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The purpose of this study is to assess the influence of institutional quality (IQ), fossil fuel efficiency, structural change and renewable energy (RE) consumption on carbon efficiency.

This research uses an econometric approach, more specifically the Autoregressive Distributed Lag model, to examine the relationship between structural change, RE consumption, IQ, fossil fuel efficiency and carbon efficiency in India from 1996 to 2019.

This study finds the positive contributions of variables like fossil fuel efficiency, technological advancement, structural transformation, IQ and increased RE consumption in fostering environmental development through enhanced carbon efficiency. Conversely, this study emphasises the negative contribution of trade openness on carbon efficiency. These findings provide concise insights into the dynamics of factors impacting carbon efficiency in India.

This study's exclusive focus on India limits the generalizability of findings. Future studies should include a broader range of variables impacting various nations' carbon efficiency. Furthermore, it is worth noting that this study examines renewable and fossil fuel efficiency aggregated. Future research endeavours could yield more specific policy insights by conducting analyses at a disaggregated level, considering individual energy sources such as wind, solar, coal and oil. Understanding how the efficiency of each energy source influences carbon efficiency could lead to more targeted and practical policy recommendations.

To the best of the authors’ knowledge, this study addresses a significant gap in the existing literature by being the first empirical investigation into the effects of IQ, fossil fuel efficiency, structural change and RE consumption on carbon efficiency. Unlike prior research, the authors consider a comprehensive IQ index, providing a more holistic perspective. The use of a comprehensive composite index for IQ, coupled with the focus on fossil fuel efficiency and structural change, distinguishes this study from previous research, contributing valuable insights into the intricate dynamics shaping India's path towards enhanced carbon efficiency, an area relatively underexplored in the existing literature.

The purpose of this study is to investigate the nonlinear effects of renewable energy (RE) consumption and economic growth on per capita CO₂ emissions during the time span from 1980 to 2020.

The study uses the logistic smooth transition autoregression (STAR) model to decipher the nonlinear relationship between RE consumption, economic growth and CO₂ emissions in the Indian economy.

The estimated results confirm a nonlinear relationship between India’s economic growth, RE consumption and CO₂ emissions. The authors found that economic growth positively impacts CO₂ emissions until it reaches a specific threshold of 1.81 (per capita growth). Beyond this point, further economic growth leads to a reduction in CO₂ emissions. Similarly, RE consumption positively affects CO₂ emissions until economic growth reaches the same threshold level, after which an increase in RE consumption negatively impacts CO₂ emissions.

The study suggests that India should optimize the balance between economic growth and RE consumption to mitigate CO₂ emissions. Policymakers should prioritize the adoption of RE during the early stages of economic growth. As economic growth reaches the specific threshold of 1.81 per capita, the economy should shift to more sustainable and energy-efficient practices to limit the effect of further CO₂ emissions on further economic growth.

To the best of the authors’ knowledge, this study represents the first-ever endeavor to reexamine the nonlinear relationship between RE consumption, economic growth and CO₂ emissions in India, using the STAR model.

Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

The main purpose of this study is to investigate the agriculture-induced environmental Kuznets curve (EKC) hypothesis in South Asian economies (SAE).

This study employs econometric techniques, including Westerlund cointegration tests, cross-sectional augmented distributive lag model (CS-ARDL) and Dumitrescu and Hurlin (DH) causality tests to investigate the relationship between renewable and non-renewable energy consumption, agriculture, economic growth, financial development and carbon emissions in SAE from 1990 to 2019.

The CS-ARDL test outcome supports the presence of the agriculture-induced EKC hypothesis in SAE. Additionally, through the application of the DH causality test, the study confirms a unidirectional causality running from renewable energy consumption (REC), fossil fuel consumption (FFC), economic growth (GDP) and squared economic growth (GDP²) to carbon dioxide (CO₂) emissions.

This study proposes that future research should extend comparisons to worldwide intergovernmental bodies, use advanced econometric methodologies for accurate estimates, and investigate incorporating the service or primary sector into the EKC. Such multidimensional studies can inform various methods for mitigating global climate change and ensuring ecological sustainability.

Environmental degradation has been extensively studied in different regions and countries, but SAE face significant constraints in addressing this issue, and comprehensive studies in this area are scarce. This research is pioneering as it is the first study to investigate the applicability of the agriculture-induced EKC in the South Asian region. By filling this gap in the current literature, the study provides valuable insights into major SAE and their environmental challenges.

The principal aim of this study is to examine the relationship between financial development and income inequality in India using the financial Kuznets curve (FKC) hypothesis.

This study uses the autoregressive distributed lag (ARDL) model and the Toda–-Yamamoto causality test to investigate the long-run and short-run relationship and causality between financial development and income inequality. In addition, this study employs a principal component analysis (PCA) to construct a comprehensive financial development index.

The study found a long-run relationship between financial development and income inequality in India for the period under consideration. Trade is found to improve the income distribution, while inflation worsens income distribution. Moreover, the empirical results revealed a feedback causality between financial development and income inequality. The study results confirm an inverted U-shaped relationship between financial sector development indicators and income inequality, thus validating the FKC hypothesis for the Indian economy.

The study draws attention of the government and policymakers, urging them to focus on building a strong financial sector by improving its efficiency. This, in turn, will lead to enhanced financial stability and a reduction in income inequality. They should prioritise the development of high-quality and sustainable financial products and services to ensure the robust growth of the financial sector.

To the best of our knowledge, this study is the latest of its kind to empirically test the financial development on income inequality and the FKC hypothesis simultaneously for the Indian economy using financial proxy variables from financial institutions (FIs) and financial markets (FMs) for the measurement of financial depth.

Purpose of study: Indian fibre cement industry is at crossroads on account of the dropping margins attributed to the dynamic internal and external challenges faced by it. The ever-changing technology, the increasing availability of substitutes and changing demographic consumption profiles have questioned the survival of this industry. Internal and external factors affecting an organization provide inputs to the strategic decision-making. Diversity of factors and prioritization is a major challenge encountered for developing a strategy for the organization.

Strategic analysis in the paper proposes to rank important key variables through SWOT-AHP methodology affecting a fibre cement company. Computed priorities of SWOT factor could help in formation of management approach to key decisions facing the firm. It is a descriptive research design. The problem itself has multiple SWOT criteria that have been evaluated in three phases with the help of industry experts and AHP criteria.

SWOT-AHP analysis has been a strategic fit for qualitative analysis of factors. The important ranked factors affecting the organization have been found to be brand name, capability of both the management and technical, quality of the product, and the efficient customer service and marketing reach/distribution.

Ranking of key SWOT factors found through AHP methodology will help the firm under study to develop and plan strategic alternatives to counter all challenges faced by them.

The management of the firm under study shall be benefited in fine-tuning the overall strategy of the organization.

This paper proposes a hybrid SWOT-AHP strategic analysis first time in this sector. The affecting factors have been quantified and ranked to identify priority factors for the firm to focus.

Compact and wideband antennas are the need of modern wireless systems that preferably work with compact, low-profile and easy-to-install devices that provide a wider coverage of operating frequencies. The purpose of this paper is to propose a novel compact and ultrawideband (UWB) microstrip patch antenna intended for high frequency wireless applications.

A square microstrip patch antenna was initially modeled on finite element method-based electromagnetic simulation tool high frequency structure simulator. It was then loaded with a rectangular slit and Koch snowflake-shaped fractal notches for bandwidth enhancement. The fabricated prototype was tested by using vector network analyzer from Agilent Technologies, N5247A, Santa Clara, California, United States (US).

The designed Koch fractal patch antenna is highly compact with dimensions of 10 × 10 mm only and possesses UWB characteristics with multiple resonances in the operating band. The −10 dB measured impedance bandwidth was observed to be approximately 13.65 GHz in the frequency range (23.20–36.85 GHz).

Owing to its simple and compact structure, positive and substantial gain values, high radiation efficiency and stable radiation patterns throughout the frequency band of interest, the proposed antenna is a suitable candidate for high frequency wireless applications in the K (18–27 GHz) and Ka (26.5–40 GHz) microwave bands.

Increasing digitalization and the advent of Industry 4.0 were scrutinized on how these may influence SDG attainment in different levels. The digitalization of the business and operational models emerged as the newer source of attaining competitive advantage in the pre-COVID-19 context. The COVID-19 pandemic has undoubtedly added more complexities to the already prevailing VUCA context of sustaining the business and development agenda irrespective of their geographic or institutional affiliations. Businesses in different industries, particularly in the developing countries, had lower or no preparedness in most cases in dealing with such uncertainties arising from the pandemic. Business firms and governments started to revise and digitalize their business models at the enterprise and institutional levels. To be very precise, MNCs in the fast-moving consumer goods industry in Bangladesh restructured their business model, at least the supply chain part, through direct and indirect digitalization of their business models, partly developing cooperation with existing e-commerce platforms. Volatile, uncertain, complex, and ambiguous focused corporate leadership and strategy helped in halting drastic slipping on the SDG millstones for Bangladesh. Despite the prevalent COVID-19 context, Bangladesh graduated to the club of the developing countries by retaining eligibility position on the gross national income index, human assets index, and economic vulnerability index. This chapter attempts to establish theoretical structuration of the links between digitalization and transformation of the business models and handling the SDG challenges.

The paper aims to explain how agility capabilities (competence, flexibility, responsiveness and speed) influence the chance of improving a small and medium-sized enterprise's (SME's) competitive position, measured by market share and profit.

Combining organisational agility with competitiveness, the authors analysed how an SME's activities in the field of agility capabilities – competence, flexibility, responsiveness and speed – influence the chance of improving their competitive position. Data were collected from 1,286 SMEs from Poland using the computer-assisted personal interviews method (CAPI). To analyse the data, the authors used logistic regression and odds ratios.

The study provides empirical evidence of the relationship between agility capabilities and an SME's competitive position. First, the results suggest that all the studied agility capabilities positively impact SMEs' competitive position. Second, the study shows that seeking to increase market share requires particular attention to flexibility, the impact of which is slightly higher than that of the other variables. Third, the findings suggest that the drive to increase profitability requires an appreciation of responsiveness and competence.

The literature contains much discussion about the relationship between agility capabilities and a company's competitive position. However, these studies refer to large companies, whilst the question of the relationship between agility capabilities and competitive position amongst SMEs remains mainly unanswered, which given SMEs' contribution to the creation of economic growth, seems difficult to understand. Although small companies are by nature highly flexible, due to the size of the companies' operations and flattened and decentralised structure, companies' competitive potential is limited as a result of the limited resources that can be devoted to developing procedures for change.

This paper aims to report a non-randomized control study undertaken to investigate prevalence and correlates of conduct disorder among male secondary education students in South-West Nigeria and to assess the impact of a problem-solving skills and attributional retraining (PSSAR) intervention with this population.

In total, 787 male students from two schools were screened for conduct disorder. All participants who met criteria for the disorder were allocated to either treatment (n = 55) or control (n = 47) groups. Outcome measures comprised the strengths and difficulties questionnaire (SDQ; teacher and student versions) and the teacher rating of students’ aggressive behaviors.

Of the sample, 13% were found to present with difficulties that met criteria for conduct disorder. The presence of these difficulties correlated with several demographic variables, including parental conflict and alcohol use. A statistically significant reduction in mean scores was observed for the treatment group in the student rating of the SDQ emotional subscale and total difficulties scores. Teacher ratings were less consistent in that conduct problems, prosocial behavior and total difficulties increased following the intervention, whereas peer problems and aggressive behavior were reported by teachers to reduce. No statistically significant change was found in the outcome measures for the control group.

In resource-constrained settings, school-based interventions are an important means through which treatment gaps in child and adolescent mental health can be addressed.

In resource-constrained settings, school-based interventions are an important means through which treatment gaps in child and adolescent mental health can be addressed. This study’s findings offer some preliminary support for the PSSAR intervention for conduct disorder in this context and indicate areas for further research.