## 1 Introduction
It can be seen that if energy demand continues to increase in the future, Thailand may face challenges related to energy security and become increasingly dependent on energy imports from neighboring countries. Moreover, the current electricity generation technologies used in Thailand significantly contribute to environmental pollution and are unsustainable in the long term [10]. Therefore, this study applies the Energy Kuznets Curve (EKC) theory to examine the relationship between economic growth and energy consumption, to understand how energy consumption in Thailand may evolve as the economy grows. The goal is to support effective energy planning that aligns with the country's energy consumption trends. In addition, the study aims to provide policy recommendations that align with the Thai government's target of increasing the share of renewable energy, such as solar, wind, and biomass, to 15-20% by 2036 [10]. Gross regional product (GRP), GRP per capita, energy consumption, and energy consumption per capita by region in Thailand are presented in Table 1. Figure 7 shows the boundaries of Thailand's 77 provinces. The region with the highest GRP and average energy consumption per capita is Bangkok and vicinity (BKK&VIC), followed by the northeastern region (NE) and the southern region (SO). The remaining regions, such as the northern region (NO), exhibit the lowest levels of economic activity [11]. However, Bangkok and vicinity (BKK&VIC), the eastern region (EA), the central region (CE), and the western region (WE) are combined in this study, as they are classified as high-economic-growth pole regions in central Thailand. In this study, we use provincial-level data from all 77 provinces of Thailand as representative data points and categorize them into the four areas mentioned above to analyze the impact of economic growth on energy consumption based on the EKC theory. This regional approach aims to provide more comprehensive and tailored policy recommendations compared to a whole-region analysis alone, taking into account variations in economic structure, geography, and environmental conditions across different regions. Figure 6: (**a**) Power generation (GWh) by fuel in 2015; (**b**) power generation (GWh) by fuel in 2022. Source: Energy Policy and Planning Office (EPPO [8]). Several studies have investigated the impact of economic growth on energy consumption and air pollution using various methodologies, such as Autoregressive Distributed Lag (ARDL) cointegration analysis, Fully Modified Ordinary Least Squares (FMOLS), and Dynamic Ordinary Least Squares (DOLS), predominantly relying on provincial-level, ground-based data in Thailand [6; 14; 15; 16; 17; 18]. Additionally, [PERSON] et al. [19] proposed the Simultaneous Smooth Transition Kink Equations (SKE) model, in which each regression parameter can exist in two different regimes, lower and upper, to examine the presence of the Kuznets Curve in Thailand's economy over the past two decades. A research gap is identified through the review of previous studies, presenting an opportunity to extend the existing literature. This study aims to fill that gap by utilizing provincial-level data in Thailand, combined with environmental data from satellite observations, to offer a more localized and specific analysis of the impacts occurring within the country. Moreover, this study employs the Spatial Dynamic Panel IV Model (SDPD IV), which captures dynamic effects over time and accounts for spatial spillover effects across regions in Thailand. Therefore, this study examines how energy consumption and economic growth in Thailand and its regions are likely to evolve in the short and long term under continued economic growth. It also seeks to assess whether energy consumption will become more sustainable. Additionally, the study investigates the characteristics of energy consumption concentration in Thailand and analyzes the relationship patterns between energy consum