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Abstract: Life cycle assessment (LCA) analysts are increasingly being asked to conduct life cycle‐based systems level analysis at the earliest stages of technology development. While early assessments provide the greatest opportunity to influence design and ultimately environmental performance, it is the stage with the least available data, greatest uncertainty, and a paucity of analytic tools for addressing these challenges. While the fundamental approach to conducting an LCA of emerging technologies is akin to that of LCA of existing technologies, emerging technologies pose additional challenges. In this paper, we present a broad set of market and technology characteristics that typically influence an LCA of emerging technologies and identify questions that researchers must address to account for the most important aspects of the systems they are studying. The paper presents: (a) guidance to identify the specific technology characteristics and dynamic market context that are most relevant and unique to a particular study, (b) an overview of the challenges faced by early stage assessments that are unique because of these conditions, (c) questions that researchers should ask themselves for such a study to be conducted, and (d) illustrative examples from the transportation sector to demonstrate the factors to consider when conducting LCAs of emerging technologies. The paper is intended to be used as an organizing platform to synthesize existing methods, procedures and insights and guide researchers, analysts and technology developer to better recognize key study design elements and to manage expectations of study outcomes.

Suggestions on how telecommunication networks can reduce their energy use are presented. The economic benefits of implementing programs aimed at reducing the environmental impact of these networks are discussed.

The construction industry is responsible for environmental impacts that include air emissions, waste generation, and the use of land, water, and energy. However, most prior construction research attention has focused on these impacts only for material selection and building operation. The focus here is the environmental and energy implications of the construction process, specifically on-site energy consumption. Existing estimates of energy use and environmental emissions for the industry are tied to large construction vehicles, but other equipment consumes energy in various forms. This research creates a broader boundary for considering the energy use and environmental impacts of engines and vehicles used for construction activities by estimating these effects and comparing them to other national-level impacts. Results indicate that fuel use estimates for construction equipment are almost double the levels suggested by government reports; accordingly, air emission impacts are 30% larger for particulate matter and almost double levels of oxides of nitrogen and volatile organic compounds. Quantitative and qualitative analysis regarding how U.S. diesel engine and fuel regulations could significantly reduce air emissions from construction sites is also provided. If fully implemented, these regulations could initiate the manufacture and use of portable generators that make on-site electricity generation comparable to the electricity grid in terms of air emissions. DOI: 10.1061/(ASCE)0733-9364(2007)133:11(846) CE Database subject headings: Construction industry; Construction equipment; Construction sites; Energy consumption; Fuels; Environmental impacts; Emissions; Electric power demand; Engines.

This article examines the task of valuating the impacts of environmental improvement methods.

Emissions inspection and maintenance (I/M) programs for light-duty motor vehicles manage ambient air quality by enforcing emissions standards and requiring non-compliant vehicles to be repaired or retired. I/M programs in the United States typically identify over-emitters through on-board diagnostics (OBD) systems and vehicles’ proprietary firmware (i.e., indirect tests), rather than through physical measurements of exhaust gases (i.e., tailpipe tests). Analyzing data from Colorado’s I/M program, this study finds the OBD test to have an accuracy of 87%, but a false pass rate of 50%, when predicting the result of a corresponding tailpipe test. As an alternative, transparent data-driven models—using logistic regression and gradient boosting machines—to consistently identify over-emitting vehicles are proposed. These models were up to 24% more accurate, or 85% more sensitive than the current OBD test in a stratified data sample. A key benefit of transparent statistical models—jurisdictions’ ability to tune the test methods to best suit program needs—is also assessed. Finally, this study shows how these results support a vision for cloud-based, selective I/M programs where statistical models are applied to OBD data—collected over-the-air from vehicles—to identify and require additional inspection for only the most probable over-emitters. [ABSTRACT FROM AUTHOR]