Managing the declining yield of non-food crops has opened new strategic challenges amidst global uncertainties. The COVID-19 scenario has increased awareness of natural lifestyle and eco-friendly products, largely dependent on non-food crop material. This strategic shift requires moving beyond traditional farm practices to improve agricultural production efficiency, and developing countries in particular have shown a consistent loss in their self-sufficiency of industrial crops despite being major exporters of non-food crop materials. However, existing studies analyze production efficiencies of non-food crops from general or theoretical aspects often by virtual estimates from breaking down the multiple factors of crop productivity. This study examined multiple factors of crop production to identify “which crop inputs have been inefficiently used overtime” by tracking efficiency changes and various input issues in overall cotton production from practical aspects, i.e., scaling non-constant returns of those multiple factors would allow for the violation of various situations. Accordingly, a stochastic frontier approach was employed to measure the production frontier and efficiency relationship using time-series data of Pakistan’s cotton production from 1971–2018—a specific non-food crop perspective from a top-ranked cotton-producing country that has recently been shifted towards being a non-exporter of cotton due to low yield. The coefficient of area, seed, and labor indicates the positive relationship with cotton production, while fertilizer, irrigation, electricity, and machinery are statistically negative. This implies that policymakers need priority-based strategies for the judicial use of synthetic fertilizers, irrigation, a subsidy policy, and technology adoption, which could significantly improve the efficiencies of cotton productivity from the same land resources. Being adaptable to other developing economies, the analysis would strategically facilitate designing and developing affordable technology-driven solutions and their customized extensions towards sustainable non-food crop production practices and Agri-Resources efficiencies.

The modern logistics industry in relation to economic growth and carbon emission has opened new strategic perspectives. Recent research work have analyzed such complex interference from a broad perspective. However, analyzing this overlap needs comprehensive insight into the logistics industry while simultaneously estimating its short-run and long-run effects from regional aspects due to continue-evolving factors and their impact on it. This paper competently analyzes logistics industry components in connection with economic prosperity, energy consumption, trade development, and carbon emission from a more specific regional perspective of a developing country. Methodologically, an autoregressive distributive lag model (ARDL) is employed using correlative evaluation of the dynamic factors and their interactive impact in short and long run on this relation, based on time-series data of Pakistan from 1990 to 2019. The study results endorse the previous studies’ outcomes by recognizing that an increase in carbon emission depends on trade development, energy usage, economic development, and the logistics industry’s various components except for air logistics. However, study results show a unidirectional long-run causality directing from economic development, logistics industry, energy utilization, and trade development to carbon emission. Moreover, these results reveal that this emission is the leading factor to introduce stringent emission standards that further overlap with regional demographics trends, i.e., carbon emission implications. These findings imply that economic development applies a substantial demand-pull impact on national logistics, i.e., regional economic development directs to the growth of the logistics industry in the corresponding region. Consequently, high-income geographical regions have higher long-run risk concerning contemporary developmental activities of the logistics industry when adhering to carbon emission standards. Particularly, the influence of upcoming emission standards must be prioritized when planning the future returns of contemporary research and development activities of the logistics industry in a given geographic area, such as CPEC. Given Pakistan’s perspective, the proposed empirical analysis can be exampled to other developing countries. This analysis may facilitate the design and development of strategies for upcoming financial funding in the modern logistics industry to seek its sustainable development-goals in developing economies.

Generally, two topics with vastly different terminology probably indicate different implied concepts. However, these topics themselves might share common references (bibliographic coupling), which suggest the underlying joint concept. Therefore, searching for these joint concepts in different topics would be of scientific interest. Previous studies have measured the similarity between topics based on comparison of the topics’ word probability distributions. In contrast, this paper presents an approach for measuring the similarity between topics based on the bibliographic coupling. Besides, the similarity is independent of the topic’s word probability distributions generated by a Latent Dirichlet Allocation (LDA) model. The proposed approach was evaluated using its counterpart (intra-topic similarity), baseline topic similarity matrices, and cosine measure. The method was exampled on brain cancer patents. A cross-topic similarity network of eight topics showcases 28 cross-topic pairs to profile which topics were associated with particular topics. Interestingly, some of the 28 combinations may be of scientific interest. For instance, the findings of the top five cross-topic pairs suggest that “growth of cancer cells” and “imbalances in the hormones” have common knowledge sources with the highest similarity value. These two entirely different concepts may suggest some common causative factors within the field. We believe that finding such an association between unrelated innovative inventions across various industries may help public and private research units in planning research direction and serve as a reference for future research.

Apparently, two different fields might share common knowledge. Such shared knowledge might have better strategic priority for commercial enterprises working on applying solutions from one area to another domain. For instance, when using solutions from "artificial intelligence" (AI) to given "social media marketing" (SMM) programs, this shared-information might accelerate cross-functional cooperation to attain completive advantage — an area of study that is mainly under-researched. In this context, the current study presents a sustainable design model to reveal the range of linkage options between AI and SMM from commercial prospects. Methodologically, the patent database related to AI and SMM is employed to generate topics independently, using the classic topic model, Latent Dirichlet allocation. Then, those topics are mutually paired through bibliographic coupling. The study’s findings observed cross-concept networks; it reveals a range of concept pairs, and each pair is linked through a unique value of coupling strength. For instance, the top five AI-SMM pairs show the highest coupling value and offer the maximum commonality of previous work(s) in the following areas: [accurate advertising; automation of customer—response]; [automation of payment transaction; accuracy of transactions; and automation of network database, storage. This study contributes to the literature in three ways. First, each bibliographic coupling-based association shows a unique association of shared knowledge — an influence of common prior art(s). Second, using citation-based linkage auto-updates to add sustainable features. Third, taking the patents database reflects the concepts covering technological activities. In summary, the current study has a specific focus, but overall analysis can be applied to other unrelated fields, making it capable of meeting complex challenges through sustainable cooperation. Bringing automation in information sharing might open promising doors for cross-field communication, capable of completing complex challenges for smart cooperation for sustainable industry development.

Artificial intelligence applications have opened new opportunities for the development of the modern logistics industry. Accordingly, Information and Communication Technology (ICT) concerning economic growth and carbon emission need comprehensive insight into the logistic industry— an area mainly analyzed from a general perspective by existing literature. Against this backdrop, this paper analyzes the short-run and long-run effects of dynamic variables and their interaction on investment in the logistics industry in connection with economic prosperity, Information and communication technology (ICT), trade, and carbon emission. Accordingly, the autoregressive distributive lag model (ARDL) is applied to observe a close regional perspective of a developing economy like Pakistan using time-series data from 1990 to 2019. The study findings observe a unidirectional long-run causality running from economic prosperity, logistics industry, Information and communication technology (ICT), and trade development to carbon emission. Moreover, regional economic growth exerts a significant demand-pull effect on corresponding regional economic development. These findings imply that high-income geographical regions will continue to face the higher long-run futuristic risks of the contemporary investment in logistic industry development when adhering to ever-evolving carbon emission standards under regional preferences. Accordingly, these regions should be given urgent attention concerning high-technology and artificial intelligence applications in developing economies. Given the availability of technological and general resource efficiencies in developing economies, such applied business-oriented scope of this statistical analysis might facilitate geo-specific strategies concerning future outcomes of current investment in the traditional logistic industry in those countries. We believe the overall analysis can be applied to other developing economies to facilitate sustainable development goals.