Due to the high-quality development of the Chinese economy, the improvement of environmental efficiency in Chinese industries has become a significant task. Understanding the environmental efficiency of the logistics industry is essential for implementing effective environmental policies. This study aims to explore the impacts of economic growth, foreign direct investment (FDI), and innovation on the environmental efficiency of the logistics industry. In this paper, we apply the undesirable SBM model to calculate the environmental efficiency of the logistics industry and use the Tobit model to analyze the impacts of economic growth, foreign direct investment (FDI), and innovation on the environmental efficiency of the logistics industry in provinces along the Belt and Road in China from 2009 to 2018. Based on the results indicate the average environmental efficiency of the logistics industry in Chinese provinces along the Belt and Road is 0.7880, indicating that the environmental efficiency of the logistics industry was generally low in some regions along the belt and road. Innovation and FDI were found have a significant impact on the environmental efficiency, while economic growth fails to significantly impact on the environmental efficiency of the logistics industry in provinces along the Belt and Road. Therefore, we should encourage improvement of the level of environmental efficiency of the logistics industry. It is necessary to realize the co-ordinated development of the logistics industry and the environment through optimization of the development of innovation and transforming the FDI model in provinces along the Belt and Road.

Agricultural carbon emissions are one of the major causes of global climate change. As some of the world’s largest agricultural producers and consumers, countries along the route of the Belt and Road initiative produce significant agricultural carbon emissions. An in-depth study on the efficiency of agricultural carbon emissions in countries along the route can help countries reduce environmental load while improving agricultural production, optimizing resource use, improving agricultural production efficiency, and achieving sustainable development goals, which is significant for global climate change mitigation. Based on the relational data and network perspective, this paper takes the agricultural carbon emission efficiency of 34 countries along the route of the Belt and Road Initiative from 1995 to 2020 as the research object. It integrates the social network analysis method and other methods to realize the expansion of agricultural carbon emission efficiency in the research method. The study shows that (1) agricultural carbon emission efficiency has more room for improvement and presents complex spatially linked network characteristics; (2) the spatial correlation network of agricultural carbon emission efficiency is relatively well connected, and there is a general spatial correlation and spatial spillover effect among countries; and (3) similar differences in the proportion of primary industries and differences in informatization levels help establish spatial correlations between regions and produce spatial spillover effects. It is imperative to change global economic growth, social development, and lifestyles through green development. This study is conducive to the international community’s formulation of differentiated agricultural carbon emission reduction support mechanisms for different countries to help the countries realize the transformation of agriculture and even overall economic development as soon as possible. At the same time, accelerating the pace of emission reduction and reducing the negative impact of agricultural carbon emissions are conducive to better responding to the challenges posed by global climate change.

Decision support technology has become a key link in modern information strategy. With the deepening of research, introduced expert systems have been introduced into decision support systems. In this way, decision support systems gradually become more uncertain and capable of handling uncertainties. The development direction of decision support system is typically based on qualitative analysis. Intelligent decision support system is a system that combines decision support system with artificial intelligence technology. This study attempts to assess in an innovative way the relationship between financing constraints, entrepreneurship, and agricultural firms. The most recently proposed intelligent decision support system, AI-assisted Intelligent Decision Support System (AIIDSS), is used to predict the impact of entrepreneurship on corporate performance. The paper constructs an entrepreneurship index from five aspects: innovation, competitiveness, human capital accumulation, management capability, and adventurous spirit. The method intends to construct the Kaplan–Zingales (KZ) index to evaluate financing constraints. Through an empirical study, it was found that entrepreneurship can significantly promote the growth of listed agricultural companies. The study can drastically reduce the difficulties involved in financing constraints normally faced by agricultural companies. The impact paths include increasing agricultural company operating cash flow, improving stock liquidity, and increasing debt financing. The research suggests that if listed agricultural companies are to improve financing constraints, entrepreneurs must improve their own competitiveness and management capabilities. This will help in reasonably controlling research and development investment besides the impulse to take risks. As the growth of an enterprise relies on considering the determinants of financing constraints, this research provides an effective investigation technique. Moreover, the findings of the study will help entrepreneurs, particularly agricultural companies, to bear most of the risks and to avail most of the opportunities.

The digital revolution changes the perception of the economy and business. The emergence of the industrial internet of things (IIoT) poses a large impact on established business models of manufacturing companies. As an important economic partner of China in South Asia, Pakistan plays an important role in the Belt and Road Initiative. This paper based on the three models: trade specialization coefficient (TSC) index, revealed comparative advantage (RCA) index, and trade complementarity (TC) index, analyzes the competitiveness and complementarity of Chinese Fruit and Vegetable (F&V) trade between China and Pakistan. The results showed that China-Pakistan had their own comparative advantages in F&V export, and there was a big difference in the types of F&V, and the competitiveness of F&V export between them was weak. The import and export structure of F&V between China and Pakistan is consistent, and there is strong trade complementarity. Therefore, it is suggested that China-Xinjiang and Pakistan should establish an import and export cooperation mechanism for F&V and export F&V with strong complementarity to each other. We should build a well-connected logistics network, which relies on the geographical advantages of Xinjiang, and comprehensively upgrade the level and quality of agricultural cooperation with Pakistan to enhance the agricultural cooperation between China and Pakistan. At the same time, China-Pakistan should strengthen cooperation in agricultural technology, trade, and equipment so as to achieve win-win results.

Whether or not consumers consider information about origin in their routine food purchase decisions is a contentious issue. Usage of social networks has redefined lifestyle and culture, and firms are finding it difficult to understand their customers. This study aims to investigate the effect of social networks (digital marketing) on Pakistani consumers’ understanding of the CoO (country-of-origin) label for F&V (fruit and vegetable) products and its relative importance in daily purchase decisions of fruit and vegetable products. In a random survey of 797 consumers in two Pakistani cities, we found that the relationship between the CoO information and the daily F&V product choices of Pakistani consumers was less important than other factors. Consumers are mistaken about the CoO label, with only one-third of respondents correctly understanding the difference between labels of “made in...” and “product of....” Therefore, mandatory CoO label policies may increase costs and reinforce consumers’ misinterpretation of the meaning of these labels; obtaining a “user-friendly” CoO label serves only those consumers who properly understand the information. This study contributes to understanding of the extent to which consumers are competent in their knowledge and understanding of these informational labels.