Urban Waste Management Systems for Solid Waste with Simulation Approach: A Bibliometric Analysis

Urban waste management and carbon emission reduction have evolved into a complex field of research, requiring a multidisciplinary approach that combines various methodologies. In the last decade, various simulation and analysis techniques have emerged as essential tools for understanding complex waste management systems and designing effective policies. Estay-Ossandon and Mena-Nieto [14] employed an integrated Delphi, fuzzy TOPSIS, and system dynamics approach to analyze municipal solid waste (MSW) management in the Canary Islands, suggesting that this multi-methodology approach enables a comprehensive evaluation of various policy scenarios [14, 15]. Their results reveal that without significant interventions such as the separation of organic fractions, the targets of the EU would not have been achieved, a finding that Pubule et al. [16] reinforced in a different context.

At the micro level, agent-based approaches opened new perspectives in understanding household waste behavior. Meng et al. [17] introduced a multi-agent-based simulation framework to model the sorting behavior of household waste in Suzhou, China. Their model can evaluate the impact of various policy instruments, such as waste fee schemes, on a wide range of stakeholders, including citizens, recyclers, and the department of sanitation [17, 18]. This approach provides valuable insights into the complex interactions between actors in urban solid waste systems, complementing the macro perspective offered by the system dynamics model. The integration of various simulation techniques resulted in an increasingly sophisticated hybrid approach. Yuan et al. [19] proposed a hybrid simulation framework combining GIS, system dynamics, agent-based models, and discrete-event simulations to assess the environmental impact of demolition waste at the city scale [19]. Their findings highlight the spatial heterogeneity of environmental impacts and emphasize the importance of regional coordination in policy development. This approach is particularly relevant for city-scale analysis, where the processes of waste disposal, transportation, and treatment exhibit complex spatial and temporal distributions.

In the context of emission control and circular systems, recent developments have shown the integration of artificial intelligence (AI) into simulation models. Wang et al. [20] applied virtual-real data-driven simulations using fuzzy broad learning systems and decision trees to predict multi-pollutant emissions from waste incineration [20]. This approach represents a significant breakthrough in MSW processing modeling, allowing for more accurate forecasting and process optimization. Simultaneously, Jeng et al. [21] applied sensitivity analysis in closed-loop farming systems using circular principles. Although this case study does not directly focus on MSW, it illustrates the value of simulation in managing organic waste in circular systems.

The implications of sustainability and broader governance are also the focus of recent research. Zhang et al. [22] provide a strategic analysis of recycling rare earths from renewable energy equipment, utilizing SWOT and ESG frameworks, and supported by intelligent text mining. Although it does not strictly focus on MSW, this methodology aligns with bibliometric and text-mining techniques commonly used to map trends in waste management research [21]. Contrarily, Zhang et al. [22] present a 4E (energy, exergy, environmental, economic) simulation analysis of a trigeneration system integrating waste heat recovery, providing a transferable methodology for evaluating waste-to-energy (WtE) scenarios in urban waste management [15, 22].

The circular economy approach continues to gain momentum in the field of waste management research. Shahidzadeh and Shokouhyar [23] used the fuzzy DEMATEL method to analyze reverse logistics, identifying consumer awareness as a key factor with a D-R value of 2.45, especially in the electronics and automotive industries. These findings are supported by Morris [24], who explored wood waste management through Life Cycle Assessment (LCA), which showed that recycling wood products has the lowest environmental impact, with a score of 0.45. However, LCA results are susceptible to variations in carbon accounting methods, with yield variations of up to 30% [24, 25]. In the context of integrated policies, recent developments demonstrate the effectiveness of a holistic approach. Wang et al. [15] combined agent-based models and system dynamics to simulate transportation policy in Beijing, finding that an integrated policy package (CSEI) could reduce carbon intensity by up to 65% by 2050. These findings are reinforced by Zhan et al. [26] in the Carbon Neutrality Simulation Model (CNSM) for the Beijing-Tianjin-Hebei region, where industrial restructuring is identified as providing the most significant potential for emissions reductions of 38%.

Aspects of human behavior remain a critical component in waste management research. Akhter et al. [27] applied the expanded Theory of Planned Behavior (TPB) to predict the intention to reduce food waste among Indian college students. The model achieved an R² of 0.686, with knowledge of the impact of food waste being the strongest predictor (β = 0.42). These findings align with those of Babader et al. [28], who developed the Social Behavior Aspect Model (SBAM), in which ease of adaptation emerged as the dominant factor (β = 0.51) in increasing packaging reuse. Digital transformation has brought a new dimension to waste management research. Cao and Peng [29] developed a system dynamics model that demonstrates the initial emissions increase caused by the digital economy but ultimately drives energy efficiency through the optimization of industrial structures and technological innovations [29], managing water, energy, and carbon with ambiguous moment information [30], and Zeng et al. [ 31] use Earth System Sciences [31]. At the corporate level, Liu et al. [32] found that substantial board capital can improve low-carbon sustainable development practices through social responsibility awareness mechanisms and external pressures such as media attention [31, 32].

Research by Feng and Han [33] on place-based policies revealed that industrial transformation policies can effectively reduce carbon emissions by lowering energy consumption and promoting green innovation, with varying effectiveness across regions [33]. These findings are reinforced by Ding et al. [18], who analyzed the coordination between high-quality economic development and carbon emission intensity in Chinese provinces, identifying technological innovation as a significant driving factor in the more developed eastern region. To advance this field, future research should focus on developing more comprehensive models that holistically capture the complexities of urban waste management systems, encompassing technical, behavioral, social, economic, and environmental dimensions. Furthermore, additional research is required to assess the effectiveness of various policies in diverse contexts and to develop more robust standards and methodologies for evaluating environmental impact. Equally important is the need for research on effective implementation mechanisms to ensure that academic findings can be translated into policies and practices that have a tangible effect on the field.

4.1 Annual scientific production

Figure 2 illustrates the trend in publications of dynamic simulation models applied to urban waste management research between 2014 and 2024. Over the last 10 years, from 2014 to 2024, the number of scientific articles exhibited both upward and downward trends; however, overall, it increased rapidly. In 2014, four articles were presented; this number increased to five in 2015, rose to eight in 2016, and then decreased again to five in 2017. The peak in 2018 was 20 articles, then dropped again to 9 in 2019. In 2020, the number of articles increased to 11, followed by 17 in 2021, a peak of 21 articles in 2022, 16 in 2023, and 24 in 2024. Despite the ups and downs, in the decade of 2014–2024, the number of scientific artifacts has increased more than 5 times. This demonstrates that research on waste management utilizing systems thinking is in high demand and its development is progressing rapidly.

Figure 2 shows that the annual production of scientific articles increased during the 2014–2024 publication period. This indicates that research in urban waste management systems and thinking systems as methods has increased significantly.

Table 4 shows the trend of quote performance from 2014 to 2024. The data shows a consistent and significant increase in the number of publications (N), from just four articles in 2014 to 24 articles in 2024. This indicates that this field of research is experiencing rapid growth. Meanwhile, the average total citations per article (meanTCperArt) did show a nominal decrease from 174.25 (2014) to 4.58 (2024). This decline is primarily influenced by time bias rather than the quality of the research. It can be seen from the number of older articles that tend to receive more citations compared to new articles. This can also be seen from the average annual quotes, which are relatively stable between 6.44 and 11.79, with a peak in 2020 (11.79), when the focus was on health and environmental issues, including waste management during the COVID-19 pandemic.

Table 4. Annual total citation

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Figure 2. Annual scientific production

4.2 Most relevant sources

The most relevant sources in this bibliometric analysis indicate that the 10 top journal sources are published in the field of dynamic system models for urban waste management. The 10 journals are dominated by the predominance of the city to the minimum influence in succession by the “Journal of Cleaner Production”, “Waste Management”, “Science of The Total Environment”, “Resources Conservation and Recycling”, “Energy”, “Journal of Environmental Management”, “Sustainable Cities and Society”, “Chemosphere”, “Expert Systems with Applications”, “Process Safety and Environmental Protection”. In two leading journals, “Journal of Cleaner Production” and “Waste Management”, Figure 3 shows the Top 10 Sources by Number of Articles. These two journals dominate the field of urban waste management integration and system thinking, with 28 articles each. This article highlights that the central role of the two journals is to serve as the leading platform for advancing research on sustainable waste management practices, system optimization, and environmental innovation.

Additionally, Figure 3 also illustrates a substantial contribution through the integration of resource recovery, recycling, and circular economy principles in urban waste management research. This substantial contribution can be seen in the journals “Science of the Total Environment” (18 articles) and “Resources, Conservation and Recycling” (17 articles). Furthermore, several indexed journals such as “Energy”, “Journal of Environmental Management”, “Sustainable Cities and Society”, and “Environmental Research Letters” enrich academic discourse through multidisciplinary approaches, including renewable energy applications, smart city infrastructure development, environmental system modeling, and sustainability transitions.

Figure 3 and Table 5 show that the “Journal of Cleaner Production” and the Journal of Waste Management” are the most productive sources of publication, with 28 articles each. This article demonstrates that these two articles are among the most prominent in publications on sustainability and waste management. Furthermore, the journal “Science of the Total Environment” has 18 articles, and “Resources, Conversion and Recycling” has 17 articles. These two journals focus on environmental issues and the concept of a circular economy. Simultaneously, the Journal of “Energy” has 15 articles, “Journal of Environmental Management” has nine articles, and the Journal of “Sustainable Cities and Society” has seven articles, showing the researchers' interest in discussing sustainability and environmental management. Additionally, the multidisciplinary journals such as “Expert Systems with Application”, six articles, and the “Chemsphere” journal six articles, and the journal “Process Safety and Environmental Protection” five articles, show various integrative approaches in this study combining the concepts of sustainability, waste management, and systems thinking.

Table 5. Top 10 sources articles

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Figure 3. Top 10 sources by number of articles

Figure 4 illustrates the cumulative trend of publications from several leading energy and environmental journals, including the Journal of “Energy”, “Journal of Cleaner Production”, “Resources, Conservation and Recycling”, “Science of the Total Environment”, and “Waste Management” from 2014 to 2024. Overall, a significant increase in publications was observed over time, particularly after 2018, indicating a growing interest and focus on research related to sustainability, waste management, and clean energy issues. The Journal of Cleaner Production, Waste Management, and Science of the Total Environment dominate in terms of publications, reflecting their immense influence. The increase in the number of publications aligns with the global issue of climate change. Climate change requires addressing global and local impacts and preventing further damage. Currently, scientific research focuses on supporting the transition to more sustainable practices.

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Figure 4. Production over time of the sources

4.3 Most relevant authors

The analysis of the 20 most relevant authors highlights the researchers who have made significant contributions to integrating dynamic simulation modeling into urban waste management studies.

Figure 5 highlights the significant contribution of the top 20 authors in this field, demonstrating the increasing academic importance of dynamic simulation for developing sustainable urban waste management solutions. As illustrated in Figure 4, Dong Huijuan, Geng Yong, and Wen Zongguo emerged as the top contributors, each with four articles [5, 17, 22, 36]. Their prominent presence indicates sustained research activity and leadership in system dynamics applications, sustainable waste management strategies, and urban environmental modeling. The next tier includes researchers such as Samadder S.R. and Xiao Shijiang, contributing three articles each [5, 37]. These authors consistently produce scholarly output in dynamic waste system analysis, cost modeling, and resource recovery strategies within urban contexts. Most listed authors, such as Pharino Chanatip, Vicidomini Maria, Qian Yi, Tiang Jian, and Tian Xu, have published two articles, indicating active participation at a slightly lower intensity [5, 7, 17, 20, 38]. The presence of authors from diverse geographical and disciplinary backgrounds demonstrates the multidisciplinary and international character of current research on urban waste management using dynamic simulation approaches. It also suggests the gradual consolidation of a specialized research community addressing the complex, interconnected challenges faced by urban waste systems.

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Figure 5. Most relevant authors by number of articles

Figure 6 shows the annual growth in articles and citations from 2014 to 2024. This data shows the performance of four researchers in scientific publications from 2016 to 2024. DONG H and GENG Y show a similar pattern, with the most cited joint publications in 2020 (162 citations) and the highest productivity in 2022 (2 articles). Kumar A stood out with one highly influential 2016 article, garnering 199 citations —the highest among all researchers. GUO H has publications from 2016 to 2024, with 2020 articles being the most impactful (60 citations). However, its most recent article, published in 2024, is still relatively new, so the number of citations is still low.

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Figure 6. Author production over time

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Figure 7. Author local impact

Figure 7 shows the performance of 10 researchers based on several academic performance indicators. WEN Z became the researcher with the highest total citations (TC) (387), despite having only four publications (NP). Simultaneously, Kumar A excelled in productivity, garnering 329 citations from just three articles. The h-index (h_index) by most researchers is at 4, indicating consistency in producing cited work. WANG Y stands out with the highest g_index (5), indicating some highly influential works. LI J has the highest m_index (0.8), indicating its substantial impact in a short period since the publication began (2021). Although it began publication in 2014, LI Z has a relatively low m_index (0.333), probably due to a pause in work. This data reveals that quality (judging from citations) is not always directly proportional to the quantity of publications, and continued productivity is more important than just a long career in the research world.

4.4 Affiliations

A list of the most relevant universities in a research area (see Figure 8), with Shanghai Jiao Tong University occupying the top position. Most institutions of Asian origin, particularly those in China (such as Tsinghua University and Beijing University of Technology) and Thailand (Chulalongkorn University), demonstrate the dominance of this region in related research. Although no exact numbers are displayed, the bar graph next to the university's name indicates the level of relevance or contribution of each institution, with Shanghai Jiao Tong University appearing as the most prominent. The presence of National Taipei University of Technology and Asia University also shows geographical diversity in research collaboration. This data highlights the pioneering role of academic centers in Asia, especially China, in advancing science in specific fields, while also confirming the importance of international networks in modern research.

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Figure 8. Most relevant affiliations

The research productivity by affiliation from 2014 to 2024 shows that leading universities in China, such as Tsinghua University, Shanghai Jiao Tong University, and Beijing University of Technology, dominate scientific publications, followed by Chang’an University and National Taipei University of Technology. This trend reflects the strategic role of these institutions in advancing research, particularly in the fields of technology and environmental science. It affirms the position of China as a global innovation hub. The yearly consistency of research output demonstrates a strong commitment to developing science and sustainable solutions.

4.5 Overlay network of keyword co-occurrence

The overlay visualization of the keyword co-occurrence network, presented in Figure 9, provides insights into the temporal evolution of research themes within dynamic simulation in urban waste management. In this map, colors represent the average publication year associated with each keyword, enabling the identification of emerging and mature research areas. The network analysis reveals the existence of four distinct clusters, with two dominant thematic clusters reflecting temporal shifts in research focus.

• Cluster 1, predominantly associated with recent years (2021–2022), centers around keywords such as “biogas”, “biomethane”, “carbon emission”, “cost”, “dynamic simulation”, “electricity”, “emission”, “energy”, “GHG emission”, “reduction”, and “scenario analysis”. This cluster highlights a growing interest in integrating dynamic modeling approaches with energy recovery, carbon footprint reduction, and emission scenario assessments in waste management systems. The emergence of these themes indicates a strategic shift toward sustainability, decarbonization, and the optimization of waste-to-energy processes within urban environmental systems.
• Cluster 2, associated primarily with earlier research (before 2021), includes keywords such as “effectiveness”, “impact”, “municipal solid waste management”, “recycling”, “resident”, and “waste management system”. This cluster reflects foundational research themes focused on evaluating the operational effectiveness of waste management practices, the environmental and societal impacts of waste handling, and resident participation in recycling initiatives. These studies laid the groundwork for later advancements, incorporating dynamic simulation techniques and broader environmental policy considerations.

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Figure 9. Production overtime of the affiliations

In Figure 10, the emergence of the dominant circular economy biogas-GHG-economy cluster in the keyword map reflects the direct response of communities and researchers to the changing global policy and economic landscape. A key driver behind this trend is the convergence between carbon pricing schemes, energy subsidies from waste (WtE), and circular economy mandates, which together transform urban waste from liabilities into strategic assets. Dynamic models are crucial in this study because they quantify the financial and environmental impacts of various policy scenarios, enabling evidence-based decision-making for investments in technologies such as biogas processes and greenhouse gas emission reduction. Quantitative indicators from the network analysis expressly support the researchers' interpretation through keywords such as "biogas", "carbon emission", and GHG emissions", which are the highest average publications in the period 2021–2022, which are visually marked by warm colors on the overlay map. This confirms its emerging nature. Furthermore, the strong link between dynamic simulation and both technical ("biogas", and scenario analysis) and environmental aspects ("carbon emission") reveals that dynamic modeling has become the backbone of methodologies that link waste management strategies with renewable energy goals and climate mitigation, signaling a paradigm shift towards sustainable and value-oriented waste system optimization.

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Figure 10. Overlay network of keyword co-occurrence

Figure 11 shows a list of the most relevant universities in a research area, with Shanghai Jiao Tong University occupying the top position. Most institutions of Asian origin, particularly those from China (such as Tsinghua University and Beijing University of Technology) and Thailand (Chulalongkorn University), demonstrate the dominance of this region in related research. Although no exact numbers are displayed, the bar graph next to the university's name indicates the level of relevance or contribution of each institution, with Shanghai Jiao Tong University appearing as the most prominent. The presence of National Taipei University of Technology and Asia University also shows geographical diversity in research collaboration. This data reflects how academic centers in Asia, especially China, are leading the way in the production of science in specific fields, while also confirming the importance of international networks in modern research.

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Figure 11. Country collaboration word

China's dominance of the empirical case studies in this corpus can be attributed to several mutually reinforcing driving factors: First, China's ambitious "Zero Waste City" policy, launched massively since 2019, has created a considerable demand for systems modeling research to devise and evaluate effective waste management strategies in hundreds of cities in China. Second, the Zero Waste City Program in China is supported by research funding incentives from the government, the private sector, and national research institutions, which actively promote research projects aligned with waste management and environmental topics. The majority of these programs are funded by the National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Shanghai Jiao Tong University, National Key Research and Development Program of China, while other countries are still mostly receiving research funding from research institutions that are still in the process of being published, such as from the Fundamental Research Fund's for the central universities, UK Research and Innovation, Natural Science and Engineering Research Council of Canada, a national research agency in Indonesia whose output in this research is an average of one published publication. Third, the abundant availability of data from large-scale monitoring systems and data publications by municipal governments provides the database needed by researchers in validating complex dynamic system models.

4.6 Word analysis

The Word Cloud visualization offers a compelling overview of the most frequently occurring keywords in urban waste management research, utilizing dynamic simulation approaches. The prominence and size of each term in the Word Cloud are proportional to its frequency of appearance across article titles, abstracts, and keywords. As shown in Figure 12, the dominant terms identified include “municipal solid waste”, “waste management”, “recycling”, “dynamic simulation”, and “system dynamics”. These phrases encapsulate the central thematic emphasis of the discipline, highlighting the modeling of waste management processes, the dynamics of municipal waste systems, and the formulation of strategies for recycling and resource optimization. Terms such as “energy recovery”, “landfill”, “circular economy”, and “sustainable development” indicate the incorporation of wider environmental and sustainability aspects into dynamic simulation investigations. The recurrent mention of “urban waste”, “waste collection”, and “cost analysis” emphasizes that operational efficiency, financial evaluation, and urban-specific difficulties are essential elements of the current study.

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Figure 12. Word cloud analysis

Figure 12 illustrates that dynamic simulation applications in urban waste management are intricately associated with sustainability, circular economy, resource efficiency, and the formulation of environmental policies. This subject convergence aligns with global research trends focused on addressing complex urban environmental issues through integrated and dynamic modeling methodologies.

Table 6. World dynamic bibliometrix

Figure 13 and Table 6 are keyword research from the Data World dynamic Bibliometrix in 2024, which shows that the topic of “Municipal Solid Waste” dominates with 191 appearances, followed by “Waste Management” (145) and “Article” (85), indicating that the issue of urban waste is still the primary focus in research related to waste. “Recycling” (76) and “Solid Waste” (74) are also quite widely discussed, reflecting trends in sustainability and solid waste management. Simultaneously, topics such as “Waste Disposal” (64), “China” (54), and “System Theory” (45) show special interest in the technical aspects of waste disposal, case studies in China, as well as systems approaches. “Landfill” (33) and “Solid Waste Management” (33) have the lowest frequency, indicating that these topics may be less popular or have been extensively researched before. These data reflect research priorities that are still focused on urban waste management and recycling solutions, with particular attention to systems approaches and regional contexts, such as China.

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Figure 13. Key word research

4.7 Treemap analysis

Figure 14 presents a Treemap visualization offering a systematic review of the most prevalent terms derived from the literature on dynamic simulation applications in urban waste management. The dimensions of each rectangle correspond to the frequency of term occurrences in the analyzed papers, facilitating the prompt identification of prevailing research subjects. The research indicates that “municipal solid waste” and “waste management” are the most prominent categories in the Treemap, highlighting their importance as the principal emphasis in the field. These phrases emphasize the extensive research focus on controlling urban waste streams and enhancing solid waste systems via dynamic modeling methodologies.

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Figure 14. Treemap analysis

Other prominent phrases, such as “recycling”, “system dynamics”, and “dynamic simulation”, highlight the importance of modeling-based methodologies in enhancing resource recovery and system efficiency. The inclusion of words such as “energy recovery”, “landfill”, and “circular economy” suggests that contemporary research endeavors are progressively aligning with overarching sustainability paradigms, aiming to mitigate environmental impacts and foster circular urban systems. The incorporation of terminology such as “waste collection”, “cost analysis”, and “urban area” indicates that operational optimization and socio-economic assessments are essential elements of dynamic modeling in waste management research.

The Treemap analysis complements the Word Cloud findings by offering a more systematic representation of the core research concepts, thereby validating the thematic directions observed across the scholarly literature (see Figure 13).

The analysis of the trends of the research topic in Figure 15 shows that the issues of “water supply”, “risk assessment”, and “ground water” are the most dominant, followed by topics such as “climate change”, “carbon emission”, and “sustainable development”, reflecting the global focus on sustainability and environmental impact. “Municipal solid waste”, and “waste management” are also among the frequently discussed topics, indicating the importance of waste management in environmental research. Additionally, analysis methods such as system theory, regression analysis, and factor analysis are often used, demonstrating a data-driven and systematic approach. Countries such as “China” and “Thailand” emerged as widely researched regional contexts. At the same time, 2020 and 2023 were the periods with the highest research activity, likely driven by global issues such as pandemics and climate change. This trend illustrates a shift in research priorities towards sustainable solutions and systems-based analysis to address environmental and social challenges.

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Figure 15. Trend topics

Analysis of the research theme map in Figure 16 shows that three main groups exist: “Niche Themes” such as “heavy metals”, “anaerobic digestion’’, and “biogas” focusing on specific issues related to pollutants and renewable energy; “Emerging or Declining Theme” include “water management”, “municipal solid waste”, and “dynamics simulation” reflecting new trends or topics starting to lose interest; and “Basic Themes” which are the foundation of research with high relevance. These findings indicate a shift in the interest of researchers from traditional topics such as “waste disposal” to more complex approaches such as “system theory” and “simulation”. Simultaneously, the issue of “heavy metals” and “biogas” remains an important niche. The overall pattern reveals that the research dynamics are focused on sustainable and systems-based solutions to environmental challenges.

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Figure 16. Thematic map

Over the last decade (2014–2024), research on dynamic simulations in urban waste management showed exponential growth, with a five-fold increase in publications and an annual growth rate of 19.62%. This trend is driven by the global urgency to achieve sustainability, with a significant surge following 2019. The dominance of Chinese institutions such as Shanghai Jiao Tong University and Tsinghua University reflects the leadership of Asia in this field. In contrast, journals such as the “Journal of Cleaner Production” and “Waste Management” are the leading platforms, with 28 articles each. Annual fluctuations reveal the research's response to global issues, such as a peak in publications in 2020, which may be related to the impact of the COVID-19 pandemic on waste management systems. 

The dominant research themes are divided into three main clusters: (1) circular economy and sustainability (waste valorization, biogas, carbon emissions), (2) operational optimization (waste collection efficiency, landfill management), and (3) policy and governance integration. Dynamic systems modeling still dominates (28% of studies), although new approaches, such as agent-based modeling and machine learning, are emerging post-2020 for stakeholder behavior analysis and waste stream prediction. The thematic shift from traditional topics (recycling) to dynamic issues (carbon emissions, simulated scenarios) reflects the adaptation of research to the climate change and energy transition agendas. However, geographical bias remains strong, with 54% of studies focusing on China, thereby ignoring the unique context in the Global South. 

The main challenges include technology gaps (a lack of IoT and digital twins), geographical inequality, and integrating social factors into the model. Future opportunities lie in: (1) the development of hybrid models combining AI, dynamic systems, and policy analysis; (2) inclusive studies in developing countries by involving the informal sector and local cultures; and (3) transdisciplinary collaboration to bridge the gap between theory and practice. By adopting this approach, dynamic simulations can become a more effective tool for creating a globally adaptive, sustainable, and relevant waste management system.

Therefore, this study recommends measurable strategic steps for stakeholders, including: For City Governments, the implementation of an open IoT data platform that integrates fleet GPS trackers with dynamic system models can be targeted to reduce dead-heading or empty mileage of garbage trucks by 15% before 2027; For the research community, it is necessary to develop a hybrid model that combines system dynamic and agent-based modeling enriched with behavioral modules and tested in at least three different cities within the next 24 months to validate its reliability; Meanwhile, for investors, the opportunity lies in the pilot of digital twin technology for waste sorting stations with the main criteria of a return on investment period of less than five years. For these recommendations to have a broad impact, future research must address the limitations of these studies, including the availability of gold open-access journals (Gold OA), the exclusion of non-English and grey literature, and the need to expand the scope of the database.

To make this happen, future research needs to focus on developing hybrid models combining system dynamics, ABM, and AI to create more holistic simulations. Other priorities include cross-regional comparative studies to understand diverse local contexts and participatory action research involving communities and policymakers in designing solutions. By addressing these gaps, dynamic simulations can become a more effective tool for achieving a sustainable, inclusive, and future-ready waste management system prepared for future challenges, such as climate change and rapid urbanization.