This research explores the implementation of streamlined licensing frameworks and consolidated procedures for promoting renewable energy generation worldwide. An in-depth analysis of the challenges faced by renewable energy developers and the corresponding solutions was identified through a series of industry interviews. The study aims to shed light on the key barriers encountered during project development and implementation, as well as the strategies employed to overcome these obstacles. By conducting interviews with professionals from the renewable energy sector, the research uncovers a range of common challenges, including complex permitting processes, regulatory uncertainties, grid integration issues, and financial barriers. These challenges often lead to project delays, increased costs, and limited investment opportunities, thereby hindering the growth of renewable energy generation. However, the interviews also reveal various solutions and best practices employed by industry stakeholders to address these challenges effectively. These solutions encompass the implementation of streamlined licensing procedures, such as single licenses and one-stop services, to simplify and expedite the permitting process. Additionally, the development of clear and stable regulatory frameworks, collaboration between public and private entities, and improved grid infrastructure were identified as key strategies to overcome regulatory and grid integration challenges. The research findings highlight the importance of collaborative efforts between policymakers, industry players, and other relevant stakeholders to create an enabling environment for renewable energy development. By incorporating the identified solutions and best practices, policymakers can streamline regulatory processes, foster public-private partnerships, and enhance grid infrastructure, thus catalyzing the growth of renewable energy projects.

Thailand and the EU started negotiating a free trade agreement (FTA) in 2005, but negotiations were subsequently suspended in 2014 after the country’s military coup. The significance of these negotiations are important because of the mutual benefit of achieving higher levels of trade and investment between the world’s largest single market and the second largest ASEAN economy. The Specific Factors (SF) model of production and trade is applied to identify potential winner and loser industries and factors of production in Thailand. The model identifies short-run loses for some labor inputs, return to capital, and output in agriculture and services. In the manufacturing and energy sectors, higher output will benefit some labor inputs and capital owners. Understanding the short-run impact of an FTA could allow policymakers in Thailand to reinforce the institutional infrastructure such as implementing trade adjustment assistance programs (TAA), to help re-train workers who may become unemployed due to free trade.

This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This research aims to delineate the ecocity indicators from the local perspectives in urban communities in the Northeast of Thailand. The research was quantitative survey research. Data was collected from a sample of 400 people who live in Khon Kaen Municipality and Udon Thani Municipality using a questionnaire. Data was analyzed by descriptive statistics and factor analysis. We found that the eco-city indicators from the perspective of people in the urban communities in the Northeast of Thailand were divided into three main criteria: a) economic perspectives; b) social perspectives; and c) environmental perspectives. When considering each aspect, it was found that the economic perspective had a total of 9 issues with an average of 3.06 out of 5.00, the social perspective had a total of 16 issues with an average of 3.76 out of 5.00, and the environmental perspective had a total of 14 issues with an average at 3.00 out of 5.00.

The study aims to investigate the relationship between ESG (Environment, Social, Governance) performance on bank value when moderated by loan loss reserves. Using all 11 Thai listed banks for the period 2017–2021, data were collected from Bloomberg database, the official website of the Stock Exchange of Thailand (SETSMART), and Bank of Thailand, totalling 55 observations. The selected CAMEL indicators served as the control variables. Multiple linear regression and conditional effect analyses were executed using Tobin’s Q as a bank value. This study carefully tested the validity of the dataset, including fixed and random effects. The research outcomes demonstrate the interaction between ESG performance and loan loss reserves has a notably negative effect on the association between ESG performance and bank value. Subsequent analysis reveals that the negative influence of ESG performance on bank value is more pronounced with higher levels of loan loss reserves. These findings have important implications for bankers, investors, and policymakers, offering insights into the dynamics of ESG and loan loss reserves considerations.

This study investigates the influence of Environmental, Social, and Governance Disclosures (ESGD) on the profitability of firms, using a sample of 385 publicly listed companies on the Thai Stock Exchange. Data from 2018 to 2022 is sourced from the Bloomberg database, focusing on ESGD scores as indicators of companies’ ESG commitments. The study utilizes a structural equation model to examine the relationships between independent variables; ESGD, Earnings Per Share (EPS), Debt to Assets ratio (DA), Return on Investment Capital (ROIC), Total Assets (TA), and dependent variables Tobin’s Q (TBQ) and Return on Assets (ROA). The analysis reveals a positive relationship between ESGD and TBQ, but not with ROA. Further exploration is conducted to determine if different ESGD levels (high, medium, low) yield consistent effects on TBQ. The findings indicate discrepancies: high and medium ESGD levels are associated with a negative impact on TBQ when EPS increased, whereas low ESGD levels correlate with an increase in TBQ with rising EPS. This nuanced approach challenges the conventional uniform treatment of ESGD in previous research and provides a deeper understanding of how varying commitments to ESG practices affect a firm’s market valuation and profitability. These insights are crucial for firm management, highlighting the importance of ESGD in relation to other financial variables and their effects on market value. This study offers a new perspective on ESGD’s impact, emphasizing the need for differentiated strategies based on ESG commitment levels.