Urban Greening As Part of District Energy Services

Urban Greening as part of District Energy Services

Sébastien MELIN

Master of Science Thesis KTH School of Industrial Engineering and Management Energy Technology EGI_2017-0011 MSC EKV 1177 Division of Heat & Power SE-100 44 STOCKHOLM

Master of Science Thesis EGI_2017-0011

MSC EKV 1177

Urban Greening as part of District Energy Services

Sébastien MELIN

Approved Examiner Supervisor 2017-03-30 Miroslav Petrov - KTH/ITM/EGI Miroslav Petrov Commissioner Contact person ENGIE Thomas Fabritius

Abstract

Work carried out during this master’s thesis is about urban greening and its close integration with district energy systems. Urban greening is the fact to develop green infrastructures (parks, street trees, ...) instead of grey infrastructures (buildings, roads, ...) in cities. Despite that the actual economic value of green infrastructure is less appreciated at first glance and very difficult to valorize, urban greening has many undeniable advantages such as reducing pollution and heat island effect. This report focuses essentially on the synergy between district cooling services and urban greening, but also on the decrease of pollution with reduction of particulate matter concentration.

This thesis is made at ENGIE, a company which strives to become the world leader in energy transition and which already operates some large district cooling systems. One of the purposes of this study is to determine the benefits for ENGIE to invest in urban greening.

To do so, a model is developed, taking into consideration an example from one district cooling system in Paris, France. All data, assumptions and models used are described in this report. Results show the clear benefits from an increased number of trees for the selected area. Economics are also part of the model, in order to evaluate the return on investment from urban greening. Globally, the model shows that urban greening as a strategy seems promising and district cooling system owners and operators like ENGIE together with city governments should invest in it. Furthermore, this could even provide more benefits in the future where green values should continue to increase.

SAMMANFATTNING Projektet som utfördes under detta examensarbete handlar om storstädernas gröna öar och dess koppling till belastningen på fjärrkylningssystem. Processen om att öka på grönskan i städer handlar naturligtvis om att utveckla miljövänlig grön infrastruktur i städer (parker, gatuträd, kantzoner, m.m.) istället för grå infrastruktur (råa byggnader, trädlösa vägar, m.m.). Trots att det ekonomiska värdet är svårt att beräkna och är mindre uppskattat vid första anblicken, den ökade grönskan i städer har många obestridda fördelar, som t.ex. en minskning av luftföroreningar och av den lokala uppvärmningseffekten under mycket varma dagar. Arbetet härmed inriktas huvudsakligen på samverkan mellan belastningen på system för fjärrkyla och mängden grönska i stora städer, men också på minskningen av föroreningar med minskad partikelkoncentration i luften. Denna rapport skrevs under handledning av ENGIE, ett företag som vill bli världsledande på energiomställning och som redan driver storskaliga system för fjärrkyla. Ett av syftena är att bestämma eventuella fördelar för ENGIE om att investera i ökad grön infrastruktur för att minska behovet för fjärrkyla i stora städer. För att kunna göra några specifika beräkningar, en modell togs fram med hänsyn tagen till ett riktigt exempel från ett fjärrkylanätverk i en stadsdel i Paris, Frankrike. All data, antaganden och parametrar som används i modellen beskrivs i denna rapport, liksom de slutgiltiga resultaten. Resultaten pekar på att det finns klara fördelar med en ökad trädmängd inom det markerade området. Ekonomin är också en del av modellen för att utvärdera avkastningen på investeringen från den ökade mängden träd. Modellen visar att investeringar i stadens grönska verkar lovande och företag som äger och driver lokala system för fjärrvärme och fjärrkyla liksom ENGIE tillsammans med stadens myndigheter bör absolut investera i det. Dessutom skulle denna process även tillföra andra mervärden i framtiden där avkastningen från de gröna öarna fortsätter att öka.

Contents

Abstract 1

List of Figures5

List of Tables 6

Introduction 7

1 District energy systems8 1.1 Global interest of district energy system...... 8 1.2 Current situation...... 9 1.3 ENGIE positioning...... 10 1.4 District cooling...... 11 1.4.1 How it works...... 11 1.4.2 Climespace...... 12

2 Urban greening 13 2.1 Advantages of urban greening...... 13 2.1.1 Increase air quality...... 14 2.1.2 Reduce heat waves...... 15 2.1.3 A cost eﬀective solution...... 16 2.2 Value of urban greening...... 17 2.2.1 Valuating urban greening...... 17 2.2.2 Willing To Pay for Green Infrastructures...... 17 2.3 French market...... 18 2.3.1 ID verde...... 19 2.3.2 Atalian...... 20 2.3.3 Segex...... 20 2.4 French legislation for urban greening...... 21

3 Urban greening as part of district energy - a new opportunity for ENGIE 22 3.1 Speciﬁc demand from the BU France Networks...... 22 3.2 Why ENGIE should look at urban greening...... 22 3.3 Strategic positioning for ENGIE...... 23

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4 Modeling urban greening 25 4.1 Assumptions...... 25 4.1.1 City choice...... 25 4.1.2 Trees...... 26 4.1.3 Other assumptions...... 27 4.2 Model used...... 27 4.2.1 Temperature reduction model...... 27 4.2.2 Maximal temperature reduction model...... 29 4.2.3 PM removal model...... 34 4.2.4 Spatial model and impact of the number of trees...... 35 4.3 Data used...... 36 4.3.1 Tree selection...... 36 4.3.2 Physical values...... 36 4.3.3 Economical values...... 37 4.3.4 Summary of all data used...... 37 4.4 Methodology...... 38 4.4.1 Space occupation...... 39 4.4.2 Evaluate temperature decrease beneﬁts...... 40 4.4.3 Evaluate PM removal beneﬁts...... 41 4.4.4 Economics...... 42

5 Results of modeling 43 5.1 Temperature decrease and cooling reduction...... 43 5.2 PM removal and air quality increase...... 45 5.3 Economics...... 47

6 Discussion 50 6.1 Back to results...... 50 6.2 Green value will increase in the future...... 51 6.3 Urban greening, a new goal for ENGIE...... 52

Conclusion 54

Bibliography 55

A ENGIE 59

B Characteristics of district cooling systems in the region Île-de-France 60

C Historical data in Paris 61

D Paris’ buildings characteristics 64

3 List of Figures

1.1 Artistic view of the "Hikari" district in Lyon [1]...... 10 1.2 Map of the district cooling network of Climespace in Paris [2]...... 12

−3 2.1 Particulate matter concentration (PM2.5 in µg.m ) for 245 cities around the world (average concentration over the years 2010 to 2014) [3]...... 15 2.2 Global turnover of French urban greening market [4]...... 19 2.3 Number of companies in France doing urban greening [4]...... 19

4.1 Map of the area of study with existing trees and district cooling network.... 26 4.2 Comparison of the diﬀerent models for the cooling eﬀect with real values [5].. 29 4.3 Cosine of Paris solar zenith angle along days and hours (on the positive value, representing solar radiation shape)...... 31 4.4 Sigmoide curve to model variations of ∆T0 with temperature T ...... 32 4.5 ∆T0 values (total, solar and temperature contributions) and temperature between April 21st and 27th ...... 33 4.6 ∆T0 values (total, solar and temperature contributions) and temperature between July 30th and August 9th ...... 33 4.7 Process of PM removal by trees [3]...... 34 4.8 Map of studied area with trees position and district cooling network...... 39

5.1 Real and apparent temperatures for buildings in the selected area, taking into account daily variations and variations from 10am to 6pm (with 148 trees)... 44 5.2 Effect of tree number over energy saved for cooling (with inside temperature at 20◦C)...... 45 5.3 Spatial variations of temperature decrease on August 1st at 12am (in a maximum scenario with 148 trees)...... 45 5.4 Effect of inside wanted temperature over energy saved for cooling (with 148 trees) 46 5.5 Effect of tree number over PM concentration decrease...... 46 5.6 Spatial variations of PM concentration (in % of maximum value) during summer and winter (in a maximum scenario with 148 trees)...... 47 5.7 Effect of tree number over Pay Back Period and Net Present Value (after 30 years) for District Cooling owner if he invests in urban greening...... 48

A.1 GDF Suez logo...... 59

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A.2 ENGIE logo and slogan...... 59

C.1 Daily variations of Paris’s Temperature in 2015 and 2016 [6], and Trappes’ Tem- perature in 2012 [7]...... 61 C.2 Hourly variations of Trappes’ temperature during the year 2012 [7]...... 62 C.3 Number of hours that a temperature was reached in Paris region in 2012.... 62 C.4 Daily variation of Paris’s concentrations in PM10 and PM2.5 [8]...... 63

5 List of Tables

2.1 Turnover and number of employees of ID verde [9]...... 20 2.2 Turnover of Atalian and City One [10]...... 20 2.3 Turnover and number of employees of Segex [11]...... 20

4.1 Measured and modeled values of cooling eﬀect from one tree linked to distance x from it (∆T (x) in ◦C). [5]...... 29 4.2 Data used for modeling...... 38 4.3 Prices used to estimate tree costs and beneﬁts...... 38

D.1 Average values for surface losses and compacity according to construction time for buildings in Paris [12]...... 64

6 Introduction

The word sustainable skyrocketed during last decades, it has become the new fashion in the energy world. Everybody is looking for a better way to produce more sustainable energies, with new technologies using non polluting sources. However, it was often forgotten that the most sustainable energy is the one we do not use. Batteries, better isolation, and all other ways to save energy are today an important vector of research. But sometime the best way to reduce consumption is just to trust nature, and a way to reach this goal is linked to urban greening. It implies less cooling during summer with better air quality, so less energy used to achieve the same level of cooling and air quality. Furthermore, it is linked to more outside activities (sport, walks in parks, ...), and so less inside activities that consume more (video games, TV, ...). It has also many beneﬁts on health, both physically and mentally, and thus implies other savings for people living close to green spaces.

Even if urban greening has many advantages, cities don’t always develop it and even reduce it. Value of urban greening is not well known and more than underestimated, only related expenses (investments and maintenance costs) are known. However, return on investment could be higher than the one of buildings or other infrastructures if well developed. Inside a district energy system, urban greening could become an asset for many cities, increase its global value and reduce some costs. As an example, street trees could be linked to district cooling systems to reduce consumption from it, but to do so a proper implementation has to be done. This is why a better understanding of district energy and its goals can help to develop more urban greening in cities. Green spaces impact must be evaluated on long term since it’s value is not totally instantaneous. But anyway, trusting nature should help more than destroying it.

7 Chapter 1 District energy systems

One of the challenges of the century is the energy transition. This transition implies many changes like: — decrease in consumption; — replacement of fossil fuels by carbon-free renewable energies; — having an environmental friendly production; — increase of energy systems eﬃciency. There are diﬀerent ways to reach those goals, district energy seems to be one of the easiest and could become the backbone of sustainable energy transition. District energy systems are global systems that include many aspects like energy production, heat and cooling systems, public lightings, digital services, green mobility, and also urban greening.

1.1 GLOBAL INTEREST OF DISTRICT ENERGY SYSTEM

Decentralized systems, like district energy systems, are used to achieve the following benefits: — Greenhouse gas emissions reduction: decrease due to fuel switching and consumption reduction; — Air quality improvements: thanks to reduction of fossil fuel consumption and cleaner energy generation; — Increase of energy efficiency: essentially linked to lower losses, Combined Heat and Power (CHP) mode production allows up to 90% of efficiency; — Use of local and renewable energies: in order to have more flexibility in fuel supply; with use of local resources, rejected heat from industries, natural water systems; — Increase energy security and reduce import dependency: reducing dependence on fuel imports and centralized generation, essentially during shortage or emergency situations;

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— Green economy: job creation, lower capital costs per project than large central, shorter construction time. Using economies of scale.

Such a system relies on distributed generation, energy storage and demand management [13]: — Distributed generation is usually an on-site generation to produce both, heat and electricity. The advantage of shifting from centralized to decentralized generation is the possibility to increase the global system eﬃciency. Indeed, heat must be produced locally because can not be transported, so heat losses from centralized generation can not be used compare to decentralized generation. — Energy storage is a key point to manage energy intermittency. To cover demand adding more generation sources would complicate the system and make more complicated to match supply and demand. However, storage would collect energy from oversupply to redistribute it during peak demand, and in a way to smooth demand. — Demand management is a new aspect to decrease consumption. With digitalization of buildings and smart metering, people can be incentivized to reduce their consumption, essentially during low supply period. Demand management must be linked to distributed generation to choose the best technologies in order to match demand. As an example, in central European countries, PV panels can be used with CHP production to supply heat and electricity. Indeed, during summer when no heat is needed during the day, only PV panels are used (as there is enough sun). During the night, CHP can be used to replace PV panels and in addition it gives some heat for cool nights. During winter, more heat is needed, even during the day, and it is less shiny, so PV panels must be compensated with CHP during the day, which would supply the necessary heat and electricity. Consequently the two technologies are complementary to each other.

But there are still challenges to overcome. Essentially technical (to choose the appropriate technologies that can match together) and financial ones (who will finance the projects: businesses, customers, authorities). The market will have to adapt too, and firms like ENGIE should lead the way to implement those systems.

1.2 CURRENT SITUATION

More and more projects are developed to make some districts cleaner, greener and even self-suﬃcient from an energy point of view. In 2013, UNEP initiated a study on cities around the world that want to become low carbon cities [14]. Its goal was to identify key factors of their success. Mains criterion to estimate the best cities are the energy eﬃciency, the renewable proportion and the greenhouse gas emissions. One of the conclusions was that district energy

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systems emerged as one of the best options to meet the objectives while having a system affordable to produce energy. As an example, the city of Lyon (in France) is currently developing the district called "Con- fluence", and among this district, a group of buildings called "Hikari" ("light" in Japanese) is one of the first energy island in Europe (excluding isolated lonely houses or buildings) that produces more energy than it consumes. To realize this mission, they mixed accommodations, offices, and shops, in order to have different energy cycles and so decrease peaks. New technologies were a key to conduct such a project [15]. But nature helped too, trees are used around buildings and even on buildings (as shown on figure 1.1), in order to get the benefits they provide like improving air quality or decreasing temperature during summer.

Figure 1.1 – Artistic view of the "Hikari" district in Lyon [1]

In many cases, district energy systems are developed by large cities having funds to support initial investments. Here ENGIE could become an asset, ﬁrst having an oﬀer cheaper thanks to uniformization of the process, and could also commit funds helped by local or national authorities.

1.3 ENGIE POSITIONING

Today ENGIE wants to become the new leader of the energy transition in the world (ap- pendixA presents more precisely the Group). ENGIE still has some large thermal power plants using conventional fuels like coal and gas. But the Group wants to switch them with production from renewable energies and also be closer to consumers oﬀering more business to customers, business to business and business to territory services (BtoC, BtoB and BtoT). District energy systems could be a solution to deal both challenges. As seen, it produces locally clean energy.

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ENGIE has already invested in district energy, essentially in France with its business unit (BU) France Networks. This BU has essentially developed heating and cooling networks in the country, in addition with some local energy production. From those existing solutions, the company can look at the possible synergies and develop it to improve its solutions. Here, urban greening could be one of the opportunities for ENGIE to improve its district cooling. Indeed, urban greening could be used to decrease consumption to cool the buildings and could be implemented along with installations for cooling network. BU France Networks has the objective to reduce its CO2 emissions as well as its ﬁne particulate matter ones. Once again, urban greening is a way to be closer to this objective. Furthermore, today demand for cooling networks isn’t as high as it used to be. Urban greening could be a new way to expand and do beneﬁts for the BU France Networks.

1.4 DISTRICT COOLING

As seen, district cooling is key point for the BU France Networks and it is also an major aspect of district energy.

1.4.1 How it works

Such a system is used to supply cooling capacity like cold water (or other fluids), to buildings around the cooling source. This is done through pipes that form an entire network. With this system, users don’t need their own chiller, they just buy cold water from the network owner. District cooling systems have three main parts [2, 16]: — Central chiller plant - it generates chilled fluid to the network; — Distribution network - it distributes chilled fluid to surroundings buildings; — User stations - it is the interface with cooling systems of buildings, or the distribution point of chilled fluid.

District cooling systems use essentially water as a fluid, which can be chilled by different ways. It can be produced from waste heat using steam turbines or absorption chillers; from natural cooling sources like rivers, seas, lakes; from electric chiller. For production from waste heat and natural sources, a backup source or a CHP is needed for peak demand. Globally, district cooling systems are more efficient than usual air-conditioning ones, they can be more than twice as efficient as individual chillers and can decrease peak consumption. In addition, it uses thermal storage to improve efficiency and reduce peaks of demand. During nights the system still works to create cold water or even ice which is stored to be used during the following days. Furthermore, those cooling systems reduce consumption of refrigerants like

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hydrochloroﬂuorocarbons (HCFCs) and hydroﬂuorocarbons (HFCs) which are hazardous for environment. This technology is more and more developed, between 2000 and 2010, energy consumption from district cooling increased by 60%. It is expected to expand by 625% until 2050 in some areas of Asia and South America [14].

1.4.2 Climespace

In Paris (and the region Île-de-France), ENGIE manages different district cooling systems through its subsidiary Climespace. It is the largest actor in the region Île-de-France, essentially situated inside Paris. Main figures about district cooling systems in the region Île-de-France are given in appendixB. Concerning Climespace, its cooling network is a closed one with two kind of pipes, one to bring chilled water at 5◦C, and one to bring it back to central chiller plant at 15◦C. Climespace has ten main central chiller plants and three storage places. Its network is more than 73km of underground pipes (figure 1.2). Globally the Group provides 470GWh of thermal energy per year for more than 600 customers [2]. Climespace (and so the BU France Networks) could use its relationships with those customers to develop new offers linked to urban greening in Paris.

Figure 1.2 – Map of the district cooling network of Climespace in Paris [2]

12 Chapter 2 Urban greening

Urban greening involves increasing the proportion of green infrastructures in cities compare to grey infrastructures. Green infrastructures are parks, gardens, all trees and plants in cities, forests, .... Compare to grey infrastructures which are buildings, roads, and all kind of installations created by humans. Urban greening could be part of district energy. Until now it wasn’t that much used since the economic value of green infrastructures is not recognized at the same as the one of grey infrastructures. A study on 245 main cities in the world revealed that 26% of them had a decrease of green spaces between 2000 and 2010, and only 16% had an increase [3]. However, there is room for green spaces and to develop urban greening. It could become one of the easiest way to improve district energy systems by decreasing consumption. Beneﬁts from urban greening would smooth demand and reduce peaks which is necessary when using distributed energy generation.

2.1 ADVANTAGES OF URBAN GREENING

Trees and plants can help to improve environment but not only, they have many other advantages. All of them are not well known and sometimes diﬃcult to determine. Most of them are social, ecological and economical ones like [17, 18, 19]: — Socially: • improve people health (physical and mental ones) and reduce diseases: thanks to more sport activities, better air quality, ... • increase access to nature and sport facilities; • less stressful environment, reduce noise; • improvement of landscapes and aesthetic; • strengthen the feeling of belonging to the community. — Ecologically:

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• natural regulation of temperature in cities and decrease heat island effects. Reduce wind break during the winter (lower need to heat); • improve air, water and ground qualities; • more biodiversity: trees and parks provide a habitat for biodiversity; • natural evacuation and filtration of rainwater, which decreases saturation of water networks and probability of flood; • prevent from erosion in some places; • green waste from green infrastructures can be used for energy production, fertilizer, wood production, food production, ... — Economically: • create local jobs (in France there are more than 28 600 companies and 91 100 people working on urban greening, with a turnover of 5.3 billion euros and 285 million invested in 2014); • increase tourism and attractiveness of cities; • housing prices increase thanks to proximity with green spaces.

Even if advantages are numerus, benefits from trees and plants are local (at least environmental ones) and disappeare within a radius of 300m for most of them. So the appropriate location must be chosen to improve the return on investment, and a difference of a factor 100 can be observed between two different neighborhoods. Globally the best places have a high population density to improve the number of people having benefits from trees. Furthermore the most polluted districts have the highest return on investment [3]. Tree planting can also focus on important places that welcome the most vulnerable people, like schools and hospitals. However, sometime a tradeoff must be done between green spaces and other infrastructures, since space must be available. In addition, trees need money and human ressources for planting and maintenance, which is not affordable for all cities. Trees are also water consuming and in arid countries it would reduce the return on investment. Among advantages listed, two are capital to improve cities, the fact that trees improve air quality and reduce heat island effect. This is why a focus is made on them. Furthermore, the ability of trees to reduce temperature could be used by ENGIE to improve its cooling district systems, and so be linked to it.

2.1.1 Increase air quality

One of the main advantages of developing tree planting is to improve quality of air. This is essentially done by captation of particulate matter by trees and sequestration of carbon dioxyde. Particulate matter are at the origin of thousand of premature deaths, and CO2 impacts environment and human health. Indeed, ﬁne particulate matter PM2.5 (smaller than 2.5µm) is estimated to be the origin of 3.2 million premature deaths per year (5% of overall premature deaths), and tens of millions of diseases. This would get worse with urbanization

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and densification of cities with more pollution. By 2050 fine particulate matter could kill 6.2 million people per year (0.068% of the population) [20]. Those fine particulate matters are particles or molecules moving in the atmosphere. PM2.5 are more dangerous than PM10 for human health as they are smaller and can go easier in lungs. In cities those particulate matter come from burning for heating and cooking, but also from transport. The highest level of concentration for PM2.5 are in China and India with high density population level (figure 2.1). Their impact on health is negative with linear relation between PM concentration and the number of cardiovascular and pulmonary diseases. So the lowest is the concentration the healthiest is the air to breath [3]. A focus on 245 cities around the world shows that today existing trees are already an asset to improve air quality. Over the 910 million people hosted by the 245 cities, trees reduce by at −3 −3 least 1µg.m PM2.5 for 52.1 million people. This figure corresponds to a reduction of 1µg.m for 5.7% of the population.

−3 Figure 2.1 – Particulate matter concentration (PM2.5 in µg.m ) for 245 cities around the world (average concentration over the years 2010 to 2014) [3]

2.1.2 Reduce heat waves

Heat waves are another problem for health. Indeed, high temperature is a risk factor for many diseases. It is the cause of 12 000 deaths annually in average. In 2003, heat waves that struck Europe killed 70 000 people. An increase of 1◦C is linked to 3.0% to 5.5% increase in all-cause mortality. And this will be worse in the future, temperature should increase by

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2-5◦C. Furthermore, heat waves should occur more often and on longer periods. It could be responsible of 260 000 deaths every year by 2050 (so nearly 22 times more than today). In addition, it will increase electricity consumption for cooling. An increase of 1◦C can result of an increase of electricity consumption for residential sector from 0.5 to 8.5%, with an average value of 4.6% increase per ◦C[21]. This ﬁgure depends essentially on the building, the cooling and heating systems, and spatial characteristics. This increase takes eﬀect when temperature is higher than 18◦C in average (between 12◦C and 23◦C according to the country). Many other factors will impact this consumption: having more inhabitants in cities, higher temperatures, longer and more often heat waves [3].

A study made on 245 cities (which host over 910 millions people), shows that trees reduce by at least 1.0◦C the temperature during summer for 68.3 millions people (which represent 7.5% of the population). This figure could be improved with more trees and a better repartition on strategic places [3]. Studies shown that a park is in average 0.94◦C cooler in a summer day than other parts of the city. This is due to shading and evapotranspiration that consume energy from solar radiation and increase latent rather than sensible heat, cooling the leaf and temperature of the air surrounding the leaf. Variation in the composition of vegetation within a park, such as the amount of trees and grass cover can be expected to affect temperature [17]. Up to 40% of the costs of cooling can be saved by shading techniques, which can be done by trees. Evapotranspiration effect would improve this figure even more. Consequently, trees can be used to decrease temperature of cities during summers. However, it can’t replace cooling systems but only complet them.

2.1.3 A cost effective solution

Globally trees are cost effective solutions. Even if it depends on the city and the type of source of PM, it is generally cheaper to invest in trees to reduce PM2.5 than in other grey infrastructures or technologies. For air temperature reduction, it depends on the technology used and the type of green space wanted. Green roof could be expensive, especially for old buildings, but parks and other green spaces on the ground are cheaper. On average price of tree planting to reduce temperature is about 420e/◦C. Combining all benefits that trees would provide, they are a much higher cost effective solution than grey alternatives. According to a study, investing 100 million dollars in trees −3 would reduce PM2.5 by more than 1µg.m for 68 million people and reduce temperature by 1◦C during summer for 77 million people. Those results could save lives and prevent many diseases. If a maximum tree planting in cities selected is made, representing an investment of 3.2 billion dollars annually, then it could save between 12000 and 37000 lives every year (just taking into consideration PM2.5 and heat waves impact). In addition, trees, cooling the surrounding air, would reduce electricity consumption by 0.9 to 4.8% annually. Carbon sequestration would also increase by 7 to 35 million tons CO2 (including impact of less electricity production) [3]. Other publications estimate even more

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beneﬁts from trees with up to 9% saved in annual heating and cooling costs for residential sector using only one tree but at the right place [22].

2.2 VALUE OF URBAN GREENING

2.2.1 Valuating urban greening

Historically, green infrastructures have been vulnerable to development pressure from grey infrastructures. Indeed, grey infrastructures are perceived as necessities with a greater ﬁnan- cial return than green infrastructures that are seen as luxuries. However, true value of green infrastructures is diﬃcult to scale, green infrastructures provide ecologically, socially and economically sustainable infrastructures. Economical valuation of green infrastructures implies two notions: use value (sports, walking spaces, ecological, future uses, ...) and non-use value (inheritance value, existing value). There are two main ways to determine those values: stated preferences (get from surveys, ...) and revealed preferences (get from existing situations, ...). But both are not ideal and it is often case by case studies that are expensive and long which are the best to evaluate a market.

Looking at advantages that trees provide, it exists some tools to estimate their economic value. As an example, the "National Tree Beneﬁt Calculator" [23] is an online and free tool to have a ﬁrst estimation of this value, and which is partially used for this report. However, it takes into consideration only some use values and not non-use values. Social aspects like people health or aesthetic are not measured, only easily scalable values are included to calculate economical value.

2.2.2 Willing To Pay for Green Infrastructures

Even if green spaces have an economic value, people will invest in it only if they want to do it. So it is necessary to have a look at the willing to pay for urban greening. This one is impacted by diﬀerent aspects[24]: — There is a relationship between size and type of greenness of an investment and people’s willing to pay for it. The more visible and greener it is, higher is the willing to pay. Shape, size and composition, all have an impact; — Higher the city is urbanized, higher is the willing to pay; — People are willing to allocate lower ﬁnancial resources from existing taxes than when paying additional taxes;

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— 95% of European planners would be willing to pay a 3% increase in taxes or rent for green infrastructures development; — There is also an impact of social classes of people, their link with the investment and their job; — Residents (the closest to invested area) are the ones prepared to pay the most; — People retired or not working are willing to pay more, so valuation is linked with frequency of use; — However green infrastructures are not the most important factors for citizens choices (public and open space: 39%; access to nature: 20% and trees: 22%, whereas clean streets: 48%; crime: 40% and transport: 43%). So economic values are supported by positive perceptions of sites ecological and social context.

2.3 FRENCH MARKET

Today French market still has potential and can be developed compare to other countries like UK. Nine out of ten French consider urban greening as necessary [19]. The different figures show that this sector has flourished since 2004, but the economic situation today in France tends to stagnate this progression.

In 2014, there were 28 600 companies (representing 91 100 people) working in urban greening, for a global turnover of 5.34 billion euros, and 284 million euros of investments [4]. Cus- tomers between 2004 and 2014 were mainly divided in three categories: individual and private demand, public market and private companies. The repartition of the turnover according to the type of customer did not change that much over last years. In 2014 the 5.15 billion euros came from: 42.7% individual and private demand, 28.2% work for public markets, 28.2% work for private companies, and 0.9% other king of customers (figure 2.2). Companies in France are mostly one-person companies (63%), 25.5% have between 2 and 5 employees (figure 2.3). From 2006 to 2012, urban greening sector increased of about 70%, and then stagnated due to a slowdown of the French economy. When comparing companies by size different conclusions can be made: — The largest firms with more than 50 employees are only 1% of a market which is largely atomized; — Companies over 10 employees represent 60% of the turnover, and the ones over 50 employees 12% of it; — Turnover per capita is largely higher with large firms (>10 employees), between 71 000 and 95 000e compare to 29 000-54 000e for the smallest ones.

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5, 6 6,0 30000 35000,0 Total: 28600 individual public markets 0 employee Total: 28400 Total: 5,15 B€ private companies other Total: 5,05 B€ 1 to 5 employees Total: 26500 11,6% 11,7% > 5 employees 3350 Total: 4,70 B€ Total: 4,70 B€ 3300 30000,0 25000 12,1% 4, 6 5,0 27,7% 28,2% 3200 1,45 B€ Total: 4,10 B€ 26,6% 1,40 B€ 26,6% 25,7% 25,3% 25000,0 1,25 B€ 1,25 B€ Total: 20100 7 300 7 250 20000 26,6% 3, 6 28,0% 4,0 15,2% 7 050 1,15 B€ 3050 Total: 16700 20000,0 28,2% 17,4% 29,7% 15000 29,8% 1,50 B€ 1,45 B€ 2900 2, 6 31,9% 3,0 31,8% 1,50 B€ 1,40 B€ 30,5% 6 400 15000,0 1,25 B€ 35,0% 10000 5 850 62,7% 62,9% 1, 6 2,0 61,3% 17800 18000 10000,0 16250 41,6% 42,7% 42,6% 2,20 B€ 53,0% 41,4% 40,4% 2,10 B€ 5000 2,00 B€ 10650 5000,0 1,70 B€ 1,90 B€ 47,6% 0, 6 1,0 7950

0 0,0 2006 2008 2010 2012 2014 2006 2008 2010 2012 2014

- 0, 4 0,0

Figure 2.2 – Global turnover of French urban Figure 2.3 – Number of companies in France greening market [4] doing urban greening [4]

— The largest companies work essentially for public market and private companies, whereas the smallest ones are essentially oriented toward individual and private demand.

Companies over 50 employees concentrate their activity towards local communities and BtoB customers . Only three players have a signiﬁcant footprint: IdVerde, Atalian, and Segex ; IdVerde being the only pure player, while Atalian is a facility manager and Segex a construction company.

2.3.1 ID verde

Key ﬁgures in 2013 for the company are: — An increase of the turnover of 5.77%; — A turnover of 277 million euros, which represents 5.2% of the French market; — 2406 employees which represents 2.64% of French working people. The company is today present in France, UK and has one agency in the USA, with more than 4 500 employees and a turnover of 380 million euros [25]. The Group acquired the UK compabies "The Landscape Group" in 2015, and "Quadron" in 2016. The Group has a stable basis now with many long term contracts (half of contracts, which can go over 10 years). Consequently, the company intends to develop at international level.

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2013 2012 2011 2009 2008 Turnover (Me) 277.2 262.1 240.4 241.8 244.8 Employees 2 406 2 359 2 304 NA 2433

Table 2.1 – Turnover and number of employees of ID verde [9]

2.3.2 Atalian

65% of the turnover is made in France, the remain is from USA (33% of international turnover), Asia (17%), Africa (2%) and Europe excluding France (48%). In 2014, the company represented about 1% of French urban greening turnover. The Group was present in 16 countries in January 2015 and wanted to add 4 new implantations per year [26]. The turnover is distributed in 5 categories [10]: — Reception: 13% — Cleaning: 55% — Multi-technical: 17% — Green spaces: 4%, which represents =79 120 000e — Security: 11%

Turnover (Me) 2016 2015 2013 2009 2005 Atalian 1 800 1 332 1 206 544 320 Atalian + City 1 978 1 494 1 321 544 320 One

Table 2.2 – Turnover of Atalian and City One [10]

2.3.3 Segex

Segex is divided in 3 "metiers": Public works and services, Environment, Energies. Its original business is construction.

2015 2014 Variation Turnover (Me) 94.6 100.2 -5.63% Employees 552 560 -1.43%

Table 2.3 – Turnover and number of employees of Segex [11]

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2.4 FRENCH LEGISLATION FOR URBAN GREENING

During the 70’s, the region "Île de France" decided to reach an ambitious objective, having 10m2 of green spaces per capita in urban areas and its surroundings, and 25m2 per capita including urban and suburban forests. Those two indicators, to scale the ambitions of the region, were inspired from rules that existed in Netherlands since 1935 and in the United Kingdom since 1944 [27]. However, in 2013 there was a lack of 45 700 hectares of public forests, 1 040 hectares of urban green spaces and 23 770 hectares of outside leisure spaces. So urban greening must continue to be developed to reach the goal of 10m2. Furthermore, the World Health Organization (WHO) recommended a minimum of 12m2 per capita near habitations, more eﬀort needs to be made.

For 2030 the region "Île de France" wants to keep ambitious objectives with creation of 2 300 hectares of parks and gardens. But it is a double problem, ﬁrst there are investments to catch up in order to reach the goals, second, the population continues to grow which globally increases green spaces needed. So the region will have to intensify urban greening, and this gives opportunities for future investments in that ﬁeld [28].

Concerning the way to get projects, it is usually by winning call for tenders. According to French legislation, demand from public administration must be done through call for tenders if the amount is higher than 15 000e. Large private companies usually go through call for tenders too [29]. So ENGIE should be able to win those call tenders to seize the market opportunity. When looking at projects repartition according to the size of the ﬁrm, usually larger ﬁrms deal with public market and private companies, whereas smaller ones are essentially oriented toward individual and private demand. Consequently ENGIE would have an advantage if it acquires a dominant actor of the market.

21 Chapter 3 Urban greening as part of district energy - a new opportunity for ENGIE

3.1 SPECIFIC DEMAND FROM THE BU FRANCE NETWORKS

Today the BU France Networks has about 40% of the heating network market in France, but the potential in the country seems still high. Indeed only 5% to 6% of consumed heat in residential sector comes from heating network, whereas it is more than 50% in many northern countries [30]. However, with the current decrease of gas prices, diﬀerence between collective heating networks and individual heating systems is not anymore so attractive. Consequently, people prefer to invest in personal heating system having lower initial investment cost. With the current situation, the BU France Networks is looking for a new source of development and diversiﬁcation, Urban greening came as an idea that could bring new opportunities for the BU and could be linked to the networks they manage.

3.2 WHY ENGIE SHOULD LOOK AT URBAN GREENING

France Networks has many objectives, among them decrease CO2 emissions, improve its relationship with customers and oﬀer new solutions to grow more. Urban greening is a perfect match to deal with those three objectives. Indeed, developing trees and parks in cities is a way to reduce temperature and so reduce cooling demande. Furthermore, green spaces can provide a better air quality and store CO2. Another point is that urban greening would be more visible than district heating and cooling and could bring ENGIE closer to its customers. In addition,

22 Master’s Thesis

the Group could use its existing customers to develop its ﬁrst oﬀers. Essentially, customers from district cooling are potential customers for urban greening. District cooling are mostly used by public markets and private companies (administrative buildings, public buildings, other large installations, ...), and those two kind of customers are the main actors of urban greening. The synergy between urban greening and district cooling should be used to create new solutions.

However, in 2014, global creation of jobs was -2 700 for urban greening, this layoﬀs are due to current economic situation in France [4]. In addition major players are investing in foreign countries like UK. This can indicated that the market growth is no more as attractive as before and it would be really complicated to start from scratch for ENGIE. The Group should acquire one of the current actors to start and then maybe create new oﬀers linking urban greening and other aspects of district energy. ENGIE could also expend an existing company in new countries. Indeed the Group is present all over the world contrary to the major players of urban greening.

3.3 STRATEGIC POSITIONING FOR ENGIE

There is a strategic fit between ENGIE activities and green infrastructure solutions. The inclusion of such services within the portfolio of the Group could take different forms and be executed step-by-step: — Landscape construction and maintenance: • Value added from such services is pretty low, but it requires very low capex and allows a very strong local anchorage; • It can include a B2B component, as existing players generally offer services to either local communities and companies; • Such a move could be done through an acquisition (i.e IdVerde in France and UK which could serve as a platfrom for further development worldwide) and a build-up strategy which could take a bit more time. — Spatial and urban planning: • Value added of design and creation for rehabilitation or creation of urban areas is higher, with an improved level of margin; • It can be built on top of the construction and maintenance activities. — Urban agriculture: • The economic model of such activities is still under construction; • Nevertheless, convergence opportunities are identified with energy activities through reuse of fatal heat, circular economy and local anchorage. — Real Estate:

23 Master’s Thesis

• Regulation, at either local or national level, incentivize real estate developers for greener buildings; • There is room on the real estate market for pure green developers to accelerate the transition of urban areas.

The local anchorage of the Group in territories, notably with heating and cooling networks, would offer a very strong fit from a commercial point of view. Green infrastructures services would also offer convergence opportunities with green roofs and walls for buildings, public lightings, and mobility solutions. In France, a large part of those activities are currently done by public employees. Outsourcing of public employees, through the transfer of full range of activities, can help cities to alleviate the burden of those costs on local budget.

But, before implementing new solutions, the impact of urban greening can be modeled to estimate its potential value. Especially, the synergy between district cooling and urban greening can be estimated with ﬁgures from current studies. Two main aspects can be modeled with existing researches, improvement of air quality (which has become a key point in some cities like Paris), and decrease of heat island eﬀect from large cities. The following part of the report will essentially focus on both of those aspects.

24 Chapter 4 Modeling urban greening

This chapter focus on the description of models used to estimate beneﬁts from urban greening. A focus is essentially made on temperature decrease eﬀect from trees to estimate the synergy that could be made with district cooling. PM removal is the second main point of analyse.

4.1 ASSUMPTIONS

4.1.1 City choice

Return on investment of urban greening depends on many aspects and essentially on the potential to decrease heat waves and improve air quality. This is why this return on investment is not the same in all cities and even between districts of the same city. Globally european cities do not have the highest return on investment, because of their location and the fact that they already have more trees than other cities in Asia, Africa or South America. However, among −3 european cities, Paris is one of the most polluted (PM10 level reached 147 µg.m in 2014 and even more the following years), has strong heat waves, and is densely populated . During the year 2016 Paris had strong peaks of pollution, which forced Paris’ mayor to reduce car use, essentially with alternating traﬃc solutions. This is why a focus on this city is made for this modeling. Furthermore, the BU France Networks has some cooling district systems in Paris (through Climespace Group), so a focus on one of them should be done to see the impact of urban greening on such a system.

It must be kept in mind that urban spaces have an impact which decrease with height. This is why it is not chosen to look at places having many skyscrapers like "La Défense" (Paris’ business center) but more to districts with "normal" size buildings like the center of Paris. One part of the district cooling system manage by ENGIE is used in the second arrondisse- ment of Paris. In this part of the city there are also some places without so much trees and where urban greening could be improved. Such an area is chosen, delimited by four streets:

25 Master’s Thesis

"Rue Vivienne", "Rue Monsigny", "Rue du 4 Septembre" and "Rue des Petits Champs". Figure 4.1 represents the chosen district, with trees already present and pipes of Climespace district cooling system. The whole area is a square of 300m by 300m (total area of 0.09km2). Concerning buildings cooled down by Climespace’s district cooling system 1, they are represented on ﬁgure 4.1.

Figure 4.1 – Map of the area of study with existing trees and district cooling network

4.1.2 Trees

A focus is made on trees since it is the type of urban greening having the most of information on literature. Beneﬁcial aspects like decrease of heat waves and reduction of PM concentration depend on tree species selected. Some studies focus on the best trees to select in order to reach those goals, but it also depends on the climat of the city and all beneﬁts willing from trees [31]. For the present model, the global impact of urban greening is wanted, not a precise study on the impact from each species of trees or other plants. This is why average values are selected, with a certain range (looking at maximum and minimum for some of them), in order to get an

1. As some data are conﬁdential, in the modeling, buildings linked to the system have been chosen near the district cooling system but are not necessary part of it.

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idea of global urban greening impact. A focus on the Platanus x hispanica is made for speciﬁc data since it is the type of tree the most present in Paris streets.

4.1.3 Other assumptions

Concerning electricity used, it is supposed that it comes from the national grid. Conse- quently electricity origin is the average one from french national grid, which is 76.3% from nuclear, 10.8% from hydropower, 4.0% from gas, 3.9% from wind turbine, 1.6% from coal, 1.4% from solar, 1.4% from bio-energies (biomass, biogas, waste) and 0.6% from oil [32]. This leads −1 2 to 51.5gCO2eq.kWh including auto-consumption (but not losses from transport) . However, there are variations along years (essentially due to weather) and this figure varied from 35 to −1 67gCO2eq.kWh from 2009 to 2015. About energy consumption for cooling in France, two sectors are really significant and in link with district cooling, it is tertiary and residential sectors. For tertiary sector, cooling represented 12% of electricity demand in 2015. Some disparities are observed, with only 7% of electricity demand for education buildings compare to 20% for offices. Concerning residential sector, cooling was only 2% of total electricity consumption in 2015 [34]. Those figures are used to estimate the part of cooling and essentially electricity reduction due to cooling effect from trees. So according to the kind of buildings cooled down, the part of cooling in electricity consumption will vary between 2% and 12% (or even 20%).

4.2 MODEL USED

This section focus on models used to estimate impact of urban greening on outside temperature and air quality.

4.2.1 Temperature reduction model

Temperature reduction thanks to trees is only local. It is made at the tree level with a certain intensity (diﬀerence between the lower temperature reached and average surrounding temperature), and then the cooler air is scattered away and mixed with hotter air, which mitigates the positive eﬀect.

Temperature reduction comes from two different phenomena: shading effect and transpirative cooling. The shading effect prevents rays to reach the ground and buildings, so it decreases

2. The relative emissions used for calculations are: 960t.GWh−1 for coal, 670t.GWh−1 for oil, 460t.GWh−1 for gas, 980t.GWh−1 for bio-energies and 0t.GWh−1 for other sources [33].

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the heat storage. Transpirative phenomenon is more or less the same as human transpiration, water is released from trees and then converted into vapor. This converts energy from the sun into latent heat instead of sensible heat, which means that temperature stops to increasing. The global intensity of cooling is higher with larger canopy and denser one, and it can vary between 0.4◦C to 3.0◦C depending on the hour, the area and the kind of tree [3,5, 35, 36].

Once the air cooled down, advection and diffusion allow to spread this cool air from trees to its surroundings. The typical diffusion length depends on the size of the green space studied and its type. For parks, the effect extend up to one park width from it. For street trees this effect keeps strong within 30m. It can be modeled by a Lorentzian curve, given by the following:

 l2 ∆T0 2 if x > d ∆T (x) = l2+(x−d) (4.1) ∆T0 if x < d where ∆T0 is the maximal temperature decrease obtained at tree level, x the distance to the tree and l the characteristic length of decay (when ∆T0/2 is reached, neglecting the distance d), and d the canopy radius. With typical lengths of l=33m and d=5.64m, and an maximal intensity of 2.15◦C, the cooling eﬀect reaches a decrease of 0.23◦C after 100m.

Other models exist, based on empirical studies, with exponential decrease or linear one. To compare models, data used for temperature decrease are summarized in table 4.1. With those values the exponential model developed is the following [5]:  1.140∆T (0.977x) if x > 5.64 ∆T (x) = 0 (4.2) ∆T0 if x < 5.64 The exponential expression is valid only beyond the tree canopy when temperature starts to decrease (here for x = d = 5.64m). It is considered that under the canopy the temperature is constant. And the linear expression is:

 ∆T0 if x < 5.64  ∆T (x) = ∆T0 − 0.0249 (x − 5.64) if 5.64 < x < 91.99 (4.3)  0 if x > 91.99 Results obtained with those models are compared to the real ones on table 4.1. Graph 4.2 illustrates the different models. It can be observed that the linear model is closed to real values only near green space and far away from it. The exponential model is more accurate in the intermediate zone, but the lorentzian one seems to be the closest to measured values. This is why this one is used to determine the cooling effect in the modeling. However, values used for the different models are valid for small size green spaces like street trees. Larger sites like parks have longer distance of influence, going up to 2km [5].

3. Using the values: l =33m and d =5.64m

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Model Tree (0m) 20m 40m 60m 80m 100m Average measured values 2.15 1.77 1.04 0.67 0.30 - Lorentzian model 3 2.15 1.81 1.03 0.58 0.35 0.23 Exponential model 2.15 1.54 0.97 0.61 0.38 0.24 Linear model 2.15 1.79 1.29 0.79 0.30 0

Table 4.1 – Measured and modeled values of cooling eﬀect from one tree linked to distance x from it (∆T (x) in ◦C). [5]

Figure 4.2 – Comparison of the diﬀerent models for the cooling eﬀect with real values [5]

4.2.2 Maximal temperature reduction model

In the temperature decrease model, it is necessary to know ∆T0, the temperature decrease at trees position. Essentially, it is important to have variations of ∆T0 along time. Indeed, the model for temperature decrease deals only with spatial variations and not temporal ones. Time aspect is contained in the parameter ∆T0 which will vary with days and even hours. This is why it is necessary to get a model to predict ∆T0.

• Origin of the phenomenon Few studies look at variations of temperature decrease with time, but the shape of temperature decrease during a day is quite similar to the shape of solar radiation [37]. During the night, there is nearly no temperature difference but during the day it can be quite high (up to 3◦C) when the solar radiation is at its maximum. To understand such a similarity, one must come back to physical reasons of temperature decrease. Indeed two phenomena are in action to explain it. The first one is the shading effect,

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which is directly linked to solar radiation. The link is quite obvious and so temperature decrease should vary like solar radiation with this phenomenon. The second effect is transpirative cooling. Once again the driving force of transpirative is radiation from the sun, which heat leaves [38]. But here, temperature as an impact too. This is why the impact of transpirative is greater during the day, but not zero during the night when temperature is still quite high. This second phenomenon explains the small tree cooling effect during the night when it is still hot outside. One last aspect to take into consideration is the limit of the temperature decrease from trees. Indeed, there is a maximum ∆T0, noted ∆Tmax, which can’t be overpassed. This is due to the limitation of solar radiation (since solar radiation can’t increase more than the maximal value of summer solstice) and limitation of transpirative effect which can’t absorbe all the energy. Transpiration needs water release and trees have a limited quantity of water and also an outflow limit. So, beyond a certain temperature no more increase in cooling can be done. Consequently it is necessary to have a way to model temperature decrease from trees, essentially impact by solar radiation and outside temperature. Value of ∆T0 from trees must vary with the three parameters, ∆Tmax, solar radiation and outside temperature T . It can be assumed a linear relation with ∆Tmax,(∆T0 ∼ ∆Tmax).

• Solar radiation part

Looking at solar radiation, its intensity is linked to the solar zenith angle θz (more precisely to its cosine, represented along days and hours in Paris on ﬁgure 4.3), which is given by:

cos (θz) = cos (φ) cos (δ) cos (ω) + sin (φ) sin (δ) (4.4) with δ the declination angle, φ the latitude of the position point and ω the hour angle. The declination and hour angles vary with days, they are given by: n − 173 π δ = arcsin 0.39759cos 2π and ω = (t − 12) (4.5) 365 12 s with n the gregorian calendar day and ts the solar time (linked to local clock time and position of the studied point) [39]. This leads to a value of ∆T0 varying with the cosine of the solar zenith angle (when its value is positive), which is itself linked to the days of the year and the hour time. Consequently this aspect will take into consideration time in the model.

• Outside temperature impact Last parameter to include is the average outside temperature (temperature without trees), to take into consideration non zero value of ∆T0 during some hot nights. But this phenomenon occurs only when the temperature is high enough, higher than a starting temperature noted Ts. Furthermore the higher is the temperature, the higher is the effect so it should vary with T −Ts when T > Ts and being null when T is lower than Ts. It should also be kept in mind that there is a limit temperature (noted Tl) beyond which one the effect can’t be increased anymore. As described before, the transpirative effect is limited by water consumption from the tree.

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1 0,8 0,6 0,4 0,2

Positive part of cos(θz) 0 1 20 39

58 21 77

96 18

115 15 134

153 12 172 191

210 9 229

248 6

0-0,2 0,2-0,4 0,4-0,6 267

286 3 305

324 0 0,6-0,8 0,8-1 343 362

Figure 4.3 – Cosine of Paris solar zenith angle along days and hours (on the positive value, representing solar radiation shape)

Another point to take into consideration is that this effect is very low when temperature are close to the starting temperature Ts, but quite important when it is closer to the limit temperature Tl. Globally the result linking the effect to temperature should be a curve with a S shape. Such a function can be obtained with a sigmoide (1/(1 + exp(−x))). Here the exponential must contained T − T ∗ variations, where T ∗ is the value when the effect is at 50% ∗ of its maximum. Consequently, T = (Ts + Tl)/2, and the curvature is determined by a factor S such as: 1 ∆T ∼ (4.6) 0 T −T ∗ 1 + exp − S

S is determined to have a value of the eﬀect lover than y% of the maximum eﬀect when T = Ts and higher than (1 − y)% of the maximum value when T = Tl. Both give the same condition on the maximum value of S which is: T − T S < l s (4.7) 2ln (1/x − 1)

For y = 10%, equation 4.7 gives S < (Tl − Ts)/4.39, and for y = 5%, S < (Tl − Ts)/5.89. ◦ ◦ As an example, with Ts = 18 C and Tl = 42 C, S = 6 gives y = 11.9% and the shape of the curve is the one of ﬁgure 4.4.

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0,9

0,8

0,7

0,6

0,5

0,4

0,3

0,2

0,1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Temperature (°C)

Figure 4.4 – Sigmoide curve to model variations of ∆T0 with temperature T

• Global model The two different phenomena must be taken into consideration into the global model. As temperature decrease from trees occurs during hot nights without solar radiation, but is higher during days with radiation and hot temperatures, it is assumed that both effects are cumulative. Furthermore, for small temperatures under Ts, there is nearly no temperature decrease. This gives the following formula for the total reduction at tree level:  ∆Tmax 1  max (cos (θ ) ; 0) + ∗ if T > T 2 z 1+exp − T −T s ∆T0 = ( S ) (4.8) 0 if not Having no experimental measurements to verify the theory, only the shape of the curve and average data from literature can be used to validate the model. Figure 4.5 and 4.6 represent the value obtained for ∆T0 (and also solar effect part and temperature effect part of ∆T0) between respectively April 21st-April 27th and July 30th-August 9th. Those figures are obtained with ◦ ◦ ◦ the following data: S = 6, Ts = 18 C, Tl = 42 C, ∆Tmax = 3 C. Temperatures are the ones of the region around Paris (shown in appendixC). Coordinates used for the solar zenith angle are Paris’ ones: Latitude : 48◦51’12” North; Longitude : 2◦20’55” East. Result obtained during summer days is quite good, with greater impact of solar radiation effect (figure 4.6). During winter days, the effect is null so the model is appropriated too. However during the transition period (with temperatures around the starting temperature Ts), some days will be overestimated and others underestimated, as shown on figure 4.5. The model is far from being perfect, but it can be assumed that overestimations can be compensated (at least partially) by underestimations and conversely.

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2,5 35 Total effect Solar effect Temperature effect Temperature

30 2

25 C) 1,5 ° 20 C) ° ( 0

ΔT 15 1 Temperature (

0,5 5

0 0 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 0 4 8 12 16 20 Hour

Figure 4.5 – ∆T0 values (total, solar and temperature contributions) and temperature between April 21st and 27th

2,5 35 Total effect Solar effect Temperature effect Temperature

30 2

25 C) 1,5 ° 20 C) ° ( 0 T Δ 15 1 Temperature (

0,5 5

0 0 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 0 3 6 9 12151821 Hour

Figure 4.6 – ∆T0 values (total, solar and temperature contributions) and temperature between July 30th and August 9th

Looking along the whole year, the average temperature decrease at tree position ∆T0 is 0.19◦C. However, this ﬁgure takes into consideration absence of reduction during winter. If only days between June 1st and September 30th are taken into consideration, then the average value is 0.50◦C. Once again, no decrease during most of nights reduce the average. When looking ◦ on the same period between 6am and 9pm the average ∆T0 is 0.73 C, and between 10am and ◦ ◦ ◦ 6pm, it is 1.09 C. Like in literature, ∆T0 reaches between 0.4 C and 3 C. In addition, the set of temperatures used is not the one inside Paris, which would be higher and will increase in the coming years. Consequently, ∆T0 should be even higher with temperatures inside Paris during next years.

33 Master’s Thesis

4.2.3 PM removal model

Trees have benefits on PM concentration, but, just like for temperature reduction, this is located around trees. This is due to the process how PM are removed. Airflow is coming with a certain concentration to the tree. Passing through the canopy of the tree, some particles are removed, then the flow with a reduction in PM concentration continues beyond the tree. However, this air flow will mix with the one that didn’t pass through the canopy and so the concentration will increase again until total uniformization that will bring back concentration about to its initial level (figure 4.7)[3].

Figure 4.7 – Process of PM removal by trees [3]

Removal of PM is made by dry deposition, which means that particles deposit on a surface. This is done essentially on leaves, with a permanent ﬁxation for many particles and a temporary one for others. Dry deposition increases with diﬀerent factors, and essentially when concentration of PM and leaf area are higher. PM removal rate is measured by the deposition velocity (in cm.s−1). This velocity depends on tree species too, but the mean deposition veloc- −1 −1 −1 ity is about 3.0cm.s for PM10 (going from 1.1 to 4.9cm.s ), and about 0.13cm.s for PM2.5 −1 (going from 0.06 to 0.19cm.s ). From this deposition velocity vd, it can be estimated the pol- −2 −1 −3 lution removal F = vdC (in µg.m .hr ) where C is the concentration in µg.m . This clearly indicates the linear relationship with local concentration and so a higher removal with a higher concentration: ∆C0 ∼ C∞ where ∆C0 is the PM concentration reduction at tree level and C∞ the PM concentration without trees (far away from trees) [40]. This leads to a reduction of PM concentration (∆C0/C∞) by 10% to nearly 40% according to the place studied, the type of green space and the season (with an average value of 24%) [3].

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After particles being trapped, PM concentration is lower in the outﬂow, however redilution occurs and makes concentration increase again. According to literature PM concentration reduction stay strong within 30m, and can be observed up to 300m [3]. To account for such a behavior a fonction like a gaussian could be used but the decrease is too fast. That is why the positive impact can be modeled by an exponential law or a Lorentzian one. Some literature use the exponential one [41] but in this report a focus is made on the Lorentzian (to take into consideration similarities with temperature reduction in subsection 4.2.1) such as:

 l2 ∆C0 2 if x > d ∆C (x) = l2+(x−d) (4.9) ∆C0 if x < d where ∆C0 is the maximal reduction in PM concentration at tree level, x the distance to the tree and l the characteristic length of decay (when ∆C0/2 is reached, neglecting the distance d), and d the canopy radius. For typical lengths chosen l =33m, and a canopy radius of d =5.64m, it is gotten that ∆C(x) = 0.012∆C0 for x=300m,. Which means that after 300m, only 1.2% of the maximal reduction is still present, so it is about the end of the beneﬁcial area.

To summarize in both cases, for PM removal and temperature decrease, two aspects must be considered, the intensity of phenomena, and their typical lengths of action. They are the values that must be chosen for the model, depending on the species selected, the city chosen, and physical characteristics.

4.2.4 Spatial model and impact of the number of trees

To take into consideration spatial variations of temperature and air quality, the selected district (described in subsection 4.1.1) must be divided into sub-areas (cells of 10m by 10m). Values for temperature and PM removal on each cells are the ones at the center of the cells. It is assumed that values are historical ones over which it is added variations due to beneﬁts from trees.

It could be determined the impact of a multi-trees effect. A park has a longer distance effect than a single tree, but the overall benefits are not the sum of benefits from all trees. Literature shows that for temperature reduction, with few trees, the difference is not really high. More precisely, during day (when solar radiation effect is the most important one), there is nearly no difference. Only at night when temperature effect is dominant, benefits can double going from 1 to 10 trees at the same position [37]. For air quality, it can also be concluded that more trees would probably improve the area of effect and improve PM removal. However, having no data on that subject and looking only at few trees systems (no parks but only trees and plants in street where district cooling is implemented), it can be assumed that the number of trees will not change that much the results. Each tree is considered independently of others.

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This is why, calculations on a cell are based on historical values on this cell, aﬀected only by the eﬀects of the closest tree.

4.3 DATA USED

4.3.1 Tree selection

Among common trees is Paris, one of them is the Platanus x hispanica, more generally called London plane. It is used as a reference for speciﬁc data needed about trees.

First aspect needed in the modeling is the size of trees and essentially of canopy. It is assumed that the mean radius is about 6m, a few smaller than in literature (going from 2.7m to 14.8m) [42]. This corresponds to large London planes, like in many district of Paris.

About sequestration value for CO2, it can be obtained from the quantity of carbon stored in trees. In cities, trees can store from 0.08 to 0.52kgC per m2 of tree canopy per year [43]. The value depends on the type of tree, its age, its location, ... To determine the quantity of CO2 captured by trees, it is used the ratio of CO2 over carbon molecular weight. For carbon it is 12.0g.mol−1 and for carbon dioxyde it is 44.0g.mol−1, which gives a ratio of 44.0/12.0=3.67. Consequently the quantity of CO2 captured every year per square meter varies between 0.293 to −2 −1 1.906 kgCO2.m .year . This result is similar to other studies where carbon dioxyde seques- −2 −1 tration varies from 0.279 to 1.903 kgCO2.m .year [44]. Among those values, the Platanus −1 x hispanica is on the top ones, with about 250kgCO2.year per tree [45], corresponding to −2 −1 4 1.9kgCO2.m .year .

4.3.2 Physical values

For PM removal model, two kind of data are needed, the intensity ∆C0/C∞ and the decay length l. For ∆C0/C∞ the values kept are 10% and 40% for respectively winter and summer. The decay length chosen is l = 33m, to be close to experimental measurements.

For temperature reduction model, just like for PM removal it is needed the intensity of maximal temperature decrease (∆Tmax) and the typical decay length l, but also the starting temperature eﬀect Ts, the limit temperature eﬀect Tl and the factor S. ◦ The range for ∆T0 is between 0.4 to 3 C, so ∆Tmax should be chosen to be between those two values. For l, the same value as the one for PM removal is chosen, l = 33m. Other data ◦ ◦ selected are: Ts = 18 C, Tl = 42 C and S = 6. 4. Value corresponding to a tree of about 6.5m of canopy radius

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Concerning solar radiation part of the eﬀect, the chosen position is the one of Paris (latitude: 28◦51’12” North; longitude: 2◦20’55” East).

For both models, historical data of temperature and PM concentrations in Paris are needed. Data used come from stations in Paris or near Paris. Data are sum up in appendixC.

For spatial characteristics, the cells are supposed to be squares of 10m side. This length is under the characteristic decrease lengths for cooling and PM removal from trees. For the whole area described in subsection 4.1.1, it gives a total of 900 cells. Concerning time division, steps of one hour are used. This gives an overall complexity for calculations of 900×24×365 = 7 884 000, which is quite high but necessary to keep spatial en temporal variations acceptable.

4.3.3 Economical values

To estimate return on investment, prices are needed, the ones linked to trees for investments, but also from different benefits like energy savings or air pollution improvement. Some online tools existe to value trees, it is the case of the "National Tree Benefit Calculator" [23] (based on i-Tree tool) that gives a first estimation of the money saved thanks to one tree. Such a tool gives benefits from stormwater, property value increase, electricity and gas consumption decrease, air quality improvement and CO2 sequestration. Values of the "National Tree Benefit Calculator" can be used to estimate PM removal benefits. In their calculations, PM10 and PM2.5 removal values are based on the prices of respectively 9 016e.ton−1 and 87 102e.ton−1.

Today carbon has a direct price, due to taxes, in order to reduce emissions. In France, in 2015 the price was 14.50e.ton−1, in 2016 it was 22e.ton−1 and it should continu to increase. Objectives are 56e.ton−1 in 2020 and 100e.ton−1 in 2030. Those prices, are used to estimate indirect beneﬁts from trees due to carbon sequestration [46].

Savings can also be made from saved energy, essentially electricity for cooling systems. Prices used are the ones of the French market, which means an average value of 0.15e.kWh−1 (ENGIE sells its electricity between 0.13640e.kWh−1 and 0.16200e.kWh−1).

Concerning trees, the initial investment in Paris is about 3300e per street tree (going from 2900e to 4100e)[47]. Maintenance is about 57.0e.tree−1.year−1, which include 48.3e from wage cost for maintenance, 3.9e from external maintenance services, 4.7e from wage cost for tree diagnostic and 0.1e from external diagnostic services [48].

4.3.4 Summary of all data used

Tables 4.2 and 4.3 sum up the diﬀerent data used for modeling. If there is no speciﬁcation given, it is the mean value that is used in the model.

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Data Unit Mean Maximum Minimum Canopy size m 6.6 14.8 2.7 LAI 3 1 8 Carbon sequestration kg.m−2.year−1 1.9 −1 PM10 Deposition velocity cm.s 3.0 4.9 1.1 −1 PM2.5 Deposition velocity cm.s 0.13 0.19 0.06 ∆C0/C∞ % 24 40 10 Concentration decay length m 33 ◦ ∆Tmax C 1.5 3 0.4 Temperature decay length m 33 ◦ Starting temperature Ts C 18 ◦ Limit temperature Tl C 42 Sigmoïde coeﬃcient S 6 −1 CO2 emissions from electricity g.kWh 51.5 67 35 Cells size m2 100 Paris Latitude ◦ 28◦51’12” North Paris Longitude ◦ 2◦20’55” East

Table 4.2 – Data used for modeling

Prices Use value Maximum Minimum Carbon taxes (e.ton−1) 22 100 22 Electricity price (e.kWh−1) 0.15 0.13640 0.16200 −1 PM2.5 price (e.ton ) 87 102 −1 PM10 price (e.ton ) 9 016 Tree planting (e.tree−1) 3 300 2 900 4 100 Tree maintenance (e.tree−1.year−1) 57.0

Table 4.3 – Prices used to estimate tree costs and beneﬁts

4.4 METHODOLOGY

Modeling is done using the different assumptions, models and data described before. Essen- tially three main points are evaluated, benefits from temperature decrease, benefits from PM removal and economics. To do so, scenarios are compared, one without tree supposing that existing trees are removed, one with existing trees and some with more trees. This allows to estimate variations of benefits with the number of trees.

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4.4.1 Space occupation

In the selected area of study, there are already some trees, but clearly not enough. Indeed, the total area is 90 000m2, so with an average density of 23 159 inhabitants per km2, it means that there are about 2000 inhabitants. However, according to French legislation (section 2.4), the goal is to reach a value of 10m2 of green spaces per capita. Considering that trees represent most of it, it would be necessary to have 17 000m2 of tree canopy surface (85% of total green spaces). With trees having 6m of canopy radius, it implies to have around 150 trees in the selected area. Those trees also need some space, and not all streets are large enough. Consequently only largest ones are selected for tree planting. This gives the maximum tree planting scenario represented on ﬁgure 4.8. The total number of trees is 148, with 36 existing trees (taking into consideration the ones close to the area), and 112 trees to add.

Figure 4.8 – Map of studied area with trees position and district cooling network

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4.4.2 Evaluate temperature decrease benefits

To evaluate benefits of cooling from trees during a whole year (essentially during summer), it is estimated the difference in cooling between different scenarios with variation in tree number or other parameters. To do so models presented in section 4.2 are used with values from section 4.3. As described in subsection 4.2.4, space is divided in cells, with some containing trees, others buildings or infrastructures where the district cooling system operates. For each cell, it is calculated the temperature reduction from trees, and this for every hour and every day along one year.

Once the diﬀerence in temperature decrease obtained, it is necessary to calculate energy savings from cooling. To do so an energy balance must be done on buildings. At equilibrium, cooling has two purposes, compensate losses from buildings and cool down fresh air needed to ˙ change the air of rooms. Consequently, energy from losses (noted Qlosses) and energy to cool ˙ ˙ down fresh air (noted Qair) are equal to cooling (noted Qc) provided by the district cooling system.

Losses are due to exchange with the outside since walls and roofs are not adiabatic. However it is necessary to estimate average losses of Parisian buildings. According to studies it varies essentially with the year of construction of the building [12]. The worst performances were just after world wars when quick reconstruction was necessary. ˙ −2 −1 In the selected area, the average surface losses are about Qs = 2.6 W.m .K . This value is the average one per Kelvin and per square meter of building parish (include roof, walls and all openings like windows and doors). As value per unit volume is necessary, compacity of buildings can be used, where compacity is the dimensionless number deﬁned by the ratio: c = S/(V 2/3) where S is the parish surface and V the building volume. In this study, compacity used is the ˙ average one in Paris, which means that c = 11.1 [12]. Values in Paris for Qs and c are summed up in appendixD, taking into account variations with construction period. ˙ ˙ 2/3 −1 This gives average losses per Kelvin of: Qlosses/K = QscV in W.K .

To change the air of a room, it is necessary to take new air from the outside and cool it down to the wanted inside temperature. The purpose of changing air is to maintain oxygen level constant, reduce CO2 concentration (due to human respiration), decrease humidity and smells, and evacuate air heated by human, computers or other devices. It is estimated that the air of a room must be changed between 5 and 10 times during an hour to have a proper system. ˙ −1 This gives average air changing requirement of: Qair/K = D.Cair in W.K , where D is air 3 −1 −3 −1 ﬂow (in m .s ) and Cair = 1 184J.m .K. the thermal capacity of air (considered constant with its value at 300K). With an air change of 7 times per hour, D = 7V/3600, which gives ˙ 0 −1 0 −3 −1 Qair/K = 7.V.Cair in W.K , where V is the room volume and Cair = 0.3289 W.m .K. .

Normally those values should be multiplied by the temperature diﬀerence between the inside and the outside of the building. However, as a comparison between diﬀerent scenarios is wanted,

40 Master’s Thesis

only the temperature difference between scenarios is needed to calculate the energy demand difference. This gives the final reduction in cooling power demand (in W):

˙ ˙ ˙ ˙ 2/3 0 Qc = Qlosses + Qair = QscV + 7.V.Cair ∆Tc (4.10) ˙ where ∆Tc is the temperature reduction due to tree that actually reduces needed cooling, Qs losses due to thermal exchanges from surfaces, c the building compacity, V the building volume 0 and Cair related to thermal capacity of air. About the value used for ∆Tc, it is given by:  min(∆T ; Tout − Tin) if Tout > Tin ∆Tc = (4.11) 0 if Tout ≤ Tin where Tout is the outside temperature without trees, Tin the inside temperature wanted, and ∆T the real temperature reduction from trees at the studied point.

For each cell and each hour of a year, it is estimated the reduction in cooling power demand ˙ Qc, which gives the total reduction of energy demand for cooling during the whole year Qc, simply by summing all values. Then characteristics of the district cooling system are used to calculate the input energy demand reduction (with a coeﬃcient of performance of 3 5) and all savings made. This includes economical savings, energy saved and CO2 emissions avoided.

4.4.3 Evaluate PM removal benefits

Just like to estimate benefits from temperature decrease, for PM removal, it is estimated the difference between different scenarios with variation in tree number or other parameters. Models and data presented in section 4.2 and 4.3 are used. Space modeling aspect is the same than for temperature decrease one. For each cell, it is estimated the decrease in concentration, and global PM removal is calculated.

For concentration reduction, model is used, giving for each cell a value, and then an average is made on the overall space studied. Time variations existe since ∆C0/C∞ vary with seasons (it is linked to leaves number). The higher value is chosen for summer (from March to October) and the lower one during winter (from November to February). This approximation neglects variations between the two phases, when new leaves appear and when leaves fall. However, those phases are short compare to time periods with or without leaves, so they can be neglected.

To estimate global PM removal in quantity, as the heigh of inﬂuence of trees is not well known, only deposition velocity is used. Indeed, as described in subsection 4.2.3, pollution −2 −1 removal is F = vdC (in µg.m .hr ), which gives the total particulate removed by trees when multiplied by the surface area. This should be the area of leaves, which is linked to canopy

5. As data from Climespace are conﬁdential, the real value is not used.

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surface by the Leaf Area Index (LAI). LAI is a dimensionless quantity deﬁned as leaf area over ground cover area. Consequently total removal per tree per second is about (in g.s−1):

m˙ PM = (LAI.S)vdC (4.12) where S = Πd2 is the tree surface area (in m2, d is canopy radius). This equation must be integrated over time to determined total quantity of PM removed. LAI value changes with seasons and can vary from 1 to 8 for Platanus x hispanica. It is considered that it has two values, on for winter time (from November to February) and one for summer time (from March to October). It is chosen the average values for LAI: LAIsummer = 4 and LAIwinter = 1 [49].

4.4.4 Economics

A simple cost-benefits analysis is done here. Costs are the ones linked to tree investments and maintenances, while benefits are many and two main approches are carried out (using values from table 4.3). First, adopting ENGIE point of view: only direct benefits are included, linked to less energy demand for cooling. Second, with a more global point of view: which would include economical benefits from PM removal and CO2 sequestration.

Indicators to evaluate the economics of the project, are the discounted Payback Period (PBP) and the Net Present Value (NPV). It has been assumed that the interest rate is 1.60% for all investments made and is constant. This rate is the one of the building French market today. Furthermore, all investments are made the ﬁrst year, then only maintenance is linked to costs.

The PBP (in years) is calculated with relation:

ln(B − C) − ln ((B − C) − i.C ) PBP = 0 (4.13) ln(1 + i) where B is the average annual beneﬁts, C the average annual costs and C0 the initial cost.

The NPV (in euros) is given by the relation:

"(1 + i)n − 1# NPV = (B − C) − C (4.14) i.(1 + i)n 0 where n is the number of years for the project.

42 Chapter 5 Results of modeling

5.1 TEMPERATURE DECREASE AND COOLING REDUCTION

With chosen values 1 and models described in chapter4, temperature decrease at tree level is in average 0.19◦C along a year, and 0.50◦C during summer (from June 1st to September 30th). Maximum value reached is 2.14◦C. Furthermore, when looking the average only during summer days (from 6am to 9pm) it goes up to 0.73◦C, or even 1.09◦C (between 10am and 18pm). Those ﬁgures corresponding to reduction close to trees can be compared to the average ones on the whole area in a maximum scenario with 148 trees. Indeed in the selected district, global reduction during the year is about 0.18◦C, 0.47◦C during summer time, 0.69◦C during summer between 6am to 9pm and 1.02◦C from 10am to 6pm. Figure 5.1 compares real temperature variations along the year with apparent temperature for buildings which corresponds to real temperature minus average cooling from trees in the area selected 2. For a particular building, the apparent temperature could be higher if trees are closed to it, or lower if they are far from it. It is also presented average value taking into consideration only the hours between 10am and 9pm.

With those values from temperature reduction, energy saved from cooling will still vary with the number of trees planted. Figure 5.2 gives the thermal energy saved thanks to trees according to the number of trees (with an inside wanted temperature of 20◦C for buildings). As it can be guessed, with more trees, reduction increases. However, it is limited by a maximum value and the more trees it is added, the less the effect increases (the curve is concave). This is due to two aspects: first, model doesn’t take into consideration correlations between trees, which would improve the effect (even if it is not additive). Second, spatial aspect limit tree influence,

◦ 1. ∆Tmax=3 C in the model. As both eﬀects (outside temperature and solar radiation ones) don’t reach ◦ their maximum, ∆T0 stay in the wanted range, between 0.4 to 3 C. 2. Temperatures on ﬁgure 5.1 are obtained with averages. Spatial variations are averaged on the whole area selected and temporal variations are averaged over 10 days.

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Apparent temperature Real temperature Apparent temperature from 10am to 6pm Real temperature from 10am to 6pm

20 C) °

15 eprtr ( Temperature

0 1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241 251 261 271 281 291 301 311 321 331 341 351 361 Day number

Figure 5.1 – Real and apparent temperatures for buildings in the selected area, taking into account daily variations and variations from 10am to 6pm (with 148 trees) like shading eﬀect that will not increase with more trees at the same position. Consequently, eﬃciency of tree cooling decreases with tree number, but should be higher in reality than with the model developed.

Figure 5.3 represents spatial variations of temperature decrease in the selected area on August 1st at midday. This ﬁgure is obtained with 148 trees (maximum scenario). Temperature decrease goes from 1.33◦C to 2.03◦C. Higher values are the ones near trees, however the decrease is quite important on the whole area. This is due to the high density of trees and a repartition as best as possible considering spatial constraints. In a maximum scenario, adding 112 trees in addition to the 36 existing ones, have a real beneﬁts for the selected district. Those trees allow to decrease energy consumption for cooling of nearly half of it (49.93% decrease compare to a no tree scenario consuming 2 276.03MWh.year−1). This represents a reduction in total electricity consumption for buildings of 3.51% 3. Such a result should encourage urban greening development and not the reverse. Even with less investments, but with well positioned trees, high results are obtained for reduction in cooling, closed to the maximum scenario.

Those results are obtained with an inside wanted temperature of 20◦C. However, large variations can be observed going from 19◦C to 25◦C for this inside temperature. Figure 5.4 represents the thermal energy saved for cooling and the percentage of cooling reduction when temperature inside buildings varies from 19◦C to 25◦C (in a scenario with 148 trees). First, it can be seen that thermal energy saved decreases when inside temperature increases.

3. Where cooling represents about 7% of electricity consumption.

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1200

1000

2,10 800 2,00 C) ° 1,90

1,80

600 1,70 1,60 1,50 1,40 290 400 decrease ( Temperature 270 1,30 250

10 230 30 Cooling energy saved (MWh/year) 210 50 190 70 170 90 150 200 110 130 130 110 150

170 90

190 70

210 50 230 30 0 250

270 10

0 20 40 60 80 100 120 140 160 290 Number of trees 1,30-1,40 1,40-1,50 1,50-1,60 1,60-1,70 1,70-1,80 1,80-1,90 1,90-2,00 2,00-2,10

Figure 5.2 – Eﬀect of tree number over energy Figure 5.3 – Spatial variations of temperature saved for cooling (with inside temperature at decrease on August 1st at 12am (in a maxi- 20◦C) mum scenario with 148 trees)

This is du to the fact that with higher inside temperature, lower cooling is needed during the year, and so lower savings are made. Furthermore, when looking at the curve, it is slightly convex which means that when reducing temperature inside buildings from 25◦C to 24◦C will need more cooling addition during a year than going from 20◦C to 19◦C. And this can be explained by the fact that more hours during the year have temperatures close to 20◦C than 25◦C (as it can be seen on ﬁgure C.3 in appendixC). The curve would be convex from 15 ◦C to 34◦C and concave from -5◦C to 8◦C. Concerning proportion of energy saved, it increases with higher inside temperature. Indeed, as less cooling is needed, the part coming from trees takes a higher proportion, and could hypothetically reached 100% when inside wanted temperature is close to the highest outside temperature (but such a situation means to stop all climatisation).

5.2 PM REMOVAL AND AIR QUALITY INCREASE

Figure 5.5 represents percentage of PM concentration reduction (compare to its maximum value), and this during summer and winter (with data and models described in chapter4). Variations occurs during the year with quantity of leaves changing. Just like for temperature decrease, it can be seen that there is a maximum limit. Once again, it can be explained by the model, but also by spatial constraints. Indeed, trees need space and this constraint limits the canopy equivalent area. Furthermore, deposition decreases when concentration decreases. For any additional tree, the apparent concentration will be lower (as trees already present reduce it), thus deposition too. This leads to the fact that additional trees will have lower impact to

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1600 90 Thermal energy saved % of cooling reduction

1400 80

70 1200

60 1000 50 800 40 600

30 % of cooling reduction

400 Thermal energy saved (MWh/year) 20

200 10

0 0 19 20 21 22 23 24 25 Inside temperature wanted (°C)

Figure 5.4 – Eﬀect of inside wanted temperature over energy saved for cooling (with 148 trees) reduce PM concentration than the ones present before. Figure 5.6 shows spatial variations of PM concentration reduction for both periods, winter and summer. Values during winter are quite low compare to summer ones, because of the absence of leaves. Peaks are also lower during winter since there are less diﬀerences between places with or without trees.

40 Winter case Summer case 35

% of PM concentration 10 reduction

0 0 20 40 60 80 100 120 140 160 Number of trees

Figure 5.5 – Eﬀect of tree number over PM concentration decrease

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Figure 5.6 – Spatial variations of PM concentration (in % of maximum value) during summer and winter (in a maximum scenario with 148 trees)

For the maximum scenario with 148 trees in the selected area, high air improvement is done. Over one year, 27.7kg of PM2.5 and 1.04ton of PM10 are removed. Trees can sequester more than 31.8ton of CO2. In addition, with energy consumption decrease for cooling, 19.5ton of CO2 emissions are avoided. This leads to a total of 51.3ton of CO2 avoided or captured during one year. Consequently, high air improvement is observed, doing nothing than taking care of trees.

5.3 ECONOMICS

Concerning economics, costs are linked to tree planting and maintenance, while beneﬁts depend on the point of view. If owner of district cooling decides to invest in urban greening, his beneﬁts will essentially be linked to energy consumption decrease. Initial investment to add 112 trees (for the maximum scenario) is about 370 000e. In addition, every year, it is necessary to spend about 8400e for tree maintenance (including the 112 new trees, but also the 36 existing ones). With all this investment, the money saved thanks to decrease in energy consumption

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for cooling, is 57 000e per year (with a temperature inside buildings of 20◦C). With those values, the Pay Back Period (period of time to refund the initial investment, taking into consideration time value of money), is about 8 years. Furthermore, for a long terme project, of 30 years, its Net Present Value is 776 000e.

However, variations occur for those two indicators (PBP and NPV) with the number of trees. Indeed, as seen in section 5.1 and 5.2, benefits vary with tree number and are not infinite. This explains the maximum observed for NPV on figure 5.7. If only economics are at stake, it would be preferable to stop investing after 90 new trees. On figure 5.7, the PBP is zero when the number of trees is under 36 since there are only existing ones, so no need to invest.

1200 9

Net Present Value Pay Back Period

1000 7,5

800 6

600 4,5 NPV (k€) PBP (years)

400 3

200 1,5

0 0 0 20 40 60 80 100 120 140 160 Number of trees

Figure 5.7 – Eﬀect of tree number over Pay Back Period and Net Present Value (after 30 years) for District Cooling owner if he invests in urban greening

Beneﬁts from trees can’t be limited to this aspect, their impact for society could be included like air improvement. When having a more global point of view for economics, taking into consideration PM removal and CO2 sequestration, NPV increases and PBP decreases. In a maximum scenario (148 trees and 20◦C inside buildings), beneﬁts every year for air quality are:

— 430e from CO2 emissions avoided thanks to energy consumption decrease for cooling;

— 700e from CO2 sequestration by trees;

— 2410e from PM2.5 removal by trees;

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— 9350e from PM10 removal by trees. This leads to a total of 12 890e benefits per year, to add to the 57 000e from cooling reduction. Consequently, air quality accounts for 18.4% of benefits is that case. And this leads to a reduction of PBP from 8.2 years to 6.4 years, and an increase of NPV (after 30 years) from 776 000e to more than one million euros (1 081 000e). Furthermore, it is not taken into consideration the fact that the price of air quality will increase in futur (carbon taxes are supposed to be multiplied by 4 before 2030). In the model, only a focus on some benefits is made, but looking at all of them could increase benefits by a factor two for society (or even more).

It can also be assumed that inhabitants of the selected area will pay for tree investments through local taxes. With about 2000 inhabitants in the area, it would implies that everybody pay an initial cost of 185e (for 112 new trees), and then 4.2e annually. This is not to high knowing beneﬁts from trees.

49 Chapter 6 Discussion

6.1 BACK TO RESULTS

Results obtained and described in chapter5 must be balanced as some assumptions were made.

First, impact of tree number is not taken into consideration (even if it can be neglected), and this would improve air temperature reduction, essentially during hot nights. But this would be compensated with overestimation of tree effect. Indeed, temperature reduction with the model will spread everywhere by the same way, even in inner courtyards. Courtyards are cooler in the model than in reality (except if they have trees). In addition, value for maximum temperature ◦ decrease ∆Tmax=3 C is issued from publications and should be instead measured inside Paris. Concerning solar part of ∆T0 for temperature reduction, shading from clouds and bad weather was not included in the model and this would probably affect results, but no measures were available to include it. It would be necessary to have access to total radiations (which depends a lot on micro climat) measured with a pyranometer, and not only beam radiations. Furthermore, for cooling, loses could be overestimated as they are the same on all walls, even the ones bonded to other buildings. But loses don’t represent the higher part of energy used for cooling, compare to cooling for air changing. Spatial aspect can also affect the curve giving variations of thermal energy saved according to the number of trees (figure 5.2). If all trees are located at the same place and not well distributed, their impact will be lower. Consequently some effects will overestimate reduction in cooling and other underestimate it. But globally results are rather in agreement with ones from other publications, giving a first good approximation.

When looking at beneﬁts from trees with regard to pollution, its was only taken into consideration CO2 storage, CO2 emissions decrease and PM removal. However, other pollutants are removed, like ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) or carbon monoxide (CO). Model developed for PM removal could be extended and adapted to those polluants. This would improve return on investment due to air quality improvement.

50 Master’s Thesis

For both, temperature reduction and PM removal, benefits have limits, which explains the maximum observed for Net present Value of tree investment. When looking only at cooling reduction, it seems useless to invest in more than 100 trees for the selected area, however, more trees would continu to improve air quality, at least for carbon storage. In addition, the model doesn’t really estimate benefits from a group of trees. Consequently investing in more than 100 trees is not a waste of money. According to the "National Tree Benefit Calculator" [23], return on investment obtained can be increased thanks to additional value from stormwater (which represents 37.4% of total benefits in average in United States for Platanus x hispanica), from property value increase (10.6% of total benefits) and from air quality improvement (in addition to CO2 sequestration and PM removal). Globally, benefits from trees could be doubled taking those other factors into account. Furthermore, the initial investment would be even more advantageous for buildings not cooled down by district cooling systems but with classical one-user cooling systems (that consume more). Local taxes could help to support the initial investment, by this way residents from the district would pay for trees as they are the ones having the highest willing to pay value (subsection 2.2.2). Here, district owner (like ENGIE), could just take part in the maintenance and make Paris pay for it. Benefits would be double for ENGIE, earn money from maintenance and cooling reduction. Last point, but not least, the impact on health has no price. Saving lives can not be included in economics but should be kept into consideration. With linear variations between PM concentration and percent increase in death (about 1.4% increase every 10µg.m−3 increase [50]), and the fact that 1◦C increase means 3.0% to 5.5% increase in all-cause mortality, it could be avoided during summer between 4.3% and 6.0% deaths due to those to aspects.

Globally, from results presented, it is totally worth to keep existing trees and develop even more. Even if modeling created for this thesis isn’t perfect, without many data on Paris and on its trees, it gives a first order of magnitude of urban greening value. However, real measurements are necessary to properly calibrate different parameters and improve models used. Furthermore, green infrastructure value should increase in the future, which means that figures obtained are probably lower limits.

6.2 GREEN VALUE WILL INCREASE IN THE FUTURE

In coming decades, there will be more people, probably more pollution and unfortunately less trees. With such a scenario for the future, value of trees will necessarily increase and will be far beyond the present value.

It is already known that CO2 taxes will increase in future (going from 22e per ton today,

51 Master’s Thesis

to 100e per ton in 2030), and it will be probably the same for other polluants. As pollution will probably increase too and that more and more studies show links between health problems and bad air quality in cities (cardiovascular and pulmonary diseases vary linearly with PM concentration), air quality value will be largely higher tomorrow than today. Heat waves are also supposed to happen more often, be longer and more intense. This would necessarily raise quantity of energy saved thanks to tree cooling. Hotter summers means also more deaths, which would raise value of cooling from trees. Other climat changes could improve value of green spaces, like stormwater if there are more and more violent storms in cities (like in June 2016 in Paris). Social and economical advantages of urban greening could also increase. Indeed, with more inhabitants in cities and higher pressure from grey infrastructures, green spaces would become increasingly scarce commodities. Willing to pay will increase as people would perceive more advantages of urban greening, even social and non-use values of urban greening would be more estimated. This is one of the reasons why green infrastructures are more and more linked to grey ones, like with green walls or roofs. In Paris, green infrastructures value starts to become more and more valued compare to grey infrastructures. Since July 20, 2016, cars are forbidden to circulate on some main lanes, instead it has become pedestrian places. It may be considered that green spaces will increase in such areas, and spread over roads. Even legislation requires more green spaces (150 trees in the area selected, which is the highest limit of the study), so it is likelihood that green and grey infrastructures will have to be mixed. Other decisions have been made by Paris city to improve green value, like giving free trees to inhabitants who have room to plant a tree. Furthermore, more and more people militate for greener and healthier cities, and some non governmental organisations are already acting for it too. Two of them have even complained against French state for not taking proper measures to ﬁght against pollution. Consequently, with higher consideration of green spaces, it would become more and more advantageous to invest and develop urban greening in near future.

6.3 URBAN GREENING, A NEW GOAL FOR ENGIE

In France, law linked to energetic transition pushes to accelerate actions in order to improve energy consumption, and this essentially for buildings. Tertiary and residential sectors are the most energy-consuming in France and energy eﬃciency is one of the main challenges. ENGIE wants to lead this transition, essentially thanks to new technologies, like connected objects. The Group wants to develop local solutions, which are innovative, linking all actors of energy transition. Their strategy is based on the "3D", which are decarbonized, decentralized and digitalized, thanks to new technologies. However, even if technologies could help, it is not the only solution. Urban greening is also a key point for future cities, and among those "3D", urban greening can answer to two

52 Master’s Thesis

of them, decarbonized (improving air quality) and decentralized (giving local cooling). This thesis demonstrates that trees could have a real impact in energy consumption. Reduction in cooling could even be higher using simple trees than sophisticated technologies. It can smooth the demand and reduce peaks during hot summers, which is a key point in energy management. Even if urban greening can’t replace district cooling systems, synergies between both should be used to improve global eﬃciency. Real value of green infrastructures has been more than underestimated during last decades and should be reconsidered for a better future.

Acquisition of a French leader in urban greening could help the Group to develop this new activity. As an example, acquiring ID Verde would imply to pay a certain amont of the EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) value (between ﬁve and ten times the EBITDA, this should be negociated), but could help to have an eﬃcient urban greening team inside the Business Unit France Networks. Synergies with district cooling are more than obvious, and developing an activity more rooted in society would be an asset for this Business Unit. Urban greening is already thought as part of district energy with green walls or roofs, but simple green spaces like trees, should not be forgotten.

53 Conclusion

This Master’s Thesis focused on urban greening and its synergies with district energy. Cities will welcome most humans tomorrow (it is estimated that more than 75% of human beings will live in town tomorrow compare to 54% today), this is why it is necessary to find a way to provide them an access to energy in the most environmentally friendly way. District energy is certainly part of the answer, and this including urban greening among it. The model developed for this thesis - focusing only on some advantages of urban greening - is already a proof that nature can’t be neglected even inside cities. The results show that benefits are not infinite but fair enough compare to investments made. In addition, some cities in the world, essentially in Asia, would get far more benefits with the same amont invested in Paris. What is obtained in this thesis could be generalized to many places. However, for each area a proper planting must be done with the proper species to select, and this would be different from one place to another. So, there is room to develop urban greening and activities linked to it. It is certain that this work is not perfect, measurements would be necessary, more data too, and models could probably be improved. However, this provides an overview of what nature can give to cities. It is also a recommandation for large firms like ENGIE to invest more in green structures, trying to mixed it more with grey technologies. Even though green infrastructures seem not that much profitable, if no investments are made today by choice, it will be probably done tomorrow having no other options.

54 Bibliography

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[14] District Energy in Cities - Unlocking the Potential of Energy Efficiency and Renewable Energy. UNEP, 2015. [15] Le Monde, A Lyon, Hikari, le premier îlot urbain à énergie positive, [On- line]. http://www.lemonde.fr/planete/article/2015/09/17/a-lyon-hikari-le-premier-ilot- mixte-intelligent-a-energie-positive_4761665_3244.html. [Accessed 12/11/2016]. [16] EnergyLand- District Coolign System (DCS), [Online]. http://www.energyland.emsd.gov.hk/en/building/district_cooling_sys/dcs.html. [Ac- cessed 27/12/2016]. [17] Diana E. Bowler, Lisette Buyung-Ali, Teri M. Knight, Andrew S. Pullin. Urban greening to cool towns and cities: A systematic review of the empirical evidence. Landscape and Urban Planning, May 2010. [18] Le verdissement montréalais: Pour lutter contre les îlots de chaleur urbains, le réchauffement climatique et la pollution atmosphérique, [Online]. http://www.cremtl.qc.ca/sites/default/files/upload/documents/publications/leverdissement montrealais.pdf. [Accessed 12/11/2016]. [19] ASTERES. Les espaces verts urbains. Union nationale des entreprises du paysage, May 2016. [20] WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide. World Health Organization, May 2006. [21] M. Santamouris, C. Cartalisb, A. Synnefab, D. Kolokotsa. On the impact of urban heat island and global warming on the power demand and electricity consumption of buildings - A review. Energy and buildings, 2015. [22] E.G. McPherson, R.A. Rowntree. Energy conservation potential of urban tree planting. Journal of Arboriculture, 1993. [23] National Tree Benefit Calculator, [Online]. http://www.treebenefits.com/calculator/. [Ac- cessed 25/11/2016]. [24] I.C.Mell, J. Henneberry, S. Hehl-Lange, B. Keskin. Promoting urban greening: valuing the development of green infrastructure investments in the urban core of Manchester. Urban Forestry & Urban Greening, 2013. [25] ID Verde, créer et entretenir le paysage, [Online]. http://www.idverde.com/actualites/38- idverde-poursuit-son-deacuteveloppement-en-grande-bretagne. [Accessed 12/11/2016]. [26] LES ECHOS, Atalian poursuit son développement international, [Online]. http://www.lesechos.fr/20/01/2015/LesEchos/21859-093-ECH_atalian-poursuit-son- developpement-international.htm. [Accessed 12/11/2016]. [27] Agence régionale des espaces verts (93). Rapport d’observations défintives, Exercices 2009 et suivants. Chambre régional des comptes d’Ile-de-France, December 2015. [28] La politique régionale en matière d’espaces verts, de forêts et de promenades en Île-de- France. Cour des comptes, Chambres régionales & territoriales des comptes, March 2016.

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[29] Paysageguide.comm, Appels d’offres et marchés publics, [Online]. http://www.paysaguide.com/site-93/vie-de-l-entreprise/activites-commerciales/appels- d-offres-et-marches-publics/article/appels-d-offres-et-marches-publics. [Accessed 12/11/2016]. [30] Les réseaux de chaleur en France, [Online]. http://reseaux-chaleur.cerema.fr/les-reseaux- de-chaleur-en-france. [Accessed 12/11/2016]. [31] J. Yang, Y. Chang, P. Yan. Ranking the suitability of common urban tree species for controlling PM2.5 pollution. Atmospheric Pollution Research, 2015. [32] Connaissance des énergies - bilan électrique de la France: que retenir de 2015?, [On- line]. http://www.connaissancedesenergies.org/bilan-electrique-de-la-france-que-retenir-de- 2015-160203. [Accessed 30/11/2016]. [33] RTE France - Eco2mix - Émissions de CO2, [Online]. http://www.rte- france.com/fr/eco2mix/eco2mix-co2. [Accessed 06/01/2017]. [34] Direction de l’économie, de la prospective et de la transparence. Bilan prévisionnel de l’équilibre offre-demande d’électricté en France. RTE, 2016. [35] N. Edward, L. Chen, Y. Wang, C. Yuan. A study on the cooling effects of greening in a high-density city: an experience from Hong Kong. Building and Environment, January 2012. [36] S. Streiling, A. Matzarakis. Influence of single and small clusters of trees on the bioclimate of a city: a case study. Journal of Arboriculture, November 2003. [37] M. F. Shahidan. Potential of individual and cluster tree cooling effect performances through tree canopy density model evaluation in improving urban microclimate. Current World Environment, July 2015. [38] Creekcare - Transpiration by trees, [Online]. https://www.ramin.com.au/creekcare/how- transpiration-works.shtml. [Accessed 26/12/2016]. [39] Rafael Guédez & Lukas Aichmayer. Renewable Energy Technology. KTH-ITM-EGI-CSP, 2016.

[40] D.J. Nowak, S. Hirabayashi, A. Bodine, R. Hoehn. Modeled PM2.5 removal by trees in ten U.S. cities and associated health effects. Environmental Pollution, July 2013. [41] H. Kuhns, D. Zhu, J. Gillies, A. Gertler. Examination of dust and air-borne sediment control demonstration projects. USDA Forest Service Pacific Southwest Research Station, November 2010. [42] J. Dahlhausen, P. Biber, T. Rötzer, E. Uhl, H. Pretzsch. Tree species and their space requirements in six urban environments worldwide. MDPI, Basel, Switzerland, May 2016. [43] D.J. Nowak, E.J. Greenfield, R.E. Hoehn, E. Lapoint. Carbon storage and sequestration by trees in urban and community areas of the United States. Environmental Pollution, 2013. [44] R.W. Gorte. U.S. Tree planting for carbon sequestration. Congressional Research Service, May 2009. [45] A. Nguyen. A benefit-cost analysis of ten urban landscaping trees in berkeley, CA. College of Natural Resources - UC Berkley, May 2005.

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[46] Le prix du carbone - levier de la transition énergétique, [Online]. www.developpement- durable.gouv.fr/IMG/pdf/01.pdf. Ministère de l’environnement, de l’énergie et de la mer, [Accessed 10/01/2016]. [47] Direction des Espaces Verts et de l’Environnement; Direction des Finances et des Achats. Modification du barème d’évaluation des dégâts occasionnés aux arbres de la Ville de Paris et des travaux effectués sur ces arbres pour le compte de tiers. Mairie de Paris, 2014. [48] A. Jiguel. Enquête sur la gestion des arbres en ville. Ville de Lyon, December 2014. [49] N.A. Hipps; M.J. Davies; J.M. Dunn; H. Griffiths; C.J. Atkinson. Effects of two contrasting canopy manipulations on growth and water use of London plane (Platanus x acerifolia) trees. Plant Soil, May 2014.

[50] J. Schwartz, F. Laden, A. Zanobetti. The Concentration–Response Relation between PM2.5 and Daily Deaths. Environmental Health Perspectives, October 2002. [51] Fédération des Services Énergie Environnement. Enquête Nationale sur les Réseaux de Chaleur et de Froid - Rapport 2014. SNCU, 2014.

58 Appendix A ENGIE

ENGIE is the new name of the company GDF Suez (logo on figure A.1), which is a french company working in the energy field (figure A.2 represents the logo of ENGIE with its slogan). In 2015, ENGIE was the third world largest group in the energy sector (non-oil). Its main shareholder is France which holds one third of the capital. In 2015, the Group had 154 950 employees in 70 countries for a global turnover of 69.9 billion euros.

ENGIE has three main activities: electricity production, gas production and energy services. Its strategy since 2014 is based on investments in energies with low CO2 emissions (wind, solar, geothermal, biomass, hydro, and natural gas), but also investments in services to customers, business, and territory (BtoC, BtoB, BtoT). In parallel, the Group disinvests in coal power plants like the one of Hazelwood in Australia (which is considered as the most polluting in the world).

Figure A.1 – GDF Suez logo

Figure A.2 – ENGIE logo and slogan

59 Appendix B Characteristics of district cooling systems in the region Île-de-France

In 2014, studies on main installations for district cooling systems in the region Île-de-France showed that for 569 MWth installed, the thermal energy delivered was 775GWh of cool air with a consumption of 182GWh of electricity [51]. This represents a coeﬃcient of performance of 4.26.

Energy used to produce cool air was electricity from national grid. Only cold-generating compressors were used for district cooling systems. Those systems emitted 7000 tons of CO2 to produce 775GWh, which represents speciﬁc emissions of 0.009kgCO2/kWh. Those emissions are the ones to produce electricity from national grid, and it must be noticed that it would change along years (and the weather).

Concerning prices, the average value for cooling was 121.4e/MWh.

60 Appendix C Historical data in Paris

Figure C.1 gives temperatures in Paris during years 2015 and 2016 [6]. It also shows temperatures in Trappes during the year 2012. Indeed, having no data precise enough for Paris (with hourly variations and not only daily variations), the ones of the closest weather station are used, which is the weather station of Trappes (27km west from Paris). Data were measured during the year 2012 and are available online [7].

35 Paris' Temperature in 2015 Paris' Temperature in 2016 Trappes' Temperature in 2012

20 C) °

15 Temperature ( 10

0 January February March April May June July August September October November December

-5

Figure C.1 – Daily variations of Paris’s Temperature in 2015 and 2016 [6], and Trappes’ Tem- perature in 2012 [7]

Figure C.1 shows that the 3 sets of temperatures are really close, so Trappes’ temperatures are appropriated. Daily variations are necessary to model temperature decrease as variations are high between days and nights. Figure C.2 is more precise and gives hourly variations of the temperature along the year 2012.

Figure C.3 also presents for each temperature how many times (in number of hours) it was

61 Master’s Thesis

20 C) °

Temperature ( 10

-5 January February March April May June July August September October November December

Figure C.2 – Hourly variations of Trappes’ temperature during the year 2012 [7] reached during the year 2012 in the region of Paris. This ﬁgure allows to understand why the impact of cooling is higher at 19◦C than at 25◦C (section 5.1).

700

600

500

400

300 Number of hours

200

100

0 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 Temperature (°C)

Figure C.3 – Number of hours that a temperature was reached in Paris region in 2012

For PM10 and PM2.5 concentrations, values measured in center Paris in 2016 are used [8]. Furthermore, Paris had more peaks pollution in 2016 than before, which would probably reﬂect

62 Master’s Thesis

−3 more future reality. Figure C.4 shows variations of PM10 and PM2.5 concentrations (in µg.m ) during the year 2016.

140 245 PM2,5 concentration PM10 concentration

120 210

100 175 ) ) 3 3 - - g,m g,m μ μ 80 140

60 105 concentration ( concentration ( 10 2,5 PM PM 40 70

20 35

0 0 January February March April May June July August September October November December

Figure C.4 – Daily variation of Paris’s concentrations in PM10 and PM2.5 [8]

63 Appendix D Paris’ buildings characteristics

In Paris, according to the construction period, buildings don’t have the same insolation, and so losses are not the same. It can be distinguished three main periods: — before 1945: old buildings, not really efficient but with few openings (windows, doors, ...) so having reasonable losses; — 1945-1975: reconstructions after world wars did not have efficiency constraints and gen- erated many energy consuming buildings; — After 1975: efficiency became an important point in construction, essentially with new regulations.

˙ Table D.1 gives values of average surface losses Qs and compacity c along years. It can be deduced from it that the average compacity is 11.1, and the average surface losses for buildings in Paris are 2.58 W.m−2.K−1.

˙ −2 −1 Time period Surface losses Qs (W.m .K ) Compacity c % of current buildings Before 1800 2.2 12.7 10% 1801-1850 2.6 13.6 15% 1851-1914 2.7 10.9 49% 1918-1939 2.9 8.8 9% 1945-1967 3.4 6.8 5% 1968-1975 3.0 7.0 3% 1976-1981 2.3 11.2 2% 1982-1989 1.8 10.9 2% After 1990 0.9 12.5 5%

Table D.1 – Average values for surface losses and compacity according to construction time for buildings in Paris [12]