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 article advocates for a fundamental shift in England’s legal approach to professional negligence, particularly within the domains of accounting and audit. English law should move away from its intricate and unclear case law surrounding professional negligence towards a clearly defined test for professional misconduct. Drawing upon a comparative analysis with the legal framework in the United States, where auditors are not shielded from liability under the law, the article highlights the need for a more consistent and accountable legal landscape in England. One of the main aspects that necessitates change is the proximity test, as set out in the Caparo case, which currently prevents auditors from being held liable for negligence to investors (as third parties)—despite investors relying on auditors for their professional skill to audit accounts. As investors rely on audited accounts when making financial decisions, a well-defined test for professional negligence should align English law with international standards and empower victims to seek compensation from the auditors themselves and/or the auditors’ professional indemnity insurance. Such a change would enhance trust and transparency in the financial domain.

Green cosmetics made from organic ingredients are becoming increasingly popular due to their environmentally friendly nature. However, research on consumer behavior towards green cosmetics is rare, especially in developing countries like Pakistan. Previous studies have primarily focused on female consumers, and little is known about the behavior of male consumers. Therefore, this research aims to investigate the behavior of both male and female consumers towards green cosmetic products and analyze the factors that affect their purchase behavior. This study employs a quantitative approach with deductive reasoning and collects data through a questionnaire from major cities in Pakistan. The study finds that eco-awareness, social influence, price-quality instructions, health consciousness, and the need for uniqueness significantly influence consumer purchase behavior when buying green cosmetics. Interestingly, price sensitivity does not significantly affect consumer purchase behavior as consumers are willing to pay for high-quality green cosmetics. Based on the findings, the study recommends promoting eco-awareness and health consciousness among consumers through educational campaigns and workshops launched by the government and the private sector. Future research can explore factors such as age, gender, and specific generations like millennials and Generation Z, as well as packaging, branding, and product design to promote environmentally friendly and health-conscious products. Additionally, comparative studies between countries can identify universal and region-specific factors, and examining the overall impact of green cosmetic products on the environment can highlight areas for improvement in sustainability.

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 Cisadane Watershed is in a critical state, which has expanded residential areas upstream of Cisadane. Changes in land use and cover can impact a region's hydrological characteristics. The Soil and Water Assessment Tool (SWAT) is a hydrological model that can simulate the hydrological characteristics of the watershed affected by land use. This study aims to evaluate the impact of land use change on the hydrological characteristics of the Cisadane watershed using SWAT under different land use scenarios. The models were calibrated and validated, and the results showed satisfactory agreement between observed and simulated streamflow. The main river channel is based on the results of the watershed delineation process, with the watershed boundary consisting of 85 sub-watersheds. The hydrological characteristics showed that the maximum flow rate (Q max) was 12.30 m³/s, and the minimum flow rate (Q min) was 5.50 m³/s. The study area's distribution of future land use scenarios includes business as usual (BAU), protecting paddy fields (PPF), and protecting forest areas (PFA). The BAU scenario had the worst effect on hydrological responses due to the decreasing forests and paddy fields. The PFA scenario yielded the most favourable hydrological response, achieving a notable reduction from the baseline BAU in surface flow, lateral flow, and groundwater by 2%, 7%, and 2%, respectively. This was attributed to enhanced water infiltration, alongside increases in water yield and evapotranspiration of 3% and 15%, respectively. l Therefore, it is vital to maintain green vegetation and conserve land to support sustainable water availability.

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.