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市場調查報告書

全球智慧城市

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 Smart Cities
出版日期: 2017年12月06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

Juniper's latest Smart Cities research highlights how the market landscape has shifted over the past 18 months, from one that was primarily technology-driven, to one where policy plays an increasingly important role. Juniper's must-read research provides unique insights into this market, providing in-depth analysis of leading global smart cities' approach to the industry along with an assessment of emerging challenges and opportunities across key service markets.

The analysis covers key industry service segments, including:

  • Smart Grid
  • Smart Traffic Management
  • Smart Parking
  • Smart Street Lighting

This research suite comprises:

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

Key Features

  • Global, US, UK Top 10 Smart Cities Analysis: Strengths, weaknesses and opportunities of the top 10 Smart Cities worldwide, in the US and the UK according to:
    • Transport
    • Public Health
    • Public Safety
    • Energy
    • Productivity
  • Sector Dynamics: Multi-segment strategic assessment and breakdown, examining key Smart City service segments:
    • Smart Grid
    • Smart Traffic Management
    • Smart Parking
    • Smart Street Lighting
  • Regional Analysis: Evaluation of Smart City market prospects, in relation to:
    • Challenges in project funding, talent acquisition, governance and innovation
    • Service segment outlook examining investment prospects
  • Interviews with leading players, including:
    • BoldIQ
    • Cisco
    • Gemalto
    • Moscow Smart City Lab
    • Nokia
    • Ruckus Wireless
    • Siemens

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: BoldIQ, Cisco, Gemalto, Nokia, Ruckus Wireless, Siemens, Smart City Lab Moscow.
  • Profiled: ABB, Accenture, Cisco, Hitachi, IBM, Intel, Nokia, Schneider Electric, Siemens, SIGFOX, Silver Spring Networks, Streetline.
  • Case Studied: BoldIQ, Gemalto, NEMA (National Electrical Manufacturers Association), Ruckus Wireless, Streetline, Smart City Lab Moscow.
  • Mentioned: Acuity Brand, Advanced Control Systems, AEREA, Alcatel-Lucent, Alliander, Altice, Amey, Amtel, ANSI (American National Standards Institute), Arqiva, ASC, ASIM, AT&T, Ausnet, AutoGrid, Avrio, Bidgely, BMTS, Bosch, Burbank, BYD, Carnegie Mellon, Cellnex, Centre for Economics and Business Research, Citi, Citrix Systems, Citymapper, Clearview Traffic, Cofely, Connode, Cyber Defense Institute, Dynamic Ratings, EESL (Energy Efficiency Services Limited), EMCALI, Engie M2M, EON, Eseye, Fastprk, FLIR, General Electric, GitHub, Honeywell, Hortonworks, Huawei, IDEO Caraïbes, IEA, IEEE, Innovari, INRIX, IO Connect, IoT Denmark, IRD, Itron, Kapsch, KEPCO, Landis+Gyr, Libelium, Living PlanIT, Master Meter, Memsic, Microsoft, Mindteck, Mitsubishi Motors, Mitsui Fudosan, Mizuho Bank, Narrownet, NASSCOM (National Association of Software and Services Companies), Nedaa, NEDO (New Energy and Industrial Technology Development Organisation), NetTrotter, NIST (National Institute for Standards and Technology), NTT DoCoMo, Numbeo, Oi Brasil, Omantel, Omnetric Group, OptiCom, Oracle, Orange, oXya, Pantascene, Pentaho, Philips, Q-Free, Qualcomm, Reliance Energy, RMS, Rongwen, Ruckus Wireless, Samsung, SAP, SELC, Sentient, SFR, Silicon Labs, Silver Spring Networks, Simple Cell, SK Telecom, Smart Parking, SmartGridCIS, Sprint, Streetlight.Vision, Tata, TDC Systems, Tele2, Telefónica, Telit, Tendril, Texas Instruments, The Weather Company, Thinxtra, T Mobile, TomTom, Trafficvision, Uber, urbancontrol, Vägverket (Swedish Road Administration), Veolia, Viktoria Swedish ICT, VITI, Vivacity Labs, VMWare, Vodafone, VT Networks, WHO (World Health Organization), Wind River, Wipro, WND.

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

Deep Dive Strategy & Competition

1. Leading Smart Cities of the World

  • 1.1 Introduction
  • 1.2 Smart City Definition
  • 1.3 A Blueprint for Smart Cities
    • Figure 1.1: Smart City Blueprint
    • 1.3.1 The Journey to Becoming a Smart City: Key Strategic Considerations for Cities
  • 1.4 Evaluating the Top 10 Smart Cities in the World
    • 1.4.1 Background
      • Table 1.2: Summary of 2016 Ranking Areas
    • 1.4.2 Ranking Data Inputs
      • i. Transport
        • Table 1.3: 2017 Smart City Index - Transport Indicators
      • ii. Healthcare
        • Table 1.4: 2017 Smart City Index - Healthcare Indicators
      • iii. Public Safety
        • Table 1.5: 2017 Smart City Index - Public Safety Indicators
      • iv. Productivity
        • Table 1.6: 2017 Smart City Index - Productivity Indicators
      • v. Energy
        • Table 1.7: 2017 Smart City Index - Energy Indicators
  • 1.5 Methodology
  • 1.6 Global Smart Cities: 2017 Ranking Results
    • Table 1.8: Top 10 Global Smart Cities, Overall Ranking, 2017
    • Table 1.9: Top 10 Global Smart Cities by Index, 2017
    • 1.6.1 Relative Performance Across Regions
      • Figure 1.10: Juniper Competitive Web: Regional Analysis of Smart City Performance Across Indices, 2017
  • 1.7 US & UK Smart Cities: The Leaders 2017
    • 1.7.1 UK Rankings: Top 10 Smart Cities
      • Table 1.21: Top 10 UK Smart Cities, Overall Rankings 2017
      • Table 1.22: Top 10 UK Smart Cities According to Index, 2017
    • 1.7.2 US Rankings: Top 10 Smart Cities
      • Table 1.23: Top 10 US Smart Cities, Overall Rankings 2017
      • Table 1.24: Top 10 US Smart Cities According to Index, 2017
  • 1.8 Regional Smart Cities Analysis
    • 1.8.1 Funding/Financing
    • 1.8.2 Talent Acquisition
    • 1.8.3 Governance
    • 1.8.4 Innovation
    • 1.8.5 Regional Readiness
      • Table 1.25: Regional Smart City Readiness Analysis

2. Smart City Service Markets

  • 2.1 State of the Smart City Market
  • 2.2 The Smart Grid
    • 2.2.1. Challenges Presented by ‘Dumb' Grids
      • i. Thermal Limitations
      • ii. Constrained Interconnections
      • iii. Renewable Energy Sources
      • iv. Non-Technical Losses
      • v. Juniper's View
    • 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 Driving Investment: 2017 Market Developments & Emerging Opportunities in Smart Grids
      • i. Paris Agreement
      • ii. Cost of Renewables
        • Table 2.2: Projected LCOE for plants Entering Service, 2022 ($ per MWh)
      • iii. Blockchain
        • Case Study: Gemalto
    • 2.2.5 Regional Smart Grid Status
      • Figure 2.3: Juniper Strategy Quadrant: Regional Smart Grid Prospects Evaluation
      • i. North America
      • ii. Latin America
      • iii. West Europe
        • Figure 2.4: Total Number of Smart Grid Projects by European Country 1994-2016
      • iv. Central & East Europe
      • v. Far East & China
      • vi. Indian Subcontinent
      • vii. Rest of Asia Pacific
      • viii. Africa & Middle East
  • 2.3 Smart Traffic Management
    • Figure 2.5: TomTom Global Top 20 Most Congested Cities
    • 2.3.1 Traffic Management Strategies
      • Figure 2.6: Smart Traffic Management Value Chain
      • Table 2.7: Road Traffic Sensor Solution Evaluation
      • i. No ‘One Size Fits All' Approach
      • ii. The Congestion Charge
        • Case Study: Stockholm
      • iii. Opportunities
        • Case Study: Rio de Janeiro
  • 2.4 Smart Parking
    • 2.4.1 Smart Parking Strategies
      • Figure 2.8: Smart Parking Value Chain
      • i. Data Acquisition
        • Table 2.9: Smart Parking Occupancy Sensor Evaluation
      • ii. Data Storage
      • iii. Analytics & Data Management
        • Case Study: Streetline
      • iv. Partnerships & Opportunities
  • 2.5 Driving Investment: 2017 Market Developments & Emerging Opportunities in Smart Traffic & Parking
    • 2.5.1 Video
      • Case Study: Moscow
    • 2.5.2 Reducing Costs
      • Case Study: Ruckus Wireless
    • 2.5.3 Open Data & MaaS
      • i. Next Steps
        • Case Study: BoldIQ
  • 2.6 Smart Traffic & Parking Regional Prospects
    • Figure 2.10: Juniper Strategy Quadrant: Regional Smart Traffic & Parking Prospects Evaluation
  • 2.7 Smart Street Lighting
    • Table 2.11: Street Light Technology Evaluation
    • 2.7.1 Smart Street Lighting Strategies
      • Figure 2.12: Smart Street Lighting Value Chain
      • i. Modules & Sensors
      • ii. Data Storage
      • iii. Analytics & Data Management
        • Case Study: Kansas City
      • iv. Caveats
        • Case Study: NEMA
    • 2.7.2 Driving Investment: 2017 Market Developments & Emerging Opportunities in Smart Street Lighting
    • 2.7.3 Smart Street Lighting Regional Prospects
      • Figure 2.13: 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 & Leaderboard

  • 3.1 Introduction
  • 3.2 Juniper Leaderboard
    • Table 3.1: Vendor Capability Assessment Criteria
    • 3.2.1 Leaderboard Scoring Results
      • Table 3.2: Juniper Leaderboard: Smart City Vendors
      • Figure 3.3: Juniper Leaderboard: Smart City Vendors
    • 3.2.2 Vendor Groupings
      • i. Established Leaders
      • ii. Leading Challengers
      • iii. Disruptors & Emulators
    • 3.2.3 Limitations & Interpretations
  • 3.3 Smart City Movers & Shakers
  • 3.4 Stakeholder Analysis
    • 3.4.1 ABB
      • i. Corporate Profile
        • Table 3.4: ABB Financial Snapshot 2014-2016 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.2 Accenture
      • i. Corporate Profile
        • Table 3.5: Accenture Financial Snapshot 2014-2016 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.3 Cisco
      • i. Corporate Profile
        • Table 3.6: Cisco Financial Snapshot FY 2015-2017 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.4 Hitachi
      • i. Corporate Profile
        • Table 3.7: Hitachi Financial Snapshot (¥ billion) 2014-2016
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.5 IBM
      • i. Corporate Profile
        • Table 3.8: IBM Financial Snapshot 2014-2016 ($m)
      • ii. Geographic Spread
      • ii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.6 Intel
      • i. Corporate Profile
        • Table 3.9: Intel Financial Snapshot 2014-2016 ($m)
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.7 Nokia Networks
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Key Strengths & Strategic Opportunities
    • 3.4.8 Schneider Electric
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.9 Siemens
      • i. Corporate Profile
        • Table 3.12: Siemens Financial Snapshot 2014-ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's 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: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.11 Silver Spring Networks
      • i. Corporate Profile
        • Table 3.13: Silver Spring Networks Financial Snapshot 2014-2016 ($000)
      • ii. Geographic Spread
      • iii. Key Clients & Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities
    • 3.4.12 Streetline
      • i. Corporate Profile
      • ii. Geographic Spread
      • iii. Key Clients & Strategic Partnerships
      • iv. Products & Services
      • v. Juniper's View: Vendor's Key Strengths & Strategic Development Opportunities

Deep Dive Data & Forecasting

1. What is a Smart City?

  • 1.1 Introduction
  • 1.2 Smart City Definition
  • 1.3 A Blueprint for Smart Cities
    • Figure 1.1: Smart City Blueprint
    • 1.3.1 The Journey to Becoming a Smart City: Key Strategic Considerations for Cities
  • 1.4 Regional Smart Cities Analysis
    • 1.4.1 Funding/Financing
    • 1.4.2 Talent Acquisition
    • 1.4.3 Governance
    • 1.4.4 Innovation
    • 1.4.5 Regional Readiness
      • Table 1.2: Regional Smart City Readiness Analysis

2. Smart City Forecasts: Baseline Figures & Summary

  • 2.1 Introduction
  • 2.1. Population
    • Figure & Table 2.1: City Population over 500,000 individuals, Split by 8 Key Regions 2017-2022
  • 2.2 Wholesale Electricity Prices
    • Figure & Table 2.2: Wholesale Electricity Prices ($/MWh) Split by 8 Key Regions 2017-2022
  • 2.3 Emissions Factors
    • 2.3.1 Electricity Generation
      • Table 2.3: CO 2 e Emissions Split by Electricity Generation Fuel
    • 2.3.2 Internal Combustion Engine Vehicles
      • Table 2.4: Emissions Factor for City Vehicles, Split by 8 Key Regions 2017-2022 (t CO 2 e/km)
  • 2.4 Economic Value of CO 2 e
    • Table 2.5: CO 2 e Emissions Value ($/t) 2017-2022
  • 2.5 Forecast Summary
  • 2.6 Cost Savings Summary
    • Figure & Table 2.6: Combined Energy & CO2e Cost Savings ($m) Split by Smart City Market Vertical 2017-2022
  • 2.7 Software Spend Summary
    • Figure & Table 2.7: Combined Annual Smart City Software Spend ($m) Split by Smart City Market Vertical 2017-2022

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 2017-2022
  • 3.4 Smart Grid Energy Savings
    • Figure &Table 3.3: Annual Smart Grid Net Energy Savings (TWh) 2017-2022
  • 3.5 Smart Grid Emissions Savings
    • Figure & Table 3.4: Smart Grid CO 2 e Emissions Savings (MMT) Split by 8 Key Regions 2017-2022
  • 3.6 Smart Grid Energy & Emissions Cost Savings
    • Figure &Table3.5: Total Annual Smart Grid Energy & CO2e Cost Savings ($m) 2017-2022
  • 3.7 Smart Grid Software Spend
    • Figure & Table 3.6: Total Annual Smart Grid Software Spend ($m) Split by 8 Key Regions 2017-2022

4. Smart Traffic Management & Parking Forecasts

  • 4.1 Introduction
  • 4.2 Methodology & Assumptions
    • Figure 4.1: Smart Traffic Management & Parking Methodology
  • 4.3 Smart Traffic Management Emissions Savings
    • Figure & Table 4.2: Annual Smart Traffic Management CO 2 e Savings (MMT) Split by 8 Key Regions 2017-2022
  • 4.4 Smart Traffic Management Cost Savings
    • Figure & Table 4.3: Annual Smart City Traffic Management Emissions Cost Savings ($m), Split by 8 Key Regions 2017-2022
  • 4.5 Smart Traffic Management Software Spend
    • Figure & Table 4.4: Total Annual Smart Traffic Management Software Spend ($m) Split by 8 Key Regions 2017-2022
  • 4.6 Smart Parking Emissions Savings
    • Figure & Table 4.5: Annual Smart Parking CO 2 e Savings (MMT) Split by 8 Key Regions 2017-2022
  • 4.7 Smart Parking Cost Savings
    • Figure & Table 4.6: Annual Smart City Smart Parking Emissions Cost Savings ($m), Split by 8 Key Regions 2017-2022
  • 4.8 Smart Parking Software Spend
    • Figure & Table 4.7: Annual Software Spend on Smart Parking Systems ($m), Split by 8 Key Regions 2017-2022
  • 4.9 Smart Traffic & Smart Parking Summary
    • Figure & Table 4.8: Combined Annual Smart City Traffic Management & Smart Parking CO 2 e Emissions Cost Savings ($m), Split by 8 Key Regions 2017-2022
    • Figure & Table 4.9: Combined Annual Smart City Traffic Management & Smart Parking Software Spend ($m), Split by 8 Key Regions 2017-2022

5. Smart Street Lighting Forecasts

  • 5.1 Introduction
  • 5.2 Methodology & Assumptions
    • Figure 5.1: Smart Street Lighting Methodology
  • 5.3 Smart Street Lighting Energy Savings
    • Figure & Table 5.2: Annual Smart Street Lighting Energy Savings (TWh), Split by 8 Key Regions 2017-2022
  • 5.4 Smart Street Lighting Emissions Savings
    • Figure & Table 5.3: Annual Smart Street Lighting CO 2 e Savings (MMT), Split by 8 Key Regions 2017-2022
  • 5.5 Smart Street Lighting Energy & Emissions Cost Savings
    • Figure & Table 5.4: Annual Smart Street Lighting Energy & Emissions Cost Savings ($m) Split by 8 Key Regions 2017-2022
  • 5.6 Smart Street Lighting Software Spend
    • Figure & Table 5.5: Annual Smart Street Lighting Software Spend ($m), Split by 8 Key Regions 2017-2022
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