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Is a Sustainable City, a Smart City?

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Competitive Differentiation
  • Sustainable Growth and Tech Trends


Publication | Update: Sep 2020

More than half of the world's people live in cities today. By 2050, nearly seven in ten people will be living in cities. Cities account for more than 70 per cent of global carbon emissions and 60 to 80 per cent of energy consumption. Rapid urbanization has created additional challenges such as social inequality, traffic congestion and water contamination and its associated health issues.

Governments and municipalities can use information and communication technologies (ICTs) and other technologies to build smarter and more sustainable cities for their citizens. 

The definition was provided by the ITU and UNECE is that a smart sustainable city is an innovative city that uses ICTs to improve quality of life, the efficiency of urban operations and services and competitiveness, while ensuring that it meets the needs of present and future generations with respect to economic, social, environmental and cultural aspects.

URENIO Research published an article, “Enhancing sustainable urban development through smart city applications,” that explores the potential contribution of smart city applications to sustainable urban development, and more specifically to environmental sustainability. Through an in-depth investigation of applications hosted on the Intelligent City Software and Solutions repository (ICOS), the applications are analyzed comparatively regarding (i) the environmental issue addressed, (ii) the associated mitigation strategies, (iii) the included innovation mechanism, (iv) the role of information and communication technologies and (v) the overall outcome.

It investigates the potential contribution of smart city approaches and tools to sustainable urban development in the environment domain. Recent research has highlighted the need to explore the relation of smart and sustainable cities more systematically, focusing on practical applications that could enable a deeper understanding of the included domains, typologies and design concepts, and this paper aims to address this research gap. At the same time, it tries to identify whether these applications could contribute to the “zero vision” strategy, an extremely ambitious challenge within the field of smart cities.

The findings suggest that the smart and sustainable city landscape is extremely fragmented both on the policy and the technical levels. Similar findings are reached for all categories of environmental challenges in cities.

Although cities where all urban systems and services are connected do not exist as of yet, many cities are already on the path to becoming smart sustainable cities. They rely on ICTs, for example, to enhance energy efficiency and waste management, improve housing and health care, optimize traffic flow and safety, detect air quality, alert police of crimes occurring on the streets and improve water and sanitation systems.

ICTs have the potential to accelerate the achievement of all 17 United Nations Sustainable Development Goals (SDGs), including SDG 11, which aims to achieve sustainable cities and communities.

According to Citi GPS Report “Sustainable Cities: Beacons of light against the shadows of unplanned urbanization,” cities facilitate organizational efficiency by giving firms access to workers and suppliers of inputs in a way that makes it easier for firms to match their varying demands for these inputs to a large pool of available supply. Cities also allow firms to share resources like infrastructure, and to benefit from the enhancement of a country’s workforce that is created by the supply of education, health, and adequate housing. There is also a political-economy argument at work here: cities offer proximity to political power, and this enhances a firms’ access to patronage and decision-making.

Another way of thinking about cities is as a form of network, a tool of analysis recently popularized by Niall Ferguson. Social networks facilitate the transfer of information to the extent that the network components — governments, workers, and firms, in this case — are connected to each other. Cities offer a way of increasing the number of linkages between those components, and this generates efficiency. This way of thinking about cities is useful because it helps to capture one important aspect of the rise of emerging market cities during the past thirty years: namely, that this rise has coincided with an era of globalization. Globalization increases the value of cities-as-networks, because globalization has the effect of creating networks out of networks: the connection between countries that globalization relies on is facilitated by the series of connections between cities which are the result of urbanization.

And as urbanization continues, it will be increasingly important to manage some of the negative externalities that the process can create: congestion, over-crowding, excess demand for infrastructure, and rising inequality. In addition to this, there are three broad factors that might constrain urbanization in the future.

The rate at which cities grow will be influenced by a number of factors such as the types of industries that characterize the city’s economy, geographical location, climate, availability of natural resources, and proximity to other cities in the region.

There is no doubt that past urbanization has lifted many people out of poverty as we will see later, but uncontrolled and unplanned urbanization has a darker side and can lead to numerous social and human issues. Given the problems of equity, the provision of basic services both physical and social, air quality, traffic congestion, and a lack of affordable housing in many urban areas, rapid urbanization could exacerbate these already serious problems unless better and sustainable planning occurs in many of these regions.

Building a sustainable city, does not need necessary mean that it needs to be ‘smart’, however being ‘smart’ enables better use of resources, more efficient transport networks, and more effective services for citizens.

Smart sustainable cities need a telecommunication infrastructure that is stable, secure, reliable and interoperable to support an enormous volume of ICT-based applications and services.

Recent developments in the Internet of Things (IoT), Artificial Intelligence (AI) and smart grids and meters are driving and supporting the development of smart sustainable cities throughout the world.

IoT—referring to the network of rapidly growing computing devices with built-in sensors and software to connect with each other and share data—enables billions of devices and objects equipped with smart sensors to connect with each other, collect real-time information and send this data, via wireless communication, to centralized control systems. These, in turn, manage traffic, reduce energy usage and improve a wide range of urban operations and services.

AI allows extremely large data sets to be analysed computationally to reveal patterns, which are used to inform and enhance municipal decision-making.

Smart grids—referring to electricity supply networks that use digital communication technology to detect and react to local changes in usage—help to optimize energy use in cities. Smart meters and sensors, equipped with Internet Protocol addresses, can communicate information about the end-users´ energy use to the energy supplier, giving end-users more control over their consumption.

While 3G and 4G networks used by mobile phones today pose a number of problems in supporting the range of services required for smart sustainable cities applications, the development of 5G, referring to the fifth generation of mobile technologies, has the potential to reliably connect devices to the Internet and other devices, transport data much more quickly and process a high volume of data with minimal delay.​

The opportunities for the development and deployment of innovative solutions offered by technology are tremendous.

 If this can be directed towards urban sustainability, and its investment can be justified by socio-economic and environmental concerns which ultimately benefit citizens, then unlocking this potential and exploiting these benefits could be the way forward. 

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Below are just a few examples showing how ICTs are helping to build smart sustainable cities:

  • In Singapore , sensors and cameras build on the city state´s existing digital system and enable the government to assess the performance and efficiency of traffic flow and identify problems such as potholes and bumpy bus rides as well as lawbreakers. For example, to strengthen security in public spaces, the city has installed more than 62,000 police cameras in public housing blocks and carparks.
  • Copenhagen, Denmark, has upgraded its street lights with efficient lamps connected by a wireless network. Smart street lights save costs because they can be programmed to dim or brighten automatically, optimizing the use of energy while lowering the risk of crime and traffic accidents.
  • São Paulo, Brazil, has developed a solution to estimate and predict air quality using AI and Big Data analytics. Aggregated, anonymized data is leveraged from the mobile network and layered with data from weather, traffic and pollution sensors. This helps calculate pollution levels 24 to 48 hours in advance, helping policy-makers, municipalities and governments to take action to prevent death and disease—for example, by redirecting traffic before air pollution hotspots strike.
  • In Holon municipality in Israel, the sewage system was plagued with problems such as frequent blockages and overflows. The municipality installed devices equipped with sensors to better manage its sewer systems and send alerts via short message service (SMS) when the level reaches low or high limits.
  • Dubai introduced an eComplaints system for citizens to regularly provide feedback on public services.​

 

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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

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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:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

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  • Enerdata: Energy Data Set, Energy Market Report, Energy Prices, LNG Trade Data and World Refineries Data

  • Euromoney: China Law and Practice, Emerging Markets, International Tax Review, Latin Finance, Managing Intellectual Property, Petroleum Economist, Project Finance, and Euromoney Magazine

  • Euromonitor International: Industry Capsules, Local Company Profiles, Sector Capsules

  • Fitch Ratings: Criteria Reports, Outlook Report, Presale Report, Press Releases, Special Reports, Transition Default Study Report

  • FocusEconomics: Consensus Forecast Country Reports

  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

  • MarketLine: Company Profiles and Industry Profiles

  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

  • The Wall Street Journal

  • The Wall Street Transcript

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  • Standard & Poor’s Industry Surveys

  • Thomson Research

  • Thomson Street Events

  • Reuter 3000 Xtra

  • OneSource Business

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  • IDC

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  • Moody’s

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  • Forrester Research

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  • SIA / SSIR

  • Kiplinger Forecasts

  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

  • Deloitte

  • Oliver Wyman

  • Faulkner Information Services

  • Accenture

  • Ipsos

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  • Bureau van Dijk’s Amadeus

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  • Berg Insight

  • ABI research

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  • MarketsandMarkets

  • GSA

  • Frost and Sullivan Analysis

  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

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M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

  • MergerStat

  • Profound

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  • ISS Corporate Governance

  • BoardEx

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  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

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Forecast methodology

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.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

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:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
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.

Revenues

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:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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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.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

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.

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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.

Elasticities
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
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.

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