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Therefore, vehicle energy consumption and emissions are affected by pavement surface properties; however, quantifying the influence of the pavement surface condition on the rolling resistance is complex. Over the last years, some efforts have been made to assess the overall impact of the use phase, particularly PVI. However, there is still a high level of uncertainty concerning the lack of validated models used to analyse the vehicle emissions and the influence of specific variables and assumptions on the results.

In order to obtain reliable results that can be interpreted by decision makers, it is necessary that methods of modelling and the assumptions adopted in LCA and carbon footprint studies are transparent. In addition, there are no significant researches involving UK case studies on the impact of the use phase on the life cycle of a pavement. By reducing the uncertainty concerning the estimation of this component, highway authorities, research organizations and other policy-making institutions can include pavement LCA in their decision-making framework with more confidence.

This paper will analyse a UK case study to investigate the impact of extending the system boundary of road pavement LCA to include the emissions due to the effect of the pavement surface properties IRI and MPD on the rolling resistance. The main aim of this study is to explore if the understanding and the knowledge of this component are sufficient to be implemented in the road pavement LCA framework. The research questions are the as follows: Are rolling resistance models ready for implementation in a pavement LCA?

Can they be applied to a UK case study? How do pavement deterioration and the models used to describe them affect the results? Based on the use of two different models present in the literature, this study will estimate the range of potential impact of the rolling resistance component, with a focus on the effect of the deterioration of pavement surface condition IRI and MPD on traffic fuel consumption and CO 2 emissions. This will allow the parameters that affect the environmental impact due to PVI rolling resistance and the magnitude of this effect to be estimated.

The relationship between pavement properties, rolling resistance and vehicle fuel consumption has been an area of study for several years. However, the inclusion of this component in the LCA system boundary is quite recent and is mainly focused on the potential for pavement management practice to reduce the net life cycle emissions of a road over the life cycle of well-maintained pavements.

In order to define the contribution of the rolling resistance, in terms of IRI and MPD, in the use phase of a pavement LCA framework, it is necessary to use both a rolling resistance model relating rolling resistance to pavement surface properties and an emission model relating traffic fuel emissions to the rolling resistance.

When the rolling resistance coefficient increases, the vehicle fuel consumption increases significantly, especially on roads with no gradient and at constant speed typically high highway speed Bendtsen The most significant pavement parameters affecting rolling resistance are macrotexture MPD , or megatexture, unevenness or roughness IRI and stiffness. Texture and unevenness affect the rolling resistance in a negative way; greater values of MPD and IRI correspond to greater rolling resistance.

The effect of roughness on rolling resistance can change with speed, while that of texture does not. How stiffness affects PVI has not been consistently explained and is as yet, uncertain. Based on these conclusions, a model describing the pavement influence on rolling resistance should take into account MPD and IRI, while the impact of stiffness is not yet clear.

Pavement unevenness and macrotexture are the deviations of a pavement surface from a true planar surface with the wavelengths of deviations ranging from 0. It includes both a model for simulating rolling resistance from IRI and MPD and an engine model to link the effects of rolling resistance to vehicle fuel consumption. The mechanistic part of HDM-4 analyses all driving resistances on the engine, based on the vehicle speed and road gradient, while the empirical part uses coefficients which convert the driving resistances to energy consumption, determined through various experiments and calibrated with measured data.

The results of this study showed that IRI and road gradient had a statistically significant relationship with fuel consumption at low and high speed, while macrotexture MPD was not statistically significant at high speed. This is contradictory to the observations of other studies, as described above. The authors explained this result by the fact that at higher speed, the air drag is the predominant component of the fuel consumption and minimizes the increase in rolling resistance due to macrotexture.

Unfortunately, the calibration factors are not published.

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Santero and Horvath Simple linear relationship between IRI and fuel consumption based on data from heavy duty trucks only, tested at low speed on test track. Model presented by Zaabar and Chatti The energy consumption variation associated with different rolling resistances of the surface layers is evaluated with laboratory tests.

During the use phase of a road pavement, pavement deterioration leads to changes in unevenness and macrotexture that vary over time based on different variables, pavement material asphalt or concrete , traffic volume and truck traffic, climate, pavement age and maintenance treatments Wang et al. Roughness IRI tends to increase over time for a specific road but the variation of the texture depth MPD can be positive or negative, depending on several mechanisms.

Unlike in the USA for instance, in the UK, new surfaces are generally produced with high initial texture depth to maintain high-speed skidding resistance and a reduction in texture depth over time is observed, especially in the more trafficked lanes. The final value that the texture depth reaches depends on the substrate of the surface dressing and the size of aggregate used for chippings. Other surfacing materials, like rolled asphalt, do not change so markedly during the first few years, but the average texture tends to reduce in subsequent years, at least in the more trafficked lanes Jacobs , UK Goverment Several studies have been performed in the UK in order to predict performance in terms of texture depth.

However, there are no general models in the UK able to predict the change of texture depth over time.

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Predictions and actual traffic growth in the UK readapted from Masters Since a high level of uncertainty characterizes this area of knowledge, both in terms of available models and in terms of parameters affecting the results, this study aims to investigate the impact of the pavement surface properties IRI and MPD during the use phase, in terms of CO 2 emissions, by using two different models in the literature VTI and UCPRC model and performing a sensitivity analysis to investigate the variables and conditions that can have an impact.

The annual average daily flow AADF in was 15, motor vehicles and HGVs, making this segment a low to medium trafficked road. The existence of previous studies focusing on the construction and maintenance phases of this road segment Galatioto et al. This will allow a better understanding of the relative environmental impact and the magnitude of the use phase, in terms of rolling resistance impact, on the LCA of this case study, by comparing it with the construction and maintenance results.

In addition, there is an appropriate level of information and data available on the history of construction and maintenance events and on the traffic flow, provided by Lincolnshire Highways Authority. UK Department for Transport In order to estimate the range of potential impact of the pavement deterioration during the use phase, different scenarios of deterioration of IRI and MPD for the same road segment are considered.

Total CO 2 emissions, divided into basic dark grey area and deterioration components light grey area , for a case without traffic growth and emission factor change. The deterioration component is particularly interesting for pavement engineering, since it is possible to reduce these emissions associated with the road surface condition, through appropriate maintenance strategies. Pavement condition improvements can be obtained rapidly to reduce traffic fuel consumption, even using available technology. On the other hand, approaches that involve technology improvements or traffic reductions can require long periods of time.

In order to better understand the behaviour of the two PVI emission models and the impact of the pavement deterioration assumptions, these components were assessed in a sensitivity analysis. Once the results in terms of CO 2 emissions are obtained from both models, it is possible to compare them, in order to understand the potential impact of the model used on the pavement LCA results.

Furthermore, in order to identify the parameters that most affect results in the use phase, a sensitivity analysis is performed for the following variables: traffic growth, IRI and MPD time progression and vehicle fuel emission factors. In order to test the sensitivity of the main inputs to the two models, different scenarios of variation of the emission factors in the UCPRC model and fuel efficiency in the VTI model will be considered.

These factors change year by year based on predictions of future fuel economy and new vehicle technologies e. The traffic growth and the pavement deterioration during the analysis period tend to increase the CO 2 emissions, while the emission factor reduction affects the results in the opposite way, as vehicles become more fuel efficient. In the UCPRC model, the deterioration component increases over time, so the absence of deterioration minimizes the total emissions. In the VTI model, the deterioration component, under the average condition of pavement deterioration, tends to decrease, producing an overall reduction in the calculated emissions.

To better understand the impact of the different variables, Fig. This is because while the traffic growth during the analysis period tends to increase the CO 2 emissions, the emission factor reduction affects the results in the opposite way, as vehicles become more fuel efficient. Therefore, even if the traffic growth and the emission factor parameters affect the results, this combined impact is not significant overall.

By contrast, the CO 2 emissions due to the pavement roughness are very sensitive to the pavement surface deterioration over time. Sensitivity analysis—impact of each variable on emissions due to pavement rolling resistance UCPRC model. Sensitivity analysis—impact of each variable on emissions due to pavement rolling resistance VTI model.


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Based on the use of two different models, this paper assessed the impact of pavement surface deterioration during the use phase of a specific UK road pavement case study, with the objective of estimating the overall impact of this component on life cycle CO 2 emissions and the parameters that can affect the results.

This makes it possible to define future research needs in this area and to understand the level of confidence possible in decision making using pavement LCA results. The results agree with previous studies in the literature Santero and Horvath ; Wang et al.

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However, the results obtained using the two models are significantly different, both considering the basic component of emissions due to PVI rolling resistance not affected by the pavement surface deterioration and the deterioration component. These considerable differences are due to the fact that the development of rolling resistance and fuel consumption models is strongly affected by methodological components such as different rolling resistance measuring methods, road surface measures, approach used to develop the models and by site-specific components weather, vehicle types and technology, type of roads, pavement design models and deterioration.

The VTI model developed in Sweden includes a rolling resistance model based on empirical data and a fuel consumption model calibrated using calculated values from VETO, a theoretical model. California and Sweden are geographical locations characterized by different climates, types of roads, pavement deterioration processes and models, traffic distribution and technology, that seriously affect the models developed and the results produced.

This difference is particularly significant in this case study, where the MPD falls over time, producing opposite results when the two models are used; in the UCPRC model, the deterioration component is positive, since the impact of the increase in IRI is larger than that due to the reduction in MPD, while for the VTI model, the deterioration component is negative. Therefore, the pavement condition deterioration over time has a strong impact on the rolling resistance, significantly affecting the results.

Butt et al. These types of study can be used to estimate impacts in stand-alone studies of a single material or process, or in comparative studies of different choices. The two models used in this study use different approaches, described above, and this results in significantly different findings, which reduces confidence in their use for all types of LCA study, which will all be sensitive to the model chosen. Traffic growth and future changes in vehicle fuel efficiency and fuel types can be expected to have a significant impact on future emissions from road transport.

Current predictions for the UK mean that these factors offset each other and combine to have little effect on the results for this case study. However, considering one factor without the other will distort the results and changes in the forecasts for these factors need to be monitored and studies updated to reflect them. The potential impact of the factors explored in this study on the results of pavement LCA including the use phase is significant.

For this reason, LCA practitioners should be careful to report the models and assumptions they use in a detailed and transparent way Huang and Parry Development of widely accepted approaches and agreement to use and declare them is a prerequisite for the development of LCA practice in this domain. The main aim of this paper was to investigate if current models of the impact of pavement surface properties on rolling resistance can be implemented in road pavement LCA.

Considering the significant impact that the pavement surface properties can have during the life cycle of a road, it is necessary that any model used to estimate this component leads to results that can be used with confidence in the decision-making process. Taking into account the results obtained in the selected case study, the use of the UCPRC and VTI models in the UK should be treated with caution because they produce significantly different results.

Further and different case studies are needed before it can be decided where they can be used. The different weight that the models give to the different pavement condition variables means the relative results from the two models are very sensitive to both level of pavement condition and its deterioration rate. This will have an impact both on stand-alone and comparative LCA studies. For UK roads, there is currently insufficient information available to predict the deterioration of roughness and texture depth over time depending on maintenance treatments, traffic volume, surface properties and materials.

This must be corrected before pavement LCA studies can be extended to the use phase. Changes in predicted future traffic levels or emission factors could change this result and should be kept under review.

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Further research is necessary before the effect of the rolling resistance can be introduced in the pavement LCA framework with confidence. In particular, for UK roads, research is needed to develop reliable pavement deterioration models and PVI rolling resistance models, before introducing this component. LCA and carbon footprint studies need to be reported in a way that makes the methods of modelling and the assumptions used transparent, before they can be interpreted by decision makers.

Standard models and procedures should be developed in the pavement LCA field to make this possible and are needed before product category rules in this domain can be extended to include the use phase. The authors would like to thank the staff at Lincolnshire County Council for providing data for the case study. Skip to main content Skip to sections. Advertisement Hide. Download PDF. Rolling resistance contribution to a road pavement life cycle carbon footprint analysis.

Open Access. First Online: 01 October Purpose Although the impact of road pavement surface condition on rolling resistance has been included in the life cycle assessment LCA framework of several studies in the last years, there is still a high level of uncertainty concerning the methodological assumptions and the parameters that can affect the results.

Methods This paper provides a review of the main models describing the impact of the pavement surface properties on vehicle fuel consumption and analyses the influence of the methodological assumptions related to the rolling resistance on the LCA results. Results and discussion The model used to calculate the impact of the pavement surface condition on fuel consumption significantly affects the LCA results. Conclusions and recommendations Existing models linking pavement condition to rolling resistance and hence vehicle emissions are not broadly applicable to the use phase of road pavement LCA and further research is necessary before a widely-used methodology can be defined.

In order to reduce this impact, in the last years, highway authorities and a growing number of organizations, companies and government institutions are introducing sustainability principles and considerations in asset management decision-making processes, by using a systematic and organized approach, called life cycle assessment LCA Korre and Durucan , Wayman et al. The use phase is one of the most critical and complex parts of a road pavement LCA, requiring specific knowledge in disparate areas Santero and Horvath Christopher explains that the way forward questions the way we have been living, with extreme changes needed to achieve the goal of zero emissions.

It can be very overwhelming at an individual level. While these things do help, Christopher explains that it will take significant adjustments to our lifestyles such as our diets, as well as the way we travel and experience the world, to really have the lasting effect needed.

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If water was needed, people would draw a bucket from the stream or well, knowing this action had an impact of one bucket of water. Similarly, when light was needed, consumption was measured by the number of candles used. Horsepower referred to how many horses needed to perform a manual activity. Consumption and the consequential impacts were extremely transparent.

Christopher urges us as individuals and responsible business owners to audit our own carbon footprint, taking time to truly understand how much carbon emissions we are personally responsible for. Once we are aware of our impact, we must work hard to continuously reduce it. The Green Ceiling refers to the limit you reach after conducting normal emission saving activities. You may have done everything you consider is within your power, with the resources available to you, to reduce your carbon footprint, but your actions are still producing carbon emissions. You may have replaced the light globes throughout your house, installed water saving shower heads and perhaps even installed solar panels.

Christopher explains that if we work with guests in the accommodation industry, together we can break through the Green Ceiling. Through his program My Green Butler, Christopher, a qualified trainer, is teaching visitors of his guest cottages at Crystal Creek Meadows how to conserve resources, educating them on effective ways to significantly reduce their carbon footprint.

Through his program, Christopher has shown that knowledge is key and when properly educated, guests are very willing to do their best to conserve resources.