Archiwa ESG - BFirst.Tech

Smart City

With increasing urbanisation around the world and increasingly important social issues such as air pollution, urban litter, the fight against climate change or over-reliance on car transport, the need to manage cities more efficiently is emerging. Modern technologies can be used to achieve this. The idea of Smart Cities is to use communication technologies to create a more interactive and efficient urban infrastructure, as well as to raise citizens’ awareness of its operation [1]. Smart Cities therefore represent a wide range of solutions that, in combination, improve the lives of residents and help combat the problems of today’s world. In the following article, we will present some of the Smart City solutions. The role of data collection in the Smart City, Smart City technologies for transport, smart energy management, as well as for combating environmental and noise pollution will be discussed.

Data collection and analysis in a Smart City

A fundamental role in the functioning of a Smart City is the collection of data through all sorts of measurement tools such as sensors, probes and cameras. The collection of real and up-to-date data on the operation of the city is crucial to the proper functioning of Smart City solutions, as their analysis allows real-time decision-making, significantly reducing resource consumption without compromising the standard of living of the inhabitants [2]. The proper collection and analysis of the vast amount of data needed for the proper operation of Smart City systems is a huge challenge.

BFirst.Tech specialises in the implementation of IoT technology, providing advanced solutions for smart monitoring, data analysis and optimisation of urban infrastructure. As a member of the United Nations Global Compact Network Poland and co-author of the Recommendations for Cities by the World Urban Forum 11 Business Council, the company actively supports the development of sustainable technologies, focusing on innovative diagnostics, environmental acoustics and data engineering systems.

Smart City in transport

One of the main areas of use of Smart City solutions is in transport. Today’s cities are able to collect far more transport data using smart tools in public transport vehicles, at important points on the road such as intersections, or through public monitoring.

The data collected in this way can then be processed accordingly and used to improve the efficiency of the city’s transport system. The collected information can be used to display timetable information and the current position of public transport vehicles with the estimated time of arrival at the stop, making public transport a very attractive alternative to the car.

*Rys. 1. Using the Smart City in Transport. Source:* *https://www.digi.com/blog/post/introduction-to-smart-transportation-benefits*

Data flowing into traffic management systems allows real-time optimisation of urban traffic to improve safety and reduce emissions. Smart parking systems make use of data on parking spaces, monitoring them and informing drivers of their availability, and allow payment for parking to be collected, improving driver comfort and also reducing pollution by reducing the time used to find a parking space [3].

Smart City solutions also help to solve the so-called first and last kilometre problem – the first and last part of a journey in a city, usually being considerably shorter than the public transport journey itself, while possibly taking a similar amount of time. Smart City systems can allow the linking of the public transport network with the use of lightweight short-distance transport modes such as bicycles or electric scooters. Properly placed hubs for such transport, combined with ease of use, can significantly facilitate urban travel and even encourage some drivers to use public transport [4].

Smart energy management

With the increasing demand for electricity, due in part to the need to decarbonise the economy as much as possible, there is a growing emphasis not only on increasing the production of energy from renewable sources, but also on using it more efficiently. The use of intelligent energy management solutions leads to less energy consumption and therefore less energy production, which can have a major impact on environmental protection.

*Rys. 2. Green energy in the city. Source:* *https://leadersinternational.org/sme4smartcities-insights/revolutionising-urban-life-how-smart-technologies-and-sustainable-energy-are-creating-the-cities-of-the-future/*

Among the Smart City systems that support better management are smart grids that monitor energy distribution and consumption, efficient systems for storing cheaply produced energy at peak production times, and smart sensors able to regulate the use of lighting according to the amount of natural light. All these solutions in combination also make it possible to create programmes that optimise when energy is used, using it mainly during the periods of lowest production costs, which is used, among other things, in the charging of electric vehicles [5].

In addition to the above-mentioned ways of using electricity more efficiently, less energy consumption can also be influenced by technical developments and new regulations for the construction and renovation of buildings so that they use as little energy as possible. This can be done, among other things, by using efficient and environmentally friendly materials, by designing buildings to minimise heat loss while allowing as much natural light as possible, or by using intelligent systems to optimise heating and lighting consumption.

Efficient energy management is one of the key aspects of the energy transition and the fight against progressive climate change. The transformation of cities into smart cities will require large amounts of electricity, which must be produced efficiently to contribute to better environmental protection [6].

BFirst.Tech has become a member of the Business Council at PRECOP29, which produced a “White Paper” providing a Polish perspective on climate issues, including energy management ahead of the United Nations Climate Change Conference 2024. BFirst.Tech offers end-to-end solutions for monitoring, diagnostics and management of big data, including energy. To learn more, explore our solutions under this link.

Smart City in the fight against pollution and noise

One of the biggest problems facing modern cities is air pollution, resulting from a number of factors, such as the burning of solid fuels in cookers and urban planning. High levels of pollution affect the health of city dwellers, reducing their productivity, occupying the raw materials of health services and reducing attractiveness for business and tourists.

In order to effectively combat air pollution, it is necessary to have accurate information on its levels and spatial distribution provided by a large number of sensors across the city. The information gathered in this way helps to make appropriate decisions on measures to improve the state of the air. In addition, properly presented information on the state of the air to residents can strengthen public awareness of the problem and increase pressure to find appropriate solutions to combat pollution [7].

In addition to air pollution, the problem of urban noise is also increasingly discussed. Traffic jams, renovations, construction of new buildings and other sources of noise in cities can sometimes pose a serious threat to human health [8], further worsening levels of concentration and focus, lowering the standard of living of residents.

*Rys. 3. Sources of noise for urban residents. Source:* *https://www.hseblog.com/noise-pollution/*

Smart sensors that are able to estimate not only the level of noise recorded but also the source of the noise can be used to combat this problem. This data can then be processed and used by experts to prepare a plan to mitigate noise levels, thus improving the lives of residents [9].

BFirst.Tech is a company with many years of expert experience in implementing solutions to combat noise pollution. BFirst.Tech offers a modern and advanced approach in the field of noise reduction, in line with the needs not only of smart cities but also of modern industry. Explore our products and solutions under this link.

Summary

Smart Cities make use of today’s advanced data acquisition, processing and storage techniques. Through their use, our cities are gaining new tools and techniques to combat the increasingly pressing problems of the modern world. These technologies can help not only with the problems of public transport, air pollution, noise and energy management mentioned in the article, but also with many others, among which are better prevention and crisis management, public safety or waste management. Which cities make the best use of them could be a key factor in their further development and the key to better meeting the needs of their inhabitants.

References

[1] https://uclg-digitalcities.org/en/smart-cities-study/2012-edition/

[2] https://www.oecd.org/en/publications/smart-city-data-governance_e57ce301-en.html

[3] https://www.teraz-srodowisko.pl/aktualnosci/przyszlosc-transport-smart-city-forum-11962.html

[4] https://smartride.pl/przyszlosc-transportu-w-smart-city-komfort-podrozy-i-czyste-powietrze/

[5] https://energy-floors.com/10-smart-city-energy-solutions-kinetic-floors/

[6] https://www.teraz-srodowisko.pl/aktualnosci/inteligentne-technologie-zarzadzanie-energia-miasta-efektywnosc-energetyczna-13055.html

[7] https://www.innovationnewsnetwork.com/the-development-of-the-smart-city-waste-management-and-air-quality-monitoring/39990/

[8] https://pmc.ncbi.nlm.nih.gov/articles/PMC6878772/

[9] https://newsroom.axis.com/blog/noise-pollution-smart-cities

ESG

In the face of climate change, growing social awareness and the need for ethical governance, there is an emerging need to set new standards for how companies operate. ESG is the actions implemented by a company through the lens of its environmental (E), social (S) and corporate governance (G) impacts. The aim of this initiative is to promote sustainability and social responsibility in the wider business community. In line with this, companies seek to strike a balance between generating profits and caring for the environment. The obligation for companies to report their ESG activities will be gradually extended, depending on the size of the entity and the specifics of its operations. Starting in 2024, the obligation will cover companies with more than 250 employees and by the end of 2027, it will also cover small and medium-sized enterprises with more than 10 employees. The purpose of this article is to show that the importance of sustainability continues to grow and ESG issues are becoming a key area of focus in business [1].

ESG indicators

The three ESG areas mentioned above—namely environment, society and corporate governance—are an integral element necessary to be taken into account by companies that care about their image as socially responsible organisations. Effective management of each of these areas, through companies taking specific actions related to them, is key to achieving this goal. A fundamental action to be taken is monitoring, which allows awareness of the intensity of the impact exerted in each area.

Environment

To effectively monitor the impact on nature, consideration should be given to areas such as:

greenhouse gas emissions,

energy consumption,

carbon footprint,

hazardous waste production,

emissions (such as substances or noise) to the environment,

emissions to the aquatic environment.

Society

In order to effectively monitor relations with employees, customers, investors and the local community, it is important to consider areas such as:

supporting diversity,

minimising disparities,

ensuring work-life balance,

respecting employee rights,

ensuring employee safety.

Corporate governance

In order to effectively monitor how the management board operates, the following areas should be taken into account:

fiscal transparency,

countering corruption,

structure of the management board,

remuneration for the management board and employees,

respect for shareholder rights [2].

Fig. 1. Graphic showing ESG indicators
Source: https://www.iberdrola.com/about-us/esg-responsible-management

Impact of ESG on companies’ operations

ESG issues have a significant impact on the actions taken by companies and their strategies. Operating a sustainable and conscious business is now a necessity in order to maintain a leading position in the market. Implementing an ESG strategy brings with it a number of valuable values for a company, as outlined below.

Increased customer loyalty

Companies that actively engage with environmental, social and corporate issues build a bond of trust with their customers. Customer loyalty increases as consumers are more likely to support companies that take action for social and environmental good.

Improving the image

ESG-compliant actions build a positive corporate image in local communities, among customers, investors and business partners. A company that cares about the environment, supports local communities and applies high ethical standards is seen as a responsible actor and a reliable partner that cares equally about social and environmental well-being.

Stable market position

Companies that effectively implement ESG strategies can enjoy a more stable position in the market. By integrating environmental, social and corporate factors into their operations, the company minimises the risk of reputational crises, which translates into operational stability and long-term growth [3].

Innovative Technologies and the Achievement of Sustainable Development Goals

Technologies such as Artificial Intelligence, Big Data and Blockchain are effective tools for monitoring and understanding an organisation’s social and environmental impact. Artificial Intelligence, used in data analysis, enables the identification of consumers’ needs, which allows companies to understand them better and plan in advance the necessary actions to be taken. Big Data analytics, on the other hand, makes it possible to process and analyse extensive data sets, enabling more “targeted” business decisions based on the non-obvious information contained in this data. Blockchain technology, on the other hand, ensures the security and immutability of data, which is key to ensuring transparency in business processes. By using these technologies, companies can develop effective sustainability strategies, taking advantage of the opportunity to digitise and automate business processes. As a result, companies can create business models that not only generate profits but are also socially responsible and environmentally friendly. Moving towards the use of advanced technologies in the area of ESG is becoming not only a trend, but also a necessity for companies wishing to be leaders in sustainability [3, 4].

ESG at BFirst.Tech

BFirst.Tech considers sustainability one of the most essential elements of the company’s strategy. With many innovative, proprietary and environmentally friendly products, we are able to meet our customers’ needs. For the second decade BFirst.Tech has been setting the standard for solutions in reducing noise pollution in working environments and urban agglomerations, generating, aggregating and providing management information (including data for non-financial ESG reporting) and monitoring and analysing information on the state of the industrial infrastructure of companies. As we are aware of the climate changes taking place, it is environmental activities that are particularly important to us, which is why we focus on them when building the company’s strategy.

Summary

ESG is a key element in building the long-term value of companies in contemporary business. Implementing an effective ESG strategy allows companies to positively influence environmental protection, stakeholder relations and governance within the organisation. It also carries a number of precious values for the company, such as a more lasting relationship with customers, an increase in the company’s reputation, or a strengthening of its position in the industry. Thus, by implementing ESG, a company can become an indispensable part of the environment positively affecting the quality of life of the community.

References

[1] About ESG—Polish ESG Association

[2] ESG co to jest? Kogo dotyczy i jaki ma wpływ na przedsiębiorstwo? (ESG—what is it? Who does it apply to and what impact does it have on a company?) (sterrn.pl)

[3] Zrozumieć ESG: Definicja, Znaczenie i Wpływ na Biznes (Understanding ESG: Definition, Importance and Impact on Business) (boringowl.io)

[4] ESG, Blockchain, and AI – Oh My! | Barnes & Thornburg (btlaw.com)

Application of Machine Learning in Data Lakes

In the digital age, there is a growing need for advanced technologies. It means not only for collecting but especially for analysing data. Companies are accumulating increasing amounts of different information that can improve their efficiency and innovation. Data Engineering offered by BFirst.Tech can play a key role in the process of using data for the benefit of a company. This is an area of sustainable products for effective information management and processing. The article presents one of the opportunities offered by the Data Engineering area. For example the integration of Machine Learning with Data Lakes.

Data Engineering – an area of sustainable products dedicated to collecting, analysing and aggregating data

Data engineering is a process of designing and implementing systems for the effective collection, storage and processing of large sets of data. This supports the accumulation of information such as website traffic analysis, data from IoT sensors or consumer purchasing trends. Firstly, the task of data engineering is to ensure that information is skillfully collected. What is more, it is stored but also easily accessible and ready for analysis. Data can be effectively stored in Lakes, Data Storages and Data Warehouses. Such integrated data sources can be used to create analyses or feed artificial intelligence engines, which ensures comprehensive use of the collected information (see the detailed description of the Data Engineering area (img 1)).

img 1 – Data Engineering

Data lakes used for storing sets of information

Data lakes enable storing a huge amount of raw data in its original, unprocessed format. Thanks to the possibilities offered by Data Engineering, data lakes are capable of accepting and integrating data from a wide variety of sources. For instance, text documents, images, IoT sensor data. It makes it possible to analyse and utilise complex sets of information in one place. The flexibility of data lakes and their ability to integrate diverse types of data make them extremely valuable to organisations facing the challenge of managing and analysing dynamically changing data sets. Unlike Data Warehouses, Data Lakes offer greater versatility in handling a variety of data types, made possible by advanced data processing and management techniques used in Data Engineering. However, that versatility also raises challenges in the area of storing and managing such complex sets of data. It requires data engineers to constantly adapt and implement innovative approaches.[1, 2]

Information processing in data lakes and the application of machine learning

The increasing volume of stored data and its diversity pose a challenge in the area of effective processing and analysis. Traditional methods are often unable to keep up with the growing complexity. What is more, they lead to delays and limitations in accessing key information. Machine Learning, supported by innovations in Data Engineering, can significantly improve those processes. Using extensive data sets, Machine Learning algorithms identify patterns, predict outcomes and automate decisions. Thanks to the integration with Data Lakes (img 2), they can work with a variety of data types. That is to say, structured to unstructured, enabling more complex analyses. Such comprehensiveness enables a more thorough understanding and use of data that would be inaccessible in traditional systems.

Applying Machine Learning to Data Lakes enables deeper analysis and more efficient processing. It facilitates the process by advanced Data Engineering tools and strategies. This enables organisations to transform great amounts of raw data into useful and valuable information. That is important for increasing their operational and strategic efficiency. Moreover, the use of Machine Learning supports the interpretation of collected data and contributes to more informed business decision-making. As a result, companies can adapt to market demands more dynamically, and create data-driven strategies in an innovative way.

img 2 – Data Lakes

Fundamentals of Machine Learning, key techniques and their application

In this paragraph, let’s discuss Machine Learning. as an integral part of the so-called artificial intelligence. It enables information systems to learn and develop based on data. Different types of learning are distinguished in that field: Supervised Learning, Unsupervised Learning and Reinforcement Learning. In Supervised Learning, each type of data is assigned a label or score that allows machines to learn. For example, to recognise patterns and create forecasts. That type of learning is used in image classification or financial forecasting, inter alia. In turn, Unsupervised Learning, in the case of which unlabeled data is used, focuses on finding hidden patterns and is useful in tasks such as grouping elements or detecting anomalies. Reinforcement Learning is based on a system of rewards and punishments. It helps machines to optimise their actions under dynamically changing conditions, e.g. games or automation. [3]

In terms of algorithms, neural networks are excellent for recognising patterns in complex data, such as images or sound. It also forms the basis of many advanced AI systems. Decision trees are used for classification and predictive analysis, for example in recommendation systems or sales forecasting. Each of those algorithms has unique applications and can be tailored to the specific needs of a task or problem. As a result, it makes Machine Learning a versatile tool in the world of data.

Examples of applications of Machine Learning

The application of Machine Learning to Data Lakes opens up a wide spectrum of possibilities. We can enumerate from anomaly detection, through personalisation of offers, to optimisation of supply chains. In the financial sector, such algorithms effectively analyse transaction patterns and identify anomalies or potential fraud in real time. That is crucial in preventing financial fraud. In retail and marketing, Machine Learning enables the personalisation of offers to customers. It happens by analysing purchase behaviour and preferences, increasing customer satisfaction and sales efficiency. [4] In industry, the algorithms contribute to the optimisation of supply chains by analysing data from various sources – as weather forecasts or market trends. It helps predicting demand and manage inventory and logistics [5].

They can also be used for pre-design or product optimisation. Another interesting application of Machine Learning in Data Lakes is image analysis. Machine Learning algorithms are able to process and analyse large sets of images and pictures. They are used in fields such as medical diagnostics, where they can help detect and classify lesions in radiological images, or in security systems, where camera image analysis can be used to identify and track objects or people.

CONCLUSIONS

The article emphasises developments in the field of data analytics, highlighting how Machine Learning, Data Lakes and data engineering influence the way organisations process and use information. Introducing such technologies into business improves existing processes and opens the way to new opportunities. The Data Engineering area introduces modernisation into information processing, characterised by greater precision, deeper conclusions and faster decision-making. That progress emphasises the growing value of Data Engineering in the modern business world, which is an important factor in adapting to dynamic market changes and creating data-driven strategies.