scholarly journals Multi-scale integrated assessment of urban energy use and CO2 emissions

2014 ◽  
Vol 24 (4) ◽  
pp. 651-668 ◽  
Author(s):  
Lijun Zhang ◽  
Gangjun Liu ◽  
Yaochen Qin
Author(s):  
Dejan R. Ostojic ◽  
Ranjan K. Bose ◽  
Holly Krambeck ◽  
Jeanette Lim ◽  
Yabei Zhang

2019 ◽  
Vol 11 (12) ◽  
pp. 3246 ◽  
Author(s):  
Yves Bettignies ◽  
Joao Meirelles ◽  
Gabriela Fernandez ◽  
Franziska Meinherz ◽  
Paul Hoekman ◽  
...  

Hosting more than half of the world population, cities are currently responsible for two thirds of the global energy use and three quarters of the global CO2 emissions related to energy use. As humanity becomes more urbanized, urban systems are becoming a major nexus of global sustainability. Various studies have tried to pinpoint urban energy use drivers in order to find actionable levers to mitigate consumption and its associated environmental effects. Some of the approaches, mainly coming from complexity science and industrial ecology disciplines, use city-scale data to find power-laws relating to different types of energy use metrics with urban features at a city-scale. By doing so, cities’ internal complexity and heterogeneity are not explicitly addressed. Moreover, to our knowledge, no studies have yet explicitly addressed the potential scale dependency of such drivers. Drivers might not be transferable to other scales and yield undesired effects. In the present study, power-law relations are examined for 10 cities worldwide at city scale and infra-city scale, and the results are compared across scales. Relations are made across three urban features for three energy use intensity metrics. The results show that energy use drivers are in fact scale-dependent and are city-dependent for intra-urban territories.


Author(s):  
Antonio Gagliano ◽  
Francesco Nocera ◽  
Maurizio Detommaso ◽  
Catalina Spataru

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Kangkang Tong ◽  
Ajay Singh Nagpure ◽  
Anu Ramaswami

AbstractIndia is the third-largest contributor to global energy-use and anthropogenic carbon emissions. India’s urban energy transitions are critical to meet its climate goals due to the country’s rapid urbanization. However, no baseline urban energy-use dataset covers all Indian urban districts in ways that align with national totals and integrate social-economic-infrastructural attributes to inform such transitions. This paper develops a novel bottom-up plus top-down approach, comprehensively integrating multiple field surveys and utilizing machine learning, to model All Urban areas’ Energy-use (AllUrE) across all 640 districts in India, merged with social-economic-infrastructural data. Energy use estimates in this AllUrE-India dataset are evaluated by comparing with reported energy-use at three scales: nation-wide, state-wide, and city-level. Spatially granular AllUrE data aggregated nationally show good agreement with national totals (<2% difference). The goodness-of-fit ranged from 0.78–0.95 for comparison with state-level totals, and 0.90–0.99 with city-level data for different sectors. The relatively strong alignment at all three spatial scales demonstrates the value of AllUrE-India data for modelling urban energy transitions consistent with national energy and climate goals.


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