Optimal endogenous carbon taxes for electric power supply chains with power plants

Nagurney, A Liu, ZG Woolley, T

MATHEMATICAL AND COMPUTER MODELLING 44:9-10 899-916

In this paper, we develop a modeling and computational framework that allows for the determination of optimal carbon taxes applied to electric power plants in the context of electric power supply chain (generation/distribution/consumption) networks. The adoption of carbon/pollution taxes both internationally and regionally has been fueled by global climate change and fuel security risks, with a significant portion of such policy interventions directed at the electric power industry. The general framework that we develop allows for three distinct types of carbon taxation environmental policies, beginning with a completely decentralized scheme in which taxes can be applied to each individual power generator/power plant in order to guarantee that each assigned emission bound is not exceeded, to two versions of a centralized scheme, one which assumes a fixed bound over the entire electric power supply chain in terms of total carbon emissions and the other which allows the bound to be a function of the tax. The behavior of the various decision-makers in the electric power supply chain network is described, along with the three taxation schemes, and the governing equilibrium conditions, which are formulated as finite-dimensional variational inequality problems. Twelve numerical examples are presented in which the optimal carbon taxes, as well as the equilibrium electric power flows and demands, are computed. The numerical results demonstrate, as the theory predicts, that the carbon taxes achieve the desired goal, in that the imposed bounds on the carbon emissions are not exceeded. Moreover, they illustrate the spectrum of scenarios that can be explored in terms of changes in the bounds on the carbon emissions; changes in emission factors; changes in the demand price functions, etc. (c) 2006 Elsevier Ltd. All rights reserved.

melatonin:methods | /neutral/methods | 181

Enhancement of lidar backscatters signal-to-noise ratio using empirical mode decomposition method

Wu, SH Liu, ZS Liu, BY

OPTICS COMMUNICATIONS 267:1 137-144

Lidar is being widely used to monitor meteorological parameters and atmospheric constituents. Applications include meteorology, environmental pollution, atmospheric dynamics and global climate change. Signal processing for lidar applications involve highly nonlinear models and consequently nonlinear filtering. In this paper, we applied a new method, empirical mode decomposition to the lidar signal processing. The denoising approach is done by removal of the proper intrinsic mode functions. The data from the simulation and measurements are analyzed to evaluate this method comparing with the traditional low-pass filter and the multi-pulse averaging. Results show that it is effective-and superior to the band-pass filter and the averaging method. The denoising method also allows less averaging laser shots which is important for the real-time monitoring and for the low cost laser transmitter. (c) 2006 Elsevier B.V. All rights reserved.

melatonin:methods | /neutral/methods | 156

Prognosis of the impact of global climate change on zonal ecosystems of the Volga river basin

Kolomyts, EG

RUSSIAN JOURNAL OF ECOLOGY 37:6 391-401

On the basis of the GISS prognostic climatic model, landscape-ecological scenarios concerning the immediate future of the region are considered in the forms of cartographic and analytical models. These scenarios predict a growing thermoarid bioclimatic trend accompanied by a general northward displacement of zonal boundaries, with corresponding acceleration of the biological cycle and increase in the productivity of boreal forests.

melatonin:methods | /neutral/methods | 144