- Energy-efficient urban forms
- Size, scaling relations, and urban metabolism
- Urban data and renewable energy potentials
- Cities, infrastructure networks and CO2 emission
How do different urban forms (mobility patterns and building typology) relate to energy consumption and renewable energy supply (e.g. solar energy)? The aim is to explore the effects of different urban configurations including compact/dispersed on the energy use and microclimate. A further aim is to assess the contribution of different urban forms to sustainability, and to provide a framework for eco-cities (e.g. green infrastructure, high density, mixed land-use, ecological and cultural diversity, and passive solar design). To quantify the complexities of urban forms in relation to energy use and their sustainable development, the following methods are used: (i) Urban entropy analysis, (ii) Statistical methods, (iii) Methods adapted from physics and engineering (such as statistical physics/information theory), (iv) GIS and spatial analysis.
One of the most salient characteristics of a city is its population size, which affects its productivity, energy use, innovation, and wealth creation. The aim is to study the importance of urban population size and scaling laws (super-linear and sub-linear relations) in cities. The topic covers the scaling relations between city size and infrastructure (transport networks and buildings), city size and resources (flows of energy, materials, waste, goods and information, etc.), but also city size and socio-economic parameters (income, innovation, disease) so as to find the regularities and systematic behavior of cities as analogies to biological and ecological systems. The aim is to develop an urban metabolism approach using statistical thermodynamics, network models and the maximum entropy method which have the potential to bring about fruitful results and new tools as to modelling energy flows in urban systems.
We live in a data-driven world and many scientific sub-disciplines have become largely data-driven, including aspects of social science, urban science, and energy management. The aim is to collect different types of energy data (satellite data, estimated and measurements data, etc.) and multi-scale urban data (neighborhood level, city and regions), as well as real time datasets – big data – so as to discover patterns in large data sets and propose new ways to map, visualize, and analyze them (average trends, statistical characteristics, deviation from average, and changes over time). GIs and geo-statistical tools, machine learning and data mining techniques can be used so as to analyse urban data and identify the current renewable resources as well as to make predictions or future decisions.
Urban infrastructure, especially transport networks, are major determinant of the overall structure, productivity, global economy, and energy efficiency of cities. Transport is also a major source of greenhouse gas emissions: CO2 emissions in the transport sector are about 30% of the total for developed countries and about 23% of total anthropogenic CO2 emissions worldwide (www.unece.org). The aim of this study is to understand the relation between mobility network structure, which connects people and places, and fuel consumption and carbon dioxide (CO2) emissions. Such a relation should provide a better understanding of how cities are organized and how they can use energy efficiently. Entropy analysis, for quantifying network structure, statistical modelling and GIS related tools use in this study.