A carbon footprint and energy consumption assessment methodology for UHI-affected lighting systems in built areas

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A carbon footprint and energy consumption assessment methodology for UHI-affected lighting systems in built areas Federico Rossi, Emanuele Bonamente, Andrea Nicolini ∗ , Elisabetta Anderini, Franco Cotana University of Perugia, CIRIAF—Interuniversity Research Center on Pollution and Environment “M. Felli”, Via G. Duranti, Perugia, 06125, Italy

a r t i c l e

i n f o

Article history: Available online xxx Keywords: Urban heat island Carbon footprint Outdoor lighting LED lamps

a b s t r a c t This paper investigates the effects of urban heat island (UHI) on outdoor lighting systems in terms of GHG emissions: a novel methodology is proposed to assess the carbon footprint (CF) change of lighting services in built areas caused by UHI-induced T with particular focus on the evaluation of the energy consumption. The methodology can be applied also to other activities affected by the UHI, such as HVAC and transport systems. In particular, CF was introduced by a two-fold approach: the quantification of the CF change due to UHI (as difference between CF in an UHI-affected case and CF for an UHI-less case) and the CF change produced by a 1 ◦ C temperature change. A focus on LED lamps was developed: the lifetime of LEDs exponentially decreases with increasing temperature and the luminous flux exponentially decays with operation time. UHI (i.e. the increase in ambient temperature) affects the lifetime and the luminous flux of lamps producing higher energy consumption and higher replacement rates. Results showed that a positive T due to UHI produces a positive CF, which also becomes economically relevant in long-term scenarios. A case study was analyzed by applying the proposed methodology to Rome outdoor public lighting. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The issue of climate change requires a greater focus on mitigating and adaptive strategies [1]. This problem needs to be addressed on both global and small scales. Urban heat island (UHI) and global warming increases the near surface ambient temperature and may cause changes in some characteristics of the atmospheric properties of cities [2]. The UHI phenomenon is well documented and relevant studies exist for most of the major cities in the world [3]. It is defined as “the relative warmth of a city compared with surrounding rural areas, associated with changes in runoff, effects on heat retention, and changes in surface albedo, changes in pollution and aerosols, and so on.” [4]. The annual mean air temperature of an urban area can be up to 3 ◦ C higher than rural areas during the day

Abbreviations: CF, Carbon footprint; CF, Carbon footprint variation; CMH, Ceramic discharge metal-halide; CO2 , Carbon dioxide; CO2eq , Equivalent CO2 ; GHG, Greenhouse gas; HVAC, Heating, ventilating, and air conditioning; LED, Lightemitting diode; UHI, Urban heat island. ∗ Corresponding author. Tel.: +39 075 5853714; fax: +39 075 5853697. E-mail addresses: [email protected], [email protected] (A. Nicolini).

and up to 12 ◦ C in the evening [5]. The rapid and uncontrolled process of urbanization, the lack of a proper urban design together with the high density of buildings, the misuse of construction materials that absorb the solar radiation more than natural surface and the anthropogenic heat release have a huge impact on urban microclimate [6–8]. Also the city size and the urban population growth are correlated to the UHI intensity [9,10]. In fact, these factors cause a higher density of built-up areas and anthropogenic activities into urban areas. Higher urban temperature increases the energy consumption for cooling and raises the peak electricity demand. The cooling energy is accompanied by the intensification of urban environmental pollution, human discomfort and greenhouse gas emissions [11]. According to the above perspective and considering that rapid and huge population growth is expected in the near future, an intensive research has been carried out in recent years in order to investigate possible innovative, effective and lowcost mitigation strategies. Many studies have been proposed on urban carbon emission analysis including the direct and indirect emission inside cities [12]; other surveys investigated the relations between the UHI phenomenon and the urban activities [13,14].

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These studies also propose new strategies focused to energy saving solutions, innovative tools for building energy efficiency and GHG emission reduction systems [15–17]. In particular: (i) Innovative materials: “cool materials” as cool roofs and cool pavements, development of the analysis and the mathematical modelization of retro-reflective materials, clathrate hydrate as phase change materials (PCM) in buildings, innovative paving [18–24]. (ii) Energy sources: new renewable energy sources aimed at reducing the use of fossil fuels, e.g. PV panels, new methods and tools about the production and the use of hydrogen as energy carrier for the electric power generation in fuel cells with low environmental impact and alternative design of the technologies for energy production and storage [25–35]. (iii) Land use change [36–38]. This paper deals with an analytical study about the UHI effect on carbon footprint of outdoor lighting systems which comprehend street lights but also lights for building fac¸ade valorization. In fact, a relevant aspect of historical and new building fac¸ade valorization is the energy consumption and environmental footprint due to the outdoor lighting, which has to be taken into account in the design phase [39,40]. In particular, a methodology is proposed in this paper and the LED technology is investigated. CF is introduced as the difference between CF for an UHI-affected case and an UHI-less case; CF produced by a 1 ◦ C temperature increase is also evaluated. Results show a significant correlation between the UHI intensity and the CF associated to outdoor lighting systems. Results were also applied to a case study and a significant impact, in terms of CF, is found for the public lighting systems of the city of Rome, Italy. This approach can also be extended for the assessment of UHI effects on different lighting systems (i.e. technologies) or other activities which are accountable for GHG emission such as transportations and HVAC. Section 2 introduces the tools currently used to assess the environmental performance of processes with a particular focus on the climate change impact category. Section 3 shows noticeable literature findings related to CF studies of urban areas. Section 4 proposes an original approach for the evaluation of the CF as a function of the Urban Heat Island effect for LED outdoor lighting. Section 5 presents the results of the analysis applied to a study case. Conclusions are given in Section 6. 2. Tools of adaptation and mitigation: LCA and CF Life Cycle Assessment (LCA) and CF methodologies are typically used to quantify the environmental impact and GHG emissions due to urban activities [41–47]. The LCA tool allows assessing the potential environmental burden of a product, a good or a service by quantifying the use of resources (e.g., energy, raw materials and water) and emissions in air, water, and soil throughout its life cycle. In particular, LCA takes into account all manufacturing phases of a product: from raw material acquisition, production, usage, end-of-life treatment, recycling and final disposal (cradle-to-grave approach). The LCA methodology is regulated by the international standards ISO 14040 [48] and ISO 14044 [49]. Furthermore, it represents a tool for decision makers that can be exploited in strategic planning in several fields, such as industry, governmental and non-governmental actions. The result of an LCA analysis is the quantification of impacts such as acidification, eutrophication, photochemical oxidant formation or toxic effects to the humans or ecosystems. The main basic indicators of an LCA analysis can be global warming potential (kgCO2eq /F.U), acidification potential (kgSO2eq /F.U.) and primary

Table 1 Fuels and electricity consumption in the city of Rome. Factor Fuels (imported) Electricity (imported) Total energy demand CF (EPA 2014)

(1015 J/yr) (1015 J/yr) (1015 J/yr) (106 tCO2eq /yr)

1962

1982

2002

2008

29.5 8.45 145 3.59

89.5 19.1 418 9

191 29.1 693 18.3

235 59 866 28.2

energy content (MJ/F.U.), where F.U. is the functional unit. Other indicators are available as outputs, and others can be further obtained by the combination of the aforementioned ones. The CF [50,51] is an indicator to assesses the Green House Gasses (GHG) emission produced by a person, an organization, an event, a product, a good, a service or an activity. All GHGs defined in the Kyoto Protocol need to be taken into account: carbon dioxide (CO2 ), methane (CH4 ), nitrous oxide (N2 O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulfur hexafluoride (SF6 ); they are all converted in CO2eq (e.g. 1 kgCH4 = 24 kgCO2 ). CO2 emissions of cities and urban context are becoming an increasing concern for the scientific community. The trends of the main drivers of urban energy requirements have been widely assessed through LCA analysis. Several interesting studies about LCA of urbanized areas can be found. In all cases, the main drivers of the increasing urban energy demand are the energy requirement for building and transportation, which are the major contributors to urban area environmental impact [48,49]. 3. LCA and CF of urban area Life-cycle assessment based studies about the carbon footprint of urban areas provide, in general, a clear description of the main drivers of the energy requirements. The city of Rome (Italy), for example, faced an urbanization process that strongly impacted the city from 1962 to 2008 [52]. The major drivers of the change in energy and resource consumption were underlined and future low-resource scenarios are indicated. Fuels (diesel, gasoline and methane) and electricity produce a remarkable impact on the city balance, as shown in Table 1. CO2 emissions related to the consumptions for fuels and electricity are calculated with EPA (2014) [53]. The impact is consistent with the annual emissions of a 10 GW power plant that operates 6000 h/yr. The main drivers of Rome environmental impact within all considered sectors have been identified as car traffic and energy consumption for winter heating and summer cooling. Other interesting examples are given from cities in a fast urbanization process and undergoing a systematic building renovation. An important industrial center with a rapidly increased urbanization, Xiamen (China), was studied in order to evaluate the CF impact of urban activities [12]. Energies taken into account refer to the main sectors including direct emissions (fossil fuel combustion, waste, industrial processes and product use, agriculture, forestry and other land use) indirect emissions (due to out-of-boundary electricity and steam use) and other indirect emissions and embodied CO2 [1]. LCA results show that the highest contributions to the total CF in Xiamen City are due to energy use, embodied CO2 , and cross-boundary transport, respectively as shown in Table 2. Table 2 Xiamen city energy-use emissions. Sector

(106 tCO2eq /yr)

% of total energy-use emissions

Households Transport Total energy-use Total city CF

2.07 0.962 14.46 24.36

14.30% 6.70% 100% –

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Table 3 Rijeka public lights electricity consumptions. Year 2003 2004 2005 2006 2007 2008

Number of public lights 11,572 11,920 12,141 12,459 12,627 12,765

Energy-efficient lights (percentage)

Average electricity consumption (kWh/yr/light)

Average CO2 emissions (tCO2eq /yr/light)

57 64 68 74 77 80

746.89 718.96 711.23 619.63 656.05 651.94

0.2412 0.2322 0.2297 0.2001 0.2119 0.2106

In 2009, the total energy-use emissions by urban end-use sectors accounted for 14.46 MtCO2eq , which represents approximately 59% of the total carbon footprint. With respect to energy use, the industrial sector produces the highest CF contribution; the household sector accounts for 2.07 MtCO2eq (approx. 14% of the total energy-use emissions); in-boundary transport accounts for about 0.962 MtCO2eq (approx. 7%). The amount of CO2 emissions, calculated according to EPA (2014) [53], is about 24.4 MtCO2eq /yr, that is consistent with data derived from energy consumption in Rome. In both cases, an approximate population of 3 million people can be considered, the resulting CF from household activities and transport is in the range of 8 to 10 tCO2eq /yr/pers. 4. Methodology for the evaluation of CF in a UHI environment The presence of a UHI produces, among other effects, a change of the Carbon Footprint as a consequence of changes in the habits of people and performance of both mechanical and electric devices. A net increase in energy required for running HVAC system and for public transportation is in general observed. Cooling demand due to air conditioning (AC) systems can consume more than 50% of the total electricity demand during extreme heat events in semiarid urban environments, with maximum consumption up to 65% of total electricity demand during peak late afternoon hours [54,55]; the humans activities and traffic produce more than 80% input of CO2 into the urban environment [56]. Public lighting represents a non-negligible source of CO2 emissions [57]. The performance of lighting bulbs (LEDs, CMH lamps, etc.) is directly affected by the local ambient temperature, in general producing a twofold effect as the operating temperature increases: the degradation of the flux and the increase of the failure rate [58]. The goal of this study is to present a procedure to quantify the increase of the urban CF associated to public lighting as a function of the outside temperature increase (i.e. as a function of the UHI). The basic conceptual approach is that each geographic area is characterized by its own CF. The presence of UHI produces a positive CF with respect to the surroundings. This change depends on the intensity of the UHI effect, and can also be predicted for future scenarios in which the local temperature is foreseen to increase, as a consequence of both local (e.g. modification of the city layout) and global (e.g. climate change) causes. A two-fold approach will be followed in the present analysis: the quantification of the CF change in the presence of a UHI with respect to the surrounding rural area (Eq. (1)), and the quantification of CF as a function of a generic ambient temperature increase (Eq. (2)). CF = CFUHI − CFnoUHI

(1)

CF = CFmodified − CFbaseline

(2)

In the present methodology, only data referred to CO2 emissions, and thus to CF evaluation, are reported. The same approach can be also extended to a comprehensive LCA assessment.

The methodology, as described in the following section, was applied to the case of public street lighting. In this context two factors determine the CF increase with the temperature: the optical efficacy (␩) decrease, and the failure rate increase. As a consequence of an outside temperature increase, more electric energy has to be provided to guarantee the same luminous flux and a more frequent replacement of the lights is necessary during the same time period. Current urban street lighting installation has a substantial room for improvements in terms of both energy efficiency and performance (e.g., color rendering index and lifetime). This is mainly due to the fast technological development [59] concerning lamps and energy supply systems. Many municipalities consider public lighting improvement as a strategic tool for achieving the “20-20-20” targets at the European level [60]. As an example, Table 3 shows public lighting CO2 emissions in Rijeka, Croatia, over the period 2003–2008, where best strategies and technologies were adopted to reduce energy consumptions [57]. The conceptual idea can be applied, with appropriate adaptations, to other systems where electricity consumptions and lifetime are related to the outside temperature, such as • electric motors; • home electrical appliances; • electrical products and components in general, and water treatment system and distribution grids. 4.1. LED streetlights The quantification of the CF as a function of the outside temperature, and hence of the UHI intensity was carried out considering commercial LED streetlights [61,62]. The methodology described here is in general applicable to other types of outdoor luminaries, and this will represent a future development of this study. LED lights are characterized by high energy efficiency, usually addressed to as luminous efficacy (i.e. the ratio between luminous flux and electric power draw). Best-performing LED lamps and luminaries are rated an efficacy up to 120 lm/W, and because of the rapid development of the solid state technology, the efficacy is foreseen to increase up to 266 lm/W for LED packages, and up to 200 lm/W for luminaries, in the next 10 years [63]. The output flux of an LED lamp degrades with time because of thermal and mechanical stress that can produce delaminations and cracks [64]. The lumen maintenance of LEDs over time can be reproduced by an exponential law [65]: t ϕ(t, Tj ) = ϕ(0, Tj ) × e− ⁄

(3)

where,  is the time constant, corresponding to the time after which the output flux is reduced by a factor e with respect to its initial value. The initial output luminous flux is dependent on the junction temperature (Tj ), according to technical specifications of the LED component. A linear relation between Tj and ambient temperature (Ta ) can be assumed [66]: Tj = Ta

(4)

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4 Table 4 LED operation vs outside temperature.

Reference scenario Modified scenario (Ta > 0) * **

Ambient temperature

Initial output flux

Final output flux

Time above threshold

Effect

Ta Ta + Ta

ϕ(0,Ta ) ϕ(0,Ta + Ta )

ϕ(tf ,Ta )* ϕ(tf ,Ta + Ta )**

tf tf − t

– Higher lamp replacement rate

Threshold value for the output flux. Output flux after tf is below threshold.

As a result, once the junction temperature is established for an LED lamp working at a given ambient temperature, it is straightforward to estimate Tj for any other Ta . Using this approach, the output flux of an LED lamp (ϕ) over the time can be estimated for different working conditions (Ta ). 4.2. CF of LED luminaries The Carbon Footprint of an LED lamp can be considered as the sum of two contributions: the CF arising from the production phase (CFP ), and the CF associated to the use-phase (CFU ): CFT = CFP +CFU

(5)

The former includes the end-of-life phases and all those contributions, assessed in a cradle-to-grave approach, excluded the electric energy needed during the use phase. The latter is dependent on the actual electricity mix used to operate the lamp. An LCA study about the CF of an LED luminary similar to that in this analysis [67] shows that a 180 W lamp, composed of 180 LEDs with 1 W nominal power consumption and 83 to 106 lm/LED output flux, has a CFP equal to 850 kgCO2eq . Since the lamp in our case is composed of 128 LED of the same type, it will be considered a CFP equal to 604.4 kgCO2eq /lamp. The CFU contribution can be assessed using the appropriate emission factor associated to the specific electricity production process, or the specific electricity mix, used in the LED application scenario. The emission factor for the grid electricity in Italy as 2012 was 373.6 gCO2eq /kWh [68]. In this study, the CF of the street lighting network is evaluated as a function of the change in the outside temperature with respect to a given baseline. A reference scenario (Ta ) can be defined as the one in which the streetlight layout (i.e. height and spacing of the lights) is designed to guarantee a sufficient light level during the entire lifetime (tf ) of the lamps. In this conditions, the minimum output flux is given by ϕ(tf ,Ta ), i.e. the output flux of a LED lamp after working a time tf at an ambient temperature Ta . After this time the output flux falls below the threshold and the lamp needs to be replaced. Since the output flux depends on the outside temperature, when a change in the local climate conditions occurs (e.g. increase of the mean ambient temperature), the initial flux changes and the threshold is reached at a different time (e.g. in a shorter time), as shown in Table 4. In case of positive temperature change, the flux reaches the threshold level earlier than the reference lifetime, and the lamp needs to be replaced more frequently. A two-fold approach is adopted to quantify the CF associated to the outside temperature change, considering (i) the CF associated to an hour of operation, and (ii) the CF associated to the production of a unit of luminous energy (i.e. 1 lmh). In the first case, the functional unit is 1 h of operation above threshold. The CFU of the reference and modified scenarios is the same, while the CFP is higher for those cases where lamps need to be replaced more frequently. In the second case, an allocation over the functional unit (1 lmh) needs to be performed. Fig. 1 shows the typical output flux of an LED lamp with respect to a threshold level (green point). Standard operation of LEDs does not include

dimming (blue curve) i.e. the power draw is constant. In this case the best functional unit to use would be time over threshold (h), since all excess light (outside the red box) is not effectively used. In case dimming is exploited (gray dashed line) the output flux is adjusted varying the LED forward current. The power draw is not constant during the entire lamp lifetime, and excess energy could be effectively used by dimming neighboring lights. In this (future) scenario the most appropriate functional unit could be the emitted luminous energy (lmh).

5. The case study 5.1. LED lamp modeling The LED lamp chosen for this study hosts 128 high-efficiency LEDs and it is characterized by a nominal power consumption of 180 W and a rated lifetime of 50,000 h, after which the output flux is 70% its initial value [61]. The resulting time constant  is equal to 140,184 h (see Eq. (3)). The sensible parameter driving the light flux is the junction temperature (Tj ). Matching the initial nominal flux of the lamp at Ta = 25 ◦ C (11,900 lm, 5000 K) to the total initial flux produced by 64 LCW-W5AM LEDs and 64 LCW-W5PM LEDs [69], the resulting estimated Tj is 85 ◦ C. Because of the linear relation between Tj and Ta (Eq. (4)), the junction temperature is assumed 60 ◦ C higher than ambient (i.e. outside) temperature. Because of the rapid increasing of commercial LED performances, especially in optical efficacy [63], the technical specifications of the reference LED lamp were updated with those of best-performing components currently available on the market. The reference lamp is then considered as composed of 64 LUWW5AM LEDs, which have a typical optical efficacy of 104 lm/W [70], and 64 LUW-W5PM LEDs, which have a typical optical efficacy of 134 lm/W [71]. The resulting initial output flux at Ta = 25 ◦ C is 18,880 lm, corresponding to an average optical efficacy of the lamp equal to 104.9 lm/W. Parameters of the LED lamp for a reference outside temperature Ta = 25 ◦ C are reported in Table 5. The LED output flux as a function of Tj is shown in Fig. 2.

Fig. 1. LED lamp output flux vs time. Typical (blue) and dimmed (gray) soperation modes are shown. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 5 LED operation parameters at Ta = 25 ◦ C. (0 h,25 ◦ C) LUW-W5PM (lm/W)

ϕ(0 h,25 ◦ C)

ϕ(30,000 h,25 ◦ C)

(0 h,25 ◦ C)

(h)

(0 h,25 ◦ C) LUW-W5AM (lm/W)

(lm)

(lm)

(lm/W)

30,000

94.3

115.5

18,880

15,250

104.9

Nominal power

Lifetime

(W) 180

ϕ(tf )/ϕ(0)

80.70%

Fig. 3. CF change (gCO2eq /h) for Ta = 1 ◦ C as a function of Ta .

Fig. 2. LED flux vs Tj .

5.2. Calculations The city of Rome is considered as an example to apply the proposed methodology. The carbon footprint change is first assessed for the urban case with respect to the surroundings (UHI vs no UHI): the average UHI on summertime and wintertime is 4 ◦ C and 3 ◦ C, respectively [72]. The mean temperature of the rural and urban areas in the period 1964–1975 was 12.4 and 15.7 ◦ C, respectively. The average temperature difference between the city and the rural areas was 3.3 ◦ C. Considering Ta = 12.4 ◦ C as the reference scenario (no UHI), the initial output flux of 180 W LED lamps is 19,570 lm. The threshold flux (i.e. the output flux at 12.4 ◦ C after 30,000 h) is 15,800 lm. The total CF associated to an LED lamp life cycle is 2622 kgCO2eq , being CFU 76.9% of the total. The total luminous energy emitted is4,129,000 lmh. When the urban temperature is considered (Ta = 15.7 ◦ C), the initial flux is 19,400 lm. In this case the threshold value is reached after 28,740 h. The total CF is 2537, the use phase contributing for 76.2%. The luminous energy emitted is 3,938,000lmh. A comparison between no-UHI and UHI scenarios was performed using the two different functional units: 1 h lighting above threshold, and 1 lmh emitted (Table 6). The CF increase is +1.01% and +1.48%, respectively. The same methodology can also be applied to assess the CF change of a given temperature increase (i.e. a fixed Ta ) starting from different reference conditions. Mean ambient temperatures ranging from 5 to 35 ◦ C were considered, and the CF was computed with respect to an increase Ta = 1 ◦ C. Because of the non-linear response of the LED lamp to the outside temperature, the carbon footprint change depends on the absolute temperature of the reference scenario. Results are shown in Table 7. The increase

Fig. 4. CF change (mgCO2eq /lmh) for Ta = 1 ◦ C as a function of Ta .

of CF vs Ta for a temperature increase of 1 ◦ C was parameterized using a quadratic polynomial: CF = a + b × Ta + cTa2

(6)

Best-fit parameters are reported in Table 8. In both cases the correlation coefficient is R = 1.00. In the first case (Fig. 3) CF is measured in gCO2eq /h, in the second case (Fig. 4) in mgCO2eq /lmh. Results in Table 7 cannot be used to extrapolate the CF for temperature change with respect to the same ambient temperature, other than Ta = 1 ◦ C. The increase of CF vs Ta for a given reference outside temperature is also non-linear. Using a fixed ambient temperature Ta = 12.4 ◦ C, CF can be parameterized as a function of Ta by a quadratic polynomial with a null constant term: CF = ˇTa + Ta2

(7)

Best-fit parameters are reported in Table 9. In both cases the correlation coefficient is R = 1.00. In the first case (Fig. 5) CF is measured in gCO2eq /h, in the second case (Fig. 6) in mgCO2eq /lmh.

Table 6 CF of UHI vs no-UHI scenarios for the city of Rome, Italy. Scenario

Mean ouside temperature (◦ C)

LED lamp lifetime (h)

CFP

CFU

CFT

CFP

CFU

CFT

(gCO2 /h)

(gCO2 /h)

(gCO2 /h)

(gCO2 /lmh)

(gCO2 /lmh)

(gCO2 /lmh)

No-UHI UHI Difference

12.4 15.7 3.3

30,000 28,740 −1260

20.15 21.03 4.38%

67.25 67.25 0%

87.4 88.28 1.01%

0.1464 0.1535 4.87%

0.4886 0.4908 0.47%

0.6349 0.6443 1.48%

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6 Table 7 CF for Ta = 1 ◦ C as a function of Ta .

Modified scenario, Ta = 1 ◦ C

Reference scenario, tf = 30,000 Ta (◦ C)

CFT (gCO2 /h)

CFT (gCO2 /lmh)

tf (h)

CFT (gCO2 /h)

Variation (%)

CFT (gCO2 /lmh)

Variation (%)

5 10 15 20 25 30 35

87.4 87.4 87.4 87.4 87.4 87.4 87.4

0.6235 0.6272 0.6311 0.6351 0.6395 0.6438 0.6484

29,671 29,659 29,647 29,635 29,622 29,609 29,596

87.62 87.63 87.64 87.64 87.65 87.66 87.67

0.26 0.26 0.27 0.28 0.29 0.30 0.31

0.6258 0.6297 0.6336 0.6378 0.6422 0.6467 0.6514

0.38 0.39 0.40 0.42 0.43 0.45 0.46

Table 8 Parameterization of CF as a function of Ta for Ta = 1 ◦ C. a Per hour Per lmh

b

(gCO2eq /h) 0.208 (mgCO2eq /lmh) 2.17

c ◦

(gCO2eq /h/ C) 3.06·10−3 (mgCO2eq /lmh/◦ C) 3.50·10−2

(gCO2eq /h/◦ C2 ) 1.58·10−5 (mgCO2eq /lmh/◦ C2 ) 3.56·10−4

Table 9 Parameterization of CF as a function of Ta for Ta = 12.4 ◦ C. ˇ Per hour Per lmh

 ◦

(gCO2eq /h/ C) 0.248 (mgCO2eq /lmh/◦ C) 2.67

(gCO2eq /h/◦ C2 ) 5.40·10−3 (mgCO2eq /lmh/◦ C2 ) 4.71·10−2

The methodology described in this section can be applied straightforwardly to quantify the carbon footprint as a change of all the sensible parameters in a multi-dimensional approach. 5.3. Results and discussion The methodology for the assessment of the Carbon Footprint change as a function of the mean ambient temperature was applied to the case of the city of Rome, Italy. The reference temperature of the surrounding rural areas, not affected by the UHI effect is 12.4 ◦ C. In this condition, a 180 W LED lamp shows a carbon footprint of 87.40 gCO2eq /h during a typical 30,000 h lifetime. The working environment in the urban areas is characterized by an average ambient temperature of 15.7 ◦ C. As a result, the time the output flux is above the same threshold level is 28,640 h (−4.5%). The corresponding CF, arising from a more frequent replacement of the LED lamps, is 0.95 gCO2eq /h (+1.09%). As of 2013, the public lighting network of the city of Rome consists of 214,359 lamps, characterized by an average power consumption of 184.7 W, and an average luminous efficacy of 82,7 lm/W [73]. The total luminous flux, 3.275 Glm, could be

Fig. 6. CF change (mgCO2eq /lmh) for Ta = 12.4 ◦ C as a function of Ta .

guaranteed by 207,267 LED lamps, as the ones analyzed in the present study, working for 30,000 h at Ta = 12.4 ◦ C. The total electric power demand would be 37.3 GW, and the total carbon footprint, considering 12 h operation per day, would be CFT = 79,340 tCO2eq /yr. Considering an average urban temperature of 15.7 ◦ C, the same threshold flux would be guaranteed for 28,740 h, with a resulting carbon footprint CFT = 80,140 tCO2eq /yr. As a result, it can be estimated that the UHI effect in Rome is responsible to an increase in GHG emissions of approx. 800 tCO2eq /yr with respect to a non-UHI scenario. Similarly, the carbon footprint change is estimated considering 15.7 ◦ C as a reference scenario. For an average temperature increase of 1 ◦ C (e.g. UHI enhancement), the increase of GHG emission would be approx. 240 tCO2eq /yr. On the other hand, a 1 ◦ C reduction of the outside temperature (i.e. UHI mitigation) would save approx. 230 tCO2eq /yr. Countermeasures such as cool roofs, cools pavements, green roofs, new high performance materials (i.e. retroreflective materials, directional reflective materials) [6,11,18–22] effectively contribute to reduce UHI and hence to save GHG emission. Economic instruments to achieve energy and climate change targets, such as Emissions Trading Systems (ETS) could also be optimize in order to include UHI countermeasures. According to the actual price, 6D /tCO2 [74], consistent economic benefits may be obtained by implementing effective UHI countermeasures. For environmental virtuous cities the incoming could be comparable to the investment (e.g., strategies for energy efficiency, envelope property and materials improvement [6,11,18–23]). Several voluntary activities and policy initiatives have been implemented as urban heat island reduction strategies [75]. This approach could be proposed as a further policy tool to stimulate and encourage the development of specific regulations to cope with the UHI effect. 6. Conclusions

Fig. 5. CF change (gCO2eq /h) for Ta = 12.4 ◦ C as a function of Ta .

A methodology for the assessment of the carbon footprint associated to outdoor lighting as a function of the ambient temperature is proposed and applied to a case study: the city of Rome, Italy.

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Among other impacts associated to the urban heat island effect (e.g. increase of energy consumption for HVAC systems and transportation), the outside temperature increase can affect performance of lamps, used for street lightning and building facades, requiring both a more frequent replacement and a higher energy to provide the same luminous flux. It is possible to evaluate the carbon footprint increase (CF) as a function to the change of the ambient temperature (Ta ) with respect to a reference scenario (Ta ). An assessment procedure is presented considering high-efficiency LED lamps. The carbon footprint is computed based on a cradle-to-grave approach, separating the contributions from the use phase (e.g. grid electricity consumption), which is dependent on the emission factor associated to the local electricity mix, from other phases. A characterization of LED lamps is carried out to project lifetime and luminosity over time as a function of the outside temperature. The assessment is performed for two functional units: hours of operation over threshold (i.e. minimum output flux to guarantee sufficient visibility) and luminous energy. The former is more representative to the current state of the art, the latter could be more relevant in case flux dimming becomes feasibly implementable. A case study is presented considering the climate conditions and the public lighting network of the city of Rome, Italy. The UHI effect is estimated in a 3.3 ◦ C temperature increase of the urban area with respect to surrounding rural areas. If the public lighting consisted of high-efficiency LED lamps, the UHI would still be responsible, at city scale, for an 800 tCO2eq /yr increase of the carbon footprint with respect to a non-UHI scenario. Starting from the actual urban scenario, the CF change is also estimated for a 1 ◦ C increase (UHI enhancement) and decrease (UHI mitigation) of the average ambient temperature. In the first case GHG emissions would increase 240 tCO2eq /yr, in the second case they would decrease 230 tCO2eq /yr. Further development of this research will include a survey on a larger sample of urban scenarios and the comparison with other lighting technologies, including an economic analysis of the strategies to mitigate the UHI-induced carbon footprint.

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