Methodologies for Assessment of Building's Energy Efficiency and Conservation: A Policy-Maker View

Recent global peer-review reports have concluded on importance of buildings in tacking the energy security and climate change challenges. To integrate the buildings energy efficiency into the policy agenda, significant research efforts have been recently done. More specifically, the public domain provides a bulk of literature on the application of buildings-related efficiency technologies and behavioural patterns, barriers to penetration of these practices, policies to overcome these barriers. From the policy-making perspective it is useful to understand how far our understanding of building energy efficiency goes and the approaches and methodologies are behind such assessment

Abating Carbon Dioxide Emissions from Electric Power Generation: Model Uncertainty and Regulatory Epistemology

Computational modeling of natural, economic, and technological systems is a primary analytical methodology in US energy and environmental regulation. Validating or otherwise evaluating such models and analyzing the uncertainties involved in their regulatory applications have become both more important and more challenging. This paper reviews these issues in the context of an important recent example involving energy, the US Environmental Protection Agency’s (EPA’s) development of regulations to reduce carbon dioxide emissions from electric power plants using a numerical model of the US electric power system. Following a summary of background information about greenhouse gas abatement policy, the paper discusses the agency’s general computational model evaluation philosophy; the history of, and current practices in, energy model evaluation; the specific model used by the EPA and its application to carbon dioxide regulation; and the concept of fundamental model uncertainty and its significance for this modeling domain

Abating Carbon Dioxide Emissions from Electric Power Generation: Model Uncertainty and Regulatory Epistemology

Computational modeling of natural, economic, and technological systems is a primary analytical methodology in US energy and environmental regulation. Validating or otherwise evaluating such models and analyzing the uncertainties involved in their regulatory applications have become both more important and more challenging. This paper reviews these issues in the context of an important recent example involving energy, the US Environmental Protection Agency’s (EPA’s) development of regulations to reduce carbon dioxide emissions from electric power plants using a numerical model of the US electric power system. Following a summary of background information about greenhouse gas abatement policy, the paper discusses the agency’s general computational model evaluation philosophy; the history of, and current practices in, energy model evaluation; the specific model used by the EPA and its application to carbon dioxide regulation; and the concept of fundamental model uncertainty and its significance for this modeling domain

Bottom-up energy modeling

Two general approaches have been used for the integrated assessment of energy demand and supply: the so-called bottom-up and top-down approaches. The bottom-up approach focuses on individual technologies for delivering energy services, such as household durable goods and industrial process technologies. For such technologies, the approach attempts to estimate the costs and benefits associated with investments in increased energy efficiency, often in the context of reductions in greenhouse gas (GHG) emission or other environmental impacts. The top-down method assumes a general equilibrium or macroeconomic perspective, wherein costs are defined in terms of losses in economic output, income, or gross domestic product (GDP), typically from the imposition of energy or emission taxes