Includes both back up capacity and spinning reserve for intermittent supply options (ISOs). Back up capacity is added to account for the **IMAGE** capacity credit (which decreases as a function Multinomial logit formulation describing GHG and other pollutants substitution for technologies and fuels of penetration of the ISOs). The costs for back up power is allocated to the ISO. Required <u>spinnning reserve of 15% assumed for ISOs.</u> The substitution between different fuels/technological options is modeled It covers all emissions from the energy The electricity generation module includes 26 based on the “gap”, which is defined in sector and the industry sector and can **PROMETHEUS** power generation technologies and their terms of the difference between energy split CO2 emissions by sector (transport, competition to cover electricity demand for demand and the amount of energy that industry, buildings, power generation, base, medium and peak load can be satisfied using existing equipment refineries, international bunkers, other) from the previous year, which is not and by fuel (coal, oil, natural gas). scrapped.

# **3.5 Main taxonomy according to applications**

Tables 3a and 3b report in addition to the treatment of uncertainty, the policy whose impact the model is able to represent and applications.

# **Policy instruments**

Top-down models are well suited to assess the macroeconomic impact of energy and environmental policies, especially market-oriented policies (carbon tax and tradable quota), on national and global scales. Because of limited representation efficiency improvements are difficult to convert explicitly into the production function of these models. For top-down models, the most efficient technology lies on the production frontier determined by market behavior. These models can help policy makers to assess the macroeconomic impacts of market instruments such as a carbon tax on energy systems or subsidies on renewable energy generation. Bottom-up models are also used to assess the energy supply and demand aspects of technology-based policies that are not driven by price (e.g., labels and standards). Theoretically, the “most efficient” technology within these models can lie beyond the production frontier determined by market behavior because customers may not actually adopt the technology. This discrepancy provides evidence of an efficiency gap. A high degree of detail regarding technology, such as its cost and efficiency, is included in bottom-up models, which allows researchers to explore the potential of decoupling economic growth from energy demands.

There are many climate-policy instruments, including carbon taxation, cap-and-trade, intensity-based targets, and subsidies (for renewable energy, research for new clean technology, emission reductions, etc.) which have been included in the modeling of IAMs. Each instrument has its advantages and disadvantages. For example, a carbon tax gives a direct price on carbon emissions, so companies can adjust their emissions based on cost-benefit analysis, but there is uncertainty in its effect on total emissions in the real world. A cap-and-trade scheme issues a number of emission allowances for the market to auction and trade, so it provides direct control over future emissions and it would be more straightforward to control temperature increase under some threshold (e.g., 2 or 1.5°C), but it is hard to estimate its economic cost. An intensitybased target scheme requires emissions per unit of economic activity to not exceed given targets, so it may be appealing to developing economies, but there is uncertainty in aggregate emissions and economic costs.

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