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Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The main purpose of this paper is to examine the role of population aging in determining the health care expenditure (HCE) in India over the period 1981 to 2018.

While establishing the linkage between population aging and HCE, the study has used economic growth, urbanization and CO2 emissions as control variables and used the autoregressive distributed lag (ARDL) approach to cointegration and VECM based Granger causality approach to estimate both the long-run and short-run relationships among the variables.

The results of the ARDL bounds test showed that there is a stable and long-run relationship among the variables. The long-run and short-run coefficients reveal that population aging and income per capita exert a statistically significant and positive effect on per capita HCE in India. The VECM causality evidence shows that there is a presence of short-run causality from economic growth and population aging to per capita HCE, urbanization to environmental degradation and further from aging to urbanization. However, the long-run causality evidence confirms unidirectional causality from population aging to the per capita HCE.

The research findings could be improved by considering the changes in mortality rate over time because of other environmental factors such as air pollution, among others as control variables. Various other variables affecting the health of an aged person could be considered for better research outcome which is not included in the present study because of the paucity of data. However, the present research findings would certainly serve effective policy instrument aiming at maximizing health gains that are highly associated with the elderly population and economic growth towards achieving sustainable development in India.

The uniqueness of the present study lies in its estimation where the relationship between population aging and HCE is looked at while considering the impact of other environmental factors separately. The causal relationship is shown among the variables using updated econometrics time-series techniques. The study tried to resolve the ambiguity associated with the relationship between aging and HCE at a macro level.

This study aims to map the “Desktop Research” (DR) output in Pakistan, as part of the growing field of research globally. It also ascertains the productive institutions and prolific authors along with their collaboration patterns.

Bibliometric techniques were used to quantitatively analyze the DR published in Pakistan. The publications from 1981 to 2021 were retrieved from Scopus. A total of 1,802 publications were retrieved and used for analysis.

Results indicated an unpredictable increase in DR output from approximately 100 to 400 records during the past five years. The year 2020 was most productive in DR research showing the excess use of secondary data by researchers in COVID-19. The focus of researchers towards DR was consistently rising. Medical journals were found to publish DR extensively. Majority of the publications were contributed by collaborative work and researchers of the USA were found as the most collaborative with Pakistani authors. Publications of single category journals, open access journals and international collaboration get more citations.

The results of the analysis rely only on a single database, Scopus, for retrieving the publication data.

The study has practical implications for the policymakers and higher education development organizations to introduce the DR as a course in academic schools.

To the best of the authors’ knowledge, this study is the first to review DR in the context of Pakistan through bibliometric analysis. This comprehensive overview provides a better understanding of the development of the field and possible practice implications.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

Nowadays, in building structures, dampers are connected to the building structure to reduce the damages caused by seismicity in addition to enhancing structural stability, and to connect dampers with the structure, joints are used. In this paper, three different configurations of double-lap joints were designed, developed and tested.

This paper aims to analyze three different categories of double-lap single-bolted joints that are used in connecting dampers with concrete and steel frame structures. These joints were designed and tested using computational, numerical and experimental methods. The studies were conducted to examine the reactions of the joints during loading conditions and to select the best joints for the structures that allow easy maintenance of the dampers and also withstand structural deformation when the damper is active during seismicity. Also, a computational analysis was performed on the designed joints integrated with the M25 concrete beam column junction. In this investigation, experimental study was carried out in addition to numerical and computational methods during cyclic load.

It was observed from the result that during deformation the double-base multiplate lap joint was suitable for buildings because the deformations on the joint base was negligible when compared with other joints. From the computational analysis, it was revealed that the three double joints while integrated with the beam column junction of M25 grade concrete structure, the damages induced by the double-base multiplate joint was negligible when compared with other two joints used in this study.

To prevent the collapse of the building during seismicity, dampers are used and further connecting the damper with the building structures, joints are used. In this paper, three double-lap joints in different design configuration were studied using computational, numerical and experimental techniques.

This study aims to present a bibliometric overview of the academic research on export performance (EP) in the business and management areas.

A bibliometric overview of 1,463 papers from 1968 to 2021, including performance analysis, science mapping analysis and graphical mapping, was conducted using the Scopus database. SciMAT software was used for thematic analysis and conceptual evolution mapping of the EP domain, and VOSviewer software was used for graphical visualization.

This study shows that EP research experienced spectacular growth, especially between 1998 and 2003, and the interest in this field continues to increase. Also, the USA and the UK appear to be the absolute leaders in EP research, with the best indicators of productivity and influence in all dimensions analyzed. The findings from the analysis through SciMAT indicate that “capabilities” and “R&D” are the main Motor themes that have contributed the most to the EP literature, whereas “global value chain” and “start-up” are emerging themes as new areas of interest.

This study develops a baseline for representing certain and exhaustive insights in the EP field and specifies trends over a period. Using a single database and excluding book chapters/conference papers are limitations of this study.

EP is a research field that has gained wide acceptance in the academic community and international marketing literature. To the best of the authors’ knowledge, no bibliometric overview has analyzed the EP literature. This study presents the first systematic quantitative analysis of academic research on EP in the business and management areas.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

This study aims to investigate whether social investment (SI) policies improve employment among single mothers.

This paper analyzes the potential effects of SI policies on vulnerable individuals and workers at the macro level by using the employment position of single mothers as a dependent variable. Time-series cross-national data from 18 OECD countries between 1998 and 2017 are analyzed. Multilevel model analysis is also used for robustness check.

I find that public spending on education and family support is positively associated with the employment rates of single mothers. In contrast, active labor market policy (ALMP) spending is negatively associated. ALMP’s negative effects stand out particularly with public spending on job training. Of all family support policies, family allowances are positively associated with single mothers’ employment, which runs counter to the conventional argument that family allowances are a disincentive for women’s or mothers’ employment. Paid leave (length and generosity) is also associated with higher employment for single mothers. There is also some tentative evidence that public spending on maternity leave benefits (spending level) may raise the odds of single mothers being employed, when individual-level factors are controlled for in multilevel analysis we implement for robustness check.

This paper does not analyze the effects of the qualitative properties of SI policies. Future research is necessary in this respect.

The effects of SI policies on employment among single mothers have not yet been examined in the literature. This paper seeks to be a first cut at measuring the effects.

This study provides a bibliometric analysis of bank efficiency and competition over the past years (from 1993 to 2022) to (1) discover the past and current state of knowledge on bank competition and efficiency, (2) identify leading and authoritative journals and scholars who made significant contributions to the distribution of knowledge and impact, (3) identify nations that made a significant contribution and impact to the literature and (4) identify the structure of collaboration that exists between scholars in the areas of bank competition and efficiency and key thematic areas.

A total number of 868 documents made up of articles, reviews, book chapters, book and conference papers from the Scopus database were gathered. This study used a bibliometric analytic approach.

The number of documents on bank competitiveness and efficiency has increased significantly, as have their total publications, citations and national output. Additionally, the most esteemed and prestigious academic journals of eminent academics who have had a significant impact on the dissemination of knowledge on bank efficiency and competition literature champion papers on banking efficiency and competition. In terms of citation performance and collaborative efforts, the United States tops the developed countries, led by China, which is also the most productive. Additionally, single-country publications predominate in the literature, with China ranking first among the top five countries with corresponding authors. While the Lerner index, H-statistic, concentration index and market power were used to measure bank competitive behaviour, the data envelopment analysis approach predominates efficiency estimation techniques that are linked to cost, profit or revenue, scale, technical and productivity indexes.

This study is one of the first to offer bibliometric evidence of both bank competition and efficiency. It also offers proof of the distribution of knowledge and intellectual structure of the concepts and concerns in bank competition and efficiency.

As of 2022, the scope of the engagement and interest of debt capital providers in ESG reporting is mainly untapped. However, a vast amount of literature has produced conflicting findings about the importance of debt capital (leverage) as a factor in sustainability reporting (SR). This is the first meta-analysis reconciling the mixed results of 85 single country studies containing 131 effect sizes across 24,482 firms conducted over past three decades (1999–2022) investigating the influence of leverage on SR. The study emphasizes the significance of contextualizing research by identifying the macro-environmental elements modifying debt's impact on SR, through the use of the institutional theory. Eleven country variables were tested on the collected dataset, spread across 36 countries.

Meta-analysis technique for aggregation of existing extant empirical work. Continuous and categorical variable-based moderator analysis to demystify the influence of country characteristics affecting the leverage–SR relationship.

Results show positive significant impact of debt capital providers on SR. Country's level of development, GDP, extent of capital constraints in a country, financial sector development within a nation, country governance factors and corruption levels, country's culture, number of sustainability reporting instruments operational in a country and geographical location proved to be significant moderators.

The study details relevant meaningful research gaps, worthy of uptake by researchers to produce targeted research.

Governments must increasingly go beyond their mandated disclosure role and acknowledge the important institutional factors that have contributed to the expansion of ESG reporting through the creation of nation-specific tools, incentive structures and disclosure-encouraging regulations. To secure a steady flow of funding and prevent negative effects on company value and cost of capital in the midst of prolonged global economic upheaval, businesses must address the information requirements of lenders. The limited total effect size emphasizes the necessity for debt providers to step up their ESG activism and exercise their maximum power and potential in stimulating extensive SR firm-level practices.

The present study is the first meta-analysis reconciling the mixed results of 85 single-country studies containing 131 effect sizes across 24,482 firms conducted over the past three decades (1999–2022) investigating the influence of leverage on SR and demystifying the macro-environmental factors affecting the leverage–SR association.