The North American Electric Grid as an Exchange Network: An Approach for Evaluating Energy Resource Composition and Greenhouse Gas Mitigation

Using a complex network framework, the North American electric grid is modeled as a dynamic, equilibrium-based supply chain of more than 100 interconnected power control areas (PCAs) in the contiguous United States, Canada, and Northern Mexico. Monthly generation and yearly inter-PCA exchange data reported by PCAs are used to estimate a directed network topology. Variables including electricity, as well as primary fuels, technologies, and greenhouse gas emissions associated with power generation can be traced through the network, providing energy source composition statistics for power consumers at a given location. Results show opportunities for more precise measurement by consumers of emissions occurring on their behalf at power plants. Specifically, we show a larger range of possible factors (∼0 to 1.3 kgCO₂/kWh) as compared to the range provided by the EPA’s eGRID analysis (∼0.4 to 1 kgCO₂/kWh). We also show that 66–73% of the variance in PCA-level estimated emissions savings is the result of PCA-to-PCA differences that are not captured by the larger eGRID subregions. The increased precision could bolster development of effective greenhouse gas reporting and mitigation policies. This study also highlights the need for improvements in the consistency and spatiotemporal resolution of PCA-level generation and exchange data reporting

A large-scale analysis of sex differences in facial expressions

<div><p>There exists a stereotype that women are more expressive than men; however, research has almost exclusively focused on a single facial behavior, smiling. A large-scale study examines whether women are consistently more expressive than men or whether the effects are dependent on the emotion expressed. Studies of gender differences in expressivity have been somewhat restricted to data collected in lab settings or which required labor-intensive manual coding. In the present study, we analyze gender differences in facial behaviors as over 2,000 viewers watch a set of video advertisements in their home environments. The facial responses were recorded using participants’ own webcams. Using a new automated facial coding technology we coded facial activity. We find that women are not universally more expressive across all facial actions. Nor are they more expressive in all positive valence actions and less expressive in all negative valence actions. It appears that generally women express actions more frequently than men, and in particular express more positive valence actions. However, expressiveness is not greater in women for all negative valence actions and is dependent on the discrete emotional state.</p></div

Frequency of facial actions in men and women.

<p>The mean fraction of videos in which inner brow raises, outer brow raises, brow furrows, lip corner pulls and lip corner depressors appeared.</p

Carbon innumeracy

<div><p>Individuals must have a quantitative understanding of the carbon footprint tied to their everyday decisions to make efficient sustainable decisions. We report research of the innumeracy of individuals as it relates to their carbon footprint. In three studies that varied in terms of scale and sample, respondents estimate the quantity of CO₂ released when combusting a gallon of gasoline in comparison to several well-known metrics including food calories and travel distance. Consistently, respondents estimated the quantity of CO₂ from gasoline compared to other metrics with significantly less accuracy while exhibiting a tendency to underestimate CO₂. Such relative absence of carbon numeracy of even a basic consumption habit may limit the effectiveness of environmental policies and campaigns aimed at changing individual behavior. We discuss several caveats as well as opportunities for policy design that could aid the improvement of people’s quantitative understanding of their carbon footprint.</p></div

A simplified visualization of study 2’s results.

<p>A simplified visualization of study 2’s results.</p

The key characteristics of the three studies.

<p>The key characteristics of the three studies.</p

Mixed-effects ANOVA outputs for estimation error.

<p>Mixed-effects ANOVA outputs for estimation error.</p

Summaries of mean estimation error and bias with 95% CIs (all experiments).

<p>Summaries of mean estimation error and bias with 95% CIs (all experiments).</p