI daily TrOC data represent the 3 d moving mean ozone column between the surface and 270 hPa under clear-sky conditions gridded at 0.5∘ latitude by 1∘ longitude resolution. Comparisons to almost 2 years of co-located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between −0.1 and +2.3 DU (<+13 %) when averaged over the tropical belt. The field of the bias is essentially uniform in space (deviations <1 DU) and stable in time at the 1.5–2.5 DU level. However, the record is still fairly short, and continued monitoring will be key to clarify whether observed patterns and stability persist, alter behaviour or disappear. Biases are partially due to TROPOMI and the reference data records themselves, but they can also be linked to systematic effects of the non-perfect co-locations. Random uncertainty due to co-location mismatch contributes considerably to the 2.6–4.6 DU (∼14 %–23 %) statistical dispersion observed in the difference time series. We circumvent part of this problem by employing the triple co-location analysis technique and infer that TROPOMI single-measurement precision is better than 1.5–2.5 DU (∼8 %–13 %), in line with uncertainty estimates reported in the data files. Hence, the TROPOMI precision is judged to be 20 %–25 % better than for its predecessors OMI and GOME-2B, while sampling at 4 times better spatial resolution and almost 2 times better temporal resolution. Using TROPOMI tropospheric ozone columns at maximal resolution nevertheless requires consideration of correlated errors at small scales of up to 5 DU due to the inevitable interplay of satellite orbit and cloud coverage. Two particular types of sampling error are investigated, and we suggest how these can be identified or remedied. Our study confirms that major known geophysical patterns and signals of the tropical tropospheric ozone field are imprinted in TROPOMI's 2-year data record. These include the permanent zonal wave-one pattern, the pervasive annual and semiannual cycles, the high levels of ozone due to biomass burning around the Atlantic basin, and enhanced convective activity cycles associated with the Madden–Julian Oscillation over the Indo-Pacific warm pool. TROPOMI's combination of higher precision and higher resolution reveals details of these patterns and the processes involved, at considerably smaller spatial and temporal scales and with more complete coverage than contemporary satellite sounders. If the accuracy of future TROPOMI data proves to remain stable with time, these hold great potential to be included in Climate Data Records, as well as serve as a travelling standard to interconnect the upcoming constellation of air quality satellites in geostationary and low Earth orbits.

Dysregulated Diurnal Cortisol Pattern and Heightened Night-Time Cortisol in Individuals with Bipolar Disorder

<b><i>Introduction:</i></b> Hypothalamic-pituitary-adrenal (HPA) axis dysregulation may contribute to the symptom burden in bipolar disorder (BD). Further characterization of cortisol secretion is needed to improve understanding of the connection between mood, sleep, and the HPA axis. Here, we observe diurnal cortisol patterns in individuals with BD and healthy controls (HCs) to determine time points where differences may occur. <b><i>Methods:</i></b> Salivary cortisol was measured at 6 time points (wake, 15, 30, and 45 min after wake, between 2:00 and 4:00 p.m. and 10:00 p.m.) for 3 consecutive days in individuals with symptomatic BD (<i>N</i> = 27) and HC participants (<i>N</i> = 31). A general linear model with correlated errors was utilized to determine if salivary cortisol changed differently throughout the day between the 2 study groups. <b><i>Results:</i></b> A significant interaction (<i>F</i> = 2.74, <i>df</i> = 5, and <i>p</i> = 0.02) was observed between the time of day and the study group (BD vs. HC) when modeling salivary cortisol over time, indicating that salivary cortisol levels throughout the day significantly differed between the study groups. Specifically, salivary cortisol in BD was elevated compared to HCs at the 10:00 p.m. time point (<i>p</i> = 0.01). <b><i>Conclusion:</i></b> Significantly higher levels of cortisol in participants with BD in the night-time suggest that the attenuation of cortisol observed in healthy individuals may be impaired in those with BD. Reregulation of cortisol levels may be a target of further study and treatment intervention for individuals with BD.

Assimilating realistically simulated wide-swath altimeter observations in a high-resolution shelf-seas forecasting system

The impact of assimilating simulated wide-swath altimetry observations from the upcoming SWOT mission is assessed using Observing System Simulation Experiments (OSSEs). These experiments use the Met Office 1.5 km resolution North-West European Shelf analysis and forecasting system. In an effort to understand the importance of future work to account for correlated errors in the data assimilation scheme, we simulate SWOT observations with and without realistic correlated errors. These are assimilated in OSSEs along with simulated observations of the standard observing network, also with realistic errors added. It was found that while the assimilation of SWOT observations without correlated errors reduced the RMSE in sea surface height (SSH) and surface current speeds by up to 20 %, the inclusion of correlated errors in the observations degraded both the SSH and surface currents, introduced an erroneous increase in the mean surface currents, and degraded the sub-surface temperature and salinity. While restricting the SWOT data to the inner half of the swath and applying observation averaging with a 5 km radius negated most of the negative impacts, it also severely limited the positive impacts. To realise the full benefits in the prediction of the ocean mesoscale offered by wide-swath altimetry missions it is crucial that methods to ameliorate the effects of correlated errors in the processing of the SWOT observations and to account for the correlated errors in the assimilation are implemented.