The Relationship Between Multidimensional Economic Complexity and Inclusive Green Growth: An Analysis of Developed and Developing Countries | ||
| پژوهشهای اقتصادی ایران | ||
| Article 14, Volume 29, Issue 100, October 2024, Pages 235-271 PDF (1.86 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22054/ijer.2024.79437.1278 | ||
| Authors | ||
| Fariba Rashnoo* 1; Ahmad Sarlak2 | ||
| 1Ph.D. Candidate in Economic Development, Arak Branch, Islamic Azad University, Arak, Iran | ||
| 2Associate Professor, Faculty of Economics, Arak Branch, Islamic Azad University, Arak, Iran | ||
| Abstract | ||
| In an era of economic complexity, where goods and services are produced using advanced technologies and with significant diversity, achieving economic growth without environmental pollution has become one of the primary goals for nations worldwide. This objective necessitates measures such as investment in knowledge-based production, which in turn relies heavily on investment in research and development. The present study aimed to examine the relationship between multidimensional economic complexity and inclusive green economic growth. Given the geographic proximity of some developed and developing countries, the research employed a spatial panel econometric method using data from these nations. The results indicated a significant relationship between inclusive green growth and economic complexity in both developed and developing countries. However, this relationship is relatively weaker in developing countries. Introduction The most crucial factor influencing the level of economic development in any country is the extent to which knowledge is generated and applied in its production processes (Kazemi, 2013). Moreover, the integration of knowledge into production significantly reduces greenhouse gas emissions (Barbieri, 2012). Economic complexity, through the knowledge channel, promotes resource efficiency, enhances the quality of production institutions, and facilitates the establishment of green productive structures (Hassan et al., 2022). Since developing countries often rely on the production of a limited range of goods, it is essential to examine their level of economic complexity. Furthermore, given the strong link between economic complexity and the technology required for renewable energy production, it is critical to examine the relationship between multidimensional economic complexity and economic growth through the technology production channel. In this context, countries like Iran must be analyzed in terms of economic complexity and compared with developed nations. Despite the importance of this issue, no study to date has explored the relationship between economic complexity and inclusive green economic growth across both developed and developing countries. The present research tried to address the following question: What is the relationship between economic complexity and green economic growth in developed and developing countries? To answer the question, the study first reviewed the theoretical foundations of green economic growth and economic complexity, followed by a discussion of the methods and models. Materials and Methods The present study relied on the model built upon the work of Mohammadi et al. (2023), who investigated the impact of economic complexity and renewable energy consumption on environmental pollution in developing countries. Spatial econometrics in Stata software was used to analyze the relationship between economic complexity and inclusive green economic growth from 2000 to 2022. This approach not only examines the relationship between independent and dependent variables but also incorporates the spatial characteristics of the locations involved, as highlighted in studies such as AbuGhunmi et al. (2023). Additionally, data from both developed and developing counties was used to conduct a comparative analysis. The first step in estimating the spatial panel model is to create the adjacency matrix. In this research, the proximity matrix for the seven OPEC member countries with common borders of spatial heterogeneity refers to the deviations in relationships between observations at different geographical locations. In this matrix, neighboring and non-neighboring countries are assigned a value of 1 and 0, respectively. Next, autocorrelation is tested through methods such as the Moran and Gray tests. Once autocorrelation is confirmed, the model type is determined through parent, multiple parent, and Akaike and Schwartz tests. Finally, the model is estimated. Results and Discussion The results of estimating the relationship between multidimensional economic complexity and green economic growth in developing countries are presented in Table 1, and those for developed countries are shown in Table 2. Table 1. Model Estimation Results With the Dependent Variable in Developing Countries Effects Variables Coefficient Probability Direct effects INF -0.08 0.000 CO2 -0.09 0.000 R&D 0.3 0.000 TECHEX 0.21 0.000 EC 0.12 0.000 Indirect effects INF -0.1 0.000 CO2 0.11 0.000 R&D 0.24 0.000 R&D(-1) 0.21 0.000 TECHEX 0.20 0.000 EC 0.2 0.000 Total effects INF -0.11 0.000 CO2 0.19 0.000 R&D 0.13 0.000 TECHEX 0.12 0.000 EC 0.21 0.000 Spatial correlation coefficient -0.236 0.039 Hausman test 7.11 0.81 Source: Research findings Table 2. Model Estimation Results With the Dependent Variable in Developed Countries Effects Variables Coefficient Probability Direct effects INF -0.06 0.000 CO2 -0.04 0.000 R&D 0.41 0.000 TECHEX 0.33 0.000 EC 0.24 0.000 Indirect effects INF -0.05 0.000 CO2 -0.06 0.000 R&D 0.36 0.000 TECHEX 0.21 0.000 EC 0.29 0.000 Total effects INF -0.03 0.000 CO2 -0.06 0.000 R&D 0.29 0.000 TECHEX 0.21 0.000 EC 0.39 0.000 Spatial correlation coefficient -0.249 0.029 Hausman test 6.99 0.78 Source: Research findings As observed in the calculations, both the direct and indirect effects, as well as the total economic complexity, have a direct and significant impact on green economic growth. As expected, this effect is stronger in developed countries than in developing ones. The effect coefficient for total economic complexity in developing and developed countries is 0.21 and 0.39, respectively. These figures indicate that the overall impact of economic complexity on economic growth is greater in developed countries. This relationship can be explained using the Kuznets curve. According to the results, economic complexity fosters green economic growth by increasing the use of technology in production and reducing emissions. Conclusion The results indicated that the impact of economic complexity on green growth is smaller in developing countries compared to developed countries. Additionally, since economic complexity reflects the use of advanced technologies and increased costs in the production process, the rise in technology use and in research and development expenditures will not only drive the production process towards greener, pollution-free methods but will also help reduce production costs over time. The coefficients presented in the table for both developed and developing countries showed the positive effect of economic complexity on green production and growth. | ||
| Keywords | ||
| Economic Complexity; Inclusive Green Growth; Developed Countries; Developing Countries | ||
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