Welcome to the ECOCITY 2022 Interactive Programme

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In recent years, implementing a circular economy in cities has been proposed by policy makers as a potential solution for achieving sustainability. One strategy for circular cities is to convert waste to resources locally, which would require cities to integrate 'circular makers' - waste-to-resource organizations such as recycling companies or manufacturers utilizing waste streams for their production processes.

Existing literature has identified a number of drivers and barriers that affect the presence of circular makers in a city, such as the affordability of land, availability of industrial sites, and proximity to various stakeholders. However, existing research is mostly based on qualitative research methods such as surveys and interviews, and lacks a spatial perspective - papers either examine individual circular makers or cities policies as a whole, rarely researching district or neighbourhood scale attributes that could explain why circular makers are clustered in certain areas in a city and not others.

The aim of this research is therefore to use GIS (Geographic Information System) spatial analysis methods to verify drivers and barriers from existing literature, in order to empirically identify the spatial factors that affect the presence of circular makers in cities.

This research will utilize data from the Dutch National Waste Registry, which records all industrial waste producers and waste flows in the Netherlands. Using this dataset, we will identify the location, scale, and industry of circular makers in the Randstad - a megalopolis consisting of the Dutch cities Amsterdam, Rotterdam, The Hague, and Utrecht. We will then use the drivers and barriers identified in existing literature to derive variables that could be used for spatial analysis. The correlation between the variables derived from literature and the presence of circular makers will be calculated using linear regression.

Using this method, we expect to identify a list of factors that are correlated with the presence of circular makers, types of circular makers that are suited for locating in urban areas, as well as types of neighbourhoods that could be well suited for circular making.

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Although cities occupy only 2% of the Earth's surface, they consume 80% of all energy generated, produce 75% of carbon emissions, and represent almost 80% of global resource use and waste release. Urban centers stand as key elements to the transition to a circular economy (CE), which has gained relevance as a new economic-environmental paradigm to overcome environmental issues by decoupling resource use and economic growth. Closing metabolic cycles within urban areas not only tackles environmental issues but also creates positive conditions for regenerating ecological systems, optimizing social and health conditions, and maximizing regional economic opportunities, by integrating urban environments with their supporting areas. However, transiting from linear to circular cities urges many challenges, given the multiple urban layers, actors and sectors, and locally-bound cultural, political, and social background. Previous research highlighted four main approaches to tackle circularity in urban areas, ranging from planning the transition from linear to circular cities; handling specific flows within a circular city or integrating flows for resource looping; to concepts on circular or regenerative urban areas, like the Cradle to Cradle (C2C) approach. From the approaches reviewed, C2C embraces the multiplicity of quantitative and qualitative requisites needed for developing circular urban areas. We drafted a C2C-inspired framework to enable optimization and integration of different flows with human activities to various urban and socioeconomic contexts. This paper refers to the first step validation of the framework, by partially applying it to a selected neighborhood to understand the main challenges for its implementation and optimization.

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Data centres (DCs) energy consumption issues have largely aroused worldwide concerns. There are considerable studies that investigate the areas on a configuration and building designs, or location-based level to save the energy consumption of the DC. However, changing the configurations or moving DC is relatively problematic. It has been recognised that there exists a trade-off relationship between the IT and the cooling energy consumption in the DC as keeping the lower temperature in the IT room would increase the energy consumed by the cooling devices but at the same time would ensure the IT computational capability, therefore, reduced the IT energy consumption, and vice versa. However, The literature lacks a sophisticated analysis of how to utilise the existing facilities in DC to minimise energy consumption by active intervention. To verify this trade-off relationship, we propose a managerial strategy that aims to optimise the DC energy consumption by controlling the combinations of Air-Conditioners (ACs) target temperatures. The enumerated existing solutions to similar questions have dropped in the fields of pure mathematical modelling or non-model-based machine learning, which have the limitations of consuming overmuch human and computational effort. Differ from the optimisation solutions to similar questions such as pure mathematical modelling, dynamic programming (DL), or Artificial Neural Network (ANN) and Reinforcement Learning (RL), we adopted the joint approach Statistical Formulated Reinforcement Learning (SFRL) based on a multivariate Vector Auto-regressive Model (VAR). VAR as a multivariate time series model formulated by analysing the real-time historical data collected from a DC locates in Turkey, and act as an approximation function to the optimisation solver set up by RL approach. This largely solved the difficulties both in the analytical modelling and machine learning fields as it reduced the manual effort of modelling the comprehensive environment by mathematical approaches as well as the computational effort of storing the infinite optimal strategies when RL makes the optimal step-solution. Our contribution to the literature mainly about the application of functional approximation to the RL decision process and its practical optimisation usage in the industry.

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Life Cycle Assessment (LCA) is a data-intensive approach that has proved its value in environmental evaluation by providing decision-makers with high-quality, multicriterial comprehensive results. For its data intensity, LCA’s application at the urban scale is additionally challenged by the scale's intricacies, which amplify significantly the amount of data required. For a complexity compromise, data collection can be reduced to a manageable amount by subdividing the studied area into smaller urban cells such as neighborhoods, by grouping the built stock and infrastructure into a limited set of aggregates with similar characteristics, and by defining archetypes to represent each group. This paper compares four different data modeling strategies bearing different data collection needs to generate neighborhood LCA input. For the building stock, top-down (TD) modeling used different benchmarks based on buildings' use typology (education, health, office, or sports). Mid-down (MD) modeling assumed the characteristics of the most common archetype and pasted its simplified bill of materials for the whole built area. Mid-up (MU) modeling used the simplified bill of materials (only envelope) of nine representative buildings (archetypes) defined by unsupervised machine learning clustering, while the bottom-up (BU) approach used the complete bill of materials of nine archetypes. For the infrastructure modeling, TD used benchmarks for street, energy, and water networks and the BU approach used GIS-supported data. The materials and energy flows were extrapolated accordingly to define the neighborhood life cycle inventory for the six approaches. K-medoids clustering, ArcGIS, and SimaPro v.9 supported the assessments. Findings show that the average variation between MD and BU scenarios was 28.3%. From the thirteen categories considered, abiotic depletion (AD, 68.5%), human toxicity (HT, 46%), and photochemical ozone potential (PCOP, 39%) varied the most, while terrestrial ecotoxicity (TE, 12%), freshwater ecotoxicity (FE, 10%) and renewable primary energy (RPE, 5.5%) were less sensible to the modeling approach. Reinforcing steel and ceramic tiles were the main impact contributors. Despite its detailed flows’ modeling, carrying out detailed BU assessments can be unfeasible in practice, and the MU seemed to best balance modeling refinement and labor intensity.