首页> 外文期刊>Energy Policy >Policy implications for achieving the carbon emission reduction target by 2030 in Japan-Analysis based on a bilevel equilibrium model
【24h】

Policy implications for achieving the carbon emission reduction target by 2030 in Japan-Analysis based on a bilevel equilibrium model

机译:日本到2030年实现碳减排目标的政策含义-基于双级平衡模型的分析

获取原文
获取原文并翻译 | 示例
           

摘要

In Japan, the National Intended Determined Contributions (INDCs) towards post-2020 GHG emission reductions indicates a reduction of 26.0% is expected by the fiscal year 2030. However, regional emissions allowances have not been fully discussed based on Japan's INDCs target, which considers the regional socio-economic features. Given this, this study points out a soft-path for a fair and efficient quota allocation by proposing an integrated bilevel equilibrium model within a hierarchical structure consisting of the national government and 47 prefectural governments. This proposed model can be changed into a single level solvable equilibrium model, which can be solved by Particle Swarm Optimization (PSO) method. The major findings show that Hokkaido, Tokyo and Osaka were allowed the largest emissions quotas, while Tottori, Saga and Okinawa had the lowest emissions quotas. And the equity is necessary to be considered along with improving the emissions efficiency when reallocating carbon emission quotas, otherwise, the initiative to reduce emissions will not take place in prefectures with higher efficiency performance. Based on the findings, energy policy implications can be generated based on the above quantitative analysis to form a fair and efficient emission quota system at a sub-national level.
机译:在日本,针对2020年后温室气体排放量的国家预定减排量(INDC)表示,预计到2030财年将减少26.0%。但是,根据日本的INDC目标,尚未对区域排放配额进行充分讨论。区域社会经济特征。鉴于此,本研究通过在国家政府和47个县政府组成的分级结构中提出了一个综合的两级均衡模型,指出了公平有效分配配额的软路径。该提出的模型可以更改为单级可解平衡模型,可以通过粒子群优化(PSO)方法求解。主要发现表明,北海道,东京和大阪的排放配额最大,而鸟取,佐贺和冲绳的排放配额最低。在重新分配碳排放配额时,必须在提高排放效率的同时考虑公平性,否则,在具有较高效率绩效的州,将不会采取减少排放的举措。基于这些发现,可以基于上述定量分析产生能源政策影响,从而在地方一级形成公平有效的排放配额系统。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号