Urban traffic dynamics and their impact on CO₂ emissions and temperature: a three-year GNSS-based study during the COVID-19 pandemic in Jakarta

Abstract

Background and Method

Urban traffic and air pollution remain strongly interconnected challenges for big cities. In Jakarta, transportation activity remains one of the primary contributors to carbon dioxide (CO₂) emissions and urban heat stress, worsened by high-density land use and limited integration of public transportation. The COVID-19 pandemic temporarily transformed these dynamics through the policy of social mobility restriction; this study therefore aims to assess how changes in traffic volume affect CO₂ intensity and thermal level across different traffic policy and behavioral contexts. This study examines the relationship between urban traffic performance, CO₂ intensity, and air temperature in Jakarta from 2021 to 2023, during both pre and post social mobility restriction periods. Real-time navigation data were logged using U-Blox F9P and M8T GNSS receivers connected with environmental sensors along two major Jakarta routes. Over 75,000 data loggings were analyzed using spatial mapping, temporal profiling, and regression modelling to analyze interactions among the vehicle speed, CO₂ level, and temperature s. The analysis applied standardized thresholds for urban air quality (380 ppm) and thermal comfort (21.6–31.6 °C) to evaluate environmental degradation under varying traffic conditions.

Results

Across the three-year study, mean CO₂ increased by 185.29 ppm (+36.8 %), air temperature by 1.74 °C (+4.9 %), and mean vehicle speed dropped by 4.93 km/h (–25.1 %). Even during the social mobility restrictions, CO₂ and temperature levels exceeded acceptable thresholds, indicating continued anthropogenic burdens beyond traffic. Regression and spatial analyses showed that reduced traffic speeds are strongly associated with increased CO₂ concentrations and temperatures, with high peak concentrations occurring in commercial, industrial, and mixed-use areas.

Conclusions

The findings indicate that short-term interventions, such as the COVID-19 pandemic's social mobility restriction policy, can temporarily reduce traffic congestion but do not address the primary causes of carbon emissions and heat stress. Persistent CO₂ and temperature exceedances highlight the need for integrated strategies targeting transport, land use, energy, and industrial activities to effectively improve air quality and climate resilience in Jakarta. These results imply that focusing solely on the transportation sector is insufficient; comprehensive policy coordination is essential for reducing urban emissions and thermal stress. The study also demonstrates the value of GNSS-based real-time monitoring as an effective tool for informing integrated approaches to low-carbon mobility, spatial planning, and climate-resilient city management.

Background

Cities are dynamic and grow and transform, causing significant impacts on the environment, including the increase of carbon dioxide (CO₂) [1], extreme heat, and reduced comfort levels. The increasing trend of urban carbon emissions [2] is closely linked to population growth [3], expansive urban development [4, 5], and consumption-driven lifestyles that prioritize human needs over environmental capacity [6]. Urban growth in a range of sectors, such as buildings, industry, and transportation, has led to an increase in carbon emissions [7, 8]. The issues occur especially in developing countries where economic and anthropogenic activities continue to rise [9–12]. Among greenhouse gases, CO₂ is the most influential cause of global warming [13–15]. Increases in CO₂ and temperature intensity worsen due to climate change [16]. Urban studies have highlighted the relationship among the increase of CO₂ emissions, temperatures, and climate change [17–19], extending to buildings and other elements of the built environment [20, 21]. Thus, the accumulation of the rise in CO₂ and urban heat stress shows how rapidly cities develop and transform.

The transportation sector is a key contributor to urban emissions [22]. In cities with high transport demand, high private vehicle use causes traffic congestion [23] and generates a high level of CO₂. Therefore, these challenges urge sustainable urban mobility solutions [24]. Over the past two decades, climate change, land use, urban form, and transportation have been identified as one of the major urban research topics [25]. Vehicle emissions and traffic congestion have been widely studied, with their downstream effects including impacts on urban air temperature, heat stress, and thermal comfort [26–33]. Beyond environmental concerns, traffic congestion and air pollution also impose serious urban social and economic issues, including time inefficiency, public-health burdens, and low productivity [34]. These factors call for the need to integrate traffic congestion management with broader strategies for sustainable and livable urban development.

Jakarta represents these challenges. As Indonesia’s capital and largest metropolitan area with an agglomeration phenomenon, the city experiences serious traffic congestion and air pollution issues [35, 36]. Traffic performance was recorded as a big issue even before the COVID-19 pandemic, with conditions worsening since 2019 [37]. Although local authorities have tried to mitigate the traffic congestion by promoting public transportation and rail investments, the problems remain due to scale, demand growth, and network integration limitations [38, 39]. Urban air pollution, driven by the transportation sector, industry, and broader anthropogenic activities, continues to bring big social, economic, and environmental issues to the city [40–44]. These issues are not purely technical, as they are closely linked to the metropolitan spatial structure, land-use planning, and the distribution of growth centers. Together, these factors create daily travel patterns and determine the concentration of emissions and heat hotspots.

Urban mobility patterns are influenced by various factors, including land-use planning, demographics, transport choices, and policy interventions [45–47]. These patterns are not firm; crises and disruptions could cause significant changes that reconfigure the travel behaviour of urban dwellers [48, 49]. Early studies report that events such as urban disasters, economic crisis or health emergencies can change urban traffic volumes and behavior [50–52]. The COVID-19 pandemic, which began in early 2020, influences almost all urban sectors at both the local and global levels 53–55]. In Jakarta, mobility levels changed during the social mobility restriction periods, with changes evident in traffic flow, travel behavior, and modal choice [56–59]. Policies such as large-scale social restrictions (locally known as PSBB) and enforcement of restrictions on community activities (locally known as PPKM) introduced strict social mobility restrictions, causing major reductions in urban mobility across cities, regions, and rural areas [61–65].

These social mobility restrictions also produced notable change of environmental quality, with many countries reporting short-term improvements in air quality during periods of reduced mobility [66–72]. In this situation, lower mobility causes lower energy consumption which also indicates a reduction in carbon emissions [73]. In Indonesia, PSBB and PPKM were linked with temporary improvement in air quality and CO₂ level [74, 75]. Social mobility restrictions also influenced some variables, including microclimates and urban heat island (UHI) dynamics [76–88]. These environmental responses were varied across time and space. The variations in policy timing, policy enforcement, land-use structures, and microclimate conditions contributed to differences in the level and duration of observed impacts. Thus, while mobility restrictions can cause short-term environmental improvements, however they do not resolve the structural drivers of urban emissions and heat stress.

Effective urban public policy needs to address both short-term crises and long-term systemic contributors to urban carbon emissions [89–91]. Evaluations of COVID-19 pandemic mobility or traffic restrictions show a range of impacts, including some cities that achieved measurable progress, while other cities indicated limited or uneven progress due to local contexts, behavioral adaptation, and baseline infrastructure conditions [92, 93]. This variability shows that the real-time and precise datasets are crucial for responsive, accurate, and place-specific urban strategies or decision-making. In Jakarta, decades of worsening urban traffic and air pollution coincided with the COVID-19 pandemic disruptions in 2020, followed by the ending of social mobility restrictions when the Indonesian government revoked PSBB/PPKM in late 2022 [94]. As urban mobility recovered, traffic congestion quickly re-emerged [95]. Again, situation causes Jakarta to be ranked among the world’s most polluted major cities again [96], while extreme heat stress and El Nino increased stress on urban spaces and systems [97]. Urban traffic congestion, carbon emissions, and elevated temperatures intensified during the COVID-19 pandemic and the subsequent transition period, emphasizing the need for evidence-based policy and investment planning.

Current research emphasizes the importance of urban form, spatial structure, and polycentricity in shaping carbon emission profiles [98–106]. These studies show that urban complexity affects varied contributions to carbon emissions and heat stress levels, including the density patterns, social background clustering, and the distribution of commercial and industrial activities that influence where and when traffic-related carbon emissions and thermal loads are concentrated. Spatial mapping and GPS-based monitoring introduce valuable understandings into these dynamics by portraying fine-scale urban spatial variability and temporal change [107–110]. However, a continual gap remains in integrating dynamic, real-time monitoring of urban traffic, carbon emissions, and thermal conditions in developing big cities, especially during periods of rapid policy and behavioral change. Therefore, to address this gap, this study uses Global Navigation Satellite System (GNSS) technology in combination with environmental sensors to investigate real-time micro and macro-scale CO₂ and temperature variations in Jakarta.

This study presents a GNSS-based investigation that linked spatial structures with carbon emissions and climate variables [111–114]. The approach focuses on the relationship between traffic performance, CO₂ intensity, and near-surface air temperature during and after COVID-19 pandemic-related social mobility restrictions. GNSS-enabled navigation provides precise, real-time positioning that can be linked with environmental measurements to reveal traffic congestion patterns, local carbon emission sources and hotspots, and thermal responses along major urban corridors. This integrated view is particularly valuable in complex urban settings where urban growth centers, transit hubs, and public areas like commercial or industrial zones create unforeseen changes in traffic behavior that affect carbon emissions and thermal levels.

Beyond improving descriptive insights, real-time, precise data directly supports analytical decision making to urban management. For example, integrated technology of GNSS with CO₂ and temperature sensors can analyze the overlapping effects of traffic flow, land-use planning, and microclimatic modifiers (e.g., canyon geometry and orientation). It can also reveal how quickly environmental conditions respond when urban mobility is disrupted, which could be seen during the imposition of COVID-19 pandemic social movement restrictions. This strength is important for evaluating the PSBB/PPKM social mobility restriction implementation and for identifying the structural conditions under which reductions in traffic congestion can create sustained environmental improvement.

The Jakarta case is no exception in this regard. The city combines high baseline demand, complex land-use structures, and intense environmental degradation, especially air pollution and heat stress. Periods of reduced urban mobility were linked with temporary improvements. However, the re-emergence of traffic congestion and increase of air pollution following the revoking of social mobility restrictions emphasize the resilience of main drivers [35–37, 95, 96]. By investigating the traffic conditions, CO₂, and temperature, this study presents the scientific evidence that short-term policy controls can cause environmental consequences.

Methodologically, this study applies GNSS-based navigation to spatially analyze a real-time and precise traffic performance, linked with CO₂ and temperature observations. While several studies in Wuhan [17], Gaza [26], and Beijing [27] explored the relationships between traffic and CO₂, few focused on tropical big cities with serious traffic congestion and heat stress like Jakarta. Moreover, these studies usually used static or satellite-based data, while this study applies mobile real-time GNSS monitoring, providing higher accuracy observation data. This approach aligns with calls for real-time and precise air quality and climate monitoring to evaluate the environmental implications of urban mobility [107–110]. While the more literatures highlight the role of spatial structure in determining emission patterns [98–106]. Few studies apply integrated dynamic navigation data with co-located environmental sensors throughout a crisis and post-crisis transition.

This study bridges a methodological gap by integrating GNSS positioning with environmental sensors to observe the real-time and precise data of dynamic traffic, CO₂, and temperature changes during and after the pandemic period. The contribution is to operationalize such an approach in a developing big city context, thereby providing an empirical basis for targeted interventions that can be adjusted or adapted to similar settings.

Data and methods

This study examines urban traffic performance using a GNSS receiver and a CO₂ sensor installed on the rooftop of a private survey vehicle (Fig. 1). The overall system architecture and data collection workflow are shown in Fig. 2. The GNSS receiver was connected to a GNSS antenna to log positioning data, including date, time, latitude, longitude, and vehicle speed. This study used two GNSS receivers, including the U-blox F9P and the U-blox M8T. Environmental data were collected using an HT-2000 meter to record date, time, CO₂ concentration, and temperature. The GNSS receiver was connected to a laptop running U-center software, and the raw GNSS data were stored in. ubx format, which was later processed using RTKLIB to produce .pos files. In parallel, the HT-2000 meter was connected to a laptop running HT software, generating data outputs in .xls format.

Fig. 1.

GNSS Receiver and CO₂ and Temperature Meter Sensor Installation on the Rooftop of Private Survey Vehicle

Fig. 2.

Architecture and Flow of Data Collection

The U-blox F9P GNSS receiver has a horizontal accuracy of ±0.014 m in RTK mode and ±1.5 m in standard single mode, while the M8T receiver provides ±2.5 m accuracy. The HT-2000 sensor measures CO₂ with an accuracy of ± (50 ppm + 3% of the reading) and temperature with an accuracy of ±0.5 °C. GNSS position data (.pos) and CO₂ data (.xls) were synchronized at one-second intervals to minimize timing differences. Outliers caused by multipath interference, such as sudden signal loss or sensor response delay, were removed using a three-standard-deviation (3σ) threshold for each variable. After data cleaning, more than 98% of the records were retained. Sensitivity analysis showed that possible sensor errors contributed less than ±5% variation in mean values, which validated the reliability of the results.

The .pos and .xls datasets were synchronized using the time variable with the VLOOKUP function in Microsoft Excel. This process generated a set of .csv files that consist of date, time, latitude, longitude, vehicle speed, CO₂ concentration, and temperature variables. The resulting CSV datasets were analyzed using GIS-based mapping and statistical methods. The field survey was conducted on two major roads in Jakarta using a private survey vehicle (Fig. 3): Blok M–Jakarta Kota (city center to northern Jakarta) and Juanda–Kalideres (city center to western Jakarta). These routes were selected due to their frequent traffic congestion behaviour and their importance as key corridors linking the city center with major suburban growth areas. Together, the routes represent a range of land-use types and urban traffic conditions.

Fig. 3.

Investigated Study Area in Two Main Roads of Jakar

Data was collected over three consecutive years, each reflecting a different pandemic stage: 2021 (during PSBB implementation), 2022 (under PPKM), and 2023 (after the revocation of pandemic-related social mobility restrictions). Surveys were conducted during morning and noon peak hours on Mondays, Fridays, and Saturdays, aligning with Jakarta commuting patterns on weekdays and recreational or shopping activities on weekends.

For analysis, vehicle speed, CO₂, and temperature data were compared against established urban standards. Regular traffic flow on main roads was defined as 30 - 40 km/h, while serious congestion was defined as 0–15 km/h [115]. Typical outdoor CO₂ concentrations range between 300 and 500 ppm; however, values exceeding 380 ppm indicate combustion-related pollution from anthropogenic sources [116, 117]. Thus, 380 ppm was used as the reference standard for acceptable urban air quality in this study. Similarly, acceptable outdoor thermal ranges for tropical hot and humid climates were set at 21.6°C–31.6°C, based on thermal comfort studies for urban dwellers [118–120]. This study also fulfils the range of methods and literature concerning the urban situations during the pandemic COVID-19 [121, 122], which had multi-dimensional impacts across different sectors of urban life.

Results discussion

This study presents the findings through spatial mapping and statistical analysis. A total of 75,160 data loggings were collected from round-trip along two main Jakarta Road corridors, and the analysis was based on the mean values of each variable. Table 1 summarizes the annual averages for year 2021, 2022, and 2023. The results indicate that the mean vehicle speed slowed down slightly between 2021 and 2022 (−2.49%) but dropped substantially between 2022 and 2023 (−23.16%). This pattern shows that the PSBB and PPKM social mobility restriction policies did not reduce traffic congestion as significantly as the post-restriction return, when traffic congestion increased in 2023. Contrary, mean CO₂ emissions increased by 16.78% from 2021 to 2022 and by 17.13% from 2022 to 2023. Air temperature also rose, with a 3.62% increase in 2022 and a further 1.21% in 2023. Overall, from 2021 to 2023, average traffic speed declined by 4.93 km/h (−25.07%), while CO₂ concentrations rose by 185.29 ppm (+36.8%) and temperature by 1.74°C (+4.88%). Figure 4 illustrates these contrasting trends, where falling vehicle speeds coincide with rising CO₂ and temperature.

Table 1.

Summary of the three years vehicle Speed, CO₂, and temperature data

Yearly data				Yearly gap of mean
2021
Parameter	Mean	Min	Max
Speed (km/h)	19.66 (moderate congestion)	0	71.06	-
CO2 (ppm)	503.5 (combustion, above standard)	428	855	-
Temperature (°C)	35.6 (above acceptable range)	27.55	45.15	-
2022
Parameter	Mean	Min	Max
Speed (km/h)	19.17 (moderate congestion)	0	65.95	−0.49
CO2 (ppm)	588.03 (combustion, above standard)	412	900	84.53
Temperature (°C)	36.89 (above acceptable range)	25.4	58.6	1.29
2023
Parameter	Mean	Min	Max
Speed (km/h)	14.73 (serious congestion)	0	59.47	−4.44
CO2 (ppm)	688.79 (combustion, above standard)	565	966	100.76
Temperature (°C)	37.34 (above acceptable range)	24.8	51.3	0.45

Fig. 4.

The Comparison of Vehicle Speed, CO₂, and Temperature in Three Years Observation

Table 1 highlights several important points. First, CO₂ levels in all three years exceeded the normal urban threshold of 380 ppm [116, 117], with averages ranging from 503.5 ppm in 2021 to 688.79 ppm in 2023. Secondly, average temperatures ranged between 35.6°C and 37.34°C, consistently surpassing the comfort range of 21.6–31.6°C for tropical hot and humid climates [118–120]. These findings suggest that even during periods classified as “moderate congestion” (19.17–19.66 km/h in 2021–2022), Jakarta’s environment was already heavily polluted and thermally stressful for urban dwellers. The weakening in 2023, when average speeds dropped to 14.73 km/h (serious congestion), further increased these issues.

Spatial mapping (Figure 5) shows that the most congested traffic was concentrated in areas with intense land use, such as commercial centers, traditional markets, transit hubs, and industrial zones, where heavy vehicles, intersections, traffic lights, and U-turns worsen stop-and-go traffic. Temporal data (Figure 6) shows that low vehicle speeds were consistently linked with higher CO₂ and temperature, while smoother flows were linked to lower values. This result confirms the strong local-scale link between traffic congestion and environmental degradation, a pattern also identified in other big cities [26–33, 40, 96].

Fig. 5.

Sample of Vehicle Speed Distribution in the Main Roads of Jakarta (a: Blok M- Jakarta Kota and b: Juanda Kalideres)

Fig. 6.

Highlights of Speed, CO₂, and Temperature Data Pattern in Round Trip Route (A morning trip, and B noon trip)

Regression analysis provides further details of correlations between variables (Fig. 7). Across all 75,160 individual data points, correlations between vehicle speed and CO₂ (R² = 0.006) and between speed and temperature (R² = 0.04) were weak. This figure may reflect the limitations of dynamic logging, sensor installation errors, or short-term variability in traffic and weather. However, when the data were summed by mean values, the relationships became strong and statistically significant: vehicle speed correlated with CO₂ (F = 33.925, p < 0.001, R² = 0.828) and with temperature (F = 34.659, p < 0.01, R² = 0.832). These findings suggest that while micro-scale variations are complex, at a sum level, traffic speed is a key driver of both carbon emissions and thermal outcomes. The consistently high CO₂ and temperature levels indicate that other anthropogenic sources such as industrial activities, residential energy use, and land-use are also major contributors [98–106].

Fig. 7.

Regression of how measured speed (km/h) relates with CO₂ (ppm) (a) and Temperature (°C) (b)

The weak correlations observed at the individual-point level (R² = 0.006 for CO₂, R² = 0.04 for temperature) rise primarily from high-frequency variability and transient effects in dynamic traffic conditions, such as sudden braking, wind turbulence, or sensor response lag. Once the data were averaged by trip and hour, these random variations were reduced, revealing statistically significant relationships between mean speed and both CO₂ and temperature (p < 0.001 and p < 0.01). This situation demonstrates that traffic congestion intensity causes a strong influence on environmental stress during combined temporal scales.

The result of this study emphasizes the strong interconnection between urban traffic and environmental conditions, confirming that traffic congestion is not only a mobility challenge but also an important driver of environmental stress, especially air pollution. Even when traffic congestion was classified as moderate in 2021 to 2022, CO₂ concentrations and temperatures consistently exceeded acceptable levels, showing that traffic is correlated to carbon emissions, air quality, and thermal comfort. This reinforces earlier studies emphasizing the relationship between transportation, emissions, and urban livability [22, 25, 44].

The findings also highlight the limited resilience of crisis-driven interventions. Although social mobility restrictions during the COVID-19 pandemic temporarily reduced traffic congestion and developed better air quality, these improvements were reduced and disappeared once the restriction policies were revoked in 2023. This finding also reflects situations from other cities worldwide [66–75, 89–93], showing that structural reforms such as low-carbon transport systems, modal shifts, and sustainable urban design are required to achieve continuing improvements.

This study also pinpoints another critical implication related to urban climate and public health. The recorded temperatures, consistently above 35°C, place urban residents under risky continuous thermal stress. This worsens the risks of heat-related illness affecting vulnerable groups, which increase issue of equity in the city [118–120]. At the same time, the concentration of traffic congestion and emissions in dense mixed-use areas reveals the significance of spatial planning. Compact growth can be a result of efficient development, but without adequate investment and concerned policy in green infrastructure and sustainable transit systems, it may also amplify localized pollution and heat stress [103–107].

Finally, the methodological contribution of this research is important. By applying GNSS-based navigation integrated with environmental sensors, this study demonstrates how dynamic, real-time, and precise monitoring can capture micro and macro-scale variations in urban conditions. Such approaches complement traditional monitoring systems and strengthen the evidence base for data-driven policymaking in rapidly urbanizing big cities [108–110].

Thus, these findings illustrate how traffic, CO₂ emissions, and temperature interact within Jakarta’s developing urban system. The clear recovery in traffic congestion and emissions after the revocation of pandemic social mobility restrictions demonstrates that temporary policy implementation alone is not effective in reversing long term environmental pressures. Moreover, the situation that both CO₂ and temperature levels remained above thresholds throughout the study period suggests that transportation is only part of the problem, while other factors, including industrial activities, energy consumption, and urban morphology, may also play critical roles in shaping the city environmental degradation.

This study’s contribution lies not only in documenting these dynamics but also in showing how advanced monitoring methods can generate effective evidence for urban governance. By situating the results within the broader field of urban studies, the research highlights the urgent need for systemic, integrated strategies that combine transport reform, land-use planning, and climate adaptation. Thus, it provides valuable recommendations for policymakers and urban researchers seeking to create more resilient, livable, and sustainable urban environments.

Conclusions

This study presents a three-year GNSS-based navigation study into the impact of urban traffic on CO₂ emissions and temperature in Jakarta. The results highlight that traffic congestion significantly increases the CO₂ and air temperature. Spatial analysis further shows that these impacts are unevenly dispersed, with hotspots concentrated in areas of intensive land use, such as commercial centers, traditional markets, transport hubs, and industrial zones with heavy traffic.

The COVID-19 pandemic mobility restriction policies (PSBB and PPKM) played a key role in influencing urban traffic patterns. These measures temporarily changed traffic behaviour and reduced congestion, but once restrictions were revoked in 2023, the traffic congestion quickly worsened with an increase in CO₂ emissions and rising temperatures. This situation demonstrates that while crisis-driven interventions can create temporary environmental benefits, urban challenges quickly re-emerge. Urban policies, therefore, need to go beyond the temporary crisis to address the systemic drivers of emissions.

This study shows that despite reduced mobility during the pandemic years, CO₂ and temperature levels remained well above the thresholds for air quality and thermal comfort. Average CO₂ levels ranged between 503.5 ppm and 688.79 ppm, far exceeding the 380-ppm level for acceptable air quality [116, 117]. The average temperatures (35.6°C–37.34°C) were consistently higher than the 21.6°C–31.6°C comfort range for tropical hot and humid climates [118–120]. The relationships between average traffic speed and both CO₂ and temperature were statistically significant (p < 0.001 and p < 0.01, respectively), confirming that traffic congestion intensity has a measurable impact on localized environmental stress. However, this study also stresses that Jakarta’s carbon emissions and thermal discomfort are not just the result of transportation issues, but are also caused by other anthropogenic activities, land-use intensity, and energy consumption.

The larger implications of this research can be considered in three ways. First, it demonstrates the strong relationship between mobility, carbon emissions, and thermal stress. This supports discussions of urban studies that transportation is an inseparable topic considering environmental and public health issues. Second, it highlights the vulnerability of big cities to crisis and rebound period cycles, stressing the need for resilient, long-term, and integrated urban policies. Third, this study presents the methodological contribution of applying the GNSS-based real-time and precise monitoring, which provides a replicable framework for portraying fine-scale spatial and temporal variations in emissions and temperature. These complement existing fixed monitoring stations and offer policymakers actionable, evidence-based solutions for decision-making.

Some limitations of this research include the following: First, GNSS signal disruption in dense urban canyons caused irregular data loss, although the error removal was done during the data processing analysis. Second, CO₂ measurements reflected microscale near-road concentrations rather than urban scale. Third, the analysis was limited to two main corridors of the city. However, the GNSS-based real-time monitoring proved to be a reliable approach for tracking dynamic traffic and environmental related observations, offering real-time, higher precision, and macro-scale datasets.

In conclusion, Jakarta's experiences show that integrated strategies are required to root out the causes of both urban carbon emissions and thermal stress problems, as opposed to just tackling the urban traffic congestion issues. This study provides urban designers, planners, and policy makers looking to develop sustainable land use planning, transportation, and environmental management interventions with a reliable framework. With the application of advanced monitoring systems and the implementation of structural urban reforms, cities can reduce carbon emissions, ultimately achieving healthier, more livable, and sustainable urban environments.

Author contributions

L.Y. research grant PI, led the research work, collected data, analysed data, data reviewer, manuscript writing, research administration, manuscript submission. G.A.N. collected data, extracted data, analysed data, data reviewer, manuscript writing, graphical designer. D.M. analysed data, data reviewer, GNSS technology supervision, research tools sponsor. O.G.A. analysed data and manuscript writing. All authors read and approved the final manuscript.

Funding

The funding of this research was supported by Universitas Indonesia, the research was conducted in collaboration with the University of Tokyo.

Data availability

No datasets were generated or analysed during the current study.

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Competing interests

The authors declare no competing interests.