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This paper analyses the factors behind the deployment of renewable energy, focusing particularly on the effect of energy efficiency policies and measures. The impact of these factors is appraised within the context of several phases of the use of renewable sources. We therefore apply the quantile regression technique to a set of 21 European Countries in two time spans: from 1990 to 1998, and from 1999 to 2006. We control variables of policy, environment, socioeconomic characteristics, and electricity generation. For the second period, energy efficiency policies and measures concerning renewable sources effectively promote renewables, namely in the take-off phase. We shed light on the lobbying effect of traditional...
Abstract: Biological indicators, particularly benthic macroinvertebrates, are widely used and effective measures of the impact of urbanization on stream ecosystems. A multimetric biological index of urbanization was developed using a large benthic macroinvertebrate dataset (n = 1,835) from the Baltimore, Maryland, metropolitan area and then validated with datasets from Cleveland, Ohio (n = 79); San Jose, California (n = 85); and a different subset of the Baltimore data (n = 85). The biological metrics used to develop the multimetric index were selected using several criteria and were required to represent ecological attributes of macroinvertebrate assemblages including taxonomic composition and richness (number...
Residential energy consumption accounts for 22% of the total energy consumption in the US. However, the impacts of local planning policies, such as increasing density and changing the housing type mix, on residential energy consumption are not well understood. Using Residential Energy Consumption Survey Data from the Energy Information Administration, quantile regression analysis was used to tease out the effects of various factors on entire distribution on the energy consumption spectrum instead of focusing on the conditional average. Results show that while housing size matters for space conditioning, housing type has a more nuanced impact. Self-reported neighborhood density does not seem to have any impact on...
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This data release is comprised of a data set that contains specific conductance and chloride concentration data in HUC-8 watersheds that intersect with areas with unconventional oil and gas plays, and a data set that contains atmospheric nitrogen deposition rates and herbaceous plant species richness values across sample sites in the USA. Associated publication: Koenker, R., Chernozhukov, V., Xuming. and Peng, L., 2017, Handbook of quantile regression: Boca Raton, FL, CRC Press.
Wind power forecasts are in various ways valuable for users in decision-making processes. However, most forecasts are deterministic, and hence possibly important information about uncertainty is not available. Complete information about future production can be obtained by using probabilistic forecasts, and this article demonstrates how such forecasts can be created by means of local quantile regression. The approach has several advantages, such as no distributional assumptions and flexible inclusion of predictive information. In addition, it can be shown that, for some purposes, forecasts in terms of quantiles provide the type of information required to make optimal economic decisions. The methodology is applied...
Residential energy consumption accounts for 22% of the total energy consumption in the US. However, the impacts of local planning policies, such as increasing density and changing the housing type mix, on residential energy consumption are not well understood. Using Residential Energy Consumption Survey Data from the Energy Information Administration, quantile regression analysis was used to tease out the effects of various factors on entire distribution on the energy consumption spectrum instead of focusing on the conditional average. Results show that while housing size matters for space conditioning, housing type has a more nuanced impact. Self-reported neighborhood density does not seem to have any impact on...
The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere–ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions...
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This data set contains atmospheric nitrogen deposition rates and herbaceous plant species richness values across sample sites in the USA, originally used for the publication by Simkin et al. (2016. Conditional vulnerability of plant diversity to atmospheric nitrogen deposition across the United States. Proceedings of the National Academy of Sciences of the United States of America 113: 4086-4091. A portion of this data set was used for example quantile regression analyses in the book chapter Cade (2017. Quantile regression applications in ecology and the environmental sciences. R. Koenker, et al., eds. Handbooks of Modern Statistical Methods: Handbook of Quantile Regression. Chapman & Hall/CRC. The data set contains...
Off-farm work is a growing reality in the US agricultural sector as a whole. Another staple program in the US agriculture is the use of crop insurance. This paper assesses hitherto unaddressed issues of fuel consumption and hence pollution generated by farm households associated with off-farm work and crop insurance. We applied a quantile regression method on a unique national farm-level survey data to address the fuel consumption issues. Results indicate that off-farm work by operators tends to decrease fuel expenses. In contrast, households with crop insurance had higher fuel consumption thereby increasing fuel usage. Finally, our study shows that the net effect of these two activities resulted in an increase...
Residential energy consumption accounts for 22% of the total energy consumption in the US. However, the impacts of local planning policies, such as increasing density and changing the housing type mix, on residential energy consumption are not well understood. Using Residential Energy Consumption Survey Data from the Energy Information Administration, quantile regression analysis was used to tease out the effects of various factors on entire distribution on the energy consumption spectrum instead of focusing on the conditional average. Results show that while housing size matters for space conditioning, housing type has a more nuanced impact. Self-reported neighborhood density does not seem to have any impact on...
Residential energy consumption accounts for 22% of the total energy consumption in the US. However, the impacts of local planning policies, such as increasing density and changing the housing type mix, on residential energy consumption are not well understood. Using Residential Energy Consumption Survey Data from the Energy Information Administration, quantile regression analysis was used to tease out the effects of various factors on entire distribution on the energy consumption spectrum instead of focusing on the conditional average. Results show that while housing size matters for space conditioning, housing type has a more nuanced impact. Self-reported neighborhood density does not seem to have any impact on...
Article Citation: James L. Carter, Alison H. Purcell, Steve V. Fend, and Vincent H. Resh (2009) Development of a local-scale urban stream assessment method using benthic macroinvertebrates: an example from the Santa Clara Basin, California. Journal of the North American Benthological Society: December 2009, Vol. 28, No. 4, pp. 1007-1021.
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Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at various time scales relevant to ecology (e.g., daily, monthly, seasonally). This data release provides supporting data for the journal article titled, "Quantifying uncertainty in coastal salinity regime for biological application using quantile regression," by Yurek et al. (2022). A spatially-resolved model was developed that derives quantile distributions of salinity related...
    map background search result map search result map Water quality, atmospheric nitrogen deposition and herbaceous plant species richness in the USA, 1970-2011 Atmospheric nitrogen deposition and herbaceous plant species richness in the USA Data Release: Modeling coastal salinity regime for biological application Data Release: Modeling coastal salinity regime for biological application Water quality, atmospheric nitrogen deposition and herbaceous plant species richness in the USA, 1970-2011 Atmospheric nitrogen deposition and herbaceous plant species richness in the USA