Abstract
The social cost of carbon dioxide (SC-CO
2
) measures the monetized value of the damages to society caused by an incremental metric tonne of CO
2
emissions and is a key metric informing ...climate policy. Used by governments and other decision-makers in benefit–cost analysis for over a decade, SC-CO
2
estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine
1
(NASEM) highlighted that current SC-CO
2
estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO
2
estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO
2
. Our preferred mean SC-CO
2
estimate is $185 per tonne of CO
2
($44–$413 per tCO
2
: 5%–95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government’s current value of $51 per tCO
2
. Our estimates incorporate updated scientific understanding throughout all components of SC-CO
2
estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO
2
values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.
•Cholera controlled in wealthy cities with major water and sanitation infrastructure.•Impoverished communities at highest risk for cholera.•Household interventions only marginally reduce cholera ...risk.•Water and sanitation infrastructure provides multiple benefits.•New approaches and institutional flexibility needed to address cholera.
Cholera has been eliminated as a public health problem in high-income countries that have implemented sanitation system separating the community’s fecal waste from their drinking water and food supply. These expensive, highly-engineered systems, first developed in London over 150 years ago, have not reached low-income high-risk communities across Asia. Barriers to their implementation in communities at highest risk for cholera include the high capital and operating costs for this technological approach, limited capacity and perverse incentives of local governments, and a decreasing availability of water. Interim solutions including household level water treatment, constructing latrines and handwashing promotion have only marginally reduced the risk of cholera and other fecally transmitted diseases. Increased research to develop and policy flexibility to implement a new generation of solutions that are designed specifically to address the physical, financial and political constraints of low-income communities offers the best prospect for reducing the burden of cholera across Asia.
Deep learning for MRI detection of sports injuries poses unique challenges. To address these difficulties, this study examines the feasibility and incremental benefit of several customized network ...architectures in evaluation of complete anterior cruciate ligament (ACL) tears. Two hundred sixty patients, ages 18–40, were identified in a retrospective review of knee MRIs obtained from September 2013 to March 2016. Half of the cases demonstrated a complete ACL tear (624 slices), the other half a normal ACL (3520 slices). Two hundred cases were used for training and validation, and the remaining 60 cases as an independent test set. For each exam with an ACL tear, coronal proton density non-fat suppressed sequence was manually annotated to delineate: (1) a bounding-box around the cruciate ligaments; (2) slices containing the tear. Multiple convolutional neural network (CNN) architectures were implemented including variations in input field-of-view and dimensionality. For single-slice CNN architectures, validation accuracy of a dynamic patch-based sampling algorithm (0.765) outperformed both cropped slice (0.720) and full slice (0.680) strategies. Using the dynamic patch-based sampling algorithm as a baseline, a five-slice CNN input (0.915) outperformed both three-slice (0.865) and single-slice (0.765) inputs. The final highest performing five-slice dynamic patch-based sampling algorithm resulted in independent test set AUC, sensitivity, specificity, PPV, and NPV of 0.971, 0.967, 1.00, 0.938, and 1.00. A customized 3D deep learning architecture based on dynamic patch-based sampling demonstrates high performance in detection of complete ACL tears with over 96% test set accuracy. A cropped field-of-view and 3D inputs are critical for high algorithm performance.
For an integer
$b\geq 2$
, a positive integer is called a b-Niven number if it is a multiple of the sum of the digits in its base-b representation. In this article, we show that every arithmetic ...progression contains infinitely many b-Niven numbers.
The response of the Antarctic ice sheet (AIS) to changing climate forcings is an important driver of sea-level changes. Anthropogenic climate change may drive a sizeable AIS tipping point response ...with subsequent increases in coastal flooding risks. Many studies analyzing flood risks use simple models to project the future responses of AIS and its sea-level contributions. These analyses have provided important new insights, but they are often silent on the effects of potentially important processes such as Marine Ice Sheet Instability (MISI) or Marine Ice Cliff Instability (MICI). These approximations can be well justified and result in more parsimonious and transparent model structures. This raises the question of how this approximation impacts hindcasts and projections. Here, we calibrate a previously published and relatively simple AIS model, which neglects the effects of MICI and regional characteristics, using a combination of observational constraints and a Bayesian inversion method. Specifically, we approximate the effects of missing MICI by comparing our results to those from expert assessments with more realistic models and quantify the bias during the last interglacial when MICI may have been triggered. Our results suggest that the model can approximate the process of MISI and reproduce the projected median melt from some previous expert assessments in the year 2100. Yet, our mean hindcast is roughly 3/4 of the observed data during the last interglacial period and our mean projection is roughly 1/6 and 1/10 of the mean from a model accounting for MICI in the year 2100. These results suggest that missing MICI and/or regional characteristics can lead to a low-bias during warming period AIS melting and hence a potential low-bias in projected sea levels and flood risks.
Strategies to manage the risks posed by future sea-level rise hinge on a sound characterization of the inherent uncertainties. One of the major uncertainties is the possible rapid disintegration of ...large fractions of the Antarctic ice sheet in response to rising global temperatures. This could potentially lead to several meters of sea-level rise during the next few centuries. Previous studies have typically been silent on two coupled questions: (i) What are probabilistic estimates of this “fast dynamic” contribution to sea-level rise? (ii) What are the implications for strategies to manage coastal flooding risks? Here, we present probabilistic hindcasts and projections of sea-level rise to 2100. The fast dynamic mechanism is approximated by a simple parameterization, designed to allow for a careful quantification of the uncertainty in its contribution to sea-level rise. We estimate that global temperature increases ranging from 1.9 to 3.1 °C coincide with fast Antarctic disintegration, and these contributions account for sea-level rise of 21–74 cm this century (5–95% range, Representative Concentration Pathway 8.5). We use a simple cost-benefit analysis of coastal defense to demonstrate in a didactic exercise how neglecting this mechanism and associated uncertainty can (i) lead to strategies which fall sizably short of protection targets and (ii) increase the expected net costs.
Graduation rates are a key measure of the long-term efficacy of academic interventions. However, challenges to using traditional estimates of graduation rates for underrepresented students include ...inherently small sample sizes and high data requirements. Here, we show that a Markov model increases confidence and reduces biases in estimated graduation rates for underrepresented minority and first-generation students. We use a Learning Assistant program to demonstrate the Markov model's strength for assessing program efficacy. We find that Learning Assistants in gateway science courses are associated with a 9% increase in the six-year graduation rate. These gains are larger for underrepresented minority (21%) and first-generation students (18%). Our results indicate that Learning Assistants can improve overall graduation rates and address inequalities in graduation rates for underrepresented students.
The response of the Antarctic ice sheet (AIS) to changing global temperatures is a key component of sea-level projections. Current projections of the AIS contribution to sea-level changes are deeply ...uncertain. This deep uncertainty stems, in part, from (i) the inability of current models to fully resolve key processes and scales, (ii) the relatively sparse available data, and (iii) divergent expert assessments. One promising approach to characterizing the deep uncertainty stemming from divergent expert assessments is to combine expert assessments, observations, and simple models by coupling probabilistic inversion and Bayesian inversion. Here, we present a proof-of-concept study that uses probabilistic inversion to fuse a simple AIS model and diverse expert assessments. We demonstrate the ability of probabilistic inversion to infer joint prior probability distributions of model parameters that are consistent with expert assessments. We then confront these inferred expert priors with instrumental and paleoclimatic observational data in a Bayesian inversion. These additional constraints yield tighter hindcasts and projections. We use this approach to quantify how the deep uncertainty surrounding expert assessments affects the joint probability distributions of model parameters and future projections.
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