In both studies, great lengths have been taken to develop rigorous physics-based vehicle models, including consideration of architectures, power train, overall accessory loads, and sensitivity to drive cycle and external ambient temperatures. Similar attention to detail has been paid to developing practical and representative EV charging profiles, reasonable mapping of standard drive cycles to real-world trips and travel behavior, and high-fidelity analyses of existing grid dispatch methods based on real-world data. By making the datasets and source codes publicly available, it is the authors’ hope that such methodologies can be expanded, and new regional applications and business use cases can be explored. To recap again here for context, the Phase I study introduced a methodology that accounts for energy and emissions during the use phase of vehicle emissions across a range of light-duty car types, including ICEV, HEV, and EV. This comparative approach enabled a head-to-head assessment of the vehicle technologies relative to a variety of private vehicle use cases. The Phase I effort revealed that EVs can contribute to reduced emissions, drying curing weed but their quantitative benefits are highly sensitive to when and how the vehicles are charged. This factor was shown to deliver results that could have a variance in the same order as the mean emissions. These initial results highlighted the importance of probing deeper into the interplay between charging profiles and vehicle classes.
The study further revealed that driving cycles and use cases are of secondary importance, which can also contribute substantially to the variance in emissions for a given vehicle type and charging profile. A third factor is the overall limit of an EV battery capacity, which is more of a determinant of whether a given EV can actually substitute for a comparable ICEV. It should be noted that EV battery range did limit any drive cycles undertaken in the passenger car comparisons, and the model can readily accommodate EVs of any specified range. Phase I revealed a few “higher order” factors that influence the relative environmental benefits, but a major takeaway is that the timing and duration of a vehicle charging event under the marginal emissions assumptions can affect the environmental impacts by up to 100%. In this Phase II effort, we deepen our investigation to include additional use cases of priority interest, while applying the original methodology developed in Phase I. The timing of this study happens to overlap with the commercial release of several high-profile light duty full-size electric pickup trucks, courier vans, transit buses, and school buses which have been publicized broadly in the media and studied extensively in research and development circles. Thus, Phase II stands to illuminate new insights via investigation of potentially important public and private use cases that leverage these new electric vehicle offerings, as a means of reducing emissions and energy. These use cases show particular promise because many small businesses operate on fairly predictable cycles and return to a central base at the end of the workday.
This Phase II study reveals that the trends observed in Phase I not only continue to be relevant but are in fact more pronounced and important. For example, the sensitivity to the time of charging is greater, accounting now for a variance in excess of 100%, as explained above. First of all, while the daily mileage experienced by a fleet vehicle may not be significantly greater than commuter-type sedan applications, due to the increased energy intensity of these larger vehicles, the energy consumption on a daily basis is considerably higher. Secondly, we forecast that fleet vehicles used for commercial purposes are likely to use Level 3 Fast Charging methods, for economic reasons, which can further intensify the variance associated with regional marginal assumptions. Finally, while previous studies have discussed this phenomenon in subjective terms, benefits for the larger vehicle classes and associated business cases have not, to our knowledge, been quantified in this way. Meaning they have not taken the approach of considering vehicle, power train and use-case characteristics in view of the larger system of charging profiles and upstream grid factors. Few studies that we are aware of have taken the full spectrum approach, leveraging specification data on new vehicles, considering rigorous energy consumption, physics-based models, real-world characteristics of a grid, dispatch, and probable electric vehicle charging profiles in a contemporary manner. These methodologies and some of the simulated results should have considerable value to fleet operators, small business owners, service-oriented, vehicle operations, as well as officials that do utility planning resource planning, and charging infrastructure. In addition, these methodologies can be extended quite broadly to consider the local grid context in other regions, as well as refined use cases that match vehicle types to a growing set of electrified transportation applications.
These studies reveal a few takeaways and signal a few notes of caution. These fleet and MD/HD vehicles are more energy-intensive and will consume more energy per day compared to LDVs, even though they may travel the same number of miles. For this reason, charge management is much more critical. A related need is more timely communication between vehicle fleets, charging service providers, and utilities to ensure charge management is transparent and mutually beneficial. Furthermore, because these larger vehicles are likely to utilize Level 3 Fast Charging in the interest of overall economic and value proposition, there is renewed attention to “get it right” from the CO2 perspective as well. The study implies that one approach that may mitigate adverse consequences is to schedule overnight charging of Medium and heavy-duty vehicles at predictable locations. Doing so could enable grid dispatch operators to increase intermediate loads with the use of highly efficient combined cycle plants or via the release of stored energy from renewables. This would obviously require more advanced planning and data acquisition but could readily be achieved through well-orchestrated pilot programs involving fleet operators, charge managers, and utility operators.Another potential benefit that seems impactful from the use of light-duty, electrified service trucks and vans is that as EVs become more prevalent, the randomization of charging events could potentially become advantageous by spreading out uncertainty and lowering the potential for adverse peaks. Again, if this information were made available to grid operators, more optimal planning decisions could be made. This would not only yield more cost-effective dispatch on a daily basis, it would also reduce emissions and ensure that longer-term investments into local electric vehicle supply equipment , grid distribution, transmission, and generation assets become optimized from the standpoint of society as well as individual consumers and businesses. Primary contributions of this effort are therefore the development of new methodologies, integration of sub-system models and independent data sources, and enhanced tools for quantifying CO2 impacts associated with vehicle electrification. The Phase II study refines the methodology and assesses EV use cases that show particular near-term promise.Given evidence for the prevalence and the human and economic burden of non-communicable diseases, behaviors that may contribute to the incidences of such diseases are of increasing academic, political and societal concern. We debate the extent to which interventions based on behavioral theory work in the real world to contribute to addressing these concerns. This arises from a live debate at the Annual Meeting of the International Society of Behavioral Nutrition and Physical Activity held in Hong Kong in June 2018. The debate reported in this article was conducted using the same format as the ‘live’ debate, our cases were each written independently then exchanged simultaneously, and the same process repeated for the responses. Once this process was completed, we authored the joint conclusion comprising points of agreement, and areas where we disagree.Given epidemiological research associating multiple chronic disease risk with participation in health-related behaviors, industrial grow health organizations and stakeholders have sought to develop behavioral interventions that lead to practically-significant changes in these behaviors and concomitant reduction in disease risk.
Behavioral scientists propose that interventions based on theories from the behavioral sciences, particularly psychology, will be optimally effective in evoking behavior change. Despite an expanding evidence base demonstrating the efficacy of theory-based interventions in promoting sustained change in health-related behavior, my colleague will suggest that the role of behavioral theory is overstated, particularly when it comes to ‘real world’ effectiveness. In particular, he will argue that what works in ‘ideal world’ carefully-controlled conditions are not effective in ‘real world’ contexts where upscaling, logistic, cultural, and implementation factors pervade. He will cite the lack of change in physical activity participation and rates of chronic disease and obesity as evidence that interventions based on behavioral theory are not effective. Here I deconstruct these ‘straw person’ arguments, and contend that interventions based on theory can and do work in promoting behavior change in real world contexts.Behavioral theory is a broad term for a set of pre-specified ideas or predictions aimed at explaining behavior. Behavioral theories come from multiple disciplines , and identify multiple determinants or mechanisms of behavior including beliefs, motivation and intentions, individual differences, social influence, and environment and demographics. A substantive body of research has identified the effectiveness of theory-based interventions targeting change in modifiable determinants or mechanisms. For example, syntheses of evidence have indicated that interventions targeting change in social cognitive beliefs and motivation, social support and norms, and planning to be effective in promoting behavior change in randomized controlled trials. Similarly, interventions based on health-risk communications have been successful in promoting behavior change, with graphic images on tobacco products a prominent example. Research targeting change in determinants derived from social-ecological theories, encompassing environmental, community, and policy factors, have also been shown to be effective. Interventions based on choice architecture, sometimes referred to as ‘nudging’, have demonstrated effectiveness in changing behavior in laboratory and field settings. In addition, interventions adopting specific strategies such as self-monitoring, prompting social support, planning, behavioral skills, and affective appeals have been found to be particularly effective. Taken together, primary studies and research syntheses indicate that theory-based interventions are effective in changing behavior in laboratory and ‘real world’ contexts. In the interests of balance, it would be remiss not to acknowledge a number of caveats to this evidence. Meta-analyses and systematic reviews have also indicated that stated theoretical basis leads to no difference in intervention effectiveness, and, in some cases, even reverse effects. Similarly, there is research demonstrating that adoption of particular behavior change strategies does not lead to greater intervention effectiveness. So how can these two streams of evidence be reconciled? Inadequate mapping of theory on to intervention components may be a moderating factor. A distinction has been made between theory-inspired and theory-based interventions. Prestwich et al. indicated that ‘theory-inspired’ interventions provide insufficient specification of links between theory and intervention strategies. Theory-inspired interventions, therefore, pay ‘lip service’ to behavioral theory, but fail to link intervention components with relevant theoretical determinants. There are also problems with inadequate reporting of such links, which hinders researchers’ ability to evaluate the effect of theoretical basis on intervention effectiveness. There is therefore a need for researchers to become more effective in matching theoretical determinants of behavior with intervention content, and for greater transparency when reporting intervention content.If interventions based on behavioral theory work in changing behavior in ‘real world’ contexts, how have they not stemmed the tide of non-communicable disease pandemics, as my colleague will contend? Knowledge and implementation of effective interventions, whether or not they are based on theory, seems to have had limited impact in changing population-level participation in health behaviors and reducing incidence of chronic disease. Although there is substantive evidence that behavioral interventions are effective in changing behavior across multiple contexts, populations, and behaviors, and, arguably, those based on theory having greater effectiveness despite some of the aforementioned limitations, such knowledge is seldom translated to population-level change. This is largely because many behavioral interventions implemented at the community or even population level are relatively short lived, under-funded, or fail due to poor implementation, up-scaling, or translation. Funding is a key issue; many behavioral interventions receive initial investment that is not sustained. Interventions need sufficient funding for the necessary networks and providers required to implement the intervention in practice. Even though economic evaluation of many behavior change interventions has demonstrated their cost effectiveness, investment in behavioral interventions pales compared to investment in procedures aimed at treating disease. It is unrealistic to expect health care providers to identify, assimilate, and implement research findings reported in scientific outlets.