Dimensions Of The Sustainable City Future City Repost

"The GPSC builds a strong partnership with cities around the world to share cutting-edge knowledge. We look forward to working with the city of Aarhus to bring its urban sustainability knowledge and insights to many other cities," said Xueman Wang, Program Manager for the GPSC.

However, in the current climate of the unprecedented urbanization and increased uncertainty of the world, it may be more challenging for eco-cities to reconfigure themselves more sustainably, especially when it comes to energy production and consumption. The predicted 70% rate of urbanization by 2050 (UN 2015d) reveals that environmental sustainability will be a key factor in global resilience and viability to forthcoming changes. This implies that city governments in the ecologically advanced nations will face significant environmental challenges due to the issues engendered by urban growth. These include increased energy consumption, pollution, toxic waste disposal, resource depletion, inefficient management of resources, and so on, In a nutshell, urban growth raises a variety of problems that tend to jeopardize the environmental sustainability of cities, as it puts an enormous strain on energy systems as well as ecosystem services.

Dimensions of the Sustainable City Future City Repost

Download File: https://shoxet.com/2vIDwo

This paper unfolds as follows. Section 2 describes and justifies the research methodology. Section 3 provides a detailed review on the eco-city. Section 4 presents the results of the case study. Section 5 discusses the results and how they are interpreted in perspective of previous studies, Finally, this paper concludes, in Section 6, by drawing the main findings, providing some reflections, and suggesting some avenues for future research.

In the context of this paper, descriptive research involves the description, analysis, and interpretation of the present nature, composition, and processes of an eco-city district in Stockholm, Sweden, where the focus is on some prevailing conditions. That is, how this district behaves in terms of what has been realized and the ongoing implementation of plans based on the corresponding environmentally sustainable development strategies associated with sustainable energy systems and energy efficiency processes and their integration. To obtain a broad and detailed knowledge in this regard, we adopted an approach consisting of the following steps:

The key dimensions of the eco-city, which have been enacted in many cities across the world, include a variety of strategies and solutions for achieving the goals of sustainability (Bibri 2018a, 2019a, 2020; Bibri and Krogstie 2017b, 2020; Farr 2008; Jabareen 2006; Kenworthy 2006, 2019; Lynn et al. 2003), especially in relation to its environmental dimension. Table 1 presents the key strategies and solutions of the eco-city as distilled based on a recent interdisciplinary case study conducted by Bibri (2020):

Furthermore, smart eco-cities are increasingly becoming more complex with the very technologies being used to deal with their urban infrastructure as to its operational functioning and management. Hence, it is necessary to develop and apply more innovative solutions and sophisticated approaches to monitor, analyze, and plan eco-city systems. Eco-cities can only be smart if there are intelligence functions that are able to integrate and synthesize urban data to improve environmental sustainability through data-driven decisions. Especially, building models of eco-cities functioning in real time from routinely sensed data is becoming a clear prospect, and ubiquitous sensing is getting closer to providing quite useful information about longer term changes (Bibri 2020). These opportunities are part of the ongoing debate on integrating eco-cities and smart cities. This in turn relates to the current issue of sustainable cities and smart cities being extremely fragmented as landscapes and weakly connected as approaches (e.g., Angelidou et al. 2017; Bibri and Krogstie 2019a, b; Kramers et al. 2014), despite the proven role of advanced ICT and the untapped potential of the IoT and big data technologies for advancing environmental sustainability (see Bibri 2019b). Moreover, such technologies are often used in smart cities without making any contribution to environmental sustainability, while the strategies of sustainable cities fall short in considering data-driven smart solutions (Bibri and Krogstie 2017a, b).

All new buildings in SRS are being built as low-energy buildings to reduce the energy use. They are provided with a well-insulated building envelope and energy efficient installations, and the roofs are used to generate solar electricity and solar heating, which increases the production of renewable energy (Stockholm City 2020). In this respect, the Municipality of Stockholm set these energy requirements on urban developers: 55 kWh per m2 x year and 30% locally produced electricity by renewables. These energy standards are on the focus due to the awareness that greenhouse gases (GHG) emissions in Stockholm mostly come from heating (42%) and electricity (20%). The energy requirements are associated with the energy goals set by SRS as shown in Table 3.

Stockholm is the leading Nordic smart sustainable city (Akande et al. 2019). In relevance to this study, the City of Stockholm (2017) sets these targets: to use digitalization and new technologies to make it easier for residents and businesses to be environmentally friendly, and to reduce energy consumption and carbon footprint.

According to Shahrokni et al. (2015b), the prototype developed for the SRS employs a hybrid approach to the implementation of the SUM concept, with the real-time calculation engine being able to process production and consumption data (see Fig. 3). The current focus of the SRS prototype is to understand the GHG emissions resulting from the consumption of electricity, heat, water, and the production of waste in the SRS. As additionally illustrated in Fig. 3, the implementation of the SUM concept entails three phases: (1) obtaining data, (2) development of a calculation engine and data processing, and (3) development of feedback tailored to individual stakeholder requirements.

However, the most challenging barrier identified in relation to SUM is accessing and integrating siloed data from the different data owners, which is hard to overcome unless a significant value is perceived. Also, applying this framework at the city level has been limited by the lack of data at this scale (Shahrokni et al. 2015b). This is actually one of the common challenges pertaining to the implementation of big data analytics and its novel applications in the context of smart sustainable cities (Bibri 2019a; Bibri and Krogstie 2018).

In addition, long-term commitment of the Municipality of Stockholm is uncertain when political constellations change (red-green coalition versus blue coalition), despite the promising outcome of the inclusion of advanced ICT in the central governance of the City of Stockholm. Kramers et al. (2016) provide some general lessons learned as to what worked well and what did not in terms of using ICT in the planning phase of SRS as part of city governance.

d3.select(window).on("resize", resize); var width = document.querySelector('#map').offsetWidth; var mapRatio = 0.7; var height = width * mapRatio; var projection = d3.geo.mercator().scale(width / 2 / Math.PI) .rotate([-11, 0]) .translate([(width) / 2, height * 1.35 / 2]) .precision(.1); var path = d3.geo.path().projection(projection); var svg = d3.select("#map") .append("svg") .attr("preserveAspectRatio", "xMinYMin") .attr("width", width) .attr("height", height) .append("g"); var sets = [ name: 'STUDY AREA 1', url: ' =31', set: d3.set(['AGO', 'BDI', 'BEN', 'BFA', 'BWA', 'CAF', 'CIV', 'CMR', 'COD', 'COG', 'COM', 'CPV', 'ERI', 'ETH', 'GAB', 'GHA', 'GIN', 'GMB', 'GNB', 'GNQ', 'KEN', 'LBR', 'LSO', 'MDG', 'MLI', 'MOZ', 'MUS', 'MWI', 'NAM', 'NER', 'NGA', 'RWA', 'SEN', 'SLE', 'SOL', 'SOM', 'SSD', 'STP', 'SWZ', 'SYC', 'TCD', 'TGO', 'TZA', 'UGA', 'ZAF', 'ZMB', 'ZWE']) , name: 'STUDY AREA 2', url: ' =43', set: d3.set(['ARE', 'DZA', 'EGY', 'QAT', 'BHR', 'LBY', 'MAR', 'MRT', 'SAU', 'YEM', 'OMN', 'SYR', 'JOR', 'IRQ', 'KWT', 'LBN', 'PSE', 'SDN', 'TUN']) , name: 'STUDY AREA 3', url: ' =55', set: d3.set(['ALB', 'AND', 'AUT', 'BEL', 'BIH', 'BGR', 'HRV', 'CYP', 'CZE', 'DNK', 'EST', 'FIN', 'FRA', 'DEU', 'GRC', 'GRL', 'HUN', 'ISL', 'IRL', 'ISR', 'ITA', 'LVA', 'LIE', 'LTU', 'LUX', 'MKD', 'MLT', 'MDA', 'MCO', 'MNE', 'NLD', 'NOR', 'POL', 'PRT', 'ROU', 'SMR', 'SRB', 'SVK', 'SVN', 'ESP', 'SWE', 'CHE', 'GBR', 'TUR']) , name: 'STUDY AREA 4', url: ' =67', set: d3.set(['AZE', 'ARM', 'BLR', 'RUS', 'GEO', 'KAZ', 'KGZ', 'TJK', 'TKM', 'UKR', 'UZB']) , name: 'STUDY AREA 5', url: ' =79', set: d3.set(['AFG', 'BGD', 'BTN', 'IND', 'IRN', 'MDV', 'NPL', 'PAK', 'LKA']) , name: 'STUDY AREA 6', url: ' =91', set: d3.set(['AUS', 'BRN', 'KHM', 'CHN', 'COK', 'FJI', 'FSM', 'IDN', 'JPN', 'KIR', 'KOR', 'LAO', 'MHL', 'MMR', 'MNG', 'MYS', 'NRU', 'NZL', 'NIU', 'PRK', 'PHL', 'PLW', 'PNG', 'WSM', 'SGP', 'SLB', 'TLS', 'TON', 'TUV', 'VUT', 'THA', 'VNM']) , name: 'STUDY AREA 7', url: ' =103', set: d3.set(['CAN', 'USA']) , name: 'STUDY AREA 8', url: ' =113', set: d3.set(['ATG', 'ARG', 'BHS', 'BRB', 'BLZ', 'BOL', 'BRA', 'CHL', 'COL', 'CRI', 'CUB', 'DMA', 'DOM', 'ECU', 'GRD', 'GTM', 'GUY', 'HND', 'HTI', 'JAM', 'MEX', 'NIC', 'PAN', 'PRY', 'SLV', 'PER', 'KNA', 'LCA', 'VCT', 'SUR', 'TTO', 'URY', 'VEN']) ]; d3.json(" ", function (error, w) if (error) throw error; svg.append("path").datum(topojson.merge(w, w.objects.units.geometries.filter(function (d) return d.id !== 'ATA'; // Sorry Antarctica ))) .attr("class", "border") .attr("d", path); for (var i = 0; i 2ff7e9595c