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The study evaluates the effects of GVC participation on firm productivity and profitability. Hence this study aims to find evidence whether there is a clear difference between the productivity and profitability effects of simple and complex backward and forward participations for Turkish firms.

The authors employ a firm level data from the Türkiye's both first and second top 500 industrial enterprises from 1993 to 2019. In addition, the authors calculate country-sector level both backward and forward GVC participation indices with their simple and complex sub-indices for each year from 1990 to 2015 from the Full Eora data of the Eora Global Supply Chain Database. The authors estimate the model with OLS and fixed effects. To understand the role of the 2008 global crisis, the authors also undertake estimations for the pre-crisis and post-crisis. The authors also divide the data by R&D intensity of sectors.

While backward GVC participation lowers both labor productivity and profitability growth, forward GVC participation promotes both. Moreover, simple and complex backward participation have similarly negative effects on productivity and profitability growth, simple and complex forward participation have the completely opposite effects though. The authors then provide substantial evidence for the differing effects of participation on productivity and profitability growth between pre-crisis and post-crisis periods. Interestingly, backward participation has a negative impact for both hi-tech and low-tech firms while forward participation boosts the productivity growth only for low-tech firms, probably due to the relatively more upstream position of low-tech firms.

To the best of the knowledge, no previous study has yet examined the profitability effects of GVC for firms. Second, in addition to overall backward and forward GVC participation rates, the authors also calculate and utilize simple and complex GVC measures in the estimations. Third, to reveal whether the global financial crisis leads to a shift in the productivity and profitability effects of GVCs, the authors separately run the regressions for the pre- and post-crisis periods. Fourth, the authors then investigate the argument that hi-tech sectors/firms could benefit more from joining GVCs compared to firms in low-tech technology sectors.

The aggregate index and per capita index have different meanings for some countries or regions. CO₂ emissions per capita matters for China because of its huge population. Therefore, this study aims to deepen the understanding of Kuznets curve from the perspective of CO₂ emissions per capita. In this study, mathematical formulas will be derived and verified.

First, this study verified the existing problems with the environmental Kuznets curve (EKC) through multiple regression. Second, this study developed a theoretical derivation with the Solow model and balanced growth and explained the underlying principles of the EKC’s shape. Finally, this study quantitatively analyzed the influencing factors.

The CO₂ emission per capita is related to the per capita GDP, nonfossil energy and total factor productivity (TFP). Empirical results support the EKC hypothesis. When the proportion of nonfossil and TFP increase by 1%, the per capita CO₂ decrease by 0.041 t and 1.79 t, respectively. The growth rate of CO₂ emissions per capita is determined by the difference between the growth rate of output per capita and the sum of efficiency and structural growth rates. To achieve the CO₂ emission intensity target and economic growth target, the growth rate of per capita CO₂ emissions must fall within the range of [−0.92%, 6.1%].

Inspired by the EKC and balanced growth, this study investigated the relationships between China’s environmental variables (empirical analysis) and developed a theoretical background (macro-theoretical derivation) through formula-based derivation, the results of which are universally valuable and provide policymakers with a newly integrated view of emission reduction and balanced development to address the challenges associated with climate change caused by energy.

This study aims to examine the impact of oil consumption on carbon dioxide (CO₂) emissions and total factor productivity (TFP) in highly oil-consuming countries of the world from 1995 to 2019.

For this purpose, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) are applied.

FMOLS and DOLS models reveal that oil consumption, human capital, population, trade openness and nonrenewable energy have a significant positive effect on CO₂ emissions. While information and communication technology (ICT), as proxied by mobile and natural resources, has a significant negative effect on CO₂ emissions. In the case of TFP, oil consumption, ICT and natural resources have a significant positive effect on the TFP. On the other hand, trade openness, population, human capital and nonrenewable energy have a significant negative effect on TFP. The results of this study can help to provide policy recommendations to reduce CO₂ emissions in studied highly oil-consuming countries of the world.

Due to the threat to sustainable development, climate change has become a major topic for debate around the world. The influence of oil consumption on CO₂ emission and TFP is less known in the available literature. Another significance of this study is that many researchers considered aggregate energy consumption to study this relationship, but the authors have studied the effect of energy consumption, particularly from oil in the top oil-consuming countries, which is a significant shortcoming of the present research.

This study examines the performance of India's food processing sector by estimating its output growth, technical efficiency (TE) and input-driven growth (IDG)

This study used panel data from six food processing manufacturing industries for the period 2000–01 to 2017–18. Technical efficiency and input-driven growth was measured using the parametric half-normal stochastic frontier production function.

The findings of this study showed that the estimated average technical efficiency is 86.6%, which specifies that the Indian food processing sector is technically inefficient. In addition, the output growth rate is 5.5%, driven by high doses of inputs (5.7%), whereas there is no indication of constant returns to scale. However, the food processing sector has experienced more input-driven expansion than either technological or efficiency changes.

This study is limited to India's organized manufacturing food processing sector; the aggregate macro data at a three-digit level based on the national industrial classification (NIC) was used. This study provides robust estimates for industrialists and processors, as well as concrete policy formulations on how overdoses of inputs may lead to high exploitation of resources, whereas outputs can be augmented by implementing upgraded and new technologies.

Previous research has estimated the total factor productivity and technical efficiency only in order to analyze the food sector's performance, but none of the studies have evaluated the share of inputs in growth performance and efficiency. Therefore, this study contributes by measuring growth performance and the share of inputs in the growth performance of India's food processing sector.

This paper aims to investigate the performance of OECD countries' long-term productivity during the period of 1975–2018.

This study employed different approaches to evaluate how efficiency scores vary with changes in inputs and outputs: Data Envelopment Analysis (CRS, VRS and FDH), TOPSIS and TOPSIS of these scores.

The findings suggest that, during the period of this study, countries with higher freedom of religion and with Presidential democracy regimes are positively associated with higher productivity.

To the best of the authors’ knowledge, this is the first study that uses efficiency models to assess the productivity levels of OECD countries based on several contextual variables that can potentially affect it.

This study aims to examine the complex effects of foreign direct investment (FDI) on China’s agricultural total factor productivity (TFP) from 2005 to 2020. It also explores the role of absorptive capacity as a moderating factor during this period.

Employing provincial panel data from China, this research measures agricultural TFP using the Stochastic Frontier Approach (SFA)-Malmquist method. The impact of FDI on agricultural productivity is further analyzed using a nondynamic panel threshold model.

The results highlight technological progress as the main driver of agricultural TFP growth in China. Agricultural FDI (AFDI) seems to impede TFP development, whereas nonagricultural FDI (NAFDI) shows a distinct positive spillover effect. The study reveals a threshold in absorptive capacity that affects both the direct and spillover impacts of FDI. Provinces with higher absorptive capacity are less negatively impacted by AFDI and more likely to benefit from FDI spillovers (FDISs).

This study provides new insights into the intricate relationship between FDI, absorptive capacity and agricultural productivity. It underscores the importance of optimizing technological progress and research and development (R&D) to enhance agricultural productivity in China.

The main aim of this study is to explore the role of multi-dimensional human capital on the economic growth of the Indian economy.

The study used the methodology given by World Bank, 2018) in calculating the human capital index (HCI). The HCI has been constructed at a regional level for all 28 Indian states and 8 Union Territories (UTs) for the period of 2015–2016. The study explored the linkages between HCI and per capita gross state domestic product (PGSDP). The study further employed OLS (Ordinary Least Square) for overall significance and Spearmen’s Rank correlation coefficient test for establishing the linkage between HCI and PGSDP.

The results indicate that quality education, expected year of schooling, and infant mortality rate play a significant role in the improvement of HCI which further impacts the productivity rate of the upcoming generation and the inclusive growth of the country. The findings show that Mizoram, Chandigarh and Kerala are better performing states while the Bihar and Uttar Pradesh are the worst performers. The results also show that there is a positive and statistically significant correlation between PGSDP and HCI and its components. Further, the results show that public expenditure on health and education has significant effect on HCI.

The results of this study would be useful for policymakers to identify the determinants and improve the position of Indian states in HCI. The results show that policymakers should focus on quality education and health to improve the productivity of future generation workers for sustainable and inclusive growth.

The study is the pioneering study to analyze the state-wise HCI in India using methods mentioned by the World Bank. Unlike previous studies, variables such as expected year of schooling, under-5 mortality rates and survival rates are constructed more pragmatically.

This study examines the differences in the economic stimulus effects, transmission mechanisms, and output multipliers of government consumption, government traditional investment, and government science and technology investment.

This study constructs and estimates a New Keynesian model of endogenous technological progress embedded in the research and development (R&D) and technology transfer sectors. Using Chinese macroeconomic time series data from 1996 to 2019, this study calibrates and estimates the model and analyzes the impulse response function and a counterfactual simulation of expenditure structure adjustment.

The results show that compared with the traditional dynamic stochastic general equilibrium (DSGE) model, the endogenous process of technological progress amplifies the impact of government consumption shock and traditional government investment shock on the macroeconomy, leading to greater economic cycle fluctuations. As government investment in science and technology has positive external spillover effects on firm R&D activities and the application of innovation achievements, it can promote more sustainable economic growth than government consumption and traditional investment in the long run.

This study constructs an extended New Keynesian model with different types of government spending, which includes endogenous technological progress within the R&D and technology transfer sectors, thereby linking fiscal policy, business cycle fluctuations and long-term economic growth. This model can study the macroeconomic impact of fiscal expenditure structure adjustment when fiscal expansion is limited. In the Bayesian estimation of model parameters, this study not only uses macroeconomic variables but also adds a sequence of private R&D investment.

The research objective of this paper is to investigate the direct and indirect impacts of green finance on agricultural carbon total factor productivity (ACTFP) within the framework of the carbon peaking and carbon neutrality (dual carbon) goals, while also identifying the driving factors through an exponential decomposition of ACTFP, aiming to provide policy recommendations to enhance financial support for low-carbon agricultural development.

In this paper, the Global Malmquist Luenberger (GML) Index method was employed to analyze and decompose the ACTFP, while the direct and spillover effects of China’s green finance pilot policy (GFPP) on ACTFP were assessed using the difference-in-differences (DID) method and the spatial differences-in-differences (SDID) method, respectively.

After the implementation of the GFPP, the ACTFP in the pilot area has experienced significant improvement, with the enhancement of technical efficiency serving as the main driving force. In addition, the GFPP exhibits a positive low-carbon spatial spillover effect, indicating it benefits ACTFP in both the pilot and adjacent areas.

Within the framework of the dual carbon goals, the paper highlights agriculture as a significant carbon emitter. ACTFP is assessed by considering the agricultural carbon emission factor as the sole non-desired output, and the impact of the GFPP on ACTFP is investigated through the DID method, thereby providing substantial validation of the hypotheses inferred from the mathematical model. Subsequently, the spillover effects of GFPP on ACTFP are analyzed in conjunction with the spatial econometric model.

This study aims to examine the impact of renewable energy consumption on agricultural productivity while accounting for the effect of financial inclusion and foreign direct investment in Brazil, Russia, India, China and South Africa (BRICS) countries during 2000–2020.

The study has used the latest data from World Bank and International Monetary Fund databases. The dependent variable in the study is agricultural productivity. Renewable energy consumption, carbon emissions, financial inclusion and foreign direct investment are independent variables. Autoregressive distributed lag (ARDL) approach was used to examine the short-run and long-run impact of renewable energy consumption, carbon emissions, foreign direct investment and financial inclusion on agricultural productivity.

The findings imply that consumption of renewable energy, carbon emissions and foreign direct investment have a positive impact on agricultural productivity while financial inclusion in terms of access does not seem to have any significant impact on agricultural productivity. Providing farmers, access to financial services can be beneficial, but its usage holds more importance in impacting rural outcomes. The problem lies in the fact that there is still a gap between access and usage of financial services.

Policymakers should encourage the increase in the usage of renewable energy and become less reliant on non-renewable energy sources which will eventually help in tackling the problems associated with climate change as well as enhance agricultural productivity.

Most of the earlier studies were based on tabular analysis without any empirical base to establish the causal relationship between determinants of agricultural productivity and renewable energy consumption. These studies were also limited to a few regions. The study is one of its kind in exploring the severity of various factors that determine agricultural productivity in the context of emerging economies like BRICS while accounting for the effect of financial inclusion and foreign direct investment.