The Role of Environmental Innovation, Financial Development and Financial Risk on Renewable Energy in Iran

Purpose: Energy plays a crucial role in the progress of societies, as a necessary component towards achieving economic development and prosperity. Energy sources can be categorized into renewable and non-renewable ones. As the world encounters crises of environmental pollution, and climate change leading to long-term negative effects on ecosystems and global economy, there is an increasing focus on renewable and clean energy sources. In response to these challenges, many countries are working to enhance the proportion of renewable energy in their environmental cycles. In this regard, Iran has been identified as one of the economies confronting high pollution in the region; therefore, it is essential to investigate the factors influencing the consumption of clean energy in this country. This research aims to study the independent effects and the interactive roles of environmental innovation, financial development, and financial risks on the consumption of renewable energy in Iran. The findings of the research can provide valuable insights for policymakers and planners, assisting them in formulating and implementing effective financial, energy, and environmental policies as the main purpose of these policies is to enhance sustainable economic growth and stability while addressing environmental challenges.
Methodology: In order to conduct the research based on the objectives of the study, our experimental model is specified and tested in the form of following four equations.
In equations 1 to 4, the dependent variable is REC (Renewable Energy Consumption), representing the percentage of total final energy consumption attributed to renewable sources. The main independent variables are:

EI (Environmental Innovation), measured by the number of the patents recorded in the environment field
FD (Financial Development), reflecting the level of financial development
FR (Financial Risk), indicating the degree of financial risk

The above independent variables also include their interaction variables.
Additionally, the control variables are:

EG (Economic Growth), represented by the GDP per capita in 2015 constant price in US dollars
EP (Environmental Pollution), measured by the total greenhouse gas emissions in kilotons of CO2 equivalent
TO (Trade Openness), representing trade as a percentage of GDP

The data for REC, EG, TO, and EP are sourced from the World Bank. The variable EI is obtained from the database of the Organization for Economic Cooperation and Development (OECD). FR is sourced from Political Risk Services (PRS). The variable FD is extracted from the International Monetary Fund (IMF). Empirical research models were analyzed using time series data to cover the years 1990 to 2022. The Dynamic Ordinary Least Square (DOLS) method was employed for the analysis, allowing for a comprehensive testing of the relationships among the variables over the specified time period.
Findings and Discussion: The stationary of variables, assessed by the Augmented Dickey-Fuller test (ADF), indicates that REC, TO, EG, FD, and FR variables are stationary in order one I(1), while EP and EI variables are stationary I(0). This suggests that cointegration relationship of the data is of orders I(0) and I(1). The optimal lag was determined using the Bayesian Information Criterion (SBC); it is lag one. The Johansen-Juselius Cointegration Test was employed to examine the existence of long-term relationships among the research variables. The results confirm at least one long-term relationship among the research variables in various model specifications. In the first model, the FD variable is positive and statistically significant (P = .01). A one-unit increase in FD corresponds to a 2.92 unit increase in the REC variable. In the second model, the FR index is positive and statistically significant (P = 0.1). A one-unit decrease in FR leads to a 0.5-unit increase in the REC variable. For model 3, the interactive variable of EI and FD is statistically significant (P = .01). A one unit increase in this interactive variable results in a 0.82-unit increase in REC. In model 4, the interactive variable of EI and FR is significant (P = .01) with a positive sign, indicating that a one-unit increase in this interactive variable leads to a 0.07-unit increase in REC. The results also show that the parameter of EG is significant at the 5% probability level. A one percent increase in EG corresponds to REC increases of 1.62, 2.24, 3.42, and 2.38 units for models 1, 2, 3 and 4, respectively. The EP variable has a negative effect on REC (P = .01). A one percent increase in EP results in REC increases of 2.44, 3.58, 3.16, and 3.08 units for models 1, 2, 3 and 4, respectively. The TO variable, with a positive sign, is significant at the 1% probability level. A one-unit increase in TO corresponds to REC increases of 0.03, 0.03, 0.02 and 0.02 units for models 1, 2, 3 and 4, respectively. The DOLS method was employed to estimate the research models.
Conclusions and Policy Implications: The results indicate that the EI (Environmental Innovation) variable has a positive impact on REC (Renewable Energy Consumption). The application and development of technology plays a crucial role, empowering investors to enhance energy efficiency and environmental quality through the implementation of new innovations in production and the expansion of clean energy development infrastructure. Furthermore, the findings demonstrate that FD (Financial Development) has a positive effect, while financial risk (FR) has a negative effect on REC. In addition, the simultaneous combined impact of FD, FR, and EI on REC is significant and positive effects. The results suggest that, in countries like Iran, where the share of renewable energy consumption is smaller, improvements in financial indicators and encouragement of environmental innovations have a more noticeable effect on strengthening the development of the renewable energy industry. The study also highlights that FD amplifies the effect, while FR lessens the effect of EI on the REC variable. This implies that, in countries with a smaller share of renewable energy consumption, the influence of enhancing financial development combined with environmental innovations on the development of the renewable energy sector is more considerable. The results suggest that technological innovation is a potential solution to boost the consumption and development of renewable energy, particularly in the field of improved financial development. A developed financial system, as indicated by the study, provides various financing methods and risk management tools for companies and households, facilitating access to funds with reduced financial risks. Moreover, innovative technology can enhance the production and distribution of renewable energies and raise the standard level of manufacturing industries. In conclusion, creating conditions to improve the productivity of innovative technologies in production with minimal pollution and expansion the combination of renewable energy and technological innovation are the approaches suggested in this study. This dual strategy can mitigate environmental damage while promoting sustainable energy consumption and development.