How can smart technology improve the sustainability performance of policies, plans and projects? Howard Waples reports.
Whether or not you understand and embrace the latest technology, you can be sure that your life has already been fundamentally affected by it. Ever-evolving and overlapping technologies mean the not-so-distant future will involve a paradigm shift in the way we live on almost every level. It also means a brand new set of opportunities and challenges for meeting our sustainable development goals.
With respect to the technologies referred to in this article, there are numerous definitions, which can be summarised as follows:
- Internet of things (IoT) – a network of physical devices (such as street lighting, vehicles and building sensors) that are embedded with electronic sensors, software, actuators and network connectivity, which enable them to connect and exchange data
- Artificial intelligence (AI) – where machines mimic natural ‘cognitive’ functions such as learning and problem solving
- Data/big data – any information collected from a variety of sources (including from IoT-connected sensors), and the use of predictive analytics, user behaviour analytics, etc. to extract value from data
- Smart technology – a broad term for utilising IoT-connected devices and exploring the use of AI and improved data analytics to provide users with greater control and functionality and better decision making.
This article explores some of the existing applications for smart technology (and its underpinning devices and processes) with respect to how it can be used to drive sustainability. It also highlights some of the major concerns associated with its implementation and governance to allow them to be avoided or minimised.
Current examples of smart technology
Smart technology is already well established in some ways, and examples of how it is implemented today are all around us.
Most people are familiar with smartphones – 1.47bn of them were sold in 2016 (International Data Corporation, 2017). Smartphones do not only connect people (email, phone calls, social media, etc.), facilitating the sharing of information for business and social uses (potential economic and social benefits); they also communicate with IoT-connected devices and data collection and analytics systems.
Examples of IoT-connected devices include remote central heating and domestic appliances. These can improve energy efficiency by ensuring that, for example, an empty space isn’t heated, or that a washing machine finishes its cycle at a specific time. This can provide users with improved levels of comfort and more time to use as they wish.
Even bins can be smart. Big Belly Bins, for instance, use solar panels to harness solar energy and sensors to continually compact the waste that is deposited, increasing capacity by up to 700%. It can communicate information on fill levels to ensure rubbish is collected only when the bin is full, reducing waste collection by up to 85% – which cuts congestion and traffic emissions. They can also help to collect and analyse area-specific data on waste volumes for better planning, and even increase WiFi coverage with their function as a free public WiFi hotspot.
One of the most useful applications for smart technology is in lighting. Streetlights can use sensors to optimise how and when they illuminate, and can also provide a platform for other smart uses. This technology is being used in several cities, including Copenhagen (Denmark), Wipperfurth (Germany) and in the London Borough of Barking and Dagenham. Advantages include
- Reducing power consumption by remotely adjusting usage
- Improving citizen safety with sensors
- Connecting nearby sensors that monitor water flows and drainage capacity in order to proactively manage flood risk
- Housing cameras and sensors that can provide data to a traffic management system or emergency services
- Electric vehicle charging points
- Providing public WiFi hotspots and other IoT devices.
Smart urban street furniture can help cities and communities to increase the attractiveness of public spaces by providing public services, information, and connectivity, while at the same time enabling the collection of valuable data (such as usage and maintenance) for optimising processes and reducing costs.
Smart leakage detection systems can include sensors that detect water leaks, both at a domestic level or across a whole network, which can wirelessly alert the homeowner or utility company and automatically shut off areas connected to the leak. This can make a big difference in the amount of this essential resource that is wasted (and the damage this can often cause) – particularly in areas of severe water stress.
Demand side response (DSR) involves intelligent energy use whereby businesses and consumers can connect their energy-using (and generating) devices to a system; this can instruct them to reduce energy use (and sell energy generated) at times of peak demand and use more when outside of peak times. This not only helps to reduce costs, but also reduces the need for peaking power stations and their associated infrastructure and emissions.
Building Information Modelling (BIM) is an example of a smart process, enabling the use of technology that promotes collaborative design, construction and operation of buildings. One of its key strengths is that it provides a common platform upon which different specialists (structural and M&E engineers, architects and sustainability professionals, etc.) can build while being able to see and annotate aspects that relate to one anothers’ disciplines. This can increase design efficiency, reduce resource usage and improveme facilities management.
Smart building design can involve the installation of numerous wirelessly interconnected sensors and actuators (IoTs) that send their data to a smart control system, which can be programmed to respond in different circumstances (closing some solar shading at certain sun directions and intensities, for example). This can save money and resources, and lead to improved user comfort, wellbeing and productivity.
The IoT will consist of 30bn uniquely identifiable objects by 2020 (Nordrum, 2016), the international ‘Smart Cities’ movement is beginning to unlock multi-stakeholder and planning obstacles, more and more data is being collected every year, and AI is becoming increasingly sophisticated. Combined, these things raise the likelihood of a fundamental shift in planning, design and environmental and sustainability management.
Smart technology can be applied to a range of spatial scales and for a range of purposes. For instance, autonomous flying drones can be used as an ‘eye in the sky’ to monitor crop growth, deforestation and so on. The ‘industrial internet of things’ (IIoT) applies to manufacturing, and the resource efficiency it unlocks is expected to lead to the next industrial revolution.
Reasons to be cautious
Smart technology is not without some potentially major problems.
Technical issues such as a lack of international standards can lead to the platforms that systems run on becoming fragmented, and not interoperable. This could lead to increased waste as higher rates of technology become obsolete, or stop being compatible with other hardware or systems.
The potential threat to privacy and security is enormous. Computer systems, phone, and even cars can be hacked and compromised, and there is the risk of security breaches – not only on a personal level (such as within a smart home) but also on a city level, and even internationally.
Ethically, there is potential for social control and political manipulation, or invasion of privacy – for instance, retailers collecting shopping data to target individuals with personalised advertising, which is instinctively less easy to ignore.
How much control should be given to ‘the machines’? This may sound like something out of a sci-fi film, but a fully autonomous system that not only monitors and feeds back data but also actions a response could take away an element of perceived control, making human operators and decision-makers obsolete.
In fact, the biggest potential driver of global change is the social and economic harm involved when a vast number of people are made redundant. Just as the agricultural and industrial revolutions fundamentally changed the way humans lived (and not always for the better), the technological revolution brings a whole new set of risks. The rate of change is so fast, as data collection is growing exponentially, that existing political, economic and social mechanisms cannot evolve at the same pace.
Therefore, a degree of caution is essential, and mechanisms should be put in place to prevent abuses of the technology and sensitive data and encourage the transparency of decision-making.
Smart devices, smart buildings, smart cities, smart planet
Smart technology can have applications at all spatial levels and planning hierarchies. However, smart devices require the development of appropriate infrastructure to exist – for example, a source of power and an ability to connect to a wider network (through something like a WAN or WiFi network). A smart building can meet those needs to a degree, but in order for buildings to connect to each other and the space and people around them, an approach that considers the city scale is required. Make enough cities smart, and the planet will follow.
With more of the world’s population moving to urban areas, there is a need to make these areas more efficient (quicker or less travel; more interpersonal interactions ), better planned (bringing together stakeholders; engaging with local people) and more sustainable (using less resources; doing more with less space; reducing health and wealth inequalities).
The smart cities movement is gaining momentum around the globe, and tries to encapsulate the three aspects mentioned above. It is clear that a city isn’t going to become smart by itself, or as a result of a single major project or initiative – a smart city should be constantly evolving.
Momentum is building across UK cities and beyond, with a large number of organisations such as the Future City Catapult leading the way with respect to innovation and interpretation of data to prove some of the benefits. This leads to greater confidence in the technology, and encourages not only investment, but also political and institutional support. The Mayor of London’s’s draft Environment Strategy identifies a ‘smart digital city’ as one of its four strategic approaches ‘to make the most of environmental opportunities now and in the future’. It is reasonable to expect that this policy direction will unlock smart interventions at a city scale, and that, over time, the policies will be adopted at a local level – potentially introducing requirements for projects to be ‘IoT ready’.
Using data and presenting it spatially through its Infrastructure Mapping Application, London has detailed picture of proposed infrastructure development (transport and utilities) and how this relates to an area’s deprivation, economic and population growth, and broader plans for urban development (such as opportunity areas). Added to the GLA’s own data – demographic, economic, labour market projections – the platform contains around 8,000 data points from a range of infrastructure providers and developers, to deliver a range of benefits:
- Utilities and infrastructure providers are better equipped to develop evidence-based business plans that meet London’s needs
- Developers are able to see the infrastructure pipeline with greater certainty, which helps unlocks development sites more easily
- Boroughs are able to form more cohesive local development plans
- Training providers are better able to respond to future industry requirements.
All of this will encourage better and more efficient planning. However, there is already a range of established assessment types that can adapt to incorporate smart technology and demonstrably drive sustainability improvements. The following table explores opportunities for implementing smart technologies into the different types of assessment, the likely problems and barriers and the interventions required.
| Assessment Type | Examples of how smart tech can improve assessments | Likely issues and interventions required |
| Policies, plans and programmes |
| Sustainability appraisal/strategic environmental assessment | The collection of more detailed monitoring data can allow more sustainability indicators to be included, giving more accuracy and reliability to the effectiveness of a policy or programme.
The data collected from previous monitoring can be used to provide more certainty to the evidence base relied upon, including determining trends. Public presentation of monitoring data to enhance transparency. |
Consultation on the most appropriate data to use (considering data privacy issues, etc).
Infrastructure/platforms in place to allow collection of data, or purchase of third party data. Data analytics and governance procedures. |
| Health impact assessment/equality impact assessment | Obtaining new data sources, including those relating to the use of active travel measures.
The use of existing sensors (and installation of further ones) to monitor air quality data in real time and to include a wider range of pollutants and give a better resolution. Use of improved epidemiological data (such as dose-response relationships) and use of better and higher resolution health data to form policy decisions on. Use of smart applications to obtain broader wellbeing data from individuals in different locations and circumstances. Use of smart technology to engage with more people about how a policy could affect them. |
Lag time of academic research to support wellbeing data analysis trends and conclusions.
Requirement to cover full range of demographics. Need for more consistent approach to defining and categorising ‘wellbeing’. Concerns over personal data privacy. |
| Project level |
| Habitats regulations assessment | Use of sensors to monitor environmental factors (such as noise and dust deposition rates) and protected species individuals and populations in real time.
Sharing of data to improve understanding and accuracy of future assessments |
Power source and connectivity for sensors, given the rural areas they will likely be located in. |
| Environmental impact assessment | Incorporation of environmental constraints into BIM modelling.
Use of sensors to monitor environmental impacts, to prove or disprove what was reported in the ES and the effectiveness of mitigation. Use of smart technology to enhance beneficial environmental impacts (such as automated management of a green roof’s moisture content) and to mitigate impacts (such as the use of anemometer sensor connected to building management system to provide screening of areas during high wind speeds). |
Perceived costs of installing sensors and ongoing monitoring (including potentially for topics that were reported to have no significant effects).
Requirement to share monitoring data with regulators and stakeholders. Perceived commercial disadvantage of sharing lessons and data with other developers. |
| Health impact assessment/equalities impact assessment | Use of improved community and stakeholder consultation to reach out to a wider demographic and enable ‘heat map’ approaches to presenting data.
Development and use of personalised data collection devices (such as heart rate monitors, step counting and self-reported opinions of health and wellbeing). Opportunities for better data analysis and more accurate correlations between environment and health. |
Lag time of academic research to support wellbeing data analysis trends and conclusions.
Concerns over personal data privacy. |
Assuming the issues are resolved, the adoption of smart technology into different types of impact assessment will enable greater data collection, sharing and analysis of data. This could create a richer evidence base to demonstrate the effectiveness of an implemented policy against associated indicators, and also a way to monitor the effectiveness of environmental mitigation and enhancement measures of a range of projects.
Howard Waples is account manager, EIA at BWB Consulting Limited
Image credit: iStock
