High resolution inventory of GHG emissions of the road transport sector in Argentina

Puliafito, Salvador Enrique; Allende, David Gabriel; Pinto, Sebastián; Castesana, Paula

doi:10.1016/j.atmosenv.2014.11.040

Cited by 56 publications

(37 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…National inventories of GHG emissions are indispensable for monitoring and control of climate change but give no information on the spatial pattern of emissions. Methods of spatial inventories have proved to be helpful for policymakers, particularly at regional level (Olivier et al 2005;Andres et al 2009;Gurney et al 2009;Gurney et al 2012;Raupach et al 2010;Rayner et al 2010;Maksyutov 2011, 2015;Puliafito et al 2015;EDGAR 2013;Hutchins et al 2017, Oda et al 2018. Another important area where spatial inventories are indispensable is the modelling of local GHG dispersion in the atmosphere in order to compare the results with the atmospheric concentration measurements, checking the inventory accuracy or improving the emission estimates (Oda et al 2018;Peylin et al 2011;Liu et al 2017).…”

Section: Introductionmentioning

confidence: 99%

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

Charkovska

Halushchak

Bun

et al. 2019

Mitig Adapt Strateg Glob Change

View full text Add to dashboard Cite

Industrial processes cause significant emissions of greenhouse gases (GHGs) to the atmosphere and, therefore, have high mitigation and adaptation potential for global change. Spatially explicit (gridded) emission inventories (EIs) should allow us to analyse sectoral emission patterns to estimate the potential impacts of emission policies and support decisions on reducing emissions. However, such EIs are often based on simple downscaling of national level emission estimates and the changes in subnational emission distributions do not necessarily reflect the actual changes driven by the local emission drivers. This article presents a high-definition, 100-m resolution bottom-up inventory of GHG emissions from industrial processes (fuel combustion activities in energy and manufacturing industries, fugitive emissions, mineral products, chemical industries, metal production and food and drink industries), which is exemplified for data for Poland. The study objectives include elaboration of the universal approach for mapping emission sources, algorithms for emission disaggregation, Mitigation and Adaptation Strategies for Global Change (2019) 24:907-939 https://doi.estimation of emissions at the source level and uncertainty analysis. We start with IPCCcompliant national sectoral GHG estimates made using Polish official statistics and, then, propose an improved emission disaggregation algorithm that fully utilises a collection of activity data available at the national/provincial level to the level of individual point and diffused (area) emission sources. To ensure the accuracy of the resulting 100-m resolution emission fields, the geospatial data used for mapping emission sources (point source geolocation and land cover classification) were subject to thorough human visual inspection. The resulting 100-m emission field even holds cadastres of emissions separately for each industrial emission category. We also compiled cadastres in regular grids and, then, compared them with the Emission Database for Global Atmospheric Research (EDGAR). A quantitative analysis of discrepancies between both results reveals quite frequent misallocations of point sources used in the EDGAR compilation that considerably deteriorate high-resolution inventories. We also use a Monte-Carlo method-based uncertainty assessment that yields a detailed estimation of the GHG emission uncertainty in the main categories of the analysed processes. We found that the above-mentioned geographical coordinates and patterns used for emission disaggregation have the greatest impact on the overall uncertainty of GHG inventories from the industrial processes. We evaluate the mitigation potential of industrial emissions and the impact of separate emission categories. This study proposes a method to accurately quantify industrial emissions at a policy relevant spatial scale in order to contribute to the local climate mitigation via emission quantification (local to national) and scientific assessment of the mitigation effort (national to global). Apart from the abov...

show abstract

Section: Introductionmentioning

confidence: 99%

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

Charkovska

Halushchak

Bun

et al. 2019

Mitig Adapt Strateg Glob Change

View full text Add to dashboard Cite

show abstract

“…It indicated that transportation intensity and energy efficiency have significant influences on carbon dioxide emissions. Puliafito et al [32] calculated the carbon emissions data of Argentina's road transportation industry from 1960 to 2010 and predicted the data from 2011 to 2050, and Monte Carlo sensitivity analysis and scenario analysis methods were applied to analyze the relations between energy demand and greenhouse gas emissions. Melo [33] applied both the spatial and non-spatial panel data models and introduced ten influence factors, such as urbanization, vehicle ownership, and income levels, etc., to analyze the causal relationship between demand-led, as well as supply-led, factors and carbon emissions of the road transportation industry.…”

Section: Introductionmentioning

confidence: 99%

A Research on Driving Factors of Carbon Emissions of Road Transportation Industry in Six Asia-Pacific Countries Based on the LMDI Decomposition Method

Zhu

2019

Energies

View full text Add to dashboard Cite

The transportation industry is the second largest industry of carbon emissions in the world, and the road transportation industry accounts for a large proportion of this in the global transportation industry. The carbon emissions of the road transportation industry in six Asia-Pacific countries (Australia, Canada, China, India, Russia, and the United States) accounts for more than 50% of this in the global transportation industry. Therefore, it is of great significance to study driving factors of carbon emissions of the road transportation industry in six Asia-Pacific countries for controlling global carbon emissions. In this paper, the Logarithmic Mean Divisia Index (LMDI) decomposition method is adopted to analyze driving factors on carbon emissions of the road transportation industry in six Asia-Pacific countries from 1990 to 2016. The results show that carbon emissions of the road transportation industry in these six Asia-Pacific countries was 2961.37 million tons in 2016, with an increase of 84.43% compared with those in 1990. The economic output effect and the population size effect have positive driving influences on carbon emissions of the road transportation industry, in which the economic output effect is still the most important driving factor. The energy intensity effect and the transportation intensity effect have different influences on driving carbon emissions of the road transportation industry for these six Asia-Pacific Countries. Furthermore, the carbon emissions coefficient effect has a relatively small influence. Hence, in order to effectively control carbon emissions of the road transportation industry in these six Asia-Pacific countries, it is necessary to control the impact of economic developments on the environment, to reduce energy intensity by promoting the conversion of road transportation to rail and water transportation, and to lower the carbon emissions coefficient by continuously improving vehicle emission standards and fuel quality.

show abstract

“…Spatially explicit data are also useful for scientists and policy makers at provincial and local levels to identify the main sources of emissions, their shares in the total emissions, and the composition of emitted GHGs. The compilation of spatial data is an area of considerable interest as evidenced by many recent studies (Andres et al 2009;Gosh et al 2010;Gurney et al 2009;Hutchins et al 2017;Oda and Maksyutov 2011;Olivier et al 2005;Pétron et al 2008;Puliafito et al 2015;Raupach et al 2010;Rayner et al 2010;Denier van der Gon et al 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Spatially explicit GHG emission inventories have also been developed at the regional level, e.g. fossil fuel CO 2 emissions (Maksyutov et al 2013;Raupach et al 2010), fossil fuel combustion CO 2 emission fluxes for the USA (Gurney et al 2009), as well as data of emission sector or category such as power generation (Pétron et al 2008), North American methane emissions (Turner et al 2015), or the road transport sector in Argentina (Puliafito et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Bun

Nahorski

Horabik-Pyzel

et al. 2018

Mitig Adapt Strateg Glob Change

View full text Add to dashboard Cite

Greenhouse gas (GHG) inventories at national or provincial levels include the total emissions as well as the emissions for many categories of human activity, but there is a need for spatially explicit GHG emission inventories. Hence, the aim of this research was to outline a methodology for producing a high-resolution spatially explicit emission inventory, demonstrated for Poland. GHG emission sources were classified into point, line, and area types and then combined to calculate the total emissions. We created vector maps of all sources for all categories of economic activity covered by the IPCC guidelines, using official information about companies, the administrative maps, Corine Land Cover, and other available data. We created the algorithms for the disaggregation of these data to the level of elementary objects such as emission sources. The algorithms used depend on the categories of economic activity under investigation. We calculated the emissions of carbon, nitrogen sulfure and other GHG compounds (e.g., CO 2 , CH 4 , N 2 O, SO 2 , NMVOC) as well as total emissions in the CO 2 -equivalent. Gridded data were only created in the final stage to present the summarized emissions of very diverse sources from all categories. In our approach, information on the administrative assignment of corresponding emission sources is retained, which makes it possible to aggregate the final results to different administrative levels including municipalities, which is not possible using a traditional gridded emission approach. We demonstrate that any grid size can be chosen to match the aim of the spatial inventory, but not less than 100 m in this example, which corresponds to the coarsest resolution of the input datasets. We then considered the uncertainties in the statistical data, the calorific values, and the emission factors, with symmetric and asymmetric (lognormal) distributions. Using the Monte Carlo method, uncertainties, expressed using 95% confidence intervals, were estimated for high point-type emission sources, the provinces, and the subsectors. Such an approach is flexible, provided the data are available, and can be applied to other countries.

show abstract

High resolution inventory of GHG emissions of the road transport sector in Argentina

Cited by 56 publications

References 32 publications

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

A high-definition spatially explicit modelling approach for national greenhouse gas emissions from industrial processes: reducing the errors and uncertainties in global emission modelling

A Research on Driving Factors of Carbon Emissions of Road Transportation Industry in Six Asia-Pacific Countries Based on the LMDI Decomposition Method

Development of a high-resolution spatial inventory of greenhouse gas emissions for Poland from stationary and mobile sources

Contact Info

Product

Resources

About