The Challenges of Urban Living

The Challenges of Urban Living

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

Urbanization or “urban transition” refers to “a shift in a population from one that is dispersed across small rural settlements, in which agriculture is the dominant economic activity, towards one that is concentrated in larger and denser urban settlements characterized by a dominance of industrial and service activities” (UN).  

Urbanization generally occurs as a result of one or more of the following processes:

  • natural population growth;
  • when more people move from rural to urban areas;
  • when the boundaries of what is considered urban are extended: and/or
  • from the creation of new urban centres.

Urban growth comes from demographic growth and international and internal migration, (IOM). Approximately one in five international migrants are estimated to live in just 20 cities -Beijing, Berlin, Brussels, Buenos Aires, Chicago, Hong Kong SAR, China, London, Los Angeles, Madrid, Moscow, New York, Paris, Seoul, Shanghai, Singapore, Sydney, Tokyo, Toronto, Vienna and Washington DC. For 18 of these cities, international migrants represented around 20 per cent of the total population. Dubai has an foreign born population of close to 83 per cent, while in Brussels it is 62 per cent, in Toronto 46 per cent, New York 37 per cent, and Melbourne 35 per cent. In some countries, rural-to-urban migration and reclassification of what is considered urban together accounted for more than half of the urban growth, such as in China and Thailand (80%), Rwanda (79%), Indonesia (68%) and Namibia (59%). Circular and temporary migration is found in many urban parts of fast-urbanizing Asian and African countries, especially China and India as well as Ghana and Kenya


Around 10 billion trips are made every day in urban areas around the world. Of these a significant and increasing proportion are undertaken using private vehicles; 80% of the increase in global transport emissions since 1970 has been due to road vehicles. It is also estimated that 90% of the increase in carbon emissions from transport is expected to come from developing cities. Besides emissions, cars are also responsible for high air pollution levels which could contribute to an increase in public health problem.

The World Health Organization estimates that more than 80% of people living in urban areas that monitor air pollution levels are exposed to air quality levels that exceed their limits. Ninety-eight percent of cities in low- and middle-income countries with more than 100,000 inhabitants do not meet their air quality standards — this decreases to 56% in high-income countries. Not all this pollution is attributed to motorized vehicles cars; however they are responsible for a large portion of this pollution. 

By 2050, cities will add more than 2.5 billion people and global car ownership could reach 2 billion, nearly double today’s level. More people in cities could potentially mean more cars. This could lead to more congestion, less economic productivity, and a decrease in air quality levels. For example in the United States, it is estimated that commuters waste 4.8 billion hours in traffic each year, translating to $ 101 billion in lost economic productivity.

INRIX conducted a study on global traffic in 1,360 cities in 38 countries and ranked the cities studied by the number of peak hours that drivers spent in congestion. Los Angeles topped the list with drivers spending on average 102 peak hours in congestion, costing drivers on average $ 2,830 and equalling more than $ 19.2 billion to the city as a whole. This includes direct (opportunity cost of time lost due in congestion, additional fuel cost and social cost of emissions released by vehicle) and indirect costs (borne by households through the increase in prices of goods and services due to congestion faced by businesses).

In London drivers spent on average 71 hours in traffic costing London drivers £2,430 each and the city as a whole £9.5 billion from direct and indirect costs. INRIX’s study does not cover cities in China and India, however in Beijing the cost of congestion and air pollution are estimated at 7%-15% of GDP.

Cars also require space for parking — an additional 45,000-70,000 km2 would be required for car parking alone by 2050, which is a huge waste of land, given that cars are left idle for the majority of time. In the U.S., it is estimated that the average car is parked for 96% of the time.


Case Study: Hong Kong

Hong Kong with a land area of 1,105 km2 has a population of more than 7 million people. The majority travel by public transport — in fact over 12.6 million passenger journeys are made on the public transport system every day which includes railways, trams, buses, minibuses, taxes, and ferries. The backbone of public transport in Hong Kong is the railways/metro system which accounts for 41% of all trips made on public transport each day. It is managed and run by MTR Corporation which was established in1975 with a mission to construct and operate an urban metro system in Hong Kong. The metro system in Hong Kong first opened in 1979 and now operates over 217km of track as well as more than 155 stations including 87 railway stations and 68 light rail stops. The average trip costs between $ 0.50 to US $ 3 and the system makes back over 180% of its operational costs on fares alone.

Today’s sustainability challenges call for new urban solutions which in its turn require experimentation on suitable scales and with multiple stakeholders. This is where urban living labs -sites devised to design, test and learn from social and technical innovation in real time - have a key role to play. They are proliferating across Europe and around the world as a means for testing innovations in many areas – from energy transition to community gardening.

Urban living labs showcase five key characteristics:

  1. Geographical embeddedness: Urban living labs tend to be placed or embedded in a geographical area.
  2. Experimentation and learning: Urban living labs test new technologies, solutions and policies in real world conditions in highly visible ways.
  3. Participation and user involvement: Co-design and engagement with stakeholders often appears in all stages of the urban living labs approach.
  4. Leadership and ownership: It appears that having a clear leader or owner is crucial for urban living labs, although a delicate balance should exist between steering and controlling.
  5. Evaluation of actions and impact: Evaluation of tested solutions is used in order to facilitate formalized learning.

According to Professor Anthony B. L. Cheung is currently the Research Chair Professor of Public Administration at the Education University of Hong Kong. there is a great disconnect between city/urban governance research and practice. According to a study, (Nuno F. da Cruz, Philipp Rode & Michael McQuarrie (2019) New urban governance: A review of current themes and future priorities, Journal of Urban Affairs) the constraints and challenges faced by urban governance as viewed by academics and activists are vastly at variance with urban managers. Of most concern to the former are issues of citizen participation, political engagement, institutional shortcomings and capacity constraints - insufficient public budgets, politicization of local issues, complexity of urban problems, and maladapted or outdated policy silos caused by inflexible bureaucracies and rigid rules.

Housing and transport are highly pertinent to urban living and well-being. They directly determine the degree of spatial inequality and mobility, that in turn affects the distribution of resources and incomes.

According to Urban Transitions 2020, integrating urban and transport planning, environment and health for healthier urban living, cities suffer from many environmental, climate change and health problems. Poor urban and transport planning is part of the problem, but can also be part of the solution. There is great potential for improvement through targeted and integrated policies. However, the urban environment is a complex interlinked system. Decision-makers need enhanced understanding of the linkages involved, cities need better knowledge, and multi-sectorial approaches are needed to tackle the current problems.

Urban Living, Density and Pandemics

More than half of the world’s population lives in highly populated urban areas. While cities have provided numerous opportunities for growth and development, our urbanization processes in recent decades have intensified many of humanity’s challenges. COVID-19 has laid bare – and indeed heightened – both these challenges and these opportunities. With an estimated 90 percent of all reported COVID-19 cases, urban areas have become the pandemic’s epicenter.             

The world’s cities are generally reacting quickly to this urban humanitarian crisis, and how they respond is critical to protect their populations, halt the pandemic.

Associate Professor at the University of Copenhagen and co-chair at the Copenhagen Center for Disaster Research, Emmanuel Raju, and Architect and Urban Planner from the University of Americas in Chile, Francisco Vergara Perucich talked about the different perspectives of urban living, density, and inequality in cities during the COVID-19 pandemic.

The COVID-19 pandemic has shown how inequality in cities worldwide has aggravated the risk of infection for millions living in informal settlements and on the brink of poverty. The pandemic highlights the need for a new urban agenda. This pandemic is already exacerbating the urban divide that has resulted from a long-term failure to address fundamental inequalities and guarantee basic human rights. The post-COVID-19 response will require these failures to be addressed, and all urban residents provided with essential services - especially health care and housing - to ensure everyone can live with dignity and be prepared for future crises. Local authorities will have to be the driving force in reducing inequality, supported by national government policies that increase the resilience of cities and their residents.

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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


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

  • EMIS: EMIS Insight and EMIS Dealwatch

  • 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

  • Bloomberg

  • Standard & Poor’s Industry Surveys

  • Thomson Research

  • Thomson Street Events

  • Reuter 3000 Xtra

  • OneSource Business

  • Hoover’s

  • MGI

  • LSE

  • MIT

  • ERA

  • BBVA

  • IDC

  • IdExec

  • Moody’s

  • Factiva

  • Forrester Research

  • Computer Economics

  • Voice and Data

  • SIA / SSIR

  • Kiplinger Forecasts

  • Dialog PRO

  • LexisNexis

  • ISI Emerging Markets

  • McKinsey

  • Deloitte

  • Oliver Wyman

  • Faulkner Information Services

  • Accenture

  • Ipsos

  • Mintel

  • Statista

  • Bureau van Dijk’s Amadeus

  • EY

  • PwC

  • Berg Insight

  • ABI research

  • Pyramid Research

  • Gartner Group

  • Juniper Research

  • MarketsandMarkets

  • GSA

  • Frost and Sullivan Analysis

  • McKinsey Global Institute

  • European Mobile and Mobility Alliance

  • Open Europe

M&A and Risk Management | Regulation

  • Thomson Mergers & Acquisitions

  • MergerStat

  • Profound

  • DDAR

  • ISS Corporate Governance

  • BoardEx

  • Board Analyst

  • Securities Mosaic

  • Varonis

  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

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.


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.


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


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.


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.