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There are papers on association about natural resources, economic expansion, and their effect on the quality of the environment in Central Asia. However, no study has investigated the effect of human capital in this nexus concerning the Central Asian states. Thus this study investigates the link between natural resources, economic expansion, human capital, and CO2 in Central Asian states by employing Pooled Mean Group (PMG) and the Dumitrescu and Hurlin (DH) causality tests from 1995 to 2018. The outcomes from the PMG model showed that human capital has an opposite and significant association with CO2 in the short and long run. Therefore, it is stated that local human capital with higher environmental awareness through education and training makes a positive impact on environmental quality. With the growth of the natural resources index and expansion of the economy, Central Asian emissions will rise. With a positive and substantial coefficient of the natural resources, the finding suggests that, even though Central Asian states have plenty of natural resources, they are unable to exploit them efficiently to reap good rent advantages to reduce the emissions. The outcomes of the causality test stated that the index of natural resources, economic expansion, and human capital have one-way causality with emissions. The important policies and recommendations for maintaining the economic and environmental sustainability of this country are provided at the end of this paper.

The geography of Kazakhstan is characterized by a diverse landscape and a small population. Therefore, certain automobile roads pass through unpopulated mountain regions, where physical road diagnostics are rare or almost absent, while landscape factors continue to affect the road. However, modern geo-information approaches and remote sensing could effectively provide the road diagnostics necessary to make timely control decisions regarding a road’s design, construction, and maintenance. To justify this assumption, we researched the deformation of a mountain road near Almaty city. Open access satellite images of and meteorological archival data for the region were processed. The resulting data were compared to validate the road’s deformation triggers. Extreme weather conditions’ impacts could be identified via road destruction (nearly 40 m longitudinal cracks, 15 m short transversal cracks, and two crack networks along a 50 m road section). The remotely sensed parameters (vertical displacement velocity, slope exposure, dissections, topographic wetness index, aspect, solar radiation, SAVI, and snow melting) show the complexity of triggers of extensive road deformations. The article focuses only on open access data from remote sensing images and meteorological archives. All the resulting data are available and open for all interested parties to use.

Many regions, not only in the Europe, introduce plans for the modernization of energy systems so that in a few or several years most of the demand for electricity was being able to cover using renewable energy sources. The aim of this paper is to present the possibility of estimation of appropriate power supply based on the renewable energy sources in the context of the whole energy system in the annual balance, taking into account the technical and the economic optimization strategies. The article presents also the simplified structure of the 100% renewable energy system supported by energy storage systems and the production of synthetic fuels.

The renewable biomass material obtained from rice husk, a low-cost agricultural waste, was used as a precursor to synthesize a highly porous graphene-based carbon as electrode material for supercapacitors. Activated graphene-based carbon (AGC) was obtained by a two-step chemical procedure and exhibited a very high specific surface area (SSA) of 3292 m2 g−1. The surface morphology of the synthesized materials was studied using scanning and transmission electron microscopy (SEM, TEM). Furthermore, the AGC was modified with nickel hydroxide Ni(OH)2 through a simple chemical precipitation method. It was found that the most significant increase in capacitance could be reached with Ni(OH)2 loadings of around 9 wt.%. The measured specific capacitance of the pure AGC supercapacitor electrodes was 236 F g−1, whereas electrodes from the material modified with 9 wt.% Ni(OH)2 showed a specific capacitance of up to 300 F g−1 at a current density of 50 mA g−1. The increase in specific capacitance achieved due to chemical modification was, therefore 27%.

The present research is dedicated to the study of anaerobic co-digestion process of different biomass materials. Anaerobic co-digestion of digested sludge, grass silage, haylage and cattle manure was evaluated in mesophilic tank reactors in the lab-scale experiment. Twelve laboratory scale tank reactors (1.5 L) were used during the incubation period of 45 days. First triplet of reactors was fed with pure digested sludge and the other three with different mixtures having the volumetric ratios of 30/35/25/10, 40/30/20/10 and 50/25/15/10 for digested sludge/corn silage/grass haylage/cattle manure. Methane production was analyzed for all labscale reactors individually. The resulting specific methane production of above-mentioned batches was 336.34, 238.1 and 233.23 LSTP[CH4] kg-1 [TVS], respectively. Other results such as cumulative biogas and methane yield, volumetric biogas and methane yield, volumetric biogas and methane yield per day were also assessed. These results had the highest meaning when complex substrate had no more than 30% of inoculum

Since 2013, a series of air pollution prevention and control (APPC) measures have been promulgated in China for reducing the level of air pollution, which can affect regional short-term electricity power demand by changing the behavior of power users electricity consumption. This paper analyzes the policy system of the APPC measures and its impact on regional short-term electricity demand, and determines the regional short-term load impact factors considering the impact of APPC measures. On this basis, this paper proposes a similar day selection method based on the best and worst method and grey relational analysis (BWM-GRA) in order to construct the training sample set, which considers the difference in the influence degree of characteristic indicators on daily power load. Further, a short-term load forecasting method based on least squares support vector machine (LSSVM) optimized by salp swarm algorithm (SSA) is developed. By forecasting the load of a city affected by air pollution in Northern China, and comparing the results with several selected models, it reveals that the impact of APPC measures on regional short-term load is significant. Moreover, by considering the influence of APPC measures and avoiding the subjectivity of model parameter settings, the proposed load forecasting model can improve the accuracy of, and provide an effective tool for short-term load forecasting. Finally, some limitations of this paper are discussed.

In the emerging field of perovskite solar cells, rational hole selective layer development is considered a double engine of this progress. To tap into the full potential and accelerate the commercialization path, machine learning (ML) is being tasked for perovskite screening. However, sincere efforts have not led to the design of hole selective layers based on the different organic core groups to yield efficient solar cells. Herein, it is demonstrated how ML can be applied to the advancement of hole transport materials (HTMs). The influence of HTMs with various core groups on the optoelectronic features and photovoltaic performance is evaluated and it is validated using both the random forest model and AutoML framework, General Automated Machine Learning Assistant (GAMA). To this end, the GAMA is utilized to predict the suitability of HTMs and it returns a 0.0542 ± 0.0470 RMSE score for 15 different materials on average. Correlation between experimental and predicted results is established, and GAMA is implemented for HTM suitability prediction. This paves the way for judicious and effective ways of the development of HTMs. In particular, the prediction approach from GAMA is an effective, reliable, and fast methodology and is pioneering in the field of HTM screening.

This article examines the environmental problems of the Western region in the Republic of Kazakhstan. The authors emphasize that for many decades the oil fields of Kazakhstan have developed mainly a raw material management system with extremely high technogenic loads on the environment. It is noted that for Kazakstani economy, oil and gas production and especially their export play a key role in generating income and growth within the gross product. At the same time, the authors point out that the impact of the oil and gas field on the environment in recent years has been characterized by its intensity, diversity and significant scale. The issue of developing new hydrocarbon raw materials deposits is considered, which is accompanied by geological exploration, drilling and construction works, laying of pipelines and roads. The authors come to the conclusion that a strong anthropogenic impact on all components of the environment causes an active change in the chemical and physicochemical properties of the soil, disrupts the hydrological regime of territories, leads to impoverishment and changes in the species composition, structure and productivity of phytocenoses, a reduction in the spatial distribution and number of animal populations. In conclusion, the authors put forward a number of fundamental tasks for solving environmental problems of the industry.