昆明市城镇家庭消费碳排放特征及影响因素分析

根据IPCC国家温室气体（GHG）碳排放计算清单指南,研究和修正昆明碳排放系数,对2000～2008年昆明市家庭城镇消费进行调查与碳排放量核算。结果表明：昆明市城镇家庭碳排放总量变化趋势为平稳上升趋势。碳排结构中能源碳排量占比最大,其排放主体为家庭用电、家用燃料,而物质消费碳排放主体为衣着类、粮食、肉类。另外,人口特征和经济特征因素与城镇家庭碳排放量具有显著相关性。     

1999~2011年鄂尔多斯市温室气体足迹动态分析

采用基于IPCC的《省级温室气体编制指南》和国际公认的《2006年IPCC国家温室气体清单指南》推荐的方法对鄂尔多斯市的温室气体足迹进行了动态分析.结果表明:1999~2011年鄂尔多斯市温室气体排放呈快速上升趋势,12年间温室排放量从518.10′104t上升为11730.10′104t,年均增幅29.69%.增幅最高的部门是能源(年均增幅35.08%)、水泥(21.94%)、农业(5.15%),林业固碳较低(28.84′104t),废弃物碳排放波动变化.从温室气体来源构成看,能源占57.5%~93.7%,水泥占3.35%~7.01%,农业占14.6%~32.6%,林业固碳占0.25%~5.57%,废弃物处理占0.44%~1.00%.可见能源消费的增加是导致鄂尔多斯市温室气体排放增加的主要原因.万元GDP温室气体排放量呈现波动变化;人均、单位面积温室气体排放量和温室气体排放指数增速很快,年均增幅分别达25.60%、30.12%和25.67%.12年间鄂尔多斯市温室气体排放等级持续上升,从较低(Ⅰc)升高到极高等级(Ⅲc),目前距应对气候变暖目标(Ⅰb)已高出了7个亚级.鄂尔多斯市温室气体排放亟待降低.     

2007年火电行业温室气体排放量估算

为了解我国火电行业温室气体排放情况,参考《IPCC国家温室气体排放清单指南》中固定源燃烧温室气体排放量计算方法学部门方法的相关内容,利用实测的温室气体排放因子以及2007年火电行业活动水平数据,计算火电行业温室气体排放量. 排放因子测算及排放量计算过程均遵循IPCC关于温室气体排放计算的质量保证和质量控制内容. 结果表明,2007年我国火电行业CO₂与N₂O排放量分别为2.81×109和1.56×105 t.同时使用参考方法,利用国家级能源统计数据直接计算火电行业CO₂排放量.将部门方法与参考方法计算结果进行比对发现,原煤、原油和天然气燃烧温室气体排放量2种方法的相对偏差分别为7.5%,98.8%和1.6%,除原油外,原煤和天然气燃烧CO₂排放量与参考方法相差并不大.      

An overview of the revised 1996 IPCC guidelines for national greenhouse gas inventory methodology for nitrous oxide from agriculture

The IPCC Guidelines for National Greenhouse Gas Inventories provide default methodologies for estimating emissions of the most important greenhouse gases at a national scale. The methodology for estimating emissions of nitrous oxide (N₂O) from agriculture was revised in 1996 by an international working group. Here we summarize this new methodology and apply it to the global data. The new method aims at assessing the full nitrogen cycle and takes into account N₂O formation in agricultural fields (direct emissions), animal waste management systems (AWMSs) as well as indirect emissions taking place at remote places after nitrogen is lost from the agricultural fields. Using the IPCC method, we estimated that global agricultural N₂O emissions almost doubled between 1960 (3.5 Tg N₂O-N) and 1994 (6.2 Tg N₂O-N). Direct emissions, animal waste management systems and indirect emissions make about equal contribution to total current emissions.     

城市废弃物处理温室气体排放研究:以厦门市为例

城市废弃物处理是城市人为活动产生温室气体的来源之一.参考IPCC国家温室气体清单指南2006推荐的方法建立了厦门市废弃物处理的温室气体排放计算模型,对厦门市2005～2010年废弃物处理的温室气体排放情况进行了估算,包括固体废弃物填埋、焚烧以及污水处理等过程.结果表明,2005年温室气体总排放量折合二氧化碳当量(CO2e)为406.3 kt,2010年温室气体总排放量(以CO2e计)达到704.6 kt,随着废水处理工艺的提高和城市生活垃圾量的迅速增长,主要排放源由废水处理转变为固体废弃物填埋.2005年填埋产生的温室气体排放占固体废弃物处理排放量的90%左右,2010年所占比例下降到75%.厦门市废水处理温室气体排放量2007年最高,以CO2e计达到325.5 kt,化学原料及化学品制造业从2005～2010年一直是厦门市CH4排放量最高的产业,占工业废水处理CH4排放总量的55%以上.     

深圳市温室气体排放清单研究

根据深圳市相关统计资料收集到的活动水平数据,参照《2006年IPCC国家温室气体清单指南》温室气体核算方法,建立了深圳市温室气体排放清单,并且与其他城市的温室气体排放水平进行了对比. 结果表明:2008年深圳市温室气体总排放量(以CO₂排放当量计)为6 569.4×104 t,能源部门的温室气体排放量占总排放量的比例最大,达80.8%;工业过程、废物处理处置部门和农林和其他土地利用(AFOLU)部门排放所占比例分别为16.5%、5.1%和-2.4%. 深圳市温室气体人均排放量为7.49 t/人,单位GDP的温室气体排放量为0.84 t/104元,二者均低于北京、上海、天津和无锡的平均排放水平,但高于重庆市.      

江浙沪地区能源及转化业温室气体排放及减排措施

根据ＩＰＣＣ提供的方法，对１９９０年江浙沪地区能源及转化业中能源的消耗量和温室气体的排放进行了统计计算，对数据进行了进一步的评价和分析，江浙沪地区能源及转化业能源消耗中固体燃料占８１．７％，液体燃料占１５．４％，气体燃料占２．９％，该地区该部门燃料消耗中温室气体排放为ＣＯ２：８８７３７Ｇｇ，ＣＨ４：０．８４９８Ｇｇ，Ｎ２Ｏ：５．８２４Ｇｇ。针对能源及转化业部门的具体情况提出了相应的减排措施。     

广东电力生产的温室气体排放趋势和演变特征

通过文献调研收集广东电力生产最新的能源消费数据和排放因子,采用“自上而下”方法估算1995—2011年广东电力行业的直接和间接GHG(温室气体)排放量,量化直接排放量的不确定性,绘制GHG排放流向图,并且根据GHG排放特征提出减排建议. 结果表明:①虽然受经济、环境和能源政策的影响,与1995年相比,2011年广东电力生产的GHG总排放量仍增长438%,达3.44×108 t,其中直接排放量达2.78×108 t,不确定性为±11%. ②从发电能源结构角度考虑,燃煤发电是电力生产的最大GHG排放源,2011年其排放量占总排放量的76%;而从用电终端考虑,工业用电是最大的GHG排放源,2011年其排放量占电力生产GHG总排放量的66%. ③1995—2011年,用电终端总体电力GHG排放强度下降了16%,居民用电人均GHG排放量上升了260%,单位综合发电量的GHG排放系数微升了1%. ④发电能源结构和终端产业结构的低碳化以及控制居民用电的GHG排放量等措施可减排2011年广东电力生产GHG总排放量的44%.      

江苏省温室气体排放研究

根据IPCC Guidelines(1995)提供的方法,对1990年江苏省温室气体排放清单统计计算,分析该地区能源、工业及农业CO_2、CH_4等温室气体排放量的状况.江苏省年人均排放CO_2为1970kg、CH_4为22.65kg、N_2O为0.11kg,与全国平均水平接近、为全球均值一半.能源消耗是江苏省各项活动中CO_2的排放主要因素,占总排放量的91.6%;CH_4的排放主要来自水稻田,占总排放量的44.1%.     

Forgotten carbon: indirect CO2 in greenhouse gas emission inventories

National governments that are Parties to the United Nations Framework Convention on Climate Change (UNFCCC) are required to submit greenhouse gas (GHG) inventories accounting for the emissions and removals occurring within their geographic territories. The Intergovernmental Panel on Climate Change (IPCC) provides inventory methodology guidance to the Parties of the UNFCCC. This methodology guidance, and national inventories based on it, omits carbon dioxide (CO₂) from the atmospheric oxidation of methane, carbon monoxide, and non-methane volatile organic compounds emissions that result from several source categories. The inclusion of this category of “indirect” CO₂ in GHG inventories increases global anthropogenic emissions (excluding land use and forestry) between 0.5 and 0.7%. However, the effect of inclusion on aggregate UNFCCC Annex I Party GHG emissions would be to reduce the growth of total emissions, from 1990 to 2004, by 0.2% points. The effect on the GHG emissions and emission trends of individual countries varies. The paper includes a methodology for calculating these emissions and discusses uncertainties. Indirect CO₂ is equally relevant for GHG inventories at other scales, such as global, regional, organizational, and facility. Similarly, project-based methodologies, such as those used under the Clean Development Mechanism, may need revising to account for indirect CO₂.     

黄花和脱水污泥厌氧消化的温室气体减排研究

采用联合国政府间气候委员会(IPCC)推荐的方法,对植物加拿大一枝黄花和污泥生物质厌氧消化产沼气的环境和能量影响进行了评估.环境影响评估重点关注温室气体(GHG)减排,将加拿大一枝黄花和脱水污泥的中温小试厌氧消化的甲烷产率数据运用于GHG减排潜质的计算;能量评估包括污泥贮存及传播、消化液的沼气逃逸,及化学肥料和化学药剂的投加几个过程中的直接和间接的能量输入.结果表明,一枝黄花和脱水污泥联合厌氧消化净GHG排放量随着SRT的缩短和黄花添加比例的增加而逐渐下降,在二者以挥发性固体(VS)比例1:1混合,停留时间为20d时的能量效率较高,GHG减排量较大,与基础方案相比,添加黄花可以实现78%的净GHG减排.     

Different accounting approaches to harvested wood products in national greenhouse gas inventories: their incentives to achievement of major policy goals

The 2006 IPCC Guidelines for National Greenhouse Gas Inventories provide four accounting approaches to harvested wood products (HWP). These differ in the way they define system boundaries. Therefore, reported national carbon emissions differ according to the accounting approach used, and the implications of each accounting approach differ for different countries. This paper investigates four IPCC accounting approaches, as well as the 1996 IPCC default approach, to determine whether they provide incentives to achievement of major policy goals related to climate, forest, trade, and waste, taking into account indirect effects of wood use change (i.e., the effects on forest carbon stocks and on carbon emissions from the use of other fuels and materials). Conclusions are as follows: (1) The analyses produced many different results from those of previous studies. These differences appear to be attributable to whether or not the indirect effects of wood use change are taken into account and the reference scenarios that are assumed; (2) The best approaches for achieving each policy goal differ, and the best approaches for particular policy goals might pose problems for other policy goals; (3) Overall, the IPCC default approach is the best accounting approach from the viewpoint of greater compatibility with, or integration across, the array of policy goals, although it does not address the issue of an increasing global carbon stock in HWP.     

Carbon accounting of material substitution with biomass: Case studies for Austria investigated with IPCC default and alternative approaches

There is evidence that the replacement of carbon-intensive products with bio-based substitutes (‘material substitution with biomass’) can be highly efficient in reducing greenhouse gas (GHG) emissions. Based on two case studies (CS1/2) for Austria, potential benefits of material substitution in comparison to fuel substitution are analysed. GHG savings are calculated according to default IPCC approaches (Tier 2 method assuming first-order decay) and with more realistic approaches based on distribution functions. In CS1, high savings are achieved by using wood residues for the production of insulating boards instead of energy. The superiority of material substitution is due to the establishment of a long-term carbon storage, the high emission factor of wood in comparison to natural gas and higher efficiencies of gas-fired facilities.The biomass feedstock in CS2 is lignocellulosic ethanol being used for bio-ethylene production (material substitution) or replacing gasoline (fuel substitution). GHG savings are mainly due to lower production emissions of bio-ethylene in comparison to conventional ethylene and significantly lower than in CS1 (per unit of biomass consumed). While CS1 is highly robust to parameter variation, the long-term projections in CS2 are quite speculative.To create adequate incentives for including material substitution in national climate strategies, shortcomings of current default accounting methods must be addressed. Under current methods the GHG savings in both case studies would not (fully) materialize in the national GHG inventory. The main reason is that accounting of wood products is confined to the proportion derived from domestic harvest, whereas imported biomass used for energy is treated as carbon-neutral. Further inadequacies of IPCC default accounting methods include the assumption of exponential decay and the disregard of advanced bio-based products.     

市政污水厂典型A2O工艺低碳运行的系统性评估

在“碳达峰、碳中和”的目标下，系统评估典型A²O工艺运行的碳排放当量及其组成，对我国市政污水厂的低碳运行具有重要的指导意义.以焦作市第一污水厂2020年的运行资料为研究案例，基于相关指南，引入水温因素构建阿伦尼乌斯公式模型用于核算直接碳排放过程，从电能消耗、药剂投加和污泥运输这3个方面核算间接碳排放过程.结果表明，CH₄和N₂O日排放强度为(115±56)kg·d^-1和(30±18)kg·d^-1;生化处理工段的能耗和药剂间接碳排放占比分别达到48.4%和51.3%; 2020年污水厂总计碳排放当量(以CO₂eq计)为2.17×10⁴t,单位污水碳排放当量(0.63±0.07)kg·m^-3;不同碳排放占比的大小顺序为：污水能耗(36.5%)>污水药剂(26.6%)>N₂O直接(15.4%)>污泥药剂(9.6%)>污泥能耗(6.7%)>CH₄直...     

杭州市城市生活垃圾处理主要温室气体及VOCs排放特征

为了解城市生活垃圾处理过程中主要温室气体及VOCs排放的变化特征,基于《2006年IPCC国家温室气体清单指南》《浙江省市县温室气体清单编制指南》和《大气挥发性有机物源排放清单编制技术指南》推荐的方法,估算了2005-2016年杭州市生活垃圾处理主要温室气体及VOCs排放量.结果表明:2005-2016年杭州市生活垃圾处理过程中温室气体排放占绝对主导地位,VOCs排放只占极少一部分.杭州市生活垃圾处理主要温室气体和VOCs排放量总体上呈上升趋势,与2005年相比,2016年杭州市生活垃圾处理主要温室气体排放量增长了68.8%,VOCs排放量增长了134.0%.从生活垃圾处理方式来看,杭州市生活垃圾填埋处理的温室气体排放量远高于焚烧处理方式,但填埋处理的VOCs排放量却低于焚烧处理方式（2007年和2008年除外）.杭州市生活垃圾填埋处理和焚烧处理的温室气体排放强度分别为0.72~0.86、0.18~0.23.从排放贡献和排放强度来看,采用填埋处理方式有利于减少垃圾处理过程中VOCs的排放,而采用焚烧处理方式更有利于温室气体的减排.随着人均生活垃圾产生量的上升,无论是温室气体还是VOCs,杭州市人均垃圾处理排放量总体呈现稳步上升的态势.研究显示,深入垃圾分类回收、控制人均生活垃圾产生量、优化垃圾焚烧处理方式,可以实现生活垃圾处理主要温室气体和VOCs的协同减排.      

燃气-蒸汽联合循环发电CO2排放量量化方法比较

为探究排放因子法与监测法两类量化方法对燃气-蒸汽联合循环发电CO₂排放源排放量量化的差异和影响因素,采用《温室气体排放核算与报告要求第1部分:发电企业》(下称《核算报告要求》)和《2006年IPCC国家温室气体清单指南》(下称《IPCC指南》)两种排放因子法,以及一种基于红外吸收光谱原理的排放源监测法,对某燃气-蒸汽联合循环发电CO₂排放源排放量进行4次量化,分别得出监测法、《核算报告要求》以及《IPCC指南》下限值、缺省值和上限值5组量化值.结果表明:① 采用监测法得出的CO₂排放源排放量量化值明显小于两种指南排放因子法量化结果;② 采用《核算报告要求》得出的CO₂排放源排放量量化值介于《IPCC指南》缺省值和下限值的量化值之间;③《核算报告要求》和《IPCC指南》中的天然气排放因子值分别超出此次监测法量化值折算出的天然气排放因子值的22%、19%、23%和28%,证明存在因高估排放因子导致高估CO₂排放量的可能;④ 装置运行负荷率越高,采用排放因子法得出的量化值越趋近于监测法量化值.研究显示,在监测条件良好的情况下,宜采用监测法对燃气-蒸汽联合循环发电CO₂排放源排放量进行量化,可避免燃料燃烧特性值和装置负荷率对排放因子法量化准确性的干扰,能更好地支撑企业和管理部门的统计量化工作.      

生活垃圾制备衍生燃料(RDF-5)——以重庆市为例

以重庆市生活垃圾为例,通过分选、破碎和干燥预处理后,得到以塑料、纸类、竹木、织物和厨余为主要成分的制备衍生燃料(RDF-5)的原料,测试了不同含水率和成型压力条件下制得的RDF-5的理化性质.结果表明,原料含水率为8%,成型压力为10 MPa时,RDF-5的延展率和耐性指数良好,便于贮存和运输.燃烧试验表明,RDF-5燃烧后灰渣的热灼减率满足《生活垃圾焚烧污染控制标准》(GB 18485—2014)的要求(5%).另外,根据2006 IPCC国家温室气体名录导则和生命周期评价(LCA),制备RDF-5对温室气体排放贡献为455.92 g·kg-1RDF-5(以CO2当量计).     

火力发电行业温室气体排放因子测算

为了解我国火力发电行业温室气体排放状况及排放因子,利用U23多组分红外气体分析仪及TH880F烟尘分析仪对全国30台具有代表性的火力发电机组排放的CO₂和N₂O进行了在线监测;监测及后续的数据处理阶段均遵循了联合国政府间气候变化专门委员会(IPCC)关于温室气体排放计算的质量保证和质量控制原则.利用统计学方法对数据进行处理,给出了CO₂和N₂O 3种表达方式的排放因子. 结果表明:CO₂排放因子主要受装机容量、燃料及机组使用年限与维护质量的影响;常规煤粉机组的N₂O排放因子随装机容量的增加逐渐变小,循环流化床机组N₂O排放因子最大;与IPCC缺省排放因子的比较表明,烟煤、褐煤的CO₂和N₂O排放因子均在IPCC缺省因子95%置信区间内,贫煤CO₂和N₂O的排放因子均大于IPCC缺省因子;天然气CO₂和N₂O排放因子与IPCC缺省因子相差不大.      

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (− 0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.     

Stepwise multiple regression method of greenhouse gas emission modeling in the energy sector in Poland

The energy sector in Poland is the source of 81% of greenhouse gas (GHG) emissions. Poland, among other European Union countries, occupies a leading position with regard to coal consumption. Polish energy sector actively participates in efforts to reduce GHG emissions to the atmosphere, through a gradual decrease of the share of coal in the fuel mix and development of renewable energy sources. All evidence which completes the knowledge about issues related to GHG emissions is a valuable source of information. The article presents the results of modeling of GHG emissions which are generated by the energy sector in Poland. For a better understanding of the quantitative relationship between total consumption of primary energy and greenhouse gas emission, multiple stepwise regression model was applied. The modeling results of CO₂ emissions demonstrate a high relationship (0.97) with the hard coal consumption variable. Adjustment coefficient of the model to actual data is high and equal to 95%. The backward step regression model, in the case of CH₄ emission, indicated the presence of hard coal (0.66), peat and fuel wood (0.34), solid waste fuels, as well as other sources (-0.64) as the most important variables. The adjusted coefficient is suitable and equals R² = 0.90. For N₂O emission modeling the obtained coefficient of determination is low and equal to 43%. A significant variable influencing the amount of N₂O emission is the peat and wood fuel consumption.