Integration of GIS, Big Data, and Artificial Intelligence in Modern Waste Management Systems—A Comprehensive Review

Abstract

This article presents a narrative, traditional literature review summarizing current research on the integration of digital technologies in waste management. The study examines how intelligent technologies, including Geographic Information Systems, Big Data analytics, and artificial intelligence, can improve energy efficiency, support sustainable resource use, and enhance the development of low emission and circular waste management systems. The reviewed research shows that the combination of spatial analysis, large-scale data processing, and predictive computational methods enables advanced modeling of waste distribution, the optimization of collection routes, intelligent sorting, and the forecasting of waste generation. Geographic Information Systems support spatial planning, site selection for waste facilities, and environmental assessment. Big Data analytics allows the integration of information from Internet of Things sensors, global positioning systems, municipal databases, and environmental registries, which strengthens evidence-based decision making. Artificial intelligence contributes to automatic classification, predictive scheduling, robotic sorting, and the optimization of recycling and energy recovery processes. The study emphasizes that the integration of these technologies forms a foundation for intelligent waste management systems that reduce emissions, improve operational efficiency, and support sustainable urban development.

1. Introduction

According to a meta-analysis conducted by Kafle and colleagues, humanity generates slightly more than 2 billion tons of waste annually. It is assumed that if current trends continue, this volume may increase to levels ranging from 3.4 to 4.2 billion tons by the middle of the 21st century [1,2,3]. High-income countries, which account for a relatively small share of the world’s population (16% of the population), generate about one-third of all waste [3]. In contrast, low-income countries are responsible for only a few percent of global waste production [4]. However, forecasts suggest that by 2050, it is low and middle-income countries where the rate of per capita waste growth will be the most dynamic [5]. It is assumed that tensions related to the growing amount of waste occur especially where urbanization outpaces the development of infrastructure [6].

In light of these trends, waste management is becoming one of the key challenges of our time because it brings together environmental, health, climate, and economic impacts. Improper waste handling leads to soil and water pollution, biodiversity loss [6], and the spread of microplastics [7,8]. An additional environmental burden is that landfills emit methane (CH₄), contributing to climate change [9], and uncontrolled combustion generates toxic air pollutants, which translates into health burdens for communities [10]. From a financial perspective, collection and treatment systems represent a significant cost for local governments [11], and failure to meet legal requirements results in additional sanctions in many countries [12]. Finally, the linear “take, use, dispose” model means wasting resources and greater import dependency instead of building resilience through a circular economy [13]. Therefore, accelerating investment in infrastructure, waste prevention, and material recovery, as well as integrating modern instruments into waste management systems, is a condition for the safe, healthy, and competitive development of cities and regions [14,15].

It is worth emphasizing that effective waste management is a key component in achieving the United Nations Sustainable Development Goals (SDGs), which are a comprehensive set of seventeen goals adopted in 2015 as part of the 2030 Agenda [16]. Waste management aligns, in particular, with SDG 11 (especially target 11.6) by reducing the environmental impact of cities and improving the quality of life for their inhabitants. It also contributes to SDG 12 (specifically 12.5) [17] through actions aimed at waste prevention, support for reuse, and increasing recycling rates. Additionally, SDG 13 can be advanced through measures such as reducing methane emissions and integrating climate action into waste management planning [18,19,20]. The interdependencies between energy transition, climate regulation, and technological innovation are also a significant context for waste management, as confirmed by analyses of changes taking place in European energy policy [21]. In practice, this means reducing pollution, increasing resource recovery, and lowering the carbon footprint of cities, which brings real, measurable benefits for both the environment and the quality of life of local communities.

Effective and efficient waste management requires the integration of diverse management instruments, including organizational, economic, technological, and logistical tools [15] into a coherent multidimensional system. In contemporary literature, there is an increasing emphasis on the need to integrate digital technologies into waste management systems, driven by the growing complexity of processes, pressure on resource efficiency, and the necessity of ensuring transparency in public actions. In this context, the integration of Geographic Information System (GIS) computational tools, including Big Data analytics, and artificial intelligence, represents one of the key directions for the development of modern waste management models [22,23], enabling more precise planning, forecasting, and control of material flows in the spirit of sustainable development [24].

The study is designed as a narrative (traditional) literature review summarizing current technological and methodological advancements in waste management. Therefore, the aim of this article is to review current trends, solutions, and research directions related to the use of digital technologies in waste management, with a particular focus on the integration of GIS, Big Data, and artificial intelligence in decision-making and operational processes.

The authors’ intention in this undertaking is to move away from approaches dominant in the existing literature, which tend to focus on analysing individual solutions within waste management. Instead, we adopt an integrative perspective in which GIS, Big Data, and AI are treated as interrelated management instruments that together form a coherent and flexible ecosystem supporting waste management across all stages of its operation, from data acquisition and integration to operational and strategic decision-making. An additional contribution is the systematic structuring of views present in the scientific discourse on the implementation of the discussed technologies, including the identification of key barriers of a technological, organizational, and regulatory-legal nature. This approach is intended to facilitate an interpretation of why solutions widely described in the literature do not always translate into effective application in real-world waste management systems.

2. Systems and Criteria for Waste Classification in National and International Contexts

2.1. Criteria for Waste Classification and the Challenges of Standardization

The classification of waste is a fundamental aspect of effective waste management and handling. Depending on the country, region, or administrative unit, classification criteria differ in order to meet the current needs of the local economy. Important criteria in this classification include the sources and places of origin of waste, such as municipal, construction, industrial, medical, or hazardous waste [25]. Additionally, the literature identifies chemical composition and physicochemical properties (toxicity, flammability, reactivity) as classification criteria, which encompass the presence of hazardous substances, recyclable materials, and biodegradable content.

Waste classification systems are designed to categorize waste according to its properties, risks, and potential for recovery. The authors indicate the recycling potential as an important criterion, meaning the possibility of reusing or recovering resources within the economic cycle [26,27]. In particular, this includes the potential for material recovery (e.g., plastics, glass, metals), energy recovery (e.g., through RDF combustion), the efficiency and cost of recycling (the feasibility of reprocessing), as well as alignment with the principles of the circular economy (reducing the extraction of primary raw materials). A summary of the main classification criteria is presented in Figure 1.

The literature emphasizes that the lack of clear classification leads to difficulties in planning recovery and disposal processes, as well as in collecting environmental data [28]. The absence of unified classification standards results in discrepancies in the data reported by local authorities’ businesses and supervisory institutions, which in turn creates challenges in analyzing waste streams and comparing circular economy performance indicators [29].

Figure 1. Waste classification criteria, including their source of origin, physicochemical properties, recycling and recovery potential, legal and regulatory aspects, and key problems and challenges related to their categorization [29,30,31,32].

Figure 1. Waste classification criteria, including their source of origin, physicochemical properties, recycling and recovery potential, legal and regulatory aspects, and key problems and challenges related to their categorization [29,30,31,32].

2.2. Waste Classification Systems in European, International, and National Contexts

As a result, the ability to make data-driven strategic decisions in waste management is limited, as is the effective monitoring of environmental and climate policy goals [30].

For this reason, the authors of many studies point to the need for implementing uniform interoperable waste classification systems based on European and international standards (EWC Basel Codes HS Codes), which can be integrated with digital tools for monitoring and reporting material flows [31].

In practice, many waste classification systems are used. In Europe, the European Waste Catalogue (EWC) is widely applied, assigning waste to codes based on its source and type. Additionally, there are international classifications, such as the United Nations Dangerous Goods Classification, as well as national regulations that may introduce their own definitions and category codes.

As noted in the analyses of “smart waste classification” systems, the classification framework must be compatible with digital tools to enable waste stream monitoring and reporting [29,30,31,32]. In the United States, the waste classification system is primarily based on the regulations of the Environmental Protection Agency (EPA), which govern waste management at the federal level [33]. In addition to hazardous waste, the EPA also maintains records and classifications for other types of waste, including Municipal Solid Waste (MSW), Industrial Waste, Universal Waste, and Medical Waste. All data is collected in the Resource Conservation and Recovery Act (RCRA) Info system, used for the registration, monitoring, and reporting of waste flows, including generator identification, transport, and disposal. This system is the U.S. counterpart of the European BDO and enables data interoperability across states. It requires all waste codes (F K P U) to be clearly assigned to generated and transported streams. Although the U.S. system is independent of the European Waste Catalogue (EWC), the Basel Convention Codes are also used in international waste trade for the export and import of hazardous waste, and the UN Dangerous Goods Codes are applied for transporting hazardous waste in accordance with United Nations regulations [34].

Research confirms that differences in classification systems between the EU and the USA hinder the exchange of waste data and the comparability of environmental reports [35,36]. There is a recognized need for digital harmonization of classification systems to improve global monitoring of waste streams and support the implementation of the circular economy [37].

In China, the waste classification system is based on national and local regulations. In major cities such as Shanghai, Beijing, and Shenzhen, a four-category system is used: Recyclable Waste, Hazardous Waste, Household Food Waste (Wet), and Residual Waste (Dry). Since 2020, the digital National Solid Waste and Chemical Management Information System has been in operation, integrating data on waste generators, transport, and processing with local databases, supported by GIS and blockchain technologies. The system is partially aligned with Basel Convention Codes, the UN Dangerous Goods Classification, and HS Codes. However, regional differences and a lack of full standardization for special industrial waste categories still persist [37,38,39,40].

In Australia, due to its federal system, there is no central waste classification system. Regulations are coordinated under the National Waste Policy, the Australian Waste Classification System (AWCS), and the National Environmental Protection Measures (NEPMs). AWCS classifies waste based on its source, physical properties, and hazard level into the following categories: General Solid, Restricted Solid, Hazardous, and Liquid Waste. The classification is based on seven criteria (H1–H7) aligned with United Nations standards (UN Recommendations on the Transport of Dangerous Goods). Data is collected and reported through the National Waste Data Reporting System (NWD-RS), which is integrated with GIS and smart waste solutions [41,42,43].

In the Republic of South Africa (RSA), a waste code system operates within the Waste Information System (WIS). Each code specifies the sector waste type and hazard class (Type 0–3). An Extended Producer Responsibility (EPR) system is being implemented, requiring digital reporting of waste at the source. The national platform South African Waste Information System (SAWIS) maintains a registry of generators and processors, reports quantities and treatment methods, and integrates data with GIS systems [44].

In Chile, the waste classification system is governed by Law No. 20.920 (Ley REP), which establishes extended producer responsibility (EPR) and the waste management hierarchy. The main data platforms include the Information System for Waste Management under Extended Producer Responsibility (SIDREP), the National Waste Declaration System (SINADER), and the National Environmental Enforcement Information System (SNIFA), which support waste reporting, monitoring, and enforcement. Together, these systems form an integrated information ecosystem that underpins the implementation of the National Circular Economy Strategy 2040 and progress toward the Sustainable Development Goals, particularly SDG 12 [45,46,47,48].

The selected continents and countries were chosen to reflect the diversity of global waste management systems with respect to waste generation levels, recycling performance, and the degree of infrastructure development. The sample includes regions operating under different regulatory frameworks and representing a range of income levels, which allows for meaningful comparisons between highly advanced systems and those facing fundamental organizational challenges [49,50]. An additional key criterion was the availability of reliable and comparable data, enabling a robust and credible comparative analysis. Overall, this selection ensures a representative regional cross-section that captures global trends, challenges, and best practices in waste management [51,52].

The inclusion of the United States, China, Australia, Europe, and Chile is therefore not incidental. These regions share several relevant characteristics essential for comparative analysis, including high volumes of waste generation, diverse legal and regulatory approaches, good data availability, global representativeness, and varying stages of economic development [53,54].

The examples discussed in this study—such as Europe, the United States, and China—can serve as a useful reference point for developing countries; however, their applicability is conditional and indirect rather than directly transferable. This is due to substantial differences in infrastructure development, institutional capacity, and broader socio-economic conditions. On the one hand, the analyzed regions provide valuable comparative insights, as they represent mature or rapidly evolving waste management systems characterized by advanced processing technologies, well-established waste classification schemes, and relatively stable regulatory frameworks [55]. This makes it possible to identify key components of effective systems, such as the role of coherent waste classification, the influence of regulation on waste streams, and the importance of reliable data for policy planning and monitoring. In this respect, these cases illustrate long-term development pathways and potential outcomes of a systemic approach to waste management [56].

On the other hand, many developing countries in Africa, South Asia, and Southeast Asia operate under markedly different conditions, including limited technical infrastructure, constrained financial resources, weaker regulatory enforcement, and a significant role of the informal sector in waste management. Under such circumstances, the direct transfer of solutions implemented in Europe, the United States, or China may prove impractical or inefficient. Advanced waste classification systems and treatment technologies typically require stable institutions, substantial investment, and strong administrative capacity [57].

Consequently, the examined cases should primarily be understood as reference frameworks and sources of good practices rather than as ready-to-implement models. Their relevance for developing countries lies in the possibility of selectively adapting certain elements—such as simplified classification systems, gradual regulatory implementation, or the integration of formal and informal waste management structures—in ways that are tailored to local conditions. Thus, the analyzed examples constitute a logically justified point of reference, provided that differences in development levels and the need for context-sensitive policy design are fully acknowledged [58].

To provide a concise summary of the differences and similarities between national and regional waste classification systems, a comparison was developed, covering key institutional, legal, and functional aspects. This overview presents the main features of the systems in terms of their structure, classification types, alignment with international conventions, and the level of digitization and integration with digital tools.

Table 1. Comparison of waste classification systems in selected countries/regions.

Country Region	Legal and Institutional Basis	Classification Structure	Main Categories	Code System Example	Digital Registry Systems	Convention Alignment	Ref.
European Union	Waste Framework Directive (2008/98/EC); European Waste Catalogue (EWC)	Hierarchical 20 sections by source and type of waste	Municipal, industrial, hazardous, construction	20 03 01—mixed municipal waste	BDO (PL) EIONET (EU)	Basel OECD UNDG	[59,60]
United States	Resource Conservation and Recovery Act (RCRA 1976); EPA Regulations (40 CFR 261)	Federal sector-based	Hazardous, non-hazardous, universal, medical	F001 K039 U001	RCRA Info (EPA)	Basel (partial) UNDG	[33,34]
China	Solid Waste Pollution Prevention and Control Law (2020); National Catalogue of Hazardous Wastes (2021)	Two-tier (municipal/industrial)	Recyclable, hazardous, wet, dry	18-digit code HW08 900-002-08	National Solid Waste & Chemicals Info System	Basel UNDG	[39,61]
Australia	National Waste Policy (2018); Australian Waste Classification System (AWCS)	Federal state-based	General, restricted, hazardous, liquid	Category A–C state codes	NWD-RS (National Waste Data Reporting System)	Basel OECD UNDG	[62,63]
South Africa	National Environmental Management: Waste Act (2008); Waste Classification and Management Regulations (2013)	National typological	General, hazardous (Type 0–3)	01-03-1 metallic waste type 1	SAWIS (South African Waste Information System)	Basel Stockholm Rotterdam	[64,65]
Chile	Ley REP (No. 20.920/2016)—Extended Producer Responsibility Law Reglamento de Residuos Peligrosos (DS 148/2003)	Centralized (Ministry of the Environment)	Non-hazardous, hazardous, special, recyclable	Code per DS 148/2003 Y01–Y45 (Basel-compliant)	SINADER (Sistema Nacional de Declaración de Residuos)	Basel OECD UNDG	[66,67]

enables the identification of development directions and the degree of harmonization of waste classification systems in selected countries and regions.

2.3. Global Trends in the Digitization and Harmonization of Waste Classification

In recent years, there has been a clear transformation in the way countries and international organizations approach the recording, classification, and monitoring of waste. With increasing environmental pressure and legislative changes, waste management systems are evolving from traditional reporting models toward integrated digital environmental data ecosystems. This trend aligns with the concept of smart waste management [68] and the Circular Economy Monitoring Framework promoted by the European Union and the Organization for Economic Cooperation and Development (OECD) [69,70].

At the global level, a convergence of technological and systemic approaches is emerging. Solutions are being implemented that combine automated waste classification with public policy frameworks and environmental reporting. Examples include the United States, where the EPA Waste Data System is being developed with the integration of Internet of Things (IoT) sensor data under the Smart Cities for Sustainability program [71]. In Japan, the e-Manifest Waste Tracking system is based on blockchain technology and public cloud solutions and is applied in the industrial sector [72]. On the African continent, an important initiative launched by the African Union is the Africa Waste Data Observatory (2023), which aims to create a shared waste data repository for the entire continent [73].

In China, Australia, and South Africa, smart waste classification approaches are being developed that integrate Internet of Things (IoT) sensors, artificial intelligence (AI) analytics, and blockchain technologies to track waste streams [74,75]. In contrast, Chile and the European Union emphasize standardized reporting within the circular economy cycle through frameworks such as the Circular Economy Monitoring Framework.

From a global perspective, modern systems for recording and classifying waste are becoming a fundamental tool for environmental data management, supporting the transition from reactive waste policies to proactive resource management aligned with circular economy principles [76,77]. In this context, the IoT refers to a network of interconnected physical devices equipped with sensors and communication technologies that enable the real-time collection, transmission, and analysis of data, including information related to waste generation, segregation, and movement.

The examples discussed in the study (e.g., Europe, the United States, and China) represent advanced waste-management systems that operate under conditions of a high level of technological development and well-established political and regulatory frameworks [78]. However, it is not clear whether these cases provide a sufficiently useful reference point for developing countries, particularly those in Africa, South Asia, and Southeast Asia. These regions face significant infrastructural, financial, and institutional constraints that fundamentally differentiate their waste-management contexts from those of developed economies. The composition of waste streams, the dominant socio-economic conditions, and policy priorities also differ substantially, which means that models adopted in developed countries often cannot be directly transferred without major adaptations [79].

2.4. The Importance of Proper Waste Classification for Technology Costs and Reporting

Proper waste classification is a fundamental component of an effective waste management system, shaping the technical, economic, and environmental aspects of treatment processes. It enables the appropriate selection of processing technologies, supports the evaluation of costs and environmental impacts, and ensures reliable reporting and recordkeeping. Incorrect or imprecise classification can lead to the use of inappropriate disposal technologies, increased emissions, or suboptimal utilization of recovered fractions [80].

A review of life cycle assessment (LCA) in waste management found that incorrect assignment of waste codes results in discrepancies in comparative results and environmental conclusions. Waste classification, understood as assigning the correct code, category, or characteristic to a specific waste stream, is not only a formal requirement but also the foundation for selecting the appropriate recovery or disposal technology [81,82].

Studies on municipal waste classification in automated systems (AI-based sorting) have shown that precise identification of material fractions can increase recycling efficiency by 15–20%, compared to a classification based solely on administrative declarations [83].

3. Structure and Functioning of Waste Management System

3.1. Life Cycle of Waste and Its Significance for Management Systems

A waste management system covers the entire life cycle of waste from the moment it is generated to its final disposal or recovery [84]. The typical stages of this process include collection, transport, segregation, and selective collection and processing, which may involve recycling, composting, or energy recovery, as well as landfilling or final recycling [85].

Waste collection includes the pickup of municipal waste from households, as well as industrial and commercial waste. At this stage, the frequency and method of collection are crucial, as they affect the quality and purity of waste streams. The next stage is transport, during which waste is moved to processing facilities or temporary storage locations [86].

The efficiency of transport depends on logistics and infrastructure, as well as on source separation, which helps minimize the risk of mixing fractions [87]. Segregation or selective collection is also an essential stage for further processing. Proper segregation allows for an increase in the share of recyclable materials and a reduction in mixed waste fractions [88]. The literature also emphasizes the importance of advanced optimization models that enable multi-criteria design of energy recovery processes, as in the case of algorithms supporting the production of fuel from selected combustible fractions of municipal waste [89]. However, in many countries, waste is still often mixed, which leads to reduced quality of secondary raw materials and lower efficiency of recovery processes [90].

Processing includes mechanical and chemical recycling, composting of organic fractions, and energy recovery from waste, for example, through combustion or anaerobic digestion. The effectiveness of these processes largely depends on the quality and homogeneity of waste streams.

Disposal or final recycling applies to waste that cannot be recovered or recycled. Such waste is sent to landfills or neutralized using other methods, such as thermal treatment in incineration plants. Even at this stage, it is possible to recover some materials and energy through end-of-life recovery. These processes and their efficiency are illustrated in the waste generation model shown in Figure 2.

The waste life cycle diagram (Figure 2) illustrates the stages where technologies supporting waste management can be applied. In the Waste Generation phase, GIS systems enable the mapping of waste sources. During the Collection stage, AI algorithms and Big Data analysis are used to optimize routes and forecast waste quantities. In the Processing phase, GIS and AI technologies support process monitoring and efficiency analysis. The Disposal stage can be supported by GIS spatial analysis for planning and locating landfill sites.

3.2. Models of the 3R, 4R, and 5R Action Hierarchies

In environmental literature, the standard waste management hierarchy typically includes the 3R model: Reduce, Reuse, Recycle [91]. Contemporary approaches expand this hierarchy with additional R’s such as Recovery (energy recovery) or Responsibility (producer responsibility), resulting in 4R or 5R models [92]. These extensions help incorporate energy recovery technologies and highlight the role of producers in the product life cycle.

In studies focused on organic waste, it is emphasized that effective recovery systems require both waste prevention and maximizing the use of waste as a secondary resource. Research has shown that the implementation of strategies involving reduction, selective collection, and biological processing significantly improves material and energy recovery efficiency, while reducing the environmental pressure of the system [93].

Additionally, an increasing number of studies indicate that traditional waste hierarchy models are gradually being complemented by circular economy (CE) concepts, where the emphasis shifts from recycling to preventive actions, eco-friendly product design, and closing material loops at the production stage [94]. The literature also highlights the need to integrate hierarchy principles with life cycle assessment (LCA) and material flow analysis (MFA). This integration enables a more precise determination of the actual environmental impact of various 4R–5R strategies [95].

Research has shown that strategies based on extended producer responsibility (EPR) can increase the share of waste returned to the economy, improve sorting technology efficiency, and reduce long-term systemic costs [96]. At the same time, in the context of biogenic waste, there is a recognized need to combine energy recovery methods, such as anaerobic digestion, composting, and material recycling into integrated systems that allow processes to be flexibly adapted to local environmental and economic conditions [97].

As a result, contemporary waste hierarchy models are becoming increasingly multidimensional, incorporating technological, social, and regulatory aspects, which broadens their application in advanced waste management systems.

3.3. Main Challenges in Waste Management Systems

3.3.1. Inefficient Waste Segregation Practices

Modern waste management systems face several challenges that limit their environmental and operational efficiency. One of the key issues is incorrect waste sorting, which lowers the quality of recyclable materials and reduces the potential for effective recovery. Another important barrier is the limited integration of information systems. This results in fragmented data flows, lower transparency and reduced opportunities for process optimization.

Addressing these challenges requires coordinated improvements in both organizational and technological areas. Environmental education can strengthen correct segregation practices and increase public awareness. Integrated management systems support smoother coordination between different actors in the waste sector. Data analysis and artificial intelligence improve forecasting and operational decision-making. Smart IoT containers provide real-time information on waste levels and help optimize collection schedules. These problems and their main solution pathways are illustrated in Figure 3.

Despite existing theoretical and regulatory frameworks, practical waste management systems face significant challenges. One of the main issues is improper and inefficient sorting at the source, especially at the level of households and service units. This leads to the mixing of different waste fractions, which significantly reduces the purity and quality of secondary raw materials. As a result, contaminated materials are often unsuitable for recycling and are instead directed to residual waste streams or landfills, leading to the loss of resource potential and increased environmental and economic costs [98,99].

Contamination of secondary raw materials such as paper, glass, plastics, and metals negatively affects their physicochemical properties and limits their usability in recycling processes. The presence of organic residues in paper waste leads to the degradation of cellulose fibers, while the inclusion of fats or chemicals in plastic waste complicates thermal processing and lowers the quality of the resulting regranulate [100]. Mixing different types of plastics, such as polyethylene terephthalate (PET) and polyvinyl chloride (PVC), results in poorer viscosity characteristics and mechanical instability of recyclates [101].

In the context of the circular economy (CE), effective source segregation is a fundamental requirement for maintaining high-quality material streams. Insufficient purity of fractions intended for recycling leads to lower recovery rates, increased sorting and processing costs, and reduced economic feasibility of recycling operations. Studies indicate that to achieve economic and environmental efficiency, the purity level of waste streams should exceed 90 percent, especially for paper and plastics [102,103].

To improve the effectiveness of collection systems, integrated actions are required, including environmental education for the public, the introduction of economic and regulatory instruments to support proper segregation behavior, and modernization of collection infrastructure, such as the use of smart bins with fill-level sensors and radio frequency identification (RFID) identification systems [104]. Implementing such solutions contributes to improved quality of secondary raw materials, reduced emissions, and increased efficiency across the entire waste management chain, in line with circular economy principles.

3.3.2. Limitations in Data Quality Within Waste Management Systems

Insufficient data control and low data quality hinder environmental and economic analysis, as well as progress monitoring. The effectiveness of waste management systems and environmental policies largely depends on the quality, consistency, and reliability of data collected at all stages of waste life cycle management. A lack of proper data control mechanisms, or poor data quality, represents a significant barrier to conducting reliable environmental and economic analyses and consequently to assessing the effectiveness of circular economy (CE) strategies [105,106].

Incomplete or inconsistent data on waste volumes and composition, recycling rates, pollutant emissions, or operational costs make it difficult to develop reliable predictive models and life cycle assessments (LCAs). The absence of precise information can lead to incorrect conclusions about the environmental and economic performance of specific processes, which in turn affects strategic decision-making by local authorities, public institutions, and private sector actors [107].

The literature emphasizes the need for integrated data management systems that ensure interoperability between different stakeholders, such as municipalities, waste processing facilities, and regulatory institutions. In this context, the implementation of digital monitoring tools, such as database platforms, blockchain technologies, or IoT systems, becomes particularly important. These technologies can enhance information transparency, reduce the risk of errors, and improve the credibility of reporting [108].

3.3.3. Barriers to Digital System Integration

Limited integration of information systems is a significant barrier to effective waste management. The lack of interoperability between logistics platforms, databases, and reporting systems prevents full utilization of the potential offered by technology-supported management. Studies on the concept of smart waste management indicate that automated classification and real-time data analysis can contribute to reducing operational costs and increasing resource recovery rates [109,110]. Integrated information systems form the foundation of this concept, relying on digital technologies, such as the Internet of Things (IoT), real-time data analytics, and artificial intelligence (AI), which enable the optimization of waste collection, transport, and processing [111].

The literature highlights that incompatibility between information systems, including municipal platforms, logistics operators, and waste processing facilities, leads to data duplication, reporting delays, and difficulties in analyzing system efficiency [112]. Research on intelligent waste management emphasizes that automated classification and real-time data analysis can significantly reduce operational costs and increase resource recovery levels [113,114]. The use of IoT systems with container fill-level sensors enables dynamic route planning, which can reduce fuel consumption and greenhouse gas emissions by 20–30% [115]. At the same time, machine learning algorithms improve sorting accuracy and the quality of recovered materials.

This is because machine learning is an advanced data analysis tool that enables the simulation of the learning process by identifying relevant features, analyzing complex patterns, and formulating reliable predictions or decisions based on multidimensional and often unstructured data sets [116,117].

The key aspects of information system integration in waste management and their relevance to the smart waste management concept are presented in

Table 2. Integration of information systems in waste management and their significance for the concept of smart waste management.

Aspect	Description, Significance	Technologies and Tools	Effects, Benefits	References
Limited integration of information systems	Lack of interoperability between logistics platforms, databases, and reporting systems hinders data exchange and process monitoring	Fragmented databases, lack of standardized data exchange protocols	Data duplication, reporting delays, limited system efficiency analysis	[109,112]
Automation of classification and data analysis	Use of AI and real-time data analytics to support operational decision-making	Artificial intelligence (AI), machine learning (ML), real-time analytics	Reduction of operational costs, increased recovery of secondary raw materials	[110,113,114]
Integrated information systems	Core element of the “smart waste management” concept; integration of data from various stages of waste handling	Internet of Things (IoT), GIS systems, cloud platforms, APIs	Optimization of waste collection, transport, and processing	[111,115]
IoT systems and fill-level sensors	Monitoring container fill levels and dynamically planning collection routes	IoT sensors, predictive algorithms, telematics	Reduction of fuel consumption by 20–30%, lower greenhouse gas emissions	[115]
Integration within the digital waste management ecosystem	Development of a unified data infrastructure enabling cooperation between authorities, operators, and recycling companies	Interoperable platforms, databases, blockchain, APIs	Transparency, data standardization, improved oversight and circular economy implementation	[118]
Implementation of data-driven solutions	Use of real-time data for strategic and operational decision-making	Big data analytics, AI decision systems	Enhanced efficiency and sustainability of waste management systems	[118]

.

Data integration within a shared IT infrastructure supports the creation of a digital waste management ecosystem, where information between stakeholders is transparent and standardized [118]. This approach facilitates monitoring the achievement of circular economy goals and the development of smarter, more efficient, and sustainable waste management systems.

3.4. The Significance of Technological Integration for Effective Waste Management

The integration of information and logistics technologies in waste management systems is becoming increasingly crucial. On the one hand, it enables real-time monitoring of waste streams and landfill conditions; on the other, it supports life cycle analysis, transport route optimization, and intelligent segregation systems. For example, the use of artificial intelligence algorithms in waste classification automates the sorting process, which can increase the share of recoverable materials and reduce operational costs [119].

In the context of the circular economy, technology becomes a tool for shifting the focus from end-of-life disposal to maximizing recovery and reuse. The literature indicates that achieving these goals requires both technological investment and high-quality data, which in turn highlights the importance of early classification and proper data system design [120].

Defining appropriate classification criteria using standardized coding systems and ensuring data quality are prerequisites for initiating technological and logistical processes. However, classification alone is only the first step. Effective waste utilization depends on the efficient functioning of all life cycle stages and the implementation of waste hierarchy models such as 3R, 4R, or 5R [121].

Current challenges, such as inefficient segregation, poor IT system integration, and a lack of reliable data, point to the need for integrating technological, logistical, and informational solutions. Only such a holistic approach will allow for the optimal use of secondary raw materials and support the transition toward a circular economy model [122].

4. Review of Digital Technologies in Waste Management

In recent years, waste management has increasingly adopted digital solutions that support the monitoring, optimization, and automation of processes across all stages of the waste life cycle. The most commonly used digital technologies in this field include the Internet of Things (IoT), data analytics, and artificial intelligence (AI)-based systems; GIS technologies and GPS-based location systems; digital platforms for data management; as well as blockchain solutions and intelligent decision-support systems. These technologies enable, among others, the monitoring of bin fill levels, optimization of collection routes, automated waste sorting, forecasting of waste streams, and improved transparency and efficiency of waste management systems. The remainder of this section provides a detailed review of individual digital technologies, their applications, and their potential benefits and limitations in the context of modern waste management.

4.1. Application of Geographic Information Systems in Waste Management

4.1.1. The Significance of GIS in Waste Planning and Management

GIS is widely regarded as one of the most promising techniques for automating waste planning and management [123,124]. It enables the storage, retrieval, analysis, and processing of large volumes of data, as well as the visualization of results with short response times [125,126]. GIS provides an effective means for importing, organizing, and analyzing geographic data, which form the basis for planning collection systems. It is used for locating and monitoring landfills, accurately collecting data on solid waste, and identifying links between landfill sites and other environmental factors [18,127].

Furthermore, public geoportals have been shown to be valuable tools in preliminary environmental assessment and in supporting planning processes, as confirmed by recent studies [109]. By integrating spatial parameters into quantitative models, it is possible to determine optimal routes, taking into account minimum distances, vehicle capacity, and terrain features, thus enhancing operational efficiency [128,129]. Consequently, GIS can be used to reduce costs and improve the efficiency of waste collection and transport.

4.1.2. Optimization of Routes and Facility Locations Using GIS

The optimization of waste collection routes depends on numerous factors, including the location of waste containers, collection schedules, vehicle types, travel impedances, and road network integrity [130]. The problem of municipal waste collection and transport can be considered a vehicle routing problem, in which the goal is to find the shortest route from waste sources to disposal or processing sites [131,132]. For example, Toufaili et al. [133] analyzed solid waste logistics in the Beirut region and found the system to be logistically inefficient. Similarly, the studies by Cavallin et al. [134] and Vu et al. [135] provided valuable insights into optimizing municipal waste collection in urban areas, including optimal placement of collection points, waste depot design, vehicle configurations, and GIS-based route planning.

Vehicle routes can also be designed by integrating GIS with mathematical algorithms [136]. In addition, the use of network analysis and geospatial softwares enables the modeling of realistic road conditions. Malakahmad et al. [137] identified key road factors, such as the number of turns, height restrictions, speed limit variations, and local traffic conditions. A solid waste management model using GIS for planning waste collection, transport, and fuel consumption did not account for dynamic traffic flows and resident behavior, instead relying on static assumptions [138,139]. However, it is recommended that dynamic traffic conditions, influenced by factors such as time, road-user behavior, and weather, be included as critical aspects of the analysis [132]. The structure of the GIS layers used in such analyses, typically including road networks, administrative boundaries, hydrology, land cover, and a topographic base, can be visualized as shown in Figure 4, where multiple thematic layers are combined to form a comprehensive spatial model used in route optimization.

The effectiveness of combining GIS with a mathematical programming model to develop a multi-criteria mixed model for determining waste collection vehicle routes and schedules was also demonstrated by Chang et al. [131].

To enhance the capabilities of GIS-based systems, a model based on agent-based modeling (ABM) has been proposed [140]. The waste transport network can be modeled as a multi-agent system, where each agent has its own objective (destination) and must coordinate to avoid conflicts. A dynamic multi-agent system operating in real time to determine optimized collection routes was proposed by Nambiar and Idicula [141]. In this model, each agent collects and transmits real-time data on used and remaining vehicle capacity, which—combined with GIS data—helps identify an optimal travel plan for each vehicle. The integration of GIS and multi-agent modeling not only enhances optimization of municipal waste collection and transport, but also enables the simulation of various route and landfill location scenarios [142].

The optimization of waste collection is also closely linked to the placement of waste containers. A GIS-based model, combined with mixed-integer programming, for locating optimal waste container and transfer station sites, proposed by Chang and Lin [143], resulted in predicted reductions in direct costs and simplified operational management. A GIS-based location-allocation method, used to redesign existing waste container sites, allowed for the verification of improperly placed bins and the generation of an optimized layout [144].

The use of GIS to determine potential locations for solid waste disposal sites in Bahir Dar was demonstrated by Ebistu and Minale [145]. They integrated field surveys, site observations, internet resources, reports, journals, and government documents using ArcMap, remote sensing techniques, and multi-criteria analysis methods to generate maps of suitable disposal areas. In a method for identifying waste landfill sites developed by Muttiah et al. [146], a simulated annealing algorithm based on GIS and Markov chains was used. In Tanzania, GIS systems were also effectively utilized to identify landfill sites with minimal environmental impact [147].

One of the most significant outcomes of using GIS in waste management is its economic impact. In the United Arab Emirates, a shortest-path GIS-based technique for optimizing waste collection routes led to a 19% reduction in costs [148]. Waste collection optimization in Migori, Kenya, resulted in a 30% reduction in fuel consumption [149]. Zsigraiova et al. [150] applied a GIS system to the collection and transport of glass waste in Barreiro, achieving a 43% decrease in fuel use.

A study conducted in Sri Lanka showed that optimized route planning could reduce weekly trips by 19% and weekly travel distance by 36% [151]. In India, the use of ArcGIS reduced route distances by nearly 10% [152].

4.2. Big Data Analysis

4.2.1. The Role of Big Data Analysis in Waste Management

Big Data Analysis (BDA) refers to the processing and analysis of large data repositories using a variety of available tools [153]. In the context of waste management, vast amounts of diverse information are collected and then analyzed to detect patterns, correlations, and insights. These insights form the basis for forecasting future trends in waste management and supporting sustainable planning [154].

Based on available scientific literature, a diagram was created (Figure 5) outlining the general stages of BDA.

Analyzing Figure 5 begins with a comprehensive data assessment that provides a holistic view of waste generation and management. At this stage, data from various sources are integrated and analyzed, including sensors, geospatial and climate data, socio-economic information, and historical records. Detailed analysis enables the identification of relationships that might not be detectable using traditional methods [155,156]. Examples of such relationships include seasonal and geographic variations, as well as economic and demographic shifts and their impact on waste generation [157,158]. At the same time, this analysis is continuously updated with real-time data.

Real-time data transmission typically relies on sensors such as volumetric sensors, IoT devices, RFID, and GPRS systems, which are installed in waste bins, collection vehicles, and processing facilities [159]. These tools enable continuous monitoring of waste levels, collection routes, and facility operations, thereby increasing operational efficiency through rapid identification of inefficiencies [160,161]. Dynamic adjustments can be made as a result, leading to reduced operational costs and improved service optimization for residents [162,163].

4.2.2. Predictive Modeling and Limitations of BDA

The next stage of BDA is predictive modeling, which allows for forecasting future waste management trends based on historical data and real-time inputs. This form of data utilization enables the prediction of changes in the volume and composition of waste produced [164,165] and facilitates proactive measures to manage these changes effectively. It is important to note that large datasets originating from diverse sources require integration and interdisciplinary analysis [166]. Pau et al. [167] emphasized that a Big Data architecture for municipal waste management needs to handle complex, heterogeneous data and ensure scalability. Research by Munir et al. [168,169] identified challenges related to both data quality and insufficient data volume, which can impact effective waste management.

Among predictive modeling techniques, time series analysis methods such as Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA) are commonly used, especially when large amounts of historical data are available [169,170]. In addition, statistical analysis (e.g., linear and nonlinear regression models) and machine learning algorithms (e.g., random forests, support vector machines, and neural networks) are applied.

In any case, as Yang and his co-authors note, machine learning has gone through four stages of development: from the germination phase, through the initial formulation period, to rapid development, and finally to the intensive growth observed today, which has had a significant impact on the development of AI [171].

Statistical analysis enables forecasting based on detected relationships, while machine learning algorithms enhance the accuracy of predictions [172]. The significant role of advanced machine learning algorithms in data-driven forecasting has also been demonstrated by Marinakis [173].

Based on the conducted analyses, practical conclusions are drawn that inform strategic decisions regarding resource allocation, infrastructure development, and policy design [174]. Waste collection routes, processing schedules, and facility utilization are optimized. Furthermore, by providing transparent information about waste trends and management practices, collaboration among policymakers, businesses, and communities becomes possible, promoting joint action towards sustainable waste management [175,176].

Although BDA is increasingly applied in waste management, there are limitations that may hinder its implementation. These include challenges related to data accuracy and integration, limited access to advanced analytical techniques, and concerns about privacy and data protection [160,177]. Therefore, it is crucial to not only develop the technical infrastructure but also ensure adequate regulatory frameworks.

4.3. Artificial Intelligence in Automated Waste Management Systems

4.3.1. Data Analysis and Decision Support in Waste Management

The rapid development of artificial intelligence in recent years has enabled its application in waste management, primarily through data analysis, pattern recognition, and decision-making processes [178,179]. As demonstrated by Abdallah et al. [180], research on AI in waste management focuses mainly on modeling waste mass and composition, forecasting waste generation based on economic and socio-demographic parameters, and managing collection systems, monitoring waste containers, and identifying landfill locations. Consequently, AI models contribute to improving the efficiency of managing various types of waste [181,182]. It is important to emphasize that AI can be applied at every stage of the waste management process [183].

Studies have shown that AI performs particularly well in the case of smart waste bins. Real-time analysis of waste levels in containers using AI algorithms increases collection efficiency [184]. Additionally, AI algorithms draw on historical data and waste generation indicators to generate optimal collection routes [185,186]. These improvements have both economic benefits, through reduced operational costs, and social benefits, by ensuring timely waste removal [182].

AI is also applied in waste sorting, using machine vision and image recognition technologies, to identify different types of waste [187]. This leads to high sorting accuracy for recyclable and organic materials [188,189]. Robot-assisted sorting systems, controlled by AI models, have also shown high effectiveness in separating materials [190]. This is especially crucial for recyclable materials, where maximizing recovery rates is essential [191]. The combined use of AI algorithms and robotic systems helps reduce or eliminate human error and improve throughput and recycling efficiency [192].

The applications of artificial intelligence in waste management are diverse, encompassing planning, processing, and recycling stages.

Table 3. Applications of artificial intelligence in waste management.

AI Application Area	Function, Application Description	Technologies, Models Used	Benefits	Ref.
Waste Quantity Modeling and Forecasting	Predicting waste mass and composition based on economic and social data	Machine learning (ML) models, regression, neural networks	Effective planning of collection and recycling systems	[180,181,182]
Optimization of Waste Collection and Transport Systems	Real-time analysis of data from smart bins and sensors	AI + IoT, sensor networks, predictive routing models	Cost reduction, fuel savings, improved punctuality	[183,184,185,186]
Automatic Waste Sorting and Separation	Image recognition and classification of waste materials	Computer Vision, CNNs (e.g., DenseNet121, AlexNet, SqueezeNet)	Increased sorting efficiency, reduced human error	[187,188,189,190,191,192]
Waste Classification in the Recycling Process	Differentiation of waste types based on images and sensor data	Deep Learning, Graph-LSTM, MobileNetV2, MDTLDC-IWM, RWC-EPODL	Up to 97% classification accuracy, improved recycling quality	[193,194,195,196,197,198,199,200,201,202,203]
Integrated Systems (AI + IoT + Big Data)	Real-time management of waste generation, collection, and disposal	Hybrid AI models, predictive systems, environmental sensors	Resource allocation optimization, support for metropolitan planning	[204,205,206,207]
Limitations and Challenges of AI in Waste Management	High computational requirements, lack of data, model transferability issues, security and privacy concerns	Data processing tools, ETL integration, cybersecurity systems, XAI models, complexity-reduction techniques, interoperability standards	Need for standardization, better datasets, improved validation methods	[160,177,208,209,210,211,212]

provides a synthesized overview of the main areas of AI application, along with descriptions of their functions, technologies used, and the achieved benefits.

4.3.2. Classification Deep Learning and Limitations of AI

For optimal waste segregation, accurate waste classification is essential. Recent studies on waste classification have highlighted the effectiveness of artificial intelligence methodologies, particularly deep learning and convolutional neural networks (CNNs). These techniques have been successfully applied to datasets such as TrashNet, Waste Picture, and HPU_-WASTE, enabling precise classification of various waste types [193].

Research into AI-based standard waste classification has shown that CNN and Graph-LSTM systems achieve high accuracy in identifying common waste categories, while the RWC-EPODL model performs effectively in classifying recyclable waste for bioenergy production [194,195]. Similar performance in waste identification and classification has been demonstrated using the MDTLDC-IWM model and MobileNetV2 [196,197]. Waste categorization is also feasible through the use of CNN architectures such as AlexNet, SqueezeNet, and especially DenseNet121, which has demonstrated high levels of accuracy [198]. Gan and Zhang [199] proposed a municipal solid waste classification and recycling algorithm based on deep learning technology. Hybrid deep learning approaches suggested by Rahman and Das [200], as well as Ramsurrun et al. [201], achieved accuracy rates of approximately 97% and 88%, respectively, in solid waste recycling. However, the high computational demands of deep learning and the need for more comprehensive datasets highlight the necessity for further research to refine these models [202,203].

In recent years, comprehensive waste management systems have been developed, particularly for large urban areas. These systems are based on real-time monitoring using integrated IoT devices and sensor networks. Collected data on waste generation, collection, and disposal, combined with historical records, are used to predict waste generation patterns, optimize resource allocation, and inform the planning of optimal waste management strategies [204,205]. However, as with municipal waste management, numerous authors have identified limitations related to data management, the size of the area under analysis, and challenges in transferring case study methods to other cities or regions [206]. Additional limitations include data availability and quality, privacy and security concerns, costs of implementation and infrastructure requirements, and the complexity of analyzing a large number of scenarios [207,208,209]. Abdallah et al. [180] also noted a lack of studies on the application of AI to simulate and optimize the management of petroleum-based waste incineration processes and biogas production.

5. Integration of GIS, Big Data, and AI in Waste Management

5.1. Instruments and Tools Supporting the Digitalization of Waste Management

Effective and efficient waste management should be supported by diverse management instruments, understood as a set of various means, methods, processes, and tools used to achieve the goals set by the waste management system and to address emerging operational and strategic challenges [210]. Management instruments enable the implementation of core management functions, including planning, organizing, motivating, and controlling, thereby creating an integrated mechanism that enhances the efficiency and effectiveness of the entire system [211].

Among contemporary instruments, digital tools are gaining particular importance, especially those based on the integration of spatial data analytics and artificial intelligence, such as Smart Waste Management Systems (SWMS) [212], integrated decision support systems leveraging GIS and AI [213,214], and Internet of Things (IoT) platforms that use machine learning to optimize waste collection and transport processes [215,216]. These solutions, often combined with technologies such as blockchain, operate at the intersection of information technology and management, support the development of adaptive, transparent, and efficient waste management systems [217,218].

An example of an implementation supporting operational decision-making in the area of waste management is the solution implemented by LIPOR, an association dealing with waste management in Portugal. This solution uses an online platform that centralizes data, with modules for database management, statistical analysis, and monitoring of field operations. The platform was largely based on RFID technology and enables real-time data collection to support decision-making [219].

5.2. Modeling Integrated Waste Management Systems

In contemporary scientific literature, there is a growing consensus that effective and efficient waste management requires modeling integrated systems that combine different types of data with strategic and operational tools [220,221]. These models suggest integrating spatial data (GIS), sensor-based data (IoT), and predictive models based on artificial intelligence to enable seamless transitions from monitoring to analyzing and forecasting waste flows [220,221,222,223]. Blockchain technology can also be incorporated to ensure transparency and immutability of records related to collection, transport, and processing, thereby enhancing public trust and operational control [218].

Such systems allow for dynamic planning of waste collection routes, adaptation to changing operational conditions, and improved transparency and responsiveness to unforeseen events [214,224]. As a result, logistical, technological, and decision-making aspects become more coherent, leading to increased efficiency in waste management systems and better integration with sustainable development goals [216]. An example of an end-to-end approach is the ProWaste platform, described as a solution integrating IoT with a machine learning module used for proactive prioritization of servicing collection points/centers, which clearly illustrates the transition from collected data (sensors + API) to operational recommendations [225].

5.3. Key Integration Technologies: SWMS, AI–GIS DSS, IoT

As part of integrating various instruments and modeling integrated waste management systems, at least three principal groups of technological solutions can be distinguished, which reflect different levels of data and decision integration in modern waste management systems. These are Smart Waste Management Systems (SWMSs), AI-assisted GIS decision support systems (AIGISDSSs), and IoT-based waste collection with machine learning.

Smart Waste Management Systems (SWMSs) are comprehensive information systems that oversee and encompass the entire waste management cycle, from generation, separation, collection, transfer, and transport to processing, disposal, reduction, reuse, recycling, or recovery [226,227,228]. They use multi-sourced data and analytical algorithms to manage processes in near real time [229,230]. The core of SWMS is the integration of IoT sensors (measuring, for example, fill level, weight, GPS position, or using Radio Frequency Identification (RFID) for tracking object data) with GIS data and AI/BigData methods, optionally supported by blockchain data storage. This allows effective planning and forecasting of waste flows, scheduling, as well as detecting operational anomalies and taking rapid actions [231,232,233]. The effects are lower transport costs and emissions, reduced “empty runs,” higher resource recovery rates, and transparent performance indicators.

In practical terms, SWMS can be described as a coherent chain of activities consisting of (i) data acquisition (e.g., from IoT/RFID/GPS and registers), (ii) centralization and integration of operational data with GIS layers, (iii) analytics and models (monitoring, prediction, anomaly detection, optimization), (iv) decision support (dashboards/KPIs, alerts, recommendations), and (v) feedback through operational activities and evaluation of effects. This allows us to see how the integration of the technologies in question covers the entire process, from waste generation and collection to transport and stream management to treatment facilities [5].

The importance of geospatial factor analysis in environmental process modeling and locational optimization has also been confirmed by recent studies using GIS to analyze spatial factors in energy and environmental systems [234]. The potential of spatial analysis in modeling environmental and energy processes is also supported by studies on the impact of local wind conditions and spatial characteristics on wind turbine blade geometry [235]. AI GIS DSS is a specialized subcategory of decision support systems (DSSs) in which spatial data (GIS) are combined with artificial intelligence (AI) and multi-criteria decision making to plan and optimize selected elements of the waste management system. In practice, this includes location selection (e.g., landfills, selective waste collection points, or various processing facilities) using GIS integration with fuzzy logic, AHP/MCDA methods, and their variants (fuzzy AHP). This approach allows heterogeneous environmental, social, and economic criteria to be transformed into suitability maps and location rankings [236]. Another area that can be supported by AI GIS DSS systems is route modeling and optimization based on road networks and operational data. Such solutions deploy AI algorithms to optimize complex routing problems and integrate decisions on “where to locate collection or disposal sites and how to access them” [237,238,239]. AI GIS DSS systems also enable scenario analysis and forecasting related to the capacity of waste management components. Combining GIS layers with machine learning models allows the prediction of waste flows, assessment of service coverage, and evaluation of spatial impacts, such as emissions or noise [240,241,242]. These solutions shift decision-making from static maps to predictive, multi-criteria spatial analyses, supporting everything from network configuration and location selection to operational routing and impact assessments. As a result, they deliver measurable benefits in waste system efficiency and transparency.

Other promising waste management solutions are those based on the Internet of Things (IoT) technology supported by machine learning algorithms. Their main goal is the optimization of waste collection and transport processes by transforming sensor data into actionable knowledge for real-time decision making [240,243,244]. Sensors installed in bins and vehicles (ultrasonic, weight, GPS, RFID) provide information on fill level, location, waste weight, and environmental parameters [213,224,225]. These data are transmitted to analytical systems where machine learning algorithms predict bin overflow moments and recommend optimal collection routes [214,245,246,247,248,249]. It should be emphasized that these systems can function independently or be modularly combined to form a comprehensive Smart Waste Management System.

5.4. Benefits, Efficiency, and Significance of Technological Integration

Integration of modern analytical and digital tools enables the transformation of traditional waste management systems into intelligent, self-learning decision-making ecosystems characterized by a high degree of automation, operational flexibility, and process transparency. The interaction of technologies such as Geographic Information Systems (GISs), Big Data analytics, artificial intelligence (AI), and blockchain allows for a comprehensive approach to managing material, informational, and decision flows within waste management systems.

Integrating GIS, Big Data, artificial intelligence (AI), the Internet of Things (IoT), and blockchain technologies brings tangible benefits in terms of efficiency, transparency, and the sustainable functioning of waste management systems [244,245]. The application of Big Data analytics and machine learning algorithms allows for predictive planning and decision optimization, increasing the flexibility and adaptability of systems in response to changing conditions [241,250]. The integration of GIS and AI supports improved spatial and strategic planning, enabling the siting of collection points and processing facilities based on environmental, economic, and social criteria [251,252,253]. Meanwhile, the use of blockchain technology enhances data transparency and reliability, ensuring full traceability of waste flows and minimizing the risk of fraud within the value chain [218,254]. Consequently, the integration of these solutions leads to the development of intelligent, resilient, and sustainable waste management systems characterized by higher resource efficiency, a reduced environmental footprint, and increased public trust [255].

5.5. Barriers, Challenges, and Conditions for Effective Digitalization

In the literature, it is emphasized that despite the significant technological potential of integrating GIS, Big Data, AI, IoT, and blockchain in waste management, the implementation of such solutions encounters several critical barriers of technical, organizational, and regulatory nature [256,257]. Among the most frequently mentioned limitations are the low quality and fragmentation of data, resulting from dispersed sources, lack of information exchange standards, and limited interoperability between systems operated by different entities [256,257,258]. Another major barrier is the high cost of implementing and maintaining digital infrastructure, including sensors, connectivity, cloud computing, and system security, which poses substantial challenges, especially for municipalities with limited budgets. Moreover, cybersecurity and data protection have become increasingly crucial issues due to the risk of attacks targeting IoT infrastructure and the necessity to comply with privacy regulations [259,260,261].

Another significant challenge lies in the complexity of cross-sectoral cooperation, particularly in public–private partnerships, where differences in interests, responsibilities, and funding models hinder effective system integration [262,263,264]. Additionally, regulatory changes, such as the introduction of deposit-refund systems or “pay-as-you-throw” schemes, affect waste flows and require continuous adaptation of predictive algorithms [265,266,267,268]. Finally, the literature points out a shortage of digital and analytical competencies within public administration, along with the need to establish appropriate data governance structures and develop organizational cultures that support digital transformation [269,270].

Consequently, effective digitalization of waste management requires not only investments in technology but also the standardization of data and the development of skilled personnel, ensuring cybersecurity and establishing stable frameworks for institutional cooperation. Only such a comprehensive approach will enable the full potential of GIS–Big Data–AI–IoT and blockchain integration to be realized in building modern, resilient waste management systems.

The digital transformation of waste management is progressing unevenly worldwide due to differences in regulatory frameworks and levels of political support. In Brazil, coordinated environmental and digital policies, including the National Solid Waste Policy, the National Circular Economy Policy and the Digital Government Strategy 2024–2027, facilitate the adoption of tools, such as WebGIS and digital monitoring systems, enabling the development of zero-waste approaches. In the European Union, digitalization is embedded in the European Green Deal; however, the absence of harmonized data standards limits the integration and scalability of technological solutions in the waste sector. In Poland, strong environmental regulations are not accompanied by mandatory digital requirements, slowing the adoption of intelligent collection and monitoring systems. Recommended actions include mandatory digital waste reporting, data standardization, alignment of environmental and digital policies, and increased support for innovation measures that can significantly enhance the efficiency of waste management systems.

6. Conclusions and Future Research

The article assumes that the integration of GIS, Big Data, and AI can serve as an important tool for waste management. As shown in the literature review on current trends, solutions, and research directions in the application of digital technologies in waste management, with a particular focus on GIS and Big Data integration, these solutions can be effectively combined in this field, enabling the optimization of multiple processes and tasks. These benefits can be observed within the five core management functions: planning, organizing, coordinating, leading, and controlling.

The advantages of integrating the discussed technologies are especially noticeable in the planning function, which should be based on reliable data and on forecasting the outcomes of planned activities. It is in this area that it becomes possible to optimize the location of various elements of the waste management system, forecast waste streams, assess service coverage, system component loads and capacities, and plan collection routes and schedules.

Within the organizing function, GIS–Big Data–AI integration makes the following possible:

• Build an integrated SWMS architecture;
• Increase interoperability and data integration among municipalities, operators, and facilities;
• Create registries and improve information flows;
• Enhance digital competencies and establish data-driven organizational cultures.

In the coordinating function, these digital tools enable dynamic routing and synchronization of operations in near real-time, rapid anomaly detection and intervention, improved coordination among waste system actors, and optimized decision-making.

Finally, in the controlling function, the integration of these tools allows for the following:

• Continuous monitoring of processes and key operational performance indicators;
• Detection of deviations thanks to GIS–Big Data–AI integration; the leading function benefits from increased stakeholder trust, greater acceptance of decisions, and the promotion of an organization-wide data culture and irregularities;
• Verification of outcomes.

The analysis identified three main categories of barriers to integrating GIS–Big Data–AI solutions into waste management systems: technological, organizational, and regulatory/legal. To address these challenges, the authors propose recommendations for local governments/operators, waste management entities, and policymakers to enable broader implementation of these solutions.

To counteract technological barriers, it is suggested to conduct the following:

• Develop unified data standards, including formats and protocols for information exchange among municipalities, companies, and institutions in the waste management system;
• Create regional or national GIS/Big Data repositories to ensure interoperability;
• Improve and automate data collection and reporting through IoT sensors, RFID systems, and online registries;
• Provide financial support for modernizing digital infrastructure and system security;
• Support research and pilot projects on GIS–Big Data–AI integration and benchmarking of existing implementations.

To overcome organizational barriers, the authors recommend developing digital and analytical competencies in public administrations and waste management sectors, establishing data governance teams in public institutions, municipal enterprises, and waste management entities, fostering data-driven organizational cultures, and strengthening cross-sector collaboration through public–private partnerships, joint pilot projects, and the exchange of best practices.

To address regulatory and legal challenges, it would be beneficial to implement the following:

• Ensure stable and predictable regulations;
• Develop and adopt legal guidelines for digital technologies (IoT, blockchain) in waste management;
• Strengthen data cybersecurity;
• Ensure compliance with GDPR and other privacy regulations to protect residents’ rights.

The integration of GIS–Big Data–AI in waste management also requires further research and scientific analysis in several key areas. One research direction should involve literature and secondary source studies, such as registries, on data standardization and interoperability, as well as ensuring data quality and governance in waste management systems. These studies should aim to characterize and develop methods for harmonizing data schemas, implement open standards for data exchange, and establish mechanisms for ensuring data quality and multi-source fusion, potentially supported by agent-based AI models.

Another significant research area, relevant to practical implementation, involves the scalability and generalizability of AI models in waste management. It is worth exploring the transferability of developed models (e.g., from one city or region to another), minimizing computational costs and data requirements, and applying AI in less studied areas, such as petroleum-based waste, incineration processes, or biogas production.

The integration of GIS–Big Data–AI technologies in waste management requires further research and scientific analysis in several key areas.

In the authors’ opinion, literature studies and studies based on secondary sources (e.g., registers) concerning data standardization and interoperability are important, as are studies in the field of data quality assurance and data management methods within the waste management system. As a result, methods for harmonizing data schemas, open standards for data exchange, and mechanisms for ensuring data quality and integration (merging) of multi-source information streams, including spatio-temporal data, should be characterized and developed.

In addition, an important area of research, crucial from the perspective of the practical implementation of the technologies discussed, is the scalability and generalizability of artificial intelligence models in waste management. In this regard, it is reasonable to work on the transferability of developed models (e.g., from one city or region to others), reducing computational costs and data requirements, as well as developing benchmarking approaches that enable reliable and repeatable comparison of implementation results in different organizational, legal, and infrastructural conditions. This benchmarking should be based on standardized performance indicators and uniform evaluation procedures, which will allow for the assessment of the comparability of solutions and the identification of good practices. At the same time, it is worth developing AI applications in less frequently analyzed areas, such as petroleum-derived waste, combustion processes, and biogas production.

Thirdly, a separate and increasingly important area is research into data security and privacy in Internet of Things (IoT) systems used in waste management. This research should include testing the resilience of systems and developing methods for anonymizing and protecting data from sensors, containers, and vehicles to ensure the safety of residents and compliance with regulatory requirements, while not limiting the usefulness of data for analysis and operational decisions.