The nexus between foreign direct investment, natural resource endowment, and their impact on sustained economic growth, is contentious. This study investigates the resource curse hypothesis and the effects of FDI on economic growth in Kazakhstan. The study covers the period from 1990 to 2022 and employs the Autoregressive Distributed Lag (ARDL) model and Toda-Yamamoto causality methods. The Bounds cointegration results reveal the existence of long-term equilibria between per capita GDP and the predictors. The findings reveal a significant impact of oil rents on economic growth, contradicting the resource curse hypothesis and suggesting a resource boon instead. In stark contrast, the impact of FDI on Kazakhstan’s economic growth is found to be insignificant, despite the presence of a causal nexus. Furthermore, economic freedom and export diversification have a positive significant impact on economic growth, while inflation exhibits a negative but significant impact. Although governance has a direct impact on GDP per capita, it is deemed insignificant, as the negative average governance index implies poor governance. Expectedly, the result establishes a causal effect between export diversification, economic freedom, governance, oil rents, and economic growth. This underscores the fundamental role played by the interplay of diversification, economic freedom, governance, and oil rents in fostering sustainable economic growth. In addition, economic freedom stimulates gross fixed capital formation, indicating that it enhances domestic investment. Notably, the findings refute the crowding-out effect of FDI on domestic investment in Kazakhstan. Consequently, to escape the resource curse and the Dutch disease syndrome, the study advocates for enhancing good governance capabilities in Kazakhstan. Thus, we recommend that good governance could reconcile the twin goals of economic diversification and deriving benefits from oil resources, ultimately transforming oil wealth into a boon in Kazakhstan.

The application of optimization algorithms is crucial for analyzing oil and gas company portfolio and supporting decision-making. The paper investigates the process of optimizing a portfolio of oil and gas projects under economic uncertainty. The literature review explores the advantages of applying various optimizers to models that consider the mean and semi-standard deviations of stochastic multi-year cash flows and revenues. The methods and results of three different optimization algorithms are discussed: ranking and cutting algorithms, linear (Simplex) and evolutionary (genetic) algorithms. Functions of several key performance indicators were used to test these algorithms. The results confirmed that multi-objective optimization algorithms that examine various key performance indicators are used for efficient optimization in oil and gas companies. This paper proposes a multi-criteria optimization model for investment portfolios of oil and gas projects. The model considers the specific features of these projects and is based on the Markowitz portfolio theory and methodological recommendations for project assessment. An example of its practical application to oil and gas projects is also provided.

This study aims to examine and challenge the impact of local government policy governance on the oil palm plantation sector in Riau Province, Indonesia. It was discovered that 1,628 million hectares of illegal oil palm plantations are located within forest areas. Plantation area and crop harvest areas are declining due to the increase in damaged old plants, low productivity of plantation crops, inadequate facilities and infrastructure conditions, low technology application, plantation business licensing, limited downstream plantation industry and marketing, assistance in changing the attitudes, behavior, and skills of farmers. The methodology used was exploratory qualitative to explore this topic, and the determination of research topics was conducted using Biblioshiny application analysis. Then, the data was analyzed using Nvivo 12 Plus software. The results of this study discovered that the policy governance of the oil palm plantation sector as a leading commodity in Riau Province, Indonesia, is influenced by three dimensions: firstly, the actor dimension; secondly, the structural dimension; and third, the empirical dimension of governance. This research contributes as a knowledge reference to oil palm plantations.

Herein, we report a facile preparation of super-hydrophilic sand by coating the sand particles with cross-linked polyacrylamide (PAM) hydrogels for enhanced water absorption and controlled water release aimed at desert agriculture. To prepare the sample, 4 wt% of aqueous PAM solution is mixed with organic cross-linkers of hydroquinone (HQ) and hexamethylenetetramine (HMT) in a 1:1 weight ratio and aqueous potassium chloride (KCl) solution. A specific amount of the above solution is added to the sand, well mixed, and subsequently cured at 150 °C for 8 h. The prepared super-hydrophilic sands were characterized by Fourier-transform infrared spectroscopy (FT-IR) for chemical composition and X-ray diffraction (XRD) for successful polymer coating onto the sand. The water storage for the samples was studied by absorption kinetics at various temperature conditions, and extended water release was studied by water desorption kinetics. The water swelling ratio for the super-hydrophilic sand has reached a maximum of 900% (9 times its weight) at 80 °C within 1 h. The desorption kinetics of the samples showed that the water can be stored for up to a maximum of 3 days. Therefore, super-hydrophilic sand particles were successfully prepared by coating them with PAM hydrogels, which have great potential to be used in sustainable desert agriculture.

One of the core problems in soil erosion research is the estimation of soil erosion. It is a feasible method and technical approach to estimate soil erosion in Loess Plateau region by using USLE model, GIS and RS technology and using DEM data, meteorological data and land-use type data. With the support of GIS and RS technology, the USLE factors and soil erosion in Loess Plateau region were estimated, and the soil erosion intensity was classified according to the Chinese soil erosion intensity classification standard. The results can provide reference for the development of soil erosion control measures in the Loess Plateau.

Root turnover is a key process of terrestrial ecosystem carbon cycle, which is of great significance to the study of soil carbon pool changes and global climate change. However, because there are many measurement and calculation methods of root turnover, the results obtained by different methods are quite different, and the current research on root turnover of forest ecosystem on the global regional scale is not sufficient, so the change law of root turnover of global forest ecosystem is still unclear. By collecting literature data and unifying the calculation method of turnover rate, this study integrates the spatial pattern of fine root turnover of five forest types in the world, and obtains the factors affecting fine root turnover of forest ecosystem in combination with soil physical and chemical properties and climate data. The results showed that there were significant differences in fine root turnover rate among different forest types, and it gradually decreased with the increase of latitude; the turnover rate of fine roots in forest ecosystem is positively correlated with annual average temperature and annual average precipitation; fine root turnover rate of forest ecosystem is positively correlated with soil organic carbon content, but negatively correlated with soil pH value. This study provides a scientific basis for revealing the law and mechanism of fine root turnover in forest ecosystem.