Books by Vladimir Potashnikov
The book presents how the world leading countries can transition, on a technological, socio-econo... more The book presents how the world leading countries can transition, on a technological, socio-economic and policy “pathway”, to a low-carbon economy consistent with the internationally agreed goal of limiting anthropogenic warming to less than 2 degrees Celsius. Achieving this goal requires that the world cut global net emissions of greenhouse gases so that they approach zero between 2050 and 2075. The publication provides update of the findings of 16 national research groups, to be used for analytical support of the UNFCCC COP21, where the historic Paris Climate Agreement was signed, and further development of decarbonization strategies.
The book summarizes findings of research of 16 country teams on deep decarbonization pathways, ai... more The book summarizes findings of research of 16 country teams on deep decarbonization pathways, aiming at limiting the global warming by 2 degrees Celsius.
This supplementary material contains case studies presenting specific aspects of the DDPP country... more This supplementary material contains case studies presenting specific aspects of the DDPP country pathways. They illustrate and complement the cross-cutting analysis included in the 2015 DDPP synthesis report
Papers by Vladimir Potashnikov
The paper pursues two goals. First, to apply Bayesian statistics for updating IO tables for 1996-... more The paper pursues two goals. First, to apply Bayesian statistics for updating IO tables for 1996-2004 period, i.e. within "old" definition of industries. Second, to estimate IOT for 2004-2010, in new definition of activities, based on national accounts and industries-level data. Both goals are experimental since as we know Bayesian statistics is not yet in common use here. However, we believe, that this approach has several advantages over R.A.S. and Maximum entropy methods. First, it is a natural and flexible way to incorporate any kind and amount of information either as a prior distribution or observable data. Second, Bayesian methods provide full density profile on estimated parameters with covariates. And third, from computational perspective minimizing highly dimensional function with hundreds of parameters, like the cross entropy measure, might be much harder than evaluation of posterior distribution using modern sampling algorithms, such as Markov Chain Monte Carlo...
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Books by Vladimir Potashnikov
Papers by Vladimir Potashnikov