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• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

Applying artificial intelligence in education is relevant to addressing the current educational crises. Many available solutions apply Convolutional Neural Networks (CNNs) to help improve educational outcomes. Therefore, a series of works have been developed integrating techniques in different educational contexts, for instance, in online teaching practices. Given the various studies and the relevance of CNNs for educational applications, this paper presents a systematic literature review to discuss the state-of-the-art. We reviewed 133 papers from the IEEE Xplore, ACM Digital Library, and Scopus databases. Based on our revision, we discuss characteristics of studies such as publication venues, educational context, datasets, types of CNNs models, and performance of models. We evidence that the literature regarding CNNs still misses more studies discussing educational problems faced by Global South students, considering both teaching and learning perspectives. Such a population cannot be neglected during experiments due to specific educational weaknesses (for example, basic skills) demanding personalized solutions. • A systematic literature review of 133 published papers. • China covered most publications, followed by India. • There is a research gap regarding using CNNs in some regions of the Global South. • The main educational context with CNN applications is students' performance. [ABSTRACT FROM AUTHOR]

China and India are two major contributors to the growth of global energy demand. As energy is one central part of the geopolitics, the energy security for both countries get complicated by the political environment. The paper investigates the positions of China and India in the trading relationships with different regions of the world. The paper also examines recent events that affect the energy security of China and India. The conclusion is that during the past decade, China and India have diversified sources of energy, especially from the Middle East, though China demonstrates a higher level of diversification and occupies a less passive position in its trading relationships. Shale revolution, the US energy sanctions on Iran and Venezuela and the US-Chinese trade conflicts, can bring China, India and Russia closer to each other due to the shrinking market influence of Russia in Europe and the needs of India and China to further diversify import sources so that energy supplies will not be disrupted by political chaos. [ABSTRACT FROM AUTHOR]

Microplastics (MPs) are non-biodegradable substances that can sustain our environment for up to a century. What is more worrying is the incapability of modern technologies to annihilate MPs from om environment. One ramification of MPs is their impact on every kind of life form on this planet, which has been discussed ahead; that is why these substances are surfacing in everyday discussions of scholars and researchers. This paper discusses the overview of the global occurrence, abundance, analysis, and remediation techniques of MPs in the environment. This paper primarily reviews the event and abundance of MPs in coastal sediments and agricultural soil of three major Asian countries, India, China, and Japan. A significant concentration of MPs has been recorded from these countries, which affirms its strong presence and subsequent environmental impacts. Concentrations such as 73,100 MPs/kg in Indian coastal sediments and 42,960 particles/kg in the agricultural soil of China is a solid testimony to prove their massive outbreak in our environment and require urgent attention towards this issue. Conclusions show that human activities, rivers, and plastic mulching on agricultural fields have majorly acted as carriers of MPs towards coastal and terrestrial soil and sediments. Later, based on recorded concentrations and gaps, future research studies are recommended in the concerned domain; a dearth of studies on MPs influencing Indian agricultural soil make a whole sector and its consumer vulnerable to the adverse effects of this emerging contaminant. [Display omitted] • Film & fibre shaped microplastics (MPs) are more abundant in India, China and Japan. • Plastic mulching is the root cause of major MPs pollution in soil and sediment. • Estuarian soil/sediment are more prone to MPs pollution than terrestrial and coastal. • FT-IR is highly used for MPs identification; mass spectrometry & microscopy are least. • MPs work as a vector for metals, PPCPs, surfactants and antibiotic resistance genes (ARGs). [ABSTRACT FROM AUTHOR]

Objective: This paper shall first investigate the informatics areas and applications of the four Traditional Medicine systems - Traditional Chinese Medicine (TCM), Ayurveda, Traditional Arabic and Islamic Medicine and Traditional Malay Medicine. Then, this paper shall examine the national informatics infrastructure initiatives in the four respective countries that support the Traditional Medicine systems. Challenges of implementing informatics in Traditional Medicine Systems shall also be discussed.Methods: The literature was sourced from four databases: Ebsco Host, IEEE Explore, Proquest and Google scholar. The search term used was "Traditional Medicine", "informatics", "informatics infrastructure", "traditional Chinese medicine", "Ayurveda", "traditional Arabic and Islamic medicine", and "traditional malay medicine". A combination of the search terms above was also executed to enhance the searching process. A search was also conducted in Google to identify miscellaneous books, publications, and organization websites using the same terms.Results: Amongst major advancements in TCM and Ayurveda are bioinformatics, development of Traditional Medicine databases for decision system support, data mining and image processing. Traditional Chinese Medicine differentiates itself from other Traditional Medicine systems with documented ISO Standards to support the standardization of TCM. Informatics applications in Traditional Arabic and Islamic Medicine are mostly ehealth applications that focus more on spiritual healing, Islamic obligations and prophetic traditions. Literature regarding development of health informatics to support Traditional Malay Medicine is still insufficient. Major informatics infrastructure that is common in China and India are automated insurance payment systems for Traditional Medicine treatment. National informatics infrastructure in Middle East and Malaysia mainly cater for modern medicine. Other infrastructure such as telemedicine and hospital information systems focus its implementation in modern medicine or are not implemented and strategized at a national level to support Traditional Medicine.Conclusion: Informatics may not be able to address all the emerging areas of Traditional Medicine because the concepts in Traditional Medicine system of medicine are different from modern system, though the aim may be same, i.e., to give relief to the patient. Thus, there is a need to synthesize Traditional Medicine systems and informatics with involvements from modern system of medicine. Future research works may include filling the gaps of informatics areas and integrate national informatics infrastructure with established Traditional Medicine systems. [ABSTRACT FROM AUTHOR]

We aimed to examine the rate and characteristics of retracted articles in infectious diseases and clinical microbiology. Using the Retraction Watch Database, we conducted a cross-sectional study for retracted publications categorized as 'Infectious Disease' or 'Microbiology' until June 30 2022. We included publications for which citation information was available through the Web of Science database. Study characteristics, retraction trends and number of citations before and after the retraction year were analysed. Overall, 1004 retracted publications were included, retracted between August 1968 and June 2022. The number of retractions climbed through the years, peaking in 2020–2021. A total of 614 retractions originated from USA, China, and India, of total 183 736 PubMed publications from these countries. Overall, 378 (38%) were retracted because of errors; 182 (18%) because of plagiarism; and 142 (14%) because of falsification/fabrication. Specific reasons included 'concerns/issues about data' (158, 16%); 'duplication of image' (127, 13%); and 'unreliable results' (116, 12%). Of the 347 retractions during 2020 to June 2022, 91 (26%) were COVID-19 related. Fifty of 895 (5.6%) first authors had two retracted papers, and 14 (1.6%) had ≥2 retractions. Of 824 publications cited at least once, 466 (57%) were cited more often after retraction. Retractions of infectious diseases and clinical microbiology publications are increasing. Concerning reasons such as plagiarism, falsification/fabrication and errors are not uncommon. Nonetheless, these publications continue to be commonly cited after being retracted. [ABSTRACT FROM AUTHOR]

Air pollution in the form of particulate matter (PM) is becoming one of the greatest current threats to human health on a global scale. This paper firstly presents a Bayesian space–time hierarch piecewise regression model (BSTHPRM) which can self-adaptively detect the transitions of local trends, accounting for spatial correlations. The spatiotemporal trends of the approximately anthropogenic PM 2.5 removed natural dust (PM 2.5_No Dust) concentrations and the corresponding population's PM 2.5_No Dust exposure (PPM 2.5 E) in the global continent from 1998 to 2016 were investigated by the presented BSTHPRM. The total areas of the high and higher PM 2.5_No Dust -polluted regions, whose spatial relative magnitude of PM 2.5_NoDust pollution to the global continental overall level was between 1.89 and 14.68, accounted for about 13.4% of the global land area, and the corresponding exposed populations accounted for 56.0% of the global total population. The spatial heterogeneity of the global PM 2.5_NoDust pollution increased generally from 1998 to 2016. The areas of hot, warm, and cold spots with increasing trends of PM 2.5_NoDust concentration initially contracted and then later expanded. The local trends of the global continental PM 2.5_NoDust concentrations and PPM 2.5 E can be parted into three changing stages, early, medium, and later stages, using the BSTHPRM. The area proportions of the regions experiencing a decreasing trend of PM 2.5_NoDust concentrations and PPM 2.5 E were greater in the medium stage than in the early and later stages. The local trends of PM 2.5_NoDust concentration and PPM 2.5 E in the two higher PM 2.5_NoDust polluted areas, northern India and eastern and southern China, increased in the early stage and then decreased in the medium stage. In the later stage (recent years), northern India displayed a stronger increasing trend; nevertheless, the follow-up decreasing trend still occurred in eastern and southern China. In the first two stages, more than half of the areas in Europe experienced a decreasing trend of PM 2.5_NoDust concentration and PPM 2.5 E; later, more than half of areas in Europe exhibited increasing trends in the later stage. North America and South America experienced a similar local trend of PPM 2.5 E to Europe. The PPM 2.5 E trend in Africa generally increased during the study period. • First presentation of a Bayesian space-time hierarchy piecewise regression model detecting self-adaptively transitions. • Firstly applies a developed Bayesian space-time model in investigating the global population's exposure to PM 2.5. • Deeply analyses the spatiotemporal trends of the global continental PM 2.5 pollution removed natural dust. • Details the spatiotemporal trends of the global population's exposure to anthropogenic PM 2.5. [ABSTRACT FROM AUTHOR]

Abstract: Carbon footprint has become a term often used by the media in recent days. The human carbon footprint is professed to be a very serious global threat and every nation is looking at the possible options to reduce it since its consequences are alarming. A carbon footprint is a measure of the impact of human activities on earth and in particular on the environment; more specifically it relates to climate change and to the total amount of greenhouse gases produced, measured in units of carbon dioxide emitted. Effort of individuals in minimizing the carbon footprint is vital to save our planet. This article reports a study of the carbon footprint of various types of shopping bags (plastic, paper, non-woven and woven) using life cycle impact assessment (LCIA) technique in two stages. The first stage (baseline study), comprised the study of the impact of different types of shopping bags in the manufacturing phase, without considering their usage and disposal phases (cradle to gate stage). The LCIA was accomplished by the IPCC 2007 method, developed by the Inter Panel on Climate Change in SIMAPRO 7.2. The GWP (Global Warming Potential) values calculated by the IPCC 2007 method for 100 years were considered as a directive to compare the carbon footprint made by the different types of shopping bags under consideration. The next stage was the study of the carbon footprint of these bags including their usage and disposal phases (cradle to grave stage) and the results derived were compared with the results derived from the baseline study, which is the major focus of this research work. The values for usage and end-of-life phases were obtained from the survey questionnaire performed amongst different user groups of shopping bags in China, Hong Kong and India. The results show that the impact of different types of shopping bags in terms of their carbon footprint potential is very high if no usage and disposal options were provided. When the carbon footprint values from different disposal options were compared, the carbon footprint values were lower in the case where a higher percentage of reuse is preferred to recycling and disposing to landfill. The results indicate that a higher percentage of reuse could significantly scale down the carbon footprint. Once the shopping bags reached the point where they can no longer be reused, they must be forwarded to recycling options, rather than being disposed to landfill. At this juncture, consumer’s perceptions and behaviours in connection with the respective government’s policies in promoting & facilitating recycling systems could be critical in reducing the carbon footprint of various shopping bags. [Copyright &y& Elsevier]

COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is "if the disease can travel faster information has to travel even faster". The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan-European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered). The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using the Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation. • Proposing a Multi-criterion Intelligent DSS • Epidemiology modeling of data using a SEIFR Model • Our approach applies data to get trained using multiple parameters to handle and serve significant challenges existing especially in healthcare. [ABSTRACT FROM AUTHOR]

Recently, some high-skilled workers tend to escape from areas with severe air pollution. The phenomenon is more prominent in two emerging economies, China and India, and they are facing the brain drain caused by air pollution. This paper establishes a partial equilibrium model about the relationship of air pollution and the flow of technological innovative professionals (TIP), and for the first time, examines the effect of air pollution on the accumulation of technological innovative human capital based on cross-country data from China and India. We find that air pollution will reduce the stock of TIP in some regions. The estimation results show that a 1% increase in the PM 2.5 concentration of China's cities leads to an approximate 146 people decrease in the stock of TIP, while a 1% increase in the PM 10 concentration of Indian states leads to a 0.127% decrease in the stock of TIP. Further analysis of regional differences indicates that air pollution has a significant negative effect on the stock of TIP in economically developed areas of China, but has little effect on that in underdeveloped areas of China. Moreover, the results of robustness tests prove the stability of the findings in this study. This paper concludes with suggestions on how the country, local governments, enterprises, and research institutions could better deal with air pollution, attract and retain TIP, and effectively reduce brain drain. • Air pollution negatively affects the accumulation of innovative human capital. • Air pollution will reduce the stock of technological innovative professionals (TIP). • A 1% increase in PM 2.5 in China's cities decreases the stock of TIP by 146 people. • A 1% increase in PM 10 in Indian states reduces the stock of TIP by 0.127%. • TIP in economically developed areas are more sensitive to air pollution. [ABSTRACT FROM AUTHOR]

Since coal will be widely used in the next decades, mainly in the developing countries such as China and India, carbon capture and storage (CCS) technologies will have a key role in the containment of global warming. This paper presents a techno-economic comparison between the most promising power generation technologies for a CO 2 -free power generation in a short-term future. In particular, three different power generation technologies have been considered in their conventional (without CCS) and CO 2 -free configurations: (a) ultra supercritical (USC) pulverized coal combustion, (b) oxy-coal combustion (OCC) and (c) integrated gasification combined cycle (IGCC). Process simulation, based on Aspen Plus and Gate Cycle commercial tools, allows to calculate plant performance, including the energy penalty due to the CCS system (10.9% points for USC and 8.7% points for IGCC). In parallel, a detailed economic assessment shows that, among the commercial-ready technologies, USC could be the most convenient solution for power generation without CCS (presenting a levelized cost of electricity – LCOE – of 38.6 €/MW h, significantly lower than 43.7 €/MW h of IGCC), whereas IGCC becomes competitive for CO 2 -free systems (with a LCOE of 59.6 €/MW h, to be compared with 63.4 €/MW h of USC). Moreover, oxy-coal combustion, which is currently not mature enough for commercial-scale applications, promises to become strongly competitive for CCS applications due to its relatively low levelized cost of electricity (62.8 €/MW h). This kind of analysis typically presents strong uncertainties, due to the variability of several key parameters (e.g. fuel and CCS prices, determined by the fluctuation of the international markets, or an improvement of the technologies). Therefore, a sensitivity analysis has been done to determine the effects of these potential fluctuation or the improvement on the economic performance of the plant. [ABSTRACT FROM AUTHOR]

An airport network forms the backbone of any air transportation system. The relationships among the origins and destinations of flights result in a complex network of routes which can be complemented with information associated with the routes themselves, for instance, traffic load and distance. In this paper, we modeled the Australia's civil domestic airport infrastructure as a network and analyzed the resulting network structure and its features using complex network tool. This case study identifies and investigates complex network measures, such as the degree distribution, characteristics path length, clustering coefficient and centrality measure as well as the correlations among them to understand the topology of an airport network. This analysis of the Australian Airport Network (AAN) indicates that it has a cumulative degree distribution described by the power-law function. As it has an average path length of 2.90, it is considered to have small-world properties. It is also found that it has a clustering coefficient of 0.50 which is higher than that of a random network of the same size which indicates that the transitivity and cohesiveness of AAN is different from a random network. In contrast to the World-wide Airport Network (WAN), the AAN is found to have disassortative mixing similar to the airport networks of China and India. [ABSTRACT FROM AUTHOR]

The past decades have seen planning and implementation of built infrastructure in all over the developed and developing world growing in large scales. This has been influenced by economic and population growths, urbanization and industrialization, which in turn have put increasing stress in provision of services. The paper reviews the policy dimensions of water infrastructure development and financing in the two largest economies at present, China and India, including planning, implementation and decision-making processes. Findings indicate that main challenges for infrastructure development have been limited sources of financing, but also policies and their implementation. The high levels of investment in water infrastructure in the two countries have been impressive, mainly in China. However, they still have not necessarily addressed efficiency over the long term, supported more inclusive and higher economic growth or improve social and environmental conditions in all cases. [ABSTRACT FROM AUTHOR]

Renewable energy has a critical role in limiting the greenhouse gas (GHG) emissions. This paper assesses the implication of aligning renewable energy deployment target with national emission reduction target for mitigation cost. The assessment methodology uses Asia-Pacific Integrated Assessment/computable general equilibrium (AIM/CGE) model to determine the mitigation cost in terms of GDP and welfare loss under alternative renewable targets in different climate-constrained scenarios. A range of country-specific emission constraints is taken to address the uncertainties related to global emission pathway and emission entitlement scheme. Comparative results show that China needs to increase its share of non-fossil fuel significantly in the primary energy mix to achieve the stringent emission reduction target compared to India. The mitigation cost in terms of economic and welfare loss can be reduced by increasing the penetration of the renewable energy to achieve the same emission reduction target. The modeling results show that coordinated national climate and renewable energy policies help to achieve the GHG emission reduction target in an efficient and cost-effective manner. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak has led to a substantial loss of human life throughout the world and has a tremendous impact on healthcare services. Industry 4.0 technologies have established effective supply chain management towards the fulfillment of customized demands in the healthcare field. In addition, the internet of things, artificial intelligence, big data analytics, and 3D printing have been extensively used to combat the COVID-19 pandemic and assist in providing value-added services in the healthcare sector. Henceforth, this paper presents a scientometric analysis on the literature of aforementioned Industry 4.0 technologies in the context of COVID-19. It provides extensive insights into co-citation and co-occurrence analysis of high cited publications, participating countries, influential authors, prolific journals, and keywords using the CiteSpace tool. The analyses reveal that China has produced the highest research outputs, although India is the most collaborative country in this field. The current research hotspots include supply chain, 4D printing, and social distancing technologies. Furthermore, it explores emerging trends, intellectual structure of publications, research frontiers, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [Display omitted] • Industry 4.0 technologies have emerged in the healthcare domain as an area of growing interest in academia and industry discipline. • Artificial intelligence, big data analytics, internet of things and 3D printing have been extensively used to control the COVID-19 spread and assist in providing value-added services in the healthcare sector. • It explores emerging trends, intellectual structure of publications, and potential research directions for further work in the Industry 4.0 assisted healthcare domain. [ABSTRACT FROM AUTHOR]

Abstract: In a world increasingly affected by global environmental changes, Low Income countries will play an ever more central role in determining the future health of the biosphere. While global use of the biosphere''s capacity has increased over the past 45 years, per capita demand for biocapacity, as measured by the Ecological Footprint, has only increased in high-income countries and has remained constant or fallen in middle- and low-income nations. Consumption has increased faster than population in high-income nations, while population growth has been the dominant factor in middle- and low- income countries. Although listed in the middle-income group of countries, China showed atypical trends in the past 45 years, with a rapid increase in per capita Ecological Footprint that outstrip its gains in income. Typical trends were instead noticed for India, whose per person Ecological Footprint has fallen slightly. The results of this paper show that decisions made in China and India will be of fundamental importance for future global sustainability. [Copyright &y& Elsevier]

Abstract: Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and almucantar retrievals (single scattering albedo (SSA) and aerosol size distribution) from 2005–2009 in Beijing and Kanpur are used to analyze differences and similarities in aerosol optical properties over these two regions. The examination of monthly mean AOD (440 nm) shows that maximum and minimum values occurred in summer and winter, respectively, for Beijing, while the range in AOD in Kanpur was lower. Precipitation in both Beijing and Kanpur peaked in summer; however, the columnar water vapor (CWV) exhibited a high correlation with AOD in Beijing (R 2 = 0.79) but had a weak relationship with AOD in Kanpur (R 2 = 0.13). The Angstrom exponent (α, 440–870 nm) generally increased linearly as the fine mode fraction (FMF) of AOD (500 nm) increased for FMF < 90% in both regions, with a high correlation of R 2 > 0.96. However a clear decrease in α for FMF > 90% found in Beijing is not shown distinctly in Kanpur, and is mainly due to the higher aerosol loading in this FMF bin at Beijing (AOD at 440 nm > 2.2) which results in a stronger coagulation of fine mode particles. Bimodal seasonally-averaged size distributions reveals similar aerosol mixtures composed of fine pollution particles and coarse dust particles in both regions. The analysis of spectral SSA as a function of α is emphasized in this paper. The average SSA at 440 nm in both regions shows a similar low dynamic range of ∼0.03 for α < 1.4. The obvious increase in SSA at 440 nm for α > 1.4 in both regions can be attributed to a higher FMF leading to fine mode coagulation. However, the distinctly smaller increase in Kanpur suggests that fine mode aerosols at Beijing are less absorbing than those at Kanpur. The visibly lower SSA at 675 nm at Kanpur compared to that at Beijing for α > 0.4 is due to a larger find-coarse mode separation radius of ∼0.76 μm in Beijing versus a value lower than ∼0.58 μm in Kanpur. Another reason lies in the weaker absorption by fine mode aerosols in Beijing. The distinctly lower near-infrared SSA in Kanpur when α < 1.4, compared to Beijing, may be attributed to lower aerosol concentrations in all α bins, resulting in less aggregation of absorbing black carbon particles on coarse particles in Kanpur. The classification of aerosol properties shows that the AOD accumulation mode in all seasons, even including spring, in Beijing and in post-monsoon and winter seasons in Kanpur can be attributed to fine particle coagulation or hygroscopic growth; during pre-monsoon and monsoon seasons in Kanpur, it is due to coarse mode particle accumulation or cloud contamination. [Copyright &y& Elsevier]

Abstract: This paper presents an overview of the current status of the main CCS initiatives in the major emerging economies: China, India, Brazil, Mexico, Russia, and South Africa. This study first covers all six countries as a group and describes their natural resources. The second part of this study embraces each country individually and includes current initiatives and current legal and technological status of CCS. At the end, this study summarizes the main findings in the emerging economies and the potential of including CCS as part of the CDM. [Copyright &y& Elsevier]

Abstract: It is well known that China and India have been recycling centers of WEEE, especially printed circuit boards, and that serious environmental pollution in these countries has been generated by improper recycling methods. After the governments of China and India banned improper recycling by the informal sector, improper recycling activities spread to other places. Then, these governments changed their policies to one of promoting proper recycling by introducing a scheme, under which E-waste recycling requires a license issued by the government. In this paper, the effectiveness of that license scheme is examined by means of an economic model. It can be shown that the license scheme can work effectively only if disposers of E-waste have a responsibility to sell E-waste to license holders. Our results run counter to the idea that international E-waste trade should be banned and provide an alternative solution to the problem. [Copyright &y& Elsevier]

As the world gets ready to begin the second decade of the twenty-first century, global climate change has been recognized as a real threat to civilization as we know it. The rapid and successful economic growth of developing nations, particularly China and India, is contributing to climate change. The route to initial economic success in China followed that of the developed nations through the development of industries. Unfortunately, China''s environmental protection efforts have not been the same as in developed countries because China is vastly different culturally, socially, economically and, especially, politically from developed nations. When China started to deal with environmental concerns in the late 1970s, it took advantage of the experiences of other countries in establishing environmental standards and regulations, but it did not have a model to follow when it came to implementing these standards and regulations because of the abovementioned differences. Economically, China is transitioning from an agricultural base into an industrial base; however, even now, 60% of the population remains farmers. China has been and still is heavily dependent upon coal for energy, resulting in serious atmospheric particulate pollution. While growing efforts have been expended on the environment, at this juncture of its economic development, China would be well served to revisit the traditional “develop first and clean up later” approach and to find a balance between development and protecting the environment. Against this backdrop, a reflective look of the effort to manage air quality from 1949–2008 (with an emphasis on the past 30years) in China is presented in this paper. The environmental component of the 2008 Olympic Games is examined as a special example to illustrate the current measures being used to improve air quality in China. [Copyright &y& Elsevier]

According to the in situ measured data of the air temperature and ground temperature inside of Kunlun mountain tunnel on Qing–Tibet railway, China, the thawed extent of the permafrost surrounding the tunnel is very large, caused by the latent heat of the cast in situ concrete lining insulation during excavation in winter and other artificial activities. In this paper, taking the coupled effects of moisture transfer and heat conduction into account, a finite element formula of this problem is derived from the governing differential equations with phase change and moisture transfer equations using Galerkin''s solution. Using the computer program, predictions for the refreezing of Kunlun mountain permafrost tunnel are made. The analysis results show that the vault of Kunlun mountain tunnel would have been refrozen by 31 March 2004, the arch bottom by 30 September 2004 and the total tunnel by 30 September 2005, without insulation material. On the other hand, if the tunnel is fitted with insulation material, whose thermal conductivity is equal to 0.03 W/m.K and thickness of 0.05 m, the vault of Kunlun mountain tunnel would have been refrozen by 31 March 2004, the arch bottom by 31 December 2004, the left wall by 31 March 2006, and the right wall by 30 September 2006. Thus, insulation material will delay the refreezing of Kunlun mountain permafrost tunnel. The thawed extent of the permafrost surrounding the tunnel in cold regions caused by construction must be considered, and the time of the field observation should be extended, or observational results, such as their temperature and stress field, will greatly be different from results in stability. [Copyright &y& Elsevier]

This paper presents a synthesis of assessment of sustainable biomass production potential in six Asian countries—China, India, Malaysia, Philippines, Sri Lanka and Thailand, and is based on the detailed studies carried out in these countries under the Asian Regional Research Programme in Energy, Environment and Climate (ARRPEEC). National level studies were undertaken to estimate land availability for biomass production, identify and evaluate the biomass production options in terms of yield per hectare and financial viability, estimate sustainable biomass production for energy, and estimate the energy potential of biomass production in the six Asian countries. Sustainable biomass production from plantation is estimated to be in the range of 182.5–210.5, 62–310, 0.4–1.7, 3.7–20.4, 2.0–9.9 and 11.6–106.6 Mt yr−1 for China, India, Malaysia, Philippines, Sri Lanka and Thailand, respectively. The maximum annual electricity generation potential, using advanced technologies, from the sustainable biomass production is estimated to be about 27, 114, 4.5, 79, 254 and 195 percentage of the total electricity generation in year 2000 in China, India, Malaysia, Philippines, Sri Lanka and Thailand, respectively. Investment cost for bioenergy production varies from US$381 to 1842 ha−1 in the countries considered in this study; investment cost for production of biomass varies from US$5.1 to 23 t−1. [Copyright &y& Elsevier]

Eco-efficiency composite indicators are widely accepted as the ratio of environmental impact to created economic value. These indicators are realistic measures for assessing sustainability performance considering the economy and environment. The weights reflect the importance of indicators to the aggregated environmental impacts. Estimating the relative weight of indicators is highly subjective, and therefore the search for a single unique weighting method has been going on for years. The regression-based weights are one of the most recent trends in sustainability modeling. Since these methods are designed initially to investigate the impact of multiple variables on a response variable rather than to estimate weights, some drawbacks are associated with their potential to provide proper weights. This paper presents a novel weighting approach integrating linear mixed-effect models with Johnson's relative weights to address these drawbacks and provide meaningful relative weights for eco-efficiency composite indicators. The proposed approach's operational and computational procedures are illustrated using a real example, and the eco-efficiency of food consumption of 38 countries is estimated for the period between 1990 and 2012 using a consumption-based sustainability accounting method. The findings have shown that energy use and GHG indicators are the most critical contributor to global food consumption's environmental impacts. The country-based eco-efficiency performance in this work has shown that China, India, and Russia are located in the low eco-efficiency score class. The Spatio-temporal analysis downgraded the geographical location's significance on the trends of eco-efficiency behavior in time and space. On the other hand, it revealed the different types of emerging hot spots over the world. • Consumption-based environmental footprint analysis is conducted. • Dynamic ecoefficiency of food consumption is estimated for 38 countries. • A novel weighting approach integrating Johnson's relative weights is proposed. • China, India, and Russia are located in the low ecoefficiency-score cluster. [ABSTRACT FROM AUTHOR]

Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

Developing more effective carbon mitigation strategies requires accurate forecasting of carbon emissions. This is especially true for China, the United States and India, which produce over half of the world's carbon emissions. China, the United States, and India represent three different types of carbon emissions data. The first data type is typical for China: rapid grows which is thereafter slowing down. The US carbon emissions data show volatile growth and decline. The third data type is common for India's data: accelerated growth. To deal with the volatility of the data and to improve the forecasting accuracy, this paper combines Metabolic Nonlinear Grey Model (MNGM) with Autoregressive Integrated Moving Average (ARIMA) to develop the combined MNGM-ARIMA model, and MNGM with Back Propagation Neural Network (BPNN) to develop the new combined MNGM-BPNN model. In this way, ARIMA and BPNN could correct the modelling residual error of MNGM, to decrease the forecasting error. Through the quantitative data study of the countries in focus, we have made the following findings: First, the mean relative percent errors of the proposed MNGM-ARIMA and MNGM-BPNN models are 1.35% and 1.57%, which are lower than the traditional MNGM, ARIMA, and BPNN models. Besides, it is projected that during the period of 2019–2030, the US′ carbon emissions will keep a downward trend, while carbon emissions in China and India will continue to grow. Finally yet importantly, in the future periods of time, the carbon emissions growth rate in India will be faster than that for China. The above findings have led us to the following conclusions: (1) Error values of those two proposed approaches have proven that the strategy of correcting the forecasting error of the previous models with the latter models can effectively improve the forecasting accuracy. (2) The proposed two combined forecasting techniques have shown sound performance for the three types of data. It is reasonable to believe that new approaches will help to improve insight into better forecasting carbon emissions in other countries and regions of the world. (3) China and India will remain the major sources of global carbon emissions, and should therefore continue their efforts on carbon emission reduction. Image 10221 • China, the United States and India contribute half of global carbon emissions. • Forecasting the carbon emissions is conducive to global carbon emission reduction. • Strategy of "error correction + quadratic modeling" and new models are proposed. • Mean relative errors of the two proposed models achieved 1.35% and 1.57%. • Future carbon emissions growth will be concentrated in developing countries. [ABSTRACT FROM AUTHOR]

Following India's efforts to mitigate the emission of greenhouse gases, there has been a transition from fossil fuels to clean energy sources, such as solar and wind, in the energy portfolio. The renewable energy sector is widely acknowledged as a fertile ground for employment creation, as it is labour-intensive. Against this backdrop, the main objective of this paper is to provide an empirical explanation of how the growth of renewable energy sources, particularly solar photovoltaic (PV) technology, affects employment creation in India. The findings reveal that, despite a substantial investment in India's solar PV technology, the number of jobs created by the sector declined marginally from 164000 in 2017–114000 in 2018. India's estimated employment elasticity is just 0.004, implying that the level of employment does not show a significant positive response to changes in installed capacity. Almost 60 per cent of the global solar PV workforce is in China, which has become the leading country both in terms of installed capacity of global solar PV technology and manufacturing of solar PV cells and modules. The wide disparity in solar PV employment between India and China is explained by the former's excessive dependence on China for solar PV cells. Alongside climate conservation, India should, therefore, place more emphasis on revitalising the domestic solar manufacturing industry to bring about efficient use of the nation's untapped resources. [ABSTRACT FROM AUTHOR]

• Experimental studies of the slotted-in dowelled bamboo-to-steel joint with/without infilling grout. • Failure behaviours and load-displacement curves for various specimens were predicted by FEM. • Analytical design models were proposed based on the results from test and FEM for the joints. Bamboo has been widely used for structural and non-structural purpose, for example as scaffolding and cladding for many centuries in South East Asian countries such as China and India, due to its high strength-to-weight ratio, low price and recyclability. In today's trend of sustainable development, there is a renewed interest in the use of bamboo for modern prefabricated structures. In this paper, dowel-type bamboo joints are introduced for prefabricated bamboo structures. An experimental study has been undertaken to investigate the structural behaviour and the failure mechanisms of the dowelled joints with varying configurations. Test results reveal that the ductile failure mode of the bamboo occurred when large end spacing was applied, and the infilling grout can significantly increase the load-carrying capacity of the dowelled joint. Three-dimensional finite element models were established and validated using available experimental data. Extensive parametric studies were carried out based on the simulation approach to investigate the effects of key influencing variables involved, i.e. hole clearance, friction coefficient, bolt strength, bamboo thickness, grout strength and end spacing. Moreover, analytical models for designing the dowelled joints are proposed and compared with the current codified approach. [ABSTRACT FROM AUTHOR]

Abstract: Asia’s largest biofuels producers are currently Indonesia, Malaysia, the Philippines, Thailand, China and India. This paper first examines the current status of biofuels in these countries, including the national development targets, strategies, incentives and policies. It then identifies the fundamental driving forces behind the development and promotion of these industries and considers their economic, social and environmental impacts. [Copyright &y& Elsevier]

Abstract: The last decade has seen a surging demand for biofuels in the wake of increasing oil prices and rising environmental concerns. The most common biofuel is bio-ethanol accounting for more than 90% of total biofuel usage. It is increasingly produced from sugar cane making cane a strategic crop for biofuels. Given the growing demand for “green” fuels, bio-ethanol production has been supported by energy policies in the past decade, which have consequently been accused of contributing to the global trend of rising food prices and thus jeopardising food security. However, while biofuel policies are an important driver, prices as much as food security will ultimately be determined by supply constraints of strategic crops. This paper hence investigates drivers of and constraints to sugar cane production in China and India and shows that supply side constraints vary significantly in the two countries. China and India both face serious limitations with regard to suitable available land for the further expansion of sugar cane production. Equally they are both faced with challenges to increasing yield output per hectare, albeit different ones. With regard to productivity, China achieved 2.7% annual yield growth since 1997, while India has seen yield decreases of −0.1%p.a. over the same period. The authors conclude that cane used as a feedstock to meet the rising energy demand will come at the expense of converting fertile land for non-food purposes. [Copyright &y& Elsevier]