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

主题2：高水平论文的写作与发表

主讲人：朱帮助教授

时间：2018年1月11日（周四）晚上7：00

地点：金盆岭校区7-217

主讲人简介：朱帮助，暨南大学教授、博士生导师，致力于能源市场与碳市场、能源经济与气候政策研究。青年珠江学者（2016）、广东省杰出青年基金获得者（2014）、广东省高校“千百十工程”省级培养对象（2012）、广东省高校优秀青年教师培养对象（2014）和暨南大学英才计划第一层次（2016）。主持国家自然科学基金3项、省部级科学基金7项。以第一或通讯作者在Springer出版专著1部，在Omega、Ecological Economics等发表SSCI、SCI论文20余篇。获省部级等政府奖励6项。

讲座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.