Faced with an increasing amount of industrial solid waste (ISW) in the process of rapid industrialization, it is indispensable to carry out ISW metabolism study to realize source and waste reduction. In this study, a new composite waste input-output (WIO) model is developed to examine ISW production and production relationships among different sectors. In particular, the extended methods of network control analysis and network utility analysis are used in the ecological network analysis under two ISW scenarios (i.e. common industrial solid waste (CISW) and hazardous waste (HW) scenarios). Furthermore, comprehensive utilization analysis is first developed to evaluate the ISW utilization level and to guide the planning of sectors with large proportion of ISW production. A case study of Guangdong, China shows that indirect flow analysis can be used to understand the internal ISW metabolism structure. The mining sectors produce a large amount of direct ISW and perform a low level of comprehensive utilization, but they have mutualism relationships with other sectors. The energy transformation (EH) sector in the CISW system has high direct generation intensity and plays as a main controller. The situation of paper manufacturing (MP) sector in HW system is similar to that of EH. Therefore, it is expected that the results of this study will provide scientific foundations for these sectors to formulate future ISW reduction policies.

The uptake of polycyclic aromatic hydrocarbons and their cellular effects were investigated in the mangrove Bruguiera gymnorrhiza. Seedlings were subjected to sediment oiling for three weeks. In the oiled treatment, the ƩPAHs was higher in roots (99%) than in leaves (1%). In roots, PAHs included phenanthrene (55%), acenaphthene (13%), fluorine (12%) and anthracene (8%). In leaves, PAHs possessed two to three rings and included acenaphthene (35%), naphthalene (33%), fluorine (18%) and phenanthrene (14%). In the roots, oil caused disorganization of cells in the root cap, meristem and conducting tissue. Oil contaminated cells were distorted and possessed large and irregularly shaped vacuoles. Ultrastructural changes included loss of cell contents and fragmentation of the nucleus and mitochondrion. In the leaves, oil caused dilation and distortion of chloroplasts and disintegration of grana and lamellae. Oil targets critical organelles such as nuclei, chloroplasts and mitochondria which are responsible for cell vitality and energy transformation.

Solar energy is a promising form of energy that has the potential to meet all of the world’s energy needs. Only half of the sun’s energy reaches the earth’s surface, even though it is more enough for meeting the world’s energy need. Though there is a great deal of solar energy utilization technologies available, solar parabolic dish collector system got researchers focus because of its higher thermal energy conversion efficiency and its unique advantages. Several researchers have been enlightening new and emerging technologies in several countries. Hence, the authors would like to emphasize the progress in this while exercising an extensive review of different solar concentrating techniques using solar parabolic dish collector in order to produce heat and electrical power using direct and indirect energy conversion devices with wide range of applications. Their design advancement and progress applications in recent years particularly in related field are discussed too.

It is of crucial importance to identify the driving factors for emission changes since China’s commitment to reduce carbon intensity in 2009. Hence, the spatial-temporal variation of carbon intensity of China’s 30 provinces from 2010 to 2017 is explored by applying a Spatial-temporal Index decomposition analysis (ST-IDA) model combined with energy input-output analysis. Industrial structure, energy intensity, energy structure, and carbon emission coefficient are identified as driving factors; simultaneously, a new factor, energy conversion efficiency, is also introduced based on the energy input-output analysis, which is of significance as China is vigourously pushing electricitification. The results show that the carbon intensity of economic sectors in most provinces declined from 2010 to 2017. Energy intensity is the biggest contributor to both the temporal decline of carbon intensity and its spatial difference for economic sectors, followed by industrial structure, energy conversion efficiency, energy structure and carbon emission coefficient, while the rank of inhibition of each factor is the same as above. Meanwhile, the carbon intensity of the residential sector is mainly affected by per capita GDP and per capita energy consumption. Related policy suggestions are given.

Inkjet printing of metal nanoparticles is an attractive method for front-side metallization of silicon solar cells. It is owing to noncontact, low-cost, low-waste, and simple process. In this work, we proposed the ink-jet printing and electroless technology to fabricate the seed layer and electrode layer, respectively. Furthermore, we used electroplating method to increase the electrode conductivity. In this way, the energy conversion efficiency up to 12.22% without AR coating can be obtained on 100 × 100 mm c-Si cell.

Nowadays, modern industries generate their energy by using renewable solar. The rapid increase in photovoltaic (PV) module installations provides a better energy conversion, but their life cycle is a major concern. This research paper focuses on the recycling process for solar PV modules using the Internet of Things in industries. The smart bin with the Internet of Things (IoT) utilizes a machine learning approach to collect solar waste. The proposed smart bin uses k-Nearest Neighbor’s algorithm (k-NN) and Long Short-Term Memory (LSTM), a network-based learning algorithm. These algorithms are useful in updating the level of the bin via alert messages. It also helps in identifying the type of waste material. The k-NN algorithm provides 83% accuracy in predicting the bin level in a real-time testing environment. The smart dust bin classifies the waste materials, and notifies its level to the collection center through the IoT platform when the level reaches a prescribed threshold, the signal corresponding to the level is passed to the common waste collection unit. IoT is connected to Cloud Server. It helps to predict the level of the smart bin. Delay is introduced in the order of 3–8 s while the alert message is sent to the common waste collection unit. The system monitors the smart bin levels and sends the notifications to alert and initiate the collection unit. Real-time mobile app monitors the bin’s level and location. The cloud IoT analytics analyze the solar e-waste in a different locations in industries.The proposed system works better and provides accurate results by using machine learning approach.

Energy consumption and CO₂ emissions are agreed as the main causes of global warming and climate change, which are causing several extreme weather events in recent decades. However, there is little understanding how humans adjust their behaviours in energy consumption and emissions in responding to these natural threats. This study aims to examine the influences of exposure, susceptibility, and vulnerability to five natural hazards on CO₂ emissions, energy intensity, renewable energy, and electricity consumption. The feasible generalized least squares model and several panel estimates are applied for a global sample of 161 countries from 2011 to 2018. The empirical results provide interesting findings. First, exposure, susceptibility, and vulnerability appear to reduce electricity usage, renewable energy consumption, energy intensity, and CO₂ emissions in the global sample. Second, the negative effects of exposure, susceptibility, and vulnerability are consistent across four income groups (high-income; upper-middle-income; lower-middle-income; and low-income) except for some interesting differences. Exposure appears to increase renewable energy consumption significantly in upper-middle and high-income, while susceptibility has a significant positive influence on renewable energy consumption in low-, upper-middle, and high-income. Third, the negative impact is also documented in seven regions, with the exception of some interesting findings: threats from nature appear to increase CO₂ emissions and energy intensity in the Middle East and North Africa, South Asia, and Europe and Central Asia, while they stimulate the use of renewable energy in Latin America and Caribbean. Interestingly, exposure and susceptibility appear to induce renewable energy transformation in Europe and Central Asia.

The effects of urbanization on carbon emissions (EUCE) are complex, while rare work has comprehensively elaborated on how various aspects affect and develop. In this study, utilizing Citespace and VOSviewer software, a global scientometric visualization analysis was conducted to excavate various impacts and future trends of urbanization on carbon emissions. Based on publications from the year 1982 to 2018, the spatial-temporal distribution of publications, collaboration, current hotspots, and future trends of EUCE were carried out. The results indicated that between 1992 and 2018, there were accelerated increasing trends of EUCE researches world widely, among which China, the USA, and UK ranked the top 3. Relevant research firstly appeared in the USA, while grew most rapidly in China. Research subjects mainly concentrate on population migration, resource consumption, land use and land cover change (LULCC), energy conservation, non-carbon greenhouse gases like CH₄ and N₂O. And attention on carbon footprint has become a hotspot for carbon mitigation. For research fronts, ecosystem service offered by urban green space has gradually evolved as a research focus. Besides, energy transformation technology is critical for mitigating carbon emissions and has become an important concern in the future development. Furthermore, the timeline visualization analysis indicates that all the research topics related to EUCE are cited and connected with each other, reflecting the necessity of interdisciplinary integration in scientific research. Overall, our study has provided a quantitative visualization on the current situation and future trends of EUCE subject, which will be helpful to subsequent research and policy guidance.

Exploring the low-carbon energy transformation pathway is vital to coordinate economic growth and environmental improvement for achieving China’s carbon peak target. Three energy-target scenarios are developed in this paper, considering the targets of energy structure, electrification rate, and carbon mitigation towards 2030 announced by the Chinese government. A dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine the economic and environmental effects under different pathways of long-term low-carbon transformation. It detects that China’s energy structure would substantially transfer to the low-carbon and clean one, whereas CO₂, SO₂, and NOX emissions in 2020–2030 would vastly abate along with all three energy-target scenarios. Different pathways would produce varying positive impacts on China’s macro-economy and achieve the different extent of double dividend effects. It is highly conceivable for China to peak its carbon emission at 12.4 GtCO₂ by 2028 if it serves the comparatively more stringent low-carbon transformation pathways.