Energy Tech and Investment Strategies for the smart cities’ realization
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
- Competitive Differentiation
- Digital Transformation
Publication | Update: Nov 2022
Smart cities use advanced technologies to improve people's productivity, sustainability, and quality of life. They are expected to lead to better safety, resource management, waste, and traffic management. The information and communication technologies used in smart cities will improve core mobility, productivity, and sustainability activities. Their respective success will depend on the management of scarce resources as urbanization will continuously increase. To this end, smart grid technology will play a crucial role through the evolution towards a more decentralized energy model where end-users will be able to generate some of their own power using renewable energy sources and selling the excessive quantities to the energy network. The decentralization of these grids will include storage options for businesses and individual consumers connected to the municipal grid.
The design of energy landscapes can thus be seen as a useful translation of the integrated design of energy systems into coherent spatial and land use planning. Equally important is an appropriate management of the corresponding transition process, in which all stakeholders should be involved from the planning phase onwards. As far as energy generation is concerned, technologies are defined by the maximum power they can produce. In this context, a distinction is made between fixed and non-fixed or variable capacity. The former refers to generators that produce electricity on demand and can be switched on and off as needed. Non-firm generation technologies cannot always produce electricity because they depend on external factors such as wind and sunlight. For optimized electricity transactions, the most efficient allocation of assets must be sought within the constraints imposed by the physical system. Distribution systems need to be strengthened and expanded in areas of low electricity demand where generation can easily exceed consumption. Similarly, heat pumps, electric vehicles and new energy-intensive equipment can significantly increase demand.
The growth of decentralized renewable energy sources, the changing consumption habits of end consumers and technological progress pose new challenges for all participants in the energy market. Finding suitable markets and business models for the new energy paradigm is a major challenge. End consumers are confronted with a variety of incentives and triggers that influence their final energy bill. The interplay of these incentives determines the end consumer's response and the available savings potential. From an integrated sustainable development perspective, these efforts should be considered not only from an environmental and economic perspective, but also from a social and governance perspective. Indeed, energy permeates all societal functions, so the social and governance pillars must be thoroughly considered to ensure a successful energy transition.
Cities need both public and private investment to achieve their smart city goals in terms of investment. Barriers to the creation and success of smart cities vary by market type. In emerging markets, corruption can be a barrier, while in developed markets, privacy can be a bigger issue. So far, much private investment has gone into the transport and mobility sector, both in terms of providing new services and improving the use of existing ones However, most private investment in technology start-ups
is are made directly or through venture capital funds, with institutional investors either not required to invest directly or limited to providing small amounts of venture capital. Partnerships and coordination with national planning authorities, investors, citizens, and other stakeholders will lead to the far better smart city development and financing. Cities need to secure projects or bonds, for example from pension funds, for debt financing, which might be proven to be appropriate for various other markets, governance conditions and investors. The development of secondary markets and improved liquidity in infrastructure financing would also encourage institutional investors to invest more. It is important for those who want to successfully invest in smart cities to be supported in navigating this complex and rapidly changing investment landscape. Combining global best practices with local knowledge, especially in emerging markets, is extremely important for risk management, whether investing in city-led smart city projects or in companies offering smart city technologies.
 Hawkins, L.E., Versace, C., Abssy, M. (2022, June). World reimagined: The potential of Smart Cities. Nasdaq. Retrieved from: https://www.nasdaq.com/articles/world-reimagined%3A-the-potential-of-smart-cities
 European Commission. (2021). The impact of the EU’s changing electricity market design on the development of smart and sustainable cities and energy communities. SCIS/SCM Policy paper. Retrieved from: https://smart-cities-marketplace.ec.europa.eu/insights/publications/impact-eus-changing-electricity-market-design-development-smart-and
 The Economist Intelligence Unit Limited. (2018). Smart Cities: Investing in the future. Retrieved from: https://impact.economist.com/perspectives/sites/default/files/EIU%20Invesco%20report_V3.pdf
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.