1. Introduction

The world’s population continues to grow, reaching 8 billion in 2022 with over 50% living in metropolitan areas. The number of residents in the city is expected to increase by 20% in 2050, alerting a need for urbanization and sustainable development of urban areas (United Nations, 2023). Some of the main challenges that arise with rapid urbanization are energy consumption, pollution, and population density; especially with the cities occupying only 3% of the total land on Earth, but utilizing 60-80% of energy consumption and 75% of carbon emissions (QS Impact, 2024). This fast-paced development of cities is making it difficult to catch up with the needs of our society, which becomes exceptionally vulnerable to the global south, specifically the Southeast Asian region.

To tackle this, Singapore, located in Southeast Asia, has become one of the world’s leading countries in the field of artificial intelligence (AI) and smart cities, ranking number 2 in Government AI Readiness Index 2023 but also showing a high achievement score of Sustainable Development Goal (SDG) 11 (Sustainable Cities and Communities) by implementing AIs into its urban development track (Hankins et al., 2023; Sustainable Development Report, 2024). With its Smart Nation Initiative launched in 2014, it aims to “build better, meaningful, and fulfilled lives, enabled seamlessly by technology” (Smart Nation Singapore, 2024). These developments influence not merely cities but also relationships between people and workforce patterns by incorporating contemporary technology to tackle social challenges including lowering pollution levels, energy savings, and infrastructural management.

This shift in development closely line up with the concept of technological determinism, which states that the development of technology is a key influence on social and cultural change (Wengenroth et al., 1998). This implies that developments like AI and smart city applications have the potential to drastically alter how cities operate in the larger scheme of urban development-- tackling issues like pollution, governance of infrastructure, and resource utilization. Although technological determinism highlights AI's assurance to build smarter, healthier cities, it also acknowledges the unanticipated repercussions of rapid technological development, which can include questions of ethics and job market disturbances.

By examining AI’s contribution to Singapore’s urban change under the prism of technological determinism, this study aims to investigate these trade-offs by striking a balance between workforce inclusion and sustainability.

2. Research Objectives and Hypotheses

This paper focuses on accomplishing particular research aims in order to comprehend the dual consequences of AI in urbanization through quantitative measures. While technology aids in safeguarding the planet and promoting sustainability (SDG 11), it also poses problems for mankind by making it harder for low-skilled jobs to stay employed (SDG 8).

The precise objectives of this project are to investigate how AI-driven technical advancement in smart cities maintains an equilibrium between workforce inclusion and environmental sustainability; with the specific aims of: (1) Study how AI technologies in transportation, garbage disposal, and energy optimization can contribute to the achievement of SDG 11 by increasing resource efficiency, lowering emissions, and strengthening infrastructure in cities; (2) Explore how AI-driven automation threatens SDG 8's objective of decent work and economic growth, by upending low-skilled jobs in industries like waste management and public transportation; (3) Offer policy suggestions that strike a balance between the advancements in AI technology, moral issues, and workforce diversity, with an emphasis on re-training, the creation of green jobs, and the fair application of AI. These goals will bring clarity on how to achieve balanced growth within smart cities powered by AI.

In order to assess AI's dual impact, this study follows the three major hypotheses based on the research objectives: (H1) SDG 11 is advanced by Singapore’s AI-driven solutions in waste management, energy optimization, and mobility, which dramatically improve urban sustainability and lead to quantifiable gains in important metrics including trash reduction, energy efficiency, and carbon emissions; (H2) Significant labor displacement results from AI-driven automation in low-skilled industries like waste management and public transportation, which contradicts SDG 8 by limiting access to good employment options; (H3) The social implications of adopting AI are somewhat reduced by regulatory applications, such as rehabilitation programs and sustainable job efforts, but their efficacy is constrained by their scale and accessibility.

3. Methodology

To compile this research, this study uses secondary data from several reliable sources. Industry reports on AI applications, sustainability measures, and workforce dynamics are available from groups such as Statistics Singapore, the Ministry of Transport, and the National Environment Agency. Official data on workforce and sustainability in cities can also be found in government publications such as the Ministry of Manpower.

Academic resources, including Semantic Scholar, JSTOR, and Google Scholar, access publications and conference papers. These resources include terms such as "waste management," "sustainability," “traffic congestion in Singapore,” and "AI in Singapore." This multi-source methodology ensures a thorough examination of AI's dual effects on workforce inclusion and environmental sustainability.

The gathered information was arranged and examined methodically. For comparison, quantitative information was standardized, including energy use, trash reduction, and job displacement measures. To find recurrent themes including labor issues, governance problems, and sustainability enhancements, qualitative data—including insights from government publications and scholarly discussions—was coded thematically. To analyze AI's dual impact, the analysis integrated critical review within the context of technological determinism, comparative analysis to examine pre- and post-AI metrics, and descriptive statistics to summarize trends.

As Singapore is a highly advanced city with one of the region's greatest economies and a reputation as an international leader in technological advances, it was selected as the subject of this study. Singapore, which bills itself as the world's most advanced Smart Nation, is a perfect case study for analyzing the dual effects of AI on workforce dynamics and sustainability due to its purposeful integration of AI into government and urban systems. Its ranking highlights its sophisticated infrastructure and AI-driven projects as the second most prepared government in the world–and the only Asian country in the top five–in the Government AI Readiness Index 2023, which reflects its leadership (Hankins et al., 2023). This research offers perspectives on the potential benefits of an AI-driven revolution by examining Singapore, which can be used as a template for additional research in the region.

Lastly, 2014 was chosen as the year of change because it represented the start of Singapore's Smart Nation Initiative, a national initiative to incorporate digital and artificial intelligence (AI) into the country's energy, waste, and transportation systems. By using 2014 as a starting point, it will be feasible to assess the consequences of this technological change by clearly comparing pre- and post-AI patterns in labor impacts, such as job growth and displacement, and sustainability measurements, like greenhouse gas emissions and resource utilization.

4. Results

This section presents the findings of the study, comparing pre- and post-AI metrics to evaluate AI’s impact on sustainability (SDG 11) and workforce inclusivity (SDG 8).

4.1. Transportation

As shown in Table 1 and Figure 1, the average personal car travel time was 30 minutes, which was consistent from 2010 to 2015 (Singapore Department of Statistics, 2011; Singapore Department of Statistics, 2016). However, by 2020, it had risen to 40 minutes, a 33% increase over the previous ten years (Singapore Department of Statistics, 2021). The trip time for public buses similarly remained at 30 minutes until 2015, but it increased significantly to 45 minutes in 2020, a 50% increase (Singapore Department of Statistics, 2016; Singapore Department of Statistics, 2021). From 30 minutes in 2010 to 37 minutes in 2015 and subsequently, to 45 minutes in 2020, MRT/LRT travel times grew progressively, increasing by 50% overall (Singapore Department of Statistics, 2011; Singapore Department of Statistics, 2016; Singapore Department of Statistics, 2021).

The average speed of cars on Singapore's motorways varied from 2010 to 2020, rising from 62.3 km/h in 2010 to 64.1 km/h in 2015 before falling to 60 km/h by 2020, suggesting that traffic was still heavy even after brief increases as shown in Table 2 and Figure 2 (Data.gov.sg, 2024; Ministry of Transport, 2024).

Table 3 and Figure 3 show that strong advances in sustainability were indicated by the 15% drop in average transportation-related CO2 emissions between 2010 and 2020 (Worldometer, 2022.).

As evident through Table 4, jobs in the transportation and storage industry varied throughout the course of the decade, going from 191,300 in 2010 to 187,600 in 2015 before sharply increasing to 214,800 in 2020. This is a net gain of 12.3% (Singapore Department of Statistics, 2024).

4.2. Waste Management

Over time, Singapore's total garbage generation dropped by 25.1%, from 7,851,000 tonnes in 2013 to 5,880,000 tonnes in 2020, visible in Table 5 and Figure 4 (National Environment Agency [NEA], 2024).

Between 2013 and 2020, the amount of waste recycled decreased by 37%, from 4,826,000 tonnes to 3,040,000 tons. This data can be seen through Table 5 and Figure 4 (NEA, 2024). The recycling rate decreased by 10% from 62% in 2013 to 52% in 2020, indicating that recycling is less effective even while overall garbage creation is lower. This data can also be seen through Table 5 and Figure 4 (NEA, 2024).

Table 3 and Figure 5 show that waste management-related carbon dioxide emissions rose 16.2% from 35,390 tonnes in 2010 to 41,120 tonnes in 2020 (Worldometer, 2022.).

As evident through Table 4; from 37,500 in 2010 to 23,200 in 2015 and then to 21,200 in 2020, employment in this sector progressively decreased, resulting in a 43.5% overall fall (Singapore Department of Statistics, 2024).

4.3. Energy Efficiency

As illustrated through Table 6 and Figure 6, between 2010 and 2020, the amount of electricity used for commercial purposes grew by 19.7% (Singapore Department of Statistics, 2024). Between 2010 and 2020, home electricity consumption increased by a substantial 24.2%. (Singapore Department of Statistics, 2024).

Table 3 and Figure 7 show that building-related CO2 emissions went down by 5.5% from 624,250 tonnes in 2010 to 589,600 tonnes in 2020 (Worldometer, 2022.).

Evident in Table 4, Construction employment rose from 104,000 in 2010 to 110,600 in 2015 before plummeting to 97,200 in 2020, a 6.5% decline over the decade (Singapore Department of Statistics, 2024).

5. Discussion and Evaluation

To begin with the transportation sector, the integration of AI-induced mobility positively influenced Singapore’s CO2 emissions by 17%, as supported by David Rolnick in his research: “Tackling Climate Change with Machine Learning”, proving that the implementation of AI and technology can significantly contribute to climate change (Rolnick et al., 2022). However, despite these environmental benefits, AI has not yet solved the problem of persistent congestion as average travel times for individual cars increased by 33% over 2010-2020 and by 50% for public buses and MRT/LRT. The increase in traffic congestion on roads indicates an underlying limitation of the existing AI systems to tackle the issues, and emphasizes the need to address extended issues such as infrastructure or population density that contribute to the volume of traffic. A relevant factor is Singapore’s population density in 2020, of which was recorded at 7,810 people per square kilometer–a 9.29% rise from 2010 (Singapore Department of Statistics, 2024). Being one of the smallest countries in the world, road space is inherently scarce in this crowded metropolitan environment, making it difficult to meet the increasing requirements for traffic, maximize the effectiveness of public transportation, and lessen the negative environmental effects of congestion. Furthermore, even if AI has improved some traffic patterns within the city, it is unable to completely alleviate the congestion brought on by a shortage of land and an expanding population.

In order to reduce traffic, national policies such as the Electronic Road Pricing (ERP) system introduced in 1998 have been essential in lowering the emissions of carbon dioxide by discouraging the use of personal vehicles (Kearns et al., 2014). This system forces all automobiles to pay a fee when entering Singapore’s urban regions, with the costs varying constantly depending on the volume of traffic at any given time–improving the efficiency of traffic congestion management (Land Transport Authority, 2024). The system was first put into place with a total of 33 ERP gantries in 1998, but by 2017, it had grown up to 78 gantries (Data.gov.sg, 2024). ERP’s positive environmental impacts are well-established; from 1998 to 2008, ERP alone is predicted to have cumulatively reduced CO2 emissions by 3,738,000 tonnes, keeping cars from stalling in traffic and cutting down on wasteful fuel use and pollution (Kearns et al., 2014). In the latest developments, the government has now planned ERP 2.0, a satellite-based congestion pricing system that will launch in 2025 (Government of Singapore, 2025). This sustained dependence on pricing approaches emphasizes how important government action is in addition to AI-based solutions.

Aside from traffic congestion, employment in transportation increased by 12.3%, meaning that this number might show a turning towards more skilled professions rather than growth in low-skilled jobs. Frey and Osborne report in their research: “The Future of Employment: How Susceptible are Jobs to Computerization?” that transportation, storage, and distribution managers have a 0.59 probability of computerization, while bus drivers have a 0.89 probability of getting replaced by AIs (Frey & Osborne, 2017). The limitation of my research comes from the restricted availability of data; the number provided by the Ministry of Manpower & Singapore Department of Statistics vaguely names this group “transportation and storage”, making it unclear whether this group includes high and low-skilled workers or just managers in general. Should the 12.3% increase in employment within the 'transportation and storage' sector relate to the managerial layer of employment and have a correspondingly low 0.59 probability of being computerized, the figure might well coincide with Osborne's findings.

Moving on to waste management, overall waste generation was reduced by 25.1% from the year 2013 to 2020. However, AI-driven advancement in waste management is not the only factor responsible for this drop. Singapore’s Zero Waste Masterplan (2019) was incorporated to introduce a “circular economy” method, which shifts from the conventional “take-make-dispose” model to a system in which materials are recycled and reinvented throughout the production network; hence reducing the total waste output (Ministry of the Environment and Water Resources [MEWR], 2020). Businesses were urged to salvage materials from wastes and incorporate them into production cycles as a result of this change, which reduced the need for waste dumping (MEWR, 2020). Additionally, initiatives across the industry to support environmentally friendly production and usage were strengthened by the government's waste elimination targets, which included reducing landfill waste per capita by 30% by 2030 (MEWR, 2020). Companies were encouraged to use resources wisely and engage in a mutually beneficial way which turns waste from one industry into raw materials for another (MEWR, 2020). The decrease in garbage generation shown during 2015 and 2020 was accelerated by these societal shifts, which were an added benefit to AI-driven waste administration advancements.

Recycling rates shortened by 10 percentage points in spite of the decrease in overall waste generation. This points to inefficiencies in infrastructural constraints and public recycling practices rather than AI-driven waste sorting. A survey by the Singapore Environment Council (SEC) in 2017-2018 evaluated respondents’ knowledge of which plastic products could be recycled in Singapore (Singapore Environment Council, 2018). The results showed that the general population knew very little about recyclable plastics; just 29.21% of the respondents accurately classified all reusable plastic goods, while the remainder gave only partially right or wrong answers, despite Singapore National Environment Agency’s (NEA) specific recycling instructions (Singapore Environment Council, 2018). Incorrect discarding practices and faulty recycling classification continue to be major barriers to raising Singapore's overall recycling percentage, despite the country's sophisticated waste management technologies.

There was also an increase of 16.2% in carbon emissions from waste, which could be explained by Singapore’s incineration system. All of Singapore’s general waste gets incinerated to reduce the amount of landfill waste, however, this process adds a considerable amount of carbon dioxide emissions (MEWR, 2020). Singapore prioritizes waste-to-energy conversion, which explains the increase in emissions despite decreasing trash volumes, as Semakau Landfill–Singapore’s current only landfill–is predicted to reach capacity by 2035 (NEA, 2024). Finally, The sharp 43.5% reduction in jobs in waste management indicates Frey and Osborne’s realization that very predictable, low-skilled work, such as garbage collection, holds much potential for automation (0.93) (Frey & Osborne, 2017). As Singapore moves toward a more computerized waste framework, jobs may also be lost as a result of centralized incinerators and automated garbage collection, which will lessen the need for human sorting and landfill management positions.

Lastly with energy efficiency, electricity consumption grew 19.7% for commercial use and 24.2% for households during the period 2010-2020, but building-associated CO₂ emissions declined by 5.5%. This contrast suggests that AI driven efficiencies were crucial in offsetting emissions, even if factors including urbanization, increased business activity, and households’ growing dependence on electrical devices were the main drivers of increased energy demand. Singapore’s fast urbanization and economic expansion are important causes of rising energy consumption, as they raise the need for commercial activities, digital services, and smart technologies. Verifiably, the Annual Electricity Report published by the International Energy Agency (IEA) predicts that data centres, cryptocurrencies, and artificial intelligence have consumed about 460TWh of electricity worldwide in 2022, which is equivalent to almost 2% of total global electricity demand (International Energy Agency, 2024). Even though AI is proven to increase energy consumption, it manages to optimize wasteful electricity use through its dual role, guaranteeing that energy will only be utilized when necessary through predictive energy management and automation. Such reductions in building-related emissions demonstrate the potential of AI-driven energy optimization toward achieving sustainability goals. Construction employment also has declined by 6.5% as evidence to support that the old job cards in construction might have decreased demand with poor activity levels in infrastructure projects and increased automation.

6. Global Comparison

As Singapore's AI-powered Smart Nation project has considerably enhanced sustainable urban development, a comparison of other smart cities shows various AI applications that provide insightful information. Yet, it is crucial to understand that variations in economic systems, policy contexts, and urban populations affect the use of AI and its effects.

6.1. AI in Traffic Management: Singapore vs. Pittsburgh

AI and Singapore's Electronic Road Pricing (ERP) system automatically modify toll prices to control traffic. Nevertheless, road congestion continues to grow worse even with AI adjustments, with individual vehicle travel times rising by 33% between 2010 and 2020. Pittsburgh's AI-powered adaptive traffic light system, SURTRAC, enables each traffic signal to independently change in response to current traffic circumstances, in contrast to Singapore's road evaluation system (Smith et al., 2018). SURTRAC has decreased overall travel time by 25.79% and emission levels from cars by 21.48% (Smith et al., 2018). Singapore’s real-time transportation management could be improved by a hybrid strategy that incorporates Pittsburgh’s AI-powered traffic lights into its ERP system.

6.2. AI in Waste Management: Singapore vs. South Korea

Both Singapore and South Korea have utilized AI in handling garbage; Singapore concentrates on AI-driven collections and waste site efficiency, while South Korea stresses public involvement. Through route optimization and landfill outflow reduction, Singapore's garbage sorting technologies powered by AI have increased the accuracy of waste collection. However, with its main focus on the backstage rather than public engagement, the recycling rates in Singapore continues to drop despite its efforts. South Korea's AI supported recycling bin system, Nephron, allows consumers to dispose of recyclables and obtain credits that can be exchanged for prizes by using image recognition systems to sort waste (Kim, 2022). With a 46% rise in recyclables compiled in a single year, a 30% increase in participation, and 9.96 million transactions annually, this approach has substantially boosted recycling engagement (Superbin, 2024). In terms of ecological effects, 6,372 tons of recyclables have been recuperated in 2023, which is equal to the plantation of 259 million pine trees (Superbin, 2024). AI-powered garbage sorting by itself will not address the problem if changes are stagnant in public involvement. A hybrid strategy that combines user benefits with automated sorting may increase recycling rates and reduce CO2 emissions associated with garbage.

6.3. AI in Energy Efficiency: Singapore vs. Amsterdam

The instances of Singapore and Amsterdam have employed AI to improve energy efficiency, but Singapore's Smart Nation efforts might benefit from Amsterdam's sophisticated automation and predictive modeling. Amsterdam has shown the promise of AI in predictive energy management, especially in structures like The Edge (Ajayi et al., 2024). Using an ecosystem of IoT sensors, the Edge gathers data on climate, lighting, and activity in true time to automatically control energy consumption (Ajayi et al., 2024). When compared to traditional commercial buildings, this has led to a 70% decrease in energy use, demonstrating the potential of AI to create genuinely sustainable environments (Ajayi et al., 2024). Not only this, but AI-powered smart grid technologies in Amsterdam secures the prioritization of solar and wind energy during peak availability; lessening the dependency on natural gas and preventing wasteful energy consumption by effective allocation of power use (Ajayi et al., 2024). Singapore on the contrary, has not yet successfully implemented AI-based demand response systems, which could be an area for future development.

7. Conclusion and Policy Recommendations

In conclusion, this paper examines the influence between AI technologies and SDG 11 and 8, under Singapore’s Smart Nation Initiative. The research has shown that AI has the ability to make positive changes towards CO2 emissions in the transportation and energy efficiency sector but shows that there are risks in job losses in low-skilled positions such as waste management and transportation. Aside from AI’s influence, this study also highlights the recognition that when assessing the outcomes of smart city efforts, it is important to take into account additional social aspects including infrastructure, legislation, and socioeconomic situations when evaluating the results. These findings show the critical need to find the balance between development, technology, and workforce inclusivity, making sure that it creates a sustainable future for all.

To improve the current issues related to SDG 8, the government should improve the current existing SkillsFuture Singapore program by focusing on low-skilled job fields that have been automized by AIs, to make sure unskilled workers can also have a chance to rehabilitate without requiring high knowledge. Companies should also implement job retention rules, if in case of transitioning to AI in their operation. Businesses working with AIs can offer training for new or transitioning employees, to ensure that low-skilled workers can follow up with tech-based positions. To compensate for the low participation rate in training programs, the government can provide scholarships or stipends for the courses for those who are laid off, making sure that the cost does not become a barrier to the transition to technology-based jobs. Additionally, Singapore may keep learning and developing by observing other nations that have effectively put in place reskilling initiatives to increase labor flexibility in the age of digitalization. For academia and researchers, cooperation with government and businesses will help in developing AI systems to discover new sustainable job positions, or even reduce the unemployment rate in the fast-changing job market. AI itself cannot completely address job issues; effective reskilling necessitates collaboration between institutions, people, and technology.

Moving on to sustainable cities, the government must promote the creation of regional recycling centers and create financial incentives for businesses to adopt AI-driven energy and waste management platforms. Alongside this, businesses should upgrade their facilities to environmentally friendly systems or AI-powered energy systems, to ensure that emissions are decreased from businesses. Long-lasting achievement for smart city sustainability initiatives depends on solid framework upgrades and engaged public engagement, which makes sure that technical developments meet ecological and societal standards. Providing educational and awareness-raising programs for the public population about the environment can encourage citizens to make more environmentally friendly decisions for their future. All in all, academia and researchers should also continue to test, research, and study artificial intelligence in different fields to maximize its efficiency. As artificial intelligence is a relatively new technology that recently began gaining popularity, it is important to continue researching and studying these technologies to guarantee safe and efficient use for all.

Lastly, for governance and ethics, governments should create standards and rules for AI adoption that reduce societal bias, provide equity, and include commitments to reskilling and job creation to create sustainable harmony for all. Not only that but businesses should carefully identify and look into the dangers and hazards of AI technologies prior to deployment, making sure that the AI can be used safely in the workplace. There must be a form of open debate or chat on AI usage for the community, to highlight problems, concerns, or complaints for safe use of AIs. Building up on this, researchers can use this resource to investigate certain fields and create frameworks for the implementation of AI.

As seen from the numbers, it is important to find a balance between innovation and workforce inclusivity, but also important that there is some sort of governance to remain sustainable, just, and resilient. In order to fully realize AI's promise, humans, machines, and governments must work together harmoniously to ensure that innovation meets both societal and economic demands. Singapore can improve its Smart Nation strategy and incorporate the latest technologies to build a more flexible, accessible, and viable future by taking inspiration from international AI programs. With the proposals and policies proposed in this paper, built upon the existing issues and careful examination, Singapore’s Smart Nation Initiative can become a global role model for other countries to learn, and adopt.

NOTES

Funding

This research was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflict of interests

The author declares no conflicts of interest related to this study.

Data Availability

The data used in this study are derived from publicly available sources, including government publications and academic research. Specific datasets are cited in the manuscript and are available upon request.

Figure 1.

Average Travel Time to Work by Mode of Transport for Singapore's Residents (2010-2020)

Figure 2.

Average Speed of Vehicles on Singapore Expressways during Peak Hours (2010-2020)

Figure 3.

2010-2020 Singapore CO2 Emissions from Transportation (tonnes)

Figure 4.

Overall Waste and Recycling Statistics in Singapore (2013-2020)

Figure 5.

2010-2020 Singapore CO2 Emissions from Waste (tonnes)

Figure 6.

Electricity Comsumption Data in Singapore (2010-2020)

Figure 7.

2010-2020 Singapore CO2 Emissions from Buildings (tonnes)

References

• Ajayi, F., Ademola, O. M., Amuda, K. F., & Alade, B. (2024). Ai-driven decarbonization of buildings: Leveraging predictive analytics and automation for Sustainable Energy Management. World Journal of Advanced Research and Reviews, 24(1), 061-079. https://doi.org/10.30574/wjarr.2024.24.1.2997Article
• Data.gov.sg. (2024). Average speed during peak hours. data.gov.sg. https://data.gov.sg/datasets/d_26f6afadf2f86b2004f9a1e28f5564cc/view
• Data.gov.sg. (2024). Number of LTA road facilities. data.gov.sg. https://data.gov.sg/collections/376/view
• Department of Statistics Singapore. (2024). Electricity Generation And Consumption. Tablebuilder.singstat.gov.sg. https://tablebuilder.singstat.gov.sg/table/TS/M890841
• Department of Statistics Singapore. (2024). Employed Residents Aged 15 Years And Over By Industry And Occupation, (June). Tablebuilder.singstat.gov.sg. https://tablebuilder.singstat.gov.sg/table/TS/M182081
• Department of Statistics Singapore. (2024). Indicators on Population. Tablebuilder.singstat.gov.sg. https://tablebuilder.singstat.gov.sg/table/TS/M810001
• Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation. Technological Forecasting and Social Change, 114, 254-280. https://doi.org/10.1016/j.techfore.2016.08.019Article
• Government of Singapore. (2025). Electronic Road Pricing (ERP). Ministry of Transport. https://www.mot.gov.sg/what-we-do/motoring-road-network-and-infrastructure/Electronic-Road-Pricing
• Hankins, E., Rahim, S., Grau, G., Grau, L., & Nettel, P. F. (2023). Government AI readiness index 2023. Oxford Insights. https://oxfordinsights.com/wp-content/uploads/2023/12/2023-Government-AI-Readiness-Index-1.pdf
• International Energy Agency. (2024). Electricity 2024 - Analysis and forecast to 2026. International Energy Agency. https://iea.blob.core.windows.net/assets/18f3ed24-4b26-4c83-a3d2-8a1be51c8cc8/Electricity2024-Analysisandforecastto2026.pdf
• Kearns, S., Eliasson, J., Xian, G. S., Buckeye, K., Komanoff, C., & Moroni, S. (2014). International best practices for congestion charge and low emissions zone. Energy Foundation China. https://www.efchina.org/Attachments/Report/reports-20140812-en/reports-20140812-en
• Kim, M. (2022). 돈이 되는 AI쓰레기통 “네프론” 이용해 보셨나요? [Have you tried using the money-making AI trash can “Nephron”?]. Seoul, Republic of Korea: Seoul Metropolitan Government. https://opengov.seoul.go.kr/mediahub/26666506
• Land Transport Authority. (2024). Electronic road pricing (ERP). Land Transport Authority. https://onemotoring.lta.gov.sg/content/onemotoring/home/driving/ERP/ERP.html
• Ministry of the Environment and Water Resources. (2020). Zero Waste Masterplan Singapore. Singapore. https://isomer-user-content.by.gov.sg/23/85029cae-9ba1-4144-8a52-26c7f86c04ba/zero-waste-masterplan.pdf
• Ministry of Transport. (2024). Written reply to parliamentary questions on data on average speed of vehicles on expressways during peak hours from 2019 to 2023. Written Reply to Parliamentary Questions on Data on Average Speed of Vehicles on Expressways during Peak Hours from 2019 to 2023. https://www.mot.gov.sg/news/Details/written-reply-to-parliamentary-questions-on-data-on-average-speed-of-vehicles-on-expressways-during-peak-hours-from-2019-to-2023
• National Environment Agency. (2024). Solid Waste Management Infrastructure. National Environment Agency. https://www.nea.gov.sg/our-services/waste-management/waste-management-infrastructure/solid-waste-management-infrastructure#:~:text=Singapore’s%20growing%20population%20and%20booming,after%2030%20years%20of%20operation
• National Environment Agency. (2024). Waste Statistics and Overall recycling. National Environment Agency. https://www.nea.gov.sg/our-services/waste-management/waste-statistics-and-overall-recycling
• QS ImpACT. (2024). Sustainable Cities and Communities. QS Impact Charity Organization. https://qsimpact.org/sdg/sustainable-cities-and-communities/
• Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., Luccioni, A. S., Maharaj, T., Sherwin, E. D., Mukkavilli, S. K., Kording, K. P., Gomes, C. P., Ng, A. Y., Hassabis, D., Platt, J. C., & Bengio, Y. (2022). Tackling Climate Change with Machine Learning. ACM Computing Surveys, 55(2), 1-96. https://doi.org/10.1145/3485128Article
• Singapore Department of Statistics. (2011). Census of Population 2010- Release3. Statistics Singapore. https://www.singstat.gov.sg/-/media/files/publications/cop2010/census_2010_release3/cop2010sr3.ashx
• Singapore Department of Statistics. (2016). General Household Survey 2015. Statistics Singapore. https://www.singstat.gov.sg/-/media/files/publications/ghs/ghs2015/findings.ashx
• Singapore Department of Statistics. (2021). Census of Population 2020 Statistical Release 2. Statistics Singapore. https://www.singstat.gov.sg/-/media/files/publications/cop2020/sr2/cop2020sr2.ashx
• Singapore Environment Council. (2018). Understanding Plastic Waste Ecosystem in Singapore. Singapore Environment Council. https://sec.org.sg/pdf/reports/new-plastic-economy.pdf
• Smart Nation Singapore. (2024). Our smart nation vision. Smart Nation Singapore. https://www.smartnation.gov.sg/vision/
• Smith, S. F., Barlow, G., Xie, X.-F., & Rubinstein, Z. B. (2018). SURTRAC: Scalable urban traffic control. KiltHub Repository. https://kilthub.cmu.edu/articles/journal_contribution/SURTRAC_Scalable_Urban_Traffic_Control/6561035?file=12043343
• Superbin. (2024). 수퍼빈, 2024년 자원순환 성과 대폭 증가 [Superbin, a significant increase in resource circulation performance in 2024]. Seoul, Republic of Korea: Superbin. https://www.superbin.co.kr/en/news/244
• Sustainable development report 2024. Sustainable Development Report. (2024). https://dashboards.sdgindex.org/map/goals/SDG11
• United Nations. (2023). Cities - United Nations Sustainable Development Action 2015. United Nations. https://www.un.org/sustainabledevelopment/cities/
• Wengenroth, U., Smith, M. R., & Marx, L. (1998). Does technology drive history. the dilemma of technological determinism. Technology and Culture, 39(4), 755-758. https://doi.org/10.2307/1215849Article
• Worldometer. (2022). Singapore CO2 emissions. Worldometer. https://www.worldometers.info/co2-emissions/singapore-co2-emissions/#:~:text=CO2%20emissions%20per%20capita%20in,in%20CO2%20emissions%20per%20capita

Figure & Data

References

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