Verification of compliance with GHG emission targets: annex B countries

Bun, A.; Hamal, K.; Jonas, M.; Lesiv, Myroslava

doi:10.1007/s10584-010-9906-6

Cited by 7 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Assessing the uncertainty of greenhouse gas (GHG) inventories is a complex problem that has been investigated for many years; however, no commonly accepted solution has been found. Low uncertainty of GHG emission inventories, namely, high accuracy and precision of emission estimates, is key to setting reduction targets for climate treaties (Jonas et al 2010), monitoring treaty implementation (Bun et al 2010), and establishing reliable emission trading schemes (Ermolieva et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time

Jarnicka

Żebrowski

2019

Mitig Adapt Strateg Glob Change

View full text Add to dashboard Cite

This paper addresses the problem of learning in greenhouse gas (GHG) emission inventories understood as reductions in uncertainty, i.e., inaccuracy and/or imprecision, over time. We analyze the National Inventory Reports (NIRs) submitted annually to the United Nations Framework Convention on Climate Change. Each NIR contains data on the GHG emissions in a given country for a given year as well as revisions of past years' estimates. We arrange the revisions, i.e., estimates of historical emissions published in consecutive NIRs into a table, so that each column contains revised estimates of emissions for the same year, reflecting different realizations of uncertainty. We propose two variants of a two-step procedure to investigate the changes of uncertainty over time. In step 1, we assess changes in inaccuracy, which we consider constant within each revision, by either detrending the revisions using the smoothing spline fitted to the most recent revision (method 1) or by taking differences between the most recent revision and the previous ones (method 2). Step 2 estimates the imprecision by analyzing the columns of the data table. We assess learning by detecting and modeling a decreasing trend in inaccuracy and/or imprecision. We analyze carbon dioxide (CO 2) emission inventories for the European Union (EU-15) as a whole and its individual member countries. Our findings indicate that although there is still room for improvement, continued efforts to improve accounting methodology lead to a reduction of uncertainty of emission estimates reported in NIRs, which is of key importance for monitoring the realization of countries' emission reduction commitments.

show abstract

Section: Introductionmentioning

confidence: 99%

Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time

Jarnicka

Żebrowski

2019

Mitig Adapt Strateg Glob Change

View full text Add to dashboard Cite

show abstract

“…The application of a number of existing techniques to analyze emissions and emission changes against their uncertainty could provide useful knowledge that countries would like to have available prior to agreeing to emission targets (Jonas et al 2004(Jonas et al , 2010aBun et al 2010). At the same time, countries should strive to provide more accurate results regarding GHG emissions.…”

Section: Introductionmentioning

confidence: 99%

Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15

Lesiv

Bun

Jonas

2015

Uncertainties in Greenhouse Gas Inventories

View full text Add to dashboard Cite

Total uncertainty in greenhouse gas (GHG) emissions changes over time due to "learning" and structural changes in GHG emissions. Understanding the uncertainty in GHG emissions over time is very important to better communicate uncertainty and to improve the setting of emission targets in the future. This is a diagnostic study divided into two parts. The first part analyses the historical change in the total uncertainty of CO 2 emissions from stationary sources that the member states estimate annually in their national inventory reports. The second part presents examples of changes in total uncertainty due to structural changes in GHG emissions considering the GAINS (Greenhouse Gas and Air Pollution Interactions and Synergies) emissions scenarios that are consistent with the EU's "20-20-20" targets. The estimates of total uncertainty for the year 2020 are made under assumptions that relative uncertainties of GHG emissions by sector do not change in time, and with possible future uncertainty reductions for non-CO 2 emissions, which are characterized by high relative uncertainty. This diagnostic exercise shows that a driving factor of change in total uncertainty is increased knowledge of inventory processes in the past and prospective future. However, for individual countries and longer periods, structural changes in emissions could significantly influence the total uncertainty in relative terms.

show abstract

“…Countries' compliance with emission targets cannot be scientifically proved when the emission reduction achieved is of the same order as, if not greater than, the emission reduction to which the country has committed. Analysis of compliance and emission trading rules under the Kyoto Protocol based on uncertainty of emission estimates has been performed in some recent studies, see Bun et al (2010), Jonas et al (2004), Jonas et al (2010), Nahorski and Horabik (2010). Indeed, the uncertainties of inventory data and the problem of reduction of uncertainties are now of great interest in the scientific community.…”

Section: Introductionmentioning

confidence: 99%

“…Country-scale inventory results are useful for tracing countries' adherence to international agreements, analyzing historical emission change trends, and grading countries according to their emission levels, etc. On the other hand it is an advantage for government bodies in all countries to have a tool that enables them to analyze the separate constituents of the many-sided processes of greenhouse gas emissions and absorptions and thereby find optimum ways of solving a number of economic or environment protection problems (Bun et al 2006). Therefore, when we talk about emissions from the point of view of a single country, it is important to have knowledge about the spatial distribution of inventory data and their structure.…”

Section: Introductionmentioning

confidence: 99%

Spatial GHG inventory at the regional level: accounting for uncertainty

et al. 2010

Self Cite

View full text Add to dashboard Cite

Methodology and geo-information technology for spatial analysis of processes of greenhouse gas (GHG) emissions from mobile and stationary sources of the energy sector at the level of elementary plots are developed. The methodology, which takes into account the territorial specificity of point, line, and area sources of emissions, is based on official statistical data surveys. The spatial distribution of emissions and their structure for the main sectors of the energy sector in the territory of the Lviv region of Ukraine are analyzed. The relative uncertainties of emission estimates obtained are calculated using knowledge of the spatial location of emission sources and following the Tier 1 and Tier 2 approaches of IPCC methodologies. The sensitivity of total relative uncertainty to change of uncertainties in input data uncertainties is studied for the biggest emission point sources. A few scenarios of passing to the alternative energy generation are considered and respective structural changes in the structure of greenhouse gas emissions are analyzed. An influence of these structural changes on the total uncertainty of greenhouse gas inventory results is studied.

show abstract

Verification of compliance with GHG emission targets: annex B countries

Cited by 7 publications

References 5 publications

Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time

Learning in greenhouse gas emission inventories in terms of uncertainty improvement over time

Analysis of change in relative uncertainty in GHG emissions from stationary sources for the EU 15

Spatial GHG inventory at the regional level: accounting for uncertainty

Contact Info

Product

Resources

About