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In summer 2020 the energy company St1 carried out its second stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing an automatic phase picking on the ISUH 2020 event and waveform data catalogue (doi:10.23729/cdfd937c-37d5-46b0-9c16-f6e0c10bc81f) using an algorithm based on machine learning (doi: 10.1785/0220210068). The dataset provided by IMS was later transferred to formats used by ISUH. The resulting dataset comprises of phase pickings and relevant waveforms of 85 induced earthquakes that occurred between 8 March 2020 to 8 December, 2020, with local magnitudes between -1.1 and 1.4. Note that the event location and other metadata of the resulting dataset are still based on the ISUH 2020 catalogue in order to preserve the consistency within the dataset as some events did not have enough automatic phase picks for reliable relocation. Waveform, location and timing data have been produced at ISUH using seismic stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) including the Helsinki local broadband network, the temporary HEL broadband network in Helsinki and Espoo, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201925.1; SmartSolos and Refteks, doi: 10.1785/0220210195). The deployment is described in Rintamäki et al., 2021, A Seismic Network to Monitor the 2020 EGS Stimulation in the Espoo/Helsinki Area, Southern Finland, doi:10.1785/0220210195. Event data, event metadata, and station metadata are provided in distinct directories, and for event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards.

In summer 2018 the energy company St1 carried out its first stimulation of deep geothermal wells in Otaniemi, Espoo, in the Helsinki metropolitan area, southern Finland. The Institute of Seismology of University of Helsinki (ISUH) monitored the induced seismicity during the stimulation, and also months before and after it. In the second half of 2022 ISUH consulted the Australian company Institute of Mine Seismology (IMS, https://www.imseismology.org) for providing seismic location and time data based on ISUH waveform data, and the IMS algorithm that is based on machine learning (doi: 10.1785/0220210068). The IMS location and time data were later transferred to formats used by ISUH. The resulting dataset consists of seismic waveforms of 483 induced earthquakes that occurred between 5 June 2018 to 26 July 2018, with local magnitudes between -0.7 and 2.0. Seismic data derive from stations of the Finnish National Seismic Network (doi: 10.14470/UR044600) the temporary HEL broadband network, the temporary borehole network of St1 (doi: 10.1785/0220190253), and a pool of lightweight mobile seismic instruments operated by ISUH (GIPP data cubes, doi: 10.5880/GIPP.201802.1, ). The deployment is described in Hillers et al., 2020, The 2018 Geothermal Reservoir Stimulation in Espoo/Helsinki, Southern Finland: Seismic Network Anatomy and Data Features, doi:10.1785/0220190253. Event data, event metadata, and station metadata are provided in distinct directories. For event data, each event is assigned a subdirectory. Data formats follow generally accepted seismological standards. Due to an unfortunate miscommunication, the IMS amplitude readings in this data product need to be halved in order to be compatible with the ISUH 2018 dataset. This also has the effect of reducing the local magnitude values reported herein by ~0.25 units.

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.

Long-term spatiotemporal Land Use and Land Cover (LULC) analysis is an objective tool for assessing patterns of sustainable development (SD). The basic purpose of this research is to define the Driving Mechanisms (DM) and assess the trend of SD in the Burabay district (Kazakhstan), which includes a city, an agro-industrial complex, and a national natural park, based on the integrated use of spatiotemporal data (STD), economic, environmental, and social (EES) indicators. The research was performed on the GEE platform using Landsat and Random Forest. The DM were studied by Multiple Linear Regression and Principal Component Analysis. SD trend was assessed through sequential transformations, aggregations, and integrations of 36 original STD and EES indicators. The overall classification accuracy was 0.85–0.97. Over the past 23 years, pasture area has changed the most (−16.69%), followed by arable land (+14.72%), forest area increased slightly (+1.81%), and built-up land—only +0.16%. The DM of development of the AOI are mainly economic components. There has been a noticeable drop in the development growth of the study area in 2021, which is apparently a consequence of the COVID-19. The upshots of the research can serve as a foundation for evaluating SD and LULC policy.

The territory of West Kazakhstan is an intensively developing region. The main oil and gas fields are concentrated there. In addition, this region is well-known as a region of nomad cattle breeding. Both of industry and agriculture demand a lot of employees, working in the open air in wintertime. Severe winter conditions, primary very low temperatures, and strong winds characterize the region. In this work, we calculated and analyzed the spatial and temporal distributions of effective temperatures in the region and their dynamics due to the global warming in the last decades. To calculate the equivalent temperature (WCET) was used the method of OFCM 2003. Nowadays, it is known as a common method for similar studies. It was shown that in the observed region, WCET is significantly lower than the ambient temperature. Repeatability of WCET, corresponding to «increasing risk», «high risk» is high in the main part of the region. Global warming in the region results in returning extremely high temperatures of the air, decreasing repeatability of the average gradation of WCET approximately on 4%, but there is no any visible changing repeatability of extreme WCET. Obtained results can be used for planning any construction work in the open air and agriculture branches.

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.

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.