Empirical Findings

I make two empirical estimations of this probabilistic Kaya model, one longitudinal for 1990-2014 as well as one cross-sectional for 2014.

Longitudinal Analysis

I make an empirical estimation of this probabilistic Kaya model—the longitudinal test for 1990-2014, World data 1990-2015 (Fig. 1):

The Kaya model findings show that total GHG:s go with larger total GDP. To make the dilemma of energy versus emissions even worse, we show in Fig. 1 that GDP increase with the augmentation of energy per capita. This makes the turn to a sustainable economy (Sachs 2015a, b) unlikely, as nations plan for much more energy in the coming decades.

Global GDP-CO link

Fig. 1 Global GDP-CO2 link: y = 0.80x + 5.96; R2 = 0.97 (N = 59)

Decarbonisation is the promise to undo these dismal links by making GDP and energy consumption rely upon carbon neutral energy resources, like modern renewables and atomic energy.

We need to model this energy-emission dilemma for the countries of the COP21 project. To understand the predicament of Third World countries, we need to know whether GHC:s or CO2:s are still increasing (Goal I) and what the basic structure of the energy mix is (Goal II). Thus, I suggest:

as a model of the decarbonisation feasibility in some Third World countries, to be analysed below, following the so-called “Kaya” model. The first concept taps the feasibility of Goal I: halting the growth of GHG:s or CO2:s, whereas the other concepts target the role of fossil fuels and wood coal like charcoal.

Cross-Sectional Analysis

In a stochastic form with a residual variance, all to be estimated on data from some 59 countries, I make an empirical estimation of this probabilistic Kaya model—the cross-sectional test for 2014:

Note that: where:

Dummy for fossils = 1 if more than 80% fossil fuels; k4 not significantly proven to be non-zero, all others are (N = 59).

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