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Title of Journal: Stoch Environ Res Risk Assess

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Abbravation: Stochastic Environmental Research and Risk Assessment

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Springer-Verlag

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DOI

10.1016/0041-008x(91)90202-p

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1436-3259

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The impact of the risk environment and energy pric

Authors: Hyungho Youn Byung In Lim Eui Young Lee YongRok Choi
Publish Date: 2010/02/23
Volume: 25, Issue: 3, Pages: 323-330
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Abstract

Energy is closely related to environmental risk A rising fuel price in the 1970s had hurt consumers and caused disturbance to the natural environment Households could not afford high imported energy prices and thus resorted to fuel wood Land competed for fuel wood and agricultural crops and thus high fuel prices strained the environment with respect to the use of land If human health and safe housing were included in environmental risk a high energy price would induce broader environmental risk Households with limited income would not be able to use expensive fossil energy to warm their houses and would depend on only electric mats or blankets to keep warm Such insufficient warming methods would not only threaten their health but would also worsen the condition of their houses The abrupt increase in energy prices in 2007 and 2008 had significantly impacted environmental risk It forced low income households to spend more on energy leaving less for other expenditure segments but had left high income households generally intact This contrasting effect between different income groups had increased the sustainability of the energy risks at the high prices This study shows how risks associated with the household economy have increased in response to the recent dramatic increases in energy prices We develop a method for assessing risk by using the variance of ratios of energy expenditure to current income We then examine how differently the economic change has increased risk across expenditure segments We find energy expenditure as the biggest contributor to the risk In addition we illustrate how energy expenditure has changed the risk profile for each income group with the first group ie the lowest income group experiencing the greatest increase This group hurts the most during days of high energy prices

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  2. Meta-elliptical copulas for drought frequency analysis of periodic hydrologic data
  3. Global sensitivity analysis for a numerical model of radionuclide migration from the RRC “Kurchatov Institute” radwaste disposal site
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  6. Comparison of three updating models for real time forecasting: a case study of flood forecasting at the middle reaches of the Huai River in East China
  7. Moment and Bayesian wavelet regression from spatially correlated functional data
  8. Risk factors contributing to motor vehicle collisions in an environment of uncertainty
  9. Simulation-based risk assessment of contaminated sites under remediation scenarios, planning periods, and land-use patterns—a Canadian case study
  10. Prediction of daily rainfall state in a river basin using statistical downscaling from GCM output
  11. Equivalent diffusion coefficient of clay-rich geological formations: comparison between numerical and analytical estimates
  12. A deformation/blurring-based spatio-temporal model
  13. Regional frequency analysis of rainfall extremes in Southern Malawi using the index rainfall and L-moments approaches
  14. Necessity for post-processing dynamically downscaled climate projections for impact and adaptation studies
  15. Drought forecasting using stochastic models
  16. A new assessment approach for urban ecosystem health basing on maximum information entropy method
  17. Regionalization of precipitation characteristics in the Canadian Prairie Provinces using large-scale atmospheric covariates and geophysical attributes
  18. Statistical algorithms accounting for background density in the detection of UXO target areas at DoD munitions sites
  19. A multistage simulation-based optimization model for water resources management in Tarim River Basin, China
  20. On the performance of a new bivariate pseudo Pareto distribution with application to drought data
  21. A method to reduce the computational requirement while assessing uncertainty of complex hydrological models
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