@ARTICLE{Wong_Wong_Ming_Green_2026,
 author={Wong, Wong Ming and Wang, Xing and Liu, Tiantian and Su, Wunhong},
 volume={vol. 52},
 number={No 1},
 pages={118-135},
 journal={Archives of Environmental Protection},
 howpublished={online},
 year={2026},
 publisher={Polish Academy of Sciences},
 abstract={This study aims to examine the impact of green finance instruments on carbon dioxide emission intensity (CDEI), filling a methodological research gap across regions and time-series data. Specifically, it investigates how green finance instruments, namely green credit, green support, and green funds, together with gross domestic product (GDP), affect the CDEI across diverse regions from 2008 to 2021. A generalized additive mixed model (GAMM) was used to analyze panel data from 29 municipalities and provinces in China over this period. These municipalities and provinces were grouped into six administrative regions, allowing the model to capture the nonlinear relationships and interactions that vary across space and time. The results indicate that in Northern, Northeastern, and Northwestern China, GDP is associated with a higher CDEI. In contrast, green credit, green support, and green funds did not significantly reduce the CDEI during the study period. This study contributes to the discussion on the importance of developing region-specific green finance strategies. It proposes policy approaches tailored to local economic conditions to improve the effectiveness of green finance efforts, thereby supporting emission reduction and advancing environmental policies and sustainable business strategies.},
 title={Green Finance Instruments and Carbon Dioxide Emission Intensities: A Generalized Additive Mixed Models Analysis},
 type={Article},
 URL={http://www.journals.pan.pl/Content/138272/PDF-MASTER/Archives%20vol%2052%20art%2010.pdf},
 doi={10.24425/aep.2026.158388},
 keywords={green finance;, carbon dioxide emission intensity;, generalized additive mixed models;, gross domesticproduct; time series;},
}