Green finance (GF) is a vital strategy implemented by China to minimize carbon emissions to achieve targets of carbon peak and carbon neutrality. Thus, the objective of this study is to reduce carbon emissions by developing green finance practices in China. This study identifies, evaluates, and ranks the factors and sub-factors of green finance because it is the core issue for sustainable development. In this regard, this study utilizes the Delphi and fuzzy Analytical Hierarchy Process (FAHP) method to analyze the main factors and sub-factors of green finance. The Delphi method finalizes the 6 factors and 26 sub-factors after a thorough investigation. The FAHP method was used to assess and rank the identified factors and sub-factors of green finance. The findings show that the political (POF) is the most crucial factor of green finance in the Chinese economy. The economic (ECF) and environmental (ENF) factors are ranked second and third important factors. The further results of the FAHP reveal that ecological and political identification (POF1), political stability (POF3), and climate commitments (POF2) are the top-ranked sub-factors of green finance. The results specify that green finance development is a very crucial strategy to minimize carbon emissions.

The literature on the impact of policy uncertainty on climate change has grown rapidly in recent years as policymakers and researchers have become increasingly concerned about the potential adverse effects of policy uncertainty on environmental sustainability. This study aims to investigate the impact of economic policy uncertainty (EPU), GDP per capita, renewable energy consumption (REC), and foreign direct investment (FDI) on environmental sustainability from the perspectives of the environmental Kuznets curve (EKC) and pollution halo/haven hypotheses. The research employs panel data analysis techniques, including panel corrected standard errors (PCSE) and generalized least squares (GLS), to analyze the data from a panel of 19 developed and developing countries from 2001 to 2019. The results reveal that EPU, GDP per capita, REC, and FDI significantly impact GHG emissions, contributing to climate change. The results of the study confirm a U-shaped EKC and pollution haven hypothesis in the selected economies. The findings of this study provide valuable insights for policymakers, as they highlight the need to consider the interplay between economic growth, foreign investment, and environmental policy in addressing climate change. The results also suggest that reducing policy uncertainty and promoting sustainable economic growth can mitigate the effects of climate change and ensure environmental sustainability.

Global human pollutant activities have raised greenhouse gas (GHG) emissions, which have directly affected the climate. Fossil fuel-based energy has brought a negative impact on the environment and is considered one of the largest sources of GHG emissions. It is envisaged that GHG emissions will increase in the future due to rapid population growth and industrialization. Thus, it is imperative to mitigate climate variability and reduce GHGs by adopting renewable energy (RE) sources for electricity generation. In this regard, the multi-criteria decision analysis (MCDA) process would serve the purpose of framing out energy policy to analyze these environmentally friendly energy sources. This study uses an integrated decision methodology—a combination of Delphi, fuzzy analytical hierarchy process (FAHP), and the fuzzy weighted aggregated sum product assessment (FWASPAS)—for the adoption of RE sources for electricity generation in Turkey. Initially, the study identified five main criteria and seventeen sub-criteria using the Delphi method. Then, the FAHP method was used to evaluate and rank the main criteria and sub-criteria. Finally, the FWASPAS method was used to assess and prioritize five major RE sources for electricity generation. The FAHP analysis indicated that political criteria are the most influential, followed by economic and technical criteria. Further, the FWASPAS method revealed that wind energy is the most significant option for electricity generation. This decision-making process can help the energy planners to utilize RE sources for sustainable development.