1. Introduction
The relationship between energy use and economic development has been extensively examined and discussed in several empirical studies, by taking into account the threat of greenhouse gas (GHG) emissions and the scarcity of energy resources.
In order to ensure the sustainability of economic development, the mitigation of GHG emissions has been the focus of policymakers, as well as researchers in recent decades.
At the European Union (EU) level, European energy policy has made efforts to develop coherent strategies for establishing common goals for all countries, including reduction of pollution and increase in the use of renewable energy.
All EU countries have assumed the commitment to sustain the goals of reducing GHG emissions by 20% compared to the 1990 levels, and increasing energy efficiency by 20% within the frame of the Europe 2020 strategy, under the flagship of "Resource Efficient Europe" (decoupling economic growth from the use of resources, supporting the shift towards a low-carbon economy, increasing the use of renewable energy sources, modernizing the transport sector and promoting energy efficiency) [
1]. The strategy aims to turn the EU into a so-called ‘low-carbon economy’ based on renewable energy sources and energy efficiency. Improved energy efficiency and more renewable energy sources can reduce energy dependence and save the EU between EUR 175 and 320 billion in energy import costs per year over the next 40 years [
2]. A reduction in GHG emissions by 40% below the 1990 levels, together with other goals related to renewable energy and energy efficiency in the EU is included in the Energy Roadmap 2050 initiative [
3], which was reinforced in 2014 [
4]. This initiative proposes not only to ensure the competitiveness and security of the energy supply, but also to support the transition to a low-carbon economy and the decarbonization of the energy system by boosting innovation in energy technology.
According to EUROSTAT [
5] (p. 144), the main producers of GHG emissions in the European countries are: The supply of electricity, gas, steam and air conditioning; manufacturing; agriculture; forestry; and fishing. GHG emissions in 2016 in the EU reached 77.76% of the 1990 levels [
6] (
Figure 1). GHG emissions saw a downturn trend during the period 1990-1999, mainly due to structural changes (economic and political uncertainty and industrial activities which collapsed in the former communist countries), industrial modernization in Western countries and replacing coal with gas. Emissions increased in 1996, when a cold winter led to an increase in heating requirements [
5], but the trend reversed in 1997. From 1998 to 2007 this trend decreased due to a growth in the use of low-carbon fuels, particularly renewable energy sources, offsetting an increase in primary energy consumption, as well as the use of lower-carbon footprints and the decline in livestock and nitrogenous fertilizer use in agriculture [
7], (p. 2). The sharpest decline in energy consumption and pollutant emissions occurred between 2007 and 2009, due partly to the financial crisis (decrease in industrial production, demand and volume of transport) and also partly due to the switch from coal- to gas-fired electricity generation as a result of the adoption of the EU GHG Emission Trading Scheme (EU-ETS) [
8], (p. 91). The decline observed between 2009 and 2012 can be attributed to larger shares of renewable energy sources in the fuel mix, the economic slowdown and an improvement in the energy intensity of the EU economy [
9]. Between 2012 and 2014, the largest share of emissions reductions was achieved in the energy production sector (particularly in the case of electricity generation and heat production). In the last two years, a slight increase was noticeable, as a result of growing energy consumption from all sources, including fossil fuels, higher transport demands, increased economic growth and the increased consumption of gas used for heating in the residential sector [
10].
Within the wealthy energy-growth-environment literature, several studies have explored the link between pollutant emissions as a dependent variable and various indicators (economic, social, geographic, demographic and technological) as explanatory variables. The present paper introduces a new explanatory variable in the analysis of the determinants of environmental degradation, namely, the economic complexity. Relevant studies on economic complexity, technological innovation and their relationship with energy consumption structure and pollution are discussed below.
In a general sense, economic complexity refers to a country’s productive structure, which leads to a specific structure of energy consumption and, as result, a specific effect on the environment. It is obvious that a country’s productive structure could influence GHG emissions while the complexity level of products could harm the environment by generating pollution, but it also embeds knowledge and capabilities, research and innovation, which can help to stimulate greener products and environmentally friendly technologies.
The increase in economic complexity is related to structural transformations in economies: Diversification from agriculture and extractive industries to more sophisticated products and services [
11,
12]. Several studies [
13,
14,
15,
16,
17,
18,
19,
20,
21] in the last decade have dealt with the problem of quantifying a country’s productive structure.
According to the Center for International Development at Harvard University, the Economic Complexity Index (ECI) is an expression of the diversity and complexity of a country’s export basket [
20]. The index is calculated for 128 countries, based on data from UN COMTRADE, the International Monetary Fund and World Development Indicators.
A higher value of ECI indicates improved (advanced) capabilities of a country in the production process [
22,
23]. The process of economic development could be explained as a process of learning how to produce and export more complex products [
14,
15], in turn requiring capabilities to develop new activities or products and attain a higher level of productivity.
Economic complexity is related to a country’s level of prosperity and there is a tight relationship between economic complexity and GDP per capita. Moreover, countries tend to move towards an income level which is compatible with their overall level of productive knowledge, meaning that their income tends to reflect their embedded knowledge [
20]. The income level of countries tends to follow their productive structure [
13,
14,
15]. According to the model developed by Hausmann and Hidalgo (2010) [
24], the return to the accumulation of new capabilities increases exponentially with the number of capabilities already available in a country. The export shares of the most complex products could increase with income, while the export share of the less complex products could decrease with income. A more complex productive structure enables countries to engage in high productivity activities which lead to faster development [
25].
Economic complexity is associated with income inequality: Increases in economic complexity tend to be accompanied by decreases in income inequality and a country’s productive structure may limit its range of income inequality [
26].
As an accurate predictor of income per capita, [
24] economic complexity may be used as explanatory variable when testing the validity of the Environmental Kuznetz Curve (EKC). An example is the study developed by Can and Gozgor (2017) [
27] which noticed that the economic complexity is a significant indicator for suppressing the level of carbon emissions in France.
As an expression of a country’s innovative output, economic complexity is based on technological innovation and R&D activities in the economy, which generate not only more sophisticated and complex products, but may promote less polluting industrial technologies, efficiency in energy use and less environmental degradation.
The recent literature on energy research provides rich evidence confirming the role of technological innovation in reducing pollutant emissions and the transition to a lower-carbon economy.
The energy production sector is one of the main sources of pollutant emissions. Energy technology innovation could provide solutions which reduce emissions, augment energy resources and enhance the quality of energy services [
28]. Energy technology innovation is a process ranging from R&D on new technologies to their diffusion, whose results are reflected in the market share and other aspects related to new technology diffusion [
29]. Efforts to promote the deployment of new energy technologies can translate the results of R&D activities into changes in the energy system, with learning-by-doing being an important element of this deployment [
30]. Effective policies for energy technology innovation can encourage energy projects and stimulate the development of energy users’ markets [
31], as well as the transformation of energy consumption structure [
32]. The efficiency of energy use can be improved through technological innovation as Miao et al. (2018) highlighted in the case of strategic emerging industries in China [
33].
The gap between the patents in fossil fuel and renewable energy technology can be decreased through policies encouraging the market entrance of companies which are specialized in renewable technologies [
34]. The integration degree between different technology classes of environmental patents is important to the environmental productivity performance of companies, as highlighted by Aldieri et al. (2017) in the case of water pollution. These authors proved that specialized environmental technology fields’ spillovers have a positive impact on firms’ productivity [
35].
As an alternative in electricity production, the development of photovoltaic technologies and their participation in the renewable energy markets are driven by governments’ incentives (reduction in corporate income tax, subsidies to operators of renewable energy projects, financing lines) and energy efficiency policies. The leading countries involved in these technologies include the USA, China, Japan, Germany and South Korea [
36,
37].
Renewable energy expansion and the growth of the electric utility infrastructure have stimulated the interest in smart grid systems. A smart grid refers to an electricity network which can integrate, in an intelligent way, the actions of all connected users in order to deliver a sustainable, economical and secure electricity supply [
38]. Based on digital technology, smart grids can improve the reliability, security, and efficiency of the electrical system through the delivery network to consumers and a growing number of distributed generation and storage resources [
39].
As the chemical industry is one of the most emission-intensive sectors, R&D cooperation between chemical companies, universities and research institutes, as well as firms’ participation in industrial clusters could create knowledge spillover effects and sustainable innovation activities [
40]. It is also important to shape policies supporting sustainability, eco-innovation and resource efficiency in this industry [
41]. Eco-innovation can contribute to sustainable development [
42], while environmentally friendly and socially responsible innovation fosters technological, institutional and organizational changes to the knowledge basis of existing production systems [
43]. A number of external and internal factors is enabling eco-innovation and environmental change in companies: Technological, organizational, institutional, economical, market-related and societal [
44]. The concept of eco-innovation is explored in the environmental literature in relation to the concept of sustainability transitions, which is defined as a long-term, multidimensional and radical transformation process leading to shifts in socio-technical systems towards more sustainable modes of production and consumption. Studies in this new field of research, which are mainly focused on energy and water supply, urban environment and transport, highlight how different green technologies could be combined in order to create new products, services, organizations and business models [
45].
While the literature on the environment and innovation is focused on the contribution of innovation to reduce climate change impact, Su and Moaniba (2017) [
46] used a reverse approach and discovered that a country’s propensity to innovate and patent climate change technologies is influenced by the levels of carbon dioxide and other GHG emissions.
External energy dependence in the European countries, increasing energy demand and the decline in conventional hydrocarbon reserves underline the call for other sources of energy (i.e., renewable and nuclear energy), although fossil fuels will remain the main source of available energy [
47]. Within this context, carbon capture and storage (CCS) has been highlighted as a viable technology solution to reduce GHG emission and a mitigating strategy for climate change by Rodrigues et al. (2015) [
47], Raza et al. (2019) [
48], Mc Coy (2014) [
49] and IEA (2011) [
50]. An extension of carbon capture and storage technology is carbon capture and utilization (CCU) where the captured CO2 emission is also used as feedstock [
51]. This is seen as a solution to improve the contradiction between economic development and environmental protection [
52]. A detailed review of relevant papers on CCS and CCU technologies is provided by Yan and Zhang (2019) [
53].
The energy consumption structure refers to the combination of different energy types (solid fuel, oil, gas, renewable, waste etc) in terms of final consumption. As a non-renewable energy source, fossil fuels are the most polluting component of the energy consumption structure. Due to their depletion and harmful effect on the environment, countries are forced to conceive energy policies from a global perspective on all energy sources and take into account the demand of the socio-economic system [
54]. In order to achieve a significant reduction in their share in final consumption, energy considerations should be incorporated within the design phase of products [
55]. The diversification of energy types, increasing the share of non-fossil energy and the development of renewable sources became priorities of energy policies in several countries [
56,
57,
58,
59]. Furthermore, hybrid renewable energy systems (combining several renewable energy sources) have gained the attention of business and industry in several countries [
60,
61], as well as micro grids composed of distributed energy resources [
62,
63].
Energy consumption structure, economic structure and energy efficiency exert a significant impact on pollutant emissions and can be considered as essential factors of low-carbon development [
64,
65,
66]. Several methods have been developed in order to provide appropriate tools to analyse sectoral emission trends [
67], simulate energy consumption and GHG emissions under different conditions [
68], select economically feasible technologies [
69] and models to optimize energy consumption structure [
70] or improve energy structure in the industry [
71].
An outstanding model of the global energy consumption structure was developed by Hu et al. (2018) in the form of an evolutionary tree, including 144 countries and regions. Developed countries have the most diverse energy consumption structures, while the location of countries in the evolutionary tree can provide a basis for improving these structures [
72].
Energy consumption structure is connected with the economic structure. For example, the output of secondary industry has a higher effect on the energy consumption structure [
73,
74] than the tertiary industry [
75].
Within the context of energy policies,
energy consumption structure, as a technical term, is replaced with the concept
of energy mix, which refers to a combination of various energy sources (coal, natural gas, nuclear, wind, solar power, biofuels) used to meet the energy needs of a country. The energy mix which leads to a reduction in carbon emission depends on the development level of the economy. Following the EKC hypothesis, Kim and Park (2018) [
76] found out that the best energy policy mix for OECD countries involves a gradual decrease in their relative reliance on natural gas, nuclear power, biofuels and waste fuels in the short run, while solar and wind power energy sources can decrease carbon emissions in the long run as the economy continues to grow. The effect of energy mix on income depends on democracy level: Less democratic countries tend to become more dependent on oil and natural gas with their own development while more democratic countries tend to move away from oil and geothermal sources and encourage the development of renewable energy sources [
77].
Considering the above, it can be noted that the relationship between economic complexity and GHG emissions has not been analysed to date in the relevant literature, except for the aforementioned work of Can and Gozgor (2017) [
27] which tests the validity of the EKC hypothesis in the French economy. The aim of the paper is to examine the long-term relationship between economic complexity, energy consumption structure and GHG emissions within a panel of 25 European Union countries. In addition, the analysis is carried out within two subpanels of European economies, in order to reveal country features which could affect the intensity of the relationship between economic complexity, energy consumption structure and GHG emissions.
The first contribution to the literature of this empirical study is that it analyses the long-run dynamics of economic complexity, energy consumption structure and GHG emissions in a panel data approach. The second contribution is related to the fact that a large part of the energy-growth- environment literature, which examines this relationship by employing panel data methods, assumes that panel time-series data are homogeneous and cross-sectionally independent which could cause possible inference and forecasting errors. To fill this gap, our study employs heterogeneous panel estimation techniques with cross-sectional dependence.
To the best of our knowledge, the present study is the first to examine the relationship between the ECI, energy consumption structure and GHG emissions in a panel data model.
The organizational structure of the paper is as follows:
Section 2 provides the empirical model for this study, data sources and descriptive statistics;
Section 3 presents the econometric modelling outcome;
Section 4 discusses the results; and
Section 5 concludes the paper with a summary of the main findings and policy implications.
5. Conclusions and Policy Implications
Greenhouse gas emission is the most frequently used indicator in monitoring environmental pollution and energy policies effectiveness, as well as assessing the progress of achieving the established energy strategy goals at national and EU level.
Within this context, the paper’s main contribution consists of the use of a new variable (ECI) to analyse the determinants of environmental degradation due to greenhouse gas emission. For this purpose, our paper investigates the effects of economic complexity on greenhouse gas emission in 25 EU countries and, additionally, in two subpanels of countries with higher respectively lower level of economic complexity, over the period 1995–2016. To this end, energy consumption structure is also included in the empirical model.
The main findings of the paper are summarized as follows.
First, the speed of reducing pollutant activities is higher in the first subpanel of 15 EU countries with higher economic complexity, suggesting differences in energy efficiency and effectiveness of energy policy mix between the two subpanels.
Secondly, a positive and significant association in the long run between energy consumption structure and greenhouse gas emission was identified within the EU panel and both two subpanels, indicating that, over time, higher energy consumption in the EU countries give rise to more greenhouse gas emissions. These findings are in line with other existing studies on the relationship between energy consumption and pollutant emissions [
68]. The impact of energy consumption structure on air emissions is higher in the ten EU countries with lower economic complexity, suggesting the need to give greater priority on policies aiming to mitigate the use of energy derived from fossil fuels.
Thirdly, more economic complexity is positively associated with greenhouse gas emissions growth in the long run within all panels and this evidence is the novel contribution of our paper to the existing literature on the impact of economy on environment. The effect of economic complexity in the two subpanels is different. Within the subpanel of countries with less complex products, the effect of both variables (ECI and ECSP) is stronger as in the other group of countries, suggesting a higher risk of pollution as the economic complexity grows and the energy balance inclined in the favor of fossil energy.
The paper also suggests that complex and sophisticated products can incorporate industrial technologies that may be more pollutant. Consequently, companies and policymakers should focus on integrating energy considerations within the design stage of products and promoting environmentally friendly investments when they decide to include products with higher complexity in the export baskets. In other words, if products complexity creates more greenhouse gas emission, the import can be an alternative. However, as Can and Gozgor (2017) [
27] stated, a detailed analysis of the scale of environmental degradation in each industry is needed.
Fourthly, the results of panel causality test are not conclusive. Only within the EU panel and the first subpanel it was identified a validated causal relationship from the energy consumption structure to the GHG emissions. This leads us to the expected conclusion that the share of fossil energy consumption should be reduced in favor of other energy types within the energy mix (i.e., renewable sources) in all European countries in order to prevent extended pollution associated with the growth of products complexity.
As policy implications at the EU level, we point out the importance of adapted national actions targeting the implementation of “The Energy Roadmap 2050” initiative [
3] and European Strategic Energy Technology Plan (SET-Plan) [
146], as well as the 2030 EU energy strategy [
147] which states three key targets for 2030: At least 40% reduction in greenhouse gas emissions with respect to 1990 levels, at least 27% of final energy from renewable resources and at least 27% increase in energy efficiency. The 2030 EU climate and energy framework was amended in 2018 by a regulation applying to greenhouse gas emissions and removals from land use, land use change and forestry and setting out commitments of Member States in this regard [
148]. It should be also mentioned the European energy efficiency framework, consisting of the three EU key Directives: Ecodesign (2009/125/EC) which covers the energy efficiency of products, Energy Performance of Buildings (EPBD-2010/31/EU) and Energy Efficiency Directive (EED-2012/27/EU) [
149,
150,
151].
In addition, our paper suggests that a careful monitoring of fulfilment of the above-mentioned EU Directives specifications and regulations is required in all examined countries, as well as consideration and implementation of energy taxes combined with policies concerning innovative technologies to increase production while reducing energy consumption. Specific measures promoting financial support for the development of carbon capture and storage (CCS) and carbon capture and utilization (CCU) and zero-emissions technologies (ZETs), hybrid and electric vehicles, clean-energy large-scale production and use, as well as diversifying the range of funding instruments of R&D activities (public-private, grants, Horizon 2020 Programme) must also be considered.
As the countries of the first subpanel with higher economic complexity already have a knowledge basis, a strategic decision could be related to the development of new products by using the existing capabilities, as well as using environmentally friendly technologies. In their case, specific suggested policy measures can be referring to developing an energy policy mix that aims to reduce energy intensity through improvements in energy efficiency and conservation and to shift energy consumption away from fossil fuels (i.e., the Netherlands), inclusively by an extensive use of CC and CCU technologies. It is needed to give a high priority to the abatement of the reliance on natural gas, bio and waste fuels, and nuclear power on the short term and increase the use of eco-friendly renewable energy (solar and wind power) on the long term as suggested by Kim and Park (2018) [
76]. Policies which focus on energy efficient infrastructure and use clean energy and smart grids, such as smart home appliances, smart transportation, and smart renewable, further deployment of electric vehicles in France, Germany, Italy, Austria and the United Kingdom, as well as further support for development of photovoltaic and wind sectors in the renewable electricity production need also to be taken into account. Biomass use in households heating can be increased in France, Germany, Italy, Finland, Sweden and the Netherlands. Taking into consideration that the use of biomass for bio energy and for bio-based products creates new business opportunities in agriculture, forestry and manufacturing sectors [
117], introduction of appropriate policy instruments (i.e.certifications schemes for organic agriculture, fair trade, environmentally fair cultivation, bio fuel production) would contribute to building a green and low-carbon economy. Greater attention also needs to be paid to the diversification of R&D funding sources, by extending the share of private sources and greater involvement in the European Energy Research Alliance and ERA-NETs (European Research Area Networks), in order to offer support for development of R&D and technological diffusion within countries with less research resources (i.e., common research projects and actions under the Horizon 2020 Programme).
Regarding the countries from the second subpanel, a strategic direction may be the extension of the knowledge basis and creation of opportunities for highly skilled human capital accumulation, absorption of innovation and creating new capabilities for green products and technologies. It is important also to stimulate the shift to a green consumption through educating consumers to ask for greener and environmentally friendly products and services. An appropriate policy mix can reduce the carbon intensity in the energy consumption mix (mainly in Poland, Estonia, Romania, Lithuania, Latvia and Bugaria), speed-up the transition from coal to oil, gas and renewable and reduce the energy intensity of economic activities. Greater investments in energy efficiency can reduce emissions from the non-traded sector, power sectors and lead to a cleaner domestic heating (in Poland, Estonia, Romania, Bulgaria, Portugal and Croatia). In order to ensure a sustainable energy supply, reducing the dependence of imports from Russia (i.e., Poland, Latvia, Lithuania and Romania) should be accompanied by the diversification of energy sources and supply routes, focus on the use of existing energy resources, significant investment in infrastructure and development of electricity transmission grids. Optimal utilization of energy markets interconnections (i.e., connection between Spain, Portugal and France) and energy market integration with neighboring EU Member States, strengthening the Baltic electricity market and integration with the Nordic market would be also beneficial. The extension of the public support to clean energy technologies will increase the economic gains in the early progress of challenging abatement targets regarding GHG emission [
152]. The increase of public and private funding of R&D activities, as well as taking advantage of available EU funding sources (i.e., Horizon 2020) for infrastructure development and further research will improve energy saving and efficiency. Increased participation in ERA-NETs (European Research Area Networks) and European Energy Research Alliance mostly in the areas of renewable energy and energy efficiency would contribute also to this goal. Diversification of the mix of energy policy instruments, through introducing additional components, such as: Tenders, investment grants, fiscal measures (reduced VAT, refunds, energy tax allowance, energy tax exemptions, revenue-neutral fiscal incentives) will encourage GHG reduction and energy saving.
As a final remark, the paper highlights that economic complexity must be taken into consideration when energy and economic policies are shaped and national targets are set out by the Member States, according to their general policy commitments regarding fostering low greenhouse gas emissions development.
A possible extension of the present study could be the examination of the EKC hypothesis regarding the effect of economic complexity on the environment in a panel of EU countries.