Evolution of Green Space under Rapid Urban Expansion in Southeast Asian Cities

Nor, Amal Najihah Muhamad; Aziz, Hasifah Abdul; Nawawi, Siti Aisyah; Jamil, Rohazaini Muhammad; Abas, Muhamad Azahar; Hambali, Kamarul; Yusoff, Abdul Hafidz; Ibrahim, Nehal; Rafaai, Nur Hairunnisa; Corstanje, Ron; Harris, J. Arthur; Grafius, Darren R.; Perotto‐Baldivieso, Humberto L.

doi:10.3390/su132112024

Cited by 26 publications

(6 citation statements)

References 55 publications

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“…The increase occurred mostly in Western and Southern European cities, while the decrease happened in Eastern Europe. Some cities in Southeast Asia exhibited declining green areas (Nawar et al, 2022; Nor et al, 2021). Many authors point out that this trend is caused by growing, dynamic urbanisation and the development of transport infrastructure (Giannico et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Exploring green areas in Polish cities in context of anthropogenic land use changes

Noszczyk

Cegielska

Rogatka

et al. 2022

The Anthropocene Review

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Recent decades saw a global degradation of ecosystems and climate change caused by rapid anthropogenic socio-economic growth. The paper investigates spatio-temporal changes in green areas in Polish cities. The study involved two levels: macro (all 936 towns and cities in Poland) and micro (zooming in on Kraków and Toruń) from 2006 to 2018. The authors analysed 64,312 records of statistical data and 32,317 polygons representing areas of specific land use categories in the Urban Atlas with GIS tools and algorithms. Results for the entire country (a macro level) indicate that the area of forests in cities is in decline, but the total share of green urban areas is increasing slightly. Polish towns and cities also exhibit a positive balance of shrub count and a negative balance of tree count. At a micro level, land use analysis indicates a slight decrease in green urban areas, but an increase in the forest area. Moreover, an analysis of the spatial distributions of changes in green areas in Kraków and Toruń demonstrated specific trends. Sites exhibiting a significant decrease in green urban areas were found mainly along main transport routes. Moreover, incomplete green belts around highly urbanised zones turned out to be a characteristic component. The slight differences in results at a micro and macro level are due to a more significant generalisation at a macro level. Micro-level research focuses on an individual case. Hence, it should be used mainly to juxtapose cities, while the macro-level perspective is adequate for cross-country analyses, for example. The results are relevant to urban policies deployed by local and regional authorities, the European Green Deal and climate neutrality.

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Section: Discussionmentioning

confidence: 99%

Exploring green areas in Polish cities in context of anthropogenic land use changes

Noszczyk

Cegielska

Rogatka

et al. 2022

The Anthropocene Review

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show abstract

“…The research shows that more green-land cover helps to improve human health and has a significant negative correlation with healthcare expenditure. Muhamad et al [ 20 ], taking three Southeast Asian cities, Kuala Lumpur, Malaysia, Jakarta, Indonesia, and Manila, Philippines as examples, find that the evolution of green space under rapid expansion can provide support and experience for local urban planning. Since Brazil has one of the greatest biomass potentials in the world, Lap et al [ 21 ] uses Brazil as a case to assess how land-use change affects bioenergy demand, thereby reducing carbon emissions.…”

Section: Literature Reviewmentioning

confidence: 99%

The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization

Ding

Zhang

et al. 2022

IJERPH

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Urban-land development and utilization is one of the main sources of carbon emissions. Improving the green and low-carbon utilization efficiency of urban land (GLUEUL) under the goal of carbon neutrality is crucial to the low-carbon transition and green development of China’s economy. Combining the concept of green and low-carbon development in urban land use, carbon emissions and industrial-pollution emissions are incorporated into the unexpected outputs of the GLUEUL evaluation system. The super-efficient slacks-based measure (SBM) model, Exploratory Spatial-Temporal Data Analysis (ESTDA) method and Geographically and Temporally Weighted Regression (GTWR) model were used to analyze the spatial-temporal transition and the influencing factors of GLUEUL in 282 cities in China from 2005 to 2020. The result shows that: (1) From 2005 to 2020, the green and low-carbon land-utilization efficiency of Chinese cities shows an increasing temporal-evolution trend, but the gap between cities is gradually widening. (2) From the spatial-temporal dynamic characteristics of Local Indicators of Spatial Association (LISA), regions with the highest GLUEUL have strong dynamics and instability, while cities at the lowest level have a relatively stable spatial structure. On the whole, the local-spatial-transfer direction of GLUEUL of each city is stable, with certain path-dependent characteristics. (3) There are differences in the degree of influence and direction of action of different factors on GLUEUL. The economic development level, industrial-structure upgrading, financial support, wealth level, and green-technology-innovation ability have positive effects on overall GLUEUL, with industrial-structure upgrading promoting GLUEUL the most, while urban population size, foreign-investment scale, and financial-development level play a negative role. This study can provide some empirical and theoretical references for the improvement of GLUEUL.

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“…Distance from green urban areas (X3) ranked amongst the most influential factors of urban expansion. Muhamad Nor et al [83] indicated the importance of urban greenery despite its reduction under the expansion of urban areas.…”

Section: Mlp Neural Network Model and Impacts Of Proximity Factors On...mentioning

confidence: 99%

Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

Pour

Oja

2021

Sustainability

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Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

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Evolution of Green Space under Rapid Urban Expansion in Southeast Asian Cities

Cited by 26 publications

References 55 publications

Exploring green areas in Polish cities in context of anthropogenic land use changes

Exploring green areas in Polish cities in context of anthropogenic land use changes

The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization

Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

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