Categry GG Versin Guidebk 2009 Title General guidance Time series cnsistency Lead authr Justin Gdwin EMEP/EEA emissin inventry guidebk 2009 1
Cntents 1 Time series cnsistency...3 1.1 Intrductin...3 1.2 Ensuring a cnsistent time series...3 1.3 Reslving data gaps...6 2 Splicing techniques...7 2.1 Overlap...7 2.2 Surrgate data...9 2.3 Interplatin...10 2.4 Trend extraplatin...11 2.5 Other techniques...13 2.6 Selecting the mst apprpriate technique...13 3 Reprting and dcumentatin f trend infrmatin...14 4 Time series cnsistency QA/QC...15 5 References...16 6 Pint f enquiry...16 EMEP/EEA emissin inventry guidebk 2009 2
1 Time series cnsistency 1.1 Intrductin This guidance has been adapted frm the 2006 Intergvernmental Panel n Climate Change (IPCC) Guidelines fr Natinal Greenhuse Gas Inventries (IPCC, 2006). The basic methds and principals are the same unless therwise indicated. The time series is a central cmpnent f an air pllutant inventry because it prvides infrmatin n histrical emissins trends and tracks the effects f strategies t reduce emissins at the natinal level. As is the case with estimates fr individual years, emissin trends shuld be neither ver nr underestimated as far as can be judged. All emissins estimates in a time series shuld be estimated cnsistently, which means that, as far as pssible, the time series shuld be calculated using the same methd and data surces in all years. Using different methds and data in a time series culd intrduce bias because the estimated emissin trend will reflect nt nly real changes in emissins but als the pattern f methdlgical refinements. This chapter describes gd practice in ensuring time series cnsistency. Subsectin 1.2 prvides guidance n cmmn situatins in which time series cnsistency culd be difficult t achieve: when carrying ut recalculatins, while adding new categries, and when accunting fr technlgical change. Sectin 2 describes techniques fr cmbining r splicing different methds r data sets t cmpensate fr incmplete r missing data. Additinal guidance n reprting and dcumentatin and QA/QC f time series cnsistency is given in Sectins 3 and 4. 1.2 Ensuring a cnsistent time series 1.2.1 Recalculatins due t methdlgical changes and refinements A methdlgical change in a categry is a switch t a different Tier frm the ne previusly used. Methdlgical changes are ften driven by the develpment f new and different data sets. An example f a methdlgical change is the new use f a higher Tier methd instead f a Tier 1 default methd fr an industrial categry because a cuntry has btained site-specific emissin measurement data that can be used directly r fr develpment f natinal emissin factrs. A methdlgical refinement ccurs when an inventry cmpiler uses the same Tier t estimate emissins but applies it using a different data surce r a different level f aggregatin. An example f a refinement wuld be if new data permit further disaggregatin f a livestck enteric fermentatin mdel. Resulting animal categries culd then be mre hmgenus r have a mre accurate emissin factr applied t them. In this case, the estimate is still being develped using a Tier 2 methd, but it is applied at a mre detailed level f disaggregatin. Anther pssibility is that data f a similar level f aggregatin but higher quality data culd be intrduced, due t imprved data cllectin methds. Bth methdlgical changes and refinements ver time are an essential part f imprving inventry quality. It is gd practice t change r refine methds when: available data have changed. The availability f data is a critical determinant f the apprpriate methd, and thus changes in available data may lead t changes r refinements in EMEP/EEA emissin inventry guidebk 2009 3
methds. As cuntries gain experience and devte additinal resurces t preparing air pllutant inventries, it is expected that data availability will imprve ( 1 ); the previusly used methd is nt cnsistent with gd practice fr that categry. Inventry cmpilers shuld review the guidance fr each categry in the detailed sectr chapters; a categry has becme key. A categry might nt be cnsidered key in a previus inventry year, depending n the criteria used, but culd becme key in a future year. Fr example, many cuntries are nly beginning t calculate emissin inventries fr PM 2.5 and becming aware f the impact f the use f bifuels n PM emissins. Althugh current emissins frm this categry are lw, they culd becme key in the future based n trend r level. Cuntries anticipating significant grwth in a categry may want t cnsider this pssibility befre it becmes key; the previusly used methd is insufficient t reflect mitigatin activities in a transparent manner. As techniques and technlgies fr reducing emissins are intrduced, inventry cmpilers shuld use methds that can accunt fr the resulting change in emissins in a transparent manner. Where the previusly used methds are insufficiently transparent, it is gd practice t change r refine them. See subsectin 1.2.3 f the present chapter fr further guidance; the capacity fr inventry preparatin has increased. Over time, the human r financial capacity (r bth) necessary t prepare inventries may increase. If inventry cmpilers increase inventry capacity, it is gd practice t change r refine methds s as t prduce mre accurate, cmplete and transparent estimates, particularly fr key categries; new inventry methds becme available. In the future, new inventry methds may be develped that take advantage f new technlgies r imprved scientific understanding. Fr example, remte-sensing technlgy imprvements in emissin mnitring technlgy may make it pssible t directly mnitr mre types f emissin surces; crrectin f errrs. It is pssible that the implementatin f the QA/QC prcedures described in Chapter 6, Inventry management, imprvement and QA/QC, will lead t the identificatin f errrs r mistakes in the inventry. As nted in that chapter, it is gd practice t crrect errrs in previusly submitted estimates. In a strict sense, the crrectin f errrs shuld nt be cnsidered a methdlgical change r refinement. This situatin is nted here, hwever, because the general guidance n time series cnsistency shuld be taken int cnsideratin when making necessary crrectins. 1.2.2 Adding new categries The additin t the inventry f a new categry r subcategry requires the calculatin f an entire time series, and estimates shuld be included in the inventry frm the year emissins start t ccur in the cuntry. A cuntry shuld make every effrt t use the same methd and data sets fr each year. It may be difficult t cllect data fr previus years, hwever, in which case cuntries shuld use the guidance n splicing in Sectin 2 f this chapter t cnstruct a cnsistent time series. A cuntry may add new categries r new gases t the inventry fr a variety f reasns: ( 1 ) Smetimes cllectin f data may be reduced which can result in a less rigrus methdlgical utcme. EMEP/EEA emissin inventry guidebk 2009 4
a new emissin activity is ccurring. Sme emissin prcesses, particularly in the Industrial Prcesses Sectr, nly ccur as a result f specific technlgical prcesses; rapid grwth in a very small categry. A categry that previusly was t small t justify resurces fr inclusin in the natinal inventry culd experience sudden grwth and shuld be included in future inventries; new NFR categries. The EMEP Emissin Reprting Guidelines cntain sme categries and subcategries which were nt cvered in the previus reprting guidelines. As a result, cuntries may include new estimates in future natinal inventries. Cuntries shuld include estimates fr new categries and subcategries fr the entire time series; additinal inventry capacity. A cuntry may be able t use mre resurces r emply additinal experts ver time, and thus include new categries and subcategries in the inventry. If a new emissin-causing activity began after the base year ( 2 ), r if a categry previusly regarded as insignificant (see subsectin 1.2.1 f the present chapter regarding methdlgical chice fr reasns fr nt estimating emissins frm an existing surce) has grwn t the pint where it shuld be included in the inventry, it is gd practice t dcument the reasn fr nt estimating the entire time series. 1.2.3 Tracking increases and decreases due t technlgical change and ther factrs Emissin inventries can track changes in emissins thrugh changing activity levels r changing emissin rates, r bth. The way in which such changes are included in methdlgies can have a significant impact n time series cnsistency. Changes in activity levels Natinal statistics will typically accunt fr significant changes in activity levels. Fr example, fuel switching frm cal t natural gas in electricity generatin will be reflected in the natinal fuel cnsumptin statistics. Further disaggregatin f activity data can prvide mre transparency t indicate specifically where the change in activity is ccurring. This apprach is relevant when changes are taking place in ne r mre subcategries, but nt thrughut the entire categry. T maintain time series cnsistency, the same level f disaggregatin int subcategries shuld, as far as pssible, be used fr the entire time series, even if the change began recently. Changes in emissin rates Research may indicate that the average rate f emissins per unit f activity has changed ver the time series. In sme cases, the factrs leading t a technlgical change may als make it pssible t use a higher Tier methd. Fr example, a cke ven plant manager wh intrduces measures t reduce the frequency and intensity f fugitive cke ven leakage may als cllect plant-specific parameters that can be used t estimate a new emissin factr. This new factr might nt be apprpriate fr estimating emissins fr earlier years in the time series befre the technlgical r practice change ccurred. In these cases it is gd practice t use the updated emissin factr r ther estimatin parameters r data t reflect these changes fr the relevant years nly. Since a ( 2 ) Within the UNFCCC/IPCC (United Natins Framewrk Cnventin n Climate Change/Intergvernmental Panel n Climate Change) apprach the Base Year is a year upn which targets are based and ften represents the start year f the inventry. EMEP/EEA emissin inventry guidebk 2009 5
general assumptin is that emissin factrs r ther estimatin parameters d nt change ver time unless therwise indicated, cuntries shuld clearly dcument the reasn fr using different factrs r parameters in the time series. This is particularly imprtant if sampling r surveying ccurs peridically and emissin factrs r estimatin parameters fr years in between are interplated rather than measured. Abatement f emissins Larger pint surces such as chemical manufacturing facilities r pwer plants might generate emissins but prevent them frm being released t the atmsphere thrugh abatement and cntrl. In these cases it is gd practice t accunt fr these measures and t apply different emissin factrs fr different years and t dcument the reasns why these factrs are different. 1.3 Reslving data gaps 1.3.1 Issues with data availability Fr a cmplete and cnsistent time series, it is necessary t determine the availability f data fr each year. Recalculating previus estimates using a higher Tier methd r develping estimates fr new categries will be difficult if data are missing fr ne r mre years. Examples f data gaps are presented belw. Peridic data: natural resurce r envirnmental statistics, such as natinal frest inventries, waste statistics and agricultural statistics, may nt cver the entire cuntry n an annual basis. Instead, they may be carried ut at intervals such as every fifth r tenth year, r regin-by-regin, implying that natinal level estimates can nly be directly btained nce the inventry in every regin has been cmpleted. When data are available less frequently than annual, several issues arise. First, the estimates need t be updated each time new data becme available, and the years between the available data need t be recalculated. The secnd issue is prducing inventries fr years after the last available data pint and befre new data are available. In this case, new estimates shuld be extraplated based n available data, and then recalculated when new data becme available. Changes and gaps in data availability: a change in data availability r a gap in data is different frm peridically available data because there is unlikely t be an pprtunity t recalculate the estimate at a later date using better data. In sme cases, cuntries will imprve their ability t cllect data ver time, s that higher Tier methds can be applied fr recent years, but nt fr earlier years. This is particularly relevant t categries in which it is pssible t implement direct sampling and measurement prgrams because these new data may nt be indicative f cnditins in past years. Sme cuntries may find that the availability f certain data sets decreases ver time as a result f changing pririties within gvernments, ecnmic restructuring, r limited resurces. Sme cuntries with ecnmies in transitin may n lnger cllect certain data sets that were available in the base year, r if available, these data sets may cntain different definitins, classificatins and levels f aggregatin. 1.3.2 Nn-calendar year data When using nn-calendar year data, it is gd practice t use the same cllectin perid cnsistently ver the time series as described in Chapter 3, Data cllectin. Cuntries shuld nt use different cllectin perids within the same time series because this culd lead t a bias in the trend. EMEP/EEA emissin inventry guidebk 2009 6
2 Splicing techniques Splicing in this cntext refers t the cmbining r jining f mre than ne methd t frm a cmplete time series. Several splicing techniques are available if it is nt pssible t use the same methd r data surce in all years. This sectin describes techniques that can be used t cmbine methds t minimise the ptential incnsistencies in the time series. Each technique can be apprpriate in certain situatins, as determined by cnsideratins such as data availability and the nature f the methdlgical mdificatin. Selecting a technique requires an evaluatin f the specific circumstances, and a determinatin f the best ptin fr the particular case. It is gd practice t perfrm the splicing using mre than ne technique befre making a final decisin and t dcument why a particular methd was chsen. The principal appraches fr inventry recalculatins are summarised in Table 2-1. 2.1 Overlap The verlap technique is ften used when a new methd is intrduced but data are nt available t apply the new methd t the early years in the time series, fr example when implementing a higher Tier methdlgy. If the new methd cannt be used fr all years, it may be pssible t develp a time series based n the relatinship (r verlap) bserved between the tw methds during the years when bth can be used. Essentially, the time series is cnstructed by assuming that there is a cnsistent relatinship between the results f the previusly used and new methd. The emissin estimates fr thse years when the new methd cannt be used directly are develped by prprtinally adjusting the previusly develped estimates, based n the relatinship bserved during the perid f verlap. In this case, the emissins assciated with the new methd are estimated accrding t Equatin 1 ( 3 ). Equatin 1: Where: 1 n y ( ) i y0 = x0 (1) n m + 1 i= m xi y 0 = the recalculated emissin estimate cmputed using the verlap methd x 0 = the estimate develped using the previusly used methd y i and x i = the estimates prepared using the new and previusly used methds during the perid f verlap, as dented by years m thrugh n A relatinship between the previusly used and new methds can be evaluated by cmparing the verlap between nly ne set f annual estimates, but it is preferable t cmpare multiple years. This is because cmparing just ne year may lead t bias and it is nt pssible t evaluate trends. ( 3 ) Overlap Equatin 1 is preferred t the equatin described in Gd Practice Guidance fr Natinal Greenhuse Gas Inventries (IPCC, 2000): n n y0 = x0 y i xi i = m i = m This is because the latter gives mre weight t verlapping years with the highest emissins. Hwever, in practical cases the results will ften be very similar and cntinued use f the previus equatin is cnsistent with gd practice where its use gives satisfactry results. EMEP/EEA emissin inventry guidebk 2009 7
Figure 2-1 shws a hypthetical example f a cnsistent verlap between tw methds fr the years in which bth can be applied. In Figure 2-2 there is n cnsistent verlap between methds and it is nt gd practice t use the verlap technique in such a case. Other relatinships between the ld and new estimates may als be bserved thrugh an assessment f verlap. Fr example, a cnstant difference may be bserved. In this case, the emissins assciated with the new methd are estimated by adjusting the previus estimate by the cnstant amunt equal t the average difference in the years f verlap. 20 Overlap - Cnsistent Relatinship Emissins 18 16 14 12 10 8 6 4 2 Tier 1 Splice Tier 2 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year Figure 2-1 Cnsistent verlap Emissins 20 18 16 14 12 10 8 6 4 2 Tier 1 Tier 2 0 Figure 2-2 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Incnsistent verlap Year EMEP/EEA emissin inventry guidebk 2009 8
2.2 Surrgate data The surrgate methd relates emissins t underlying activity r ther indicative data. Changes in these data are used t simulate the trend in emissins. The estimate shuld be related t the statistical data surce that best explains the time variatins f the categry. Fr example, mbile surce emissins may be related t trends in vehicle distances travelled, emissins frm dmestic wastewater may be related t ppulatin, and industrial emissins may be related t prductin levels in the relevant industry. See Chapter 3, Data Cllectin. In its simplest frm, the estimate will be related t a single type f data as shwn in Equatin 2: ( s ) y = / (2) 0 yt 0 s t where: y = the emissin estimate in years 0 and t s = the surrgate statistical parameter in years 0 and t Althugh the relatinship between emissins and surrgate can be develped n the basis f data fr a single year, the use f multiple years might prvide a better estimate. Bx 1 prvides an example f the use f surrgate data fr estimating methane emissins frm undergrund cal mining in the United States. In sme cases, relating emissins t mre than ne statistical parameter may develp mre accurate relatinships. Regressin analysis may be useful in selecting the apprpriate surrgate data parameters. Using surrgate methds t estimate therwise unavailable data can imprve the accuracy f estimates develped by the interplatin and trend extraplatin appraches discussed belw. EMEP/EEA emissin inventry guidebk 2009 9
Bx 1 Case study f surrgate data Methane emissins frm undergrund cal mining in the United States On a quarterly basis, the U.S. Mine Safety and Health Administratin (MSHA) measures methane emissins levels at undergrund mines with detectable levels f methane in their ventilatin air. USEPA uses these measurements as a basis fr calculating natinal emissins frm undergrund cal mining. These data were nt available fr the years 1991 1992, hwever, because f restructuring within the Department f Labr. T estimate emissins fr these years, USEPA used ttal undergrund cal prductin as a surrgate data set. The graph belw shws the relatinship between undergrund cal prductin and measured emissins, which are clsely but nt perfectly crrelated. Differences reflect the fact that individual mines vary greatly in their emissin rates, and as prductin levels at mines change ver time, the weighted average emissin rate als changes. USEPA applied Equatin 2 t estimate emissins fr 1991 and 1992 using Tier 3 emissins data and cal prductin fr 1990. These data pints are crssed by the dashed line in the graph. Nte that this prcedure is very similar t an verlap with the Tier 1 methd because cal prductin is the recmmended activity data fr Tier 1. Cmparisn f implied emissin factrs frm estimates using surrgate data with Tier 1 default factrs wuld be a useful QA/QC check. Surrgate Data fr Cal Mining in the United States 450,000 120 400,000 350,000 100 Thusand Metric Tns 300,000 250,000 200,000 150,000 80 60 40 Billin Cubic Feet Cal Prductin Measured Emissins 100,000 50,000 20-1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Year 0 Surce: USEPA (2004) 2.3 Interplatin In sme cases it may be pssible t apply a methd intermittently thrughut the time series. Fr example, necessary detailed statistics may nly be cllected every few years, r it may be impractical t cnduct detailed surveys n an annual basis. In this case, estimates fr the intermediate years in the time series can be develped by interplating between the detailed estimates. If infrmatin n the general trends r underlying parameters is available, then the surrgate methd is preferable. EMEP/EEA emissin inventry guidebk 2009 10
Figure 2-3 shws an example f linear interplatin. In this example, data fr 1994 and 1995 are nt available. Emissins were estimated by assuming a cnstant annual grwth in emissins frm 1993 1996. This technique is apprpriate in this example because the verall trend appears stable, and it is unlikely that actual emissins fr 1994 and 1995 are substantially different frm the values predicted thrugh interplatin. Fr categries that have vlatile emissin trends (i.e. they fluctuate significantly frm year t year), interplatin will nt be accrding t gd practice and surrgate data will be a better ptin. It is gd practice t cmpare interplated estimates with surrgate data as a QA/QC check. Emissins 20 18 16 14 12 10 8 6 4 2 0 Figure 2-3 Linear Interplatin 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Linear interplatin Year Methd Interplatin 2.4 Trend extraplatin When detailed estimates have nt been prepared fr the base year r the mst recent year in the inventry, it may be necessary t extraplate frm the clsest detailed estimates. Trend extraplatin is cnceptually similar t interplatin, but less is knwn abut the actual trend. Extraplatin can be cnducted either frward (t estimate mre recent emissins) r backward (t estimate a base year). Trend extraplatin simply assumes that the bserved trend in emissins during the perid when detailed estimates are available remains cnstant ver the perid f extraplatin. Given this assumptin, it is clear that trend extraplatin shuld nt be used if the change in trend is nt cnstant ver time. In this situatin it will be mre apprpriate t cnsider using extraplatins based n surrgate data. Extraplatin shuld als nt be used ver lng perids f time withut detailed checks at intervals t cnfirm the cntinued validity f the trend. In the case f peridic data, hwever, extraplatins will be preliminary and the data pint will be recalculated at a later stage. Bx 2 in this sectin shws an example in which activity data fr frests are available nly at peridic intervals, and data fr the mst recent years are nt yet available. Data fr recent years can be extraplated n the basis f a cnsistent trend, r n the basis f apprpriate data. It shuld be nted, hwever, that the uncertainty f the extraplated estimates increases in prprtin t the length f time ver which the extraplatin is made. Once the latest set f peridic data becmes EMEP/EEA emissin inventry guidebk 2009 11
available, it will be necessary t recalculate the part f the time series that had been estimated using trend extraplatin. The example in Bx 2 assumes a linear extraplatin, which is likely t be apprpriate fr the frestland categry. Nn-linear extraplatins are pssible, and may be mre apprpriate given an bserved trend, (e.g. expnential grwth in the use f ODS (znsphere damaging substances) substitutes). Cuntries using nn-linear extraplatin shuld prvide clear dcumentatin fr the chice and explain why it is mre apprpriate than linear extraplatin. Bx 2 Case study n peridic data, using extraplatin Cnsider a case where a natinal frest inventry is cnducted every five years. Estimates f several types f required data (e.g. tree grwth) will therefre nly be btained at certain intervals. On the assumptin that grwth is n average reasnably stable between years, inventry estimates fr the years after the last available data shuld be made using extraplatins f past estimates (i.e. treegrwth trends). As shwn in the figure belw, a bimass estimate fr 2005 fr a plt is btained in this way, althugh the latest measurement was made in 2000. The trend between 1995 and 2000 is simply extraplated linearly. In practice, a lg scale might be used t accmmdate expnential behaviur, but this is nt cnsidered fr this simple example. Als, extraplatin can be imprved using surrgate data r mre sphisticated mdelling taking int accunt parameters influencing the parameter we want t extraplate. Linear Extraplatin in AFOLU Actual (Peridic) Data) Original Extraplatin 65 60 Tree Grwth 55 50 45 40 1985 1990 1995 2000 2005 Year NB: AFOLU = Agriculture Frestry and Land Use Unlike peridically available data, when data are nt available fr the first years in the time series (e.g. base year and pre base year data n, fr example, waste dispsal and land use) there is n pssibility f filling in gaps with future surveys. Trend extraplatin back in time is pssible but shuld be dne in cmbinatin with ther splicing techniques such as surrgate data and verlap. Sme cuntries that have undergne significant administrative and ecnmic transitins since 1990 d nt have cnsistent activity data sets fr the entire time series, particularly if natinal data sets cvered different gegraphic areas in previus years. T extraplate backwards in these cases, EMEP/EEA emissin inventry guidebk 2009 12
it is necessary t analyse the relatinship between different activity data sets fr different perids, pssibly using multiple surrgate data sets. 2.5 Other techniques In sme cases, it may be necessary t develp a custmised apprach t best estimate the emissins ver time. Fr example, the standard alternatives may nt be valid when technical cnditins are changing thrughut the time series (e.g. due t the intrductin f mitigatin technlgy). In this case, it will be necessary t carefully cnsider the trends in all factrs knwn t influence emissins ver the perid. Where custmised appraches are used, it is gd practice t dcument them thrughly, and in particular t give special cnsideratin t hw the resultant emissins estimates cmpare t thse that wuld be develped using the mre standard alternatives. 2.6 Selecting the mst apprpriate technique The chice f splicing technique invlves expert judgement, and depends n an expert assessment f the vlatility f emissins trend, the availability f data fr tw verlapping methds, the adequacy and availability f surrgate data sets, and the number f years f missing data. Table 2-1 summarises the requirements fr each technique and suggests situatins in which they may r may nt be apprpriate. Cuntries shuld use Table 2-1 as a guide rather than a prescriptin. Table 2-1 Summary f splicing techniques Apprach Applicability Cmments Overlap Data necessary t apply bth the previusly used and the new methd must be available fr at least ne year, preferably mre. Surrgate data Interplatin Trend extraplatin Other techniques Emissin factrs, activity data r ther estimatin parameters used in the new methd are strngly crrelated with ther well-knwn and mre readily available indicative data. Data needed fr recalculatin using the new methd are available fr intermittent years during the time series. Data fr the new methd are nt cllected annually and are nt available at the beginning r the end f the time series. The standard alternatives are nt valid when technical cnditins are changing thrughut the time series (e.g. due t the intrductin f Mst reliable when the verlap between tw r mre sets f annual estimates can be assessed. If the trends bserved using the previusly used and new methds are incnsistent, this apprach is nt gd practice. Multiple indicative data sets (singly r in cmbinatin) shuld be tested in rder t determine the mst strngly crrelated. Shuld nt be dne fr lng perids. Estimates can be linearly interplated fr the perids when the new methd cannt be applied. The methd is nt applicable in the case f large annual fluctuatins. Mst reliable if the trend ver time is cnstant. Shuld nt be used if the trend is changing (in this case, the surrgate methd may be mre apprpriate). Shuld nt be dne fr lng perids. Dcument custmised appraches thrughly. Cmpare results with standard techniques. EMEP/EEA emissin inventry guidebk 2009 13
mitigatin technlgy). 3 Reprting and dcumentatin f trend infrmatin If the same methd and data surces are used thrughut the time series, and there have been n recalculatins, then fllwing the reprting guidance fr each categry shuld be sufficient t ensure transparency. Generally, cuntries shuld explain inventry trends fr each categry, giving particular attentin t utliers, trend changes, and extreme trends. Cuntries shuld prvide additinal dcumentatin if they have recalculated previus estimates and if they have used the techniques in this chapter t splice methdlgies. Recalculatins: in additin t fllwing the categry-specific guidance n each categry prvided in the sectral vlumes, cuntries shuld clearly dcument any recalculatins. The dcumentatin shuld explain the reasn fr the recalculatin and the effect f the recalculatin n the time series. Cuntries can als include a graph that shws the relatinship between the previus data trend and the new data trend. Table 3-1 prvides an example f hw recalculatins can be dcumented either fr reprting purpses r fr internal tracking. Table 3-1 Categry/Gas Previus Data (PD) Latest Data (LD) Categry-specific dcumentatin f recalculatins Emissins (Gg) Difference in percent =100 [(LD PD)/PD] Dcumentatin (reasn fr recalculatin): 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 Splicing techniques: cuntries shuld prvide dcumentatin f any splicing techniques used t cmplete a time series. The dcumentatin shuld identify the years in which data fr the methd were nt available, the splicing technique used, and any surrgate r verlap data used. Graphical plts, such as thse shwn in subsectin 2.1 f the present chapter, can be useful tls fr dcumenting and explaining the applicatin f splicing techniques. Mitigatin: the categry-specific guidance in the sectral vlumes prvides targeted guidance n specific infrmatin that shuld be reprted fr each categry, including mitigatin and reductins. Generally, cuntries shuld dcument the apprach used t track mitigatin activities and prvide all relevant parameters such as abatement utilisatin, destructin efficiency, updated emissin factrs, etc. EMEP/EEA emissin inventry guidebk 2009 14
4 Time series cnsistency QA/QC The mst effective way t ensure the quality f a time series is t apply bth general and categryspecific checks t the entire time series (see Chapter 6, Inventry management, imprvement and QA/QC). Fr example, the utlier and implied emissin factr checks in Chapter 6 will help t identify pssible incnsistencies in the time series. Categry-specific checks are particularly imprtant because they are targeted t unique features f each categry. As described abve, pltting and cmparing the results f splicing techniques n a graph is a useful QA/QC strategy. If alternative splicing methds prduce different results, cuntries shuld cnsider which result is mst realistic. In sme cases, additinal surrgate data can be used t check the spliced time series. A side-by-side cmparisn f recalculated estimates with previus estimates can be a useful check n the quality f a recalculatin. This can be dne thrugh a tabular cmparisn as shwn in Table 3-1, r as a graphical plt. It is imprtant t nte, hwever, that higher Tier methds may prduce different trends than lwer Tier methds because they mre accurately reflect actual cnditins. Differences in trends d nt necessarily suggest a prblem with the recalculated estimate. Where it is pssible t use mre than ne apprach in tracking the effects f mitigatin activities, cuntries shuld cmpare the results f multiple appraches. If the results differ by mre than wuld be expected, it is gd practice t explain the reasn fr the differences and evaluate whether r nt a different apprach shuld be used. Fr disaggregated higher Tier estimates, implied emissin factrs can be a useful tl fr checking the cnsistency f the trend and the plausibility f mitigatin estimates. In sme cases activity data cllectin may have been interrupted r drastically changed. This situatin causes challenges fr time series cnsistency. In this situatin it is gd practice t examine clsely dcumentatin f the previus data cllectin system t get a gd understanding f hw changes in data cllectin, including definitins and delimitatins, have affected the data used in the inventry and any implicatins fr incnsistencies in time-series. If apprpriate dcumentatin is nt available, an alternative is t cmpile indicatrs (e.g. emissins per unit prductin r emissins per car) and cmpare these between cuntries with a similar ecnmic structure, acrss time series and in the verlap f the tw data cllectin methds. In sme cases a cuntry may have undergne changes in gegraphical cverage, e.g. a cuntry may have divided int tw r mre new cuntries. In this situatin it is gd practice t cmpare the inventry data with estimates frm reginal statistics fr the years prir t the split. It can als be recmmended t cllabrate with ther cuntries that were nce part f the same cuntry t ensure cmpleteness and avid-duble cunting. If reginal statistics are nt available and such cllabratin is nt pssible, it is gd practice t cmpare apprpriate indicatrs as described abve fr the cuntry prir t a split with the data used in the inventry. If incnsistencies are identified, it is gd practice t crrect them and, if necessary, apply apprpriate splicing techniques as described in this chapter. EMEP/EEA emissin inventry guidebk 2009 15
5 References IPCC (2006), 2006 IPCC Guidelines fr Natinal Greenhuse Gas Inventries, IPCC Natinal Greenhuse Gas Inventries Prgramme, Glbal Envirnmental Strategies (IGES), Hayama, Japan, (www.ipcc-nggip.iges.r.jp/public/2006gl/index.htm). IPCC (2000), Gd Practice Guidance and Uncertainty Management in Natinal Greenhuse Gas Inventries, IPCC Natinal Greenhuse Gas Inventries Prgramme, the Institute fr Glbal Envirnmental Strategies (IGES), Hayama, Japan, (www.ipcc-nggip.iges.r.jp/public/gp/english/). USEPA (2004), Inventry f U.S. Greenhuse Gas Emissins and Sinks: 1990 2003, United States Envirnmental Prtectin Agency (USEPA), Natinal Service Center fr Envirnmental Publicatins (NSCEP), (www.epa.gv/glbalwarming/publicatins/emissin) 6 Pint f enquiry Enquiries cncerning this chapter shuld be directed t the c-chairs f the Task Frce n Emissin Inventries and Prjectins (TFEIP). Please refer t the TFEIP website (www.tfeip-secretariat.rg/) fr the cntact details f the current c-chairs. EMEP/EEA emissin inventry guidebk 2009 16