Energy System Models (partial equilibrium. Energy system models based on a partial equilibrium framework take into account the economic activities of the energy sector or parts thereof. They, therefore, do not consider the implications of energy-related investments on other parts of the economy, e.g., labour or other investment requirements, in contrast to general equilibrium models.[12] The framework can be further categorized into simulation and optimization methodologies (such as PRIMES and the TIMES framework).[13,14] Optimization models aim to identify the least-cost solution and simulation models aim to replicate the development of a specific sector, accounting for the decision making of different actors. The general objective function of the optimization model relies on linear programming and minimizes the discounted total energy system costs, subject to various constraints (e.g., energy or emission balances, efficiency relationships, utilization constraints, reserve capacity, greenhouse gas mitigation targets, emissions, renewable quotas, etc.) as described in Equation S1.[13] The techno-economic framework of the partial equilibrium models is typically applied to study the long-term effects of a transition to a low carbon economy yet often does not include consideration for distributional impacts.[15] Distributional impacts have typically been assessed through linking with other models, e.g., macroeconomic, or by increasing the level of detail in a sector through disaggregation.
Energy System Models (partial equilibrium. This section describes how distributional effects are traditionally considered in specific partial equilibrium energy system models. The review explores different modeling research by geographic scope, policies evaluated, sectors or actors modelled and the methods employed to incorporate consideration for distributional impacts. This is followed by an assessment of the strengths and weaknesses of different methods and their overall effectiveness in capturing distributional impacts. Various indicators are used to measure the distributional impacts. This section concludes with an overview of the usefulness of this type of modeling framework for assessing the distributional effects of climate policies. Methodologies to assess distributional impacts of energy and climate policies Partial equilibrium analyses based on an optimization modeling framework aim to identify the least-cost pathway and the development of technology diffusion under policy targets across the whole energy system. The framework aims to balance prices and quantities across one or more markets to the point of equilibrium between energy supply and demand. Concerning distributional impacts, a key limitation of this methodology is that it only considers what occurs within the energy system boundaries. Consequently, there is no inherent feedback loop to better estimate the impacts on the broader economy, e.g., increased energy costs, or the collection and distribution of carbon taxes. A second issue concerns the focus of such a framework on technological and economic factors, where the primary objective is to identify longer-term technology pathways towards achieving specific policy targets with limited focus on the role of policy design in the near term and sectoral implications of policy interventions. A more aggregate representation of sectors means that socio-economic differences within sectors are not often represented. A third issue is the optimization paradigm. This approach strongly favours the economic efficiency of a solution with less appreciation of the inertia and barriers inherent across different sectors, and differences between actors. Given their techno-economic focus, the simplification of behavioural representation, and the long- term analytical time frame, such models are less able to simulate the impacts of sector- specific policies, and their distributional impacts.[15,32] Xxxxx et al. [33] conducted a review to assess the potential of long-term energy system models to addr...
