Open-data based carbon emission intensity signals for electricity generation in European countries – top down vs. bottom up approach

Unnewehr, Jan Frederick; Weidlich, Anke; Leonhard, Gfüllner,; Schäfer, Mirko

doi:10.1016/j.cles.2022.100018

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…Generally, regional data is only available with a low temporal resolution (monthly or yearly) through statistical reporting [18]. Different reporting schemes for aggregated statistical data as compared to temporally resolved time series further hinder validation (see for instance the discussion in [12] or [24]). ENTSO-E publishes generation and load time series for the four different control areas in Germany (operated by the transmission system operators Amprion GmbH, TransnetBW GmbH, TenneT TSO GmbH and 50Hertz Transmission GmbH, respectively), which do not coincide with federal states.…”

Section: Resultsmentioning

confidence: 99%

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Sundblad¹,

Fürmann²,

Weidlich³

et al. 2023

2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)

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Electricity generation and demand time series often are only available on a national scale. In this contribution, we derive regionalization factors to allocate publicly available national generation and demand time series for Germany to the federalstate level. We compare two different types of regionalization approaches: Static factors are based on the regional distribution of capacities or population and GPD, whereas dynamic factors take plant-specific generation time series, regionally resolved weather patterns or compositions of different load profiles into account. We observe that dynamic regionalization factors show significant temporal variability, emphasizing the limitations of static regionalization factors for a spatio-temporally more detailed representation of power system time series.

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Section: Resultsmentioning

confidence: 99%

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Sundblad¹,

Fürmann²,

Weidlich³

et al. 2023

2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)

Self Cite

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“…As illustrated by Unnewehr et al these two approaches can yield different results. 55 Finally, the UBA takes a different approach to electricity trade: an UBA grid EF that takes trade into account is larger than one that does not, while the opposite is true for this study. This effect can be observed when comparing the values for UBA 1 (w/o trade) and UBA 2 (with trade), and explains why the difference between this study and the official value is largest for UBA 2.…”

Section: Energy and Environmental Sciencementioning

confidence: 89%

“…The review produced 48 primary research articles. 10–57 The nine aspects that most frequently appeared in these articles and were found to have an impact on the resulting grid EF are:…”

Section: State Of Researchmentioning

confidence: 99%

Towards standardized grid emission factors: methodological insights and best practices

Schäfer,

Cerdas,

Herrmann

2024

Energy Environ. Sci.

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“…A study took a telecommunications company in Slovenia as the research object, calculated the greenhouse gas emissions within enterprise scope 3, and took the purchase of electricity, the commuting of employees, the use of vehicles owned by the company and heating as the measured objects [24]. In addition, the research calculates the grid emission factors, deduces the emission factors of specific European countries, and calculates the carbon intensity based on the power generation of power plants [25]. The other is the exploration of the carbon emissions reduction path.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Coupling Effect and Space-Time Difference between China’s Digital Economy Development and Carbon Emissions Reduction

Shi

Kang

2023

IJERPH

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Previously conducted studies have established that the digital economy has a one-way inhibition effect on carbon emissions. Against this background, this paper aims to analyze the coordinated development effect of the interaction between the digital economy and carbon emissions reduction. The entropy weight method, coupling and coordination degree model, Dagum Gini coefficient and Moran’s I index have been carried out as research methods in this paper. The results showed that: (1) The coupling and coordination of China’s digital economy and carbon emissions reduction shows an overall growth trend, but the coupling and coordination among regions, provinces and cities show a large imbalance. (2) In the sample period, the overall difference in the coupling and coordination between digital economy development and carbon emissions reduction shows an expanding trend, and the overall difference results are attributed to regional differences. (3) There is a significant spatial correlation in the coupling and coordination degree of digital economy development and carbon emissions reduction among cities. The paper systematically grasps the status of coupling and coordination development, the source of difference and spatial correlation between the digital economy and carbon reduction in Chinese cities. A dependence relationship has been established, which is digital economy development and carbon emissions reduction, and an interactive promotion pattern has been revealed between the digital economic system and the carbon emissions reduction system.

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Open-data based carbon emission intensity signals for electricity generation in European countries – top down vs. bottom up approach

Cited by 13 publications

References 30 publications

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Towards standardized grid emission factors: methodological insights and best practices

Analysis of the Coupling Effect and Space-Time Difference between China’s Digital Economy Development and Carbon Emissions Reduction

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