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

Abstract

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.

Errors and uncertainties in a gridded carbon dioxide emissions inventory

“…This subsection describes the two spatially explicit CO 2 emission data used in this study: (1) the Open-source Data Inventory for Anthropogenic CO 2 (ODIAC, Oda et al 2010, 2018Maksyutov 2011, 2015) and (2) the geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU, Bun et al 2018;Charkovska et al 2018Charkovska et al , 2019Danylo et al 2019). Table 1 compares the specifications of ODIAC and GESAPU.…”

“…While the multi-resolution modeling approach is considered to be the best approach to achieve emission estimates at policy-relevant scales, their development is extremely laborintensive and such EIs are only available for limited places and times. A few other spatially explicit EIs that employ a multi-resolution modeling approach (e.g., Gurney et al 2012;Bun et al 2018;Mori et al 2015) also share these difficulties, and none of them cover the full globe to support global climate mitigation. It is important to note that large-scale, top-down GHG emission verification support systems, such as the one proposed by Pinty et al (2017), assume the use of a disaggregation-based EI such as the Emission Database for Global Atmospheric Research (EDGAR, Janssens-Maenhout et al 2012, not of the detailed bottom-up estimates based on multi-resolution modeling.…”

High-resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector

“…Total emissions To calculate specific total CH 4 and N 2 O emissions in categories, for different types of animal and manure systems, as well as to easily calculate total emissions in CO 2 -equivalent for different territories, the results are aggregated in a regular grid of 2 km × 2 km, as described in detail in Bun et al (2018). Subsequently, the results were aggregated to the larger areas, for example, the provinces, when needed.…”

“…Trombetti et al (2017). This is one of main reasons why estimation of emissions with high spatial resolution is a subject of many studies, but a vast majority of them dealt with emissions from fossil fuel consumption; see the list of references in Bun et al (2018). At the same time, well-focussed and more intensive emission mitigations, when applied widespread, will have in effect a positive impact on achieving the global target limit of GHG concentration in the atmosphere.…”

High-resolution spatial distribution of greenhouse gas emissions in the residential sector

“…The outputs from this method are independent of any grid size (see Hogue et al (2017) in this special issue) so can be converted to a grid of any size, for the calculation of total emissions from very diverse emission sources (i.e., point-, line-, and area-type sources in other categories of human activity). This paper complements the high-resolution spatially explicit emission inventory undertaken for the agricultural sector (see Charkovska et al (2018) in this special issue) and for all sectors (see Bun et al (2018) in this special issue) in Poland. These studies were conducted within the European Union FP7 Marie Curie Actions IRSES project no.…”