Tropical peat swamp is an essential ecosystem experiencing increased degradation over the past few decades. Therefore, this study used the social-ecological system (SES) perspective to explain the complex relationship between humans and nature in the Sumatran Peatlands Biosphere Reserve. The peat swamp forest has experienced a significant decline, followed by a significant increase in oil palm and forest plantations in areas designated for peat protection. Human systems have evolved to become complex and hierarchical, constituting individuals, groups, organizations, and institutions. Studies on SES conducted in the tropical peatlands of Asia have yet to address the co-evolutionary processes occurring in this region, which could illustrate the dynamic relationship between humans and nature. This study highlights the co-evolutionary processes occurring in the tropical peatland biosphere reserve and provides insights into their sustainability trajectory. Moreover, the coevolution process shows that biosphere reserve is shifting toward an unsustainable path. This is indicated by ongoing degradation in three zones and a lack of a comprehensive framework for landscape-scale water management. Implementing landscape-scale water management is essential to sustain the capacity of peatlands social-ecological systems facing disturbances, and it is important to maintain biodiversity. In addition, exploring alternative development pathways can help alter these trajectories toward sustainability.

The electoral campaign that led Trump to win the presidential election focused on attacking the elites and using nationalist rhetoric, highlighting issues such as illegal immigration and economic globalization. Once in power, his trade policies, based on perceptions of unfair competition with countries like China, resulted in the imposition of high tariffs on key products. These measures were justified as necessary to protect domestic industries and jobs, although they triggered trade wars at the international level. This article examines the economic consequences of the protectionist policies implemented by the United States under the Trump administration. The protection of less competitive sectors aims to reduce imports, negatively affecting production and income in exporting countries, and limiting U.S. exports to these markets. Although some countries have experienced an increase in real income due to trade diversion, overall, income fluctuations have been negative.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

The objective of this study is to explore the relationship between changing weather conditions and tourism demand in Thailand across five selected provinces: Chonburi (Pattaya), Surat Thani, Phuket, Chiang Mai, and Bangkok. The annual data used in this study from 2012 to 2022. The estimation method is threshold regression (TR). The results indicate that weather conditions proxied by the Temperature Humidity Index (THI) significantly affect tourism demand in these five provinces. Specifically, changes in weather conditions, such as an increase in temperature, generally result in a decrease in tourism demand. However, the impact of weather conditions varies according to each province’s unique characteristics or highlights. For example, tourism demand in Bangkok is not significantly affected by weather conditions. In contrast, provinces that rely heavily on maritime tourism, such as Chonburi (Pattaya), Phuket, and Surat Thani, are notably affected by weather conditions. When the THI in each province rises beyond a certain threshold, the demand for tourism in these provinces by foreign tourists decreases significantly. Furthermore, economic factors, particularly tourists’ income, significantly impact tourism demand. An increase in the income of foreign tourists is associated with a decrease in tourism in Pattaya. This trend possibly occurs because higher-income tourists tend to upgrade their travel destinations from Pattaya to more upscale locations such as Phuket or Surat Thani. For Thai tourists, an increase in income leads to a decrease in domestic tourism, as higher incomes enable more frequent international travel, thereby reducing tourism in the five provinces. Additionally, the study found that the availability and convenience of accommodation and food services are critical factors influencing tourism demand in all the provinces studied.

Providing and using energy efficiently is hampered by concerns about the environment and the unpredictability of fossil fuel prices and quantities. To address these issues, energy planning is a crucial tool. The aim of the study was to prioritize renewable energy options for use in Mae Sariang’s microgrid using an analytical hierarchy process (AHP) to produce electricity. A prioritization exercise involved the use of questionnaire surveys to involve five expert groups with varying backgrounds in Thailand’s renewable energy sector. We looked at five primary criteria. The following four combinations were suggested: (1) Grid + Battery Energy Storage System (BESS); (2) Grid + BESS + Solar Photovoltaic (PV); (3) Grid + Diesel Generator (DG) + PV; and (4) Grid + DG + Hydro + PV. To meet demand for electricity, each option has the capacity to produce at least 6 MW of power. The findings indicated that production (24.7%) is the most significant criterion, closely followed by economics (24.2%), technology (18.5%), social and environmental (18.1%), and structure (14.5%). Option II is strongly advised in terms of economic and structural criteria, while option I has a considerable advantage in terms of production criteria and the impact on society and the environment. The preferences of options I, IV, and III were ranked, with option II being the most preferred choice out of the four.

The impact of crude oil price fluctuations on the real effective exchange rate (REER) has been widely debated, but specific evidence, particularly for developing countries in Southeast Asia, is scarce and inconclusive. This issue, especially concerning both short- and long-term relationships, remains inadequately addressed, affecting these countries for risk management related to oil price fluctuations. This study aims to fill this gap by examining these relationships in Thailand context to provide more evidence on how the REER in Southeast Asia responds to changes in crude oil prices. Monthly data of crude oil prices in Dubai market and the Thai baht REER from 2000 to 2019 were employed. Johansen co-integration test and Vector Error Correction Model (VECM) were used for analyzing long-term and short-term relationships, respectively. The results indicate a significant negative long-term relationship between crude oil prices and the REER, with a 0.31% reduction in the REER for every 1% increase in the real price of oil. However, in the short term, VECM analysis reveals significant movements in the REER in response to external shocks. On average from 2000–2019, the significant fluctuations in the REER are quickly alleviated and adjusted to its long-run equilibrium, typically by 2% in the following month following external shocks such as crude oil price fluctuations. Given these findings, which highlight the long-term relationship between the REER and crude oil prices and its short-term adjustment, it is suggested that when there is a shock from the crude oil prices, the government can strengthen short-term oil price controls or monetary subsidies to mitigate the extensive repercussions of energy market fluctuations, as such interventions would have a lesser impact on the long-term equilibrium of the REER.