煤炭清单与减排政策研究
|
摘要
能源在我国经济社会发展过程中占有举足轻重的地位,同时寸能源也是CO2排放的几乎全部来源。目前我国CO2排放量已经超过美国,成为世界上排放量最多的国家,占比接近1/4。未来我国能源需求的动力依然较强,相应产生的排放问题将愈来愈成为国际关注的焦点,面临较大的国际压力。煤炭是我国CO2排放的最主要来源,占比70%以上,因此,研究我国煤炭相关CO2排放的构成、选择关键领域并实施减排政策,将大大减缓我国CO2排放增长的进程。
CO2排放清单是指一定时期内用于记录和报告人为产生的CO2排放量的详细列表,周期通常为一年。CO2排放清单的编制范围包括国家层面、区域和城市层面、企业层面以及产品生命周期过程等。政府间气候变化专门委员会(IPCC)发布的一系列温室气体清单指南为研究和编制CO2排放清单提供了指导原则。将煤炭这一特定的能源品种作为单独的部分编制相应的清单,对我国而言,具有特别重要的意义,因此本文提出了煤炭清单的概念,煤炭清单,即国家煤炭利用相关的CO2排放清单,是指一定时期内(通常为一年)记录和报告一国范围内因煤炭利用而产生的CO2排放的详细列表。从研究对象上来看,煤炭清单是国家CO2排放清单的一部分。从研究方法上看,一种是在国家CO2排放清单体系下,基于煤炭消费侧的排放清单方法；另一种是基于煤炭生产和销售侧的排放清单方法；而国家C02排放清单则是基于能源消费侧的排放清单方法；产品CO2排放清单是基于产品生命周期过程的排放清单方法。
本文详细分析了当前我国煤炭相关统计体系的主要特点,指出其对于编制煤炭清单的挑战和优势,认为在我国目前的统计体系下,难以应用基于煤炭消费侧的排放清单方法；而我国当前的煤炭生产和销售侧数据相比更适合应用基于煤炭生产和销售侧的排放清单方法来编制煤炭清单,该方法在理论上是IPCC推荐的参考方法的扩展；其现实依据是我国煤炭基本处于自给自足的状态,煤炭销售量主要流向国内用煤部门,进出口量相比生产消费量非常小
本文通过研究发现,2005年电力部门耗煤产生的CO2排放量占煤炭相关排放总量的49%,相比1994年的37.2%有较大提高。由于电力是能源的高级形式,能源电化比例越高,能源效率也较高,因此上述比例在未来还将有不断提高的趋势。将电力作为我国未来减排的重点领域,符合我国的基本国情和未来发展的基本趋势。
本文对电力部门未来CO2排放趋势和减排政策的分析分别采用环境经济学分析和能源系统工程两种方法,前者对于确定未来我国排放总量趋势、煤炭相关排放趋势以及电力相关排放趋势有重要意义,主要采用环境库兹涅茨曲线的基本思想,着重分析CO2排放强度(而非CO2排放量)和人均GDP(即经济发展阶段)之间的关系,结合我国“十二五”期间CO2排放强度下降17%的规划目标,并基于政策一致性原则,确定未来我国CO2的排放趋势及其排放峰值。分析结果表明,我国将在2038年达到CO2排放的峰值,煤炭利用相关CO2排放的峰值将在该年份之前达到,电力相关CO2排放峰值亦将在该年份左右达到。环境经济学方法从政策角度很好地评估了我国CO2排放强度下降目标的涵义,分析表明,我国“十二五”期间提出的排放强度下降日标相比国际经验更为激进,反映了我国在控制C02排放方面所做出的积极努力。
但环境经济学方法仍存在一定局限性,特别是从能源利用到CO2产生的作用过程来看,经历了终端需求、能源转化和能源供应等过程,影响排放的各复杂因素无法体现出来,因此,对具体实施CO2减排政策的指导作用甚微。
能源系统工程方法在电力系统上的应用则分别从微观角度分析了电力的终端需求、发电技术的评价以及发电能源的供应等问题,在电力系统分析中应用两种框架——基于情景设置的核算框架(情景分析框架)和优化分析框架。前者主要基于电力需求主要因素、电力技术发展主要因素的情景设置,对电力需求量和电力供应结构进行“自底向上”核算；分析结果表明,基准情景下,2020、2030、2040和2050年的电力需求分别为5004.00,6418.65,8095.83和9443.16 TWh。从供给侧来看,综合技术、经济、管理、政策以及环境等因素,设置了电力供给结构情景,结果表明,各种技术呈现渐进式发展,水电、核电对传统煤电的替代作用较为明显,碳捕集与碳封存(CCS)技术发展缓慢。本文还特别分析了能源技术成本随着技术规模的变化而变化,应用能源技术学习曲线拟合这种关系,对风电和碳捕集技术进行分析和比较,结果显示,风电的单位CO2减排成本略高于碳捕集技术,但考虑了碳运输和封存成本之后,后者将略高于前者。
优化分析框架是在电力需求量和CO2排放量约束条件下,研究使得电力系统成本最小化的电力供应结构。优化结果表明,除了需要继续发展水电之外,未来我国还需姚优先增加超(超)临界等高参数燃煤机组的比例,不断降低低参数燃煤机组比例。另外,更严格的CO2排放约束使得核电机组的成本有效性也逐渐体现出来。从2040年之后超(超)临界燃煤机组基础上的CCS技术才具有经济竞争力。相比较而言,风电机组由于其自身成本较高、寿命较短等问题,难以有较大的发展,且风电由于寿命较短,其装机规模呈现波动状态。
能源系统工程方法为政府制定电力减排规划政策提供了指导,情景分析过程更为主观,且对技术的评价静态、独立,没有考虑技术特征的动态变化及技术之间的选择过程,但其考虑的因素更为全而,特别是在政策引导、管理因素的考虑上,这些因索是难以量化的；优化分析框架更为客观、科学,考虑各种技术之间的动态变化、选择和互动,但政策目标单一,且由于很多因素难以量化,导致分析可能产生片而性。本文提出了规划政策所需要遵循的主要原则,即系统性、科学性、目标性、完整性和可靠性原则,指出了它们之间的关系。认为当前主要任务是,需要在优化分析框架基础上,继续考虑其它更多、更为复杂的因素,将之量化并纳入到该框架中,即在系统性、科学性和目标性前提下,不断提高完整性。本文基于Lingo软件开发了优化分析程序,为进一步拓展相关研究奠定了重要技术基础。
由于可靠性原则是最难以得到保证的原则,必须依赖健全的能源与碳排放统计、报告和核查体系。这也是当前规划政策所面临的最大障碍。可靠性原则同样也是决定我国能否有效实施基于市场的机制——碳排放交易机制的最重要因素。只有在基于配额拍卖的碳排放交易机制下,可以通过企业的自我选择来解决数据和信息的可靠性问题,但其必须依赖完善的市场体系,这在我国还有相当长一段路需要走。
本文采用层层递进的过程,首先采用基于生产和销售侧的排放清单方法对煤炭清单进行研究,选择关键领域和类别——电力,然后针对电力系统进行环境经济、技术经济以及系统工程学分析,并对相关政策进行评价。主要创新点如下：
第一,提出煤炭清单的概念,并对其理论体系进行初步阐释,划分基于消费侧的排放清单方法与基于生产和销售侧的排放清单方法。在我国煤炭相关生产、销售、消费等活动水平数据、排放因子数据的相关统计体系基础上,认为基于煤炭生产和销售侧的排放清单方法编制煤炭清单具有一定优势,据此编制了2005年煤炭清单,并对其进行不确定性分析。
第二,本文将已经出台的CO2排放强度规划目标纳入到相关环境经济学模型框架中,将目标进行分解,得到不同分解路径下的CO2排放总体趋势。在当前国内外相关研究中尚属首次,该方法具有可移植性,在能耗强度目标甚至其它各种规划目标的分析中都具有参考价值。
第三,本文尝试建立一套应用于电力系统的能源系统工程分析框架,研究其政策涵义。该分析框架包含基于情景设置的核算框架与有约束条件下的优化框架。传统的能源系统工程分析虽然都涉及了各主要方面,但主要针对的是整个能源系统,如LEAP和MARKAL模型,且在电力需求分析中通常采用计量经济分析方法,忽略了形成电力需求的基本本要素,本文采用“拟专家判断”的方法,影响电力需求的各基本要素进行细致分析,包括文献综述、实地调研等,形成一套“拟专家判断”数据,根据相关数据的分布来分析电力需求的情景；电力供应情景分析与一般的专家判断法不同,主要采用因素综合分析法,即厘清影响电力供应技术的技术、经济、管理、政策以及环境等因素,并设置各自的权重,据此对各种技术进行综合判断并设置相应的情景；电力供应优化分析以约束条件下的最优化为基本思想,与MARKAL模型电力系统模块不同的是,本文考虑的是电力系统成本最小化而非投资费用的最小化,且将技术学习曲线纳入到分析框架中。本文还开发了基于Lingo软件的电力供应优化分析程序,该程序具有相当大的灵活性,不仅方便计算,还便于在以后的研究中将更多、更为全面的约束条件考虑在内。
第四,能源系统工程分析方法依赖的重要基础是主要电力技术的评价,在电力技术经济评价中,本文引入了技术学习曲线,结合技术推广路径,对我国风电技术和碳捕集技术进行投资支出、推广时间以及减排成本分析和比较。这一分析路径和方法在目前国内能源研究中还较少见到。
本文从总体上尝试为煤炭利用相关的CO2排放问题提供全面、细致的分析框架,从侧重历史排放的排放清单研究到侧重未来发展的环境经济分析、情景分析与优化框架；从宏观趋势分析到基于详细技术特征的“自底向上”分析；从技术自身的评价到技术战略选择再到技术支持政策与机制分析。对于具有明显交叉特点的能源环境学科,上述分析框架为其建设提供了一定参考,具有重要理论意义。当然,本文没有能力处理能源环境学的方方面面,在具体分析上,侧重于煤炭清单研究与电力系统的情景与优化分析等,这对我国以煤为基础、电为中心的能源结构具有重要现实意义。
Energy plays a very important role in the development of economics and society in our country, almost all of CO2 emissions are from energy use. China has been the largest CO2 emissions country in the world, which accounted for almost a quarter of the world's total emissions after she exceeded the level of US in 2008. The Chinese energy demand in the future will remain strong, and the related CO2 emissions will be more and more focused and pressed by the international society. Coal is the main origination of CO2 emissions in China, accounted for 70% of the total energy related emissions. So research on the coal related CO2 emissions and focus on the main source will largely slow down the growth rate of CO2 emissions in our country.
CO2 emissions inventory is a kind of detailed list, which used for recording and reporting the anthropogenic CO2 emissions during a period, usually, a year. The scope of CO2 emissions inventory includes the national, regional and city-wide, corporate, and the lifecycle of product. The Intergovernmental Panel on Climate Change (IPCC) released several GHG Inventory Guidelines for the purpose of inventory making. It is of special significance to make CO2 emissions inventory related to coal consumption. So the concept of coal inventory is put forward in this thesis, which means the CO2 emissions inventory related to coal consumption in a country. As to the object, coal inventory is a part of the national CO2 emission inventory; but there are two inventory making paths, one is based on the coal consuming side as the national CO2 emissions inventory making, and the other is based on the coal producing and sale side. Lifecycle emissions inventory of a product is based on the lifecycle emissions of the producing process.
This research analyzes the main characteristic of Chinese coal related statistic system, figuring out the challenges and advantages when used for coal inventory making. We conclude that currently it is hard to use the coal consuming based inventory making path, but it is more suitable for coal producing and sale based path to apply in Chinese coal inventory. This path is the extension of the IPCC reference method in theory, in practice it is decided by Chinese coal situation, which characterized as self-dependant.
The result of the coal inventory shows that, coal power related CO2 emissions accounted for 49% of the total CO2 emissions in 2005, which is higher than 37.2% in 1994. For power is the advanced energy product, more energy transformed to power the higher energy efficiency is. So we can estimate that the percentage of coal power related CO2 emissions will even go higher. Take the power sector as the key emissions sources will largely slow down the growth rate of CO2 emissions in China.
The thesis use environmental economics method and energy system engineering method to forecast the power sector related CO2 emissions and analyze the emissions reduction policy. It is of great significance to use the environmental economic method to forecast the total CO2 emissions, coal use related CO2 emissions and power related CO2 emissions. This method based on the Environmental Kuznetz Curve (EKC) to analyze the relationship between CO2 emission intensity and GDP per capita. Then we will combine this relationship to the 17% CO2 emissions intensity target put forward in the national 12th five year plan to get the CO2 emission path based on the policy consistence. The result shows that China will reach the CO2 peak in the year 2038; coal use related CO2 emission will reach the peak before 2038 and coal power related CO2 emissions will reach the peak after the coal related emissions peak. In addition, the environmental economic method can also evaluate the CO2 emissions reduction target, which shows that the reduction target in the 12th five year plan is more aggressive than the international experience, which means China has make active efforts to control the CO2 emission.
But there are limits related to the environmental economic method. The most noticeable is that it cannot reflect the process during which CO2 is produced. The process including power demand, power transformation, and primary energy supply. So the environmental economic method can hardly guide the technology chose and policy making.
Energy system engineering method is used to solve these problems in the power system. It will be based on the micro-characteristic of the power demand sector, power transformation sector and primary energy supply sector. We will apply two analysis frameworks to the power system:the accounting framework based on the scenario setting (scenario analysis) and the optimization framework. The scenario analysis framework based on the setting of the factors that influence the power demand, power transformation technology to calculate the power demand and power supply structure and power related CO2 emissions. The result shows that power demand in 2020,2030,2040 and 2050 will reach 5004.00,6418.65,8095.83 and 9443.16 TWh under the reference scenario. In the supply side, we synthesize the technology factor, economic factor, management factor, policy factor and environmental factor to set the power technology development scenario. We can find that hydro power and nuclear power will increase quickly to substitute the traditional low efficiency coal power, and the development of carbon capture and storage (CCS) is slow. In addition, in this part we discuss the technology learning curve, which explain the fact that the technology cost will reduce as the scale increase. We use the learning curve to analyze the wind power and carbon capture technology, the conclusion of which is that reduction cost of wind power is higher than the carbon capture technology; but when carbon transportation and storage cost included, the latter will be higher than the former.
The optimization framework is considering the total cost of power system under the condition of power demand and CO2 emissions control. The optimization result shows that China need to increase the scale of supercritical or ultra supercritical coal power besides to hydro power. When CO2 emissions control become stricter, the economic advantage of nuclear power will be emerge. CCS technology will become economic competitive after 2040 because of the technological learning effect. For the short lifecycle and high cost, the wind power can hardly account for large percent of the total energy supply.
The energy system engineering method provides suggestions for the government reduction plan. We can find that the scenario analyze is more subjective, static and independent in the technology evaluation. But the factors it considered are more complete, including some measureless factors, such as management factors and policy factors. The optimization framework is more objective and scientific, which consider the dynamic interaction of technologies. But this framework has just one objective and many factors that cannot be measured and included, so the result will inevitably ex parte. So the thesis proposes five principles for policy plan, which are integrated, scientific, targeted, complete and reliable. Currently we need to base on the optimization framework, consider more and more factors which are hard to be measured. The Lingo program developed in this thesis will pave the way for the further research.
The reliable principle is hard to satisfy, which depends on the robust data gathering, reporting and verification system of energy and CO2 emissions. This also is the biggest challenge of the emissions reduction plan. The reliable principle also determines the feasibility of CO2 emissions trading system, which is the market based mechanism. Only under the auction of credits can the emissions trading system realize its economic effective. But this system has to be guaranteed by a mature market mechanism, which has a long way to go in China.
This thesis has the following innovations:
Firstly, the coal inventory concept is put forward and its theoretical framework is explained. Two inventory paths, including coal consuming based path and coal producing and sale based path, are introduced. Based on the current coal related statistical system in China, we suggest coal producing and sale based path is more suitable for China coal inventory making. China coal inventory in 2005 is made using this path.
Secondly, we put the CO2 emissions intensity target into the environmental economic framework to forecast the CO2 emissions, coal use related CO2 emissions and power related CO2 emissions. We also use this model to evaluate the CO2 emissions intensity target. This kind of analysis is not common in the related researches.
Thirdly, the thesis tries to establish an energy system engineering framework in applying to the power system, and evaluate the policy implication. The framework including accounting framework based on scenario setting and optimization framework under several controls. The traditional energy system engineering framework often consider the whole energy system, such as LEAP and MARKAL. In power demand part, former researches mainly use the econometric method to forecast. In this thesis we base on the influential factors that determine each power demand sector, use the accounting method to calculate the power demand. Based on the evaluation in large amount of papers and reports, we get the distribution of each factor and use them to do uncertainty analysis to power demand. Power supply scenario analysis is based on the technological, economic, management, policy and environmental factors that influence the development of each power technology. In the optimization part, we use the optimization method to minimize the system cost, which is different from MARKAL power system module, which targets the minimization of the investment.!n addition, we also include the technological learning curve to the optimization model. Based on the Lingo we develop the program not only for facilitating the calculation, but also for further research.
Fourthly, energy system engineering method is based on the evaluation of each power technology. The thesis introduces the technological learning curve to evaluate the movement of the technology cost. A comparable analysis is used to analysis the cost of wind power and carbon capture technology. This kind of analysis is not common in current research.
The thesis try to provide a comprehensive and meticulous framework for the coal use related CO2 emissions and power sector emissions reduction. From historical analysis on emissions inventory to future analysis including environmental economic analysis, scenario analysis and optimization analysis; from the trend forecast to the bottom up analysis based on the technological characteristic; from the technological evaluation to technological choice and strategy. The framework theoretically provides an important reference for the energy and environmental research. The thesis of course cannot apply it to each subject of energy and environmental research, why we choose coal inventory and power system analysis is based on the energy reality of China, which is coal-foundation and power-centralization.
CO2排放清单是指一定时期内用于记录和报告人为产生的CO2排放量的详细列表,周期通常为一年。CO2排放清单的编制范围包括国家层面、区域和城市层面、企业层面以及产品生命周期过程等。政府间气候变化专门委员会(IPCC)发布的一系列温室气体清单指南为研究和编制CO2排放清单提供了指导原则。将煤炭这一特定的能源品种作为单独的部分编制相应的清单,对我国而言,具有特别重要的意义,因此本文提出了煤炭清单的概念,煤炭清单,即国家煤炭利用相关的CO2排放清单,是指一定时期内(通常为一年)记录和报告一国范围内因煤炭利用而产生的CO2排放的详细列表。从研究对象上来看,煤炭清单是国家CO2排放清单的一部分。从研究方法上看,一种是在国家CO2排放清单体系下,基于煤炭消费侧的排放清单方法；另一种是基于煤炭生产和销售侧的排放清单方法；而国家C02排放清单则是基于能源消费侧的排放清单方法；产品CO2排放清单是基于产品生命周期过程的排放清单方法。
本文详细分析了当前我国煤炭相关统计体系的主要特点,指出其对于编制煤炭清单的挑战和优势,认为在我国目前的统计体系下,难以应用基于煤炭消费侧的排放清单方法；而我国当前的煤炭生产和销售侧数据相比更适合应用基于煤炭生产和销售侧的排放清单方法来编制煤炭清单,该方法在理论上是IPCC推荐的参考方法的扩展；其现实依据是我国煤炭基本处于自给自足的状态,煤炭销售量主要流向国内用煤部门,进出口量相比生产消费量非常小
本文通过研究发现,2005年电力部门耗煤产生的CO2排放量占煤炭相关排放总量的49%,相比1994年的37.2%有较大提高。由于电力是能源的高级形式,能源电化比例越高,能源效率也较高,因此上述比例在未来还将有不断提高的趋势。将电力作为我国未来减排的重点领域,符合我国的基本国情和未来发展的基本趋势。
本文对电力部门未来CO2排放趋势和减排政策的分析分别采用环境经济学分析和能源系统工程两种方法,前者对于确定未来我国排放总量趋势、煤炭相关排放趋势以及电力相关排放趋势有重要意义,主要采用环境库兹涅茨曲线的基本思想,着重分析CO2排放强度(而非CO2排放量)和人均GDP(即经济发展阶段)之间的关系,结合我国“十二五”期间CO2排放强度下降17%的规划目标,并基于政策一致性原则,确定未来我国CO2的排放趋势及其排放峰值。分析结果表明,我国将在2038年达到CO2排放的峰值,煤炭利用相关CO2排放的峰值将在该年份之前达到,电力相关CO2排放峰值亦将在该年份左右达到。环境经济学方法从政策角度很好地评估了我国CO2排放强度下降目标的涵义,分析表明,我国“十二五”期间提出的排放强度下降日标相比国际经验更为激进,反映了我国在控制C02排放方面所做出的积极努力。
但环境经济学方法仍存在一定局限性,特别是从能源利用到CO2产生的作用过程来看,经历了终端需求、能源转化和能源供应等过程,影响排放的各复杂因素无法体现出来,因此,对具体实施CO2减排政策的指导作用甚微。
能源系统工程方法在电力系统上的应用则分别从微观角度分析了电力的终端需求、发电技术的评价以及发电能源的供应等问题,在电力系统分析中应用两种框架——基于情景设置的核算框架(情景分析框架)和优化分析框架。前者主要基于电力需求主要因素、电力技术发展主要因素的情景设置,对电力需求量和电力供应结构进行“自底向上”核算；分析结果表明,基准情景下,2020、2030、2040和2050年的电力需求分别为5004.00,6418.65,8095.83和9443.16 TWh。从供给侧来看,综合技术、经济、管理、政策以及环境等因素,设置了电力供给结构情景,结果表明,各种技术呈现渐进式发展,水电、核电对传统煤电的替代作用较为明显,碳捕集与碳封存(CCS)技术发展缓慢。本文还特别分析了能源技术成本随着技术规模的变化而变化,应用能源技术学习曲线拟合这种关系,对风电和碳捕集技术进行分析和比较,结果显示,风电的单位CO2减排成本略高于碳捕集技术,但考虑了碳运输和封存成本之后,后者将略高于前者。
优化分析框架是在电力需求量和CO2排放量约束条件下,研究使得电力系统成本最小化的电力供应结构。优化结果表明,除了需要继续发展水电之外,未来我国还需姚优先增加超(超)临界等高参数燃煤机组的比例,不断降低低参数燃煤机组比例。另外,更严格的CO2排放约束使得核电机组的成本有效性也逐渐体现出来。从2040年之后超(超)临界燃煤机组基础上的CCS技术才具有经济竞争力。相比较而言,风电机组由于其自身成本较高、寿命较短等问题,难以有较大的发展,且风电由于寿命较短,其装机规模呈现波动状态。
能源系统工程方法为政府制定电力减排规划政策提供了指导,情景分析过程更为主观,且对技术的评价静态、独立,没有考虑技术特征的动态变化及技术之间的选择过程,但其考虑的因素更为全而,特别是在政策引导、管理因素的考虑上,这些因索是难以量化的；优化分析框架更为客观、科学,考虑各种技术之间的动态变化、选择和互动,但政策目标单一,且由于很多因素难以量化,导致分析可能产生片而性。本文提出了规划政策所需要遵循的主要原则,即系统性、科学性、目标性、完整性和可靠性原则,指出了它们之间的关系。认为当前主要任务是,需要在优化分析框架基础上,继续考虑其它更多、更为复杂的因素,将之量化并纳入到该框架中,即在系统性、科学性和目标性前提下,不断提高完整性。本文基于Lingo软件开发了优化分析程序,为进一步拓展相关研究奠定了重要技术基础。
由于可靠性原则是最难以得到保证的原则,必须依赖健全的能源与碳排放统计、报告和核查体系。这也是当前规划政策所面临的最大障碍。可靠性原则同样也是决定我国能否有效实施基于市场的机制——碳排放交易机制的最重要因素。只有在基于配额拍卖的碳排放交易机制下,可以通过企业的自我选择来解决数据和信息的可靠性问题,但其必须依赖完善的市场体系,这在我国还有相当长一段路需要走。
本文采用层层递进的过程,首先采用基于生产和销售侧的排放清单方法对煤炭清单进行研究,选择关键领域和类别——电力,然后针对电力系统进行环境经济、技术经济以及系统工程学分析,并对相关政策进行评价。主要创新点如下：
第一,提出煤炭清单的概念,并对其理论体系进行初步阐释,划分基于消费侧的排放清单方法与基于生产和销售侧的排放清单方法。在我国煤炭相关生产、销售、消费等活动水平数据、排放因子数据的相关统计体系基础上,认为基于煤炭生产和销售侧的排放清单方法编制煤炭清单具有一定优势,据此编制了2005年煤炭清单,并对其进行不确定性分析。
第二,本文将已经出台的CO2排放强度规划目标纳入到相关环境经济学模型框架中,将目标进行分解,得到不同分解路径下的CO2排放总体趋势。在当前国内外相关研究中尚属首次,该方法具有可移植性,在能耗强度目标甚至其它各种规划目标的分析中都具有参考价值。
第三,本文尝试建立一套应用于电力系统的能源系统工程分析框架,研究其政策涵义。该分析框架包含基于情景设置的核算框架与有约束条件下的优化框架。传统的能源系统工程分析虽然都涉及了各主要方面,但主要针对的是整个能源系统,如LEAP和MARKAL模型,且在电力需求分析中通常采用计量经济分析方法,忽略了形成电力需求的基本本要素,本文采用“拟专家判断”的方法,影响电力需求的各基本要素进行细致分析,包括文献综述、实地调研等,形成一套“拟专家判断”数据,根据相关数据的分布来分析电力需求的情景；电力供应情景分析与一般的专家判断法不同,主要采用因素综合分析法,即厘清影响电力供应技术的技术、经济、管理、政策以及环境等因素,并设置各自的权重,据此对各种技术进行综合判断并设置相应的情景；电力供应优化分析以约束条件下的最优化为基本思想,与MARKAL模型电力系统模块不同的是,本文考虑的是电力系统成本最小化而非投资费用的最小化,且将技术学习曲线纳入到分析框架中。本文还开发了基于Lingo软件的电力供应优化分析程序,该程序具有相当大的灵活性,不仅方便计算,还便于在以后的研究中将更多、更为全面的约束条件考虑在内。
第四,能源系统工程分析方法依赖的重要基础是主要电力技术的评价,在电力技术经济评价中,本文引入了技术学习曲线,结合技术推广路径,对我国风电技术和碳捕集技术进行投资支出、推广时间以及减排成本分析和比较。这一分析路径和方法在目前国内能源研究中还较少见到。
本文从总体上尝试为煤炭利用相关的CO2排放问题提供全面、细致的分析框架,从侧重历史排放的排放清单研究到侧重未来发展的环境经济分析、情景分析与优化框架；从宏观趋势分析到基于详细技术特征的“自底向上”分析；从技术自身的评价到技术战略选择再到技术支持政策与机制分析。对于具有明显交叉特点的能源环境学科,上述分析框架为其建设提供了一定参考,具有重要理论意义。当然,本文没有能力处理能源环境学的方方面面,在具体分析上,侧重于煤炭清单研究与电力系统的情景与优化分析等,这对我国以煤为基础、电为中心的能源结构具有重要现实意义。
Energy plays a very important role in the development of economics and society in our country, almost all of CO2 emissions are from energy use. China has been the largest CO2 emissions country in the world, which accounted for almost a quarter of the world's total emissions after she exceeded the level of US in 2008. The Chinese energy demand in the future will remain strong, and the related CO2 emissions will be more and more focused and pressed by the international society. Coal is the main origination of CO2 emissions in China, accounted for 70% of the total energy related emissions. So research on the coal related CO2 emissions and focus on the main source will largely slow down the growth rate of CO2 emissions in our country.
CO2 emissions inventory is a kind of detailed list, which used for recording and reporting the anthropogenic CO2 emissions during a period, usually, a year. The scope of CO2 emissions inventory includes the national, regional and city-wide, corporate, and the lifecycle of product. The Intergovernmental Panel on Climate Change (IPCC) released several GHG Inventory Guidelines for the purpose of inventory making. It is of special significance to make CO2 emissions inventory related to coal consumption. So the concept of coal inventory is put forward in this thesis, which means the CO2 emissions inventory related to coal consumption in a country. As to the object, coal inventory is a part of the national CO2 emission inventory; but there are two inventory making paths, one is based on the coal consuming side as the national CO2 emissions inventory making, and the other is based on the coal producing and sale side. Lifecycle emissions inventory of a product is based on the lifecycle emissions of the producing process.
This research analyzes the main characteristic of Chinese coal related statistic system, figuring out the challenges and advantages when used for coal inventory making. We conclude that currently it is hard to use the coal consuming based inventory making path, but it is more suitable for coal producing and sale based path to apply in Chinese coal inventory. This path is the extension of the IPCC reference method in theory, in practice it is decided by Chinese coal situation, which characterized as self-dependant.
The result of the coal inventory shows that, coal power related CO2 emissions accounted for 49% of the total CO2 emissions in 2005, which is higher than 37.2% in 1994. For power is the advanced energy product, more energy transformed to power the higher energy efficiency is. So we can estimate that the percentage of coal power related CO2 emissions will even go higher. Take the power sector as the key emissions sources will largely slow down the growth rate of CO2 emissions in China.
The thesis use environmental economics method and energy system engineering method to forecast the power sector related CO2 emissions and analyze the emissions reduction policy. It is of great significance to use the environmental economic method to forecast the total CO2 emissions, coal use related CO2 emissions and power related CO2 emissions. This method based on the Environmental Kuznetz Curve (EKC) to analyze the relationship between CO2 emission intensity and GDP per capita. Then we will combine this relationship to the 17% CO2 emissions intensity target put forward in the national 12th five year plan to get the CO2 emission path based on the policy consistence. The result shows that China will reach the CO2 peak in the year 2038; coal use related CO2 emission will reach the peak before 2038 and coal power related CO2 emissions will reach the peak after the coal related emissions peak. In addition, the environmental economic method can also evaluate the CO2 emissions reduction target, which shows that the reduction target in the 12th five year plan is more aggressive than the international experience, which means China has make active efforts to control the CO2 emission.
But there are limits related to the environmental economic method. The most noticeable is that it cannot reflect the process during which CO2 is produced. The process including power demand, power transformation, and primary energy supply. So the environmental economic method can hardly guide the technology chose and policy making.
Energy system engineering method is used to solve these problems in the power system. It will be based on the micro-characteristic of the power demand sector, power transformation sector and primary energy supply sector. We will apply two analysis frameworks to the power system:the accounting framework based on the scenario setting (scenario analysis) and the optimization framework. The scenario analysis framework based on the setting of the factors that influence the power demand, power transformation technology to calculate the power demand and power supply structure and power related CO2 emissions. The result shows that power demand in 2020,2030,2040 and 2050 will reach 5004.00,6418.65,8095.83 and 9443.16 TWh under the reference scenario. In the supply side, we synthesize the technology factor, economic factor, management factor, policy factor and environmental factor to set the power technology development scenario. We can find that hydro power and nuclear power will increase quickly to substitute the traditional low efficiency coal power, and the development of carbon capture and storage (CCS) is slow. In addition, in this part we discuss the technology learning curve, which explain the fact that the technology cost will reduce as the scale increase. We use the learning curve to analyze the wind power and carbon capture technology, the conclusion of which is that reduction cost of wind power is higher than the carbon capture technology; but when carbon transportation and storage cost included, the latter will be higher than the former.
The optimization framework is considering the total cost of power system under the condition of power demand and CO2 emissions control. The optimization result shows that China need to increase the scale of supercritical or ultra supercritical coal power besides to hydro power. When CO2 emissions control become stricter, the economic advantage of nuclear power will be emerge. CCS technology will become economic competitive after 2040 because of the technological learning effect. For the short lifecycle and high cost, the wind power can hardly account for large percent of the total energy supply.
The energy system engineering method provides suggestions for the government reduction plan. We can find that the scenario analyze is more subjective, static and independent in the technology evaluation. But the factors it considered are more complete, including some measureless factors, such as management factors and policy factors. The optimization framework is more objective and scientific, which consider the dynamic interaction of technologies. But this framework has just one objective and many factors that cannot be measured and included, so the result will inevitably ex parte. So the thesis proposes five principles for policy plan, which are integrated, scientific, targeted, complete and reliable. Currently we need to base on the optimization framework, consider more and more factors which are hard to be measured. The Lingo program developed in this thesis will pave the way for the further research.
The reliable principle is hard to satisfy, which depends on the robust data gathering, reporting and verification system of energy and CO2 emissions. This also is the biggest challenge of the emissions reduction plan. The reliable principle also determines the feasibility of CO2 emissions trading system, which is the market based mechanism. Only under the auction of credits can the emissions trading system realize its economic effective. But this system has to be guaranteed by a mature market mechanism, which has a long way to go in China.
This thesis has the following innovations:
Firstly, the coal inventory concept is put forward and its theoretical framework is explained. Two inventory paths, including coal consuming based path and coal producing and sale based path, are introduced. Based on the current coal related statistical system in China, we suggest coal producing and sale based path is more suitable for China coal inventory making. China coal inventory in 2005 is made using this path.
Secondly, we put the CO2 emissions intensity target into the environmental economic framework to forecast the CO2 emissions, coal use related CO2 emissions and power related CO2 emissions. We also use this model to evaluate the CO2 emissions intensity target. This kind of analysis is not common in the related researches.
Thirdly, the thesis tries to establish an energy system engineering framework in applying to the power system, and evaluate the policy implication. The framework including accounting framework based on scenario setting and optimization framework under several controls. The traditional energy system engineering framework often consider the whole energy system, such as LEAP and MARKAL. In power demand part, former researches mainly use the econometric method to forecast. In this thesis we base on the influential factors that determine each power demand sector, use the accounting method to calculate the power demand. Based on the evaluation in large amount of papers and reports, we get the distribution of each factor and use them to do uncertainty analysis to power demand. Power supply scenario analysis is based on the technological, economic, management, policy and environmental factors that influence the development of each power technology. In the optimization part, we use the optimization method to minimize the system cost, which is different from MARKAL power system module, which targets the minimization of the investment.!n addition, we also include the technological learning curve to the optimization model. Based on the Lingo we develop the program not only for facilitating the calculation, but also for further research.
Fourthly, energy system engineering method is based on the evaluation of each power technology. The thesis introduces the technological learning curve to evaluate the movement of the technology cost. A comparable analysis is used to analysis the cost of wind power and carbon capture technology. This kind of analysis is not common in current research.
The thesis try to provide a comprehensive and meticulous framework for the coal use related CO2 emissions and power sector emissions reduction. From historical analysis on emissions inventory to future analysis including environmental economic analysis, scenario analysis and optimization analysis; from the trend forecast to the bottom up analysis based on the technological characteristic; from the technological evaluation to technological choice and strategy. The framework theoretically provides an important reference for the energy and environmental research. The thesis of course cannot apply it to each subject of energy and environmental research, why we choose coal inventory and power system analysis is based on the energy reality of China, which is coal-foundation and power-centralization.
引文
[1]2050中国能源和碳排放研究课题组.2050中国能源和碳排放报告[M].北京：科学出版社,2009.
[2]GB/T 213-2008.煤的发热量测定方法[S].北京：中国标准出版社,2008.
[3]GB/T 3715-1996.煤质及煤分析有关术语[S].北京：中国标准出版社,1996.
[4]GB/T 4754-2002.国民经济行业分类[S].北京：中国标准出版社,2002.
[5] IPCC.1996年IPCC国家温室气体清单指南修订本
[6] IPCC. IPCC国家温室气体清单优良作法和不确定性管理
[7] IPCC土地利用、土地利用变化和林业优良作法指南
[8] IPCC.2006年IPCC国家温室气体清单指南
[9]ISO 14064-1.组织层次上对温室气体排放和清除量的量化和报告的规范及指南.2006
[10]ISO 14064-2.项目层次上对温室气体减排和清除增加的量化、监测和报告的规范及指南.2006
[11]ISO 14064-3.温室气体声明审定与核查的规范及指南.2006
[12] UNFCCC.联合国气候变化框架公约
[13] UNFCCC,非《公约》附件一所列缔约方初次国家信息通报的第六份汇编和综合报告,FCCC/SBI/200E/18/Add.2,25 October 2005.
[14]UNFCCC,《公约》附件一所列缔约方1990-2007年期间国家温室气体清单数据报告,FCCC/SBI/2009/12,21 October 2009.
[15]WBCSD, WRI.温室气体议定书企业核算与报告准则(修订本).2004.
[16]温家宝.2011政府工作报告[EB/OL].新华网,2011-3-15:http://news.xinhuanet.com.
[17]阿维纳什·K.迪克西特著,冯曲,吴桂英译.经济理论中的最优化方法[M].上海：上海三联书店,上海人民出版社,2006.
[18]包能胜,徐军平,倪维斗.以大型风电场为核心的多能源互补发电系统[J].中国能源,2006,(8)：.
[19]滨川圭弘,西川炜一等.能源环境学[M].北京：科学出版社,2003.
[20]布鲁斯·米切尔,资源与环境管理[M].北京：商务印书馆,2005.
[21]蔡博峰,刘春兰,陈操操等.城市温室气体清单研究[M].北京：化学工业出版社,2009.
[22]陈春根,罗国勋.我国电力需求趋势预测及发展对策[J].数量经济技术经济研究,2003(6)：64-67.
[23]陈红敏.中国产业部门的能耗强度特征及节能减排的分类实现路径[J].资源科学,31(7)：1226-1231.
[24]陈红敏.我国对外贸易的能源环境影响——基于隐含流的研究[D].上海：复旦大学,2009.
[25]陈鹏.中国煤炭：性质、分类和利用[M].北京：化学工业出版社,2007.
[26]陈诗一.节能减排与中国工业的双赢发展：2009-2049[J].经济研究,2010年第3期.
[27]陈文颖,吴宗鑫.用MARKAL模型研究中国未来可持续能源发展战略[J].清华大学学报(自然科学版),2001,12.
[28]陈文颖,高鹏飞,何建坤.用MARKAL-MACRO模型研究碳减排对中国能源系统的影响[J].清华大学学报(自然科学版),2004
[29]陈迎等.中国外贸进出口商品中的内涵能源及其政策含义[J].经济研究,2008
[30]戴树桂等.环境化学[M].北京：高等教育出版社,2006.
[31]戴维·R·安德森,丹尼斯·J·斯维尼,托马斯·A·威廉斯著,于淼等译.数据、模型与决策[M].北京：机械工业出版社,2003.
[32]戴星翼,诸大建等.2050上海低碳发展路线图[M].北京：科学出版社,2011.
[33]高鹏飞,陈文颖,何建坤.中国的二氧化碳边际减排成本[J].清华大学学报(自然科学版),2004.9.
[34]郭朝先.中国碳排放因素分解：基于LMDI分解技术[M].中国人口资源与环境,2010年第12期.
[35]国家发展和改革委组织编制.中国应对气候变化国家方案[EB/OL].:新华网,2007-6-4:http://news.xinhuanet.com.
[36]国家发展和改革委员会能源研究所,国家气候变化对策协调小组办公室.中国温室气体清单研究[M].北京：中国环境科学出版社,2007.
[37]国家发展和改革委员会能源研究所.中国2050年低碳发展之路——能源需求暨碳排放情景分析[M].北京：科学出版社,2009.
[38]国家煤矿安全监察局,煤炭信息研究院.中国煤炭工业年鉴2005[R].中国煤炭工业出版社,2007.
[39]国家人口发展战略研究课题组.国家人口发展战略研究报告摘要[N].中国人口报,2007.1.3
[40]国家统计局.2009年国民经济和社会发展统计公报[EB/OL].国家统计局网站,2010-2-25:http://www.stats.gov.cn.
[41]国家统计局.2010年国民经济和社会发展统计公报[EB/OL].国家统计局网站,2011-2-28:http://www.stats.gov.cn.
[42]国家统计局.“十一五”经济社会发展成就系列报告之三：人口总量平稳增长就业形势保持稳定[EB/OL].:国家统计局网站,2011-3-2:http://www.stats.gov.cn.
[43]国家统计局能源统计司.中国能源统计年鉴(2009年)[M].北京：中国统计出版社,2010.
[44]国家统计局能源统计司.中国能源统计年鉴(2010年)[M].北京：中国统计出版社,2011.
[45]国民经济与社会发展第十二个五年规划纲要[EB/OL].2011-3-16: http://news.xinhuanet.com
[46]黄蕊,朱永彬,王铮.上海市能源消费趋势和碳排放高峰估计[J].上海经济研究,2010年第10期.
[47]黄忠,李瑞峰.“十二五”期间我国煤炭供需分析与预测[J].中国煤炭,2010年8月.
[48]姜克隽,胡秀莲,庄幸,刘强.中国2050年低碳情景和低碳发展之路[J].中外能源,2009
[49]姜英等.我国商品褐煤低位发热量回归式的推导[J].煤炭学报,2004
[50]焦树建.关于电厂发电成本计算方法的探讨[J].燃气轮机技术,2000,13(3)：7-10.
[51]李华林,陈文颖,吴宗鑫,吴欧密.西部可持续能源开发利用模型[J].清华大学学报(自然科学版),2006第9期.
[52]李华林,陈文颖,吴宗鑫.用内生技术学习曲对西部能源系统的分析[J].清华大学学报(自然科学版),2007第12期。
[53]李红等.中国温室气体排放清单信息库软件及网页的开发[J].城市环境与城市生态,2000
[54]李力,王凤.中国制造业能源强度因素分解研究[J].数量经济技术经济型究,2008,(10)：66-74.
[55]李为正.MARKAL模性用于电力系统规划中若干问题的研讨[J].数量经济技术经济研究,1986(10)：34-39.
[56]林伯强,经济变化、效率改进与能源需求预测——以中国电力行业为例[J],经济研究,2003(5)57-65
[57]林伯强,蒋竺均.中国二氧化碳的环境库兹涅茨曲线预测及影响因素分析[J].管理世界,2009年第4期.
[58]林伯强,刘希颖.中国城市化阶段的碳排放：影响因素与减排策略[J].经济研究,2010年第8期
[59]刘嘉,陈文颖,刘德顺.中国ESDPM模型及其在交通需求预测中的应用[J].中国人口资源与环境,2011,21(3)：71-75.
[60]刘嘉,陈文颖,刘德顺.中国能源服务需求预测模型[J].清华大学学报(自然科学版),2010,50(3)：481-484.
[61]刘佳明.风电成本的简易估算[J].内蒙古电力技术,2007第4期.
[62]罗杰·伯曼,马越等著,张涛等译.自然资源与环境经济学[M].中国经济出版社,2002.
[63]马忠海,潘自强,贺惠民.中国煤电链温室气体排放系数及其与核电链的比较[J].核科学与工程,1999(9)：268-274.
[64]倪维斗,焦树建,赵十杭,陈宾墨.现代文明的动力[M].山东：山东科学技术出版社,2001
[65]倪维斗,李政,薛元.以煤气化为核心的多联产能源系统—资源/能境/环境整体优化与可持续发展[J].中国工程科学,2000(8)：59-68.
[66]潘克西.煤炭产业组织研究[D].上海：复旦大学,2002.
[67]潘克西.低碳经济与清洁发展——理想·现实·行动方案[J].上海节能,2009年第9期.
[68]彭佳雯,黄贤金,钟太洋,赵雲泰.中国经济增长与能源碳排放的脱钩研究[J].资源科学,2011,33(4)：626-633.
[69]祁国燕,曾红颖.一些国家人均GDP从800美元到3000美元能源、汽车、钢铁消费变化[J].中国经贸导刊,2003(20)：26-27.
[70]齐晔等.中国进出口贸易中的隐含碳估算[J].中国人口资源与环境,2008.
[71]渠慎宁,郭朝先.基于STIRPAT模型的中国碳排放峰值预测研究[J].中国人口资源与环境,2010年第12期.
[72]让-雅克·拉丰,大卫·马赫蒂摩.激励理论(第一卷)：委托-代理模型[M].北京：中国人民大学出版社,2002.
[73]孙宇飞.城市碳排放清单及其相关因素分析[D].上海：复旦大学,2011.
[74]涂华等.利用固定碳计算我国无烟煤的含碳量[J].煤炭科学技术,2003.
[75]托马斯·思德纳蒋.张蔚文等译.环境与自然资源管理的政策工具[M].上海三联书店,上海人民出版社,2005.
[76]王克,王灿,吕学都,陈吉宁.基于LEAP的中国钢铁行业CO2减排潜力分析[J].清华大学学报(自然科学版),2006年第12期.
[77]魏一鸣等.能源-经济-环境复杂系统建模与应用进展[J].管理学报,2005,(3)：
[78]吴敬琏.中国应当走一条什么样的工业化道路?[J].管理世界,2006,(8)：1-7.
[79]吴宗鑫,刘滨,齐志新.美国和日本能源消费比较对我国的启示[J].当代石油石化, 2005,13(9)：25-37.
[80]夏德建,任玉珑,史乐峰.中国煤电能源链的生命周期碳排放系数计量[J].统计研究,2010,(8)：82-89.
[81]晓斌等.中国洁净煤发电的生命周期清单分析[J].洁净煤技术,2005.
[82]谢金星,薛毅.优化建模与LINDO/LINGO(?)软件[M].北京：清华大学出版社,2005.
[83]邢芳方,欧阳志云等.北京终端能源碳消费清单与结构[J].环境科学,2007,28(9)：1918-1923.
[84]徐亮.2010年、2015年中国煤炭需求预测研究[J].中国煤炭,2010年5月.
[85]杨宏伟,IPCC能源清单指南进展及其对中国的影响[J].气候变化研究进展,2006.
[86]杨玉峰等.温室气体排放总量计算的不确定性及对清洁发展机制的影响[J].上海环境科学,2001
[87]杨光,黄文杰.计入碳排放因素的中国核电与煤电平准化成本比较[J].经济管理,2010,(3)：7-12.
[88]虞继舜.煤化学[M].北京：冶金工业出版社,2000.
[89]张斌.2010-2020年我国能源和电力发展前景分析[J].中国能源,2010年5月.
[90]张仁健等.中国二氧化碳排放源现状分析[J].气候与环境研究,2001.
[91]张树伟.非化石能源份额与排放强度变化的互动研究[J].能源技术经济,2010第11期.
[92]张颖,王灿,王克,陈吉宁.基于LEAP的中国电力行业C02排放情景分析[J].清华大学学报(自然科学版),2007,47(3)：365-368.
[93]郑洪弢,以煤气化多联产为基础的能源系统及合成气城市的研究[D].北京：清华大学,2004
[94]郑爽,我国煤层甲烷类温室气体排放及清单编制[J].中国煤炭,2002.
[95]郑照宁,刘德顺,中国风电投资成本变化预测[J].中国电力,2004第7期.
[96]郑照宁,刘德顺,中国光伏组件价格变化的学习曲线模型及政策建议[J].太阳能学报,2005第1期.
[97]中国电力企业联合会,全国电力工业统计快报(历年[EB/OL].中国电力企业联合会网站,http://www.cec.org.cn.
[98]中国科学院可持续发展战略研究组.2009中国可持续发展战略报告——探索中国特色的低碳道路[M].北京：科学出版社,2009.
[99]钟流举,郑君瑜等.大气污染物排放源清单不确定性定量分析方法及案例研究[J].环境科学研究,2007年第4期.
[100]周大地,戴彦德,郁聪等.2020中国可持续能源情景[M].北京：中国环境科学出版社,2003.
[101]朱法华等.燃煤电厂SO2排放量确定方法研究[J].电力环境保护,2009.
[102]朱勤,彭希哲,傅雪.我国未来人口发展与碳排放变动的模拟分析[J].人口与发展,2011,17(1)：2-15.
[103]庄贵阳.低碳经济：气候变化背景下中国的发展之路[J].北京：中国气象出版社,2009
[104]庄幸等.煤炭产品从矿井到用户的能源含量分析[J].中国能源,2009
[105]王庆一主编.能源词典[M].北京：中国石化出版社,2005.
[106]生物质和生物质能手册
[107]能源统计报表制度
[108]中国煤炭远销协会.中国煤炭需求分析与预测研究报告[R].2008
[109]中国煤炭远销协会
[110]燃料燃烧领域排放清单编制工作,2003
[111]中国实施二氧化碳捕集与封存的参考意见
[112]中国产业与能源协调发展研究课题报告,2006年7月
[113]中国国家发展和改革委.节能中长期专项规划[EB/OL],发改环资[2004]2505号.国家发展和改革委网站,2004-11-25, http://www.sdpc.gov.cn.
[114]中国国际工程咨询公司,国家发展和收革委员会能源研究所.中国能源效率分析及所际比较研究[R].北京：2006.
[115]中国国家发展和改革委.核电中长期发展规划(2005～2020年)[EB/OL].国家发展和改革委网站,2007-11-2, http://www.sdpc.gov.cn.
[116]中国国家发展和改革委.可再生能源中长期发展规划[EB/OL]国家发展和改革委网站,2007-9-4, http://www.sdpc.gov.cn.
[117]中国煤田地质总局.全国煤炭资源预测和评价(第三次全国煤田预测)研究报告[R].1997年
[118]中国能源中长期发展战略研究项目组.中国能源中长期(2030,2050)发展战略研究[M].科学出版社,2011.
[119]朱永彬,王铮等.基于经济模拟的中国能源消费和碳排放高峰预测[J].地理学报,2009年8月.
[120]Ang B. W.,2004, Decomposition Analysis for Policy Making in Energy:Which is the Preferred Method? [J] Energy Policy, Vol.32, pp.1131-1139.
[121]Arrow, K.J.,1962, The economics implications of learning by doing [J], Review of Economic Studies,29,155-173.
[122]BP, BP Statistical Review of World Energy [R].2010.
[123]Cai Wenjia, Wang Can, Wang Ke, Zhang Ying, Chen Jining. Scenario Analysis on CO2 Emissions Reduction Potential in China's Electricity Sector [J]. Energy Policy 35 (2007) 6445-6456
[124]Carney, S.,2006. The development and application of a stakeholder led approach for both estimating and exploring the potential for greenhouse gas emissions mitigation on the governmental office regional scale [D]. PhD thesis, University of Manchester, Manchester.
[125]Carney, S. and Shackley, S., the greenhouse gas regional inventory project (GRIP): designing and employing a regional greenhouse gas measurement tool for stakeholder use [R]. Energy Policy 37 (2009) 4293-4302.
[126]Commission of the European Communities. Proposal for a directive on the geological storage of carbon dioxide, Impact Assessment, Annex XII:Indicative transport and storage networks for CO2 at Member State level [R].2008.
[127]Dales, j. Pollution property and prices [M]. Toronto:University of Toronto Press,1968.
[128]Deborah Seligsohn. Doing more than you think [J]. China Economic Quarterly, Sept. 2008.
[129]Deborah Seligsohn, et al.2010. CCS in China:toward an environmental, health, and safety regulatory framework [R]. WRI Issue Brief.
[130]Deborah Seligsohn. China's system for measuring, monitoring, and reporting energy and climate data [R]. World Resources Institute,2010.
[131]Dietz T., Rosa E. A. Rethinking the Environmental Impacts of Population, Affluence, and Technology [J], Human Ecology Review,1994, Vol.1, pp.277-300.
[132]Eddy H.C., Haining G., et al.,2010. Performance improvement and reduction of emissions from coal-fired utility boilers in China [R]. Energy for Sustainable Development 14, 206-212.
[133]Ehrlich, Holdren. The impact of population growth [J]. Science,1971,171:1212-1217.
[134]EIA (Energy Information Agency). International Energy Outlook 2010 [M].2010
[135]EU. The EU Flagship Programme-the key to making CO2 Capture and Storage (CCS) commercially viable by 2020 [R].2007
[136]Ferioli, F., K. Schoots, B.C.C. van der Zwaan. Use and limitations of learning curves for energy technology policy:a component-learning hypothesis [J], Energy Policy,2009,37, 2525-2535.
[137]Frey H C, Zheng J. Probabilistic analysis of driving cycle-based highway vehicle emission factors [J]. Environ Sci Technol,2002,36 (23):5184-5191
[138]Frey H C, Bharvirkar R, Zheng J. Quantitative analysis of variability and uncertainty in emission estimation [M]. Research Triangle Park, NC:North Carolina State University for the US Environmental Protection Agency,1999
[139]Friedl, B., Getzner, M. Determinants of CO2 Emissions in a Small Open Economy [J], Ecological Economics,2003, Vol.45, pp.133-148.
[140]Gibbins and Chalmers. Carbon capture and storage [J], Energy Policy 2008,36, 4317-4322
[141]Global Wind Energy Council (GWEC). Global wind 2009 report [R].2009.
[142]Greenpeace. Carbon capture and storage (CCS) and coal-fired power [R].2007. From: http://www.greenpeace.org/raw/content/usa/press-center/reports4/carbon-dioxide-captur e-and-sto.pdf
[143]Greenpeace. Energy revolution [R].2008
[144]Grossman, G. M. and Krueger, A. B. Environmental Impacts of a North American Free Trade Agreement [J], National Bureau of Economic Research Working Paper.1991, No.3914.
[145]Heleen de Coninck and Groenenberg,2007. Incentivising CCS in the EU [EB/OL], Presentation, stakeholder meeting DG ENV, from: http://ec.europa.eu/environment/climat/ccs/pdf/incentivisationoptions.pdf
[146]IEA (International Energy Agency). World Energy Outlook 2009 [M]. Paris:OECD Publishing,2009.
[147]IEA-ETSAP. Documentation for the MARKAL Family of Models [EB/OL]. Energy Technology Systems Analysis Programme. http://www.etsap.org/Tools.asp,2004
[148]IEA-ETSAP. Documentation for the TIMES Model [EB/OL]. Energy Technology Systems Analysis Programme. http://www.etsap.org/Tools.asp,2005
[149]IEA. Technology roadmap:carbon capture and storage [R].2009a www.iea.org/papers/2009/CCS_Roadmap.pdf
[150]IEA. CO2 capture and storage:a key carbon abatement option executive summary [R]. 2008a.
[151]IEA. World Energy Outlook 2009 [M]. Paris:OECD Pubiishing,2009b.
[152]IEA. World energy outlook 2008 [M]. Paris:OECD Publishing,2008b.
[153]IPCC (Intergovernmental Panel on Climate Change). IPCC special report on carbon capture and storage [R], New York:Cambridge University Press.2005.
[154]IPCC. SRCCS (IPCC Special Report on Carbon Oxide Capture and Storage Summary for Policy Makers) [R], Cambridge University Press,2005
[155]IPCC, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories [R].1997.
[156]ISO 14064-1. Specification with guidance at the organization level for quantification and reporting of greenhouse gas emission and removel.2006
[157]ISO 14064-2. Specification with guidance at the project level for quantification, monitoring and reporting of greenhouse gas emission reductions or removal enhancements. 2006
[158]ISO 14064-3. Specification with guidance for the validation and verification of greenhouse gas assertions.2006
[159]Junginger, M., A. Faaij, W. C. Turkenburg. Global experience curves for wind farms [J]. Energy Policy,2005,33,133-150.
[160]Kramer, G. J. and M. Haigh. No quick switch to low-carbon energy [J]. Nature,2009, Vol 462.
[161]Lantz V., Feng Q. Assessing Income, Population, and Technology Impacts on CO2 Emissions in Canada, Where's the EKC? [J]. Ecological Economics,2006, vol.57, pp.229-238.
[162]Liu and Gallagher. Driving carbon capture and storage forward in China [J]. Energy Procedia,20091,3877-3884.
[163]Liu, H and Gallagher, K. S. Catalyzing strategic transformation to a low-carbon economy: a CCS roadmap for China [J]. Energy Policy,2010.
[164]Liu, P, Efstratios N.Pistikopoulos, ZhengLi. An energy systems engineering approach to the optimal design of energy systems in commercial buildings [J]. Energy Policy 38 (2010) 4224-4231
[165]Lund, P.D. An improved market penetration model for wind energy technology forecasting [R]. EWEA Special Topic Conference, the Economics of Wind Energy, Finland, 1995
[166]M. van den Broek, Ric Hoefnagels, E. Rubin, Wim Turkenburg, Andre Faaij. Effects of technological learning on future cost and performance of power plants with CO2 capture [J]. Progress in Energy and Combustion Science,2009,35,457-480.
[167]Ma. Chunbo, David I. Stern. China's Changing Energy Intensity Trend:A Decomposition Analysis [J]. Energy Economics,2008, (30):1037-1053.
[168]Mackay, R.M., Probert, S.D. Likely market-penetrations of renewable-energy technologies [J], Applied Energy,1998,59,1-38.
[169]Mannion, A.M, Carbon and its domestication [M]. Springer,2006
[170]Martinez-Zarzoso, I., Bengochea-Morancho, A. Pooled Mean Group Estimation for an Environmental Kuznets Curve for CO2 [J]. Economics Letters,2004, Vol.82, pp.121-126.
[171]Maurice B., et al. Uncertainty analysis in life cycle inventory. Application to the production of electricity with French coal power plants [J]. Journal of Cleaner Production. 2000
[172]Martin Wagner. The Carbon Kuznets Curve:A Cloudy Picture Emitted by Bad Econometrics? [J]. Resource and Energy Economics,2008, Vol.30, pp.388-408.
[173]McKinsey & Company. China's Green Revolution:Prioritizing Technologies to Achieve Energy and Environmental Sustainability [R]. Shanghai:McKinsey & Company,2009.
[174]Moomaw W. R and Unruh G. C. Are Environmental Kuznets Curve Misleading US? The Case of CO2 Emissions, Special Issue on Environmental Kuznets Curves [J]. Environmental and Development Economics,1997, Vol.2, pp.451-463.
[175]Neij, et al. Experience curves:a tool for energy policy assessment [R]. Extol project final report, environmental and energy systems studies, Lund, Sweden,2003
[176]OECD, IEA Database
[177]Panayotou, T. Empirical Tests and Policy Analysis of Development [J]. ILO Technology and Employment Programme Working Paper, WP238,1993.
[178]Peters, G. P. From production-based to consumption-based national emission inventory [J]. Ecological Economics.2008
[179]Pew Charitable Trusts and the Clean Energy Economy. Who's winning the clean energy race? Growth, competition and opportunity in the world's largest economics [R],2010
[180]Pistikopoulos, E.N. Energy systems engineering—an integrated approach for the energy systems of the future [R]. In:European Congress of Chemical Engineering-6, Copenhagen, Denmark,2007.
[181]Praetorius, B., et al. Carbon capture and storage, Innovation for sustainable electricity systems,2008, P77-114
[182]Riahi, K., Edward S. Rubin, Leo Schrattenholzer. Prospects for carbon capture and sequestration technologies assuming their technological learning [J]. Energy,2004a,29, 1309-1318.
[183]Riahi, K., Edward S. Rubin, Margaret R. Taylor, Leo Schrattenholzer, David Hounshell. Technological learning for carbon capture and sequestration technologies [J]. Energy Economics,2004b,26,539-564
[184]Rubin, E. S., Yeh, S., Antes, M., Berkenpas, M., Davison, J. Use of experience curves to estimate the future cost of power plants with CO2 capture [J]. International Journal of Greenhouse Gas Contro,2007, I1,188-197.
[185]Rypdal Kristin, et al. Uncertainties in greenhouse gas emission inventories---evaluation, comparability and implications [J]. Environmental Science & Policy,2001
[186]Sarah M. Forbes. FutureGen and the Department of Energy's advanced coal programs [R]. Testimony, Hearing before the U.S. House of Representatives Science and Technology Subcommittee on Energy and the Environment,2009. www.wri.org.
[187]Sarah Forbes. A bad bet on carbon? Another view on CCS. NYTimes, May 14,2010.
[188]Shafik, N., Bandyopadhyay, S. Economic Growth and Environmental Quality:Time Series and Cross-country Evidence [J]. World Bank Policy Research Working Paper, No.904,1992
[189]Shindler B., K. A. Cheek and G. H. Stankey. Monitoring and Evaluating Citizen-Agency Interactions:A Framework Developed for Adaptive Management [R]. General Technical Report PNW-GTR-452, Portland, Oregon, US Department of Agriculture, Forest Service, Pacific Northwest Research Station,1994.4.
[190]Stockholm Environment Institute (SEI), Tellus Institute,2005. LEAP:Long Range Energy Iternatives Planning System, User Guide for LEAP 2005.
[191]Sun J.W. Is CO2 emission intensity comparable? [J]. Energy Policy,2000,28 (1), PP.1081-1084.
[192]Sun J.W. The sufficient condition needed to make CO2 emission intensity lower than energy intensity [J]. Energy Policy,2002,30 (1), PP.165-166.
[193]Sun J.W. The natural and social properties of CO2 emission intensity [J]. Energy Policy, 2003,31(1), PP.203-209.
[194]Wang, T. and J. Watson. China's Energy Transition:Pathways for Low Carbon Development [R]. Brighton:University of Sussex Energy Group,2009.
[195]WBCSD, WRI. The Greenhouse Gas Protocol A Corporate Accounting and Reporting Standard (Revised Edition) [R],2004.
[196]Wright, T.P. Factors affecting the cost of airplanes [J], Journal of Aeronautical Sciences, 1936,3,122-128.
[197]World Nuclear Association,2010
[198]Xiaoping Wang, et al. A high-resolution emission inventory for eastern China in 2000 and three scenarios for 2020 [J].Atmospheric Environment.2005
[199]Yoichi Kaya. Impact of Carbon Dioxide Emission on GNP Growth:Interpretation of Proposed Scenarios, Presentation to the Energy and Industry Subgroup, Response Strategies Working Group, IPCC, Paris,1989
[200]York, R., Rosa, E.A., Dietz, T. STIRPAT, IPAT and ImPACT:analytic tools for unpacking the driving forces of environmental impacts [J]. Ecological Economics,2003,46,351-365.
[201]Zheng, Nina, Zhou, N., Fridley, D. comparative analysis of modeling studies on China's future energy and emissions outlook [R]. LBNL-4032E,2010.9.
[202]Zhou, N. et al. China's Energy and Carbon Emissions Outlook to 2050 [R]. Lawrence Berkeley National Laboratory Report (LBNL-4472E).2011.4
[203]Sarah Forbes. A bad bet on carbon? Another view on CCS. NYTimes, May 14,2010.
[204]Song Li. Development and demonstration of a methodology for characterizing and managing uncertainty and variability in emission inventories [D]. Dr.2002.
[2]GB/T 213-2008.煤的发热量测定方法[S].北京：中国标准出版社,2008.
[3]GB/T 3715-1996.煤质及煤分析有关术语[S].北京：中国标准出版社,1996.
[4]GB/T 4754-2002.国民经济行业分类[S].北京：中国标准出版社,2002.
[5] IPCC.1996年IPCC国家温室气体清单指南修订本
[6] IPCC. IPCC国家温室气体清单优良作法和不确定性管理
[7] IPCC土地利用、土地利用变化和林业优良作法指南
[8] IPCC.2006年IPCC国家温室气体清单指南
[9]ISO 14064-1.组织层次上对温室气体排放和清除量的量化和报告的规范及指南.2006
[10]ISO 14064-2.项目层次上对温室气体减排和清除增加的量化、监测和报告的规范及指南.2006
[11]ISO 14064-3.温室气体声明审定与核查的规范及指南.2006
[12] UNFCCC.联合国气候变化框架公约
[13] UNFCCC,非《公约》附件一所列缔约方初次国家信息通报的第六份汇编和综合报告,FCCC/SBI/200E/18/Add.2,25 October 2005.
[14]UNFCCC,《公约》附件一所列缔约方1990-2007年期间国家温室气体清单数据报告,FCCC/SBI/2009/12,21 October 2009.
[15]WBCSD, WRI.温室气体议定书企业核算与报告准则(修订本).2004.
[16]温家宝.2011政府工作报告[EB/OL].新华网,2011-3-15:http://news.xinhuanet.com.
[17]阿维纳什·K.迪克西特著,冯曲,吴桂英译.经济理论中的最优化方法[M].上海：上海三联书店,上海人民出版社,2006.
[18]包能胜,徐军平,倪维斗.以大型风电场为核心的多能源互补发电系统[J].中国能源,2006,(8)：.
[19]滨川圭弘,西川炜一等.能源环境学[M].北京：科学出版社,2003.
[20]布鲁斯·米切尔,资源与环境管理[M].北京：商务印书馆,2005.
[21]蔡博峰,刘春兰,陈操操等.城市温室气体清单研究[M].北京：化学工业出版社,2009.
[22]陈春根,罗国勋.我国电力需求趋势预测及发展对策[J].数量经济技术经济研究,2003(6)：64-67.
[23]陈红敏.中国产业部门的能耗强度特征及节能减排的分类实现路径[J].资源科学,31(7)：1226-1231.
[24]陈红敏.我国对外贸易的能源环境影响——基于隐含流的研究[D].上海：复旦大学,2009.
[25]陈鹏.中国煤炭：性质、分类和利用[M].北京：化学工业出版社,2007.
[26]陈诗一.节能减排与中国工业的双赢发展：2009-2049[J].经济研究,2010年第3期.
[27]陈文颖,吴宗鑫.用MARKAL模型研究中国未来可持续能源发展战略[J].清华大学学报(自然科学版),2001,12.
[28]陈文颖,高鹏飞,何建坤.用MARKAL-MACRO模型研究碳减排对中国能源系统的影响[J].清华大学学报(自然科学版),2004
[29]陈迎等.中国外贸进出口商品中的内涵能源及其政策含义[J].经济研究,2008
[30]戴树桂等.环境化学[M].北京：高等教育出版社,2006.
[31]戴维·R·安德森,丹尼斯·J·斯维尼,托马斯·A·威廉斯著,于淼等译.数据、模型与决策[M].北京：机械工业出版社,2003.
[32]戴星翼,诸大建等.2050上海低碳发展路线图[M].北京：科学出版社,2011.
[33]高鹏飞,陈文颖,何建坤.中国的二氧化碳边际减排成本[J].清华大学学报(自然科学版),2004.9.
[34]郭朝先.中国碳排放因素分解：基于LMDI分解技术[M].中国人口资源与环境,2010年第12期.
[35]国家发展和改革委组织编制.中国应对气候变化国家方案[EB/OL].:新华网,2007-6-4:http://news.xinhuanet.com.
[36]国家发展和改革委员会能源研究所,国家气候变化对策协调小组办公室.中国温室气体清单研究[M].北京：中国环境科学出版社,2007.
[37]国家发展和改革委员会能源研究所.中国2050年低碳发展之路——能源需求暨碳排放情景分析[M].北京：科学出版社,2009.
[38]国家煤矿安全监察局,煤炭信息研究院.中国煤炭工业年鉴2005[R].中国煤炭工业出版社,2007.
[39]国家人口发展战略研究课题组.国家人口发展战略研究报告摘要[N].中国人口报,2007.1.3
[40]国家统计局.2009年国民经济和社会发展统计公报[EB/OL].国家统计局网站,2010-2-25:http://www.stats.gov.cn.
[41]国家统计局.2010年国民经济和社会发展统计公报[EB/OL].国家统计局网站,2011-2-28:http://www.stats.gov.cn.
[42]国家统计局.“十一五”经济社会发展成就系列报告之三：人口总量平稳增长就业形势保持稳定[EB/OL].:国家统计局网站,2011-3-2:http://www.stats.gov.cn.
[43]国家统计局能源统计司.中国能源统计年鉴(2009年)[M].北京：中国统计出版社,2010.
[44]国家统计局能源统计司.中国能源统计年鉴(2010年)[M].北京：中国统计出版社,2011.
[45]国民经济与社会发展第十二个五年规划纲要[EB/OL].2011-3-16: http://news.xinhuanet.com
[46]黄蕊,朱永彬,王铮.上海市能源消费趋势和碳排放高峰估计[J].上海经济研究,2010年第10期.
[47]黄忠,李瑞峰.“十二五”期间我国煤炭供需分析与预测[J].中国煤炭,2010年8月.
[48]姜克隽,胡秀莲,庄幸,刘强.中国2050年低碳情景和低碳发展之路[J].中外能源,2009
[49]姜英等.我国商品褐煤低位发热量回归式的推导[J].煤炭学报,2004
[50]焦树建.关于电厂发电成本计算方法的探讨[J].燃气轮机技术,2000,13(3)：7-10.
[51]李华林,陈文颖,吴宗鑫,吴欧密.西部可持续能源开发利用模型[J].清华大学学报(自然科学版),2006第9期.
[52]李华林,陈文颖,吴宗鑫.用内生技术学习曲对西部能源系统的分析[J].清华大学学报(自然科学版),2007第12期。
[53]李红等.中国温室气体排放清单信息库软件及网页的开发[J].城市环境与城市生态,2000
[54]李力,王凤.中国制造业能源强度因素分解研究[J].数量经济技术经济型究,2008,(10)：66-74.
[55]李为正.MARKAL模性用于电力系统规划中若干问题的研讨[J].数量经济技术经济研究,1986(10)：34-39.
[56]林伯强,经济变化、效率改进与能源需求预测——以中国电力行业为例[J],经济研究,2003(5)57-65
[57]林伯强,蒋竺均.中国二氧化碳的环境库兹涅茨曲线预测及影响因素分析[J].管理世界,2009年第4期.
[58]林伯强,刘希颖.中国城市化阶段的碳排放：影响因素与减排策略[J].经济研究,2010年第8期
[59]刘嘉,陈文颖,刘德顺.中国ESDPM模型及其在交通需求预测中的应用[J].中国人口资源与环境,2011,21(3)：71-75.
[60]刘嘉,陈文颖,刘德顺.中国能源服务需求预测模型[J].清华大学学报(自然科学版),2010,50(3)：481-484.
[61]刘佳明.风电成本的简易估算[J].内蒙古电力技术,2007第4期.
[62]罗杰·伯曼,马越等著,张涛等译.自然资源与环境经济学[M].中国经济出版社,2002.
[63]马忠海,潘自强,贺惠民.中国煤电链温室气体排放系数及其与核电链的比较[J].核科学与工程,1999(9)：268-274.
[64]倪维斗,焦树建,赵十杭,陈宾墨.现代文明的动力[M].山东：山东科学技术出版社,2001
[65]倪维斗,李政,薛元.以煤气化为核心的多联产能源系统—资源/能境/环境整体优化与可持续发展[J].中国工程科学,2000(8)：59-68.
[66]潘克西.煤炭产业组织研究[D].上海：复旦大学,2002.
[67]潘克西.低碳经济与清洁发展——理想·现实·行动方案[J].上海节能,2009年第9期.
[68]彭佳雯,黄贤金,钟太洋,赵雲泰.中国经济增长与能源碳排放的脱钩研究[J].资源科学,2011,33(4)：626-633.
[69]祁国燕,曾红颖.一些国家人均GDP从800美元到3000美元能源、汽车、钢铁消费变化[J].中国经贸导刊,2003(20)：26-27.
[70]齐晔等.中国进出口贸易中的隐含碳估算[J].中国人口资源与环境,2008.
[71]渠慎宁,郭朝先.基于STIRPAT模型的中国碳排放峰值预测研究[J].中国人口资源与环境,2010年第12期.
[72]让-雅克·拉丰,大卫·马赫蒂摩.激励理论(第一卷)：委托-代理模型[M].北京：中国人民大学出版社,2002.
[73]孙宇飞.城市碳排放清单及其相关因素分析[D].上海：复旦大学,2011.
[74]涂华等.利用固定碳计算我国无烟煤的含碳量[J].煤炭科学技术,2003.
[75]托马斯·思德纳蒋.张蔚文等译.环境与自然资源管理的政策工具[M].上海三联书店,上海人民出版社,2005.
[76]王克,王灿,吕学都,陈吉宁.基于LEAP的中国钢铁行业CO2减排潜力分析[J].清华大学学报(自然科学版),2006年第12期.
[77]魏一鸣等.能源-经济-环境复杂系统建模与应用进展[J].管理学报,2005,(3)：
[78]吴敬琏.中国应当走一条什么样的工业化道路?[J].管理世界,2006,(8)：1-7.
[79]吴宗鑫,刘滨,齐志新.美国和日本能源消费比较对我国的启示[J].当代石油石化, 2005,13(9)：25-37.
[80]夏德建,任玉珑,史乐峰.中国煤电能源链的生命周期碳排放系数计量[J].统计研究,2010,(8)：82-89.
[81]晓斌等.中国洁净煤发电的生命周期清单分析[J].洁净煤技术,2005.
[82]谢金星,薛毅.优化建模与LINDO/LINGO(?)软件[M].北京：清华大学出版社,2005.
[83]邢芳方,欧阳志云等.北京终端能源碳消费清单与结构[J].环境科学,2007,28(9)：1918-1923.
[84]徐亮.2010年、2015年中国煤炭需求预测研究[J].中国煤炭,2010年5月.
[85]杨宏伟,IPCC能源清单指南进展及其对中国的影响[J].气候变化研究进展,2006.
[86]杨玉峰等.温室气体排放总量计算的不确定性及对清洁发展机制的影响[J].上海环境科学,2001
[87]杨光,黄文杰.计入碳排放因素的中国核电与煤电平准化成本比较[J].经济管理,2010,(3)：7-12.
[88]虞继舜.煤化学[M].北京：冶金工业出版社,2000.
[89]张斌.2010-2020年我国能源和电力发展前景分析[J].中国能源,2010年5月.
[90]张仁健等.中国二氧化碳排放源现状分析[J].气候与环境研究,2001.
[91]张树伟.非化石能源份额与排放强度变化的互动研究[J].能源技术经济,2010第11期.
[92]张颖,王灿,王克,陈吉宁.基于LEAP的中国电力行业C02排放情景分析[J].清华大学学报(自然科学版),2007,47(3)：365-368.
[93]郑洪弢,以煤气化多联产为基础的能源系统及合成气城市的研究[D].北京：清华大学,2004
[94]郑爽,我国煤层甲烷类温室气体排放及清单编制[J].中国煤炭,2002.
[95]郑照宁,刘德顺,中国风电投资成本变化预测[J].中国电力,2004第7期.
[96]郑照宁,刘德顺,中国光伏组件价格变化的学习曲线模型及政策建议[J].太阳能学报,2005第1期.
[97]中国电力企业联合会,全国电力工业统计快报(历年[EB/OL].中国电力企业联合会网站,http://www.cec.org.cn.
[98]中国科学院可持续发展战略研究组.2009中国可持续发展战略报告——探索中国特色的低碳道路[M].北京：科学出版社,2009.
[99]钟流举,郑君瑜等.大气污染物排放源清单不确定性定量分析方法及案例研究[J].环境科学研究,2007年第4期.
[100]周大地,戴彦德,郁聪等.2020中国可持续能源情景[M].北京：中国环境科学出版社,2003.
[101]朱法华等.燃煤电厂SO2排放量确定方法研究[J].电力环境保护,2009.
[102]朱勤,彭希哲,傅雪.我国未来人口发展与碳排放变动的模拟分析[J].人口与发展,2011,17(1)：2-15.
[103]庄贵阳.低碳经济：气候变化背景下中国的发展之路[J].北京：中国气象出版社,2009
[104]庄幸等.煤炭产品从矿井到用户的能源含量分析[J].中国能源,2009
[105]王庆一主编.能源词典[M].北京：中国石化出版社,2005.
[106]生物质和生物质能手册
[107]能源统计报表制度
[108]中国煤炭远销协会.中国煤炭需求分析与预测研究报告[R].2008
[109]中国煤炭远销协会
[110]燃料燃烧领域排放清单编制工作,2003
[111]中国实施二氧化碳捕集与封存的参考意见
[112]中国产业与能源协调发展研究课题报告,2006年7月
[113]中国国家发展和改革委.节能中长期专项规划[EB/OL],发改环资[2004]2505号.国家发展和改革委网站,2004-11-25, http://www.sdpc.gov.cn.
[114]中国国际工程咨询公司,国家发展和收革委员会能源研究所.中国能源效率分析及所际比较研究[R].北京：2006.
[115]中国国家发展和改革委.核电中长期发展规划(2005～2020年)[EB/OL].国家发展和改革委网站,2007-11-2, http://www.sdpc.gov.cn.
[116]中国国家发展和改革委.可再生能源中长期发展规划[EB/OL]国家发展和改革委网站,2007-9-4, http://www.sdpc.gov.cn.
[117]中国煤田地质总局.全国煤炭资源预测和评价(第三次全国煤田预测)研究报告[R].1997年
[118]中国能源中长期发展战略研究项目组.中国能源中长期(2030,2050)发展战略研究[M].科学出版社,2011.
[119]朱永彬,王铮等.基于经济模拟的中国能源消费和碳排放高峰预测[J].地理学报,2009年8月.
[120]Ang B. W.,2004, Decomposition Analysis for Policy Making in Energy:Which is the Preferred Method? [J] Energy Policy, Vol.32, pp.1131-1139.
[121]Arrow, K.J.,1962, The economics implications of learning by doing [J], Review of Economic Studies,29,155-173.
[122]BP, BP Statistical Review of World Energy [R].2010.
[123]Cai Wenjia, Wang Can, Wang Ke, Zhang Ying, Chen Jining. Scenario Analysis on CO2 Emissions Reduction Potential in China's Electricity Sector [J]. Energy Policy 35 (2007) 6445-6456
[124]Carney, S.,2006. The development and application of a stakeholder led approach for both estimating and exploring the potential for greenhouse gas emissions mitigation on the governmental office regional scale [D]. PhD thesis, University of Manchester, Manchester.
[125]Carney, S. and Shackley, S., the greenhouse gas regional inventory project (GRIP): designing and employing a regional greenhouse gas measurement tool for stakeholder use [R]. Energy Policy 37 (2009) 4293-4302.
[126]Commission of the European Communities. Proposal for a directive on the geological storage of carbon dioxide, Impact Assessment, Annex XII:Indicative transport and storage networks for CO2 at Member State level [R].2008.
[127]Dales, j. Pollution property and prices [M]. Toronto:University of Toronto Press,1968.
[128]Deborah Seligsohn. Doing more than you think [J]. China Economic Quarterly, Sept. 2008.
[129]Deborah Seligsohn, et al.2010. CCS in China:toward an environmental, health, and safety regulatory framework [R]. WRI Issue Brief.
[130]Deborah Seligsohn. China's system for measuring, monitoring, and reporting energy and climate data [R]. World Resources Institute,2010.
[131]Dietz T., Rosa E. A. Rethinking the Environmental Impacts of Population, Affluence, and Technology [J], Human Ecology Review,1994, Vol.1, pp.277-300.
[132]Eddy H.C., Haining G., et al.,2010. Performance improvement and reduction of emissions from coal-fired utility boilers in China [R]. Energy for Sustainable Development 14, 206-212.
[133]Ehrlich, Holdren. The impact of population growth [J]. Science,1971,171:1212-1217.
[134]EIA (Energy Information Agency). International Energy Outlook 2010 [M].2010
[135]EU. The EU Flagship Programme-the key to making CO2 Capture and Storage (CCS) commercially viable by 2020 [R].2007
[136]Ferioli, F., K. Schoots, B.C.C. van der Zwaan. Use and limitations of learning curves for energy technology policy:a component-learning hypothesis [J], Energy Policy,2009,37, 2525-2535.
[137]Frey H C, Zheng J. Probabilistic analysis of driving cycle-based highway vehicle emission factors [J]. Environ Sci Technol,2002,36 (23):5184-5191
[138]Frey H C, Bharvirkar R, Zheng J. Quantitative analysis of variability and uncertainty in emission estimation [M]. Research Triangle Park, NC:North Carolina State University for the US Environmental Protection Agency,1999
[139]Friedl, B., Getzner, M. Determinants of CO2 Emissions in a Small Open Economy [J], Ecological Economics,2003, Vol.45, pp.133-148.
[140]Gibbins and Chalmers. Carbon capture and storage [J], Energy Policy 2008,36, 4317-4322
[141]Global Wind Energy Council (GWEC). Global wind 2009 report [R].2009.
[142]Greenpeace. Carbon capture and storage (CCS) and coal-fired power [R].2007. From: http://www.greenpeace.org/raw/content/usa/press-center/reports4/carbon-dioxide-captur e-and-sto.pdf
[143]Greenpeace. Energy revolution [R].2008
[144]Grossman, G. M. and Krueger, A. B. Environmental Impacts of a North American Free Trade Agreement [J], National Bureau of Economic Research Working Paper.1991, No.3914.
[145]Heleen de Coninck and Groenenberg,2007. Incentivising CCS in the EU [EB/OL], Presentation, stakeholder meeting DG ENV, from: http://ec.europa.eu/environment/climat/ccs/pdf/incentivisationoptions.pdf
[146]IEA (International Energy Agency). World Energy Outlook 2009 [M]. Paris:OECD Publishing,2009.
[147]IEA-ETSAP. Documentation for the MARKAL Family of Models [EB/OL]. Energy Technology Systems Analysis Programme. http://www.etsap.org/Tools.asp,2004
[148]IEA-ETSAP. Documentation for the TIMES Model [EB/OL]. Energy Technology Systems Analysis Programme. http://www.etsap.org/Tools.asp,2005
[149]IEA. Technology roadmap:carbon capture and storage [R].2009a www.iea.org/papers/2009/CCS_Roadmap.pdf
[150]IEA. CO2 capture and storage:a key carbon abatement option executive summary [R]. 2008a.
[151]IEA. World Energy Outlook 2009 [M]. Paris:OECD Pubiishing,2009b.
[152]IEA. World energy outlook 2008 [M]. Paris:OECD Publishing,2008b.
[153]IPCC (Intergovernmental Panel on Climate Change). IPCC special report on carbon capture and storage [R], New York:Cambridge University Press.2005.
[154]IPCC. SRCCS (IPCC Special Report on Carbon Oxide Capture and Storage Summary for Policy Makers) [R], Cambridge University Press,2005
[155]IPCC, Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories [R].1997.
[156]ISO 14064-1. Specification with guidance at the organization level for quantification and reporting of greenhouse gas emission and removel.2006
[157]ISO 14064-2. Specification with guidance at the project level for quantification, monitoring and reporting of greenhouse gas emission reductions or removal enhancements. 2006
[158]ISO 14064-3. Specification with guidance for the validation and verification of greenhouse gas assertions.2006
[159]Junginger, M., A. Faaij, W. C. Turkenburg. Global experience curves for wind farms [J]. Energy Policy,2005,33,133-150.
[160]Kramer, G. J. and M. Haigh. No quick switch to low-carbon energy [J]. Nature,2009, Vol 462.
[161]Lantz V., Feng Q. Assessing Income, Population, and Technology Impacts on CO2 Emissions in Canada, Where's the EKC? [J]. Ecological Economics,2006, vol.57, pp.229-238.
[162]Liu and Gallagher. Driving carbon capture and storage forward in China [J]. Energy Procedia,20091,3877-3884.
[163]Liu, H and Gallagher, K. S. Catalyzing strategic transformation to a low-carbon economy: a CCS roadmap for China [J]. Energy Policy,2010.
[164]Liu, P, Efstratios N.Pistikopoulos, ZhengLi. An energy systems engineering approach to the optimal design of energy systems in commercial buildings [J]. Energy Policy 38 (2010) 4224-4231
[165]Lund, P.D. An improved market penetration model for wind energy technology forecasting [R]. EWEA Special Topic Conference, the Economics of Wind Energy, Finland, 1995
[166]M. van den Broek, Ric Hoefnagels, E. Rubin, Wim Turkenburg, Andre Faaij. Effects of technological learning on future cost and performance of power plants with CO2 capture [J]. Progress in Energy and Combustion Science,2009,35,457-480.
[167]Ma. Chunbo, David I. Stern. China's Changing Energy Intensity Trend:A Decomposition Analysis [J]. Energy Economics,2008, (30):1037-1053.
[168]Mackay, R.M., Probert, S.D. Likely market-penetrations of renewable-energy technologies [J], Applied Energy,1998,59,1-38.
[169]Mannion, A.M, Carbon and its domestication [M]. Springer,2006
[170]Martinez-Zarzoso, I., Bengochea-Morancho, A. Pooled Mean Group Estimation for an Environmental Kuznets Curve for CO2 [J]. Economics Letters,2004, Vol.82, pp.121-126.
[171]Maurice B., et al. Uncertainty analysis in life cycle inventory. Application to the production of electricity with French coal power plants [J]. Journal of Cleaner Production. 2000
[172]Martin Wagner. The Carbon Kuznets Curve:A Cloudy Picture Emitted by Bad Econometrics? [J]. Resource and Energy Economics,2008, Vol.30, pp.388-408.
[173]McKinsey & Company. China's Green Revolution:Prioritizing Technologies to Achieve Energy and Environmental Sustainability [R]. Shanghai:McKinsey & Company,2009.
[174]Moomaw W. R and Unruh G. C. Are Environmental Kuznets Curve Misleading US? The Case of CO2 Emissions, Special Issue on Environmental Kuznets Curves [J]. Environmental and Development Economics,1997, Vol.2, pp.451-463.
[175]Neij, et al. Experience curves:a tool for energy policy assessment [R]. Extol project final report, environmental and energy systems studies, Lund, Sweden,2003
[176]OECD, IEA Database
[177]Panayotou, T. Empirical Tests and Policy Analysis of Development [J]. ILO Technology and Employment Programme Working Paper, WP238,1993.
[178]Peters, G. P. From production-based to consumption-based national emission inventory [J]. Ecological Economics.2008
[179]Pew Charitable Trusts and the Clean Energy Economy. Who's winning the clean energy race? Growth, competition and opportunity in the world's largest economics [R],2010
[180]Pistikopoulos, E.N. Energy systems engineering—an integrated approach for the energy systems of the future [R]. In:European Congress of Chemical Engineering-6, Copenhagen, Denmark,2007.
[181]Praetorius, B., et al. Carbon capture and storage, Innovation for sustainable electricity systems,2008, P77-114
[182]Riahi, K., Edward S. Rubin, Leo Schrattenholzer. Prospects for carbon capture and sequestration technologies assuming their technological learning [J]. Energy,2004a,29, 1309-1318.
[183]Riahi, K., Edward S. Rubin, Margaret R. Taylor, Leo Schrattenholzer, David Hounshell. Technological learning for carbon capture and sequestration technologies [J]. Energy Economics,2004b,26,539-564
[184]Rubin, E. S., Yeh, S., Antes, M., Berkenpas, M., Davison, J. Use of experience curves to estimate the future cost of power plants with CO2 capture [J]. International Journal of Greenhouse Gas Contro,2007, I1,188-197.
[185]Rypdal Kristin, et al. Uncertainties in greenhouse gas emission inventories---evaluation, comparability and implications [J]. Environmental Science & Policy,2001
[186]Sarah M. Forbes. FutureGen and the Department of Energy's advanced coal programs [R]. Testimony, Hearing before the U.S. House of Representatives Science and Technology Subcommittee on Energy and the Environment,2009. www.wri.org.
[187]Sarah Forbes. A bad bet on carbon? Another view on CCS. NYTimes, May 14,2010.
[188]Shafik, N., Bandyopadhyay, S. Economic Growth and Environmental Quality:Time Series and Cross-country Evidence [J]. World Bank Policy Research Working Paper, No.904,1992
[189]Shindler B., K. A. Cheek and G. H. Stankey. Monitoring and Evaluating Citizen-Agency Interactions:A Framework Developed for Adaptive Management [R]. General Technical Report PNW-GTR-452, Portland, Oregon, US Department of Agriculture, Forest Service, Pacific Northwest Research Station,1994.4.
[190]Stockholm Environment Institute (SEI), Tellus Institute,2005. LEAP:Long Range Energy Iternatives Planning System, User Guide for LEAP 2005.
[191]Sun J.W. Is CO2 emission intensity comparable? [J]. Energy Policy,2000,28 (1), PP.1081-1084.
[192]Sun J.W. The sufficient condition needed to make CO2 emission intensity lower than energy intensity [J]. Energy Policy,2002,30 (1), PP.165-166.
[193]Sun J.W. The natural and social properties of CO2 emission intensity [J]. Energy Policy, 2003,31(1), PP.203-209.
[194]Wang, T. and J. Watson. China's Energy Transition:Pathways for Low Carbon Development [R]. Brighton:University of Sussex Energy Group,2009.
[195]WBCSD, WRI. The Greenhouse Gas Protocol A Corporate Accounting and Reporting Standard (Revised Edition) [R],2004.
[196]Wright, T.P. Factors affecting the cost of airplanes [J], Journal of Aeronautical Sciences, 1936,3,122-128.
[197]World Nuclear Association,2010
[198]Xiaoping Wang, et al. A high-resolution emission inventory for eastern China in 2000 and three scenarios for 2020 [J].Atmospheric Environment.2005
[199]Yoichi Kaya. Impact of Carbon Dioxide Emission on GNP Growth:Interpretation of Proposed Scenarios, Presentation to the Energy and Industry Subgroup, Response Strategies Working Group, IPCC, Paris,1989
[200]York, R., Rosa, E.A., Dietz, T. STIRPAT, IPAT and ImPACT:analytic tools for unpacking the driving forces of environmental impacts [J]. Ecological Economics,2003,46,351-365.
[201]Zheng, Nina, Zhou, N., Fridley, D. comparative analysis of modeling studies on China's future energy and emissions outlook [R]. LBNL-4032E,2010.9.
[202]Zhou, N. et al. China's Energy and Carbon Emissions Outlook to 2050 [R]. Lawrence Berkeley National Laboratory Report (LBNL-4472E).2011.4
[203]Sarah Forbes. A bad bet on carbon? Another view on CCS. NYTimes, May 14,2010.
[204]Song Li. Development and demonstration of a methodology for characterizing and managing uncertainty and variability in emission inventories [D]. Dr.2002.