Effects of COVID-19 restriction measures in Indonesia: A comparative spatial and policy analysis of selected urban agglomerations

Handayani, Wiwandari; Insani, Tia Dianing; Fisher, Micah R.; Gim, Tae‐Hyoung Tommy; Mardhotillah, Santi; Adam, Urban El-fatih

doi:10.1016/j.ijdrr.2022.103015

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…The concentration increase was concurrent with the first implementation of large scale social restriction in Jakarta Province that was started in April 2020 (24), as reported by the previous study that fine particulate (PM 2.5 ) level in Jakarta was increasing about 14 percent during January-June 2020 and the Air Quality Index (AQI) in 2020 was higher than 2 years before (18). Despite the global decreasing trend of PM 2.5 level in majority of cities in the world during Covid19 lockdown implementation, comparing to the concentration before the restriction policy imposed, it was however, Jakarta together with Tokyo, Singapore, Hanoi and Kathmandu had approximately 11% higher in PM 2.5 level (25).…”

Section: Cross Correlation Analysismentioning

confidence: 99%

Profiling Temporal Pattern of Particulate Matter (PM10) and Meteorological Parameters in Jakarta Province during 2020-2021

Husnina

Wangdi²,

Puspita³

et al. 2023

JKL

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Introduction: Jakarta has recorded heightened air pollution for years, and particulate matter (PM10) is one of the pollutants that could bring health burden in population. This study described the distribution of PM10 as well as analysed the correlation with meteorological parameters during 2020–2021 in Jakarta Province. Methods: Air quality standard index daily data from January 1st 2020 to March 31st 2021 was retrieved from the official data portal (https://data.jakarta.go.id/). The Spearman Rank correlation was employed to understand the correlation between PM10 Index with meteorological factors. Autoregressive Integrative Moving Average (ARIMA) model was constructed and Akaike Information Criterion (AIC) selected the model. Cross-correlation analysis explored the association between PM10 with meteorological parameters at multiple time lags. Results and Discussion: PM10 Index started to increase in April 2020 and reached its peak in August 2020. PM10 was positively correlated with temperature (p-value <0.05, R2: 0.134), but it was negatively correlated with humidity and wind speed (p-value <0.05, R2: -0.392 and -0.129). The high cross-correlation coefficients were found between PM10 and temperature at lag 0, humidity at lag 1 and wind speed at lag 1 (rho: 0.42, -0.38 and -0.24). The time series model ARIMA with parameter (p,d,q) (1,1,1) describes the fluctuation of PM10 index data with AIC 3552.75. Conclusion: PM10 concentration in Jakarta is significantly correlated with meteorological factors. The implementation of social restriction in Jakarta need to be supported by pollution control in the neighbouring areas in order to be able to reduce PM10 pollution level.

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Section: Cross Correlation Analysismentioning

confidence: 99%

Profiling Temporal Pattern of Particulate Matter (PM10) and Meteorological Parameters in Jakarta Province during 2020-2021

Husnina

Wangdi²,

Puspita³

et al. 2023

JKL

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“…Family clustering in Greater Jakarta is also a critical issue. Handayani et al found that the stricter health protocol in Greater Jakarta was rarely applied, especially by those who lived in the same house, resulting in family members infected with the rest of their family from workplace cluster cases [48].…”

Section: Covid-19 In Greater Jakartamentioning

confidence: 99%

Exploring People’s Perception of COVID-19 Risk: A Case Study of Greater Jakarta, Indonesia

Pelupessy

Jibiki

Sasaki

2022

IJERPH

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This study aims to understand people’s perceptions of COVID-19 risk in Greater Jakarta, Indonesia. In response to the COVID-19 pandemic, the Indonesian government enacted a health protocol campaign and highlighted the community as an important unit of protocol compliance. We hypothesized that people’s perception of the likelihood of being infected with COVID-19 is associated with health protocol compliance at the community level and their perception of community resilience. As the number of infected persons drastically increased, the “family cluster” also became a significant issue in the pandemic response, especially in Indonesia. In this study, we explored both community and family aspects that influence people’s perceptions. We conducted an online survey in March 2021 with 370 respondents residing in the Greater Jakarta area. The respondents were classified into four age groups (20s, 30s, 40s, and 50-and-over), with gender-balanced samples allocated to each group. We used a questionnaire to measure the perception of COVID-19 risk along with the Conjoint Community Resiliency Assessment Measure (CCRAM). Multiple regression analysis revealed that family factors have a much larger influence on the individual perception of the likelihood of contracting COVID-19 than community factors. The results suggest that the link between family-level efforts against COVID-19 and individual-level perceptions cannot be separated in response to the pandemic.

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“…There is an emerging trend in that cities are becoming increasingly interconnected with the rapid development of inter-city transportation, leading to increased urban agglomerations that cross city boundaries and contain multiple urban areas [20]. Urban agglomeration involves close links between cities in terms of labor resources, technological elements, and production processes [21]. Through cross-regional trade in goods and services, water resources are virtually redistributed across regions.…”

Section: Introductionmentioning

confidence: 99%

Urban Low-Carbon Consumption Performance Assessment: A Case Study of Yangtze River Delta Cities, China

et al. 2022

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Urban low carbonization has been an essential element in China’s carbon peak and carbon neutrality strategies. An assessment of urban low-carbon performance could provide valuable information for monitoring and guiding the low-carbon transition in cities. However, due to cross-regional carbon transfer, the actual level of achievement would be masked, if the assessment was based only on a production-based index such as carbon emission intensity (CEI). Focusing, instead, on consumption-based low-carbon performance, this study calculated levels of urban carbon consumption intensity (CCI) based on city-level carbon footprint accounting, investigated the patterns and drivers of changes in CCI of 26 Yangtze River Delta (YRD) cities from 2012 to 2015, and conducted a comparative analysis of CEI and CCI data from both static and dynamic viewpoints. It was found that the CCI of YRD cities decreased from 1.254 to 1.153 over the period. Cities at higher economic levels were found to have lower CCI values. Decomposition results show that shifts in production structure, intensity of emissions and changing consumption patterns contributed to the decline in CCI of the YRD area. Richer cities were found to show greater declines in CCI due to decarbonizing structures in production and consumption. The comparative results show that although the CEI and CCI of cities were generally correlated in both static level and dynamic change, the net carbon transfer impacted the correlation sensitivity between various cities. Finally, our findings provide practical guidance on achieving coordinated emission reductions at an inter-city level from both production and consumption perspectives.

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Effects of COVID-19 restriction measures in Indonesia: A comparative spatial and policy analysis of selected urban agglomerations

Cited by 9 publications

References 19 publications

Profiling Temporal Pattern of Particulate Matter (PM10) and Meteorological Parameters in Jakarta Province during 2020-2021

Profiling Temporal Pattern of Particulate Matter (PM10) and Meteorological Parameters in Jakarta Province during 2020-2021

Exploring People’s Perception of COVID-19 Risk: A Case Study of Greater Jakarta, Indonesia

Urban Low-Carbon Consumption Performance Assessment: A Case Study of Yangtze River Delta Cities, China

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