• Energy Research
• 13. Climate action close
• 12. Responsible consumption close
• KG close
• University of Central Asia close

Knowledge of farmers’ perceptions of and adaptations to climate change is important to inform policies addressing the risk of climate change to farmers. This case study explored those issues in the Melamchi Valley of Nepal through a survey of 365 households and focus group discussions in 6 communities using a Community-Based Risk Screening Tool–Adaptation and Livelihoods (CRiSTAL). Analysis of climate trends in the study area for 1979–2009 showed that mean annual temperatures rose by 1.02°C and the frequency of drought increased measurably after 2003. Farmers reported increases in crop pests, hailstorms, landslides, floods, thunderstorms, and erratic precipitation as climate-related hazards affecting agriculture. They responded in a variety of ways including changing farming practices, selling livestock, milk, and eggs, and engaging in daily wage labor and seasonal labor migration. With more efficient support and planning, some of these measures could be adjusted to better meet current and future risks from climate change.

Energy generation is currently evolving into a smart distribution system that incorporates several green energy resources at a distributed level, ensuring that clean energy is generated without releasing harmful gases, that operational procedures are consistent, and that energy management and supervision arrangements are improved. This paper proposes a multi-agent system-based microgrid energy management and proper control in distributed systems. For the complexity of energy management in distributed systems, a multi-agent system-based decentralized control architecture was developed. The proposed technique is based on several smart agents, each agent is based on the microgrid data for energy management and frequency control. The proposed energy management system based on the multi-agent system was tested by simulation under renewable resource fluctuations and seasonal load demand. The simulation results show that the proposed energy management system proved to be more resilient and high-performance controls than conventional centralized energy control systems.

Internet of Things (IoT) has attracted tremendous research attention in the recent past fromindustry and academia. IoT is quite helpful in uplifting living standards by transforming conventional technology into smart systems. Greenhouse production is considered as an ultimate solution for rising global food demands with the growing population. Greenhouse provides a year-round production facility for fresh vegetables with around 50% increased production rate in comparison to open-air cultivation. However, energy consumption and labor cost in greenhouses account for more than 50% of the cost of greenhouse production. In this paper, we have proposed a novel optimization scheme that aims to achieve a trade-off between energy consumption and desired climate setting in greenhouse i.e. temperature, $${\mathrm{CO}}_2$$ level, and humidity. For performance evaluation of the proposed system, we have developed an ad-hoc emulator of the greenhouse environment. For the proposed model validation and experimental analysis, we have used 15 days of external environmental data collected in Jeju, South Korea. Proposed optimization scheme results are compared with a baseline scheme. Comparative analysis of experimental results shows that our proposed model maintains desired indoor environment for maximizing crop production with 26.56% reduced energy consumption than the baseline scheme. Furthermore proposed model achieve a 27.76% cost reduction when compared to the baseline scheme. Better optimization results of the proposed scheme give us the confidence to further investigate its effectiveness in a real environment for achieving improved energy efficiency.

Abstract This study analyzes the socio-economic impact of electrification with special attention to gender dynamics using evidence from the Badakhshan Province, Afghanistan. We employ survey data and stakeholder interviews to evaluate changes in education, health and economic opportunities. We find that women in grid-supplied communities are on average 27% more literate and complete more years of schooling compared to women in off-grid communities. Households in grid-supplied communities report a lower incidence of diseases, lower child mortality, improved access to ante-natal care, and higher vaccination rates, but they also endure a higher incidence of respiratory diseases due to cooking indoors. We show that switching from off-grid to grid-supplied electricity would allow households to power either 4,200 hours of LED light, 1,340 hours of TV, or 55 hours of hotplate usage at no additional cost. Access to grid-supplied electricity also enables economic opportunities which is reflected in the average higher income by a factor of 5.9 compared to households using solar panels. Moreover, households with grid-electricity save time on the collection of biomass fuel, e.g. for households using firewood, the daily time saving potential amounts to 5 hours. Consequently, men spend on average more time with their children and are more likely to provide help with household tasks that are typically in the female domain in grid-supplied households. Nonetheless, we find that access to grid electricity has mixed effects on education, health and economic outcomes, especially with regards to gender dynamics. Therefore, we challenge the assumption of a linear relationship between electrification and associated impacts, and stress that structural, institutional and power dynamics exert greater influence on education, health and economic outcomes than access to electricity alone.

To help answer questions about availability, accessibility, sustainability and other dimensions of energy security, the vulnerability approach concentrates the attention of policymakers on the assessment of risks associated with natural, technical, political and economic factors. This understanding, combined with a focus on energy services (e.g. lighting, heating, telecommunications, mobility, etc.) helps to prioritize actions to achieve the goal of energy security. This paper conceptualizes energy security as low vulnerability of vital energy systems and sustained provision of modern energy services. Taking Tajikistan as a case, this paper highlights key vulnerabilities including neglect of environmental conditions, insufficient energy production capacity, unreliable and expensive energy imports, dwindling power infrastructure causing technical and economic losses, inadequate transparency in the power sector, lack of regional cooperation in energy and water resources sharing, and inadequate financial resources to address these challenges. Three major proposals presented by the World Bank, the United Nations Development Program, and the Government of Tajikistan to achieve energy security in Tajikistan are evaluated. Specifically, they lack a focus on energy services and therefore overlook people's socio-cultural context and appropriate energy needs. This paper highlights energy services as critical to people’s wellbeing and socio-economic development.

Based on a case study in central Kyrgyzstan, this paper examines the links between the environment, rural livelihoods, and labor migration. The low diversity of income-generating activities in rural areas increases vulnerability to climate change impacts, other environmental stresses, and market failures. As a result, additional livelihood strategies such as labor migration and engagement in trade or other business ventures have become essential coping strategies for rural households. Remittances sent by migrants contribute not only to individual rural households but also to rural community development. Remittances help repay loans that have been taken out by households for different purposes, particularly for running or expanding farming and animal husbandry. When remittances are spent to increase livestock herds, the resulting intensive use of nearby pastures often leads to overgrazing and land degradation.

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

Central Asia is a vital link in the huge Chinese Belt and Road Initiative (BRI) that follows much of the ancient Silk Road routes in this region. Other than the economic expansion and trade benefits associated with this complex infrastructure system, little is known about the many of the exogenous issues and direct environmental and socioeconomic impacts surrounding the BRI in Central Asia. This opinion paper takes a sharper look at some of these externalities and potential effects through a sustainability lens. Major environmental concerns focus on the proliferation of spur roads that will develop off upgraded or new Belt Roads to promote exploitation of natural resources. Steep, high-elevation landscapes in the Pamirs and Tien Shan pose problems for road location and construction, and the history of road building in less formidable terrain in Yunnan, China is unsustainable, leading to epic landslide and gully erosion, which degrade river systems. Furthermore, many socioeconomic issues may arise like debt dependencies of poor countries, spread of communicable diseases into remote communities, depletion of mineral resources, and implicit compliance with pro-China policies. While some of the poorer post-Soviet nations can reap short-term benefits from BRI plans, it is urged that they assess the long-term sustainability of BRI development and play an active role in determining the conditions for implementation.

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7-10.8 %, 3.7-7.5 %, and 1.6-14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

This paper provides a comprehensive yet concise overview of the potential, deployment, outlook, and barriers to renewable energy including small-scale hydropower, solar, wind, geothermal and bioenergy for the five Central Asian countries of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. Small-scale hydropower potential estimates range from 275 MW to 30,000 MW, solar PV from 195,000 MW to 3,760,000 MW, wind from 1500 MW to 354,000 MW, geothermal from 2 MW to 54,000 MW, and bioenergy from 200 MW to 800 MW. However, deployment is minuscule: 5–225 MW in small-scale hydropower across five countries, whereas only Kazakhstan deployed large-scale solar PV (>800 MW) and wind (>300 MW). Though there are strategies and programs to increase deployment, with Kazakhstan in the lead, other countries lag behind in their efforts. A number of barriers hinder the development of renewable energy in the region, including issues with regulatory framework, infrastructure, finances, expertise, awareness and support, and data and information. Because these obstacles are interrelated, a cross-sector and integrated approach is necessary to overcome them. Renewable energy can help Central Asian countries satisfy a growing energy demand and avoid the negative environmental impacts of using fossil fuels.