スマートシティ：主要プラットフォーム、セグメント分析＆予測 2019-2023年
Smart Cities
この調査レポートは、世界のスマートシティ市場を詳細に調査し、市場に関する解説や詳細な予測な結果などを掲載しています。
主な掲載内容
世界の主要スマートシティプラットフォーム戦略、ビ... もっと見る
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サマリー
この調査レポートは、世界のスマートシティ市場を詳細に調査し、市場に関する解説や詳細な予測な結果などを掲載しています。
主な掲載内容
-
世界の主要スマートシティプラットフォーム戦略、ビジネスモデル革新、将来展望
-
スマートグリッド
-
スマート街灯
-
スマートアーバンモビリティ
-
スマート交通管理
-
スマートパーキング
-
スマート医療
-
調達分析
-
P3分析（Public-Private-Partnership）
-
投資回収
-
ベンチマーク産業予測
-
ソフトウェア支出
-
ハードウェア支出
-
エネルギー削減
-
排出削減
Report Details
Regions:
8 Key Regions - includes North America, Latin America, West Europe, Central & East Europe, Far East & China, Indian Subcontinent, Rest of Asia Pacific and Africa & Middle East
Countries:
Canada, China, Denmark, Germany, Japan, South Korea, Norway, Portugal, Spain, Sweden, UK, USA
Overview
Juniper’s latest Smart Cities research takes a deep dive into the evolving platform landscape across the market; highlighting multiple vendors’ and cities’ strategies aligned with a series of recommendations and opportunities for stakeholders.
Juniper’s must-read research provides unique insights into this market; providing in-depth analysis of key smart city segment market forces and future outlook for the market.
The analysis covers key industry service segments, including:
-
Smart Grid
-
Smart Urban Mobility
-
Smart Traffic Management
-
Smart Parking
-
Smart Street Lighting
-
Smart Health
This research suite includes:
-
Deep Dive Strategy & Competition (PDF)
-
5-Year Deep Dive Data & Forecasting (PDF & Excel)
-
Executive Summary & Core Findings (PDF)
-
12 months' access to harvest online data platform
-
Sector Dynamics: Multi-segment strategic assessment and breakdown; examining key Smart City platform strategies, business model innovation and future outlook:
-
Smart Grid
-
Smart Street Lighting
-
Smart Urban Mobility
-
Smart Traffic Management
-
Smart Parking
-
Smart Health
-
Procurement Analysis: Evaluation of Smart City business models, in relation to:
-
P3 (Public-Private-Partnership) analysis
-
Investment recovery models
-
Interviews with leading players, including:
-
Dimonoff
-
EnergyHub
-
IoT Living Lab
-
Nokia
-
Passport
-
Ruckus Networks
-
Trafi
-
Juniper Leaderboard: Key player capability and capacity assessment for 15 Smart City platform vendors:
-
AT&T
-
Bosch
-
Cisco
-
Ericsson
-
GE
-
Hitachi
-
Huawei
-
IBM
-
Intel
-
Itron
-
Nokia
-
Oracle
-
Schneider Electric
-
Siemens
-
Verizon
-
Benchmark industry forecasts: market segment forecasts for key Smart City verticals, including:
-
Software Spend
-
Hardware Spend
-
Energy Saving
-
Emissions Saving
-
Cost Saving
-
Who are the leading Smart City platform vendors, and how do they differentiate strategically?
-
Which strategies should cities look to apply when developing Smart City projects?
-
What are the key trends shaping Smart City policy and service markets?
-
What are the prospects for city smart energy, transport services and lighting markets?
-
What is the economic opportunity for smart city projects?
Interviewed: Dimonoff, EnergyHub, IoT Living Lab, Nokia, Passport, Ridecell, Ruckus Networks, Trafi.
Profiled: AT&T, Bosch, Cisco, Ericsson, GE, Hitachi, Huawei, IBM, Intel, Itron, Nokia, Oracle, Schneider Electric, Siemens, Verizon.
Case Studied: CityBridge Consortium, Con Edison, Didi Chuxing, Dimonoff, EnergyHub, Huawei, IoT Living Lab, Neusoft, Passport, Ruckus Networks, Surtrac, Trafi.
Mentioned: ABB, Accenture, Acuity Brands, Advanced Control Systems, AGT International, Alcatel‑Lucent, Alibaba, Alphabet, Alstom SA, Amazon, Apple, Argonne National, Arris, AutoGrid, Avrio, Bell Canada, Bharti Infratel, Bidgely, BlaBlaCar, Black & Veatch, Boeing, Bouygues Telecom, Broadcom, Burbank, BVG (Berliner Verkehrsbetriebe), Capgemini, Chicago Innovation Exchange, China Communications Standards Association, Choice! Energy Management, Citi Bike, CITIXL (City Innovation Exchange Lab), Citrix Systems, Citybeacon, CivicSmart, Colorado Smart Cities Alliance, Comark, Connode, Control Group, Cyber Defense Institute, Cyber Security Association of China, Deloitte. Deutsche Telekom, Downer, Dynamic Ratings, EnergyHub, Esri, Estuate, FIDO Alliance, Ford, Freescale Semiconductor, Frost Data Capital, G+D, Genetec, GlobalLogic, Google, Görlitz, Grab, HKSTP (Hong Kong Science and Technology Parks Corporation), Honda, Honeywell, i2O Water, Infosys, INRIX, IRCTC (Indian Railway Catering and Tourism Corporation), JDA, KPMG, Landis+Gyr, Living PlanIT, London Health Commission, Lyft, Masdar Institute of Science and Technology, Master Meter, Meridium, Microsoft, Mizuho Bank, Monaco Telecom, Moniteye, NACTO, National Science Foundation, NEC, Nedaa, NEDO (New Energy and Industrial Technology Development Organisation), NHS (National Health Service), NTT DoCoMo, NXP, Oi Brasil, Ola, OPower, OSIsoft, oXya, Pantascene, Parkeon, Paytm, Philips, Plug and Play, PVT, Qualcomm, Reliance Energy, Renault, Reuters, Rongwen, Samsung, SAP, SAS, SELC, Sensity Systems, Sentient, Shotspotter SST, Silver Spring Networks, SmartGridCIS, SoftBank, Software AG, Songas, Southern Company, Sprint, Streetline, Synchronoss, Tapp, Tata, Tech Mahindra, Telit, Telstra, Tencent, Tendril, TfL (Transport for London), Titan, Trainline, Ubicquia, urbancontrol, Veolia, VMWare, Vodafone, Waymo, Weiqiao Pioneering Group, Wheaton Franciscan, WHO, Wind River, Wipro, World Wide Web Consortium, Zain, Zaphod.
Data & Interactive Forecast
Juniper’s Smart Cities forecast suite includes:
-
Segment splits for:
-
Smart Grid
-
Smart Traffic Management
-
Smart Parking
-
Smart Street Lighting
-
Regional splits for 8 key regions, as well as country level data splits for:
-
Canada
-
China
-
Denmark
-
Germany
-
Japan
-
Norway
-
Portugal
-
Spain
-
Sweden
-
South Korea
-
UK
-
US
-
Interactive Scenario Tool allowing users to manipulate Juniper’s data for 6 different metrics.
-
Access to the full set of forecast data of 77 tables and over 10,300 datapoints.
Juniper Research’s highly granular interactive Excels enable clients to manipulate Juniper’s forecast data and charts to test their own assumptions using the Interactive Scenario Tool, and compare select markets side by side in customised charts and tables. IFxls greatly increase clients’ ability to both understand a particular market and to integrate their own views into the model.
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目次
Table of Contents
1. Deep Dive Strategy & Competition
1. Smart Cities: Evolving Market Dynamics
1.1 Introduction . 6
1.1.1 The Journey to Becoming a Smart City: Key Strategic
Considerations for Cities . 6
1.1.2 Talent Acquisition . 7
1.1.3 Governance . 7
1.1.4 Innovation. 7
Case Study: Amsterdam IoT Living Lab . 8
1.2 Smart City Procurement . 8
1.2.1 P3 Analysis . 9
1.2.2 Investment Recovery Through Advertising .11
Figure 1.1: LinkNYC Kiosk. 11
Case Study: LinkNYC . 11
i. Procurement . 11
ii. Investment Recovery . 11
iii. Jun iper’s View . 11
1.3 Smart City Platform Analysis .12
1.3.1 Strategic Approach .12
i. Security Considerations . 13
1.4 Connectivity .14
1.4.1 Low Power Wide Area Networks .14
Figure 1.2: Total Low Pow er Smart City Connections in Service (m), Split by
Wireless Technology, 2018 & 2022 . 14
i. Device Roaming.14
1.4.2 The Emergence of 5G .15
1.4.3 5G Smart Cities: Strategic Recommendations .16
i. Cultivate Framew orks that Promote Open Source Elements, Adaptable Machine
Learning & Data Analytics Systems .16
ii. Promotion of Public-Private Partnerships .16
iii. 5G Smart City Pricing Strategies .16
1.4.4 Converged Networks .17
Case Study: Ruckus Networks.18
2. Market Segment Analysis
2.1 Introduction .20
2.2 Smart Energy.20
2.2.1 Smart Grid Business Model Innovation .21
Case Study: Brooklyn Microgrid .21
i. Combatting Lost Revenue.22
2.2.2 Smart Street Lighting .24
Case Study: Dimonof f .25
2.2.3 Smart Street Lighting City Innovation.26
Case Study: City of Pittsburgh .26
Case Study: Huawei PoleStar 2.0 .27
2.2.4 Smart Street Lighting Challenges.28
i. Energy Savings .28
ii. Interoperability.28
iii. Adjacent Services .28
2.2.5 Smart Energy Market Investment Trends & Regulatory Environment .29
i. The US. 30
ii. EU . 30
iii. Far East & China . 31
2.3 Smart Transport.31
2.3.1 City Strategy .32
Case Study: Didi Chuxing Smart Transportation Brain . 33
Case Study: Traf i . 34
2.3.2 Urban Mobility Development Strategies.35
i. Ridesharing. 35
ii. Ticketing. 36
Figure 2.1: Global Mobile & Online Transport Ticketing Users (m), Transaction
Volume (m) 2015-2018 . 36
iii. Bikesharing . 37
iv. Autonomous Vehicles . 38
v. Electric Vehicles . 39
Case Study: EnergyHub . 40
vi. Smart Traff ic Management. 41
Case Study: Surtrac. 42
vii. Smart Parking . 42
Case Study: Passport . 44
2.4 Smart Health .45
2.4.1 Platform Strategies.45
Case Study: Ningbo Cloud Hospital. 46
Case Study: Array of Things, Chicago. 47
2.4.2 City Strategy .48
i. Key Considerations .48
Case Study: DigitalHealth.London .49
3. Smart City Platform Vendor Analysis & Leaderboard
3.1 Introduction .51
3.2 Juniper Leaderboard .51
Table 3.1: Vendor Capability Assessment Criteria .52
3.2.1 Leaderboard Scoring Results.53
Table 3.2: Juniper Leaderboard: Smart City Platform Vendors .53
Figure 3.3: Juniper Leaderboard: Smart City Platform Vendors.54
3.2.2 Vendor Groupings .55
i. Established Leaders.55
ii. Leading Challengers .55
iii. Disruptors & Emulators.56
3.2.3 Limitations & Interpretations.57
3.3 Smart City Movers & Shakers .58
3.4 Stakeholder Analysis .60
3.4.1 AT&T .60
i. Corporate Prof ile .60
Table 3.4: AT&T Financial Snapshot ($m) 2016-2018 .60
ii. Geographic Spread .60
iii. Key Clients & Strategic Partnerships .60
iv. High Level View of Offerings .61
v. Juniper’s View : Key Strengths & Development Opportunities .61
3.4.2 Bosch .62
i. Corporate. 62
Table 3.5: Bosch Financial Snapshot (?m/$m) 2015-2017 . 62
ii. Geographic Spread . 62
iii. Key Clients & Strategic Partnerships. 62
iv. High Level View of Offerings . 62
v. Juniper’s View : Key Strengths & Development Opportunities. . 63
3.4.3 Cisco .63
i. Corporate Prof ile. 63
Table 3.6: Cisco Financial Snapshot ($m) FY 2016-2018. 64
ii. Geographic Spread . 64
iii. Key Clients & Strategic Partnerships. 64
iv. High Level View of Offerings . 64
v. Juniper’s View : Key Strengths & Strategic Development Opportunities . 66
3.4.4 Ericsson .66
i. Corporate Prof ile. 66
Table 3.7: Ericsson Financial Snapshot (SEK m) 2016-2018 . 66
ii. Geographic Spread . 67
iii. Key Clients & Strategic Partnerships. 67
iv. High Level View of Offerings . 67
v. Juniper’s View : Key Strengths & Development Opportunities . 68
3.4.5 GE .68
i. Corporate Prof ile. 68
Table 3.8: GE Financial Snapshot ($bn) 2016-2018 . 68
ii. Geographic Spread . 69
iii. Key Clients & Partnerships. 69
iv. High Level View of IoT Of ferings .70
v. Juniper’s View : Key Strengths & Strategic Development Opportunities .70
3.4.6 Hitachi .71
i. Corporate Prof ile .71
Hitachi Financial Snapshot (\ billion) 2015-2017 .71
ii. Geographic Spread .71
iii. Key Clients & Strategic Partnerships .71
iv. High Level View of Offerings .72
v. Juniper’s View : Key Strengths & Strategic Development Opportunities .73
3.4.7 Huawei.73
i. Corporate .73
Table 3.9: Huaw ei Financial Performance Snapshot ($m) 2015-2017 .73
ii. Geographical Spread .74
iii. Key Clients & Strategic Partnerships .74
iv. High Level View of Offerings .74
v. Juniper’s View : Key Strengths & Development Opportunities .75
3.4.8 IBM .75
i. Corporate Prof ile .75
Table 3.10: IBM Financial Snapshot ($m) 2015-2017 .75
ii. Geographic Spread .75
iii. Key Clients & Strategic Partnerships .76
iv. High Level View of Offerings .76
v. Juniper’s View : Key Strengths & Development Opportunities .77
3.4.9 Intel .77
i. Corporate Prof ile .77
Table 3.11: Intel Financial Snapshot ($m) 2016-2018.77
ii. Geographic Spread . 78
iii. Key Clients & Strategic Partnerships. 78
iv. High Level View of Offerings . 78
v. Juniper’s View : Key Strengths & Development Opportunities . 79
3.4.10 Itron .79
i. Corporate Prof ile. 79
Table 3.12: Itron Financial Snapshot ($m) 2016-2018 . 79
ii. Geographic Spread . 80
iii. Key Clients & Strategic Partnerships. 80
iv. High Level View of Offerings . 80
v. Juniper’s View : Key Strengths & Development Opportunities . 81
3.4.11 Nokia .81
i. Corporate Prof ile. 81
Table 3.13: Nokia Financial Snapshot 2016-2018 (?m) . 81
ii. Geographic Spread . 82
iii. Key Clients & Partnerships. 82
iv. High Level View of Offerings . 83
v. Juniper’s View : Key Strengths & Opportunities. 84
3.4.12 Oracle .84
i. Corporate Prof ile. 84
Table 3.14: Oracle Financial Snapshot ($m) 2016-2018. 84
ii. Geographic Spread . 85
iii. Key Clients & Strategic Partnerships. 85
iv. High Level View of Offerings . 85
v. Juniper’s View : Key Strengths & Development Opportunities . 86
3.4.13 Schneider Electric .86
i. Corporate Prof ile .86
Table 3.15: Schneider Electric Financial Snapshot (?m) 2016-2018.86
ii. Geographic Spread .87
iii. Key Clients & Strategic Partnerships .87
iv. High Level View of Offerings .87
v. Juniper’s View : Key Strengths & Development Opportunities .88
3.4.14 Siemens .88
i. Corporate Prof ile .88
Table 3.16: Siemens Financial Snapshot (?m) 2016-2018 .88
ii. Geographic Spread .88
iii. Key Clients & Strategic Partnerships .88
iv. Products & Services .89
v. Juniper’s View : Key Strengths & Development Opportunities .90
3.4.15 Verizon .91
i. Corporate Prof ile .91
Table 3.17: Verizon Financial Snapshot ($m) 2016-2018 .91
ii. Geographic Spread .91
iii. Key Clients & Strategic Partnerships .91
iv. High Level View of Offerings .92
v. Juniper’s View : Key Strengths & Development Opportunities .92
2. Deep Dive Data & Forecasting
1. Smart Cities: Evolving Market Dynamics
1.1 Introduction . 4
1.1.1 The Journey to Becoming a Smart City: Key Strategic
Considerations for Cities . 4
1.1.2 Talent Acquisition . 5
1.1.3 Governance . 5
1.1.4 Innovation . 5
2. Smart City Forecasts: Baseline Figures & Summary
2.1 Introduction . 7
2.1 Population . 7
Figure & Table 2.1: City Population over 500,000 individuals, Split by 8 Key
Regions 2018-2023 . 8
2.2 Wholesale Electricity Prices . 8
Figure & Table 2.2: Wholesale Electricity Prices ($/MWh) Split by 8 Key Regions
2018-2023 . 9
2.3 Emissions Factors . 10
2.3.1 Electricity Generation . 10
Table 2.3: CO2e Emissions Split by Electricity Generation Fuel . 10
2.3.2 Internal Combustion Engine Vehicles . 10
Table 2.4: Emissions Factor for City Vehicles, Split by 8 Key Regions 2018-2023
(t CO2e/km) . 11
2.4 Economic Value of CO2e . 11
Table 2.5: CO2e Emissions Value ($/t) 2017-2022 . 11
2.5 Forecast Summary . 11
2.6 Cost Savings Summary . 12
Figure & Table 2.6: Combined Energy & CO2e Cost Savings ($m) Split by Smart
City Market Vertical 2018-2023 . 12
2.7 Software Spend Summary . 13
Figure & Table 2.7: Combined Annual Smart City Software Spend ($m) Split by
Smart City Market Vertical 2018-2023 . 13
3. Smart Grid Forecasts
3.1 Introduction . 15
3.2 Methodology & Assumptions . 15
Figure 3.1: Smart Grid Forecast Methodology . 17
3.3 City Energy Consumption . 18
Figure & Table 3.2: Annual City Energy Electricity Consumption (TWh) Split by 8
Key Regions 2018-2023 . 18
3.4 Smart Grid Energy Savings . 19
Figure & Table 3.3: Annual Smart Grid Net Energy Savings (TWh) 2018-2023 . 19
3.5 Smart Grid Emissions Savings . 20
Figure & Table 3.4: Smart Grid CO2e Emissions Savings (MMT) Split by 8 Key
Regions 2018-2023 . 20
3.6 Smart Grid Energy & Emissions Cost Savings . 21
Figure & Table 3.5: Total Annual Smart Grid Energy & CO2e Cost Savings ($m)
2018-2023 . 21
3.7 Smart Grid Software Spend . 22
Figure & Table 3.6: Total Annual Smart Grid Software Spend ($m) Split by 8 Key
Regions 2018-2023 . 22
4. Smart Traffic Management & Parking Forecasts
4.1 Introduction . 24
4.2 Methodology & Assumptions . 24
Figure 4.1: Smart Traffic Management & Parking Methodology . 26
4.3 Smart Traffic Management Emissions Savings . 27
Figure & Table 4.2: Annual Smart Traffic Management CO2e Savings (MMT) Split
by 8 Key Regions 2018-2023. 27
4.4 Smart Traffic Management Cost Savings . 28
Figure & Table 4.3: Annual Smart City Traffic Management Emissions Cost
Savings ($m), Split by 8 Key Regions 2018-2023 . 28
4.5 Smart Traffic Management Software Spend. 29
Figure & Table 4.4: Total Annual Smart Traffic Management Software Spend ($m)
Split by 8 Key Regions 2018-2023 . 29
4.6 Smart Traffic Management Hardware Spend . 30
Figure & Table 4.5: Total Annual Smart Traffic Management Hardware Unit
Spend ($m) Split by 8 Key Regions 2018-2023 . 30
4.7 Smart Parking Emissions Savings . 31
Figure & Table 4.6: Annual Smart Parking CO2e Savings (MMT) Split by 8 Key
Regions 2018-2023 . 31
4.8 Smart Parking Cost Savings . 32
Figure & Table 4.7: Annual Smart City Smart Parking Emissions Cost Savings
($m), Split by 8 Key Regions 2018-2023. 32
4.9 Smart Parking Software Spend . 33
Figure & Table 4.8: Annual Software Spend on Smart Parking Systems ($m), Split
by 8 Key Regions 2018-2023. 33
4.10 Smart Parking Hardware Spend . 34
Figure & Table 4.9: Annual Hardware Unit Spend on Smart Parking Systems
($m), Split by 8 Key Regions 2018-2023. 34
4.11 Smart Traffic & Smart Parking Summary . 35
Figure & Table 4.10: Combined Annual Smart City Traffic Management & Smart
Parking CO2e Emissions Cost Savings ($m), Split by 8 Key Regions 2018-2023 35
Figure & Table 4.11: Combined Annual Smart City Traffic Management & Smart
Parking Software Spend ($m), Split by 8 Key Regions 2018-2023 . 36
Figure & Table 4.12: Combined Annual Smart City Traffic Management & Smart
Parking Hardware Unit Spend ($m), Split by 8 Key Regions 2018-2023 . 37
5. Smart Street Lighting Forecasts
5.1 Introduction . 39
5.2 Methodology & Assumptions . 39
Figure 5.1: Smart Street Lighting Methodology . 41
5.3 Smart Street Lighting Energy Savings . 42
Figure & Table 5.2: Annual Smart Street Lighting Energy Savings (TWh), Split by
8 Key Regions 2018-2023 . 42
5.4 Smart Street Lighting Emissions Savings . 43
Figure & Table 5.3: Annual Smart Street Lighting CO2e Savings (MMT), Split by 8
Key Regions 2018-2023 . 43
5.5 Smart Street Lighting Energy & Emissions Cost Savings . 44
Figure & Table 5.4: Annual Smart Street Lighting Energy & Emissions Cost
Savings ($m) Split by 8 Key Regions 2018-2023 . 44
5.6 Smart Street Lighting Software Spend . 45
Figure & Table 5.5: Annual Smart Street Lighting Software Spend ($m), Split by 8
Key Regions 2018-2023 . 45
5.7 Smart Street Lighting Hardware Spend . 46
Figure & Table 5.6: Annual Smart Street Lighting Hardware Unit Spend ($m), Split
by 8 Key Regions 2018-2023 . 46
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Summary
この調査レポートは、世界のスマートシティ市場を詳細に調査し、市場に関する解説や詳細な予測な結果などを掲載しています。
主な掲載内容
-
世界の主要スマートシティプラットフォーム戦略、ビジネスモデル革新、将来展望
-
スマートグリッド
-
スマート街灯
-
スマートアーバンモビリティ
-
スマート交通管理
-
スマートパーキング
-
スマート医療
-
調達分析
-
P3分析（Public-Private-Partnership）
-
投資回収
-
ベンチマーク産業予測
-
ソフトウェア支出
-
ハードウェア支出
-
エネルギー削減
-
排出削減
Report Details
Regions:
8 Key Regions - includes North America, Latin America, West Europe, Central & East Europe, Far East & China, Indian Subcontinent, Rest of Asia Pacific and Africa & Middle East
Countries:
Canada, China, Denmark, Germany, Japan, South Korea, Norway, Portugal, Spain, Sweden, UK, USA
Overview
Juniper’s latest Smart Cities research takes a deep dive into the evolving platform landscape across the market; highlighting multiple vendors’ and cities’ strategies aligned with a series of recommendations and opportunities for stakeholders.
Juniper’s must-read research provides unique insights into this market; providing in-depth analysis of key smart city segment market forces and future outlook for the market.
The analysis covers key industry service segments, including:
-
Smart Grid
-
Smart Urban Mobility
-
Smart Traffic Management
-
Smart Parking
-
Smart Street Lighting
-
Smart Health
This research suite includes:
-
Deep Dive Strategy & Competition (PDF)
-
5-Year Deep Dive Data & Forecasting (PDF & Excel)
-
Executive Summary & Core Findings (PDF)
-
12 months' access to harvest online data platform
-
Sector Dynamics: Multi-segment strategic assessment and breakdown; examining key Smart City platform strategies, business model innovation and future outlook:
-
Smart Grid
-
Smart Street Lighting
-
Smart Urban Mobility
-
Smart Traffic Management
-
Smart Parking
-
Smart Health
-
Procurement Analysis: Evaluation of Smart City business models, in relation to:
-
P3 (Public-Private-Partnership) analysis
-
Investment recovery models
-
Interviews with leading players, including:
-
Dimonoff
-
EnergyHub
-
IoT Living Lab
-
Nokia
-
Passport
-
Ruckus Networks
-
Trafi
-
Juniper Leaderboard: Key player capability and capacity assessment for 15 Smart City platform vendors:
-
AT&T
-
Bosch
-
Cisco
-
Ericsson
-
GE
-
Hitachi
-
Huawei
-
IBM
-
Intel
-
Itron
-
Nokia
-
Oracle
-
Schneider Electric
-
Siemens
-
Verizon
-
Benchmark industry forecasts: market segment forecasts for key Smart City verticals, including:
-
Software Spend
-
Hardware Spend
-
Energy Saving
-
Emissions Saving
-
Cost Saving
-
Who are the leading Smart City platform vendors, and how do they differentiate strategically?
-
Which strategies should cities look to apply when developing Smart City projects?
-
What are the key trends shaping Smart City policy and service markets?
-
What are the prospects for city smart energy, transport services and lighting markets?
-
What is the economic opportunity for smart city projects?
Interviewed: Dimonoff, EnergyHub, IoT Living Lab, Nokia, Passport, Ridecell, Ruckus Networks, Trafi.
Profiled: AT&T, Bosch, Cisco, Ericsson, GE, Hitachi, Huawei, IBM, Intel, Itron, Nokia, Oracle, Schneider Electric, Siemens, Verizon.
Case Studied: CityBridge Consortium, Con Edison, Didi Chuxing, Dimonoff, EnergyHub, Huawei, IoT Living Lab, Neusoft, Passport, Ruckus Networks, Surtrac, Trafi.
Mentioned: ABB, Accenture, Acuity Brands, Advanced Control Systems, AGT International, Alcatel‑Lucent, Alibaba, Alphabet, Alstom SA, Amazon, Apple, Argonne National, Arris, AutoGrid, Avrio, Bell Canada, Bharti Infratel, Bidgely, BlaBlaCar, Black & Veatch, Boeing, Bouygues Telecom, Broadcom, Burbank, BVG (Berliner Verkehrsbetriebe), Capgemini, Chicago Innovation Exchange, China Communications Standards Association, Choice! Energy Management, Citi Bike, CITIXL (City Innovation Exchange Lab), Citrix Systems, Citybeacon, CivicSmart, Colorado Smart Cities Alliance, Comark, Connode, Control Group, Cyber Defense Institute, Cyber Security Association of China, Deloitte. Deutsche Telekom, Downer, Dynamic Ratings, EnergyHub, Esri, Estuate, FIDO Alliance, Ford, Freescale Semiconductor, Frost Data Capital, G+D, Genetec, GlobalLogic, Google, Görlitz, Grab, HKSTP (Hong Kong Science and Technology Parks Corporation), Honda, Honeywell, i2O Water, Infosys, INRIX, IRCTC (Indian Railway Catering and Tourism Corporation), JDA, KPMG, Landis+Gyr, Living PlanIT, London Health Commission, Lyft, Masdar Institute of Science and Technology, Master Meter, Meridium, Microsoft, Mizuho Bank, Monaco Telecom, Moniteye, NACTO, National Science Foundation, NEC, Nedaa, NEDO (New Energy and Industrial Technology Development Organisation), NHS (National Health Service), NTT DoCoMo, NXP, Oi Brasil, Ola, OPower, OSIsoft, oXya, Pantascene, Parkeon, Paytm, Philips, Plug and Play, PVT, Qualcomm, Reliance Energy, Renault, Reuters, Rongwen, Samsung, SAP, SAS, SELC, Sensity Systems, Sentient, Shotspotter SST, Silver Spring Networks, SmartGridCIS, SoftBank, Software AG, Songas, Southern Company, Sprint, Streetline, Synchronoss, Tapp, Tata, Tech Mahindra, Telit, Telstra, Tencent, Tendril, TfL (Transport for London), Titan, Trainline, Ubicquia, urbancontrol, Veolia, VMWare, Vodafone, Waymo, Weiqiao Pioneering Group, Wheaton Franciscan, WHO, Wind River, Wipro, World Wide Web Consortium, Zain, Zaphod.
Data & Interactive Forecast
Juniper’s Smart Cities forecast suite includes:
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Segment splits for:
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Smart Grid
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Smart Traffic Management
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Smart Parking
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Smart Street Lighting
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Regional splits for 8 key regions, as well as country level data splits for:
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Canada
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China
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Denmark
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Germany
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Japan
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Norway
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Portugal
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Spain
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Sweden
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South Korea
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UK
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US
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Interactive Scenario Tool allowing users to manipulate Juniper’s data for 6 different metrics.
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Access to the full set of forecast data of 77 tables and over 10,300 datapoints.
Juniper Research’s highly granular interactive Excels enable clients to manipulate Juniper’s forecast data and charts to test their own assumptions using the Interactive Scenario Tool, and compare select markets side by side in customised charts and tables. IFxls greatly increase clients’ ability to both understand a particular market and to integrate their own views into the model.
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Table of Contents
Table of Contents
1. Deep Dive Strategy & Competition
1. Smart Cities: Evolving Market Dynamics
1.1 Introduction . 6
1.1.1 The Journey to Becoming a Smart City: Key Strategic
Considerations for Cities . 6
1.1.2 Talent Acquisition . 7
1.1.3 Governance . 7
1.1.4 Innovation. 7
Case Study: Amsterdam IoT Living Lab . 8
1.2 Smart City Procurement . 8
1.2.1 P3 Analysis . 9
1.2.2 Investment Recovery Through Advertising .11
Figure 1.1: LinkNYC Kiosk. 11
Case Study: LinkNYC . 11
i. Procurement . 11
ii. Investment Recovery . 11
iii. Jun iper’s View . 11
1.3 Smart City Platform Analysis .12
1.3.1 Strategic Approach .12
i. Security Considerations . 13
1.4 Connectivity .14
1.4.1 Low Power Wide Area Networks .14
Figure 1.2: Total Low Pow er Smart City Connections in Service (m), Split by
Wireless Technology, 2018 & 2022 . 14
i. Device Roaming.14
1.4.2 The Emergence of 5G .15
1.4.3 5G Smart Cities: Strategic Recommendations .16
i. Cultivate Framew orks that Promote Open Source Elements, Adaptable Machine
Learning & Data Analytics Systems .16
ii. Promotion of Public-Private Partnerships .16
iii. 5G Smart City Pricing Strategies .16
1.4.4 Converged Networks .17
Case Study: Ruckus Networks.18
2. Market Segment Analysis
2.1 Introduction .20
2.2 Smart Energy.20
2.2.1 Smart Grid Business Model Innovation .21
Case Study: Brooklyn Microgrid .21
i. Combatting Lost Revenue.22
2.2.2 Smart Street Lighting .24
Case Study: Dimonof f .25
2.2.3 Smart Street Lighting City Innovation.26
Case Study: City of Pittsburgh .26
Case Study: Huawei PoleStar 2.0 .27
2.2.4 Smart Street Lighting Challenges.28
i. Energy Savings .28
ii. Interoperability.28
iii. Adjacent Services .28
2.2.5 Smart Energy Market Investment Trends & Regulatory Environment .29
i. The US. 30
ii. EU . 30
iii. Far East & China . 31
2.3 Smart Transport.31
2.3.1 City Strategy .32
Case Study: Didi Chuxing Smart Transportation Brain . 33
Case Study: Traf i . 34
2.3.2 Urban Mobility Development Strategies.35
i. Ridesharing. 35
ii. Ticketing. 36
Figure 2.1: Global Mobile & Online Transport Ticketing Users (m), Transaction
Volume (m) 2015-2018 . 36
iii. Bikesharing . 37
iv. Autonomous Vehicles . 38
v. Electric Vehicles . 39
Case Study: EnergyHub . 40
vi. Smart Traff ic Management. 41
Case Study: Surtrac. 42
vii. Smart Parking . 42
Case Study: Passport . 44
2.4 Smart Health .45
2.4.1 Platform Strategies.45
Case Study: Ningbo Cloud Hospital. 46
Case Study: Array of Things, Chicago. 47
2.4.2 City Strategy .48
i. Key Considerations .48
Case Study: DigitalHealth.London .49
3. Smart City Platform Vendor Analysis & Leaderboard
3.1 Introduction .51
3.2 Juniper Leaderboard .51
Table 3.1: Vendor Capability Assessment Criteria .52
3.2.1 Leaderboard Scoring Results.53
Table 3.2: Juniper Leaderboard: Smart City Platform Vendors .53
Figure 3.3: Juniper Leaderboard: Smart City Platform Vendors.54
3.2.2 Vendor Groupings .55
i. Established Leaders.55
ii. Leading Challengers .55
iii. Disruptors & Emulators.56
3.2.3 Limitations & Interpretations.57
3.3 Smart City Movers & Shakers .58
3.4 Stakeholder Analysis .60
3.4.1 AT&T .60
i. Corporate Prof ile .60
Table 3.4: AT&T Financial Snapshot ($m) 2016-2018 .60
ii. Geographic Spread .60
iii. Key Clients & Strategic Partnerships .60
iv. High Level View of Offerings .61
v. Juniper’s View : Key Strengths & Development Opportunities .61
3.4.2 Bosch .62
i. Corporate. 62
Table 3.5: Bosch Financial Snapshot (?m/$m) 2015-2017 . 62
ii. Geographic Spread . 62
iii. Key Clients & Strategic Partnerships. 62
iv. High Level View of Offerings . 62
v. Juniper’s View : Key Strengths & Development Opportunities. . 63
3.4.3 Cisco .63
i. Corporate Prof ile. 63
Table 3.6: Cisco Financial Snapshot ($m) FY 2016-2018. 64
ii. Geographic Spread . 64
iii. Key Clients & Strategic Partnerships. 64
iv. High Level View of Offerings . 64
v. Juniper’s View : Key Strengths & Strategic Development Opportunities . 66
3.4.4 Ericsson .66
i. Corporate Prof ile. 66
Table 3.7: Ericsson Financial Snapshot (SEK m) 2016-2018 . 66
ii. Geographic Spread . 67
iii. Key Clients & Strategic Partnerships. 67
iv. High Level View of Offerings . 67
v. Juniper’s View : Key Strengths & Development Opportunities . 68
3.4.5 GE .68
i. Corporate Prof ile. 68
Table 3.8: GE Financial Snapshot ($bn) 2016-2018 . 68
ii. Geographic Spread . 69
iii. Key Clients & Partnerships. 69
iv. High Level View of IoT Of ferings .70
v. Juniper’s View : Key Strengths & Strategic Development Opportunities .70
3.4.6 Hitachi .71
i. Corporate Prof ile .71
Hitachi Financial Snapshot (\ billion) 2015-2017 .71
ii. Geographic Spread .71
iii. Key Clients & Strategic Partnerships .71
iv. High Level View of Offerings .72
v. Juniper’s View : Key Strengths & Strategic Development Opportunities .73
3.4.7 Huawei.73
i. Corporate .73
Table 3.9: Huaw ei Financial Performance Snapshot ($m) 2015-2017 .73
ii. Geographical Spread .74
iii. Key Clients & Strategic Partnerships .74
iv. High Level View of Offerings .74
v. Juniper’s View : Key Strengths & Development Opportunities .75
3.4.8 IBM .75
i. Corporate Prof ile .75
Table 3.10: IBM Financial Snapshot ($m) 2015-2017 .75
ii. Geographic Spread .75
iii. Key Clients & Strategic Partnerships .76
iv. High Level View of Offerings .76
v. Juniper’s View : Key Strengths & Development Opportunities .77
3.4.9 Intel .77
i. Corporate Prof ile .77
Table 3.11: Intel Financial Snapshot ($m) 2016-2018.77
ii. Geographic Spread . 78
iii. Key Clients & Strategic Partnerships. 78
iv. High Level View of Offerings . 78
v. Juniper’s View : Key Strengths & Development Opportunities . 79
3.4.10 Itron .79
i. Corporate Prof ile. 79
Table 3.12: Itron Financial Snapshot ($m) 2016-2018 . 79
ii. Geographic Spread . 80
iii. Key Clients & Strategic Partnerships. 80
iv. High Level View of Offerings . 80
v. Juniper’s View : Key Strengths & Development Opportunities . 81
3.4.11 Nokia .81
i. Corporate Prof ile. 81
Table 3.13: Nokia Financial Snapshot 2016-2018 (?m) . 81
ii. Geographic Spread . 82
iii. Key Clients & Partnerships. 82
iv. High Level View of Offerings . 83
v. Juniper’s View : Key Strengths & Opportunities. 84
3.4.12 Oracle .84
i. Corporate Prof ile. 84
Table 3.14: Oracle Financial Snapshot ($m) 2016-2018. 84
ii. Geographic Spread . 85
iii. Key Clients & Strategic Partnerships. 85
iv. High Level View of Offerings . 85
v. Juniper’s View : Key Strengths & Development Opportunities . 86
3.4.13 Schneider Electric .86
i. Corporate Prof ile .86
Table 3.15: Schneider Electric Financial Snapshot (?m) 2016-2018.86
ii. Geographic Spread .87
iii. Key Clients & Strategic Partnerships .87
iv. High Level View of Offerings .87
v. Juniper’s View : Key Strengths & Development Opportunities .88
3.4.14 Siemens .88
i. Corporate Prof ile .88
Table 3.16: Siemens Financial Snapshot (?m) 2016-2018 .88
ii. Geographic Spread .88
iii. Key Clients & Strategic Partnerships .88
iv. Products & Services .89
v. Juniper’s View : Key Strengths & Development Opportunities .90
3.4.15 Verizon .91
i. Corporate Prof ile .91
Table 3.17: Verizon Financial Snapshot ($m) 2016-2018 .91
ii. Geographic Spread .91
iii. Key Clients & Strategic Partnerships .91
iv. High Level View of Offerings .92
v. Juniper’s View : Key Strengths & Development Opportunities .92
2. Deep Dive Data & Forecasting
1. Smart Cities: Evolving Market Dynamics
1.1 Introduction . 4
1.1.1 The Journey to Becoming a Smart City: Key Strategic
Considerations for Cities . 4
1.1.2 Talent Acquisition . 5
1.1.3 Governance . 5
1.1.4 Innovation . 5
2. Smart City Forecasts: Baseline Figures & Summary
2.1 Introduction . 7
2.1 Population . 7
Figure & Table 2.1: City Population over 500,000 individuals, Split by 8 Key
Regions 2018-2023 . 8
2.2 Wholesale Electricity Prices . 8
Figure & Table 2.2: Wholesale Electricity Prices ($/MWh) Split by 8 Key Regions
2018-2023 . 9
2.3 Emissions Factors . 10
2.3.1 Electricity Generation . 10
Table 2.3: CO2e Emissions Split by Electricity Generation Fuel . 10
2.3.2 Internal Combustion Engine Vehicles . 10
Table 2.4: Emissions Factor for City Vehicles, Split by 8 Key Regions 2018-2023
(t CO2e/km) . 11
2.4 Economic Value of CO2e . 11
Table 2.5: CO2e Emissions Value ($/t) 2017-2022 . 11
2.5 Forecast Summary . 11
2.6 Cost Savings Summary . 12
Figure & Table 2.6: Combined Energy & CO2e Cost Savings ($m) Split by Smart
City Market Vertical 2018-2023 . 12
2.7 Software Spend Summary . 13
Figure & Table 2.7: Combined Annual Smart City Software Spend ($m) Split by
Smart City Market Vertical 2018-2023 . 13
3. Smart Grid Forecasts
3.1 Introduction . 15
3.2 Methodology & Assumptions . 15
Figure 3.1: Smart Grid Forecast Methodology . 17
3.3 City Energy Consumption . 18
Figure & Table 3.2: Annual City Energy Electricity Consumption (TWh) Split by 8
Key Regions 2018-2023 . 18
3.4 Smart Grid Energy Savings . 19
Figure & Table 3.3: Annual Smart Grid Net Energy Savings (TWh) 2018-2023 . 19
3.5 Smart Grid Emissions Savings . 20
Figure & Table 3.4: Smart Grid CO2e Emissions Savings (MMT) Split by 8 Key
Regions 2018-2023 . 20
3.6 Smart Grid Energy & Emissions Cost Savings . 21
Figure & Table 3.5: Total Annual Smart Grid Energy & CO2e Cost Savings ($m)
2018-2023 . 21
3.7 Smart Grid Software Spend . 22
Figure & Table 3.6: Total Annual Smart Grid Software Spend ($m) Split by 8 Key
Regions 2018-2023 . 22
4. Smart Traffic Management & Parking Forecasts
4.1 Introduction . 24
4.2 Methodology & Assumptions . 24
Figure 4.1: Smart Traffic Management & Parking Methodology . 26
4.3 Smart Traffic Management Emissions Savings . 27
Figure & Table 4.2: Annual Smart Traffic Management CO2e Savings (MMT) Split
by 8 Key Regions 2018-2023. 27
4.4 Smart Traffic Management Cost Savings . 28
Figure & Table 4.3: Annual Smart City Traffic Management Emissions Cost
Savings ($m), Split by 8 Key Regions 2018-2023 . 28
4.5 Smart Traffic Management Software Spend. 29
Figure & Table 4.4: Total Annual Smart Traffic Management Software Spend ($m)
Split by 8 Key Regions 2018-2023 . 29
4.6 Smart Traffic Management Hardware Spend . 30
Figure & Table 4.5: Total Annual Smart Traffic Management Hardware Unit
Spend ($m) Split by 8 Key Regions 2018-2023 . 30
4.7 Smart Parking Emissions Savings . 31
Figure & Table 4.6: Annual Smart Parking CO2e Savings (MMT) Split by 8 Key
Regions 2018-2023 . 31
4.8 Smart Parking Cost Savings . 32
Figure & Table 4.7: Annual Smart City Smart Parking Emissions Cost Savings
($m), Split by 8 Key Regions 2018-2023. 32
4.9 Smart Parking Software Spend . 33
Figure & Table 4.8: Annual Software Spend on Smart Parking Systems ($m), Split
by 8 Key Regions 2018-2023. 33
4.10 Smart Parking Hardware Spend . 34
Figure & Table 4.9: Annual Hardware Unit Spend on Smart Parking Systems
($m), Split by 8 Key Regions 2018-2023. 34
4.11 Smart Traffic & Smart Parking Summary . 35
Figure & Table 4.10: Combined Annual Smart City Traffic Management & Smart
Parking CO2e Emissions Cost Savings ($m), Split by 8 Key Regions 2018-2023 35
Figure & Table 4.11: Combined Annual Smart City Traffic Management & Smart
Parking Software Spend ($m), Split by 8 Key Regions 2018-2023 . 36
Figure & Table 4.12: Combined Annual Smart City Traffic Management & Smart
Parking Hardware Unit Spend ($m), Split by 8 Key Regions 2018-2023 . 37
5. Smart Street Lighting Forecasts
5.1 Introduction . 39
5.2 Methodology & Assumptions . 39
Figure 5.1: Smart Street Lighting Methodology . 41
5.3 Smart Street Lighting Energy Savings . 42
Figure & Table 5.2: Annual Smart Street Lighting Energy Savings (TWh), Split by
8 Key Regions 2018-2023 . 42
5.4 Smart Street Lighting Emissions Savings . 43
Figure & Table 5.3: Annual Smart Street Lighting CO2e Savings (MMT), Split by 8
Key Regions 2018-2023 . 43
5.5 Smart Street Lighting Energy & Emissions Cost Savings . 44
Figure & Table 5.4: Annual Smart Street Lighting Energy & Emissions Cost
Savings ($m) Split by 8 Key Regions 2018-2023 . 44
5.6 Smart Street Lighting Software Spend . 45
Figure & Table 5.5: Annual Smart Street Lighting Software Spend ($m), Split by 8
Key Regions 2018-2023 . 45
5.7 Smart Street Lighting Hardware Spend . 46
Figure & Table 5.6: Annual Smart Street Lighting Hardware Unit Spend ($m), Split
by 8 Key Regions 2018-2023 . 46
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