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The purpose of this paper is to focus on modeling economy growth with indicators of knowledge-based economy (KBE) introduced by World Bank for a case study in Iran during 1993-2013.

First, for grouping and reducing the number of variables, Tukey method and the principal component analysis are used. Also for modeling, 67 per cent of data is used for training in the two approaches of ARDL bounds testing and gene expression programming (GEP) and 33 per cent of them for testing the models. Then, the result models are compared with fitness function and Akaike information criteria (AIC).

The GEP model with fitness 945.7461 for training data and 954.8403 for testing data from 1000 is better than ARDL bounds testing model with fitness 335.5479 from 1000. In addition, according to model comparison tools (AIC), the GEP model has an extremely larger weight in comparison with ARDL bounds model. Therefore, the GEP model is introduced for future use in academia.

Knowledge and information is one of the most basic sources of wealth in economists’ sight. Thus, using KBE indicators appears essential in economic growth regarding daily progress in knowledge processes and its different theories. It is also extremely important to determine an appropriate model for KBE indicators which play a highly important role in the allocation of the economic resources of the country in an optimal manner.

This paper introduced a novel expression for economy growth using KBE indicators. All the data and the indicators are extracted from Word Bank service between 1993 and 2013.

This paper analyses the relationship between financial sector development (FSD) and life insurance inclusion in India during the period from 1971–1972 to 2016–2017. The study analyses the effect of financial deepening on life insurance inclusion in India.

The study employs augmented Dickey–Fuller (ADF) unit roots test to check the stationarity properties of the time series data. It estimates a life insurance inclusion model using the auto-regressive distributed lag model (ARDL) bounds testing approach to cointegration.

The study finds evidence of a cointegrating relationship between financial deepening and life insurance inclusion in India. A significant error correction coefficient indicates automatic adjustments to short-run disequilibrium, reinforcing the cointegrating relationship between financial sector and life insurance inclusion.

A major limitation of the study is that it excludes the first-time sum assured (FSA) contributed by the private sector life insurance companies due to lack of data availability.

The results of the study call for faster expansion of the financial sector and provision of a low interest rate regime in the Indian economy. The study invokes the need for sufficient training to the personnel in the banking and non-banking institutions to cater to the complex needs of life insurance buyers.

The paper estimates the link between FSD and life insurance inclusion and introduces a new measure of life insurance demand, the life insurance inclusion, measured using the FSA.

This paper investigates the relationship between domestic gold prices and inflation in Vietnam based on the monthly series of the gold price index and consumer price index over the period of December 2001–July 2020.

The co-integration between the domestic gold price and inflation is examined within the autoregressive distributed lag-error correction (ARDL bounds testing) framework. This paper also applies the vector error correction model (VECM) and impulse response function analysis to explore the causal relationship between these two variables. Moreover, since both gold and inflation series are likely to have structural changes over time, a unit root test controlling for significant breaks is employed in this paper.

Findings from the ARDL bounds testing model suggest the presence of a co-integration between the underlying variables. The VECM indicates that shocks in inflation lead to a negative response to gold prices in the long run. In the short term, only fluctuations in gold prices impact inflation, and this causality is unidirectional.

Gold is regarded as a critical financial asset to preserve wealth from inflation pressure in the case of Vietnam. These findings propose implications for both investors and policymakers.

Empirical results suggest that inflation has a long-term impact on gold prices in the Vietnamese market. In the existence of a permanent inflationary shock, domestic prices of gold respond negatively to this shock; hence, gold can act as a good hedge against inflation in Vietnam.

This study aims to develop a parsimonious model to estimate US aggregate hotel industry revenue using domestic trips, consumer confidence index, international inbound trips, personal consumption expenditure and number of hotel rooms as predictor variables. Additionally, the study applied the model in six sub-segments of the hotel industry – luxury, upper upscale, upscale, upper midscale, midscale and economy.

Using monthly aggregate data from the past 22 years, the study adopted the auto-regressive distribute lags (ARDL) approach in developing the estimation model. Unit root analysis and cointegration test were further utilized. The model showed significant utility in accurately estimating aggregate hotel industry and sub-segment revenue.

All predictor variables except number of rooms showed significant positive influences on aggregate hotel industry revenue. Substantial variations were noted regarding estimating sub-segment revenue. Consumer confidence index positively affected all sub-segment revenues, except for upper upscale hotels. Inbound trips by international tourists and personal consumption expenditure positively influenced revenue for all sub-segments but economy hotels. Domestic trips by US residents added significant explanatory power to only upper upscale, upscale and economy hotel revenue. Number of hotel rooms only had significant negative effect on luxury and upper upscale hotel sub-segment revenues.

Hotel operators can make marketing and operating decisions regarding pricing, inventory allocation and strategic management based on the revenue estimation models specific to their segments.

It is the first study that adopted the ARDL bound approach and analyzed the predictive capacity of macroeconomic variables on aggregate hotel industry and sub-segment revenue.

The purpose of this paper is to explore and investigate the electricity consumption and production and its linkage to economic growth in Pakistan.

The authors used an augmented Dickey–Fuller unit root test to check the stationarity of the variables, while an autoregressive distributed lag (ARDL) bounds testing approach and causality test were applied to investigate the variables long-term association with the economic growth.

The study results show that electricity consumption in the agriculture, commercial and industrial sector has significant association with economic growth, while electricity consumption in the household and street lights demonstrate a non-significant association with the economic growth. Furthermore, results also exposed that electricity production from coal, hydroelectric, natural gas, nuclear and oil sources have significant association with the economic growth of Pakistan.

This study made a contribution to the literature regarding electricity consumption and production with economic growth in Pakistan by using an ARDL bounds testing approach and causality test. This study provides a guideline to the government of Pakistan that possible steps are needed to improve the electricity production and supply to fulfill the country demand.

The main purpose of this paper is to empirically investigate the effect of macroeconomic uncertainty on environmental degradation in India over the period 1971–2016. Additionally, this paper considers the role of financial development, energy consumption intensity and economic growth in explaining the variation of environmental degradation in India.

The authors applied the power generalized autoregressive conditional heteroskedasticity model to measure inflation volatility and used it as a proxy for macroeconomic uncertainty. From a methodological perspective, the authors employ the autoregressive distributive lag bound testing model to establish the long-run equilibrium association between the variables. The Toda–Yamamoto causality approach has been used to examine the direction of causality between the variables.

Findings suggest that macroeconomic uncertainty exerts a positive effect on carbon emissions, indicating that higher inflation volatility, as a proxy for macroeconomic uncertainty, hinders India's environmental quality. Financial development, economic growth and energy consumption intensity have also adversely impacted environmental quality.

The negative association between macroeconomic uncertainty and environmental degradation calls for some stringent policy actions. While formulating policies to promote growth and maintain stability, policymakers and government stakeholders should take into account the environmental effects of macroeconomic policies. There is a need to implement more environmental-friendly technologies in the financial sector that could reduce carbon emission.

To the best of the authors' knowledge, this study is the first that considers the role of macroeconomic uncertainty along with financial development and energy intensity in an emerging economy like India.

This study aims to assess the nexus among oil price (OP), renewable energy consumption (REC) and trade balance (TB) for India using annual time series data for the time period 1985–2019. In particular, the authors examine whether REC improves India's TB in the context of high oil import dependence.

The study uses autoregressive distributed lags (ARDL) bound testing approach that has the advantage of yielding estimates of long-run and short-run parameters simultaneously. Moreover, the small sample properties of this approach are superior to other multivariate cointegration techniques. Fully modified ordinary least square (FMOLS) and dynamic ordinary least squares (DOLS) are also applied to test the robustness of the results. The causality among the series is investigated through block exogeneity test based on vector error correction model.

The findings based on ARDL bounds testing approach indicate that OPs exert a negative impact on TB of India in both long run and short run, whereas REC has a favorable impact on the TB. In particular, 1% increase in OPs decreases TBs by 0.003% and a 1% increase in REC improves TB by 0.011%. The results of FMOLS and DOLS corroborate the findings from ARDL estimates. The results of block exogeneity test suggest unidirectional causation from OPs to TB; OPs to REC and REC to TB.

The study underscore the importance of renewable energy as a potential tool to curtail trade deficits in the context of Indian economy. Our results suggest that the policymakers must pay attention to the hindrances in augmentation of renewable energy usage and try to capitalize on the resulting gains for the TB.

Climate change is a major challenge for developing countries like India. Renewable energy sector is considered an important instrument toward attaining the twin objectives of environmental sustainability and employment generation. This study underscores another role of REC as a tool to achieve a sustainable trade position, which may help India save her valuable forex reserves for broader objectives of economic development.

To the best of the authors’ knowledge, this is the first study that probes the dynamic nexus among OPs, REC and TB in Indian context. From a policy standpoint, the study underscores the importance of renewable energy as a potential tool to curtail trade deficits in context of India. From a theoretical perspective, the study extends the literature on the determinants of TB by identifying the role of REC in shaping TB.

This study aims to empirically assess how infrastructure development influenced economic growth in Zambia from 2000 to 2020.

The study uses data from the World Development Indicators (WDI), spanning from 2000 to 2020. The selection of this time period was determined by the availability of data related to the research. The Autoregressive Distributed Lag (ARDL) bounds testing approach was used for data analysis.

The findings show that economic growth is cointegrated with capacity to generate electricity, proving the existence of a long-run equilibrium relationship between them. Furthermore, the empirical results established that electricity generation capacity had a positive and significant impact on economic growth. Similarly, in the short run, electricity generation capacity, and mobile cellular services had a positive impact on economic growth.

Policy measures should prioritise increasing capacity for producing electricity and expanding access to energy by relevant economic sectors. Increased access to energy by these sectors can raise productivity, spur economic growth and accelerate industrialisation. Also, in the light of climate change, it is crucial that policymakers explore alternate sources of electricity generation, such as green and renewable sources. Furthermore, policy initiatives should prioritise expanding mobile cellular infrastructure, given that mobile cellular technology has become a vital component of economies and continues to offer unprecedented opportunities for economic growth.

This study presents novel empirical evidence on the unique relationship between infrastructure and economic growth in Zambia, highlighting electricity generation and mobile cellular services as pivotal factors for enhancing productivity and spurring industrial development.

Foreign direct investment (FDI) has a critical role in boosting agricultural productivity and the growth of emerging economies. The relationship between FDI inflows and environmental factors has not received much attention in identifying its impact on agricultural output. Using annual time series data from 1990 to 2023, this study examines the causal association and short- and long-run effects of FDI inflows, forest coverage and CO2 emissions on the agricultural productivity of the India, China and US (ICU) economies. The autoregressive distributed lag (ARDL) results confirmed that FDI inflows have a significant and positive impact on Indian and Chinese agriculture productivity, whereas CO2 emissions adversely affect US agriculture productivity in the long run. In the short run, CO2 emissions led to agricultural productivity in both China and the US economies. The bound test and error correction model (ECM) result also confirmed the long-run connection and convergence of the equilibrium path among the studied variables except India. The findings of the Granger causality test showed a unidirectional causal link between agriculture productivity and FDI inflows and forest coverage in India and a bidirectional causal link between CO2 emission and agricultural yield and forest coverage and CO2 emission in the Chinese agriculture sector. The study also revealed a unidirectional causal association between forest coverage and agricultural output and between FDI, CO2 emissions and forest coverage in the US agriculture sector. Policymakers were advised to encourage FDI in the agriculture sector and expand the use of environment-friendly technology to decrease carbon emissions and promote forest coverage for sustainable growth and higher agricultural production.

The climate change effects on agricultural output in different regions of the world and have been debated in the literature of emerging economies. Recently, the agriculture sector has influenced globally through climate change and also hurts all sectors of economies. This study aims to examine and explore the impact of global climate change on agricultural output in China over the period of 1982-2014.

Different unit root tests including augmented Dickey–Fuller, Phillips–Perron and Kwiatkowski, Phillips, Schmidt and Shin are used to check the order of integration among the study variables. The autoregressive distributed lag (ARDL) bounds testing approach to cointegration and the Johansen cointegration test are applied to assess the association among the study variables with the evidence of long-run and short-run analysis.

Unit root test estimations confirm that all variables are stationary at the combination of I(0) and I(1). The results show that CO₂ emissions have a significant effect on agricultural output in both long-run and short-run analyses, while temperature and rainfall have a negative effect on agricultural output in the long-run. Among other determinants, the land area under cereal crops, fertilizer consumption, and energy consumption have a positive and significant association with agricultural output in both long-run and short-run analysis. The estimated coefficient of the error correction term is also highly significant.

China’s population is multiplying, and in the coming decades, the country will face food safety and security challenges. Possible initiatives are needed to configure the Chinese Government to cope with the adverse effects of climate change on agriculture and ensure adequate food for the growing population. In concise, the analysis specifies that legislators and policy experts should spot that the climate change would transmute the total output factors, accordingly a county or regional specific and crop-specific total factor of production pattern adaptation is indorsed.

The present empirical study is the first, to the best of the authors’ knowledge, to investigate the impact of global climate change on agricultural output in China by using ARDL bounds testing approach to cointegration and Johansen cointegration test.