PROPOSING A MODEL OF GREEN SUPPLY CHAIN MANAGEMENT BASED ON NEW PRODUCT DEVELOPMENT (NPD) IN AUTO INDUSTRY

Purpose: The present study is an attempt to provide a model of green supply chain management based on new product development (NPD) in the auto industry. Methodology: In the present study, the method of grounded theory has been used to develop a model. The required data were collected through interviews with academic experts, auto industry managers, and a review of documented meta-data and relevant reports. The sampling continued until the saturation of categories using the theoretical sampling method. The research data were analyzed simultaneously with the data collection during the three stages of open, axial, and selective coding. This process led to the development of the green model of supply chain management following NPD in the auto industry based on the grounded theory. Main Findings: The findings of this study suggest that managers must carefully consider all categories and subcategories identified in this study and have the necessary and sufficient information about each item to implement the green supply chain management successfully based on NPD in the automotive industry. Application of Study: This research indicates the need to pay attention to green supply chain management based on the development of new products in the automotive industry. Novelty/Originality: Development of the green model of supply chain management following NPD in the auto industry based on the grounded theory.

Green Automotive Industry: Global Trends and Challenges for Russian Car Manufacturers

The transition to the “green” model of the economy is a complex strategic task that requires a combination of two development vectors: maintaining dynamic economic growth and long-term preservation of the natural environment. One of the key directions for resolving existing environmental problems is associated with decarbonization and improving the eco-friendliness of vehicles. Almost all the world’s automobile corporations are actively developing electric transport technologies and launching an increasing number of new models into mass production, offering alternative fuel sources. This paper investigates trends in the greening of the global automotive industry, the resulting threats and risks of the development of the national automotive industry, identifies the reasons for the environmental instability of the Russian automotive industry.

The Performance of Adaptive Approach in Lean and Green Operations

In recent years, the manufacturing sector has been pressured with global warming and resource scarcity. This issue has triggered the industry to seek for solutions to improve the sustainability of their production. Based on literature study, the main components of an organisation consists of manpower, machine, money, material and environment. Thus, the fundamentals need to be addressed to improve the production performance. In this study, an adaptive lean and green approach is presented to identify the priority areas that can improve the organisation performance. Backpropagation algorithm is incorporated into the adaptive model to analyse the dynamic performance of the organisation. However, the input of industry expert is required to prioritise the initial input of the main components. This is relatively important as prioritisation of main components defer from sectors. A case study will be illustrated with the adaptive lean and green model. Operation improvements shall be observed through the implementation of the proposed method.

Visible Light-Driven Photocatalytic Activity of Magnetic Recoverable Ternary ZnFe2O4/rGO/g-C3N4 Nanocomposites

ZnFe2O4/rGO/g-C3N4 ternary nanocomposite photocatalysts with different ZnFe2O4/g-C3N4 weight ratio (0.5, 0.75, 1) were prepared by a stepwise solvothermal method using ethylene glycol as the solvent. Physicochemical methods such as X-ray diffraction, UV-Vis diffuse reflectance spectroscopy and photoluminescence spectroscopy were applied in order to characterize the composites. The formation of a meso-/macroporous structure with specific surface area between 67 and 77 m2 g–1 was confirmed by N2 adsorption/desorption. The bandgap of the composites was found to be lower (2.30 eV) than that of g-C3N4 (2.7 eV). In contrast to pure g-C3N4, the composites showed no fluorescence, i.e. no recombination of e–/h+ took place. All samples, including pure g-C3N4 and ZnFe2O4, were tested for adsorption and photocatalytic degradation of aqueous malachite green model solutions (10–5 M) under visible light irradiation (λ > 400 nm). The results show that the prepared nanocomposites have higher absorption and photocatalytic activity than the pristine g-C3N4 and ZnFe2O4 and can be successfully used for water purification from organic azo-dyes.

Rhodamines as Photocatalyst in Organic Synthesis

Abstract:: Organic synthesis under environment friendly conditions has great impact in the sustainable development. In this context, visible light photocatalysis has emerged as a green model as this offers an energy-efficient pathway towards the organic transformation. Different transition-metal catalysts (Ir-, Ru-, Cu- etc) and organic dyes (eosin Y, rose bengal, methylene blue etc) are well-known photocatalysts in organic synthesis. Apart from the well-known organophotoredox catalysts, rhodamines (Rhodamine B and Rhodamine 6G) have been also employed as efficient photocatalysts for different organic transformations. In this review, we will focus on the photocatalysis by rhodamines in organic synthesis. Mechanistic pathway of the methodologies will also be discussed. We believe this review will stimulate the employment of rhodamines in the visible light photocatalysis for efficient organic transformations in the future.

Large-Scale and Refined Green Space Identification-Based Sustainable Urban Renewal Mode Assessment

Urban sustainable renewal has received extensive attention in a wide range of fields, including urban planning, urban management, energy management, and transportation. Given that environmental resource conservation is critical to urban sustainability renewal, this study highlighted the imbalance among green space, urban development, and transportation accessibility. Here, a novel node-place-green model is presented to measure sustainable urban development; meanwhile, deep learning is utilized to identify and extract the green space to measure the environmental index. Based on the generated node, place, and green value, urban developing status could be classified into nine modes for further analysis of transportation, urban function, and ecological construction. The experimental results of Harbin reveal the feasibility of the proposed method in providing specific guidelines for urban planning and policies on sustainable development.