Rising environmental concerns and globalization of supply chain made their control and management difficult. Blockchain technology, as a distributed digital ledger technology, guarantees security, visibility, traceability, and transparency promises ease for environmental and global supply chain problems. In this research work, blockchain technology and business analytics techniques were critically analyzed in the context of supply chain. Global and local governmental bodies, consumers, and communities are putting pressure to meet sustainability goals, which prompts us to investigate how these technologies can address and aid remanufacturing and recycling practices and sustainability in supply chain. The results illustrate that advanced technologies have a key role in the implementation of remanufacturing and recycling practices. The results also indicate that the remanufacturing and recycling practices substantially improve automobile firm performance. Moreover, the results also indicated that Covid-19 pandemic has positive moderating effect between advanced technology and remanufacturing and recycling practices while having insignificant effect between remanufacturing and recycling practices and automobile firm performance.
In ecological and environmental sampling the quantification of units is either difficult or overly demanding in terms of the time, money, workload, it requires. For this reason efficient and cost-effective sampling methods need to be devised for data collecting. The most commonly used method for this purpose is the Ranked Set Sampling (RSS). In this paper, a sampling scheme called Improved Paired Ranked Set Sampling (IPRSS) is proposed to estimate the population mean. The performance of the proposed IPRSS is evaluated under perfect and imperfect rankings. A simulation study based on selected hypothetical distributions and a real-life data set showed that IPRSS is more precise than RSS, Paired RSS (PRSS) or Extreme RSS (ERSS).
The United Nations’ Sustainable Development Goal (SDG) number seven expressly calls for universal access to affordable and sustainable energy. Energy sustainability will require a reduction in energy consumption, including embedded energy consumption in sectoral demand and supply chains. However, few studies have estimated the amount of coal, petroleum, and gas (fossil fuel) embedded in demand-and-supply chains (FFEDS). Furthermore, the inter-and intra-sectoral energy linkages are understudied. This study quantifies China’s FFEDS, the world’s largest energy consumer. According to the findings, the highest levels of coal, natural gas, and petroleum consumption (CNGPC) are embedded in the construction sector’s input demand. “Electricity and steam production and supply” total intermediate exports (internal plus inter-sectoral) stimulated the highest coal consumption. “Crude petroleum products and natural gas products” and “railway freight transport” aggregate supplies induced the highest volume of natural gas and petroleum consumption. Compared to intra-sectoral demand, inter-sectoral demand stimulated significantly larger CNGPCs. In contrast, CNGPC’s inter- and inter-sectoral supplies were nearly identical. Modifying current carbon taxation and credit mechanisms to include energy embedded in demand and supply can help to achieve SDG 7.
Today’s world is changed; the only constant thing is digital technologies galloping and enveloping all walks of life; blockchain is the most pertinent of the available technologies. Due to the high demand for the technology, this research tests blockchain technology (BTT) and its influence on organizational performance (ORP) while incorporating recycling and remanufacturing (RRM), green design (GDN), and green purchasing (GPP) as mediators to ascertain the relation between the two constructs. The data for the research is collected from the Malaysian manufacturing sector. The data was collected from four hundred enterprises, and regression analysis was used for statistical inference through Smart PLS. Significant results are found between BTT and RRM, BTT and GDN, BTT and GPP, RRM and ORP, and GDN and ORP. The study’s result also confirms that no significant value was found between GPP and ORP.
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