@ARTICLE{26543116_722059414_2022, 
	author = {Gilbert Ahamer}, 
	keywords = {, energy foresight, global modelling, Global Change Data Base, scenarios, heuristic modelling, fuel mix, trends extrapolation, land use change, saturationautopoietic systems},
	title = {Scenarios of Systemic Transitions in Energy and Economy},
	journal = {Foresight and STI Governance},
	year = {2022},
	volume = {16},
	number = {3},
	pages = {17-34},
	url = {https://foresight-journal.hse.ru/en/2022-16-3/722059414.html},
	publisher = {},
	abstract = {For the energy economics sector, earlier forecasting approaches (e.g., a Kaya identity or a double-logarithmic function) proved too simplistic. It is becoming necessary to systemically include the emergence of new discrete evolutionary changes. This paper provides a novel quantitative forecasting method which relies on the Global Change Data Base (GCDB). It allows for the generation and testing of hypotheses on future scenarios for energy, economy, and land use on a global and country level. The GCDB method envisages systemic variables, especially quotients (such as energy intensity), shares (such as GDP shares, energy mix), and growth rates including their change rates. Thus, the non-linear features of evolutionary developments become quantitatively visible and can be corroborated by plots of large bundles of time-series data. For the energy industry, the forecasting of sectoral GDP, fuel shares, energy intensities, and their respective dynamic development can be undertaken using the GCDB method.},
	annote = {For the energy economics sector, earlier forecasting approaches (e.g., a Kaya identity or a double-logarithmic function) proved too simplistic. It is becoming necessary to systemically include the emergence of new discrete evolutionary changes. This paper provides a novel quantitative forecasting method which relies on the Global Change Data Base (GCDB). It allows for the generation and testing of hypotheses on future scenarios for energy, economy, and land use on a global and country level. The GCDB method envisages systemic variables, especially quotients (such as energy intensity), shares (such as GDP shares, energy mix), and growth rates including their change rates. Thus, the non-linear features of evolutionary developments become quantitatively visible and can be corroborated by plots of large bundles of time-series data. For the energy industry, the forecasting of sectoral GDP, fuel shares, energy intensities, and their respective dynamic development can be undertaken using the GCDB method.}
}