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As smart cities become a global topic, interest in smart mobility, the core of smart cities, is also growing. The technology that comes closest to general users is “autonomous driving”. In particular, the successful market entry and establishment of some private companies proved that “autonomous driving” is not technology of the future but imminent reality. However, safety in autonomous vehicles that rely on sensors instead of the driver’s five senses has been the focus of attention from the beginning and continues to be so. In this study, we attempted to counter this interest. Based on the actual data of thirty traffic accidents, assuming the AEBS (Autonomous Emergency Braking System) was installed to assist the driver in safe driving, it was reinterpreted through simulation to see what changes occurred in the accident. In the computer program, PC-Crash, the results were first analyzed through simulation using Euro NCAP (New Car Assessment Program)’s AEBS test standards. Subsequently, the other variables in the AEBS were controlled and the accident was reinterpreted by changing only the angle of the radar detection sensor. As a result, it was confirmed that a total of 27 accidents out of thirty accidents could have been prevented with the AEBS. In addition, it proved that the crash avoidance rate of vehicles gradually increased as the radar angle increased.

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

A sustainable financial and economic system is the need of the hour, especially in the context of a deteriorating environment. Environment quality is very important for healthy labour life, contributing to economic growth more effectively. China is one of the largest economies in the world, which is the residency of 18.47% of total humans on planet earth. Keeping environment is a major policy concern; this study investigates the relationship between environmental quality and economic growth in the presence of human capita and renewable energy use. The study uses the data for the time period of 1995–2017. While conducting nonparametric tests, the study applied QMMR regression approach to hand the issues of cross-sectional heterogeneities and endogeneity simultaneously. The study found that GDP is positively associated with carbon emissions throughout all the quantiles. Foreign trade is showing mixed results, most probably due to changes in trade patterns, and a hidden heterogeneity across provinces in China.

Agriculture is the major source of livelihood in rural areas and is considered the backbone of the Indian economy. In Sikkim, agriculture is being practiced by 80% of the rural population, and having no other major livelihood options has created immense pressure on the farmers and agricultural land. Agriculture sector is under great stress as the farmers are being confronted by various challenges in Sikkim Himalaya in recent years, such as land degradation, climate change and socio-economic problems. Despite the number of indigenous agriculture management methods being practised in Sikkim Himalaya, the agricultural production system is weakening. In this context, this paper presents an analysis of challenges faced by indigenous communities, local farmers and potential sustainable strategies for their management in Rani Khola watershed of Sikkim Himalaya. Data and information were collected by field observation, questionnaire surveys of 300 households, key informant interviews and focus group discussions conducted during 2017–18. Data processing and analysis were carried out with a combination of techniques, such as the application of remote sensing (RS), geographic information system (GIS)-based data processing and descriptive statistics. Major challenges identified in the watershed are water scarcity (80%), climate change (88%), soil erosion and runoff (72%), higher investment cost (100%), lack of irrigation facilities (77%), fragmentation and size of landholdings (100), human–wildlife conflict (59%) and pests and disease (60%). Some possibilities and innovations that could address these problems are the use and retaining of various indigenous soil and water conservation (SWC) measures, diversified farming systems, community involvement in the government development process, better irrigation facilities, strengthening the local economy, coordinated planning between stakeholders and development of market feedback mechanism within the system.

Today, smart cities offer many significant benefits. As a result, the smart city is usually described in highly benign terms, and it is often uncritically assumed that its implementation must lead to widescale improvements, at all levels of society. Yet, while smart cities undoubtedly offer advantages, they also carry associated risks and dangers which could outweigh the benefits. This raises the important question of to which extent the ordinary citizen is aware of these risks and dangers. This study sets out to examine this question. While the ‘downsides’ of smart cities have been the subject of research over the past few years, there have been no studies which explore the perception of smart city disadvantages among citizen-stakeholders in the GCC countries. This study seeks to fill this gap in the literature, by examining the perspectives of a representative sample of citizens from the GCC. The results show that, despite the ‘whitewashing’ effect of public messaging, the study’s participants have a range of significant concerns about smart cities which could affect their acceptance of such initiatives. The findings will inform smart city development bodies in the GCC and contribute to strategies for promoting smart cities and citizen engagement.

The brick kiln industry is one of the largest and most highly unregulated industrial sectors in developing countries. Most of the kilns use low-quality coal as primary fuel along with small quantities of bagasse, rice husk, and wooden chips. As a result of inefficient methods of combustion in conventional brick kilns, such as fixed chimney Bull’s trench kilns (FCBTKs), harmful pollutants are emitted in high quantities, which ultimately deteriorate the environment and are widely in operation in Pakistan. The most prominent harmful pollutants include carbon dioxide (CO2), carbon monoxide (CO), sulphur dioxide (SO2), black carbon (BC), and particulate matter less than 2.5 microns (PM2.5). Over the years, new technologies have been adopted by developed countries for the reduction of environmental burdens. One of these technologies is induced draught zigzag kilns (IDZKs), or zigzag kilns (ZZKs), technology, which effectively improves the combustion across the path of bricks stacked in a zigzag pattern. For the mass adoption of this technology, environmental assessment and comparison of both technologies is a crucial step. Both types of kiln sites are investigated for the analysis of their emissions and their environmental impact in this work. Carbon mass balance equations are used for the calculation of emission factors. Collected inventory data is then used for the life cycle assessment of both types of kilns using open LCA (version 1.10.3) and the Eco-invent database. According to the study, ZZK technology outperforms FCBTK in all aspects. The analysis of the specific energy consumption (SEC) of fired bricks for each kiln type reveals that ZZKs require 30% less energy than the conventional FCBTK. This implies that ZZKs demand lesser fuel than FCBTKs. The zigzag technology adoption scenario, in particular, can lead to approximately 30% lower CO2 emissions, which can be further reduced by up to 80% when taking into account black carbon (BC) emissions. Additionally, the adoption of zigzag technology can result in a 35% decrease in PM2.5 emissions. The study shows that adopting ZZK technology significantly reduces impact categories, such as particulate matter formation (PMF), photochemical oxidant formation (POF), and terrestrial acidification (TA) by 63%, 93%, and 95%, respectively.

Assessment of the global burden of disease is based on epidemiological cohort studies that connect premature mortality to a wide range of causes, including the long-term health impacts of ozone and fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5). It has proved difficult to quantify premature mortality related to air pollution, notably in regions where air quality is not monitored, and also because the toxicity of particles from various sources may vary. Here we use a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments. In accord with the global burden of disease for 2010 (ref. 5), we calculate that outdoor air pollution, mostly by PM2.5, leads to 3.3 (95 per cent confidence interval 1.61-4.81) million premature deaths per year worldwide, predominantly in Asia. We primarily assume that all particles are equally toxic, but also include a sensitivity study that accounts for differential toxicity. We find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM2.5, with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.