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The present study is motivated by the need to decouple economic growth from environmental degradation given the new wave of chase for higher economic growth trajectories comes with its environmental cost implications, especially among developing blocs like the Emerging 7 (E7) countries. There is a consistent trade-off between economic growth versus environmental quality. Government apparatus are perpetually on the chase for low-carbon emission policies via the pursuit for green economy. To this end, this present study extends the conventional environmental Kuznets curve (EKC) argument by incorporating the role of institution in emerging industrialized economies (E7) and using second-generation panel analysis methods like mean group (MG), augmented mean group (AMG), common correlated effects mean group (CCEMG), and the Dumitrescu and Hurlin causality test for more robust estimates and inferences. To this end, we explore the long-run and causality relationship between economic growth, quadratic form of economic growth, institutional quality, trade flow, investment in energy sector, and financial development in an EKC environment. Empirical analysis established a long-run equilibrium relationship among the outlined variables over the study period. The long-run regression shows the presence of EKC in the E7. Thus, suggesting the preference for GDP growth over environmental quality at the earlier stage of growth curve. Interestingly, investment in energy, trade flow dynamics across the blocs, and financial development dampens the detrimental effect of environmental pollution as we observed negative relationship with the ecological footprint. On the contrary, quality of institution is weak as institutional quality increase (worsen) the quality of environment in the E7 economies. From a policy perspective, this current study proposed the need for more stringent environmental treaties and regulations and promotion of green economy without compromising economic growth. In the conclusion part of the study, more details and specifics about the policy blueprint are presented.

Abstract Gold nanoparticles (Au-NPs) seeded plasmonic nanofluids (PNFs) have shown promising results in overall performance enhancement of direct absorption solar collector (DASC) due to localized surface plasmon resonance (LSPR) effect. For the work presented here, Au-NPs were synthesized by the wet chemical method and were utilized to prepare plasmonic nanofluid. The surface plasmon resonance peak of Au-NPs was observed at 531 nm using UV–Visible spectrophotometer study. The testing for performance enhancement of gold plasmonic nanofluid (GPNF) laden DASC so far is limited to laboratory scale setups or simulation studies. Considering the dearth of outdoor experimental studies, an attempt has been made in the present study to evaluate the thermal performance of Au-NPs (∼40 nm) based nanofluid (∼0.0002 wt%) in full scale DASC. The experiments have been performed at different flow rates under clear sky outdoor conditions in winter season at Jalandhar, India. The maximum collector outlet temperature was measured to be 55 °C with GPNF which is about 7 °C higher than the maximum outlet temperature obtained with de-ionized water as working fluid. Thermal efficiency with GPNF is about 33% higher than de-ionized water at the optimal flow rate of 0.030 kg/s.

LoRA stands for Long Range communication. Lo-RAwan is the specification used for the battery operated and wireless systems. The main aim of the LoRA is to target the Internet Of Things requirement and also Bidirectional communication. In order to prove the proof of concept, here we are creating a smart prepaid electric meter which will consist of lora module along with it. All the data related to the meters i.e voltage, energy power etc will be send to a gateway. Gateway is the central unit or router which collects all the data from all the meters and it is connected with the local storage device or PC which will create the database for all the data, Now this data is again uploaded to the cloud by this pc to the public network, which can be accessed from any of the place. The most important part of this is that this concept of communication can be extended to other application in which the data transmitted bits is less. As a result of this we can overcome the disadvantage of the 3G/4G GSM modules and low power WAN i.e. LORA can be used for communication.

Indian agriculture has made a significant progress in recent years, but of late it is facing many challenges due to the adverse effect of climate change. Moreover, the increasing population pressurizes the agricultural sector for enhanced food production. To face the challenges of food security and climate change, the country needs to reorient its land use and agriculture with the state-of-the-art technologies and policy initiatives. DST through its research initiatives, has partnered with three institutions viz., Tamil Nadu Agricultural University, Coimbatore; International Crop Research Institute on Semi-Arid Tropics, Hyderabad and Indian Agricultural Research Institute, New Delhi to develop potential techniques and technologies for adaptation in agriculture to increase resilience against climate change in sustaining crop production. The paper briefly presents outcome of these studies.

This article presents a model of development, civil war and climate change. There are multiple interactions. Economic growth reduces the probability of civil war and the vulnerability to climate change. Climate change increases the probability of civil war. The impacts of climate change, civil war and civil war in the neighbouring countries reduce economic growth. The model has two potential poverty traps – one is climate-change-induced and one is civil-war-induced – and the two poverty traps may reinforce one another. The model is calibrated to sub-Saharan Africa and a double Monte Carlo analysis is conducted in order to account for both parameter uncertainty and stochasticity. Although the IPCC Special Report on Emission Scenarios (SRES) is used as the baseline, thus assuming rapid economic growth in Africa and convergence of African living standards to the rest of the world, the impacts of civil war and climate change (ignored in SRES) are sufficiently strong to keep a number of countries in Africa in deep poverty with a high probability.

AbstractThis paper proposes a methodology to assess the wind risk of electrical transmission towers considering the coupling of the tower with the cables and a failure mechanism based on capacity. ...

Abstract DC microgrids have received wide interest in recent times because of the advantages related to improved power transfer capability, high efficiency, compatibility with distributed energy resources (DER) and reduced losses. However, the adoption of DC microgrids has been restricted because of the challenges in designing a reliable protection scheme. The challenge results from the high magnitude of the fault current without zero crossing, coupled with the requirement of high rapidity in fault detection. The scenario is further complicated during the variation in operational dynamics of microgrid either due to weather intermittency or network reconfiguration arising out of DER outage. Non-addressal of the above issues results in reduced grid resilience because of possible relay malfunction during stressed scenarios. In this regard, an ensemble classifier-based protection scheme has been proposed for DC microgrid with adaptability to system reconfiguration and weather intermittency. The adaptiveness has been achieved by online identification of network topology, while immunity to weather variation is attained by stochastic modelling of solar irradiance and wind speed. The use of local information for executing the protection tasks avoids the issues related to data loss and latency in the communication link. The task of operating mode detection, topology identification, fault detection/classification and section identification have been formulated as a set of classification problems and further solved using a random subspace sampling-based ensemble classifier technique. The reliability of the proposed scheme has been extensively validated for a wide range of fault scenarios involving wide variation in network topology, weather condition and fault parameters.

This paper describes the application of IoT Technology for monitoring different parameters of battery of electric vehicle. Electric vehicle totally depends upon the source of energy from the battery. In this project, the idea of monitoring the performance of the vehicle using IoT techniques is proposed, so that monitoring can be done easily and directly. The objective of the project is to promote green power and to improve smartness of electric vehicle by monitoring the battery parameters such as voltage, temperature, current and charge avaibility. Also, these values displayed in cloud, which brings the concept of Internet of Things (IoT). The IoT based battery monitoring system consist of two major parts i) Monitoring device and ii) User interface. Based on experimental results, the system is capable to detect battery performance.

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Glacier retreat is evident on all continents, and the rate of retreat has accelerated during recent decades. Accurate, spatially explicit information on the position of glacier margins over time is useful for analyzing patterns of glacier retreat and measuring reductions in glacier surface area. This information is also essential for evaluating how mountain ecosystems are evolving due to climate warming and the attendant glacier retreat. Here, we present a non-comprehensive spatially explicit dataset showing multiple positions of glacier fronts since the Little Ice Age (LIA) maxima, including many data from the pre-satellite era. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings, and aerial or satellite images with a supplement of geochronology; and own field data. We provide ESRI shapefiles showing 728 past positions of 94 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8).