Impact of climatic shocks on child human capital: evidence from young lives data

This study attempts to assess the impact of two different climatic shocks, drought and excessive rainfall/flood, as perceived by households on child human capital across four countries: Ethiopia, India, Peru and Vietnam – countries with diverse socio-economic backgrounds. Human capital, in this context, subsumes both child learning and health outcomes. The data source is the Young Lives Study and cross-sectional household data on the older cohort of children (between 14 and 16 years of age) are utilized, for the year 2009. The enrolment rate, Peabody Picture Vocabulary Test, Cloze test and Math test scores are used as proxies for child learning outcomes. The health outcome variables being studied are the WHO-defined Body Mass Index z-scores and Height for Age z-scores. The overall objective is to study heterogeneity in responses to shock in terms of the development of human capital across the set of countries considered. Additionally, the study elaborates on the role of institutional help, during crisis, and the role of household characteristics in buffering these climatic shocks.     

Predictors of turnover amongst volunteers: A systematic review and meta-analysis

Volunteers represent a global workforce equivalent to 61 million full-time workers. A significant decline in volunteering has highlighted the urgency to better understand and address turnover amongst volunteers. To address this, we conducted a systematic review and meta-analysis of turnover amongst volunteers. We also examined whether staying or leaving has different predictors. The meta-analysis integrated and synthesized 117 studies, encompassing 1104 effect sizes across 55 335 volunteer workers, to identify and quantify relationships between turnover and the broad range of variables that have been examined in the volunteer work domain. Amongst the strongest predictors of volunteer turnover were attitudinal variables, in particular, job satisfaction (ρ = −.58), affective commitment (ρ = −.58), engagement (ρ = −.54) and organizational commitment (ρ = −.54). Contextual variables that showed the largest effects included communication (ρ = .62), organizational support (ρ = −.61) and the quality of the relationship between volunteers and their leader (leader-member exchange, ρ = −.55). We synthesize our findings into an integrative framework delineating the predictors of volunteer turnover. In doing so, we extend turnover research to consider non-remunerated work contexts and provide a basis for developing turnover theory that is responsive to the unique experience of volunteers.     

Arsenic in North Carolina: public health implications

Arsenic is a known human carcinogen and relevant environmental contaminant in drinking water systems. We set out to comprehensively examine statewide arsenic trends and identify areas of public health concern. Specifically, arsenic trends in North Carolina private wells were evaluated over an eleven-year period using the North Carolina Department of Health and Human Services database for private domestic well waters. We geocoded over 63,000 domestic well measurements by applying a novel geocoding algorithm and error validation scheme. Arsenic measurements and geographical coordinates for database entries were mapped using Geographic Information System techniques. Furthermore, we employed a Bayesian Maximum Entropy (BME) geostatistical framework, which accounts for geocoding error to better estimate arsenic values across the state and identify trends for unmonitored locations. Of the approximately 63,000 monitored wells, 7712 showed detectable arsenic concentrations that ranged between 1 and 806 μg/L. Additionally, 1436 well samples exceeded the EPA drinking water standard. We reveal counties of concern and demonstrate a historical pattern of elevated arsenic in some counties, particularly those located along the Carolina terrane (Carolina slate belt). We analyzed these data in the context of populations using private well water and identify counties for targeted monitoring, such as Stanly and Union Counties. By spatiotemporally mapping these data, our BME estimate revealed arsenic trends at unmonitored locations within counties and better predicted well concentrations when compared to the classical kriging method. This study reveals relevant information on the location of arsenic-contaminated private domestic wells in North Carolina and indicates potential areas at increased risk for adverse health outcomes.     

Accurate modeling of harvesting is key for projecting future forest dynamics: a case study in the Slovenian mountains

Maintaining the provision of multiple forest ecosystem services requires to take into consideration forest sensitivity and adaptability to a changing environment. In this context, dynamic models are indispensable to assess the combined effects of management and climate change on forest dynamics. We evaluated the importance of implementing different approaches for simulating forest management in the climate-sensitive gap model ForClim and compared its outputs with forest inventory data at multiple sites across the European Alps. The model was then used to study forest dynamics in representative silver fir–European beech stands in the Dinaric Mountains (Slovenia) under current management and different climate scenarios. On average, ForClim accurately predicted the development of basal area and stem numbers, but the type of harvesting algorithm used and the information for stand initialization are key elements that must be defined carefully. Empirical harvesting functions that rigorously impose the number and size of stems to remove fail to reproduce stand dynamics when growth is just slightly under- or overestimated, and thus should be substituted by analytical thinning algorithms that are based on stochastic distribution functions. Long-term simulations revealed that both management and climate change negatively impact conifer growth and regeneration. Under current climate, most of the simulated stands were dominated by European beech at the end of the simulation (i.e., 2150 AD), due to the decline of silver fir and Norway spruce caused mainly by harvesting. This trend was amplified under climate change as growth of European beech was favored by higher temperatures, in contrast to drought-induced growth reductions in both conifers. This forest development scenario is highly undesired by local managers who aim at preserving conifers with high economic value. Overall, our results suggest that maintaining a considerable share of conifers in these forests may not be feasible under climate change, especially at lower elevations where foresters should consider alternative management strategies.     

Estimation and simulation for geospatial porosity and permeability data

Reservoir simulation of \(\hbox {CO}_2\) sequestration, energy recovery, and environmental contamination scenarios must be accompanied by uncertainty quantification. Typically this is done by stochastically modeling porosity and permeability fields, simulating realizations based on the model, and then numerically simulating flow and transport. The challenge is to generate simulated porosity and permeability fields with characteristics as similar as possible to those known of the reservoir under study. In this paper we focus on the first two steps above in analyzing a large 3-dimensional array of geospatial porosity data and using the results to produce simulated data with characteristics mimicking those of the original porosity observations. The spatial covariance is empirically approximated from horizontal cross sections of the data via a kernel principle component analysis yielding dimension reduction. Simulations in three dimensions are produced by linking consecutive parallel cross sections via conditioning on a small subarray of the data. The conditional simulations effectively reproduce observed channeling, an important large scale feature of interest in the sub-surface relevant to transport of contaminates. The original porosity data is non-Gaussian and requires additional analysis and transformation to generate both porosity and permeability fields.