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

Yadav, Ashutosh; Gyamfi, Bright Akwasi; Asongu, Simplice A.; Behera, Deepak Kumar

doi:10.1016/j.jenvman.2024.120906

Cited by 7 publications

(1 citation statement)

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“…The 1% significant positive connection between the regression coefficient of the CO 2 emission relaxation value and government investment indicates that government investment does not effectively contribute to reducing CO 2 emissions. Yadav et al [50] pointed out that the government should strive to promote continuous support while prioritizing the efficient allocation of investment funds, ensuring that financial inputs effectively bolster carbon emission efficiency; (5) Urbanization rate: The level of urbanization has a 1% significant negative correlation with the amount of energy consumption slack in the source-area. Zhao et al [51] suggest that as urbanization progresses, energy consumption in urban areas becomes more efficient and requires less energy compared to rural areas.…”

Section: Second Stage Analysis: Sfa Regression Evaluationmentioning

confidence: 99%

Provincial Coal Flow Efficiency of China Quantified by Three-Stage Data-Envelopment Analysis

Jiang,

Jin,

et al. 2024

Sustainability

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The exploration of regional variations in coal flow efficiency (CFE) in China and the collaborative strategies for emission reduction are vital for accelerating the progress of ecological civilization within the coal industry and achieving an optimal allocation of coal resources. To unveil the evolutionary traits of actual CFE and its decomposition, this study employs a current technology based on a combined super-efficient measure (SBM), global SBM, the stochastic frontier approach (SFA), and the global Malmquist–Luenberger index (GML) model on panel data from 2010 to 2021 across 30 provinces in China. The research conclusions are as follows. First, significant efficiency gaps are observed among provinces, showcasing superior performance in the north and east regions. Moreover, the impact of environmental factors and random disruptions on individual slack variables varies, resulting in a decrease of 0.18 and 0.43 in the CFE of source-area and sink-area when these factors are not taken into account. Thirdly, a clear distinction emerges between the technical efficiency change index (EC) and the best-practice gap change index (BPC). Lastly, the CFE displays regional disparities marked by an upward trajectory and fluctuating patterns resembling a “W” shape.

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Section: Second Stage Analysis: Sfa Regression Evaluationmentioning

confidence: 99%