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


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03-25

AUTHORS

Olha Danylo, Rostyslav Bun, Linda See, Nadiia Charkovska

ABSTRACT

The development of high-resolution greenhouse gas (GHG) inventories is an important step towards emission reduction in different sectors. However, most of the spatially explicit approaches that have been developed to date produce outputs at a coarse resolution or do not disaggregate the data by sector. In this study, we present a methodology for assessing GHG emissions from the residential sector by settlements at a fine spatial resolution. In many countries, statistical data about fossil fuel consumption is only available at the regional or country levels. For this reason, we assess energy demand for cooking and water and space heating for each settlement, which we use as a proxy to disaggregate regional fossil fuel consumption data. As energy demand for space heating depends heavily on climatic conditions, we use the heating degree day method to account for this phenomenon. We also take the availability of energy sources and differences in consumption patterns between urban and rural areas into account. Based on the disaggregated data, we assess GHG emissions at the settlement level using country and regional specific coefficients for Poland and Ukraine, two neighboring countries with different energy usage patterns. In addition, we estimate uncertainties in the results using a Monte Carlo method, which takes uncertainties in the statistical data, calorific values, and emission factors into account. We use detailed data on natural gas consumption in Poland and biomass consumption for several regions in Ukraine to validate our approach. We also compare our results to data from the EDGAR (Emissions Database for Global Atmospheric Research), which shows high agreement in places but also demonstrates the advantage of a higher resolution GHG inventory. Overall, the results show that the approach developed here is universal and can be applied to other countries using their statistical information. More... »

PAGES

1-27

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11027-019-9846-z

DOI

http://dx.doi.org/10.1007/s11027-019-9846-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112988194


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