Session 1 - Montefiore

More documents

Recommendations

Info

which were calculated with the same kind of data and the same method, can thus be easily comparable; they consume less energy per capita than home-to-work travels. The first explanation is that distances from home to school are shorter than distances from home to work. Several schools are indeed located in most Walloon towns, even in the more rural ones, whereas work locations remain concentrated in bigger cities and in some suburban business parks. Moreover, the use of public transport is higher for home-to-school travels than for home-to-work ones which could also explain the better results obtained for home-to-school travels. Home-to-station travels were included in the previous results and represent between 0.9% and 3.7% of home- to-work travels and between 1.1% and 4.8% of home-to-school ones, whereas the modal part of the train was very low. These results show that it is important to take home-to-station travels into account in suburban areas. Moreover, trying to increase the modal part of the train in suburban areas should be a good strategy, but only if alternatives to individual car are proposed for home-to-station travels. Annual home-to-work and home-to-school energy consumption was higher in the two residential districts located far away from city centers (Tintigny and Rotheux), whereas home-to-work consumption was high in Jambes, but home-to-school consumption was lower than in others districts. As Jambes is located closer to a big city center (6 kilometers), students can more easily use alternative non polluting modes of transport. Home-to-shop and home-to-leisure travels represent between 62.0% and 96.5% of the annual energy consumption for home-to-school travels, as seen in Table 3. These values mainly depend on the distances to shops, services and leisure. The more equipped the neighborhood and its surrounding are, the smaller the energy consumption rate for home-to-shop and home-to-leisure travels is. As those purposes of travels were calculated according to “type-profiles” and not according to statistical data, results were not as robust as home-to-work and home-to-school consumption but seem to give credible results. Shops and leisure, just as schools, are spread out on the whole region, even in most rural areas (rural core, suburban centers, etc.) which allow for reduced distances from home to destination. 4.3 Sensitivity analyses Several sensitivity analyses were performed to identify the most relevant indicators that act upon transport energy consumption. Since the main key indicators that seem to be highlighted by the first results were the A-F. Marique and S. Reiter, A method for evaluating transport energy consumption in suburban areas, Environmental Impact Assessment Review, 2012, Vol 33(1):p1-6. 13
distance between home and final destination and the bus services, the first sensitivity analysis deals with the location of the districts. If we consider, as a first approach, that all the studied neighborhoods keep their socio-economic characteristics but could now benefit from the same good location than the neighborhood presenting the lowest energy consumption rate (Fontaine neighborhood, close to a city center, good bus services, higher mix in functions), energy consumption relating to home-to-work and home-to-school travels decrease significantly: -55.4% in Tintigny, -22.5% in Jambes and -32.4% in Rotheux, mainly because trips by car are shorter and less numerous. These results highlight that location is paramount as far as transport energy consumption is concerned. To try to isolate the impact of the distance, we then considered that the distances between home and work and between home and school mentioned in the national census were reduced by 10% in a first theoretical calculation and by 20% in a second one. These simulations confirmed that the impact of distances on energy consumption in transport is high (see Table 4). However, these results remain purely theoretical because it is not possible to change the location of existing neighborhoods. Nevertheless, these results show the importance of promoting the implementation of future neighborhoods in areas close to large employment centers and services and increasing the population of these areas when they are already built. The third type of sensitivity analysis deals with the energy consumption of the vehicles. If we considered that the performances of all the vehicles (fuel consumption per mode) are improved by 10%, which is a credible approach, home-to-work and home-to-school energy consumption decrease from 6.6% to 9.6%. These savings are further improved if the performances of the vehicles are improved by 20%. If only the performances of public buses are improved, resulting savings for home-to-work and home-to-school travels are low: energy consumption only decreases by 1.7% to 2.7% because the modal part of the bus is very low in these districts. Therefore, improving the performances of public vehicles will only give good results in areas where buses are used by a large part of the population. To favor home-workers is also a credible strategy to reduce transport energy consumption. It was calculated that if 5% of the workers of a district are allowed to work at home, energy consumption savings (home-to- work and home-to-school travels) are in the range of 2.3% to 3.6%, according to the district. If the percentage of “home workers” rises to 10%, energy reductions can reach 6.9%. A-F. Marique and S. Reiter, A method for evaluating transport energy consumption in suburban areas, Environmental Impact Assessment Review, 2012, Vol 33(1):p1-6. 14
Page 1 and 2:
Session 1 - Supervisors : Pierre De
Page 3 and 4:
Session 3 - Supervisors : Damien Er
Page 5 and 6:
Session 5 - Supervisors : Nathalie
Page 7 and 8:
Page 9 and 10:
Page 11 and 12:
Session 11 - Supervisors : Damien E
Page 13 and 14:
Session 13 - Supervisors : Nathalie
Page 15 and 16:
Session 15 - Supervisors : Sigrid R
Page 17 and 18:
Appendix A : Sigrid Reiter Appendix
Page 20 and 21:
EEA Report No 10/2006 Urban sprawl
Page 22 and 23:
Contents Contents Acknowledgements
Page 24 and 25:
Urban sprawl — a European challen
Page 26 and 27:
from city centres, while retaining
Page 28 and 29:
growth as mentioned in Chapter 1. R
Page 30 and 31:
The extent of urban sprawl in Europ
Page 32 and 33:
esidential density, sprawl is equal
Page 34 and 35:
The extent of urban sprawl in Europ
Page 36 and 37:
3 The drivers of urban sprawl 3.1 C
Page 38 and 39: The drivers of urban sprawl Figure
Page 40 and 41: also clearly demonstrate city spraw
Page 42 and 43: Box 4 Portugal and Spain: threats t
Page 44 and 45: Box 5 (cont.) Map Development scena
Page 46 and 47: Box 6 (cont.) The drivers of urban
Page 48 and 49: emote locations and requiring trans
Page 50 and 51: 4.1.2 Natural and protected areas T
Page 52 and 53: Box 7 (cont.) The impacts of urban
Page 54 and 55: the societal cost of asthma has bee
Page 56 and 57: Box 8 Effects of residential areas
Page 58 and 59: 5.2 The European spatial developmen
Page 60 and 61: European regional forces generating
Page 62 and 63: Box 9 (cont.) Responses to urban sp
Page 64 and 65: urban development plan and focused
Page 66 and 67: Box 10 (cont.) Responses to urban s
Page 68 and 69: Annex: Data and methodological appr
Page 70 and 71: C Assessing urban sprawl at the Eur
Page 72 and 73: References and further reading Ambi
Page 74 and 75: European Commission, 1990. Green Pa
Page 76 and 77: European Environment Agency Urban s
Page 78 and 79: Appendix A.2 78
Page 80 and 81: Section 2 presents a brief review o
Page 82 and 83: intervention in these specific type
Page 84 and 85: electricity as trains in Belgium ar
Page 86 and 87: The majority of those districts are
Page 90 and 91: The last type of sensitivity analys
Page 92 and 93: (INSEE) in France, the Office for N
Page 94 and 95: Kints C. La rénovation énergétiq
Page 96 and 97: A Method to Evaluate the Energy Con
Page 98 and 99: application in many other suburban
Page 100 and 101: Our classification of buildings cur
Page 102 and 103: to work and the mode of transportat
Page 104 and 105: energy consumption of a neighborhoo
Page 106 and 107: transportation represents 11.8% to
Page 108 and 109: most important. Insulating only the
Page 110 and 111: savings highlighted in the first an
Page 112 and 113: measurements because of high variat
Page 114 and 115: Hilderson, W., E. Mlecnik and J. Cr
Page 116 and 117: FIGURE CAPTIONS AND TABLE TITLES Fi
Page 118 and 119: PLEA2012 - 28th Conference, Opportu
Page 126 and 127: ENERGY REQUIREMENTS AND SOLAR AVAIL
Page 128 and 129: year are 34.9 °C and -9,1°C. The
Page 130 and 131: The new built density of the neighb
Page 132 and 133: Appendix A.6 132
Page 134 and 135: uilding” approach. A large amount
Page 136 and 137: C. Comparison of our classification
Page 138:
In fact, as cavity walls became wid
Page 141 and 142:
METHODS, TOOLS, AND SOFTWARE Keywor
Page 143 and 144:
approaches. These methodologies hav
Page 145 and 146:
travel through the urban area of Li
Page 147 and 148:
METHODS, TOOLS, AND SOFTWARE Figure
Page 149 and 150:
as well as the ability to test fore
Page 151 and 152:
Appendix A.8 151
Page 153 and 154:
consumption is rarely taken into ac
Page 155 and 156:
interdependences between urban stru
Page 157 and 158:
These data were also used by Boussa
Page 159 and 160:
and energy consumption in the resid
Page 161 and 162:
Mean modal share (train) 14,0% 12,7
Page 163 and 164:
Figure 4: Annual transport energy c
Page 165 and 166:
23,0%, even if the annual energy co
Page 167 and 168:
or commercial purposes. “Type-pro
Page 169 and 170:
Beevers SD and Carslaw DC (2005) Th
Page 171 and 172:
Lavadinho S and Lensel B (2010) Imp
Page 173 and 174:
Appendix A.9 173
Page 175 and 176:
NOTICE The submitted manuscript has
Page 177 and 178:
Using ZEB design goals takes us out
Page 179 and 180:
Boulder, Colorado has a solar acces
Page 181 and 182:
(Mermoud 1996) to calculate the exp
Page 183 and 184:
energy is used. TDVs have been deve
Page 185 and 186:
Net Zero Energy Emissions Building
Page 187 and 188:
combined with selecting a favorable
Page 189 and 190:
Appendix A.10 189
Page 191 and 192:
Life Cycle Assessment of Buildings
Page 193 and 194:
Page 195 and 196:
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
PRe, SimaPro., 2000. Life Cycle Ass
Page 209 and 210:
Appendix A.11 209
Page 211 and 212:
396 buildings [17,26]. This review
Page 213 and 214:
398 2.2. Embodied energy and carbon
Page 215 and 216:
400 using photovoltaic panels will
Page 217 and 218:
Appendix A.12 217
Page 219 and 220:
assumptions, the indicators (Embodi
Page 221 and 222:
Table 3 Distribution of the steel w
Page 223 and 224:
the three case studies exhibit a st
Page 225 and 226:
PLEA 2011 - 27 th Conference on Pas
Page 227 and 228:
Page 229 and 230:
Page 231 and 232:
Appendix B : Pierre Dewallef Append
Page 233 and 234:
When writing your presentation, you
Page 235 and 236:
Status of Solar Tower Power Plant t
Page 237 and 238:
Appendix B.3 237
Page 239 and 240:
Page 241 and 242:
Status of biomass combustion power
Page 243 and 244:
Appendix B.5 243
Page 245 and 246:
Page 247 and 248:
Status of ocean thermal energy conv
Page 249 and 250:
Appendix B.7 249
Page 251 and 252:
Page 253 and 254:
Status of Natural Gas Combined Cycl
Page 255 and 256:
Appendix B.9 255
Page 257 and 258:
Page 259 and 260:
Status of Integrated coal Gazeifica
Page 261 and 262:
Appendix B.11 261
Page 263 and 264:
Page 265 and 266:
Status of Carbon Capture and Storag
Page 267 and 268:
Appendix B.13 267
Page 269 and 270:
Page 271 and 272:
Status of Carbon Capture and Storag
show all

Session 1 - Montefiore

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?