This research analyzes the relationship between political stability, renewable energy utilization, economic progress, and tourism in Indonesia from 1990 to 2020. We employ advanced econometric techniques, including the Fourier Bootstrap Autoregressive Distributed Lag (ARDL) approach and Fourier Toda-Yamamoto causality testing, to ensure the robustness of our results while accounting for smooth structural changes in the data. The analysis uncovers a long-term equilibrium relationship between tourism and its fundamental determinants. Our research reveals significant positive impacts of political stability and renewable energy consumption on tourism in Indonesia. A stable political environment creates a favorable climate for tourism development, instilling confidence in both domestic and international tourists. Promoting renewable energy usage aligns with sustainable tourism practices, attracting environmentally conscious travelers. Furthermore, our findings demonstrate a bi-directional causal relationship between these variables over time. Changes in political stability, renewable energy consumption, and economic growth profoundly influence the tourism sector, while the growth of tourism itself can also stimulate economic development and foster political stability. Our findings underscore the need for environmentally sustainable and politically stable tourism policies. Indonesia’s tourism sector can grow sustainably with renewable energy and stability. Policymakers can develop strategies with tourism, political stability, renewable energy, and economic prosperity in mind.

The idea of emotions that is concealed in human language gives rise to metaphor. It is challenging to compute and develop a framework for emotions in people because of its detachment and diversity. Nonetheless, machine translation heavily relies on the modeling and computation of emotions. When emotion metaphors are calculated into machine translation, the language is significantly more colorful and satisfies translating criteria such as truthfulness, creativity and beauty. Emotional metaphor computation often uses artificial intelligence (AI) and the detection of patterns and it needs massive, superior samples in the emotion metaphor collection. To facilitate data-driven emotion metaphor processing through machine translation, the study constructs a bi-lingual database in both Chinese and English that contains extensive emotion metaphors. The fundamental steps involved in generating the emotion metaphor collection are demonstrated, comprising the basis of theory, design concepts, acquiring data, annotating information and index management. This study examines how well the emotion metaphor corpus functions in machine translation by proposing and testing a novel earthworm swarm-tunsed recurrent network (ES-RN) architecture in a Python tool. Additionally, the comparison study is carried out using machine translation datasets that already exist. The findings of this study demonstrated that emotion metaphors might be expressed in machine translation using the emotion metaphor database developed in this research.

Tourism is one of the important sectors that support Indonesia’s economic growth. The tourism sector itself plays a strategic role in increasing the country’s foreign exchange. However, during the Covid-19 pandemic, tourism became one of the most affected sectors. Electronic visa on arrival (e-VOA) is a form of digital transformation in immigration services offered by the Indonesian government to increase the number of tourist arrivals during the recovery of the national economy, especially in the tourism sector, after the Covid-19 pandemic. This study provides an in-depth insight into how e-VOA functions as a digital transformation tool in the immigration and tourism sectors. By exploring the impact of e-VOA implementation, this article contributes to the understanding of how digitalisation can improve the efficiency of administrative processes and support the recovery of the tourism sector in post-pandemic Bali. This study uses qualitative approaches and methods with descriptive analysis techniques to create an objective description of a situation through numbers or statistical data. The results of this study show that e-VOA services effectively contribute to an increase in the number of foreign tourists in Bali. It also has a positive impact on the economic growth of tourism-related businesses in Bali.

This study examines the impact of innovation governance and policies on government funding for emerging science and technology sectors in Saudi Arabia, addressing key bureaucratic, regulatory, and cultural barriers. Using a mixed-methods approach, the research integrates qualitative insights from stakeholder interviews with quantitative survey data to provide a comprehensive under-standing of the current innovation landscape. Findings indicate a high level of policy awareness among stakeholders but reveal significant challenges in practical implementation due to bureaucratic inefficiencies and stringent regulations. Cultural barriers, such as a risk-averse mindset and traditional business practices, further impede innovation. Successful initiatives like the National Transformation Program (NTP) demonstrate the potential for well-coordinated efforts, highlighting the importance of regulatory reform and cultural shifts towards entrepreneurship. Strategic recommendations include streamlining bureaucratic processes, enhancing policy coordination, and fostering a culture of innovation through education and stakeholder engagement. This study contributes to the existing literature by offering actionable insights to enhance innovation governance, supporting Saudi Arabia’s Vision 2030 goals.

Climate change is the most important environmental problem of the 21st century. Severe climate changes are caused by changes in the average temperature and rainfall can affect economic sectors. On the other hand, the impact of climate change on countries varies depending on their level of development. Therefore, the aim of this paper is to investigate the relationship between climate changes and economic sectors in developed and developing countries for the period 1990–2021. For this purpose, a novel approach based on wavelet analysis and SUR model has been used. In this case, first all variables are decomposed into different frequencies (short, medium and long terms) using wavelet decomposition and then a SUR model is applied for the examination of climate change effects on agriculture, industry and services sectors in developed and developing countries. The findings indicate that temperature and rainfall have a significant negative and positive relationship with the agriculture, industry and services sectors in developed and developing countries, respectively. But severity of the negative effects is greater in the agricultural and industrial sectors in all frequencies (short, medium and long terms) compared to service sector. Furthermore, the severity of the positive effects is greater in the agricultural sector in all frequencies of developing countries compared to the industrial and services sectors. Finally, developing countries are more vulnerable to climate change in all sectors compared to developed countries.