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The adoption of alternative fuel vehicles (AFVs) has been regarded as one of the most important strategies to address the issues of energy dependence, air quality, and, more recently, climate change. Despite decades of effort, we still face daunting challenges to promote wider acceptance of AFVs by the general public. More empirical analyses are needed to understand the technology adoption process associated with different market structures, the effectiveness of regulations and incentives, and the density of infrastructure adequate to reach sustainable commercial application. This paper compares the adoption of natural gas vehicles (NGVs) in eight countries: Argentina, Brazil, China, India, Italy, New Zealand, Pakistan, and the US. It examines the major policies aimed at promoting the use of NGVs, instruments for implementing those policies and targeting likely stakeholders, and a range of factors that influence the adoption of NGVs. The findings in this paper should be applicable to other AFVs. r

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New data enable targeted policy to lessen GHG emissions

Energy system optimization models (ESOMs) are widely used to generate insight that informs energy and environmental policy. Using ESOMs to produce policy-relevant insight requires significant modeler judgement, yet little formal guidance exists on how to conduct analysis with ESOMs. To address this shortcoming, we draw on our collective modelling experience and conduct an extensive literature review to formalize best practice for energy system optimization modelling. We begin by articulating a set of overarching principles that can be used to guide ESOM-based analysis. To help operationalize the guiding principles, we outline and explain critical steps in the modeling process, including how to formulate research questions, set spatiotemporal boundaries, consider appropriate model features, conduct and refine the analysis, quantify uncertainty, and communicate insights. We highlight the need to develop and refine formal guidance on ESOM application, which comes at a critical time as ESOMs are being used to inform national climate targets.

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Existing assessments of biomass supply and demand and their impacts face various types of limitations and uncertainties, partly due to the type of tools and methods applied (e.g., partial representation of sectors, lack of geographical details, and aggregated representation of technologies involved). Improved collaboration between existing modeling approaches may provide new, more comprehensive insights, especially into issues that involve multiple economic sectors, different temporal and spatial scales, or various impact categories. Model collaboration consists of aligning and harmonizing input data and scenarios, model comparison and/or model linkage. Improved collaboration between existing modeling approaches can help assess (i) the causes of differences and similarities in model output, which is important for interpreting the results for policy-making and (ii) the linkages, feedbacks, and trade-offs between different systems and impacts (e.g., economic and natural), which is key to a more comprehensive understanding of the impacts of biomass supply and demand. But, full consistency or integration in assumptions, structure, solution algorithms, dynamics and feedbacks can be difficult to achieve. And, if it is done, it frequently implies a trade-off in terms of resolution (spatial, temporal, and structural) and/or computation. Three key research areas are selected to illustrate how model collaboration can provide additional ways for tackling some of the shortcomings and uncertainties in the assessment of biomass supply and demand and their impacts. These research areas are livestock production, agricultural residues, and greenhouse gas emissions from land-use change. Describing how model collaboration might look like in these examples, we show how improved model collaboration can strengthen our ability to project biomass supply, demand, and impacts. This in turn can aid in improving the information for policy-makers and in taking better-informed decisions.

The Bakken formation has contributed to the recent rapid increase in U.S. oil production, reaching a peak production of >1.2 × 106 barrels per day in early 2015. In this study, we estimate the energy intensity and greenhouse gas (GHG) emissions from 7271 Bakken wells drilled from 2006 to 2013. We model energy use and emissions using the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) model, supplemented with an open-source drilling and fracturing model, GHGfrack. Overall well-to-refinery-gate (WTR) consumption of natural gas, diesel, and electricity represent 1.3%, 0.2%, and 0.005% of produced crude energy content, respectively. Fugitive emissions are modeled for a “typical” Bakken well using previously published results of atmospheric measurements. Flaring is a key driver of emissions: wells that flared in 2013 had a mean flaring rate that was ≈500 standard cubic feet per barrel or ≈14% of the energy content of the produced crude oil. Resulting production-weighted mean GHG emissions in 2013 were 10.2 g of CO2 equivalent GHGs per megajoule (henceforth, gCO2eq/MJ) of crude. Between-well variability gives a 5–95% range of 2–28 gCO2eq/MJ. If flaring is completely controlled, Bakken crude compares favorably to conventional U.S. crude oil, with 2013 emissions of 3.5 gCO2eq/MJ for nonflaring wells, compared to the U.S. mean of ≈8 gCO2eq/MJ.