@ARTICLE{26543116_229725378_2018, 
	author = {Gilbert Ahamer}, 
	keywords = {, energy foresight, global modelling, Global Change Data Base, scenarios, trends extrapolation, dynamics-as-usual scenario, biomass energy, land use changesaturation},
	title = {Applying Global Databases to Foresight for Energy and Land Use: the GCDB method},
	journal = {Foresight and STI Governance},
	year = {2018},
	volume = {12},
	number = {4},
	pages = {46-61},
	url = {https://foresight-journal.hse.ru/en/2018-12-4/229725378.html},
	publisher = {},
	abstract = {Any economy strongly depends on energy trends, which, as practice shows, are non-linear. This paper proposes an efficient method for predicting these trends. It is based upon a geo-referenced approach and combines a biosphere-energy model with a Global Change Data Base (GCDB). The advantage of the considered method over "pure modeling" lies in its heuristics, dealing with the real historical dynamics of techno-socio-economic systems. Newly emerging qualities and saturation effects will be better portrayed by the proposed method, which includes first and second derivatives. The novelty of the GCDB method is in that it uses correlations of data series rather than data points. This allows for insights when contemplating swarms of data series and a heuristic examination of whether or not the widely-used hypothesis of path dependency in energy economics - and, more generally, in economic development - is applicable.The author believes that the application of the GCDB method will increase the objectivity of the collected data, enrich the knowledge in the field of «growth theory», expand the knowledge base, and increase the efficiency of public policy related to climate change.},
	annote = {Any economy strongly depends on energy trends, which, as practice shows, are non-linear. This paper proposes an efficient method for predicting these trends. It is based upon a geo-referenced approach and combines a biosphere-energy model with a Global Change Data Base (GCDB). The advantage of the considered method over "pure modeling" lies in its heuristics, dealing with the real historical dynamics of techno-socio-economic systems. Newly emerging qualities and saturation effects will be better portrayed by the proposed method, which includes first and second derivatives. The novelty of the GCDB method is in that it uses correlations of data series rather than data points. This allows for insights when contemplating swarms of data series and a heuristic examination of whether or not the widely-used hypothesis of path dependency in energy economics - and, more generally, in economic development - is applicable.The author believes that the application of the GCDB method will increase the objectivity of the collected data, enrich the knowledge in the field of «growth theory», expand the knowledge base, and increase the efficiency of public policy related to climate change.}
}