The Evolution of Smart-Cities With AI and Blockchain Technology

Abstract This article grants a comprehensive understanding of the applications and prospects of AI Blockchain technology in smart cities. I have researched and compiled information regarding the Internet of things (IoT) technologies for smart cities, smart cities as innovation ecosystems sustained by the future internet, traffic management by automation of street lights, and the future of waste management in sustainable cities. The implementation of these technologies has shown a decrease in carbon emissions, traffic, and error in recycling systems. Blockchain applications in smart cities have been extensively researched in order to clearly explain the immense influence this technology will have on all future data-related activities. This includes but is not limited to, governments, banks, hospitals, civilian services, and energy trades. The literature compiled gives a closer understanding of how AI blockchain is a viable option for batching and automating the analysis of large data packets, to allow for streamlined damage control and a massive reduction in outbreaks. The material showcases how a city's eco-system could benefit and sustain itself using these innovative IT solutions. It also gives insight into how individuals could begin profiting off tangible assets once these technologies are implemented. Introduction As the populace in cities becomes considerably denser, governments and civil engineers are looking for more modern solutions for monitoring city operations such as traffic, waste management, and air quality. Therefore when AI and Blockchain technologies were introduced to the mainstream, initiatives like Blockchain4Cities came to be [1]. Source: Axis communications The current number of people living in cities is projected to grow 25% by 2050, meaning increased pressure on managing energy and water resources, transport networks, and waste disposal. Commuting has lots of challenges, one of the most prominent being congestion, reaching costs of up to 100 billion EUR annually or 1% of the EU’s GDP [2]. To solve this issue city planners have to grasp and study how people move around the city. With this information, a more effective plan concerning bus station locations, bike routes, traffic lights, roundabouts, and traffic congestion can be created. Even though air pollution monitoring is inexpensive and already in place in most urban areas, handling the level of pollution presents difficulties to even the most competent mayors. After all, finding a balance between growing populations and controlling environmental consequences is not an easily achieved goal. The issue not only affects the populace's health but is also a significant economic deterrent. The summed costs of combating air pollution, as claimed by the National Health Service of England, was around 157 million pounds. Game theorists warn these costs could accumulate to up to 18.6 billion pounds by 2035 [3]. As the human populace grows, so do consumption rates, with the annual global sum of waste generated being over 720 billion tons. This is due to people in urban cities being reliant on industrialized packaged foods, massively adding to the amount of household waste generated daily. Even when waste is a visible issue, administrators respond with “end of pipe” measures, rather than stimulating the recirculation of used materials or avoiding the exhaustion of virgin resources. Fortunately, waste management systems involve electronic automation and can be scaled or modernized to combat these issues [4]. The greatest issue concerning urban planning seems to be electrical grid planning. All current power is supplied via large power plants with cables running distances of up to 100km, causing energy losses in the form of heat. The consumers must then pay fees with the transmission losses taking up 4.57% of the final price for households, and 4% for business owners. In Europe, these losses stay relatively low at around 4-5%, but in a densely populated country like India, with an underdeveloped power grid, these losses amount to up to 19% of the cost. One attempt to reduce carbon emissions is the growing amount of renewable energy sources. While solutions like energy panels and water mills can power a whole city, implementing them into an already existing and outdated grid is not as simple as connecting 2 wires. Depending on RES(renewable energy sources) this implementation could also cause an increase in power outages due to weather fluctuations [5]. Initial solutions are primarily implemented as a method to reduce costs, after which a data analysis system is fully fledged out. From there possibilities for improved infrastructure sustainability are noticed and taken into consideration, leading to innovations in green tech. The following chapters will showcase how AI and Blockchain are being implemented to solve these issues, with an emphasis on improving projected future outcomes and hypothetical solutions presented in expert documentation. The use of AI in smart cities The fundamental task of a smart city is to gather enormous amounts of data to analyze, in order to detect inefficiencies and provide optimal solutions. Big Data can be characterized as high volume, high-velocity, and high variety information assets. This means a large amount of information has to be processed at a high speed without restricting itself to one data type. This is achieved by combining artificial intelligence into the analysis process. AI is a broad field of science that studies methods of applying a non-human system to learn from experience and imitate human intelligent behavior. Source: DLabs.AI One task that involves predictions based on data is maintenance. Pacific Gas and Electric - California’s leading power supplier back in 2019 - filed for bankruptcy due to a catastrophe caused by a lack of monitoring and safety violations. Deteriorating circuits and gear caused wildfires that led to the death of 22 and more than 30 billion dollars in damages [6]. This could have been avoided had maintenance been prioritized and executed as planned. The reason large corporations cut corners in these activities is due to the need to halt power lines in order to proceed with maintenance. This means a loss of service to clients [7]. Artificial intelligence saves time and money by calculating the best time for maintenance activities by analyzing data trends and pinpointing the exact schedule that would least impact clients. Defects can also be detected by AI with the help of IoT devices and sensors constantly monitoring the power grid. The AI would come to understand standard operational values and detect when conditions change; notifying engineers of a need for reparations while also granting concise diagnostics to help understand the reason for deterioration [8]. Renewable energies are projected to make up 62% of all generated energies by 2050. To put this in perspective, 27% of 2019’s generated energies were green [9]. The lack of commitment to RES’s is due to their dependence on environmental conditions - not only the weather but the land has to be appropriately adjusted to permit the installation of solar panels, windmills, and watermills. Without an automated management system, the fluctuation in power supply could cause too much instability in the power grid. The solution to this issue is the storage of power in batteries and calculating when this stored power will be used... With AI as the predictor, weather and energy consumption forecasts are taken into consideration to plan when consumers require the most energy and when unused energy should be stored for later use. Cities growing larger will cause an increase in the necessary power cable range. Thus, inflated energy prices will be caused by losses in transmission. An overreliance on large power plants to power a whole city is one reason for this infrastructural issue, and one way to solve this problem is energy decentralization. By installing small local energy plants in rural areas, energy can be transmitted over short ranges, reducing both losses and the threat of localized issues like terrorist attacks. Decentralization can also prolong the lifespan of powerlines by reducing their overall burden, making the power grid harder to destabilize for bad actors. By spreading out the power supply into microgrids capable of taking on the load of damaged systems when necessary, localized issues become much easier to handle. Deciding where the microgrids are located is up to the AI. By mapping out where the most power is used and these locations' distances from one another, multiple pseudo-optimal solutions can be presented to civil engineers. A decision can then be made on where these grids should be constructed. Source: Intelligent Transport Smartphones are a great tool for tracking an individual’s data and batching it into a group of individuals with similar patterns. A good example of this ability in action is that both public transport commuters and car drivers report live traffic statuses through map apps. This real-time information can let others know of delays and jams, which should help others make changes in their commute to get to their destination quicker and help congestion free up faster. If this information is also fed into a monitored AI, it could help city planners make informed decisions on future modifications to bus schedules and stop-light locations. Categorizing people by mode of transport and at what time of day they tend to commute can make schedules more fluid and personalized. The comfort of a ride can also be improved by taking onboard sensors into account. If a bus or rental bike shakes too much on a road, then road maintenance must be scheduled and alternative routes should be planned. Another example of onboard sensors is Dubai’s attempt at monitoring bus drivers’ conditions as they work. This helped reduce accidents caused by exhaustion by up to 65% [10]. Improving traffic control should also reduce air and noise pollution due to fewer idle times. Urban mobility makes up 40% of all carbon emissions related to road transport. AI, when working in unison with IoT, creates smart machines capable of learning from patterns and acting based on set thresholds. If that tech is applied to trash cans, an AI could decide where they would be more optimally positioned. This can be achieved using data such as the speed at which a can is filled, overflow levels, and what category of trash is discarded most frequently [11]. A recycling bin could be put in place in a specific location due to a large amount of plastic bottles being discarded in that area [12]. This method was used in the South Korean city of Songdo by using RFID tags to differentiate whether trash was burnable, processable, or recyclable. Reusing and recycling our waste is the targeted issue of these solutions, but the main focus of a waste management system should not be the recycling of waste but rather the reduction of waste produced [13]. The aforementioned problems and solutions concern the storage, collection, and recycling stages of solid waste management. The other steps are transportation, treatment, and disposal in adequate form. One way to improve transportation that holds many similarities to the improvement of public transport is the calculation of a more efficient route. Achieving this will reduce fuel costs and atmospheric emissions. This can be done using geographical information systems (GIS) and Machine Learning. If the symbiosis of these technologies is well executed, the result will be a reduction in travel distance and number of vehicles needed causing improvements in both traffic and air pollution [14]. Source: cj-consultancy.com Separation of trash is currently done using image recognition technology, allowing for automatic waste classification. This massively increases the speed at which objects are recycled and greatly reduces errors such as composted or burnt plastics. By using actuators in the process, human contact with the waste becomes redundant and reduces health risks for employees [15]. The World Health Organization(WHO) reports that 9 out of 10 people around the world breathe polluted air. With this alarming information in mind, researchers have been measuring and monitoring air pollution to minimize the risk of illnesses such as cardiac issues, respiratory disorders, allergies, and breast cancer. Almost all methods of human production revolve around some form of air pollution and thus eliminating the problem is simply not an option in modern times. Since that is the case the best we can do is reduce air pollution in inhabited areas [16]. To solve any problem of this scale, huge datasets are needed. While a city could install the recommended amount of air monitoring stations and base city planning around the data gathered from those sensors, countries like India only have 160 of the recommended 4000 air monitoring stations needed to accurately monitor air quality. This hinders the possibility of setting up any new policies and leaves low air quality unaddressed. Without data, an optimal solution cannot be implemented. Computation of air quality does not solely depend on the data of these sensors but also on inhabitant activity like commutes, construction, industrialization, and garbage disposal. An AI could predict an effective city plan with this information alone [17]. Once proper air monitoring is installed comparisons can be made. Some examples include whether industrial production is proportional to growth in air pollution or if growth is too much to be permitted or how traffic at certain times of day could cause air pollution to decrease to a suboptimal standard. Once AI has made these things clear, action can be taken to keep the affected parties happy while maintaining a satisfactory air quality level [18]. One more use in local air quality analysis is the ability to offer regional medicines tailored to the medical cases of the local population. Since health problems linked to air pollution are different depending on a region’s air, having extensive data of both the patient's whereabouts and the quality of the air could help doctors have a better understanding of the illness [19]. The use of Blockchain in smart-cities Blockchains can be understood as a shared database that allows participants to accept, reject, or analyze transactions. They ease transaction recording and asset monitoring in business networks. These assets can be tangible or intangible. An example of a tangible asset would be a car or a house, while an intangible asset could be a patent or royalties. The data of these transactions are stored as data blocks which are chained to one another using cryptographic hashing. This, in combination with the decentralized nature of the ledger, makes the data practically impossible to change once saved to the chain. Thus the characteristic immutability of the blockchain ledger. Source: Edge AI and Blockchain for Smart Sustainable Cities: Promise and Potential The implementation of blockchain technology could bring about a huge transformation in how citizens treat their assets. For example, a homeowner with installed solar panels could decide to sell their surplus in stored power if they are away from home or used less than predicted [20]. A vehicle could be leased to a neighbor without the risk of theft and the comfort of concise tracking of data before and after handing off the car. If this system benefited both parties then we might see a reduction in auto ownership and result in a reduction in traffic and air pollution. According to Bank of America Merrill Lynch, this business system integrated into smart cities could bring about a metropolis capable of generating 1.29 trillion euros worldwide [21]. A suggested way to reduce traffic is to instill traffic authorities with the right to issue a finite amount of driving permits, which are first shared among drivers equally. The driver can then trade their permits on an open market for profit. This is possible thanks to smart contracts and the accessible tracking of vehicles with anything as small as an RFID to a fully equipped LIDAR system. Drivers could also bid for access to faster routes, reserve parking spots, or pay off highway tolls [22]. Blockchain could also be useful for insurance companies to predict whether a driver is reckless or not since blockchain data cannot be spoofed and would have full access to onboard vehicle sensors, street cameras, and ticket records [23]. Source: FutureBridge Vehicle-to-everything communication is how a car informs the city of street conditions, events such as accidents, and traffic congestion. This would improve transport safety and quality. A physical car key might no longer exist and would rather be possessed as a non-fungible token, allowing the owner to trade and lease the vehicle to reputable members of the service. This renders vehicle theft moot, since the vehicle is constantly monitored, and the starting of the vehicle must first be greenlighted by the blockchain. A vehicle owner could also be restricted from use of the key after committing a driving offense, thus enforcing street laws automatically [24]. Electric vehicles are well known for their low maintenance cost (due to a smaller number of moving parts) and for their environmental friendliness. These vehicles use lithium-ion batteries since they hold power very well and are relatively safe. Since the efficient manufacturing of these batteries hit the market, electrical vehicles have been on a steady rise in popularity. With increased demand comes the need for cities to adapt to supply these cars with electrical power [25]. Thus private parking spaces and other firms began offering charging services through which the client does not only pay for the parking space but the charging too. Since service providers are focused on making as much profit as possible, EV owners may prefer to trade battery charges with one another instead. This reduces the impact of EVs on the power grid and makes charging far cheaper and flexible in parking spots. A blockchain-based system was proposed to allow individual car owners to trade energy using day-ahead real-time trading markets. An EV car owner looking to trade power could offer their service on a market, deciding on a fair price based on location and charge time [26]. Blockchain technology has a multitude of applications in the energy sector. Some of the benefits are cheaper trading prices, streamlined processes, and improved interactions between client and provider [27]. As previously mentioned, a huge solution for power loss and distance inefficiencies.is the decentralization of the power grid. This can be achieved by deploying smaller power plants around urban areas, allowing individuals to trade energy, and making sure these transactions are well tracked and secured [28,29,30]. Due to this growth in energy needs, researchers globally are making an effort to investigate the possibilities, profits, and problems involved in integrating blockchain into the energy sector [31]. To start with a simple application, blockchains can be used to automate the metering and billing process in energy services. This comes with the possibility of cost reduction due to the lack of an administrator. The data stored in bills grants clients the possibility of tracking their energy use and cutting back on their costs by finding excess power use in their household. It goes without saying that the blockchain also offers a layer of protection from cyber threats [32]. These smart contracts can also be applied to water and gas bills. The first instance of blockchain use in the energy sector was when cryptocurrencies were accepted as a valid form of payment for electricity bills. BAS Nederland was the first energy company to integrate bitcoin as an acceptable form of payment [33]. Source: Encyclopedie de l’energie A more complex example of blockchain activities within the energy sector is decentralized energy trading. These trades are known as wholesale energy trading. The blockchain's role within this market is the reduction of costs, granting data access to all parties involved, getting rid of any middle-man, and possibly reducing trading volumes [34]. With purchasing parties being more aware of their energy sources and costs, consumers can now freely trade on the energy market and select the best provider. This energy trading does not necessarily constrict itself to large competitive firms. Homeowners with an abundance of renewable energy sources could also utilize a blockchain-based network to trade with their neighbors and make a profit from investments [35]. As investigated by Kand, E.S [36], the system would work as such: In the event of 2 households powered by batteries, the consumer would be equipped with miners that analyze energy use. If energy use is not enough to power the household for the rest of the day, Ethereum smart contracts would seamlessly come through with an energy trade completed under certain conditions. Energy trading conditions must be predetermined by the consumer and seller. A set price must be discussed and agreed upon by both parties. The miners should only send an energy trading request in the scenario that the consumer's stored energy depletes beyond preset margins. Seller conditions must be met by consumers before the trade may proceed. Experts predict that global demand for reliable and clean energy sources will keep growing over the next century, thus the expansion of decentralized power grids is inevitable. While this would greatly benefit our energy infrastructure, there are still hurdles. A trading model must exist that will ensure fairness in trades and net economic efficiency growth. Otherwise, the system could plummet into disarray with unfair prices, unstable and inefficient energy sources, and poor transactions [37]. Source: Blue Cloud As the populace grows and becomes more dependent on industrial foods, global waste management becomes a rising issue in city planning, giving a great impetus to improve waste management practices and make industrial foods more sustainable. While blockchain technology does not reduce the amount of waste produced, it does ensure the avoidance of landfills and more efficient waste recycling. At their core, blockchains are adept at tracking assets and storing their transactional history, allowing for a reliable data footprint of either tangible or intangible assets [38]. Blockchains can be used to enforce and ease adherence to waste disposal policies. This can be done with the help of smart bins that use QR codes or RFID tags to check whether or not a citizen has properly separated their trash. The history of waste could be used to resolve disputes regarding waste-related transactions. Smart contracts could simply automate the process of penalization, giving incentive to once again adhere to policies [39]. More direct applications to waste management can be described as a solution to supply chain management. There are 2 categories of blockchain solutions: Reward systems, incentive: With the proper disposal of waste, the acting entity is rewarded with a cryptocurrency, which can then be swapped out for special rewards, or another form of currency. The Plastic Bank is a real-world example of a blockchain reward system with a focus on making trash gathering more rewarding to the average citizen [40]. Entities would gather un-recycled plastic, bring it to a depository, weigh the waste gathered, and collect their reward as a blockchain-secured token through a mobile application. The goal was to reduce the amount of trash that gets discarded into the ocean. The immutability of the blockchain prevented any attempts at fraudulent income. The waste could either be verified and accounted for by a reverse-vending machine system, or a human. There exist other examples of this system [41]. The system was implemented. Due to there being an insufficient amount of data to properly produce an invoice for waste collectors, Arep, a subsidiary of the French national railway company, installed smart bins at their train stations. The goal was the reduction of waste collection costs by having an exact digital history of waste storage amounts in the form of a blockchain linked to each bin. While this system was implemented with the goal of cost reduction, a side goal of Arep was to analyze the data of the waste discarded and to install separation bins in the right locations. Unfortunately, the project was discontinued since waste management practices were not of importance to station management. Similar projects that have installed recycling bins with the aid of AI advice do exist and have seen a great reduction in mixed waste [42]. Monitoring/tracking of waste. These systems greatly depend on the identification of waste over a long period of time which presents some limitations [43]. While in the early stages of consumption products should be easy to identify through RFID or QR, but if this waste gets damaged, identification becomes difficult. This is not an issue for the reward system since the waste is only identified and accepted by material, but then monitoring and tracking are impeded [44]. On an industrial scale, tracking of waste can be done by measuring and tokenizing the waste by weight rather than RFID. Microplastics can also be gathered from the ocean and batched as a singular digital entity [45]. Conclusion AI and Blockchain technology can make a huge impact on smart-city security, maintenance, pollution control, vehicular traffic, commute planning, waste management, power grid management, and quality of life. Although there are some important issues that need to be addressed, this synergetic combination of modern solutions is primed to remodel our modern ways of managing businesses and governments. They can also have a ripple effect on logistics, industry, government, banking, real estate, health, education, and citizen services. With the implementation of blockchains in governments, we can finally rid our systems of bureaucracy, tax evasion, and a lack of transparency. A huge part of this article was the emphasis on “sustainable” operations. Sustainable operations tend to involve a positive influence on the environment and economy. Whether these systems will see broad adoption still relies on judicial systems and public opinion, both of which seem rather undecided. Another overlooked topic is the discarding of old systems. A good example is the process of incineration. If used to produce heat from waste to heat districts [46] then incineration is looked on favorably. On the other hand, other papers showcase the misconception of incineration [47]. A recurring subject of this article is the lack of an influence on waste production in general. Blockchain and AI can only assist in the creation of a “circular economy”, meaning waste no longer ends up in landfills, but instead circulates through every stage of production for as long as possible. While the data within the blockchain does not necessarily prevent waste production, it can be analyzed and used to update policies which in turn can cause waste reduction. The discussion of blockchain and AI implementation in smart cities can be useful by bringing obscure problems to light, but that does not mean these issues must or can be solved with blockchain and AI. Most data can be collected without blockchains and most processes can be automated without artificial intelligence. For example, blockchain is most useful in fields that cannot implement a single authoritative body, but that is not the case for waste management, power grids, or air quality control where a central authority can be trusted with the management of the field. Implementation of blockchain nonetheless could lead to a more efficient, decentralized system. One mistake all innovators should avoid is narrowing down all possible solutions to be strictly blockchain or AI-oriented. The primary goal is to solve a problem; not use blockchains and AIs specifically to solve a problem. References [1] Blockchain technology at the service of urban management https://www.iberdrola.com/innovation/blockchain-for-smart-cities-urban-management [2] 3 Key Problems IoT and AI Can Solve For Cities https://aibusiness.com/document.asp?doc_id=760656 [3] Urban Computing: Facing the Big Challenges of Big Cities https://mindthegraph.com/blog/urban-computing-challenges-big-cities/ [4] J. Gutberlet, Waste in the City: Challenges and Opportunities for Urban Agglomerations https://www.intechopen.com/chapters/57824 [5] R. Stompf, 7 major challenges of a power grid and their solutions https://fuergy.com/blog/7-problems-and-challenges-of-a-power-grid [6] N.Graham, Artificial intelligence in smart cities https://www.businessgoing.digital/artificial-intelligence-in-smart-cities/ [7] Secure and Maintain Power Grids Effectively with Artificial Intelligence . https://blog.vsoftconsulting.com/blog/secure-and-maintain-power-grids-with-artificial-intelligence#:~:text=Artificial%20Intelligence%20(AI)%20can%20be,distribution%2C%20and%20consumption%20of%20power [8] Wo Jae Lee, Haiyue Wu, Huitaek Yun, Hanjun Kim, Martin B.G.Jun, John W.Sutherland, Predictive Maintenance of Machine Tool Systems Using Artificial Intelligence Techniques Applied to Machine Condition Data https://www.sciencedirect.com/science/article/pii/S2212827118312988 [9]Renewable electricity growth is accelerating faster than ever worldwide, supporting the emergence of the new global energy economy https://www.iea.org/news/renewable-electricity-growth-is-accelerating-faster-than-ever-worldwide-supporting-the-emergence-of-the-new-global-energy-economy [10] Dubai aims to improve public transit with the help of AI https://www.intelligenttransport.com/transport-news/79089/dubai-public-transit-ai/ [11] Esmaeilian B, Wang B, Lewis K, Duarte F, Ratti C, Behdad S (2018) The future of waste management in smart and sustainable cities: a review and concept paper. Waste Management [12] An Overview of Smart City: Observation, Technologies, Challenges and Blockchain Applications Vijay Kumar Chaurasia, Alhasha Yunus, Madhusudan Singh https://link.springer.com/chapter/10.1007/978-981-15-2205-5_7 [13] How AI is creating a new future for urban waste management . https://qrius.com/ai-creating-new-future-urban-waste-management/#:~:text=AI%20technologies%20are%20increasingly%20being%20used%20abroad%20for%20urban%20waste%20management.&text=The%20world%20generates%201.3%20billion,2.2%20billion%20tonnes%20by%202025 [14] Towards Artificial Intelligence in Urban Waste Management: an early prospect for Latin America https://iopscience.iop.org/article/10.1088/1757-899X/1196/1/012030/pdf [15] Hammi B, Khatoun R, Zeadally S, Fayad A, Khoukhi L (2017) Internet of things (IoT) technologies for smart cities. https://doi.org/10.1049/iet-net.2017.0163 [16] 9 out of 10 people worldwide breathe polluted air, but more countries are taking action https://www.who.int/news/item/02-05-2018-9-out-of-10-people-worldwide-breathe-polluted-air-but-more-countries-are-taking-action [17] India’s Deadly Air Pollution Keeps Getting Worse Not Better https://www.bloomberg.com/news/articles/2022-01-11/india-s-deadly-air-pollution-keeps-getting-worse-not-better [18] M.Anand How Artificial Intelligence can help improve air quality https://yourstory.com/2020/08/artificial-intelligence-improve-air-quality/amp [19] Guidelines as to what constitutes an adverse respiratory health effect, with special reference to epidemiologic studies of air pollution C. Andrews, S. Buist, B. G. Ferris https://pubmed.ncbi.nlm.nih.gov/3994164/ https://ohsu.pure.elsevier.com/en/publications/guidelines-as-to-what-constitutes-an-adverse-respiratory-health-e-2 [20] Bagloee, S.A.; Heshmati, M.; Dia, H.; Ghaderi, H.; Pettit, C.; Asadi, M. Blockchain: The operating system of smart cities. Cities 2021, 112, 103104 https://www.sciencedirect.com/science/article/abs/pii/S0264275121000020?via%3Dihub [21] Blockchain technology at the service of urban management https://www.iberdrola.com/innovation/blockchain-for-smart-cities-urban-management [22] Bagloee, S.A.; Heshmati, M.; Dia, H.; Ghaderi, H.; Pettit, C.; Asadi, M. Blockchain: The operating system of smart cities. Cities 2021, 112, 103104. https://www.sciencedirect.com/science/article/abs/pii/S0264275121000020?via%3Dihub [23] Löffler, M.; Mokwa, C.; Münstermann, B.; Wojciak, J. Shifting gears: Insurers adjust for connected-car ecosystems. In Digital Mckinsey; Mckinsey & Company: Austin, TX, USA, 2016; pp. 1–13 https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/shifting-gears-insurers-adjust-for-connected-car-ecosystems [24] Yuan, Y.; Wang, F.Y. Towards blockchain-based intelligent transportation systems. In Proceedings of the 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), Rio de Janeiro, Brazil, 1–4 November 2016; pp. 2663–2668. https://ieeexplore.ieee.org/document/7795984 [25] Explaining Electric & Plug-In Hybrid Electric Vehicles . https://www.epa.gov/greenvehicles/explaining-electric-plug-hybrid-electric-vehicles#:~:text=Electric%20vehicles%20(EVs)%20have%20a,and%20an%20internal%20combustion%20engine [26] Huang, Z.; Li, Z.; Lai, C.S.; Zhao, Z.; Wu, X.; Li, X.; Tong, N.; Lai, L.L. A Novel Power Market Mechanism Based on Blockchain for Electric Vehicle Charging Stations. Electronics 2021, 10, 307. https://www.mdpi.com/2079-9292/10/3/307 [27] Ferreira, J.C.; Ferreira da Silva, C.; Martins, J.P. Roaming Service for Electric Vehicle Charging Using Blockchain-Based Digital Identity. Energies 2021, 14, 1686 https://www.mdpi.com/1996-1073/14/6/1686 [28] Pop, C.; Cioara, T.; Antal, M.; Anghel, I.; Salomie, I.; Bertoncini, M. Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids. Sensors 2018, 18, 162. https://pubmed.ncbi.nlm.nih.gov/29315250/ [29] Pee, S.J.; Kang, E.S.; Song, J.G.; Jang, J.W. Blockchain based smart energy trading platform using smart contracts. In Proceedings of the 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Okinawa, Japan, 11–13 February 2019; pp. 322–325. https://ieeexplore.ieee.org/document/8668978 [30] Hua, W.; Sun, H. A Blockchain-Based Peer-to-Peer Trading Scheme Coupling Energy and Carbon Markets. In Proceedings of the 2019 International Conference on Smart Energy Systems and Technologies (SEST), Porto, Portugal, 9–11 September 2019; pp. 1–6. https://ieeexplore.ieee.org/document/8849111 [31] Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 2019, 100, 143–174. https://linkinghub.elsevier.com/retrieve/pii/S1364032118307184 [32] Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 2019, 100, 174-197. https://www.sciencedirect.com/science/article/pii/S1364032118307184?via%3Dihub#bib121 [33]Casino, F.; Dasaklis, T.K.; Patsakis, C. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telemat. Inform. 2019, 36, 55–81. https://www.sciencedirect.com/science/article/pii/S0736585318306324?via%3Dihub [34] Mylrea, M.; Gourisetti, S.N.G. Blockchain for smart grid resilience: Exchanging distributed energy at speed, scale and security. In Proceedings of the 2017 Resilience Week (RWS), Wilmington, DE, USA, 18–22 September 2017; pp. 18–23. https://www.osti.gov/biblio/1434882-blockchain-smart-grid-resilience-exchanging-distributed-energy-speed-scale-security [35] Mengelkamp, E.; Gärttner, J.; Rock, K.; Kessler, S.; Orsini, L.; Weinhardt, C. Designing microgrid energy markets: A case study: The Brooklyn Microgrid. Appl. Energy 2018, 210, 870–880. https://www.sciencedirect.com/science/article/abs/pii/S030626191730805X?via%3Dihub [36] Kang, E.S.; Pee, S.J.; Song, J.G.; Jang, J.W. A Blockchain-Based Energy Trading Platform for Smart Homes in a Microgrid. In Proceedings of the 2018 3rd International Conference on Computer and Communication Systems (ICCCS), Nagoya, Japan, 27–30 April 2018; pp. 472–476. https://ieeexplore.ieee.org/document/8463317 [37] Ali, F.S.; Bouachir, O.; Ozkasap, O.; Aloqaily, M. SynergyChain: Blockchain-Assisted Adaptive Cyber-Physical P2P Energy Trading. IEEE Trans. Ind. Inform. 2021, 17, 5769–5778. https://ieeexplore.ieee.org/document/9305283/ [38] A systematic literature review of blockchain-based applications: Current status, classification and open issues Fran Casino, Thomas K.Dasaklis, Constantinos Patsakis https://www.sciencedirect.com/science/article/pii/S0736585318306324 [39] Blockchain Technology for Sustainable Waste Management Phillip Taylor, Katrien Steenmans, Ine Steenmans https://www.frontiersin.org/articles/10.3389/fpos.2020.590923/full [40] Plastic Bank (2020). Empowering the World to Stop Ocean Plastic. https://scholar.google.com/scholar_lookup?publication_year=2020&title=Empowering+the+World+to+Stop+Ocean+Plastic [41] Sanderson, P. (2017). Prismm Environmental and Parry & Evans Become First Companies to Trade Recyclable Paper using Bitcoin in UK. REB Market =Intelligence. www.rebnews.com/prismm-environmental-and-parry-evans-become-first-companies-to-trade-recyclable-paper-using-bitcoin-in-uk [42] Lidbot- building the future of waste management https://blog.iota.org/lidbot-and-iota-building-the-future-of-waste-management-680504f4e303/ [43] Sadhya, V., and Sadhya, H. (2018). “Barriers to adoption of blockchain technology,” in AMCIS 2018 Proceedings (New Orleans, LA). https://aisel.aisnet.org/amcis2018/AdoptionDiff/Presentations/20/ [44] Law, K. L., and Thompson, R. C. (2014). Microplastics in the sea. Science 345, 144-145. doi: 10.1126/science.1254065 https://www.science.org/doi/abs/10.1126/science.1254065 [45] Kouhizadeh, M., and Sarkis, J. (2018). Blockchain practices, potentials, and perspectives in greening supply chains. Sustainability 10:3652. doi: 10.3390/su10103652 https://www.mdpi.com/2071-1050/10/10/3652 [46] Malinauskaite, J., Jouhara, H., Czajczyńska, D., Stanchev, P., Katsou, E., Rostkowski, P., et al. (2017). Municipal solid waste management and waste-to-energy in the context of a circular economy and energy recycling in Europe. Energy 141, 2013-2044. doi: 10.1016/j.energy.2017.11.128 https://www.sciencedirect.com/science/article/pii/S0360544217319862 [47] Steenmans, I. (2017). Integrated infrastructure systems: strategic planning practice and problem structuring support (Ph.D. thesis). University College London, London, United Kingdom. https://discovery.ucl.ac.uk/id/eprint/1553325/