On the other hand, the reallocation of factors of production from traditional to modern economic activities can also have positive effects by developing new means of reducing pollution and producing cleaner energy (e.g., solar panels, wind turbines, hydroelectricity, etc). Green technology and eco-innovation are decisively geared at lessening, if not reversing, the negative impacts of pollution by creating new products/services and business methods. These include among others, innovations in renewable energy, recycling, wastewater treatment, and eco-friendly food processing and packaging. The world market of environmental products and services is growing and policy makers are now paying more attention to the environmental goods and services (EGS) industry which is seen as a key ingredient of industrial competitiveness, trade advantage and social stability [40, 70, 79, 120].
In recent years, the ECI has received widespread attention throughout the scientific community, mainly because it is a robust predictor of economic growth [59, 63]. Furthermore, [58] have recently shown that countries exporting complex products tend to be more inclusive and have lower levels of income inequality than countries exporting simpler products. The authors attribute part of their finding to industrialization which played a major role in the rise of a new middle class by creating new jobs and training/education opportunities for workers.
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The remainder of the paper is structured as follows. Section 2 provides a review of the relevant literature and discusses the connection between environmental performance and economic complexity. Section 3 describes the datasets used in the paper. Section 4 describes the econometric analysis for studying the effect of economic complexity on environmental outcomes and discusses the control and instrumental variables included in the econometric model. Section 5 presents the results and their robustness analysis. In Section 6, we draw our conclusions.
According to Kaufmann et al. [78] the composition of the goods that a country produces is an important determinant of its environmental performance. High economic complexity implies a shift from a low-productivity agricultural economy to higher-productivity sectors and to the production of more sophisticated products. This shift requires increased consumption of energy resources, which in turn contributes to elevated CO\(_2\) emissions and environmental degradation. On the other hand, the ECI is related to structural transformations in the economy, and reflects the amount of knowledge and advanced capabilities embedded in the production process. Hence more complex countries offer better conditions for developing technological solutions that benefit the environment [99].
Despite its potential importance, the relationship between economic complexity and the environment remains relatively unexplored.Footnote 3 Only a few papers identify the ECI as a predictor of environmental outcomes, and most of them use carbon emissions to represent environmental quality.
Can and Gozgor [20] were the first to introduce ECI in a model that tests the EKC validity in the French economy for the period from 1964 to 2014. Their results illustrate that higher economic complexity suppresses the level of CO\(_2\) emissions in the long run. Neagu and Teodoru [101] examine the relationship between the ECI and environmental pollution within a panel of 25 European Union countries spanning the period from 1995 to 2016. Their findings show that economic complexity is positively associated with greenhouse gas emissions. Dogan et al. [36] use 55 countries over the period 1971 to 2014, which they divide into three different income groups. Their results indicate that the ECI affects CO\(_2\) emissions differently at the various stages of development and income, increasing the environmental degradation in lower and higher middle-income countries and abating CO\(_2\) emissions in high-income countries.
We study the effect of economic complexity on environmental performance using datasets 1 and 3 (see Section 3). Given the availability of controls, the sample covers 88 developed and developing countries over the period of 2002-2012.Footnote 5
We estimate equation (1) using different econometric methods. First, we use pooled-OLS and then, fixed-effects-OLS. However, fixed effects estimators do not necessarily identify the effect of economic complexity on environmental performance. The estimation of the effect requires exogenous sources of variation. While we do not have an ideal source of exogenous variation recognized by previous studies, there are two promising potential instruments of ECI that we adopt in our fixed-effects 2SLS/IV analysis.
Firstly, we use the measure of the (log) number of journal articles published in scientific and technical journals in a given year. This index calculates the total number of papers in the fields of physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences. Higher values are associated with a higher level of scientific effort and output, which is directly related to the intensity of process and product innovation in the economy i.e. to the sophistication of its productive structure.
In this section, we discuss our baseline findings, i.e., the results when estimating equation (1) with different econometric techniques. Table 2 reports the results of pooled-OLS, adding additional variables from the set of controls in each step (column). In all specifications, we consider time fixed effects. In all columns except (1), we adopt a set of regional dummies for geographical heterogeneity, which is related to latitude, climatic conditions, and ecological awareness. Namely, we use the following dummies: Europe, Asia, Oceania, North America, South America. In column (8), we also adopt the dummy variable OECD to isolate the effect of high levels of economic development on environmental quality [49, 89, 92]. As expected, the sign of the estimated coefficient is positive because of the higher environmental awareness in developed countries. In all specifications, economic complexity has a positive relationship with environmental performance and the control variables enter with the expected sign. The education coefficient is negative, though its magnitude is negligible.
Column (1) in Table 4 reports the estimates using the baseline fixed-effects 2SLS/IV specification and ECI+ as an alternative measure of economic complexity (the regression includes the set of controls used in the benchmark specification and time dummies). The baseline results remain qualitatively intact. Particularly, the coefficient of ECI+ is positive and statistically significant in the instrumented regression. On average, keeping all other variables constant at their mean values, an increase of 1 point in the ECI+ increases the EPI by 9.4 points. The level of development, measured by (log) GDP per capita, again shows a non-linear impact on environmental performance. The EKC hypothesis also appears to be affirmed with the ECI+ as explanatory variable. The negative coefficient of the \(GDP\ per\ capita\) variable combined with the positive sign of its squared term confirms the inverse U-shaped relationship between pollution and economic development.
The relationship between economic growth, environmental pollution and energy consumption has been studied thoroughly in recent decades, using data from different countries and regions. Most studies are for single countries [9, 10, 55, 121, 122, 134, 142] and only a few papers have used multi-country data to investigate this relationship, producing ambiguous results [2, 42, 76, 105, 113].
Table 5 reports the estimation results of the benchmark specification but using (log) energy consumption (kg of oil equivalent per capita) as the dependent variable in column (1); (log) renewable electricity output (% of total electricity output), renew electricity, in column (2); (log) renewable energy consumption (% of total final energy consumption), renewenergy, in column (3); (log) renewable internal freshwater resources per capita (cubic meters), renew water, in column (4); (log) fossil fuel energy consumption (% of total), fossil fuel cons, in column (5). Economic complexity has a positive effect on energy use and the same effect stands for the level of income. However, for higher stages of economic development, it seems that energy consumption is less intensive (the squared term of \(GDP\ per\ capita\) has a negative coefficient). Both agriculture and industry sectors seem to be energy demanding, and as expected, a higher proportion of urban population is associated with higher energy consumption.
For the variables linked to renewable resources only the coefficient of renew energy is statistically significant, while the negative sign implies a negative effect of economic complexity on renewable energy consumption. Income has the same effect but for higher income levels the consumption of renewable energy increases. In addition, agriculture, industry and corruption have a negative effect on renew energy but trade increases the consumption of renewable energy. However, the negative sign of the fossil fuel cons coefficient in column (5) (statistically significant at the 10% level) provides evidence that complex economies tend to rely less on fossil fuels.
Our results show that economic complexity has an improving effect on all the above measures: higher economic complexity leads to (a) better health, higher life expectancy at birth and lower mortality rate under 5; (b) improved access to water and sanitation; (c) higher biodiversity and habitat (more protected terrestrial biome areas, marine protected areas, critical habitat protection). 2ff7e9595c
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