Every plant is significantly important in tackling climate change, including Makila (Litsea angulata BI) an endemic wood species found in the forest of Moluccas Provinces. Therefore, this research aimed to examine the role of the Makila plant in tackling climate change by measuring biomass content using constructing an allometric equation. The method used was a destructive sampling, where 40 units of Makila plant at the sampling level were felled, and sorted according to root, stem, branch, rating, and leaf segments. Each segment was weighed both at wet and after drying, followed by a classical assumption test in data processing, and the formulation of an allometric equation. The regression model was examined for normality and suitability in predicting independent variables, ensuring there were no issues with multicollinearity, heteroscedasticity, and autocorrelation. The results yielded a multiple linear regression, namely: Y = −1131.146 + 684.799X₁ + 4.276X₂, where Y is biomass, X₁ is the diameter, and X₂ is the tree height. Based on the results of the t-test: variable X₁ partially affected Y while variable X₂ partially had no effect on Y. The F-test indicated that variables X₁ and X₂ jointly affected Y with R Square: 0.919 or 91.9% and the rest was influenced by other unexplored factors. To simplify biomass prediction and field measurement, a regression equation that used only 1 independent variable, namely tree diameter, was used for the experiment. Allometric equation only used 1 variable, Y = −1,084,626 + 675,090X₁, where X₁ = tree diameter, Y = Total biomass with R = 0.957, and R² = 0.915. Considering the potential for time, cost, and energy savings, as well as ease of measurement in the field, the biomass of young Makila trees was simply predicted by measuring the tree diameter and avoiding the height. This method used the strong relationship between biomass, plant diameter, and height to facilitate the estimation of biomass content accurately by entering the results of field measurements.

The Trans Sumatra Toll Road (TSTR) is a mega toll road project with an assignment State-Owned Enterprise (SOE) scheme in Indonesia. In its development, TSTR has several limitations, including funding, low investment feasibility and the un-optimum implementation of land value capture (LVC). This has the impact of delaying the completion of project development, decreasing the performance of toll road developer companies and even causing bankruptcy. LVC is an alternative funding scheme proven successful in other countries such as Hongkong, England and Vietnam. Several transportation projects based on transit-oriented development have successfully achieved profits using the LVC method. With a low project feasibility, the implementation of the Road Plus Property Developer (RPPD) business model is expected to be a solution to improve investment performance in the TSTR project. RPPD is defined as an assignment scheme toll road business model based on LVC implementation. This research aims to develop policies for implementing the RPPD business model on toll road SOE-assigned schemes. The data was collected by in-depth interviews with experts in two stages. The data analysis method used is Soft System Methodology (SSM). This research produces two recommended actions: ratification of the Presidential Regulation regarding the implementation of LVC and institutional transformation of regionally owned business entities in the property sector. It is hoped that implementing the RPPD policy will become a priority in completing the TSTR project.

India’s economic growth is of significant interest due to its expanding Gross Domestic Product (GDP) and global market influence. This study investigates the interplay between production, trade, carbon dioxide (CO₂) emissions, and economic growth in India using Granger causality analysis. Also, the data from 1994 to 2023 were analyzed to explore the relationships among these variables. The results reveal strong positive correlations among production, trade, CO₂ emissions, and GDP, with production showing significant associations with export, import, and GDP. Co-integration tests confirm the presence of a long-term relationship among the variables, suggesting their interconnectedness in shaping India’s economic landscape. Regression analysis indicates that production, export, import, United States (US)-India trade, manufacturing cost of energy, and CO₂ emissions significantly impact GDP. Moreover, the Vector Error Correction Model (VECM) estimation reveals both short-term and long-term dynamics, highlighting the importance of understanding equilibrium and deviations in economic variables. Overall, this study contributes to a better understanding of the complex interactions driving India’s economic growth and sustainability.

This article emphasizes the importance of Small and Medium-Sized Enterprises (SMEs) and large companies in driving economic growth. SMEs are labour-intensive and agile, creating more jobs, while large companies are capital-intensive and rely on technology, having more resources for research and development. In the Gulf Cooperation Council (GCC) region, SMEs contribute significantly to Gross Domestic Product (GDP) and job opportunities, while large companies dominate specific sectors. The research employs a multidisciplinary approach using an extensive literature review to summarize the current literature, highlight the economic impact of SMEs and large companies in GCC, and highlight the importance of large companies in developing local citizens. Policy-makers must consider these differences to integrate these dynamic changes for effective support policies. This study examines the economic impact of SMEs and large companies in the GCC region, providing recommendations to support large businesses. It addresses challenges and opportunities related to employment, household earnings, economic output, and value addition. Promoting the economic impact of SMEs and large companies can lead to sustainable economic growth and development in the GCC region. Also, this article pointed out the importance of large companies and their economic impact in the GCC region; policy recommendations will help the governing bodies in decision-making towards promoting sustainable economic growth.

Improving the competitiveness of tourism destinations is crucial for driving local economies and achieving income growth. In light of this evidence, numerous government departments strive to assess specific factors that impact the competitiveness of tourism destinations, enabling them to issue appropriate new tourism policies that promote more effective forms of tourism business. Therefore, the primary objective of this paper is to investigate how various elements such as tourism resources, tourism support, tourism management, location conditions, and tourism demand influence regional competitiveness in the Northern Bay region of Guangxi Province in China. To accomplish this goal, an online survey was conducted to collect data from 420 visitors who had experienced North Gulf Tourism; yielding an impressive response rate of 95 percent. The findings reveal that all aforementioned factors—namely: Tourism resources, tourism support, tourism management, location conditions and tourist demand—significantly impact destination competitiveness. Notably though, it was found that among these factors influencing destination competitiveness; it is primarily determined by effective local-level management (β = 0.345). Following closely behind are tourist demand (β = 0.133) as the second most influential factor affecting destination competitiveness; followed by location conditions (β = 0.116) ranking third; then comes tourist support (β = 0.03) as fourth in line impacting destination competitiveness; finally with least impact being exerted by available tourist resources (β = 0.016). Consequently, highlighting that regional competitiveness within Guangxi’s Northern Bay area predominantly hinges on efficient local-level management practices thus strongly recommending relevant authorities formulate novel work policies aimed at enhancing levels of local-level competitive advantage within the realm of regional touristic offerings.