This causal loop diagram illustrates the interlocking financial and structural barriers slowing Egypt’s energy transition. A reinforcing loop captures how high perceived investment risk drives up the cost of capital (WACC), which in turn discourages investment in renewables and delays capacity growth. At the same time, low renewable capacity sustains fossil fuel reliance, further increasing uncertainty and reinforcing risk perception. A balancing effect could emerge as investment in renewables grows, but the dominant loop currently traps the system in a cycle of underinvestment and slow progress.

Evaluating the effect of Shale oil and gas on the balance of power globally: seeking to understand the potential impact on the Middle East in particular and on major demand centres such as North America, China and Europe

Modelling after Earth, this is a model of the greenhouse effect has in increasing the temperature. By trapping some of the radiation emitted by the planet the atmosphere can is itself a positive feedback loop.

A dynamic model of relationships between CO2, plant food production, and growth.

 2015 Springer book on systems science. See planned contents from blog. Reframed to be similar to the Understanding Systems Science Insight IM-9773

A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.

Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.

Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.

Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.

Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.

Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.

**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.

Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**

The original "standard run" version of the model is available here.

Units don't really work, not sure what to do regarding flow units (can't divide units and the conversion part doesn't make any sense)