https://www.econstor.eu/bitstream/10419/24483/1/dp0162.pdf

Technological Change in Economic Models of Environmental Policy: A Survey

Most countries, including the United States, have an array of greenhouse gas mitigation policies, which provide subsidies or restrictions typically aimed at specific technologies or sectors. Such climate policies range from automobile fuel economy standards, to gasoline taxes, to mandating that a certain amount of electricity in a state comes from renewables, to subsidizing solar and wind electrical generation, to mandates requiring the blending of biofuels into the surface transportation fuel supply, to supply-side restrictions on fossil fuel extraction. This paper reviews the costs of various technologies and actions aimed at reducing greenhouse gas emissions. Our aim is twofold. First, we seek to provide an up-to-date summary of costs of actions that can be taken now using currently available technology. These costs focus on expenditures and emissions reductions over the life of a project compared to some business-as-usual benchmark—for example, replacing coal-fired electricity generation with wind, or weatherizing a home. We refer to these costs as static because they are costs over the life of a specific project undertaken now, and they ignore spillovers. Our second aim is to distinguish between dynamic and static costs and to argue that some actions taken today with seemingly high static costs can have low dynamic costs, and vice versa. We make this argument at a general level and through two case studies, of solar panels and of electric vehicles, technologies whose costs have fallen sharply. Under the right circumstances, dynamic effects will offer a justification for policies that have high costs according to a myopic calculation.

https://pubs.aeaweb.org/doi/pdf/10.1257/jep.32.4.53

The Cost of Reducing Greenhouse Gas Emissions

Discounting and Distributional Considerations in the Context of Global Warming.

Energy planning models (EPMs) play an indispensable role in policy formulation and energy sector development. The forecasting of energy demand and supply is at the heart of an EPM. Different forecasting methods, from statistical to machine learning have been applied in the past. The selection of a forecasting method is mostly based on data availability and the objectives of the tool and planning exercise. We present a systematic and critical review of forecasting methods used in 483 EPMs. The methods were analyzed for forecasting accuracy; applicability for temporal and spatial predictions; and relevance to planning and policy objectives. Fifty different forecasting methods have been identified. Artificial neural network (ANN) is the most widely used method, which is applied in 40% of the reviewed EPMs. The other popular methods, in descending order, are: support vector machine (SVM), autoregressive integrated moving average (ARIMA), fuzzy logic (FL), linear regression (LR), genetic algorithm (GA), particle swarm optimization (PSO), grey prediction (GM) and autoregressive moving average (ARMA). In terms of accuracy, computational intelligence (CI) methods demonstrate better performance than that of the statistical ones, in particular for parameters with greater variability in the source data. However, hybrid methods yield better accuracy than that of the stand-alone ones. Statistical methods are useful for only short and medium range, while CI methods are preferable for all temporal forecasting ranges (short, medium and long). Based on objective, most EPMs focused on energy demand and load forecasting. In terms geographical coverage, the highest number of EPMs were developed on China. However, collectively, more models were established for the developed countries than the developing ones. Findings would benefit researchers and professionals in gaining an appreciation of the forecasting methods, and enable them to select appropriate method(s) to meet their needs.

/pdf/forecasting-methods-in-energy-planning-models-443zr1kmvi.pdf

Forecasting methods in energy planning models

The synthesis of bottom-up and top-down in energy policy modeling

https://www.annualreviews.org/doi/pdf/10.1146/annurev.eg.18.110193.002145

The Costs of Limiting Fossil-Fuel CO2 Emissions: A Survey and Analysis

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The Costs of Limiting Fossil-Fuel CO2 Emissions: A Survey and Analysis