The Effects on Energy Markets Subjected to Regulatory Changes Using Neural Net Methodology

Abstract

Neural net methodology has been used to model alternative scenarios of fuel utilization. Regulation and legislation to address the problems of energy related pollution such as acid rain, nuclear waste, greenhouse gases, and tailpipe pollution, will alter fuel input ratios with consequential effects in the energy using sectors. Also, alternative input scenarios using clean coal technology, natural gas, and nuclear power have been modeled. Results indicate that large relative increases of coal or nuclear fuel inputs will cause similar substantial increases in electricity generation, and substitution effects will cause a shift of petroleum uses in final consumption from the commercial and residential sectors to the transport sector. Increasing the gas fuel input relative to other fuels causes little disturbance in using sectors. Incremental increases in fuel consumption maintaining constant relative fuel input shares causes little disturbance. On the other hand, massive increases in fuel consumption inputs maintaining constant input shares is likely to be disastrous public policy.