Key building blocks of a Smart City | Smart Buildings & Smart Assets
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Connected Intelligence
- Sustainable Growth and Tech Trends
Publication | Update: Sep 2020
A Smart City provides effective integration of physical, digital and human systems in the built environment to deliver a sustainable, prosperous and inclusive future for its citizens.
The term ‘smart city’ is being used to depict a city that embraces technology to improve the efficiency of infrastructure and services provided to citizens. Even though this term has been around for decades, it particularly became popularized in 2010 when IBM launched its Smarter Cities Challenge. There are a number of definitions of smart cities: IBM defines a smart city ‘as one that makes optimal use of interconnected information available today to better understand and control its operations and optimize the use of limited resources and Cisco has a similar definition – a smart city is one that adopts ‘scalable solutions that take advantage of information and communications technology (ICT) to increase efficiencies, reduce costs, and enhance quality of life. Over the years the concept has changed from one that is focused on technologies and systems to one that is focused on citizens and the services that are provided for them.
According to Citi GPS Report “Sustainable Cities: Beacons of light against the shadows of unplanned urbanization,” several applications of a smart environment have been introduced (or are in the process of being introduced) in many urban areas, including smart homes, smart grids, smart transportation, smart infrastructure, and smart healthcare.
There are a number of cities (both developed and emerging) that have put smart technologies at the core of their strategic plans.
For example the Indian government announced its objective to build one hundred smart cities all over the countries. The first of these smart cities, Dholera is already under construction.
Dubai has also been actively seeking to become the world’s smartest city and has launched their Smart Dubai Initiative which aims to ‘embrace technological innovation’ with the aim to make the city more efficient, safe, and a greater experience for their residents and visitors.
Finland has invested a lot of thought and money in developing smarter platforms for their cities. In particular Kalasatama in Helsinki has incorporated a number of smart solutions such as smart houses with scalable technology related to heating and lighting, smart waste collection systems, smart grids, etc.
According to Mike Beevor, Field Chief Technologist, Pivot3, in an IoT For All article, “Smart Building Initiatives are the Building Blocks of a Smart City,” smart buildings have been utilizing the Internet of Things (IoT) to connect and advance systems, delivering more efficiency and data. These connected systems could include IP video camera systems, access control systems, smart meters, smart parking and/or any networked devices or IoT sensors, to name a few.
By bringing these systems together, administrators can more easily make connections between previously separate bases of data, such as pairing video footage with access management anomalies, using video for people tracking to make sure a building’s HVAC adjusts the temperature properly or observing the busiest times of an on-site parking facility.
A smart home is a home that incorporates advanced automation systems to provide inhabitants with sophisticated control over the building’s functions. The range of different smart home devices (Nest and Hive mentioned above are two examples) and technologies available is expanding rapidly with developments in sensors, computer controls, and the Internet-of-Things. In fact it is estimated that by 2022, 500 smart devices will be present in a typical family home.168 IBM have created Watson IoT that enables residents to integrate rooms, devices, and services in a home, provides residents with the ability to analyze consumption of energy, improve the health and wellness of residents, and improve the safety and security of homes.
Other IoT solutions have been designed to improve certain issues in the home. Stockrose, a Swedish property management company worked with Engia (a provider of intelligent cloud solutions) and the Azure IoT platform developed by Microsoft to track and improve the usage of hot water in their apartments. According to Microsoft this project enabled Stockrose property owners to save an estimated $ 42 million in hot water costs within 10 years.
Smart assets are equipped with sensors that track condition and behavior.
Sensors have enabled the creation of ‘smart assets’. For example infrastructure such as railway systems and sewer tunnels can be fitted with automatic, continuously transmitting networks of fiber-optic sensors. According to Lord Mair, President of the Institution of Civil Engineers these systems are able to tell us the condition of the assets as well as their behavior over time and warn the developers of the need for maintenance and repair before any failure occurs. The Crossrail project in London has been fitted with such sensors as part of their construction phase. Such technology would enable these assets to be managed efficiently over time, provide a more efficient service and ultimately save money and resources over time.
The market for smart solutions is definitely growing. Markets and Markets estimates that the smart cities market would grow by a CAGR of 23.1% and reach .2 billion by 2022.
According to Matthias Van Steendam, Principal and Didier Tshidimba, Senior Partner of Roland Berger, Brussels Office research, the digital solutions should help the city in improving the quality of life of its citizens by fulfilling their human needs: physiology, safety, belonging, esteem and self-actualization. Secondly, the solutions should help the city in preserving resources, reducing emission/waste and growing public green space to enhance the protection of the planet. Finally, Smart City applications need to be financially sustainable, fueling economic growth by increasing the efficiency of public services, increasing their value and exploiting new business opportunities.
Source: Roland Berger: How the Smart City can build itself
Technology, sensors, the Internet-of-Things, the use of big data, and others, if properly applied can fundamentally alter the quality of life in an urban environment. Using connected devices, cities can figure out how to become smarter, safer and more efficient. Data can lead to innovations in street planning and parking or provide valuable insights during crisis investigations. Furthermore, devices are flexible in addressing different use cases — systems can be optimized and customized depending on the unique needs of a local government.
Key building blocks of a Smart City
A Smart City should consist out of the following four blocks:
1) Central infrastructure, the assembly of technological choices, policies, underlying systems etc. to govern and stimulate the creation of connectivity, technologies and applications (e.g. Legal framework, business models etc.),
2) Distributed Infrastructure, the telecommunication network and data services allowing the capturing, transmission, storage, analysis and commercialization of data in a secure and efficient way (e.g. 4G, WiFi etc.),
3) Digital Enablers, the integrated set of city assets and technologies that are generating and transmitting data as input for the smart solutions (e.g. CCTV, smart waste bins etc.) and
4) Digital Solutions, the smart plug 'n play solutions improving people's quality of life, planet sustainability and city's profits (e.g. adaptive traffic lighting, optimized waste management etc.)
Role of the different players in a Smart City
For the successful development of a Smart City, a coordinated approach is needed, with significant actions to be fulfilled for the development of all building blocks. For each building block, specific players will be responsible in the overall Smart City ecosystem:
Source: Roland Berger: How the Smart City can build itself
For the Central Infrastructure, the main role is to be played by the local and national governments. They should define the long-term strategy and coordinated approach for the Smart City with clear objectives and a roadmap. They will set the rules of the game for all players, with for example an open data policy for remote management of the city assets, to be leveraged by third parties in their applications. A dedicated function within these public institutions should be created, leveraging all specialized knowledge of the specific council departments.
To develop the Distributed Infrastructure, the current capabilities in terms of big data and connectivity should be identified first. The right Telecom players should be attracted and stimulated to further develop the connectivity and data storage capabilities within the city, as key requirements for the development of applications. Investments will be needed in networks and software to cope with a significant increase in the number of smart devices and the new data traffic streams they create. Their networks will have to deliver significantly higher performance to be able to deliver critical data services.
To build the network of Digital Enablers, both technology players and other industrial players will be key partners for the governments. The network of digital enablers will consist out of both new digital connected assets (e.g. self-driving cars, parking availability sensors, waste bin sensors etc.) and existing city assets turned into a connected asset connected with the city's big data platform (e.g. CCTV images, luminaires etc.). Technology players will need to develop new smart assets and ensure that the necessary existing city assets become smart, by connecting them with the central data framework and with other smart assets.
In conclusion, cities should build ecosystems that will build self-developing smart cities. To do so, they need to take 4 steps:
1) Define the strategy and set up a central function to coordinate all efforts. Cities should determine the rules in the form of a comprehensive and coherent set of policies to govern and stimulate the usage of smart applications. To assess the maturity of the current positioning, Roland Berger has developed a Smart City Strategy Index
2) Plan for the development of big data and connectivity capabilities
3) Plan for the digitization of the city's (new and existing) assets
4) Attract and stimulate developers of digital solutions
Objectives and Study Scope
This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.
The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges.
The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period.
The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players. This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.
Research Process & Methodology
We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.
We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.
Research Portfolio Sources:
Global Business Reviews, Research Papers, Commentary & Strategy Reports
M&A and Risk Management | Regulation
The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.
Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:
As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.
Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.
Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pretax revenue and its total boughtin costs (costs excluding wages and salaries).
Forecasts of GDP growth: GDP = CN+IN+GS+NEX
GDP growth estimates take into account:
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.
Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.
Industry Life Cycle Market Phase
Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:
The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.
The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.
Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from ofﬁcial sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reﬂect different assumptions about their relative importance.
The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.