SignificanceMethane from global rice cultivation currently accounts for one-half of all crop-related greenhouse gas emissions. Several international organizations are advocating reductions in methane emissions from rice by promoting intermittent flooding without accounting for the possibility of large emissions of nitrous oxide (N2O), a long-lived greenhouse gas. Our experimental results suggest that the Indian subcontinent’s N2O emissions from intermittently flooded rice fields could be 30–45 times higher than reported under continuous flooding. Net climate impacts of rice cultivation could be reduced by up to 90% through comanagement of water, nitrogen, and carbon. To do this effectively will require a careful ongoing global assessment of N2O emissions from rice, or we will risk ignoring a very large source of climate impact.
We use a parallelized spatial analytics platform to process the twenty-one year totality of the longest-running time series of night-time lights data—the Defense Meteorological Satellite Program (DMSP) dataset—surpassing the narrower scope of prior studies to assess changes in area lit of countries globally. Doing so allows a retrospective look at the global, long-term relationships between night-time lights and a series of socio-economic indicators. We find the strongest correlations with electricity consumption, CO2 emissions, and GDP, followed by population, CH4 emissions, N2O emissions, poverty (inverse) and F-gas emissions. Relating area lit to electricity consumption shows that while a basic linear model provides a good statistical fit, regional and temporal trends are found to have a significant impact.
Background:
Regulatory analyses of air pollution policies require the use of concentration–response functions and underlying health data to estimate the mortality and morbidity effects, as well as the resulting benefits, associated with policy-related changes in fine particulate matter
(
)]. Common practice by U.S. federal agencies involves using underlying health data and concentration–response functions that are not differentiated by racial/ethnic group.
Objectives:
We aim to explore the policy implications of using race/ethnicity-specific concentration–response functions and mortality data in comparison to standard approaches when estimating the impact of air pollution on non-White racial/ethnic subgroups.
Methods:
Using new estimates from the epidemiological literature on race/ethnicity-specific concentration–response functions paired with race/ethnicity-specific mortality rates, we estimated the mortality impacts of air pollution from all sources from a uniform increase in concentrations and from the regulations imposed by the Mercury Air Toxics Standards.
Results:
Use of race/ethnicity-specific information increased
premature mortality estimates in older populations by 9% and among older Black Americans by 150% for all-source pollution exposure. Under a uniform degradation of air quality and race/ethnicity-specific information, older Black Americans were found to have approximately 3 times higher mortality relative to White Americans, which is obscured under a non–race/ethnicity-specific modeling approach. Standard approaches of using non–racial/ethnic specific information
underestimate
the benefits of the Mercury Air Toxics Standards to older Black Americans by almost 60% and
overestimate
the benefits to older White Americans by 14% relative to using a race/ethnicity-specific modeling approach.
Discussion:
Policy analyses incorporating race/ethnicity-specific concentration–response functions and mortality data relative to nondifferentiated inputs underestimate the overall magnitude of
mortality burden and the disparity in impacts on older Black American populations. Based on our results, we recommend that the best available race/ethnicity-specific inputs are used in regulatory assessments to understand and reduce environmental injustices.
https://doi.org/10.1289/EHP9001
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