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市場調查報告書

全球智慧城市

Worldwide Smart Cities

出版商 Juniper Research 商品編碼 322747
出版日期 內容資訊 英文
商品交期: 最快1-2個工作天內
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全球智慧城市 Worldwide Smart Cities
出版日期: 2016年05月17日 內容資訊: 英文
簡介

本報告提供全球智慧城市用的配合措施的相關調查,智慧城市定義和概要,主要10個智慧城市的評估,各結構技術部門趨勢、策略、引進的案例研究、市場規模 (能源/成本/排放量的削減規模、軟體投資額等) 的變化與預測,相關組織、經營者分析,實行技術的說明等彙整資料。

提供內容

  • 市場趨勢、競爭環境 (PDF)
  • 5年市場預測 (PDF & Excel)
  • 摘要整理&主要調查結果 (PDF)

市場趨勢、競爭環境

第1章 全球主要智慧城市

  • 簡介
  • 智慧城市定義
  • 調查範圍
    • 公共事業
    • 運輸基礎設施
    • 路燈
  • 主要10個智慧城市的評估

第2章 智慧城市的服務的市場

  • 簡介
  • 智慧電網
    • 「水庫」電網顯示的課題
    • 全球電力市場
    • 電力市場類型
    • 智慧電網的各地區的情形
  • 智慧流量管理
    • 流量管理策略
      • 案例研究:斯德哥爾摩
      • 案例研究:里約熱內盧
  • 智慧停車
    • 智慧停車策略
      • 案例研究:Streetline
  • 智慧流量&停車:各地區的展望
  • 智慧路燈
    • 智慧路燈策略
    • 智慧路燈:各地區的展望

第3章 智慧城市的相關利益者分析

  • 簡介
  • 供應商矩陣
    • 規定、解釋
    • 地位的矩陣
    • 供應商的群組化
  • 對智慧城市有大幅影響力的企業、組織、團體
  • 相關利益者分析
    • ABB
    • Accenture
    • Cisco
    • 日立
    • IBM
    • Intel
    • lokia Networks
    • Schneider Electric
    • Siemens
    • SIGFOX
    • Silver Spring Networks
    • Streetline

市場規模、預測

第1章 全球主要智慧城市

  • 簡介
  • 智慧城市定義
  • 調查範圍

第2章 智慧城市的預測:基準

  • 簡介
  • 電力批發價格
  • 排放相關因素
  • 預測摘要
    • 降低成本數量:摘要
    • 軟體支出額:摘要

第3章 智慧電網的預測

  • 簡介
  • 預測手法、前提條件
  • 城市的能源消費
  • 智慧電網
    • 能源削減數量
    • 排放削減數量
    • 能源&排放降低成本數量
    • 軟體支出額

第4章 智慧流量管理&停車的預測

  • 簡介
  • 預測手法、前提條件
  • 智慧流量管理
    • 排放削減數量
    • 降低成本數量
    • 軟體支出額
  • 智慧停車
    • 排放削減數量
    • 降低成本數量
    • 軟體支出額
  • 智慧流量管理 &智慧停車:摘要
    • 摘要:排放削減數量
    • 摘要:軟體支出額

第5章 智慧路燈的預測

  • 簡介
  • 預測手法、前提條件
  • 智慧路燈
    • 能源削減數量
    • 排放削減數量
    • 能源&排放降低成本數量
    • 軟體支出額

快指南

第1章 智慧城市:快指南

  • 簡介
  • 通訊技術
    • 無線技術
      • 3G/LTE技術、小型基地台
      • ZigBee、Z-Wave
      • Bluetooth
      • 低功率WAN
      • 彙整
    • 有線技術
      • 寬頻網際網路
  • 資料安全&隱私
    • 公共Wi-Fi
    • 企業&地方政府資料
  • IoT
    • 運作中的IoT:Sydney Harbour Bridge - NICTA

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目錄

Overview

Rapid urbanisation, limited resources and the desire to remain competitive has resulted in Smart Cities becoming a global phenomenon. Juniper's ground-breaking research provides unique insights into this market, examining city developments and analysis into the dynamics and key trends driving Smart City services.

This research suite comprises:

  • Market Trends & Competitive Landscape (PDF)
  • 5 Year Market Sizing & Forecast (PDF & Excel)
  • Executive Summary & Core Findings (PDF)

Key Features

  • Strengths, weaknesses and opportunities of the top 10 Smart Cities of the globe, analysed by:
    • Technology
    • Transport
    • Energy
    • Open Data
    • Economy
  • Multi-segment strategic assessment and breakdown, examining key Smart City verticals:
    • Smart Grid
    • Smart Traffic Management
    • Smart Parking
    • Smart Street Lighting
  • In-depth regional evaluation of market verticals' trends, prospects and future outlook.
  • Benchmark industry forecasts for adoption, cost saving and software spend across all verticals.
  • Interviews with leading players across the value chain, including:
    • ABB
    • Cisco
    • Nokia Networks
    • Siemens
    • Sigfox
    • Silver Spring Networks
    • Streetline

Key Questions

  • 1. What are the key strengths and weaknesses of major Smart Cities?
  • 2. Which strategies should stakeholders look to apply when developing Smart City projects?
  • 3. What are the key trends shaping Smart City policy and service markets?
  • 4. What are the prospects for city smart energy, traffic services and lighting markets?
  • 5. What is the economic opportunity for smart city projects?

Companies Referenced

Interviewed:

ABB, Nokia Networks, Siemens, SIGFOX, Silver Spring Networks, Streetline.

Profiled:

ABB, Accenture, Cisco, Hitachi, IBM, Intel, Nokia Networks, Schneider Electric, Siemens, SIGFOX, Silver Spring Networks, Streetline.

Case Studied:

Amsterdam, Barcelona, Chicago, Kansas City, London, New York, NEMA (National Electrical Manufacturers Association), Nice, Oslo, Rio de Janeiro, San Francisco, Singapore, Stockholm, Streetline, Zürich.

Mentioned:

Acuity Brand, Advanced Control Systems, AEREA, AGT International, Alcatel-Lucent, Alliander, Allianz Deustchland, Altice, Amtel, ANSI (American National Standards Institute), Arqiva, ASC (Amsterdam Smart City), ASIM, AT&T, Avrio, Bosch, BYD, Carnegie Mellon, Cellnex, Citi, Citrix Systems, Clearview Traffic, Cofely, Connode, Cyber Defense Institute, Daimler, Digi International, Dynamic Ratings, Echelon, EESL (Energy Efficiency Services Limited), Efacec, EMCALI, Engie M2M, Exceleron, Fastprk, FLIR, General Electric, Honeywell, Hortonworks, IDEO Caraïbes, Innovari, INRIX, IO Connect, IoT Denmark, IRD, Landis+Gyr, Libellium, Living PlanIT, Lockheed Martin, Memsic, Microsoft, Mindteck, Mitsubishi, Mizuho Bank, Narrownet, NASSCOM (National Association of Software and Services Companies), Nedaa, NEDO (New Energy and Industrial Technology Development Organisation), NetTrotter, New York MTA (Metropolitan Transport Association), Nexant, NIST (National Institute for Standards and Technology), NTT DoCoMo, Oi, Omantel, Omnetric Group, On Semiconductor, OptiCom, Oracle, Orange, Osram, oXya, Pantascene, Pentaho, Philips, Q-Free, Reliance Energy, RMS, Samsung, SAP, Sensity, Sentient, SFR, Silicon Labs, Simple Cell, SK Telecom, Smart Parking, SmartGridCIS, Sprint, Tata, TDC Systems, Telefónica, Telit, Tele2, Tendril, Texas Instruments, TfL (Transport for London), Thinxtra, T Mobile, Trafficvision, Vägverket (Swedish Road Administration), Veolia, Viktoria Swedish ICT, VITI, VMWare, VT Networks, WND, Whirlpool, Zain KSA.

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 55 tables and over 7,400 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.

Table of Contents

Market Trends & Competitive Landscape

1. Leading Smart Cities of the World

  • 1.1. Introduction
  • 1.2. Smart City Definition
  • 1.3. Research Scope
    • 1.3.1. Utilities
      • Figure 1.1: Smart City Utilities
    • 1.3.2. Transport Infrastructure
      • Figure 1.2: Smart City Transport Infrastructure
    • 1.3.3. Street Lighting
      • Figure 1.3: Smart City Street Lighting
  • 1.4. Evaluating the Top 10 Smart Cities of the World
    • 1.4.1. Scoring Methodology
      • Table 1.4: Smart City Assessment Scoring Weights
    • 1.4.2. Smart Cities: Scoring Results
      • Figure 1.5: Smart City Scoring Results

2. Smart City Service Markets

  • 2.1. Introduction
  • 2.2. The Smart Grid
    • 2.2.1. Challenges Presented by ‘Dumb' Grids
    • 2.2.2. Global Electricity Markets
      • Figure 2.1: Electricity Value Chain & Smart Grid Services
    • 2.2.3. Types of Electricity Market
      • i. Regulated Markets
      • ii. Deregulated Markets
      • iii. Impact of Market Status on the Smart Grid
    • 2.2.4 Regional Smart Grid Status
      • Figure 2.2: Juniper Strategy Quadrant: Regional Smart Grid Prospects Evaluation
      • i. North America
      • ii. Latin America
      • iii. West Europe
      • iv. Central & East Europe
        • Figure 2.3: Total Number of Smart Grid Projects by European Country 200-2014
      • v. Far East & China
      • vi. Indian Subcontinent
      • vii. Rest of Asia Pacific
      • viii. Africa & Middle East
  • 2.3. Smart Traffic Management
    • Figure 2.4: TomTom Global Top 20 Most Congested Cities
    • 2.3.1. Traffic Management Strategies
      • Figure 2.5: Smart Traffic Management Value Chain
      • Table 2.6: Road Traffic Sensor Solution Evaluation
      • iii. Case Study: Stockholm
      • iv. Case Study: Rio de Janeiro
  • 2.4. Smart Parking
    • 2.4.1. Smart Parking Strategies
      • Figure 2.7: Smart Parking Value Chain
      • Table 2.8: Smart Parking Occupancy Sensor Evaluation
      • iv. Case Study: Streetline
  • 2.5. Smart Traffic & Parking Regional Prospects
    • Figure 2.9: Juniper Strategy Quadrant: Regional Smart Traffic & Parking Prospects Evaluation
  • 2.6. Smart Street Lighting
    • Table 2.10: Street Light Technology Evaluation
    • 2.6.1. Smart Street Lighting Strategies
      • Figure 2.11: Smart Street Lighting Value Chain
    • 2.6.2. Smart Street Lighting Regional Prospects
      • Figure 2.12: Juniper Strategy Quadrant: Regional Smart Street Lighting Prospects
      • i. North America
      • ii. Latin America
      • iii. West Europe
      • iv. Central & East Europe
      • v. Far East & China
      • vi. Indian Subcontinent
      • vii. Rest of Asia Pacific
      • viii. Africa & Middle East

3. Smart City Stakeholder Analysis

  • 3.1. Introduction
  • 3.2. Vendor Matrix
    • Table 3.1: Vendor Capability Assessment Criteria
    • 3.2.1. Limitations & Interpretation
    • 3.2.2. Positioning Matrix Results
      • Table 3.2: Smart City Vendor Scoring Matrix
      • Figure 3.3: Smart City Vendor Positioning Matrix
    • 3.2.3. Vendor Groupings
      • i. Summary
      • ii. On Track Group
      • iii. Exceeding Expectations Group
      • iv. Further Potential Group
  • 3.3. Smart City Movers & Shakers
  • 3.4. Stakeholder Analysis
    • 3.4.1. ABB
      • i. Corporate Profile
        • Table 3.4: ABB Financial Snapshot 2013-2015 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.2. Accenture
      • i. Corporate Profile
        • Table 3.5: Accenture Financial Snapshot 2013-2015 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.3. Cisco
      • i. Corporate Profile
        • Table 3.6: Cisco Financial Snapshot 2013-2015 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.4. Hitachi
      • i. Corporate Profile
        • Table 3.7: Hitachi Financial Snapshot (¥billion) 2012-2014
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.5. IBM
      • i. Corporate Profile
        • Table 3.8: IBM Financial Snapshot 2013-2015 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.6. Intel
      • i. Corporate Profile
        • Table 3.9: Intel Financial Snapshot 2013-2015 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.7. lokia Networks
      • i. Corporate Profile
        • Table 3.10: lokia Financial Snapshot 2013-2015 (Em)
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Opportunities
    • 3.4.8. Schneider Electric
      • i. Corporate Profile
        • Table 3.11: Schneider Electric Financial Snapshot (Em) 2013-2015
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
    • 3.4.9. Siemens
      • i. Corporate Profile
        • Table 3.12: Siemens Financial Snapshot 2013-2015 (Em)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.10. SIGFOX
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Development Opportunities
    • 3.4.11. Silver Spring Networks
      • i. Corporate Profile
        • Table 3.13: Silver Spring Networks Financial Snapshot2013-2015 ($000)
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Development Opportunities
    • 3.4.12. Streetline
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Development Opportunities

Market Sizing & Forecasts

1. Leading Smart Cities of the World

  • 1.1. Introduction
  • 1.2. Smart City Definition
  • 1.3. Research Scope
    • 1.3.1. Utilities
      • Figure 1.1: Smart City Utilities
    • 1.3.2. Transport Infrastructure
      • Figure 1.2: Smart City Transport Infrastructure
    • 1.3.3. Street Lighting
      • Figure 1.3: Smart City Street Lighting

2. Smart City Forecasts: Baseline Figures & Summary

  • 2.1. Introduction
    • Figure & Table 2.1: City Population over 500,000 individuals, Split by 8 Key Regions 2016-2021
  • 2.2. Wholesale Electricity Prices
    • Figure & Table 2.2: Wholesale Electricity Prices ($/MWh) Split by 8 Key Regions 2016-2021
  • 2.3. Emissions Factors
    • 2.3.1. Electricity Generation
      • Table 2.3: C02e Emissions Split by Electricity Generation Fuel
    • 2.3.2. Internal Combustion Engine Vehicles
      • Table 2.4: Emissions Factor for City Vehicles, Split by 8 Key Regions 2016-2021 (tCO2 e/km)
    • 2.3.3. Economic Value of CO2e
      • Table 2.5: C02e Emissions Value ($/t) 2016-2021
  • 2.4. Forecast Summary
    • 2.4.1. Cost Savings Summary
      • Figure & Table 2.6: Combined Energy & CO2e Cost Savings ($m) Split by Smart City Market Vertical 2016-2021
    • 2.4.2. Software Spend Summary
      • Figure & Table 2.7: Combined Annual Smart City Software Spend ($m) Split by Smart City Market Vertical 2016-2021

3. Smart Grid Forecasts

  • 3.1. Introduction
  • 3.2. Methodology & Assumptions
    • Figure 3.1: Smart Grid Forecast Methodology
  • 3.3. City Energy Consumption
    • Figure & Table 3.2: Annual City Energy Electricity Consumption (TWh) Split by 8 Key Regions 2016-2021
  • 3.4. Smart Grid
    • 3.4.1. Smart Grid Energy Savings
      • Figure & Table 3.3: Annual Smart Grid Net Energy Savings (TWh) Split by 8 Key Regions 2016-2021
    • 3.4.2. Smart Grid Emissions Savings
      • Figure & Table 3.4: Smart Grid C02e Emissions Savings (MMT) Split by 8 Key Regions 2016-2021
    • 3.4.3. Smart Grid Energy & Emissions Cost Savings
      • Figure & Table 3.5: Total Annual Smart Grid Energy& CO2e Cost Saving ($m)
    • 3.4.4. Smart Grid Software Spend
      • Figure & Table 3.6: Total Annual Smart Grid Software Spend ($m) Split by 8 Key Regions 2016-2021

4. Smart Traffic Management & Parking Forecast

  • 4.1. Introduction
  • 4.2. Methodology & Assumptions
    • Figure 4.1: Smart TraTRc Management & Parking Methodology
  • 4.3. Smart Traffic Management
    • 4.3.1. Smart Traffic Management Emissions Savings
      • Figure & Table 4.2: Annual Smart Traffic Management C02e Savings (MMT) Split by 8 Key Regions 2016-2021
    • 4.3.2. Smart Traffic Management Cost Savings
      • Figure & Table 4.3: Annual Smart City Traffic Management Emissions Cost Savings ($m), Split by 8 Key Regions 2016-2021
    • 4.3.3. Smart Traffic Management Software Spend
      • Figure & Table 4.4: Total Annual Smart Traffic Management Software Spend ($m), Split by 8 Key Regions 2016-2021
  • 4.4 Smart Parking
    • 4.4.1. Smart Parking Emissions Savings
      • Figure & Table 4.5: Annual Smart Parking CO2e Savings(MMT)Split by 8 Key Regions 2016-2021
    • 4.4.2. Smart Parking Cost Savings
      • Figure & Table 4.6: Annual Smart City Smart Parking Emissions Cost Savings ($m), Split by 8 Key Regions 2016-2021
    • 4.4.3. Smart Parking Software Spend
      • Figure & Table 4.7: Annual Software Spend on Smart Parking Systems ($m), Split by 8 Key Regions 2016-2021
  • 4.5. Smart Traffic Management &Smart Parking Summary
    • 4.5.1. Smart Traffic Management &Smart Parking Summary Emissions Savings
      • Figure & Table 4.8: Combined Annual Smart City Traffic Management & Smart Parking CO2e Emissions Cost Savings ($m), Split by 8 Key Regions 2016
    • 4.5.2 Smart Traffic Management & Smart Parking Summary Software Spend
      • Figure & Table 4.9: Combined Annual Smart City Traffic Management & Smart Parking Software Spend ($m), Split by 8 Key Regions 2016-2021

5. Smart Street Lighting Forecasts

  • 5.1. Introduction
  • 5.2. Methodology & Assumptions
    • Figure 5.1: Smart Street Lighting Methodology
  • 5.3. Smart Street Lighting
    • 5.3.1. Smart Street Lighting Energy Savings
      • Figure & Table 5.2: Annual Smart Street Lighting Energy Savings (TWh), Split by 8 Key Regions 2016-2021
    • 5.3.2. Smart Street Lighting Emissions Savings
      • Figure & Table 5.3: Annual Smart Street Lighting C02e Savings (MMT), Split by 8 Key Regions 2016-2021
    • 5.3.3. Smart Street Lighting Energy & Emissions Cost Savings
      • Figure & Table 5.4: Annual Smart Street Lighting Energy & Emissions Cost Savings ($m) Split by 8 Key Regions 2016-2021
    • 5.3.4. Smart Street Lighting Software Spend
      • Figure & Table 5.5: Annual Smart Street Lighting Software Spend ($m), Split by 8 Key Regions 2016-2021

A Quick Guide

1. Smart City Quick Guide

  • 1.1. Introduction
  • 1.2. Communications Technologies
    • 1.2.1. Wireless Technologies
      • i. 3G/LTE Technologies & Small Cells
        • Figure 1.1: Cellular HetNet
      • ii. ZigBee and Z-Wave
        • Table 1.2: ZigBee & Z-Wave Evaluation
        • Figure 1.3: Mesh Network Diagram
      • iii. Bluetooth
        • Figure 1.4: BLE Power Consumption, Libelium Waspmote Module, Single Data Transmission Frame
        • Figure 1.5: Bluetooth Classic OR High Speed Combined with Low Energy Stack
      • iv. Low Power Wide Area Networks
      • v. Wireless Technology Conclusions
    • 1.2.2. Wired Technologies
      • i. Broadband Internet
        • Figure 1.6: ADSL Download Speed vs Distance from Exchange
  • 1.3. Data Security & Privacy
    • i. Public Wi-Fi
      • Figure 1.7: Password vs Passphrase Strength
    • ii. Enterprise & Municipal Data
  • 1.4. The Internet of Things
    • i. IoT in Action: Sydney Harbour Bridge - NICTA
      • Figure 1.8: Sydney Harbour Bridge Sensor
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