Revolutionizing sustainable economic growth in China: Harnessing natural resources, green development, and fintech for a greener future

resour. ce rich economies are the major contributors of global pollution due to over use of natural resources. The extraction of natural resources not only contaminates environment but also create ecological imbalance. Also, in the quest for economic expansion, countries tend to fuel their economies with cheaper energy resources as it may excel their revenue source. Considering the issue, the study intends to investigate the feasibility of Fintech industry for sustainable economic growth. In addition to this, green growth and natural resources are also considered a prompter of sustainable economic growth. Therefore, the present study explores the nexus among natural resources, green growth, fintech and sustainable economic growth in the context of China. The study used water levels, energy consumption and air pollution to develop an index for green growth. Dataset from 2000 to 2022 was extracted from World Development Indicator to determine the associations among the variables. For a robust analysis, the study employed the Quantile Autoregressive Distributed Lag (QARDL) model to account for asymmetric impacts of the predictors on the dependent variable. The findings provided evidence into the long-run associations between the variables. The lagged value of green growth positively and significantly impacts sustainable economic growth whereas FinTech was found to exert a positive and significance influence on sustainable economic growth. Moreover, lagged value of natural resources were also positively and significantly associated with sustainable economic growth. However, in terms of short-run impact, the analysis did not support presence of short-run influences of the predictors of sustainable economic growth. Model's adequacy and reliability was ensured by testing for autocorrelation with results confirming the absence of autocorrelation. The study contributes to existing literature by using methodological advancements in examination of dynamic association among economic factors and consequently, provides meaningful implications for policymakers.