主題1：Achieving the carbon intensity target of China: A perspective of industrial and energy structure adjustments

主題2：高水平論文的寫作與發(fā)表

主講人：朱幫助教授

時間：2018年1月11日（周四）晚上7：00

地點：金盆嶺校區(qū)7-217

主講人簡介：朱幫助，暨南大學教授、博士生導師，致力于能源市場與碳市場、能源經(jīng)濟與氣候政策研究。青年珠江學者（2016）、廣東省杰出青年基金獲得者（2014）、廣東省高校“千百十工程”省級培養(yǎng)對象（2012）、廣東省高校優(yōu)秀青年教師培養(yǎng)對象（2014）和暨南大學英才計劃第一層次（2016）。主持國家自然科學基金3項、省部級科學基金7項。以第一或通訊作者在Springer出版專著1部，在Omega、Ecological Economics等發(fā)表SSCI、SCI論文20余篇。獲省部級等政府獎勵6項。

講座1摘要：This study proposes a novel least squares support vector machine with mixture kernel function-based integrated model for achieving the China’s carbon intensity target by 2020 from the perspective of industrial and energy structure adjustments. Firstly, we predict the industrial and energy structures by the Markov Chain model and scenario analysis, GDP by scenario analysis, and energy consumption by introducing a novel least squares support vector (LSSVM) machine with mixture kernel function in which particle swarm optimization is employed for searching the optimal model parameters. Secondly, we deduce the carbon intensities and contribution potentials of industrial and energy structure adjustments to achieving the carbon intensity target by 2020 under 27 combined scenarios. The obtained results show that, compared with the LSSVM with single radial basis and polynomial kernel functions, and cointegration equation models, the proposed LSSVM with mixture kernel function can achieve a higher forecasting accuracy for energy consumption. The contribution potential of industrial structure adjustment is greater than that of energy structure adjustment to achieving the carbon intensity target. Each combined scenario can realize carbon intensity target, and the one with GDP low-speed growth, industrial structure medium adjustment and energy structure major adjustment, will be the preferred path to achieving the carbon intensity target.