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The pandemic shock puts the world on quarantine and paused economic operations that affected energy consumption and economic output. This study analyzed the impact of the COVID-19 shock on GDP, energy consumption, and climate change then forecasted the situation until 2032 using the system dynamic modeling approach. The outcomes reveal that the pandemic shock will decrease the growth by 1.3% in 2020 and 1.32% in 2021. The current shortfall, low energy consumption, and delay in completion of energy-related projects can reduce the GDP by 5.2% in 2020. The effect will penetrate the system and will cause further losses in the upcoming years. The energy consumption and quarantine situation will improve the climate situation and drop the average temperature by 0.049 and 0.021 o C in 2020 and 2021. The aggregate demand and supply side measures such as national spending, lowering the lending rate, and cuts in income taxes can help in diffusing the situation. The government should start operations on ongoing energy projects, give relaxation to SME’s with tight SOPs to secure jobs, and prevent possible GDP losses. The decline in oil prices provides an opportunity to cut fossil fuel subsidies and implement a carbon pricing mechanism.

The research explores the nexus between technological innovation and green growth in nine newly industrialized (NI) countries for the period from 2000 to 2016. Cross-sectional dependence and unit root tests are preliminary conducted. Pedroni’s panel cointegration and fully modified OLS (FMOLS) confirm the existence of long-run variables association. The outcome from IPAT, STIRPAT, and MLR reveals that production and processing-related technologies are harmful to green growth. Climate change mitigation technologies linked to transportation, water treatment, and processing have a positive impact on green growth. However, energy consumption, generation, and transmission technologies hurt the environment. Similarly, energy consumption in the agriculture sector adds in greenhouse gas emission, whereas, renewable energy is contributing positively to green production and growth. Environmental taxes and research and development budgets are playing a decisive role in the pursuit of green growth. The results have significant policy implications for policymakers, which can lead to green growth.

The green innovations, environmental policies, and carbon taxes are the tools to achieve sustainable development goals (SDGs) in the mitigation process. This study is intended to examine the impact of innovation, carbon pricing (CTAX), environmental policies (EP), and energy consumption (ECON) on PM 2.5 and greenhouse gas (GHG) emission for Central-Eastern European countries. The panel effect during 2000–2018 is tested using a dynamic panel data model while the Granger causality approach obtains country-related outcomes. The outcomes reveal that eco-friendly innovations have a more profound effect on carbon mitigation. Environmental policies reduce emissions by 2.7% in the short run and 17.4% in the long run. Similarly, CTAX mitigates GHG emissions by 8.6% in the short-run and PM2.5 by 0.9% and 5.7% in the short and long run. However, urbanization, energy consumption and trade openness are the leading polluters in the region. The main findings remain dominant in the country-specific results and find unidirectional and bidirectional causality evidence among variables. The research concludes that green innovations and strict environmental policy can lead towards achieving sustainable development goals using carbon taxes as a tool on the way. Graphical abstract

The dynamics of global emissions and the increasing focus on market-based policy instruments have prompted this research to examine the extent to which such instruments are successful in emission control. The study explores the influence of carbon taxes, eco-friendly innovations, and ecological policy in attaining sustainable development goals and fulfilling the mitigation targets of climate change for 2030. The article selected 15 EU countries from southern and western regions and tested the empirical relationship between 2000 and 2018. This work used second-generation testing approaches and error correction-based modeling approaches to analyze the relationship between the variables. The results show that eco-friendly innovations and environmental policies help reduce emissions in the long and short run. On the other hand, carbon taxes have a more prominent effect on mitigation efforts, specifically in the short run. Factors such as urbanization, economic growth, and energy consumption are the most prominent polluting elements, the results being consistent in all models. The results further show a unidirectional and bidirectional causality relationship between the variables, and outcomes are more country-specific. Given these arguments, carbon taxes are a short-term instrument in combating carbon emissions. However, the sustainable development vision 2030 relies on eco-innovations linked with research and development and the transition from gray to green energy. Graphical abstract

The exponential distribution has always been prominent in various disciplines because of its wide range of applications. In this work, a generalization of the classical exponential distribution under a neutrosophic environment is scarcely presented. The mathematical properties of the neutrosophic exponential model are described in detail. The estimation of a neutrosophic parameter by the method of maximum likelihood is discussed and illustrated with examples. The suggested neutrosophic exponential distribution (NED) model involves the interval time it takes for certain particular events to occur. Thus, the proposed model may be the most widely used statistical distribution for the reliability problems. For conceptual understanding, a wide range of applications of the NED in reliability engineering is given, which indicates the circumstances under which the distribution is suitable. Furthermore, a simulation study has been conducted to assess the performance of the estimated neutrosophic parameter. Simulated results show that imprecise data with a larger sample size efficiently estimate the unknown neutrosophic parameter. Finally, a complex dataset on remission periods of cancer patients has been analyzed to identify the importance of the proposed model for real-world case studies.

This study uses the Bayesian structural model to assess the causal effect of the futures exchange (FTX) insolvency on cryptocurrencies from October 2022 to December 14, 2022. Findings show that FTX insolvency negatively impacts cryptocurrencies. Moreover, the results indicate rapid divergence from counterfactual predictions, and the actual cryptocurrencies are consistently lower than would have been expected in the absence of the FTX collapse. Cryptocurrency is reacting strongly to the uncertainty caused by insolvency. In relative terms, the collapse of FTX has been highly detrimental to Solana and Ethereum. Furthermore, the outcomes show that cryptocurrencies would not have been negatively affected if the intervention had not occurred. FTX collapsed owing to a mismatch between the assets and liabilities. The industry is still mostly unregulated, and regulators must act quickly, highlighting the need for outstanding innovation and decentralized and trustless technology adoption.

The study uses the rolling window technique to assess the causal link between the oil price (OP) and the Baltic Dry Index (BDI) from 2000 to 2022. The results support the competitive market hypothesis, suggesting that OP and BDI have a significant relationship. The results indicate that knowledge of potential changes in OP can help to lessen the risk of abrupt changes in BDI. Similarly, information on various sources of OP changes can assist the shipping industry in taking practical measures to minimize the impact of shocks. The stability of global maritime trade is vital to the global economy because its massive shares and abnormal behaviour can negatively affect the oil market and international trade. This helps ensure timely measures are taken to avoid unforeseen events and maintain stability. Therefore, the study is a valuable contribution to the shipping industry stakeholders in terms of information about the specific period with underlying factors.

A compact, low profile, multiple-input–multiple-output (MIMO) diversity antenna with super-wideband (SWB) characteristics has been proposed. The proposed antenna comprises four symmetric monopole-radiating elements printed on low-cost FR4 substrate with the slotted ground plane. The single antenna of a monopole structure and a quad-port MIMO antenna, with the dimensions of 30 × 20 mm2 and 60 × 55 mm2, respectively, are ideal for IoT and high-speed data applications. The proposed MIMO antenna has a high diversity gain and low envelope correlation coefficient (ECC) within the frequency range. Simulated results demonstrate the performance of the MIMO-SWB antenna, which operates from 2.3 to 23 GHz, with a high isolation level over 20 dB in the achieved frequency band. Moreover, the proposed MIMO antenna has been investigated with mirror fashion and orthogonal structure. Both structures provide similar results except for mutual coupling performance. The orthogonal adjustment for high isolation achieves better results with the proposed model. Further, the prototype of the proposed antenna is fabricated and measured effectively. Simulated and measured results show good agreement for super-wideband applications.

This study aims to investigate the relationship between digital financial inclusion and carbon neutrality in different provinces of China. The findings indicate that the relationship between digital financial inclusion and carbon neutrality varies across regions. Specifically, digital financial inclusion is found to have a causal effect on carbon neutrality in one-third of the provinces, with a stronger effect observed in the middle region. Conversely, carbon neutrality is found to have a causal effect on digital financial inclusion, particularly in the western regions. However, no causal relationship is detected between digital financial inclusion and carbon neutrality in two-thirds of the regions. Promoting green technology and supporting green innovation initiatives can reduce carbon emissions and support regional innovation clusters. Investing in digital infrastructure, incorporating environmental risk assessments, and aligning digital financial services with sustainable development goals can improve access.

Since its emergence in China, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide including Pakistan. During the pandemic, whole genome sequencing has played an important role in understanding the evolution and genomic diversity of SARS-CoV-2. Although an unprecedented number of SARS-CoV-2 full genomes have been submitted in GISAID and NCBI, data from Pakistan is scarce. We report the sequencing, genomic characterization, and phylogenetic analysis of five SARS-CoV-2 strains isolated from patients in Pakistan. The oropharyngeal swabs of patients that were confirmed positive for SARS-CoV-2 through real-time RT-PCR at National Institute of Health, Pakistan, were selected for whole-genome sequencing. Sequencing was performed using NEBNext Ultra II Directional RNA Library Prep kit for Illumina (NEW ENGLAND BioLabs Inc., MA, US) and Illumina iSeq 100 instrument (Illumina, San Diego, US). Based on whole-genome analysis, three Pakistani SARS-CoV-2 strains clustered into the 20A (GH) clade along with the strains from Oman, Slovakia, United States, and Pakistani strain EPI_ISL_513925. The two 19B (S)-clade strains were closely related to viruses from India and Oman. Overall, twenty-nine amino acid mutations were detected in the current study genome sequences, including fifteen missense and four novel mutations. Notably, we have found a D614G (aspartic acid to glycine) mutation in spike protein of the sequences from the GH clade. The G614 variant carrying the characteristic D614G mutation has been shown to be more infectious that lead to its rapid spread worldwide. This report highlights the detection of GH and S clade strains and G614 variant from Pakistan warranting large-scale whole-genome sequencing of strains prevalent in different regions to understand virus evolution and to explore their genetic diversity.