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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.

1.0 Introduction The residential module implements the following end uses and characteristics to simulate the power demand in a single family home: • Water heater • Lights • Dishwasher • Range • Microwave • Refrigerator • Internal gains (plug loads) • House (heating/cooling loads) The house model considers the following four major heat gains/losses that contribute to the building heating/cooling load: 1. Conduction through exterior walls, roof and fenestration (based on envelope UA) 2. Air infiltration (based on specified air change rate) 3. Solar radiation (based on CLTD model and using tmy data) 4. Internal gains from lighting, people, equipment and other end use objects. The Equivalent Thermal Parameter (ETP) approach is used to model the residential loads and energy consumption. The following sections describe the modeling assumptions for each of the above end uses and the details of power demand calculations in the residential module.

The operation of a building connects the building, its performance and its end-users. When a mismatch occurs between users' expectations and operations processes, then complaints can arise. The use of enhanced complaint handling processes is investigated to ascertain whether this can help diagnose performance problems. Two LEED Platinum-rated office buildings form the basis for a case study of the components that make up an enhanced complaint handling process. Some of the social dynamics of complaints in buildings are consequently discussed. It is found that an enhanced complaint handling approach could contribute to a form of ongoing commissioning that goes beyond primarily reactive or dismissive treatments of complaints. Le fonctionnement d'un immeuble etablit des rapports entre l'immeuble, ses performances et ses utilisateurs finaux. Lorsqu'un decalage se produit entre les attentes des utilisateurs et les processus d'exploitation, des plaintes peuvent alors s'elever. L'utilisation de processus ameliore...

Global wind resources greatly exceed current electricity demand and the levelized cost of energy from wind turbines has shown precipitous declines. Accordingly, the installed capacity of wind turbines grew at an annualized rate of about 14% during the last two decades and wind turbines now provide ~6–7% of the global electricity supply. This renewable electricity generation source is thus already playing a role in reducing greenhouse gas emissions from the energy sector. Here we document trends within the industry, examine projections of future installed capacity increases and compute the associated climate change mitigation potential at the global and regional levels. Key countries (the USA, UK and China) and regions (e.g., EU27) have developed ambitious plans to expand wind energy penetration as core aspects of their net-zero emissions strategies. The projected climate change mitigation from wind energy by 2100 ranges from 0.3–0.8 °C depending on the precise socio-economic pathway and wind energy expansion scenario followed. The rapid expansion of annual increments to wind energy installed capacity by approximately two times current rates can greatly delay the passing of the 2 °C warming threshold relative to pre-industrial levels. To achieve the required expansion of this cost-effective, low-carbon energy source, there is a need for electrification of the energy system and for expansion of manufacturing and installation capacity.

The industrial sector accounts for 17% of end-use energy in the UK, and 54% globally. Therefore, there is substantial scope for simulating and assessing potential energy retrofit options for industrial buildings. Building Energy Modelling (BEM) applied to industrial buildings poses a complex but important opportunity for reducing global energy demand, due to years of renovation and expansion. Large and complex industrial buildings make modelling existing geometry for BEM difficult and time consuming. This paper presents a potential solution for quickly capturing and processing as-built geometry of a factory to be utilized in BEM. Laser scans were captured from the interior of an industrial facility to produce a Point Cloud. The existing capabilities of a Point Cloud processing software were assessed to identify the potential development opportunities to automate the conversion of Point Clouds to building geometry for BEM applications. In conclusion, scope exists for increasing the speed of 3D geometry creation of an existing industrial building for application in BEM and subsequent thermal simulation.

DOE-2 (DOE2 90) includes several correlation curves that predict the energy use of systems underpart load conditions. DOE-2 simulates systems on an hour-by-hour basis, so the correlations are intended to predict part load energy use (and efficiency) as a function of the part load ratio (PLR) for each hour, where PLR = Hourly Load/Available Capacity. Generally residential and small commercial HVAC equipment meets the load at off-design conditions by cycling on and off. Therefore, the part load correlations must predict the degradation due to this on and off operation over an hourly interval.