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Given the importance of green finance in a discussion of energy efficiency and clean energy, it is critical to evaluate its implications for the growth of renewable energy. This study examines the impact of green finance on renewable energy development in Singapore.

The dynamic ordinary least squares (DOLS) regression was used in this work to test such a connection.

Using the DOLS for the period 2000–2020, it was discovered that green finance aids renewable energy development in Singapore. Additionally, the findings revealed that economic growth, oil prices, energy consumption, carbon dioxide emissions and institutional factors are all positively associated with renewable energy growth, resulting in a boost in renewable energy development.

Hence, as a result, the monetary authorities of Singapore, such as financial institutions, non-governmental organisations and corporations, should prioritise renewable energy projects under green finance initiatives to boost renewable energy growth. This may assist in raising investment flows to green projects; hence, accelerating the adoption of renewable energy.

Increased Singapore's initiatives to accelerate green finance have prompted this study to examine the research question of whether green finance has a significant impact on renewable energy growth. Thus, to the best of the authors’ knowledge, this will be the first empirical study to explore the impact of green finance on renewable energy growth in the case of Singapore.

Growing food insecurity is a leading cause of fatalities, particularly in developing nations like Sub-Saharan Africa and Southeast Asia. However, the rising energy consumption and carbon dioxide (CO2) emissions are mostly associated with food production. Balancing the trade-offs between energy intensity and food security remains a top priority for environmentalists. Despite the critical role of the environment in food security, there is a scarcity of substantial studies that explore the statistical connections among food security, CO2 emissions, energy intensity, foreign direct investment (FDI) and per capita income. Therefore, this study aims to provide more precise and consistent estimates of per capita CO2 emissions by considering the interplay of food security and energy intensity within the context of emerging economies.

To examine the long-term relationships between CO2 emissions, food security, energy efficiency, FDI and economic development in emerging economies, this study employs correlated panel-corrected standard error, regression with Newey–West standard error and regression with Driscoll–Kraay standard error models (XTSCC). The analysis utilizes data spanning from 1980 to 2018 and encompasses 32 emerging economies.

The study reveals that increasing food security in a developing economy has a substantial positive impact on both CO2 emissions and energy intensity. Each model, on average, demonstrates that a 1 percent improvement in food security results in a 32% increase in CO2 levels. Moreover, the data align with the Environmental Kuznets Curve (EKC) theory, as it indicates a positive correlation between gross domestic product (GDP) in developing nations and CO2 emissions. Finally, all experiments consistently demonstrate a robust correlation between the Food Security Index (FSI), energy intensity level (EIL) and exchange rate (EXR) in developing markets and CO2 emissions. This suggests that these factors significantly contribute to environmental performance in these countries.

This study introduces novelty by employing diverse techniques to uncover the mixed findings regarding the relationship between CO2 emissions and economic expansion. Additionally, it integrates energy intensity and food security into a new model. Moreover, the study contributes to the literature by advocating for a sustainable development goal (SDG)-oriented policy framework that considers all variables influencing economic growth.

Considering the evolving importance of green finance, this study uses climate-related development mitigation finance as a proxy of green finance and investigates the impact of green finance on ecological footprint as an indicator of environmental quality along with the influence of economic growth, renewable energy, greenhouse gas emissions, trade openness and urbanization across 47 developing countries over the period 2000–2018.

After finding the presence of cross-sectional dependency among variables, the second-generation panel unit root test was employed to detect the order of integration among the variables. Since all the variables were found to be stationary, Westerlund cointegration technique was employed to detect the long-run relationship among the variables. Then, the long-run elasticity among the dependent and independent variables was tested using fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and pooled mean group–autoregressive distributed lag (PMG–ARDL) approaches.

The empirical findings suggest the presence of long-run relationship among all the variables, namely, ecological footprint, green finance, economic growth, renewable energy consumption, greenhouse gas emissions, trade openness and urbanization for the selected developing countries in the sample. Furthermore, economic growth, greenhouse gas emissions, trade openness and urbanization, all have a positive and significant impact on the ecological footprint, whereas renewable energy consumption and green finance have a significant and negative impact on the ecological footprint, which supports the view that environmental quality is improved with the greater use of renewable energy technologies and allocation of greater amounts of more green finance.

The empirical results of this study offer policymakers and regulators some implications for environmental policy for protecting the countries from ecological issues.