Does the depth of the Financial Markets matter for establishing Green Growth? Assessing Financial sector's potency in decoupling Economic Growth and Environmental Pollution

Factors That Influence the Intention of Smallholder Rice Farmers to Adopt Cleaner Production Practices: An Empirical Study of Precision Agriculture Adoption

Sustainable agriculture is crucial for achieving SDG2 and building a resilient climate-food system. This study provides a nuanced understanding of factors that influence the adoption of precision agriculture technology by Vietnamese smallholder rice farmers as a sustainable agricultural solution. The study's objectives are: (1) to provide a nuanced understanding of factors that influence adoption of precision agriculture technology by Vietnamese smallholder rice farmers; and (2) to identify factors associated with agricultural practice in Vietnam that may impact the adoption of precision agriculture technology. The study conducted 35 semi-structured interviews with smallholder rice farmers and agriculture experts. Data were analyzed using iterative thematic analysis. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used for empirical analysis. The UTAUT constructs of Performance expectancy, Effort expectancy, Government support, and Social influence were found to be determinants of adoption. Trialability and Observability impact Effort expectancy and Performance expectancy. We also discovered that the performance of agricultural cooperatives and support of lead firms play a crucial role in facilitating agricultural technology adoption by Vietnamese smallholder rice farmers. The results confirm the applicability of UTAUT in defining smallholders' behavioural intention and predicting agricultural technology adoption. The study also provides constructive feedback on the UTAUT model by highlighting the effect of agricultural cooperatives' performance as innovation intermediaries and of the support of lead firms.

The role of green finance and governance effectiveness in the impact of renewable energy investment on CO2 emissions in BRICS economies

In the context of sustainable development, this study investigates the intricate dynamics among good governance, renewable energy investment, and green finance in BRICS nations. The aim of the study is to assess how green finance and governance effectiveness moderate the impact of renewable energy investment on CO2 emissions. Utilizing the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) model, a meticulous analysis spanning two decades was conducted to unravel the relationships among key variables and CO2 emissions. The findings underscore a nuanced interplay where renewable energy investments, synergized with robust governance and strategic green finance, significantly mitigate CO2 emissions, contributing to sustainable economic development. However, the study reveals non-linear relationships, highlighting the necessity for optimal allocation and strategic planning to maximize environmental benefits. In the short-run, a government effectiveness policy threshold that should be attained in order for renewable energy investment to reduce CO2 emissions is provided. In the long-run, the negative responsiveness of CO2 emissions to renewable energy investment is further consolidated by green finance. Moreover, enhancing renewable energy investment in the long run is positive for environmental sustainability. It follows that policy makers should tailor policies aimed at enhancing renewable energy investment in the long-run as well as complementing renewable energy investment with green finance in the long-run in order to ensure environmental sustainability by means of reducing CO2 emissions. Policymakers in BRICS nations are urged to strengthen governance structures, promote renewable energy investments, leverage green finance, foster public-private partnerships, adopt a holistic approach, and address non-linear effects to accelerate the transition to a low-carbon economy.

Can using energy resources productively and promoting good governance boost carbon productivity? An economic growth-environmental degradation decoupling analysis on 116 global countries

Decoupling economic growth from environmental pollution for promoting low-carbon growth has become a global objective. Though the previous studies have mostly analyzed how environmental pollution can be reduced, not much emphasis was given to assessing how economic growth can be enhanced while limiting environmental damages in tandem. Hence, this study examines how carbon productivity is determined by energy productivity improvement, good governance, financial development, financial globalization, and international trade using data from 116 global economies. Overall, the analytical findings reveal that energy productivity improvement initially cannot decouple economic growth from environmental pollution by inhibiting carbon productivity. However, later on, using energy productively does manage to decouple economic growth from environmental pollution by boosting carbon productivity. Accordingly, the U-shaped nexus between these variables is confirmed by these statistical findings. Besides, the results also endorse the carbon productivity-boosting effects of good governance, financial development, and international trade while foreign direct investment receipts are not found to exert any significant impact on carbon productivity. On the other hand, the robustness tests' results affirm that the carbon productivity-influencing impacts are heterogeneous across countries belonging from different categories of national income, carbon productivity, energy productivity, governance, and regional locations, as well. Nevertheless, the results overall confirm that countries having comparatively higher levels of energy productivity and governance are more likely to decouple the growth of their respective economies from environmental pollution. Based on these findings, some decoupling policies are recommended.

Carbon emissions predicting and decoupling analysis based on the PSO-ELM combined prediction model: evidence from Chongqing Municipality, China

The "14th Five-Year Plan" period is a crucial phase for China to achieve the goal of carbon peaking and carbon neutrality (referred to as the "double carbon"). Thus, it is very important to analyze the main factors affecting carbon emissions and accurately predict the change of carbon emissions to achieve the goal of double carbon. For the slow data updates and the low accuracy of traditional prediction models about the carbon emissions, the key factors of carbon emissions change selected by gray correlation method and the consumption of coal, oil, and natural gas were input into four single prediction models: gray prediction model GM(1,1), ridge regression, BP neural network, and WOA-BP neural network to obtain the fitted and predicted values of carbon emissions, which serve as input to the particle swarm optimization-extreme learning machine (PSO-ELM) model together. Based on the PSO-ELM combined prediction method above and the scenario prediction indicators constructed according to relevant policy documents of Chongqing Municipality, the carbon emission values of Chongqing Municipality during the 14th Five-Year Plan period are predicted in this paper. The empirical results show that carbon emissions of Chongqing Municipality still maintain an upward trend, but the growth rate slow down compared with 1998 to 2018. In general, the carbon emission and GDP of Chongqing Municipality showed a weak decoupling state during 1998 to 2025. By calculation, the PSO-ELM combined prediction model is superior to the above four single prediction models in carbon emission prediction and has good property by the robust testing. The research results can enrich the combined prediction method about the carbon emissions and provide policy suggestions for Chongqing's low-carbon development during the 14th Five-Year Plan period.

Determinants of environmental quality in India: evidence using the bootstrapped ARDL model with structural breaks

This current study examines the impact of renewable energy consumption, agriculture, and globalization on carbon emissions in India over the period from 1980 to 2018. For long-run estimates, we apply Gregory-Hansen's co-integration test, bootstrap ARDL approaches, fully modified ordinary least squares, and dynamic OLS. The empirical results of long-run estimates indicate that a 1% increase in renewable energy consumption, agriculture, and economic globalization will increase carbon emissions by 0.764%, 1.675%, and 0.517%, respectively. Moreover, this study confirms the detrimental effect of these variables on carbon dioxide emissions. Economic globalization coefficients indicate that the scale effect is valid in India. The 2002 economic crisis slowed down the country's growth rate, which reduced the ecological pollution. Several policy recommendations are derived from the empirical findings.