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Scaling up electric vehicles (EVs) provides an avenue to mitigate both carbon emissions and air pollution from road transport. The benefits of EV adoption for climate, air quality, and health have been widely documented. Yet, evidence on the distribution of these impacts has not been systematically reviewed, despite its central importance to ensure a just and equitable transition. Here, we perform a systematic review of recent EV studies that have examined the spatial distribution of the emissions, air pollution, and health impacts, as an important aspect of the equity implications. Using the Context-Interventions-Mechanisms-Outcome framework with a two-step search strategy, we narrowed down to 47 papers that met our inclusion criteria for detailed review and synthesis. We identified two key factors that have been found to influence spatial distributions. First, the cross-sectoral linkages may result in unintended impacts elsewhere. For instance, the generation of electricity to charge EVs, and the production of batteries and other materials to manufacture EVs could increase the emissions and pollution in locations other than where EVs are adopted. Second, since air pollution and health are local issues, additional location-specific factors may play a role in determining the spatial distribution, such as the wind transport of pollution, and the size and vulnerability of the exposed populations. Based on our synthesis of existing evidence, we highlight two important areas for further research: (1) fine-scale pollution and health impact assessment to better characterize exposure and health disparities across regions and population groups; and (2) a systematic representation of the EV value chain that captures the linkages between the transport, power and manufacturing sectors as well as the regionally-varying activities and impacts.

 

Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made their mathematical analysis difficult, recent work has established important statistical properties like consistency and asymptotic normality by considering subsampling in lieu of bootstrapping. Though such results open the door to traditional inference procedures, all formal methods suggested thus far place severe restrictions on the testing framework and their computational overhead often precludes their practical scientific use. Here we propose a hypothesis test to formally assess feature significance, which uses permutation tests to circumvent computationally infeasible estimates of nuisance parameters. This test is intended to be analogous to the F-test for linear regression. We establish asymptotic validity of the test via exchangeability arguments and show that the test maintains high power with orders of magnitude fewer computations. Importantly, the procedure scales easily to big data settings where large training and testing sets may be employed, conducting statistically valid inference without the need to construct additional models. Simulations and applications to ecological data, where random forests have recently shown promise, are provided. 

Sorted by the photon fluences of short Gamma-ray Bursts (SGRBs) detected by the Fermi-Gamma Ray Burst Monitor (GBM), nine brightest bursts are selected to perform a comprehensive analysis. All GRB lightcurves are fitted well by 1 to 3 pulses that are modelled by fast-rising exponential decay profile (FRED), within which the resultant rising time is strongly positive-correlated with the full time width at half maxima (FWHM). A photon spectral model involving a cutoff power-law function and a standard blackbody function (CPL + BB) could reproduce the spectral energy distributions of these SGRBs well in the bursting phase. The CPL’s peak energy is found strongly positive-correlated with the BB’s temperature, which indicates they might be from the same physical origin. Possible physical origins are discussed to account for these correlations. 

Abstract We report the detection of a strong thermal component in the short gamma-ray burst 170206A with three intense pulses in its light curves, throughout which the fluxes of this thermal component exhibit fast temporal variability the same as that of the accompanying nonthermal component. The values of the time-resolved low-energy photon index in the nonthermal component are between about −0.79 and −0.16, most of which are harder than the −2/3 expected in the synchrotron emission process. In addition, we found a common evolution between the thermal component and the nonthermal component, E p , CPL ∝ kT BB 0.95 ± 0.28 and F CPL ∝ F BB 0.67 ± 0.18 , where E p,CPL and F CPL are the peak photon energy and corresponding flux of the nonthermal component, and kT BB and F BB are the temperature and corresponding flux of the thermal component, respectively. Finally, we proposed that the photospheric thermal emission and the Comptonization of thermal photons may be responsible for the observational features of GRB 170206A.