11 12 __________ 13 * Corresponding author 14 Email: hessam@mit.edu (H AzariJafari ) 15 2 ABSTRACT 16 We implemented a context -sensitive and prospective framework to assess the global warming 17 potential (GWP) impacts of cool pavement strategies on specific roads for different cities. The 18 approach incorporates several interconnections among different elements of the built environment, 19 such as buildings and urban road segments, as well as the transportation fleet, using spe cific 20 building and pavement information from an urban area. We show that increasing pavement albedo 21 lowers urban air temperatures but can adversely affect the building energy demand in the areas 22 with high incident radiation exposure. The heating energy sav ings and the radiative forcing effect 23 improve the GWP savings in a cold and humid climate condition. The total GWP saving intensity 24 is sensitive to the city morphology and road traffic. Our probabilistic results show that cool 25 pavement strategies can offse t 1.0-3.0% and 0.7 -6.0% of the total GHG emissions of the US cities 26 Boston and Phoenix, respectively, for a 50 -year analysis period. The worldwide range of savings 27 can be as large as 5.0 - 44.7 Gt CO 2eq. A paradigm shift in pavement strategy selection is r equired 28 in most of the neighborhoods. 29 Keywords: Built environment; urban heat island effect; vehicle fuel consumption; city morphology; 30 radiative forcing 31 3 1. INTRODUCTION 32 By mid -century, two out of every three persons on the planet will be living in urban areas 1. These 33 growing urban populations face two simultaneous climate challenges: extreme heat events 34 attributed to urban heat island (UHI) effects and global climate change (GCC). Cities want to take 35 actions that address UHI without compromising progress toward s global climate goals. 36 Variations in the magnitude of the UHI effect have been attributed to differences in urban 37 characteristics including a) capacity for evapotranspiration (EVAP) 2, b) efficiency of convective 38 heat transfer (CONV) 3, c) net radiation into the urban area (NETRAD) 3, 4, d) magnitude of 39 sensible and la tent anthropogenic heat flux (FLUX) 5, and e) heat storage capacity of the built 40 environment (HEATCAP) 6. To mitigate UHI , a number of strategies have been proposed that 41 modify these urban chara cteristics including 1) removing anthropogenic heat sources (which 42 impacts FLUX) 7, 2) alteration of urban configuration (CONV, NETRAD) 8, 3) increased urban 43 vegetation (EVAP, NETRAD, HCAP) 9 as well as installation of so -called 4) cool roofs (NETRAD) 44 10, and 5) cool pavements (NETRAD) 11. In most implementations, cool roofs and cool pavements 45 have surfaces with higher solar reflectance (albedo) and lower thermal emittance than a 46 conventional roof or road. A nalyses of most of these strategies , including reducing heat flux, green 47 and reflective roofs 10, 12 -14, and increased urban vegetation 15 have consistently reached 48 conclusions that they were both effective at mitigating UHI and would generat e corresponding 49 reductions in greenhouse gas (GHG) emissions. Analyses of cool pavements, however, have 50 reached inconsistent conclusions. We will argue in this paper that these inconsistencies arise from 51 differences in modeling scope and a failure to fully characterize the influence of urban context on 52 the result. Using a high -resolution model, we will show that while there are some instances where 53 cool pavements can generate net increases in GHG emissions, there are many more contexts where 54 the opposite is true. 55 Modeling the climate change implications of cool pavements is challenging for at least two reasons 56 – interconnectedness within and heterogeneity of the urban context. In cities, every component of 57 the built environment is interconnected – affecting the performance of each other. As such, a 58 change in one component of the built environment not only affects its own lifecycle but also alters 59 the environmental performance of other components. This is particularly true for pavements where 60 their surface an d thermal properties can alter the radiative balance (direct radiative forcing, RF) 61 and the energy use of nearby buildings (by changing local ambient temperature and irradiance) ; 62 their structural properties alter fuel use of passing vehicles (by changing the rolling resistance and 63 wasted motion energy ); and their supply chains produce emissions . A comprehensive framework 64 is required to incorporate all these interconnections when assessing the GCC impact of different 65 cool pavement solutions. Moreov er, the urban environment is highly heterogeneou s, in terms of 66 both building configuration (morphology) and traffic patterns , making it unlikely that any single 67 cool pavement solution will be optimal for the whole city. 68 Pavement albedo change alters the ea rth’s energy balance at the top -of-atmosphere , directly 69 contributing to the GCC cause -and-effect chain, which is known as the RF effect 16. Studies 70 focused on the RF effect of pavements are limited but show its possible impact. Applying global 71 average radiative characteristics, Akbari, Menon et al. 17 estimated that a pavement albedo increase 72 of 0.15 in 26m2 of pavement could create an RF equivalent to removing one ton of CO 2 from the 73 atmosphere ( -2.55 kg CO 2 equivalent/m2/0.01 albedo increase). Updating these values using a 74 global climate simulation mo del, Menon et al. 16 reported a pavement RF effect of -3.26 kg 75 4 CO 2e/m2/0.01 albedo increase. In a recent study, the authors developed a parametric model of 76 pavement RF that accounts for location -specific insolat ion and the muting effects of local shading 77 and aerosols. The results of this model suggest an RF effect of -1.1 to -1.6 kg CO 2e/m2/0.01 albedo 78 increase . Given the spatial variation within a city and consequent shading from adjacent building, 79 the amount of incoming and outgoing solar radiations might be different and as a consequence, the 80 RF effect of different roads within a city may vary 18. Moreover, these results highlight an 81 important positive consequence, but only provide a partial picture surrounding pavement albedo 82 modification. 83 Although the GHG implications of pavements have not been mapped out in a way that accounts 84 for variation in an urban context, a body of research exists on the urban energy balance and 85 temperature reduction effect of pavement albedo increase . As reviewed in Santamouris 19 and Qin 86 20, more than ten studies have been reported in the literature that estimate th e impact of pavement 87 albedo modification on ambient temperature and, in some cases, excess building energy demand 88 (EBED – the change in energy used by nearby buildings). All such studies report a decrease in 89 average ambient temperature in response to an in crease in surface albedo 19, 20 but the 90 corresponding change in modeled EBED can vary from a saving to an increase. For example, 91 Pomerantz et al. 21 estimated that the cooling EBED saving for a city -wide albedo increase of 0.2 92 is up to 2 kWh/m2 per year for the state of California. The analysis by Pomerantz et al. did not 93 include the effect of solar radiation reflected from the pavement (herein, incident radiation) on 94 EBED. In contrast, i n a study of the city of Phoenix, Yaghoobian and Kleissl 22 investigated the 95 sensitivity of the EBED to canyon aspect ratio and building characteristics and found that a 0.4 96 albedo increase in the pavement caused a 5.3–10.9% increase in the peak cooling demand. Clearly, 97 there is an unanswered question as to whether pavement albedo modification provides GCC 98 benefits (or impacts). 99 Over th e last decade, a growing body of literature has attempted to provide a more complete picture 100 of the total life cycle impacts of changes to pavement materials. The scope of these papers varied 101 significantly with some studies including RF and/or EBED effects of albedo change and some 102 including different causes of excess vehicle fuel consumption due to imperfect pavement vehicle 103 interaction (PVI). Specifically, they included PVI effects from either pavement roughness and/or 104 pavement deflection. Table S1 in the SI summarizes these differences in scope among the LCA 105 literature. For our discussion here, the most critical issue with these earlier LCA studies is that 106 most estimated the impact of albedo modification by applying global average models 23-26. In other 107 words, albedo -related impacts were estimated by assigning a constant value per unit of the area 108 regardless of the location and prevailing urban morphology. This approach precludes insight into 109 the impa ct of local context. Two papers 11, 27 diverge from this group through their use of more 110 complex simulations that allow for consideration of urban context. Using empirical models of 111 temperature change and energy balance developed by Li et al. 28, Azarijafari et al.27 analyzed the 112 lifecycle impacts of pavement albedo modification including both EBED and RF as well as PVI 113 effects . They found that increasing pavement albedo can induce a net annual GCC burden of 0.34 114 kg CO 2e/m2 per 0.2 albedo increase in a cold climate con dition (Quebec, Canada). Gilbert et al. 11 115 appli ed a single -layer urban canopy model within a broadly applied climate simulation as part of 116 a life cycle assessment of various pavement types . They found that a 0.2 albedo increase results in 117 a net EBED saving of 0.8 -1.0 CO 2e/m2 per year in the city of LA under different scenarios of 118 reflective coating and concrete overlay . PVI and RF effects were not considered. 119 5 In the end, a lthough the UHI benefits of cool pavement s in different locations is well established 120 19, 20, 29, the net GCC impact is not yet settled. This is true because previous studies have not had 121 consistent scope and did not explore the impact of local urban context. The microclimate, 122 morphology, and prevailing traffic vary significantly within a city 30-32 These differences play an 123 important role in the net impact of pavement albedo modification. 124 The objective of this study is to develop a framework that is able to capture the impact of cool 125 pavement strategies on both the UHI effect and climate change in cities considering the 126 interconnections of buildings, vehicles, and pavements. The framework can be used for individual 127 pavement segment analyses within specific neighborhoods and aggregated across an entire city. 128 Using this framework, s everal cool pavement strategies were evaluated for two cities with different 129 climates: Boston and Phoenix. Results of this analysis show that cool pavements can induce both 130 burden and benefit, but when applied appropriately both lower urban air temperatures and provide 131 net GCC benefits equal to 1-6% of the total city GHG emissions. 132 2. METHOD OVERVIEW 133 To quantify the life cycle GHG emissions associated with cool pavements, including the 134 implications of changes to s urface albedo, we develop ed a framework to model the pavement life 135 cycle from materials extraction to end -of-life including the interaction among pavements, 136 buildings, and vehicles. 137 This framework comprehensively covers the location -specific impacts of co ol pavements on 138 different parts of the built environment, including the embodied impacts associated with the 139 pavement materials, and the interaction among pavements, vehicles, and building energy 140 consumption. These elements and their interconnection are sh own in Error! Reference source 141 not found. . Some components of this framework have been described in previous publications but 142 are summarized here for completeness. We apply the modeling framework to the roads and 143 buildings in two citi es. 144 6 145 Figure 1. Overview of the methodology and life cycle components of pavements in urban areas. Considering the interconnection 146 among the elements of the built environment, a top -down approach was developed to capture the effect of cool pavement strategies 147 on the GHG balance of cities. 148 149 2.1. Analytical Scope 150 The framework applied here is intended to capture the impacts of all activities that current 151 knowledge allows to attribute to the materials choice associated with a pavement’s surface course. 152 Formally, this includes activities associated with materials extraction and refinement (including 153 any transformation and transportation to the pavement site), construction, maintenance and 154 rehabilitation (M&R), pavement use, and end -of-life (including any disposition of materials during 155 M&R). Framed in this way, and following norms within the pavement literature, the analysis 156 presented herein presumes the existence of the road and therefore excludes consequences such as 157 additional traffic because of its presence. 158 The primary nove lty of this work derives from developing and applying models that provide 159 insight into the influence of local urban context on impacts that occur during pavement use. As 160 7 such, those models are described in greater detail and modeling of all other life -cycle stages is 161 summarized in the following section. 162 2.2. Modeling: Impacts from materials, construction, and end -of-life 163 The embodied impacts of pavements include the emissions from the supply chain of construction 164 materials and reflective coatings used for increa sing pavement albedo as well as the construction 165 activities required to place those materials. Hence, bill of materials and required equipment for 166 reconstruction of the currently existing urban roads are included in the scope of analysis. Moreover, 167 the qua ntity and type of materials and equipment used for maintenance and repair of the road 168 segments are incorporated into the analysis based on established patterns of pavement deterioration 169 and pavement performance thresholds (See SI Spreadsheets S1, S3 and S4 ). The transportation 170 required to move these materials from production plants to the construction site and from the site 171 to a landfill or recycling plants were included in the analysis. Input parameters and pavement 172 designs related to the embodied impacts of pavements are presented in the SI Spreadsheet S1. 173 2.3. Modeling: Impacts during pavement use 174 During pavement use, pavement material properties, including surface albedo, lead to UHI and 175 GCC impacts both directly and through the interaction among pavements, vehicles, and buildings. 176 The framework applied here captures three of these interactive effects. These are: causing 1) excess 177 fuel consumption in vehicles due to both pavement roughness and stiffness, 2) excess building 178 energy demand, and 3) direct radiati ve forcing . 179 Vehicle excess fuel consumption 180 The interaction between pavement and vehicle occurs because pavement characteristics alter the 181 energy required for cars and trucks to move along a roadway. This effect is measured in terms of 182 excess fuel consumpt ion (EFC); that is the fuel consumption induced in the fleet in excess of what 183 it would require on an ideal pavement. EFC is attributed to differences in both pavement structural 184 response (primarily pavement deflection) 33 and surface roughness 34 (these two effects will be 185 labeled as EFC -DEF and EFC -ROUGH, respectively). Pavement materials can induce 186 significantly different amounts of EFC in passenger vehicles and trucks. Different designs will 187 deflect and deteriorate (and become rougher) at different rates. In addition, the rate of road surface 188 deterioration varies according to traffic volume and speed. Both effects are time -dependent and 189 location -specific. Mechanisms and modeling approaches for estimating the EFC induced by 190 pavement characteristics are presented in the SI document Section S1.1.1. Details on the modeling 191 of EFC including the modeling of deterioration rates for each road type are presen ted in 192 Spreadsheet SI. These models were applied to estimate pavement deterioration and EFC for every 193 pavement segment in the two case cities. 194 Excess building energy demand (EBED) 195 Pavement materials affect building energy demand by altering both the local ambient temperature 196 (the UHI effect) and the magnitude of radiative energy incident on the building. The Urban 197 Weather Generator (UWG) 35 was used to estimate ambient temperatures and precipitation for 198 each neighborhood. To generate these estimates, UWG considers differences in urban morphology, 199 material properties (including surface albedo and thermal diffusivity), sou rces of anthropogenic 200 heat (including buildings and traffic), and prevailing regional climate. 201 8 The incident radiation effect of pavements on EBED was quantified using a modeling framework 202 developed by the authors and described in 36. Because analyzing EBED for each building in a case 203 city is computationally expensive, the framework relies upon neural network metamodels that 204 estimate EBED based on a building’s geometry, the local areal density, and the adjacen t canyon 205 aspect ratio (building height to street width). These metamodels were built from synthetic datasets 206 derived from the simulation of EBED of thousands of neighborhood configurations executed in 207 coupled -physics models of energy exchange and use withi n a neighborhood. These models were 208 implemented in the visual programming environment provided in Grasshopper and Rhinoceros 3D. 209 Separate models were constructed for different types of neighborhoods, referred to as local climate 210 zones (LCZs) by Stewart and Oke 37, within each of the two case cities. 211 The procedure of assigning the EBED to each building in a case city is presented in the Application 212 of models to GIS section. The modeling details and framework are presented in the SI document, 213 Section S.1.1.2i. 214 Direct Radiative Forcing 215 RF is the change in net irradiance at the top of the atmosphere (TOA). Changes in surface albedo 216 induce an RF by perturbing the shortwave radiation budget 17. For shortwave forcing agents like 217 surface albedo changes, the instantaneous RF at the TOA is linked to surface temperature change 218 and serves as an effective proxy for the stratospheric -adjusted RF at the tropopause 38. The shading 219 effect of buildings and road traffic on pavements can alter the amount of radiation received and 220 the amount of irradiation that wil l be reflected towards the TOA thereby altering the RF impact of 221 pavements. The location -specific RF impacts due to changes in pavement albedo were estimated 222 based on a previously -developed parametric model. In this model, the albedo -induced RF is a 223 functi on of the intensity of incoming radiation, atmospheric transmittance and the change in albedo. 224 Incoming radiation estimates were based on city coordinates and taken from NASA Atmospheric 225 Science Data Center: Surface meteorology and Solar Energy database 39, the atmospheric 226 transmittance were calculated according to the shading effect of traffic and buildings on the 227 pavements. Modeling details and equations are described in section S1.1.2ii. 228 Description of GIS -based approach 229 As discussed in the methodology for estimating excess building energy demand, the developed 230 meta -models were assigned to each building based on the neighborhood and building 231 characteristics. The building, pavement, and neighborhood characteristics were collected from 232 several publicly available GIS databases (See section S2. 2 in the SI document). Using this 233 information each building was assigned to a prevailing LCZ and therefore a corresponding meta - 234 model to estimate EBED. The impact of urban morphology on EFC is also manifested in the traffic 235 volume and speed limit. In addi tion to traffic data, the building dimensions were extracted from 236 street GIS sources while the density of buildings was calculated using radial distribution function 237 40. 238 2.4. Case Study 239 In this study, we evaluated three cool pavement strategies (and one baseline pavement) applied in 240 the citi es of Phoenix, Arizona and Boston, Massachusetts in the US. These cities were chosen 241 because their climate conditions are quite different. Therefore, the analyses can provide a more 242 comprehensive assessment of the context -sensitivity of cool pavement strat egies. Previous studies 243 assessed the albedo effect of flexible (asphalt) and rigid (concrete) pavement alternatives 11. We 244 9 extend this to include strategies with larger reflectivity values. Specifically, we evaluate rigid, 245 reflective flexible (higher -albedo asphalt), and reflective rigid scenarios as alternatives for the 246 incumbent flexible road network (More than 95% of the road surface in cities are covered with 247 flexible pavements 41). The average value of rigid pavement albedo was assumed as 0.3 while for 248 both reflective alternatives, the average value was assumed as 0.4. As the pavement albedo changes 249 as a function of time, we adopted the albedo values and changing rates from previous experimental 250 and analytical studies to capture this dynamic aspect. Details of these assumptions are presented 251 in the SI document section S1.2. The scenarios were selected as the most practical solutions for 252 increasing pavement construction and reflectivity. A pairwise uncertainty analysis was then 253 performed to capture both the significance (precision) and the effect of size (relevance) of the 254 environmentally preferred scenario. The modified null hypothesis test method used for the 255 uncertainty analysis, two alternatives are declared to be different only if they exhibit a statistically 256 significant difference of at least some minimum threshold. Foll owing Cohen's guidance for 257 identifying only large effects, we set that threshold to 80% of the standard deviation of the 258 difference 42. The procedure of the probabilistic analysis is detailed in section S1.4 of the SI 259 document. Because the raw materials to create a reflective rigid pavement are not available in a 260 large capacity in all markets, we also explored the implication of having only reflective flexible 261 and rigid alternatives available. As the role of c arbonation and lighting was trivial compared to the 262 total life cycle impacts, they were excluded form the scope of this study. 263 3. RESULTS 264 The impact of cool pavement strategies on both UHI and GCC in Boston and Phoenix is presented 265 for three scenarios that involve shifting from flexible (the dominant pavement type in these cities) 266 to 1) conventional rigid (which have a higher albedo), 2) ref lective flexible, or 3) more reflective 267 rigid pavements. To better characterize the implications of pavement material change, we explore 268 those implications individually, then present the complete life -cycle result. First, a detailed 269 assessment of albedo -induced impacts of pavements is presented from two perspectives: the 270 contribution of incident radiation and of cooling ambient temperatures on EBED and, at a higher 271 level, the intensity of RF and EBED impacts in different neighborhoods. A detailed exploratio n of 272 the impact of pavement selection on vehicle emissions including the relative contribution due to 273 roughness and stiffness is presented in the SI document, Figure S11 and S12. Then, the total life 274 cycle GHG emissions of the four pavement options are pre sented. Finally, considering the existing 275 uncertainty in the parameters, the lowest -impact scenario(s) for each neighborhood are identified 276 and presented. 277 3.1. Albedo -induced temperature and climate effects of shifting to cool 278 pavements 279 The choropleth maps in Figure 2 represent the albedo -induced (only) GCC effects of shifting from 280 the existing flexible to conventional rigid pavements, aggregated at the census tract (also referred 281 to here as neighborhood) level, for both Phoenix (a) and Boston (b). Figure 2 also includes detailed 282 breakdowns of albedo -related results for two illustrative census tracts (CTs) in each city (inset bar 283 plots). Decreases in modeled peak summer temperature across the CTs within each city are noted 284 above each map ranging from 0.3 to 1.7 °C in Boston and 1.4 to 2.1 °C in Phoenix. 285 The predominant local climate zone (LCZ) for each CT in Boston and Phoenix are presented in 286 the SI document, Figure S6. In all neighborhoods, the RF effect from albedo change leads to a 287 10 GCC benefit (negative RF) . In the neighborhoods where the sur face area of pavements is relatively 288 larger than that of buildings, the benefit from the RF effect dominates the EBED results (e.g., 289 compare the green bar (RF) in Figure 2.a.i and 2.b.iv with the light blue (cooling EBED) and 290 orange (heating EBED) bars in the same figures). Therefore, we observe a net GCC benefit induced 291 by the albedo increase alone in those regions. 292 Unlike the RF effect, the impact of a lbedo change on EBED varies greatly by context. In Boston, 293 an albedo increase reduces EBED as often as it increases it (52% of CTs see a reduction, see Figure 294 S8a). Some neighborhoods may experience an increase in cooling EBED (negative savings, light 295 blue bar) and a decrease in heating EBED (orange bar, see Figure 2.b.i) or vice versa ( Figure 296 2.b.iv). This occurs because in Boston the cooling and heating loads induced by incident radiation 297 and ambient cooling are all of the same order of magnitude ( Figure 2.b.ii and iii). As such, the net 298 balance of these effects varies widely across the city depending on the local morphology . 299 Nevertheless, adding the beneficial impact of RF ( Figure 2.b.i and iv) to the varying EBED impacts 300 in Boston results in a consistent GCC benefit due to albedo change only across the CTs in Boston. 301 Trends within Phoenix a re quite different than in Boston. As shown in Figure 2.a, in the city of 302 Phoenix, the temperature reduction range is larger than that in Boston. This r esult is in line with 303 Manoli et al. 43 that showed th at surface albedo increase alleviates the intensity of UHIs more 304 effectively in dry regions. Despite this ambient cooling , in Phoenix , incident radiation -induced 305 cooling burden is consistently larger than other EBED effects. ( compare first box -and-whisker 306 with others in Figure 2.a.ii a nd iii). This results in a net GWP burden associated with EBED in 307 most Phoenix CTs (See Figure S8c for the EBED results of all CTs). As shown in Figure 2.a, 308 however, once RF benefits are considered, the total GWP implications of albedo change (only) 309 vary considerably across Phoenix CTs. Generally, neighborhoods with a high ratio of paved 310 surface area to built volume tend to show benefits in Phoenix. 311 11 Figure 2. Albedo -induced GHG savings from increasing albedo by shifting from flexible (existing) to rigid (higher albedo) 312 pavement in the city of a) Phoenix and b) Boston is shown as a heatmap (negative values imply a burden). Temperature decrease s 313 across the citi es due to the shift in pavement type are shown above each map. The waterfall charts (i and iv) represent the 314 disaggregation of the albedo effect in two different neighborhoods within each city and the box plots (ii and iii) show the 315 contribution of the inc ident and cooling effect to the total building energy demand in the corresponding census tracts. Note: the 316 scale of the values in the box plots depends on the number of buildings and building stories. 317 b) Boston, Massachusetts a) Phoenix, Arizona ΔTmax, summer = -1.4 to -2.1 °C ΔTmax, summer = -0.3 to -1.7 °C iv) i) ii) i) iii) iii) iv) ii) 12 3.2. Pavement life cycle global warming potential impacts of shifting to cool 318 pavements 319 The choropleth maps in Figure 3.a and b represent the life cycle GCC benefit (or burden) measured 320 as GWP saving (or burden , kg CO 2e/m2) of shifting from the existing flexible to a rigid pavement 321 for each CT in the cit ies of Phoenix and Boston, respectively. This result differs from that shown 322 in Figure 2 because Figure 3 results include the impact of other life -cycle stages. Breakdowns of 323 lifecycle GWP by lifecycle stage associated with both pavement designs along with two higher 324 albedo scenarios (reflective flexible and reflective rigid) are shown as stacked bar plots for selected 325 road segments in each city ( Figure 3.a.i and iv and Figure 3.b.i and ii). These results represent 326 mean impacts. The next section will explore the impact of uncertainty. 327 The first notable feature of these maps is that results can vary widely across CTs within each city, 328 reinforcing the importance of high -resolution modeling. For the city of Phoenix, most CTs (68%) 329 would benefit from replacing flexible pavements with rigid ones, but the implications range widely 330 from a b enefit of 1,400 t CO 2e to a burden of 1,100 t CO 2e per lane ·km. These benefits are largest 331 where traffic volumes are high. Deflection -induced excess fuel consumption (DEF -EFC) can be 332 quite large in Phoenix because of the impact of high ambient temperatures on the viscoelastic 333 properties of flexible pavements. In Phoenix, the magnitude of EFC benefit alone exceeds the 334 added embodied burden for rigid pavements in 97% of road lane -km (21% in Boston) . See Figure 335 S11. For Phoenix neighborhoods where both albedo -related effects result in a net burden (red - 336 colored CTs in Figure 2.a) and traffic volumes are low, replacement of flexible pavements would 337 lead to a GW P burden (red -colored choropleths in Figure 3.a). In fact, as illustrated in Figure 3.a.ii 338 and iv, in these neighborhoods, all higher albedo alternatives (rigid, reflective flexible (flexible 339 with reflective coating), and reflective rigid pavement) lead to a GWP burden. 340 For the city of Boston, although results also range from a benefit of 640 t CO 2e to a burden of 40 341 t CO 2e per lane.km, their strategic implications are uniform; rigid, reflective flexible, and reflect 342 rigid scenarios ( Figure 3bi and iii) lead to lower life cycle GWP impacts than a flexible pavement 343 in all CTs. The segment -level results of shifting from flexible to different cool pavement strategies 344 are provided in t he interactive Dashboard of Boston and Phoenix associated with this paper 44, 45. 345 13 Figure 3. Total life cycle global warming potential savings of shifting from flexible (existing) to rigid (higher albedo) pavements 346 in the cities of a) Phoenix and b) Boston on the heatmap (a negative number indicates a burden). The bar charts (i and iv for a and 347 i and ii for b) represent the life cycle GHG emissions and breakdown of those emissions associated with flexible, rigid, and higher 348 reflective scenarios for the specified road segments in the map (ii and iii for a and iii and iv for b). AADT = average annu al daily 349 traffic. 350 a) b) i) iv) ii) iii) i) iv) iii) ii) 14 3.3. Comparative analysis of pavement alternatives considering uncertainty 351 There are several sources of uncertainty and variability in any LCA analysis. These sources include 352 but are not limited to the data quality of inventory, input paramet er variability (e.g., the inherent 353 variations in the world), and modeling choices 46, 47. We account for these sources in a probabilistic 354 LCA (See SI Spreadsheet) and then we implement a modified t -test method 42 to identify 355 alternatives with statistically significant lower implications of climate change impact, or if there is 356 a statistical tie (SI section S1.4). The modified t -test identifies an alternative as preferred only if it 357 exhibits a statistically sign ificant difference that exceeds a prescribed threshold. For this analysis, 358 the threshold was set to 80% of the standard deviation of the difference, a level recommended by 359 Cohen 42 to identify difference only when large effects are present. Significance was evaluated at 360 the 0.05 level. 361 As shown in Figure 4.a and Figure 4.b, the probabilistic results present a more nuanced picture of 362 the lowest -impact alternative in ma ny CTs. Of the four scenarios tested, the reflective rigid 363 pavement (lightest blue color CTs in Figure 4) is the most frequently preferred in both cities (53% 364 of lane ·kms in Boston, 73% in Phoenix). In Boston, these results indicate that replacing 365 conventional flexible pavements with a higher albedo alternative is always the best choice 366 (reflective rigid preferred in 53% of lane ·kms; reflective rigid or re flective flexible in 45% 367 lane·kms, and rigid, reflective rigid, or reflective flexible in 2% ). In Phoenix, the results are more 368 diverse, but rigidity appears to be the most pervasive driver of change with rigid solutions being 369 the clearly preferred alterna tive in 82% of lane ·kms (73% reflective rigid and 9% rigid). For the 370 rest of Phoenix, there are 1% of lane ·kms where no change is the best alternative and 17% where 371 there is not a sufficiently large effect difference to resolve among no change (flexible), rigid, and 372 reflective rigid alternatives. 373 Considering the most environmentally preferred scenario across all segments , results indicate an 374 average savings of 285 t CO 2eq saving per lane ·km in Boston and 280 t CO 2eq saving per lane ·km 375 in Phoenix (See Figure 4c .) by shifting from the traditional technology to the preferred cool 376 pavement strategy. 377 Generally, shifting from a flexible to rigid alternative results in a larger GCC benefit in high - 378 volume roads (See SI, Figure S9). Therefore, in those CTs that mostly possess arterials and 379 interstates, the climate change impact of the scenarios is distinguished enough that the preferred 380 alternatives cannot be obscured by the introduced uncertainty to the system. While this statement 381 is true for both Phoenix and Boston high -volume roads, the saving intensity per lane ·km in low - 382 volume roads follow different intensity levels. In Phoenix, the low -traffic roads that are located in 383 sparsely built neighborhood receive larger saving. On the other hand, the dense neighborhood local 384 roads would experience a minimal GCC saving from the most preferred scenario as the EBED 385 burden is large enough to neutralize the expected savings in the embodied, RF, and PVI in the 386 alternative scenarios. In the Boston low -volume roads located in dense areas experience larger 387 GCC savings (i.e., a more significant EBED benefits) than those located in sparsely built areas. 388 389 15 390 391 Figure 4. The environmentally preferred scenario(s) (lowest life cycle GWP) co nsidering the uncertainty and variability sources 392 in the LCA of pavements in a) Boston and b) Phoenix . The difference in mean GWP impact of the most preferred scenario and 393 others is more than 0.8 standard error unit. In cases where this difference threshol d is not meant, there is a statistical tie among 394 the alternatives . In Boston, the tie is among reflective flexible, reflective rigid, and rigid. In Phoenix, the tie is among flexible, rigid, 395 and reflective flexible. c) City-wide GHG savings obtained from s hifting to the most preferred scenario . The ends of the box are 396 the 25th and 75th percentiles, the median is marked by a horizontal line inside the box, the mean is marked by an “x”, the wh iskers 397 are the two lines outside the box that extend to the minimum and maximum values. 398 4. DISCUSSION 399 As more than 95% of the GHG emissions in urban areas are attributed to building and vehicle 400 operation, several strategies have been proposed to reduce or offset these emissions 48. Many of 401 these strategies require actio ns by systems outside the control of city decision -makers (e.g., grid 402 decarbonization) or behavior change on the part of citizens. These challenges make it difficult for 403 cities to implement GHG mitigation solutions 49. Pavement material change is one strategy that 404 cities have a strong influence over (if not total control). 405 The modeling approach presented in this study has provided insight into the climate change 406 impacts of cool pavement strategies. Considering the unc ertainty and variability (5th and 95th 407 percentile values) and assuming a ten -year period for reconstructing/repairing the whole area of 408 the urban road network, the selection of the lowest -impact cool pavement strategy can offset 0.9 - 409 3.1 Mt CO 2eq from the c ity of Boston over a fifty -year period . That is the equivalent of removing 410 the emissions of driving 5,500 to 17,800 vehicles over that same fifty -year period. The saving 411 range for Phoenix is much larger at 2.2-16.7 Mt CO2eq over fifty years (given the 2.3 times and 412 4.6 times larger population and road length, respectively). The equivalent of emission from 13,700 413 to 96,000 vehicles. 414 Considering Boston’s GHG reduction pledge of carbon neutrality by 2050 50, cool pavement 415 strategies can offset 1.0 -3.0% of the total GH G emissions in the next 50 years. Considering 416 Phoenix’s net -zero goal by 2060 51, the GHG savings from implementing the lowest -impact cool 417 16 pavements can offset 0.7 -6.0% of the total GHG emissions of the city during the next 50 years. 418 Although these percenta ges are not large, they can be realized with little marginal investment by 419 selecting appropriate pavement materials during the next major reconstruction of a given roadway. 420 Globally there is around 4 billion km2 of urban area 52. Even if only 7.5% of that area is paved 421 (this ratio is 7.7% in Phoenix, 15 % in Boston), then the estimated GHG savings suggests that 422 making the appropriate pavement material choice could lead to a global reduction in impact of 5.0 423 to 44.7 Gt CO 2e saved in the next 50 years. This value range corresponds to 5th and 95th percentile 424 of the values shown in Figure 1Figure 4.c. 425 This research also clearly showed the importance of having a high -resolution tool that can capture 426 the impact of heterogeneity across urban neighborhoods to identify the preferred pavement 427 alternative. Traffic, city morphology, and pavement materials inter act to determine the pavement’s 428 impact on urban air temperatures and climate. These effects vary widely across a city. 429 There are opportunities to further improve the GHG savings from cool pavement strategies. 430 Harmonizing the building envelope characterist ics with pavement albedo may intensify the GHG 431 savings. For example, particularly in warm cities like Phoenix, increasing the building envelope 432 albedo would reflect a larger amount of incident radiation reducing the amount of energy absorbed 433 by the buildin g. In contrast, t o accentuate the heating savings expected from the albedo increase 434 in cold cities like Boston, a darker color envelope would further reduce the EBED in winter. Also, 435 the emission factors of cooling and heating energy can significantly and directly change the results. 436 More intensive grid decarbonization can extend the GHG benefits result ing from cool pavement 437 strategies in warm climates like Phoenix. 438 In the end, the results presented here suggest there are many opportunities for cool pavemen ts to 439 both mitigation UHI and GCC impacts. The framework described should allow cities to identify 440 those opportunities in a computationally efficient manner. 441 ACKNOWLEDGE MENTS 442 This research was conducted as part of the Concrete Sustainability Hub at the Massachusetts 443 Institute of Technology, which is supported by the Portland Cement Association and the Ready 444 Mixed Concrete Research and Education Foundation. The authors would like to thank Dr Xin Xu 445 and Prof. Leslie Norford for their help on the modeling s etup. 446 Supporting Information 447 Parameters, probability distributions, and statistical values as well as pavement design details and 448 performance are presented in spreadsheet SI1 (XLSX file). SI2 (PDF file) includes the details of 449 calculations and case study methodology specifications. 450