Global blue carbon accumulation in tidal wetlands increases with climate change

Coastal tidal wetlands produce and accumulate significant amounts of organic carbon (C) that help to mitigate climate change. However, previous data limitations have prevented a robust evaluation of the global rates and mechanisms driving C accumulation. Here, we go beyond recent soil C stock estimates to reveal global tidal wetland C accumulation and predict changes under relative sea level rise, temperature and precipitation. We use data from literature study sites and our new observations spanning wide latitudinal gradients and 20 countries. Globally, tidal wetlands accumulate 53.65 (95%CI: 48.52–59.01) Tg C yr⁻¹, which is ∼30% of the organic C buried on the ocean floor. Modeling based on current climatic drivers and under projected emissions scenarios revealed a net increase in the global C accumulation by 2100. This rapid increase is driven by sea level rise in tidal marshes, and higher temperature and precipitation in mangroves. Countries with large areas of coastal wetlands, like Indonesia and Mexico, are more susceptible to tidal wetland C losses under climate change, while regions such as Australia, Brazil, the USA and China will experience a significant C accumulation increase under all projected scenarios.     

Teacher Stress: What It Is,Why It's Important,How It Can be Alleviated

Teacher stress can be conceptualized as an imbalance between risk and protective factors. Stress emanates from risk factors at the personal, interpersonal, and organizational levels. When risk factors exceed protective factors, teacher ability to cope with adversity is inhibited, likely resulting in stress and pernicious consequences. In this paper we draw on empirical evidence as well as our own professional efforts at reducing stress among novice teachers to explain the phenomenon and recommend interventions.     

The relative effectiveness of private and public schools: evidence from Kenya

ABSTRACT

This paper estimates the relative effectiveness of private and public primary schools in Kenya using data from 4,433 Grade 6 schoolchildren. Using ordinary least squares as a baseline model, we use the proportion of private schools in a district as an instrument in a Heckman two-stage correction framework, as well as propensity score matching models to correct for selection bias. There is a positive private school effect across all models. In the corrected models, we find that private school pupils outperform their public school counterparts by between .24 and .52 standard deviations.