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The concept of sustainable transportation of goods as the primary paradigm for designing contemporary logistics systems assumes the use of energy-efficient and affordable modes of transport in a way that guarantees the most cost-efficient variant of the delivery scheme. That especially applies to road transport deliveries, where the number of alternatives for organizing the transportation process is numerous and the choice of the optimal solution is complicated by the multiple stochastic influences of the environment on the technological processes. In this paper, we contribute to solving the problem of shaping the sustainable delivery schemes by proposing an approach to shape the complete set of alternative transport and technological schemes for packaged cargo delivery by road transport. The developed mathematical model allows estimating the efficiency of each alternative delivery scheme for the given request and chooses the best variant that minimizes the total costs of all participants in the delivery process. The proposed algorithms are implemented in the C# programming language within the frame of a class library for modeling transport delivery processes. A case of transport processes for Delivery Ltd. (Kharkiv, Ukraine) is applied to illustrate the procedure of using the developed approach to choose the optimal transport and technological schemes for long-distance deliveries. As the result of simulating the goods transportation processes, we show the regression models that represent dependencies of the total costs for the implementation of a delivery scheme from the parameters of demand for the transportation of goods. These regression models allow estimating the most efficient delivery schemes based on the functional analysis of the obtained dependencies for the given demand parameters.

The current era of computing is witnessing a huge amount of data being generated with every passing moment. This massive data if nourished effectively can open new horizons for the computing world. The modern world is slowly but surely moving towards the automation age where every entity and object is being automated to perform desired tasks without the need of human interventions. This has made the lives of people more convenient and comfortable. Automation has taken over every single field of computing and even beyond. Smart mobility is one such example of automation wherein the users get real time information about the traffic conditions as well as alternate route suggestions in case of traffic jams. Transportation is considered as the backbone of every business. The automated intelligent transportation system (ITS) has completely transformed the way how people, goods and services are transported and is quite important for achieving sustainability. This paper provides an overview of the existing ITS system, concept of smart mobility and existing vulnerabilities in these systems. Their security concerns and scenarios are also analyzed. Furthermore, in this paper the importance and need for securing these intelligent systems is highlighted and future trends in ITS is also suggested. Although ITS and smart mobility technology are already providing convenient transportation and navigational facilities, there is still a huge scope to improve these facilities for the end users. The suggested future trends if integrated in an effective manner can provide exemplary means to provide state-of-the-art navigational facilities and smart mobility in a true sense.

The demand for electric vehicles (EVs) has significantly increased in recent years, but it represents only a tiny percentage of the total new vehicles sold globally. In 2021, the total sales of electric two-wheelers were less than 1% only. Therefore, the adoption of an electric two-wheeler (E2W) needs to be studied. This research develops a behavioral electric two-wheelers adoption intention model. The present study aimed to identify the factors that influence consumers’ intentions to adopt electric two-wheelers. The questionnaire method was employed, and 182 valid responses were collected. The partial least squares structural equation modeling (PLS-SEM) was used to test the research hypothesis. The empirical results indicate that environmental concern, perceived economic benefit, charging infrastructure, and social influence significantly impact consumers’ attitudes towards electric two-wheelers. Attitude also significantly affects the consumer’s intention to purchase an electric two-wheeler. Perceived economic benefits were found to be the main factor influencing consumers to purchase electric two-wheelers. The findings of this research also show that women are more inclined towards purchasing electric two-wheelers than men. These results offer useful information for governments and electric-two-wheelers companies to gain a better understanding of consumer behavior towards purchasing electric two-wheelers.

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of our imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1 and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the higher fossil emissions and smaller SLAND for that year consistent with El Niño conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data indicate a renewed growth in EFF of +2.0 % (range of 0.8 % to 3.0 %) based on national emissions projections for China, USA, and India, and projections of Gross Domestic Product corrected for recent changes in the carbon intensity of the economy for the rest of the world. For 2017, initial data indicate an increase in atmospheric CO2 concentration of around 5.3 GtC (2.5 ppm), attributed to a combination of increasing emissions and receding El Niño conditions. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quéré et al., 2016; 2015b; 2015a; 2014; 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017.