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

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., CO2, CH4, N2O, SO2, NMVOC) as well as total emissions in the CO2-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

Accounting uncertainty for spatial modeling of greenhouse gas emissions in the residential sector: fuel combustion and heat production.

Energy consumption in households has a great potential for energy savings as well as for greenhouse gas emission reduction. As national inventory reports provide estimates at only a country or regional level, we have developed a new GIS approach that increases the resolution of emission inventories. We consider stationary emission sources, such as fossil fuel combustion and heat production for household energy needs that cover energy demand for cooking, water and space heating. We estimate the spatial emissions of greenhouse gases based on IPCC guidelines using official statistics on fuel consumption and spatial data about population density. The heating degree-day method was then used to determine the climatic conditions and spatial variability in energy demand. The results of the spatial inventory are obtained for settlements that are presented as area-type emission sources in a geospatial database. The uncertainties in the inventory results are estimated using a Monte Carlo method. The results show that uncertainties in greenhouse gas emissions at the regional level are significantly higher than at the country level although the uncertainty of emissions in CO2-equivalent does not exceed 17.0%

High resolution spatial inventory of GHG emissions emissions from stationary and mobile sources in Poland: summarized results and uncertainty analysis

Greenhouse gases (GHG) inventories at national or regional levels include the total emissions and emissions for many categories of economic activity. The aim of our research is to analyze the high resolution spatial distributions of emissions for all categories of economic activity in Poland. GHG emission sources are classified into point-, line- and area-type sources. We created maps of such sources for all categories of economic activities covered by IPCC Guidelines, using official information of companies, administrative maps, Corine Land Cover maps, and other available data. The worst resolution is for area-type sources (100 m). We used statistical data at the lowest level as possible (regions, districts, and municipalities). We created the algorithms for these data disaggregation to the level of elementary objects for GHG spatial inventory. These algorithms depend on category of economic activity and cover all categories under investigation. We analyzed emissions of CO2, CH4, N2O, SO2, NMVOC, and others, and we calculated the total emissions in CO2-equivalent. We used a grid to calculate the summarizing emissions from the all categories. The grid size depends on the aim of spatial inventory, but it can't be less than 100 m. For uncertainty analysis we used uncertainty of statistical data, uncertainty of calorific values, and uncertainty of emission factors, with symmetric and asymmetric (lognormal) distributions. On this basis and using Monte-Carlo method the 95% confidence intervals of results' uncertainties were estimated for big point-type emission source, the regions, and the subsectors

Uncertainty associated with fossil fuel carbon dioxide (CO2) gridded emission datasets

CO2 emissions from fossil fuel combustion (FFCO2) serves as a reference in carbon budget analysis and thus needs to be accurately quantified. FFCO2 estimates from different emission inventories often agree well at global and national level, however their subnational emission spatial distributions are unique and subject to uncertainty in the proxy data used for disaggregation of country emissions. In this study, we attempt to assess the uncertainty associated with emission spatial distributions in gridded FFCO2 emission inventories. We compared emission distributions from four gridded inventories at a 1 W 1 degree resolution and used the differences as a proxy for uncertainty. The calculated uncertainties typically range from 30% to 200% and inversely correlated with the emission magnitude. We also discuss limitations of our approach and possible difficulties when implemented at a higher spatial resolution

Errors and uncertainties in a gridded carbon dioxide emissions inventory

Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges

Spatial analysis of ghg emissions in eastern polish regions: energy production and residential sector

The characteristics of territorial distribution of greenhouse gas emission sources have been analyzed for eastern Polish regions. Mathematical models and information technology for spatial analysis of greenhouse gas emissions from fossil fuel consumption of heat/power plants and households have been developed in consideration of the territorial distribution of greenhouse gas emission sources and structure of statistical data for the Polish voivodships: lubelskie, podkarpackie, podlaskie, and świętokrzyskie. The results of spatial analysis for these eastern voivodeships are presented in the form of thematic maps. Key words: information technology, spatial analysis, greenhouse gas emissions, heat/power plant, residential sector, fossil fuel

Spatial modeling of greenhouse gas emissions from stationary sources

The book presents novel approaches to spatial modelling of greenhouse gas emissions from stationary sources in the sectors of energy, industry, agriculture and for waste handling. A wide range of mathematical models for emissions of carbon dioxide, methane, nitrous oxide and other greenhouse gases in relevant sectors is described, along with geoinformation technology to create spatially distributed emission estimates. As a result, high resolution spatial greenhouse gas emissions from point- and area-type sources are computed for Poland and Ukraine, and provided in the book mostly in the graphical form. The monograph is intended for specialists in the field of mathematical modelling and GIS technology in the area of ecology and environmental protection. It may serve for students and postgraduates of relevant disciplines. [In Ukrainian

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

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., CO2, CH4, N2O, SO2, NMVOC) as well as total emissions in the CO2-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