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040 |aCaOODSP|beng|erda|cCaOODSP
043 |an-cn-on|an-cn-ab
0861 |aM154-151/2021E-PDF
1001 |aPied, Marie, |eauthor.
24510|aComparing machine learning and linear regression methods for estimating marginal greenhouse gas emission factors of electricity generation with renewables / |cprepared by Marie Pied, Sophie Pelland, Steven Wong, Dave Turcotte and Vahid Raissi Dehkordi.
264 1|a[Ottawa] : |bNatural Resources Canada = Ressources naturelles Canada, |c2021.
264 4|c©2021
300 |a1 online resource (ix, 33 pages) : |bcharts
336 |atext|btxt|2rdacontent
337 |acomputer|bc|2rdamedia
338 |aonline resource|bcr|2rdacarrier
500 |a"Date: November 30, 2021."
504 |aIncludes bibliographical references (pages 31-33).
520 |a"The electricity sector is key to reducing greenhouse gas emissions: energy supply across sectors is shifting towards electricity, and electricity production in turn is shifting towards low carbon sources. Marginal greenhouse gas emission factors (MEFs) are needed to properly quantify the impact on greenhouse gas emissions of policies and programs that modify electricity demand. We compared different methods of estimating MEFs applied to the Canadian provinces of Ontario and Alberta: a new method using artificial neural networks and existing methods using simple and multiple linear regression"--Executive summary, page i.
650 0|aElectric power|zOntario.
650 0|aGreenhouse gas mitigation|zOntario.
650 0|aClimate change mitigation|zOntario.
650 0|aElectric power|zAlberta.
650 0|aGreenhouse gas mitigation|zAlberta.
650 0|aClimate change mitigation|zAlberta.
650 6|aÉlectricité|zOntario.
650 6|aGaz à effet de serre|xRéduction|zOntario.
650 6|aClimat|xChangements|xAtténuation|zOntario.
650 6|aÉlectricité|zAlberta.
650 6|aGaz à effet de serre|xRéduction|zAlberta.
650 6|aClimat|xChangements|xAtténuation|zAlberta.
7101 |aCanada. |bNatural Resources Canada, |eissuing body.
7102 |aCanmetENERGY (Canada), |eissuing body.
85640|qPDF|s2.20 MB|uhttps://publications.gc.ca/collections/collection_2022/rncan-nrcan/M154-151-2021-eng.pdf