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This article investigates the impact of five determinants of the green supply chain practices on organizational performance in the context of Pakistan manufacturing firms. A sample of 218 firms was collected from the manufacturing industry. The green supply chain practices were measured through five independent variables including green manufacturing, green purchasing, green information systems, cooperation with customers, and eco-design. By using exploratory factor and simultaneous regression analysis, the results indicate that except green purchasing, rests of the four independent variables have been found statistically significant to predict organizational performance. However, the eco-design of green practices followed by green information systems has revealed the greatest impact on organizational performance. Therefore, the managers of the manufacturing firms should not only implement eco-design in their supply chain but also concentrate on proper monitoring and implementation of green information systems to increase their firms’ performance. A main contribution of this research from theoretical side is that it is possible to notice a negative effect of “green purchasing” towards organizational performance particularly in the scenario of Pakistan manufacturing industry. Another valuable result is that green purchasing is an important antecedent of firms economic performance in the US manufacturing firms (Green et al. 2012 ), although not significantly related to organizational performance in our study. In addition, we also discussed research limitations, areas for future research, and implications for practitioners.

The objective of the study is to examine the long-run and causal relationship between environmental logistics performance indicators (ELPI) and growth-specific factors in a panel of 15 selected global ranked logistics countries over a period of 2007–2015. This study is exclusive as we utilized a number of LPI factors including logistics performance, logistics competence, and logistics infrastructure with mediation of sustainable factors, i.e., carbon dioxide (CO 2 ), fossil fuel, and greenhouse gas (GHG) emissions in a region. The results show that the per capita income, industry, manufacturing, and service share to GDP is affected by CO 2 emissions and GHG emissions. Logistics competence and infrastructure promote economic growth and sectoral value added, while energy demand and FDI inflows both are prerequisite for sustainable agriculture in a region. The causal relationships confirm that more energy demand results in an increase in economic growth, industry value added, and the service sector (i.e., feedback hypothesis), while the sustainable supply chain system improves energy demand, FDI inflows, economic growth, and sectoral growth (i.e., conservation hypothesis) in a panel of countries.

The aim of this study is to examine the association between national economic and environmental indicators with green logistics performance in a time series data of UK since 1981 to 2016. The research used autoregressive distributed lag method to understand the long-run and short-run relationships of national scale economic (foreign direct investment (FDI) inflows, per capita income) and environmental indicators (total greenhouse gases, fossil fuel, and renewable energy) on green logistics. In the short run, the research findings indicate that the green logistics and renewable energy have positive relationship, while fossil fuel is negatively correlated with green logistics operations. On the other hand, in the long run, the results show that FDI inflows, renewable energy sources, and per capita income have statistically significant and positive association with green logistics activities, while foreign investments attracted by environmental friendly policies and practices adopted in global logistics operations, which not only increase the environmental sustainability but also enhance economic activities with greater export opportunities in the region.

Chinese classical garden rockeries have rich historical and cultural value, as they are a part of China’s cultural heritage. In recent years, while the digital research of rockeries has increased, most of it is limited to a rockery’s digital record. However, other issues have not been thoroughly discussed, such as how to digitally protect a rockery, activate it as an immovable, solid site heritage by digital means, and transform its function. To ensure the effectiveness of rockery data, this paper compares the advantages and disadvantages of digitally mapping the Huanxiu Shanzhuang rockery, which was conducted by the team at an early stage, to a convenient and accurate digital rockery data acquisition scheme that was used for the Zhanyuan South Rockery, as an example of carrying out three-dimensional digital mapping. Furthermore, from the perspective of the sustainable development of cultural heritage, this study attempts to print Chinese classical garden rockeries in three dimensions to realize their physical representation. This paper aims to provide a new way to activate, restore, and protect rockeries.

Improving the logistics ecological efficiency (LEE) has become a significant part of ensuring a sustainable development and tackling environmental pollution. Previous studies in the logistics industry seldom considered air pollutants and the association of spatial information. Therefore, innovatively considering SO 2 , NO x , and PM, this study adopted the super-SBM-undesirable model to calculate the LEE of 30 provinces in China from 2005 to 2019 and, thereafter, developed information-based matrix to explore its influencing factors by using the spatial Dubin model. The results indicated that (1) the overall LEE was low with the average of 0.657, presenting a three-stage trend of “decreasing first, then rising, and later stable,” and significant regional differences with the decreasing gradient pattern of the “Eastern-Central-Western.” (2) A spatial directionality distributed from the northeast to southwest and a significant spatial autocorrelation were observed. (3) The LEE had a significant positive spillover effect. Industrial structure, urbanization level, environmental regulation, and technological innovation level had a positive impact on the local LEE, and industrial structure displayed the most promoting effects. Energy intensity, economic level, energy structure, and opening level had a significant effect on the local LEE with varying degree of inhibition. Local energy intensity and environmental regulation had a positive influence on the LEE in neighboring areas, while local opening level had inhibiting effects. In addition, policy recommendations for enhancing the LEE were made.

Breeding of stress-tolerant plants is able to improve crop yield under stress conditions, whereas CRISPR/Cas9 genome editing has been shown to be an efficient way for molecular breeding to improve agronomic traits including stress tolerance in crops. However, genes can be targeted for genome editing to enhance crop abiotic stress tolerance remained largely unidentified. We have previously identified abscisic acid (ABA)-induced transcription repressors ( AITRs ) as a novel family of transcription factors that are involved in the regulation of ABA signaling, and we found that knockout of the entire family of AITR genes in Arabidopsis enhanced drought and salinity tolerance without fitness costs. Considering that AITRs are conserved in angiosperms, AITRs in crops may be targeted for genome editing to improve abiotic stress tolerance. We report here that mutation of GmAITR genes by CRISPR/Cas9 genome editing leads to enhanced salinity tolerance in soybean. By using quantitative RT-PCR analysis, we found that the expression levels of GmAITRs were increased in response to ABA and salt treatments. Transfection assays in soybean protoplasts show that GmAITRs are nucleus proteins, and have transcriptional repression activities. By using CRISPR/Cas9 to target the six GmAITRs simultaneously, we successfully generated Cas9-free gmaitr36 double and gmaitr23456 quintuple mutants. We found that ABA sensitivity in these mutants was increased. Consistent with this, ABA responses of some ABA signaling key regulator genes in the gmaitr mutants were altered. In both seed germination and seedling growth assays, the gmaitr mutants showed enhanced salt tolerance. Most importantly, enhanced salinity tolerance in the mutant plants was also observed in the field experiments. These results suggest that mutation of GmAITR genes by CRISPR/Cas9 is an efficient way to improve salinity tolerance in soybean.

This study aims to examine the effect of FinTech adoption on the sustainability performance of banking institutions in an emerging economy such as Bangladesh. Besides, this study also investigates the mediating role of green finance and green innovation in the relationship between FinTech adoption and sustainability performance. To examine the relationship among the study variables, this study used data from 351 employees of banking institutions operating in Bangladesh during the period January to March 2021 using a convenience sampling method. Furthermore, the study utilized a two-staged structural equation modeling and an artificial neural network (SEM-ANN) approach to analyze the data. The findings show that FinTech adoption significantly influences green finance, green innovation, and sustainability performance. Similarly, the results indicate that green finance and green innovation have a significant positive influence on sustainability performance. Furthermore, the results reveal that green finance and green innovation fully mediate the relationship between FinTech adoption and the sustainability performance of banking institutions. Moreover, the present study contributes to the existing literature on technological innovation, green finance, and sustainability performance greatly as it is the first study to examine both linear and non-linear relationships among these variables using the SEM-ANN approach. As a result, the study highlights the importance of FinTech adoption, green finance, and innovation in the attainment of sustainability performance, as well as the urgent need to incorporate new technologies, green initiatives, and financing into banking strategies to help achieve the country’s sustainable economic development.

Accurate State of Health (SOH) estimation is critical for ensuring the safe operation of lithium-ion batteries. However, current data-driven approaches face significant challenges: insufficient feature extraction and ambiguous physical meaning compromise prediction accuracy, while initialization sensitivity to noise undermines stability; the inherent nonlinearity and temporal complexity of battery degradation data further lead to slow convergence or susceptibility to local optima. To address these limitations, this study proposes an enhanced CNN-BIGRU model. The model replaces conventional random initialization with a Hiking Optimization Algorithm (HOA) to identify superior initial weights, significantly improving early training stability. Furthermore, it integrates an Attention mechanism to dynamically weight features, strengthening the capture of key degradation characteristics. Rigorous experimental validation, utilizing multi-dimensional features extracted from the NASA dataset, demonstrates the model’s superior convergence speed and prediction accuracy compared to the CNN-BIGRU-Attention benchmark. Compared with other methods, the HOA-CNN-BIRGU-Attention model proposed in this study has a higher prediction accuracy and better robustness under different conditions, and the RMSEs on the NASA dataset are all controlled within 0.01, with R2 kept above 0.91. The RMSEs on the University of Maryland dataset are all below 0.006, with R2 kept above 0.98. Compared with the CNN-BIGRU-ATTENTION baseline model without HOA optimization, the RMSE is reduced by at least 0.15% across different battery groups in the NASA dataset.

Logistics is an essential service for manufacturing because it supports the coordination and integration of global production activities, ultimately reducing costs and improving efficiency. However, it is not clear that the complex structural relationship between logistics and manufacturing under the Global Value chains (GVCs). Consequently, employing the input-output model, we develop the logistics input complexity (LIC) to capture the complex structural characteristics of logistics services embodied in manufacturing sectors and investigate its role in manufacturing comparative advantage. We find that the logistics input complexity can boost manufacturing competitiveness. Furthermore, we also confirm that the domestic and foreign groups of LIC have at least a partially complementary effect on the export competitiveness of manufacturing. Finally, we identify heterogeneity in the domestic and foreign groups' five subdivision logistics modes of LIC. It suggests that logistics service providers must precisely match the related manufacturing production chains to cultivate a comparative advantage in manufacturing.

Studying the linkage between manufacturing industry and logistics industry is conducive to explore and improve the efficiency of the common development of them. In order to study the interaction of logistics industry on the development of manufacturing industry and the development of two-industry-linkage, it first calculates the high-quality development level of logistics industry and manufacturing industry, then uses the coupling coordination model to theoretically analyze and empirically test the coupling and coordinated development level of high-quality development of logistics industry and manufacturing industry from three aspects: coupling degree, coordination degree and coupling coordination degree, and based on the perspective of integration field theory, it takes the three basic synthetic fields of logistics integrator, logistics base-nuclear and logistics connection-key as the analysis dimension, PVAR model was introduced for in-depth analysis the impact of logistics industry on manufacturing industry and the level of the two-industry-linkage. It was found that the high-quality development of China’s logistics industry and manufacturing industry is close on the whole, and the development trend is consistent, the high-quality development of them is mainly caused by the change of scale, but there is no obvious change in technical efficiency, which also provides a way for the high-quality development of the two-industry-linkage in the future. The two-industry-linkage mostly belongs to the situation of low-level mutual restriction, which has not yet reached a high level of mutual promotion, resulting in the overall coupling coordination degree basically in a state of barely coordination. The development of logistics industry and manufacturing industry need to go through certain practice and running in, when there is an error matching between the two, the logistics industry will inhibit the two-industry-linkage. When the economy develops to a certain extent, the expansion of the logistics system scale to the level of the two-industry-linkage is not necessarily beneficial, blindly exceeding the demand for logistics investment will cause a waste of resources, which is not conducive to the high-quality development of the logistics industry and the coupling and coordinated development of the two industries. In the long run, the change of the logistics basic-nuclear capacity, the logistics integrator scale and logistics connection-key level will have a positive impact on the change of green total factor productivity in manufacturing industry.