In the heart of Southeast Asia, the Bangkok Metropolitan Region (BMR) has been a hotspot for urban growth, but this rapid expansion comes at a cost. A recent study published by Nattapong Puttanapong, a researcher from the Faculty of Economics at Thammasat University, Bangkok, sheds light on the intricate relationship between urbanization and the surface urban heat island (SUHI) effect. The findings, published in the journal Geography and Sustainability, which translates to ‘ภูมิศาสตร์และการยั่งยืน’ in Thai, offer valuable insights for urban planners, policymakers, and the energy sector.
The study, spanning 36 years, utilized advanced machine learning techniques to analyze changes in land use and land cover (LULC) and their impact on the SUHI effect. The research revealed a stark correlation between urban growth and increased land surface temperatures (LST). “We found that as the city expanded, the temperature differential between urban and rural areas widened significantly,” Puttanapong explained. This phenomenon, known as the SUHI effect, can have profound implications for energy consumption and urban livability.
The SUHI effect, initially measured at 3°C in the late 1980s, peaked at 4.8°C between 2012 and 2019. However, recent urban greening initiatives have slightly mitigated this effect, bringing the temperature differential down to 4.1°C. The study underscores the critical role of green and blue infrastructure—parks and water bodies—in mitigating urban heat. These natural elements not only provide respite from the heat but also contribute to energy savings by reducing the demand for air conditioning.
For the energy sector, the implications are clear. Higher urban temperatures lead to increased energy consumption for cooling, putting a strain on power grids and contributing to higher greenhouse gas emissions. “Understanding the SUHI effect is crucial for energy planning and management,” Puttanapong noted. “By integrating green and blue infrastructure into urban design, we can create more sustainable and energy-efficient cities.”
The research also highlights the influence of economic factors on land use patterns. Events like the 1997 Asian Financial Crisis and land tax laws introduced in 2019 have shaped the urban landscape, often at the expense of green spaces and water bodies. This underscores the need for integrated urban management and sustainable land use policies to enhance climate resilience.
As cities around the world continue to grow, the lessons from Bangkok are pertinent. The study suggests that machine learning models can be powerful tools for classifying LULC changes and providing insights to support sustainable urban planning. By leveraging these technologies, cities can better manage their growth, mitigate the SUHI effect, and create more livable, energy-efficient urban environments.
The findings from Puttanapong’s research are a call to action for urban planners, policymakers, and the energy sector. As we look to the future, the integration of green and blue infrastructure, coupled with advanced machine learning techniques, will be key to building resilient, sustainable cities. The journey towards sustainable urbanization is complex, but with the right tools and policies, it is achievable. The future of our cities depends on it.