考虑要素禀赋差异的各国各行业碳排放轨迹集成比较研究及其对我国的启示

项目名称： 考虑要素禀赋差异的各国各行业碳排放轨迹集成比较研究及其对我国的启示

项目编号： No.71273027

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 廖华

作者单位： 北京理工大学

项目金额： 58万元

中文摘要： 开展碳排放轨迹的内在规律和影响因素研究是制定中长期减排战略和目标的前提。国际经验值得借鉴或警示；但各国要素禀赋条件不同导致碳排放轨迹也不同，任何单个国家的历史排放经验（不论是总量还是结构）都很难作为其他国家的参考。探索各国（特别是发达国家）碳排放轨迹差异及其形成的原因更具参考价值。以往文献基本上是碳排放总量研究或者各行业分别研究，且碳排放轨迹函数形式设定不够灵活（主观设定成EKC曲线或多项式函数形式）。本项目拟将要素禀赋结构和经济结构纳入碳排放分析框架中，构建非平衡多维面板数据样条回归模型，解决模型参数标准误估计和预测结果置信区间估计问题；将国家×行业×时间三个维度的碳排放集结在一个计量模型内，以解决经济结构或行业关联性对各行业碳排放的影响；定量研究全球各国各部门长期历史时期的碳排放演变轨迹差异及其影响因素，开展不同情景下的碳排放预测；为IAM模拟提供参考基准，并形成相应的碳减排政策建议。

中文关键词： 碳排放；多维数据；样条回归；碳峰值；国际比较

英文摘要： Investigating the internal laws or impact factors of carbon emission evolution track is the precondition of making rational CO2 emission reduction strategy and target in the long term. International experiences are worthy of referencing, but due to the different factor endowments across countries, carbon emission evolution tracks are distinct, and any ONE country's historical carbon experience, whether in aggregation or in structure, is difficult to be referenced by another. It is vital to study the differences of carbon tracks across countries. Most conventional literatures focus on aggregate analysis or sectoral study separately, and their model functions are not flexible (usually EKC curves or polynomial functions). This research project tries to (1) include the factor endowments and economic structures into the analysis framework, (2) establish the unbalanced and multi-dimensional panel data spline regression models, (3) propose the estimation method of the standard errors of the parameters and the confidence intervals of the predictors, (4) integrate the carbon emission by country by sector and by year into one econometric model, in order to eliminate the impact of economic structure or interrelationship between sectors (5) quantitatively study the differences of emission evolution tracks across countries a

英文关键词： CO2 Emission；Multi-Dimensional Data；Spine Regression；CO2 Emission Peak；International Comparatives