Renewable energy, circular economy indicators and environmental quality: A global evidence of 131 countries with heterogeneous income groups

Majeed, Muhammad Tariq; Luni, Tania Article Renewable energy, circular economy indicators and environmental quality: A global evidence of 131 countries with heterogeneous income groups Pakistan Journal of Commerce and Social Sciences (PJCSS) Provided in Cooperation with: Johar Education Society, Pakistan (JESPK) Suggested Citation: Majeed, Muhammad Tariq; Luni, Tania (2020) : Renewable energy, circular economy indicators and environmental quality: A global evidence of 131 countries with heterogeneous income groups, Pakistan Journal of Commerce and Social Sciences (PJCSS), ISSN 2309-8619, Johar Education Society, Pakistan (JESPK), Lahore, Vol. 14, Iss. 4, pp. 866-912 This Version is available at: https://hdl.handle.net/10419/228728 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc/4.0/ Pakistan Journal of Commerce and Social Sciences 2020, Vol. 14 (4), 866-912 Pak J Commer Soc Sci Renewable Energy, Circular Economy Indicators and Environmental Quality: A Global Evidence of 131 Countries with Heterogeneous Income Groups Muhammad Tariq Majeed (Corresponding author) School of Economics, Quaid-i-Azam University, Islamabad, Pakistan Email: [email protected] Tania Luni School of Economics, Quaid-i-Azam University, Islamabad, Pakistan Email: [email protected] Article History Received: 07 Sept 2020 Revised: 25 Nov 2020 Accepted: 10 Dec 2020 Published: 31 Dec 2020 Abstract Increasing global warming, degrading environmental quality, and waste ending in landfills have become threats to the sustainability of ecosystems. The circular economy (CE) offers an alternative approach to the linear economy, which lowers pressure on the ecosystem. Renewable energy, as an important pillar of CE, neither generates waste nor increases the extraction of limited resources. This study explores the dynamic links of renewable energy and CE with environmental quality. This study explains the important mechanisms of circular business models in the context of contingency theory, transaction cost theory, resource-based theory, networks-based theory, and agency theory. The empirical analysis is based on a global panel of 131 countries, including a disaggregated analysis for different groups of countries according to their income levels and European Union member countries. The 2nd generation tests namely “cross sectionally augmented IPS (CIPS), cross sectionally augmented Dickey Fuller test (CADF) and Westerlund cointegration test” are applied to test the relationships between the variables. We employ novel indicators of CE such as biowaste recycling, municipal waste recycling, e-waste recycling, packaging waste recycling, trade-in recyclables, and patents in recycling to examine their impacts on environmental quality. The results suggest that renewable energy and different measures of CE significantly improve environmental quality. Energy intensity, economic growth, and urbanization degrade the environment. The study suggests that CE measures need to be promoted to combat climate change. Keywords: biowaste recycling, CO2 emissions, municipal waste recycling, packaging waste recycling, renewable energy consumption. 1. Introduction Sustainable development goals were proposed to improve human life and environmental quality in such a way that the limited resources of the planet should be harnessed without Majeed & Luni 867 compromising their quality and availability for future generations. However, since the dawn of civilization, resources are extracted to fulfill the increasing demands of human civilization without considering the fact that the regenerating capacity of any system is limited. A disproportionate resource extraction to the resource regenerative capacity leads to deterioration and depletion of natural resources. Natural resources have been accumulated over thousands of centuries; however, their amplifying use has caused extinction within a century. A linear economy exploits virgin resources. The global extraction and use of virgin materials exceeded 100 billion tons per year since 2017. Furthermore, an estimate by the international resource panel (IRP) suggests that this unsustainable use will reach to 184 billion tons by 2050, exacerbating environmental risks (Schroder, 2020). A linear economy represents a traditional economy that is based on the extraction and use of natural resources to produce goods and services. During this production, process externalities are generated in the form of waste and pollution, which deteriorate environmental quality (see Figure 1). The waste generation hurts the environment through a reduction in availability and quality of natural capital attributable to extraction and increased pollution (Murray et al., 2017). The industrial revolution led to global growth and accelerated resource extraction and resulted in higher consumer demand, which negatively affected the circularity, and the system has stuck in the take-makedispose system (Circle Economy, 2020). Figure 1: Linear Economy Climate is changing due to higher anthropogenic influence and resulting in higher frequency and intensity of extreme weather events, hurricanes and floods, droughts, higher sea levels, all this damages infrastructure, livelihood, resources, and impact health (UN DESA, 2020; Majeed and Maria, 2019a) and ecosystem. Therefore, changing climate, degrading environmental quality, decreasing finite resources, and increasing waste, all have led to the consideration of the process that enhances the generative capacity of the planet which in turn improves environmental quality and supports life on earth. It has been emphasized in the literature that “extraction of virgin and finite resources leads to a decrease in the regenerative capacity of the earth” (Bonciu, 2014). Therefore, the concept of the linear economy “produce-consume-waste” is extractive and no longer deemed of and an effort is being made to promote circular economy (CE), which eliminates the concept of waste. A CE represents “an industrial system that is restorative or regenerative by intention and design” (see Figure 2). Externalities Waste Consumption Production Extraction Renewable Energy, Circular Economy Indicators and Environmental Quality 868 Figure 2: Circular Economy The concept of CE emphasizes on minimization of externalities (generation of waste and pollution) and decreased use of finite resources. The CE reduces the depletion of natural resources and improves resource performance (Moraga et al., 2019; Ellen MacArthur Foundation, 2013). Furthermore, the purpose of the CE is to decouple economic growth from limited (finite) resources and establish such systems that promote economic, social, and natural capital building (Ellen MacArthur Foundation, 2019; Elia et al., 2017). The CE emphasizes the need to increase the efficiency of resource use which decreases environmental impact along with increasing the wellbeing of the generations (Magnier, 2017). Shifting from linear to a CE which is restorative, reproductive, and cyclical is beneficial for sectoral, organizational, national, and international borders (Korhonen et al., 2018) as it is cost-effective, curtails down the costs associated with the production of new products, does not generate waste and decreases product loss across the value chain. The CE is based on the principle of the closed loop as it decreases the use of virgin materials. The transition to a CE should not be considered only from a material perspective but it can also have an influential effect on environmental quality and climate change (Demurtas et al., 2015). CE practices can lead to a reduction in energy demand and emissions (IRENA, 2019). One strand of the literature views decoupling of emissions from economic growth as a prime need of the contemporary world (Khan & Majeed, 2020). However, growth and emissions come together when growth is based on conventional energy sources such as coal, fossil fuels, and gas consumption. The theoretical debate on economic growth and Consume Return Reuse Produce Majeed & Luni 869 environmental sustainability could be traced back to the groundbreaking study of Grossman and Krueger (1995) which suggested “an inverted U-shaped association between per capita GDP and environmental pollution”. This association is generally referred as the “Environmental Kuznets Curve (EKC)”, and a plethora of studies has investigated its validity, however, up until now the empirical literature is not yet definitive (Majeed and Mazhar, 2020). One possible explanation could be that many studies assume the relationship between economic growth and emissions as a natural process. However, it is not correct as Grossman and Krueger (1995) consider the role of public policy that can assure its falling part. In this research, we propose that circular business models and CE practices can play a vital role in confirming emissions abating effect of economic growth. Another strand of the literature doubts the validity of CE practices for sustainable development. Murray et al. (2017) argued that CE has certain limitations and can lead to many tensions. For example, it excludes social dimension inherent in sustainable development that curbs its ethical elements, and some other inadvertent effects. Similarly, Schroder (2020) asserts its various negative consequences for the developing world, which largely depend on linear sectors. In a recent study Cotta (2020) argued that exports of used “electronic and electric equipment (EEE) and recyclable plastic materials” worsen the environmental problems of importing counties. Hence, an empirical investigation is necessary to better understand the significance of CE. This study provides empirical evidence on the new emerging debate on switching from linear economy paradigm to CE paradigm. The use of renewable resources for energy generation is a sustainable practice as it does not deplete or degrade and reduces dependence on fossil fuels which pollute the environment. Thus, renewable resources decrease extraction of fossil fuels and help to enhance environmental quality, mitigate climate change, and avoid depletion of limited virgin resources. The contemporary production and consumption practices and resource exploitation methods are not sustainable as they are putting excessive pressure on the ecosystem in terms of resource depletion, biodiversity extinction, climate change, and unsustainable development (Korhonen et al. 2018; Majeed and Mazhar, 2019a). Thus, conserving the ecosystem and achieving sustainable societies require efficient resource utilization, cooperative consumption, and decreasing costs associated with the resource and waste utilization (Korhonen et al. 2018; Kravchenko et al. 2020). The circular economy practices including biowaste recycling, municipal waste recycling, packaging waste recycling, trade in recyclables, patents related to recycling, and municipal waste generation can alleviate the pressure on the ecosystem and can conserve the environmental quality. Furthermore, such practices maximize product value by promoting product reuse and lowering the demand for new products. Consequently, new extraction for more production is decreased and prospective emissions are mitigated. Therefore, CE helps in climate neutrality and promotes the use of resources within the threshold level and does not exceed the planetary boundaries. With this background, this study investigates how the CE contributes to environmental mitigation over the period 1990-2014. This study addresses the following research questions: Renewable Energy, Circular Economy Indicators and Environmental Quality 870  How does renewable energy affect environmental quality?  Does the effect of renewable energy vary across different income groups?  Does renewable energy matter for CE practices?  How do CE indicators influence the environment?  Which dimensions of CE are more important to retain environmental quality? This research is useful for policymakers, environmental researchers, energy economists, government officials, social scientists, industrial managers, and development practitioners. This is a pioneer empirical study on the usefulness of diverse indicators of CE in a global setting. The findings of the study can be utilized to manage sustainable development goals and global emissions targets. Furthermore, clean energy management strategies can be better implemented and managed by linking the energy sector with CE practices. The empirical estimates are also useful in a comparative setting as trade-offs among alternative CE measures can be better settled. This study contributes to the literature in the following ways. First, this study is a pioneering study that blends two global issues namely renewable energy-environment nexus and circular economy-environment nexus in a single paper. Second, to the best of our knowledge, we are the first who provide an empirical analysis of CE indicators. Third, this study provides both global evidence and evidence from heterogeneous income groups on renewable energy and the environment nexus. Fourth, this study also improves the methodological part of the paper by applying the Second Gen Panel time series analysis which deals with the issue of temporal and cross-sectional dependence in panel data analysis. Fifth, this study also exploits the dynamic heterogeneous nature of relationships by using common correlated effects mean group (CCEMG) and dynamic CCEMG estimation procedures which allow slope heterogeneity and cross-sectional dependence. Sixth, this study sets a heterogeneous evaluation of renewable energy and circular economy in a comparative setting according to income levels. Seventh, this study provides analytical insights on the different dimensions of the CE indicators. The study is organized as follows: 2nd section provides a detailed discussion of the literature related to the circular economy. The 3rd section is comprised of data, variable description, and methodology. The 4th section is based on results and discussion and the 5th section will conclude the work. 2. Literature Review European Commission (2015) explains CE as “an economy where the value of products, materials, and resources is maintained in the economy for as long as possible, and the generation of waste minimized”. Thus, CE represents a process that retains value (Haupt & Hellweg, 2019) and enhances social and economic dimensions. The CE provides multiple benefits by reducing mining of virgin materials, abating soil and water pollution, controlling ecosystem damage, and discouraging the use of plastic. (Material Economics, 2018; Schroeder et al., 2019). Thus, as CE promotes circular practices and return of material in the form of input to nature and manages waste, it can save the loss of income due to endangered marine species as some 880 million people are dependent on fisheries and aquaculture for their livelihood (FAO, 2016). Another important component of CE is Majeed & Luni 871 resource efficiency. A resource-efficient economy optimizes production and consumption concerning resource use and decreases energy and material use, resource-saving (dematerialization), remanufacturing, recycling, and reusing (rematerialization) along with infrastructure transitions (IRP, 2017) and decreases waste generation. Figure 3 summarizes the importance of CE for economic, social, and environmental gains. Figure 3: Circular Economy 2.1 Theoretical Perspectives The sustainable development theory posits that anthropogenic activities need to be managed in such a way that the present generation make an efficient and sustainable utilization of natural resources and ecosystem services without compromising the need for future generations. That is, achieving and managing the dynamic allocative efficiency in the use of resources can preserve the environment and attain sustainable development. The eco-industrial development theory asserts cascading of matter energy either between industrial ecosystem members or natural system, and cooperation among the companies (so that resources can be used at a rate at which they are replenished). The waste of a company is used as input in another company, thus minimizing the concept of waste generation. Industrial ecology promotes efficiency at a regional scale rather than a firm level. The eco-industrial development promotes efficient sharing of resources including “information, materials, water, energy, infrastructure and natural habitat supporting economic and environmental gains along with human resource enhancement (Deutz & Gibbs, 2008). Renewable Energy, Circular Economy Indicators and Environmental Quality 872 The ecological modernization theory explains the links between environmental quality and CE practices. The theory advocates that initially when economies grow, due to rapid industrialization environmental quality degrades. But with further growth, technological development takes place and leads to an improved relationship between industrialization and the environment, enhancing environmental quality. Improvement in environmental quality results from technological advancements, society's role in restructuring and ecological reforms, political measures to improve environmental quality, and change in consumer and producer behavior leading to sustainable practices that enhance environmental quality. Furthermore, public awareness about the environmental importance also leads to the use of eco-friendly technologies thus resulting in improved environmental quality (Majeed & Luni, 2019; Majeed & Mazhar, 2019b; Majeed & Tauqir, 2020). The “structural and contingency theories” suggest that business corporations consistently review the best resource bundling and frequently reallocate internal resources to become compatible with environmental needs and follow circular business model (Lahti et al., 2018). The “transaction cost theory” explains how companies can close material loops and improve alliances to manage adaption and pressures ascending from sustainability requirements and environmental obligations in the value chain (Argyres, & Mayer, 2007). The “resource-based theory” postulates that constructing and complementing a firm’s resource portfolio offers a sustainable gain to initiate and adapt a circular business model (Sirmon et al., 2007). The network theory posits that new networks are developed when firms implement CE business practices and contract-related costs are lowered due to certain network feature such as trust and information sharing norms. The “agency theory” postulates that the agent (customer) can misbehave by, for example, “inappropriately handling, damaging, or overusing product” (Eisenhardt, 1989). In such situation monitoring and incentives can support CE business models by enhancing the probability that assets are recycled and reused. 2.2 Circular Economy and the Environment Climate change has raised concerns over the “take-make-waste” economy around the globe. The extractive economy uses the resources in an unsustainable way that damages the resource and emits GHGs, therefore, is a hurdle in the way of achieving the 1.5˚C target of the “Paris Agreement”. Achieving the target and minimizing the net emissions to zero by 2050 need drastic measures and a projected cost of USD 54 trillion (by 2100) will be borne by the global economy related to climate change which will keep on increasing with temperature changes (Ellen MacArthur Foundation, 2019). The unsustainable resource use in the traditional economy (take-make-dispose) poses a threat to the survival of 1 million plants and animals that are endangered due to climate change. About 90% of land and water degradation is related to resource extraction and agriculture is the major driver behind that deterioration (biodiversity loss and deteriorating quality and decreasing water availability) (Ellen MacArthur Foundation, 2019). About 55% of global emissions result from the transport and buildings sector while the remaining 45% originate from land management and output production. Thus, a quarter Majeed & Luni 873 of global emissions is caused by each: industrial sector and “Agriculture, Forestry, and Other Land Use (AFOLU)”. The take-make-waste concept of the linear economy needs to be remodeled in such a way that it has regenerative capacity. For this purpose, the CE is an important concept in this direction. For better environmental quality sustainable production and consumption patterns should be followed. Sustainable consumption means consumption that leads to less waste, societal well-being, and resource efficiency (getting more out of less) and consuming in a way that has a minimum negative environmental impact (Tunn et al. 2019). To support the CE not only sustainable consumption but business models are required that increase the life of the product and utilization, narrow down the resource loop, thus enhancing resource and economic efficiency and decreasing environmental losses. However, productivity improvement can lead to rebound effects by increasing consumption (Murray et al., 2017). 2.3 Renewable Resources and Circular Economy CE assessment comprises renewable energy use. For example, Elia et al. (2017) argued that CE requires the “increased share of renewable and recyclable resources” including renewable energy. Ellen MacArthur Foundation (2015) quoted “replacing fossil fuels with renewable energy” as an example of the principles behind CE. The expectation is that “the adoption of CE will fundamentally transform economic activities away from reliance on non-renewable and emissions-intensive carbon flows towards more sustainable production and consumption” (Korhonen et al., 2018). Schroeder et al. (2019) argued that CE economy practices such as “maximization of material and energy efficiency, creating value from waste, or applying biomimicry principles to move from nonrenewable to renewable resources” are important CE, business models. The CE not only focuses on the development of such technologies and models that keep the material in circulation, but it also comprises the concepts of “designing out waste, substituting renewable materials for non-renewable ones, and restoring natural systems” (Schroder, 2020). An effective CE needs a global approach to resource efficiency to take care of the use of raw materials and energy sources. That is, energy should be based on renewable sources. The CE, renewables, and energy efficiency are interconnected to maintain sustainable development. The firms powering the production of global resources are increasingly searching for solutions to meet market demand by minimizing energy usage and environmental problems. Many corporations are trying to “blend the CE with the bioeconomy” to align their operations towards a sustainable closed system. That is, an increasing reliance on renewables can facilitate the objectives of the CE. The growth of renewable resources supports carbon absorption. Renewable resources perform the function of carbon storage and when these resources are converted to waste (through use) they do not contribute to emissions in the atmosphere like fossil-based products (Harris & Rydberg, 2018). Among renewable resources, forestation contributes to the bioeconomy and is a major source of carbon capture. Renewable Energy, Circular Economy Indicators and Environmental Quality 880 Table 1: Variable Description Variables Symbols Definition of Variable Measurement Source Dependent Variable Co2 Emissions lCO2 “Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement and the result of anthropogenic activities”. “Metric tons per capita” WB, 2020 Independent Variables (Focused Variables) Renewable Energy LR “Renewable energy consumption is the share of renewable energy in total final energy consumption”. “% of total final energy consumpti on” WB, 2020 Municipal Waste Generation GMW “Generation of municipal waste”. Per capita Eurostat , 2020 Municipal Waste Recycling MWR “Recycling rate of municipal waste”. “%” Eurostat , 2020 Biowaste Recycling BWR “Recycling of biowaste”. “kg per capita” Eurostat , 2020 Packaging Waste Recycling PWR “Recycling rate of packaging waste by type of packaging” “%” Eurostat , 2020 Recycling Patents RP “Patents related to recycling and secondary raw materials”. “Numbers ” Eurostat , 2020 Recyclables Trade LRT “Trade in recyclable raw materials”. “ton” Eurostat , 2020 Ampwr AMPW R “Average of municipal waste recycled and packaging waste recycled”. Index “Constr ucted by authors” Labtmp LABT MP “Average of biowaste recycling, trade of recyclables, municipal waste recycled and packaging waste recycled”. Index “Constr ucted by authors” Abmp ABMP “Average of biowaste, municipal waste and packaging waste”. Index “Constr ucted by authors” E-Waste Recycling EWR “Recycling rate of e-waste”. “% of total e-waste” Eurostat , 2020 Circular Material CM “Circular material use rate”. “% of total material use” Eurostat , 2020 Majeed & Luni 881 Construction and Demolition Recovery CDR “Recovery rate of construction and demolition waste”. “% of constructi on and demolition mineral waste recycled” Eurostat , 2020 Independent Variables (Control Variables) GDP Per Capita LY “GDP per capita is gross domestic product divided by midyear population”. “Constant 2010 US$” WB, 2020 Energy Intensity LEI “Energy consumption per capita divided by GDP per capita”. “Per capita” Constru cted by authors Urbanization LU “Urban population refers to people living in urban areas”. “% of total population ” WB, 2020 Energy Use EU “Energy use refers to use of primary energy before transformation to other end-use fuels”. “kg of oil equivalent per capita” WB, 2020 Other Variables Trade T “Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product”. “% of GDP” WB, 2020 Foreign Direct Investment FDI “FDI is the net inflows (new investment inflows less disinvestment) in the reporting economy from foreign investors, and is divided by GDP”. “% of GDP” WB, 2020 Forest Area FA “Forest area is land under natural or planted stands of trees and excludes tree stands in agricultural production systems and trees in urban parks and gardens”. “% of land area” WB, 2020 Agriculture Land AL “Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures”. “% of land area” WB, 2020 Renewable Energy, Circular Economy Indicators and Environmental Quality 882 4. Results and Discussion As countries are engaged in trade and policies of one country has implications for others, therefore, cross sectional dependence should be examined in panel data. The results obtained without examining cross-sectional dependence results in biased analysis. To test for cross-sectional dependence, “BreuschPegan LM, Pesaran scaled LM, Biascorrected, and Pesaran CD test” have been used. The Breusch and Pegan (1980), “LM test” is examined by equation (5) mentioned below: (5) As n approaches infinity the “LM test” cannot be applied, therefore, “scaled version of LM” proposed by Pesaran (2004) is used (equation 6). (6) When t is finite and n is large the “BPs test” is distributed asymptotically N (0, 1), but with increase in n, normal approximation is not appropriate as BPs not centered at zero and leads to distortions. Therefore, Pesaran et al. (2008) introduced “bias-corrected scaled LM statistics” which can be estimated by the equation (7) (7) The study used Pesaran (2004) “CD test”. The “CD test” can be presented by the equation (8) (8) T represents time and N is used for the sample size, T shows the time, and indicates “correlations among errors of different cross-sections of country i and k”. Table 2 reports the outcomes of cross-sectional dependence tests. The tests conclude the presence of cross-sectional dependence as the null hypothesis of no cross-sectional dependence is rejected at a 1% level of significance by all tests. Majeed & Luni 883 Table 2: Results of Cross-Sectional Dependence Test Variables BreuschPegan LM Pesaran scaled LM Biascorrected scaled LM Pesaran CD General Panel (131) CO2 Emissions per capita 58862.06*** 385.80*** 383.07*** 22.70*** GDP per capita 133040.4*** 954.22*** 951.49*** 277.58*** Renewable Energy per capita 67241.93*** 450.02*** 474.29*** 41.34*** Energy intensity per capita 99528.40*** 697.42*** 694.69*** 151.06*** Urbanization 158445.4*** 1148.90*** 1146.17*** 214.78*** High Income (45) CO2 Emissions per capita 6227.66*** 117.70*** 116.77*** 19.72*** GDP per capita 18580.21*** 395.31*** 394.37*** 114.31*** Renewable Energy per capita 10571.88*** 215.33*** 214.39*** 64.22*** Energy intensity per capita 14794.56*** 310.23*** 309.29*** 76.75*** Urbanization 16996.83*** 359.73*** 358.79*** 49.51*** Upper Middle Income (43) CO2 Emissions per capita 5607.82*** 110.71*** 109.81*** 18.13*** GDP per capita 15024.38*** 332.29*** 331.39*** 106.25*** Renewable Energy per capita 5523.10*** 108.71*** 107.82*** 0.83 Energy intensity per capita 8768.82*** 185.09*** 184.19*** 42.47*** Urbanization 16837.19*** 374.94*** 374.05*** 77.62*** Lower Middle Income (31) CO2 Emissions per capita 4519.54*** 132.95*** 132.30*** 24.16*** GDP per capita 7900.89*** 243.83*** 243.18*** 70.75*** Renewable Energy per capita 3500.32*** 99.53*** 98.88*** 0.94 Energy intensity per capita 5036.70*** 149.91*** 149.26*** 33.82*** Urbanization 8940.88*** 277.93*** 277.28*** 56.91*** Low Income (12) CO2 Emissions per capita 486.61*** 36.61*** 36.36*** 3.95*** GDP per capita 498.29*** 37.63*** 37.37*** 4.43*** Renewable Energy, Circular Economy Indicators and Environmental Quality 884 In the next step, we proceed with 2nd generation tests. Table 3 presents the results obtained from the “cross-sectionally augmented IM Pesaran and Shin (CIPS) test” introduced by Pesaran (2007) and “cross-sectionally augmented ADF (CADF) unit root test”. This method controls the cross-sectional dependence while examining the integration order. The equation (9) used for CIPS is given as follows (9) Here, subscripts ‘i' and‘t’ represent “the intercept and time trend”. CADF test is also used to examine stationarity. The “Augmented Dickey-Fuller” regression is performed by adding lagged levels ( ) of cross-sectional averages and first difference value of all series. Equation 10 is used for the “CADF test”. (10) Here, represents the average for all observations at time t and “the equation included is a proxy of unobserved effects by common factors.” Renewable Energy per capita 478.82*** 35.93*** 35.68*** -0.82 Energy intensity per capita 634.12*** 49.45*** 49.19*** -0.36 Urbanization 1454.86*** 120.88*** 120.63*** 26.51*** European Union Member Countries (27) CO2 Emissions per capita 2560.08*** 83.38*** 82.41*** 36.48*** GDP per capita 3121.21*** 104.56*** 103.59*** 49.15*** Renewable Energy per capita 3715.26*** 126.98*** 126.01*** 60.21*** Energy intensity per capita 3669.55*** 125.25*** 124.29*** 59.45*** Urbanization 4283.93*** 148.44*** 147.48*** 15.12*** Trade in recyclable materials 1218.29*** 32.73*** 31.77*** 2.97*** Patents related to recycling 490.17*** 5.26*** 4.29*** 5.06*** Biowaste recycling 2244.05*** 71.45*** 70.48*** 19.95*** Municipal waste recycling 2496.85*** 80.99*** 80.03*** 36.68*** Packaging waste recycling 2247.44*** 71.58*** 70.61*** 40.21*** ABMP 2670.14*** 87.53*** 86.57*** 35.91*** AMPWR 3128.75*** 104.84*** 103.87*** 49.04*** LABTMP 1244.13*** 33.71*** 32.75*** 2.95*** Municipal waste generation 1323.36*** 36.40*** 25.74*** 13.49*** “Probabilities represented by * p < 0.1, ** p < 0.05, *** p < 0.01 Majeed & Luni 885 In the general panel, high-income, and lower-middle-income groups the variables are stationary at the level and 1st difference, however, the variables are stationary at the level in the upper-middle-income group and first difference in the lower-income group respectively. In EU member countries the results suggest that CO2, BWR, MWR, ABMP, and LGMW are difference stationary while lRT, RP, PWR, AMPWR, LABTMP are level stationary respectively. Renewable Energy, Circular Economy Indicators and Environmental Quality 886 Table 3: 2nd Generation Unit Root Results Variable CIPS CADF General Panel (131) Level 1st Difference Level 1st Difference CO2 emissions per capita -2.053 -4.762*** -3.462*** -35.425*** GDP per capita -2.086** -3.642*** -3.850*** -22.207*** Renewable energy per capita -2.288*** -4.683*** -6.228*** -34.491*** Energy intensity per capita -2.403*** -4.709*** -7.593*** -34.803*** Urbanization -1.934 -1.642 -2.058** -1.398 “Critical values -2, -2.05, -2.14 at 10%, 5%, 1% in CIPS and CADF having -2.000, -2.050, - 2.140 at 10%, 5%, and 1% level of significance, respectively” HY (45) CO2 emissions per capita -1.826 -4.936*** -0.460 -21.964*** GDP per capita -1.589 -3.543*** 1.186 -12.333*** Renewable energy per capita -2.455*** -4.938*** -4.806*** -21.977*** Energy intensity per capita -2.622*** -5.104*** -5.963*** -23.127*** Urbanization -0.509 -1.536 8.651 1.550 UMY (43) CO2 emissions per capita -2.469*** -4.907*** -4.793*** -21.275*** GDP per capita -2.500*** -4.046*** -5.000*** -15.452*** Renewable energy per capita -2.345*** -4.691*** -3.952*** -19.815*** Energy intensity per capita -2.263*** -4.597*** -3.404*** -19.182*** Urbanization -1.479 -1.618 1.901 0.960 LMY (31) CO2 emissions per capita -1.826 -4.885*** -0.377 -17.936*** GDP per capita -1.729 -3.790*** 0.178 -11.654*** Renewable energy per capita -1.483 -4.743*** 1.590 -17.122*** Energy intensity per capita -2.387*** -4.547*** -3.599*** -15.998*** Urbanization -2.643*** -1.450 -5.067*** 1.778 “Critical values -2.04, -2.11, -2.23 at 10%, 5%, 1% in CIPS and CADF having -2.040, -2.110, - 2.230 at 10%, 5%, and 1% level of significance respectively.” LY (12) CO2 emissions per capita -1.541 -3.977*** 0.819 -7.881*** GDP per capita -1.711 -4.184*** 0.210 -8.622*** Renewable energy per capita -1.122 -4.008*** 2.314 -7.992*** Majeed & Luni 887 Table 4 presents the results obtained from the “Westerlund (2007) cointegration test”, which considers cross-sectional dependence, unit-specific short-run dynamics, and trend and slope parameters. The test provides four results two results examine the alternative hypothesis that the whole panel is cointegrated while the other two examine that at least one unit is cointegrated (Ehigiamusoe & Lean, 2019). The test is performed with zero lag and lead and results confirms cointegration. The results support cointegration in the general panel and all income groups. Thus emissions, GDP per capita, renewable energy consumption, and urbanization are co-integrated. Energy intensity per capita -1.411 -4.227*** 1.283 -8.774*** Urbanization -0.817 -1.764 3.405 0.022 “Critical values -2.14, -2.25, -2.45 at 10%, 5%, 1% in CIPS and CADF having -2.140, -2.250, - 2.450 at 10%, 5%, and 1% level of significance respectively”. European Union Member Countries (27) CO2 emissions per capita -2.137* -4.170*** -1.971** -11.572*** GDP per capita -1.465 -2.532*** 1.203 -3.837*** Renewable energy per capita -2.230** -3.527*** -2.410*** -8.538*** Energy intensity per capita -2.180** -3.497*** -2.174** -8.392*** Urbanization -1.162 -1.848 2.638 -0.605 Direct indicators Municipal waste generation -1.868 -3.811*** -0.699 -9.875*** Municipal waste recycling -1.807 -3.422*** -0.413 -8.038*** Biowaste recycling -1.677 -3.947*** 0.201 -10.518*** Packaging waste recycling -2.855*** -4.096*** -5.360*** -11.224*** Indirect indicators Recycling patents -3.063*** -4.574*** -6.343*** -13.482*** Recyclables trade -2.311** -3.540*** -2.791*** -8.598*** Indexes AMPWR -2.509*** -3.861*** -3.729*** -10.114*** LABTMP -2.311* -3.519*** -2.793*** -8.498*** ABMP -.2.146* -3.688*** -2.011** -9.298*** “Critical values -2.07, -2.17, -2.34 at 10%, 5%, 1% in CIPS and CADF having -2.070, -2.170, - 2.340 at 10%, 5%, and 1% level of significance respectively. GP: General panel, HY: High income, UMY: Upper middle income, LMY: Lower middle income” Renewable Energy, Circular Economy Indicators and Environmental Quality 888 Table 4: Wester-Lund Cointegration Results Value Z-Value P-Value Bootstrap P-Value GP (131) Group-tau -4.323 -22.500 0.000 0.000 Group-alpha -7.767 7.752 1.000 0.040 Paneltau -106.182 -75.320 0.000 0.000 Panel-alpha -21.478 -18.375 0.000 0.000 HY (45) Group-tau -3.012 -3.983 0.000 0.000 Group-alpha -8.334 4.047 1.000 0.020 Paneltau -15.945 -1.416 0.079 0.200 Panel-alpha -6.757 2.255 0.988 0.460 UMY (43) Group-tau -4.694 -15.440 0.000 0.000 Group-alpha -8.996 3.389 1.000 0.000 Paneltau -121.351 -99.017 0.000 0.000 Panel-alpha -40.011 -26.557 0.000 0.000 LMY (31) Group-tau -3.349 -5.269 0.000 0.000 Group-alpha -5.869 5.150 1.000 0.870 Paneltau -11.254 0.654 0.743 0.350 Panel-alpha -4.334 3.651 1.000 0.870 LY (12) Group-tau -10.421 -28.933 0.000 0.000 Group-alpha -6.139 3.082 0.999 0.680 Paneltau -7.842 -0.369 0.356 0.153 Panel-alpha -5.868 1.571 0.942 0.407 “GP: General panel, HY: High income, UMY: Upper middle income, LMY: Lower middle income” Due to the presence of cointegration, the results of “FMOLS and DOLS” are presented in table 5. The FMOLS results show that renewable energy has a negative and significant impact on CO2 emissions globally and in all income groups. This finding is consistent with Samreen & Majeed (2020) who found similar effect for 89 countries over the period 1992-2014. This indicates that an increase in renewable energy will decrease CO2 emissions globally regardless of the income differences among the countries. However, the magnitude of the impact of renewable energy on emission mitigation varies across different income groups. The highest impact of renewable energy on emissions reduction is revealed in low-income economies followed by lower middle income, upper middle income, and high-income countries. As low-income countries lack clean energy access, renewable energy causes the strong impact on environmental quality. The results also suggest an increase in emissions from GDP per capita. An increase in income contributes to higher emissions in low-income economies followed by high income, lower middle income, and upper middle income. The low-income countries use Majeed & Luni 889 obsolete technologies that deteriorate their environmental quality. The contribution of energy intensity in emissions is high in low-income economies due to a lack of efficiency and modern technologies. The impact of urbanization varies across income groups. Urbanization increases emissions in low-income economies while decreases emissions in other income groups. This finding is inconsistent with Majeed & Tauqir (2020) who found similar effects across different income groups. Similarly, the findings of DOLS also support the results from FMOLS in terms of the sign as well as statistical significance. Accordingly, the regression coefficient of renewable energy is negative and statistically significant indicating that an increase in the share of renewable energy will improve environmental quality by decreasing carbon dioxide emissions. Renewable energy has a more prominent impact on the environmental quality of low-income countries. The regression coefficients of GDP per capita is highest in low-income countries suggesting that income generation is at the cost of environmental quality. The impact of income on environmental deterioration is lowest in high-income economies. Energy intensity harms the environment of all income groups, however, the effect is more severe in developing economies. The impact of urbanization across different income groups is insignificant while at the global level it is significant and depicting positive relationships. Table 5: Results of FMOLS and DOLS “Equation 3” Estimator Variables GP HY UMY LMY LY FMOLS GDP per capita 1.13*** 0.83*** 0.67*** 0.82*** 1.55*** Renewable energy per capita -0.42*** -0.12*** -0.19*** -0.79*** -2.08*** Energy intensity per capita 0.45*** 0.74*** 0.88*** 0.61*** 2.07*** Urbanization -1.49*** -0.99*** -0.58*** -0.41* 0.26*** DOLS GDP per capita 1.05*** 0.86*** 1.33*** 1.38*** 3.85*** Renewable energy per capita -0.15*** -0.10*** -0.11*** -0.49** -1.85*** Energy intensity per capita 0.80*** 0.75*** 1.16*** 0.91*** 2.90*** Urbanization 0.53*** -0.33 -0.08 -0.16 -0.18 “Probabilities represented by * p < 0.1, ** p < 0.05, *** p < 0.01, GP: General panel, HY: High income, UMY: Upper middle income, LMY: Lower middle income” The study also employed static models with heterogeneous slopes (MG, AMG, CCE). Pesaran and Smith (1995) Mean group estimator allows “intercept, slope coefficient, and error variances to differ across groups” (Blackburne III & Frank, 2007). Augmented Mean Group (AMG) estimator introduced by Eberhardt and Bond (2009) and Eberhardt (2012), considers “the effects of common shocks by including a common dynamic process” (Shafiei and Salim, 2014). The findings remain consistent with the previous results. Deployment of renewable energy boosts environmental quality and the impact is highest in low-income economies, while income and energy intensity cause environmental degradation across all income groups with severe consequences for lowincome countries due to inefficient technologies. Renewable Energy, Circular Economy Indicators and Environmental Quality 896 Table 10: Results of Competitiveness and Innovations and Secondary Raw Material Dependent Variable: Carbon Dioxide Emissions Dimensions Competitive ness and innovation Secondary raw material Indexes Indirect measures of CE Indexes Equation (4.8) Equation (4.9) Equation (4.10) Equation (4.11) Equation (4.12) 1 2 3 4 5 Variables Recycling Patents Recyclables Trade AMPWR LABTMP ABMP Circular Economy -0.017*** -0.003 -0.003*** 0.016*** -0.002*** (0.0018) (0.0053) (0.0002) (0.0044) (0.0003) GDP per capita 1.079*** 0.193 0.884*** 1.076*** 0.884*** (0.0224) (0.1923) (0.0295) (0.0322) (0.0262) Renewable energy -0.120*** -0.076*** -0.015*** -0.176*** -0.0227*** (0.0093) (0.0220) (0.0054) (0.0145) (0.0069) Energy intensity 1.048*** 1.254*** 1.036*** 0.904*** 1.075*** (0.0239) (0.0730) (0.0223) (0.0364) (0.0314) Urbanization -1.233*** -0.673* -1.035*** -1.515*** -0.834*** (0.0985) (0.3465) (0.1025) (0.1359) (0.1360) Observations 378 378 378 378 378 No of groups 27 27 27 27 27 Log Likelihood 1222.312 1210.058 1232.349 1212.119 1240.177 Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 Table 11 reports the results based on sensitivity analysis. For sensitivity analysis trade, FDI, forest area, and agriculture land are used as additional control variables. The analysis confirms that renewable energy decreases emissions globally as well as across all income groups. In EU member countries renewable energy, packaging waste recycling, municipal waste recycling, biowaste recycling, recyclables trade, and CE indexes mitigate environmental degradation while municipal waste generation increases environmental deterioration. Majeed & Luni 897 Table 11: Sensitivity Analysis Sensitivity Analysis Trade FDI Forest Area Agriculture land Groups analyzed Dependent Variable: CO2 emissions Global Panel -0.1533*** -0.1584*** -0.1296*** -0.1471*** (0.0064) (0.0064) (0.0070) (0.0064) R-Squared 0.8825 0.8826 0.8860 0.8884 High Income -0.1057*** -0.1006*** -0.0977*** -0.1033*** (0.0043) (0.0042) (0.0044) (0.0041) R-Squared 0.8047 0.8016 0.8011 0.8087 Upper Middle Income -0.1090*** -0.1107*** -0.1082*** -0.1055*** (0.0090) (0.0093) (0.0111) (0.0090) R-Squared 0.8332 0.8334 0.8325 0.8335 Lower Middle Income -0.4727*** -0.4752*** -0.4407*** -0.4765*** (0.0203) (0.0204) (0.0229) (0.0204) R-Squared 0.8330 0.8312 0.8333 0.8317 Low Income -0.9521*** -1.3996*** -1.5867*** -2.2331*** (0.1349) (0.1235) (0.1481) (0.1221) R-Squared 0.7644 0.7323 0.8292 0.8430 CE indicators European Union-Circular Economy Indicators Renewable energy -0.1443*** -0.1490*** -0.1033*** -0.1336*** (0.0128) (0.0120) (0.0156) (0.0120) R-Squared 0.7020 0.7016 0.7136 0.7103 Muncipal waste generation 0.1192** 0.1202** 0.0972* 0.1201** (0.0577) (0.0580) (0.0569) (0.0569) R-Squared 0.7051 0.7048 0.7157 0.7135 Packaging Waste Recycling -0.0005 -0.0004 -0.0022** -0.0021** (0.0009) (0.0009) (0.0010) (0.0010) R-Squared 0.7022 0.7017 0.7172 0.7135 Municipal Waste Recycling -0.0018* -0.0018* -0.0027*** -0.0030*** (0.0009) (0.0009) (0.0009) (0.0009) R-Squared 0.7046 0.7043 0.7195 0.7173 Biowaste Waste -0.0003 -0.0003 -0.0007** -0.0008*** Renewable Energy, Circular Economy Indicators and Environmental Quality 898 Recycling (0.0002) (0.0002) (0.0002) (0.0002) R-Squared 0.7031 0.7028 0.7180 0.7156 Recyclables Trade -0.026*** -0.021*** -0.0190*** -0.0332*** (0.0063) (0.0053) (0.0049) (0.0054) R-Squared 0.7143 0.7129 0.7238 0.7352 Recycling Patents -0.0001 -0.0002 -0.0003 -0.0006 (0.0005) (0.0005) (0.0005 (0.0005) R-Squared 0.7020 0.7017 0.7139 0.7113 Abmp -0.001 -0.001 -0.002*** -0.002*** (0.0006) (0.0006) (0.0006) (0.0006) R-Squared 0.7035 0.7031 0.7198 0.7177 AMPWR -0.0015 -0.0014 -0.0033 -0.0035 (0.0010) (0.0010) (0.0011) (0.0011) R-Squared 0.7034 0.7029 0.7198 0.7169 LABTMP -0.0279*** -0.0217*** -0.0195*** -0.0338*** (0.0066) (0.0055) (0.0051) (0.0056) R-Squared 0.7146 0.7128 0.7236 0.7345 Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01 Finally, the study also examined the interactive effects of direct and indirect measures of CE. The results reported in table 12 indicate that the emissions effect of municipal waste generation is significantly influenced by recyclable trade. Similarly, the results reported in table 13 suggest that the emissions impact of municipal waste generation is also influenced by recyclables patents. Recycling patents promote carbon neutrality. The interactive term of municipal waste generation and recyclables patents leads to carbon neutrality. Table 14 reports the combined impact of packaging waste recycling with recyclables trade. The interactive impact is negative and significant suggesting that emissions mitigating effect of packing waste recycling is enhanced by recyclable trades. Table 15 presents the combined impact of packaging waste recycling and recycling patents. The interactive term also appears with negative sign, but the coefficient is insignificant. This finding suggest that recycling patents are not enough to consolidate the emissions impact of packing waste recycling. Table 16 highlights the combined impact of municipal waste recycling and recyclable trade. The interactive term is negative and significant indicating environmental improvement through municipal waste recycling and recyclables trade. Table 17 demonstrates the combined impact of municipal waste recycling and recycling patents. The interactive term is insignificant while municipal waste recycling improves environmental quality through emission reduction. Table 18 reports the combined impact Majeed & Luni 899 of bio waste recycling and recyclable trade. The interactive term is negative and significant indicating reduction in emission through the implementation of CE measures. Recyclable trade is negative and significant suggesting the contribution of recyclable trade in environmental improvement. 5. Conclusion The present world is facing growing environmental issues such as increasing waste, excessive extraction of natural resources, deforestation, and loss of biodiversity. These problems are largely attributed to the linear economy, which follows the “take-makedispose” extractive industrial model. Contrary to this, CE follows the “reduce, reuse, and recycle” circular industrial model. This study investigates renewable energy, CE, and environment nexus by considering renewable energy as a key driver of environmental quality and the main pillar of circular economy practices, including CE indicators. This study covers the time 1990-2014 for global economy as well as for different groups of countries according to their income levels. Furthermore, the study analyzes the influence of various CE measures on carbon emissions of 27 European Union member countries. The results suggest that renewable energy and different measures of CE significantly improve environmental quality. Furthermore, heterogeneous panel techniques that take account of cross-sectional dependence and slope heterogeneity also support our findings that the circular economy contributes to environmental mitigation and helps in achieving sustainable development. Energy intensity, economic growth, and urbanization degrade the environment. 5.1 Contribution of the Study The sustainable use of resources offers opportunities to combat climate change and global warming. In this regard, renewable resources and CE measures support the global world in the form of reduced waste generation, decreased extraction of resources, and improved production and consumption patterns. The concerns about degrading the environmental quality and climate change have been raised across the world but studies examining the environmental mitigating role of CE indicators are not available. This study is a pioneering study and first of its kind that empirically investigates the influence of renewable energy consumption, an important pillar of CE, on the environmental quality at the global level and in different income groups. Second, the study uses different indicators of the CE like biowaste recycling, municipal waste recycling, the role of patents in recycling, and secondary raw material recycling in enhancing environmental quality in European Union member countries. Third, the study employed 2nd generation tests to analyze the effect of incorporated determinants on environmental quality. Fourth, cointegration among the variable is determined through the Wester-Lund panel cointegration test. Fifth, the study analyzed the long-run relationship among the variables using FMOLS, and DOLS. Sixth, the study also used heterogeneous panel techniques to examine the relationship among the variables which allow slope heterogeneity and crosssectional dependence. Renewable Energy, Circular Economy Indicators and Environmental Quality 900 5.2 Theoretical and Policy Implications Renewable energy reduces the extraction of fossil fuels, emissions from fossil fuels, waste ending in landfills, water degradation, and climate change. Like the use of renewables, recycling of waste also mitigates environmental degradation as it does not compromise the regenerative capacity of the system and resources are used for a longer period. Biowaste and municipal waste recycling reduce environmental degradation. An increase in the number of patents in recycling and secondary raw material also enhance environmental quality. Thus, our results support the theory of sustainable development as resources are used more efficiently. Furthermore, the findings also suggest the existence of “ecological modernization and eco-industrial development. As economies grow and industrialize, initially, technological backwardness and inefficiency in resource use degrade environmental quality, however with the development, awareness and technological advancement resulting from innovation enhance environmental quality and improve human-environment relationship. Based on our findings it can be suggested that governments of all economies should promote the circular economy and use of renewable energy as they not only ensure energy security but also shift dependence from finite non-renewable resources to those which can be sustained and are readily available in all the countries regardless of income levels. Renewable energy supports CE as it shifts the reliance from virgin resources to renewable ones whose harnessing does not have an adverse environmental impact. Among different measures of CE, competitiveness and innovations have a more profound impact on environmental quality. 5.3 Study Limitations The limitations of the study include: First, only 27 EU countries were examined for most of the CE indicators due to the unavailability of data. Second, the study collectively examined the impact of packaging waste recycling and did not examine the disaggregated impacts of individual measures, including the recycling rate of plastic waste, and the recycling rate of wooden packaging. Third, linear analysis is conducted while nonlinearities and complex relationships including direct and indirect effects are not explored. 5.4 Future Research Direction Based on the data availability future studies can examine the impacts of other indicators of CE on environmental sustainability. A comparative analysis can be conducted between the countries practicing CE measures to examine the policies and benefits in terms of reduction in resource consumption and shift to renewable resources. Asymmetries between CE and environmental quality can be examined by future studies, which will help in understanding the complex relationship between different dimensions of CE and their impacts on environmental quality. Furthermore, the robustness of the estimators is not examined in the present study which can be the focal point of future studies. Grant Support Details / Funding This research work received no research grant. Majeed & Luni 901 REFERENCES Antikainen, M., Uusitalo, T., & Kivikytö-Reponen, P. (2018). Digitalisation as an enabler of circular economy. Procedia CIRP, 73, 45-49. Argyres, N., & Mayer, K. J. (2007). Contract design as a firm capability: An integration of learning and transaction cost perspectives. Academy of Management Review, 32(4), 1060-1077. 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Renewable Energy, Circular Economy Indicators and Environmental Quality 912 Table 18: Results of Bio waste recycling and Recyclables Trade Variables Dependent Variable: lCO2 Combined effects of CE measures Equation (5.4a) (1) (2) (3) (4) (5) GDP per capita 1.030*** 1.018*** 1.006*** 1.022*** 1.009*** (0.0393) (0.0393) (0.0396) (0.0389) (0.0395) Renewable Energy per capita -0.146*** -0.130*** -0.141*** -0.130*** -0.132*** (0.0114) (0.0123) (0.0114) (0.0123) (0.0123) Energy intensity per capita 0.971*** 0.968*** 0.959*** 0.968*** 0.962*** (0.0452) (0.0447) (0.0449) (0.0447) (0.0448) Urbanization -0.403*** -0.416*** -0.369*** -0.400*** -0.363*** (0.0816) (0.0811) (0.0797) (0.0812) (0.0797) BWR*lRT -0.00004* -0.0004*** -0.00001 -0.0002* (0.00002) (0.0001) (0.00002) (0.0001) BWR -0.00001 0.0048*** 0.003* (0.0003) (0.0015) (0.0018) lRT -0.0191*** -0.0184*** -0.0121* (0.0054) (0.0056) (0.0067) Constant -3.662*** -3.576*** -3.578*** -3.618*** -3.580*** (0.341) (0.338) (0.338) (0.337) (0.337) Observations 405 405 405 405 405 R-squared 0.704 0.712 0.712 0.712 0.714 Adjusted Rsquared 0.701 0.707 0.707 0.708 0.709 “Standard errors in parentheses; Probabilities represented by * p < 0.1, ** p < 0.05, *** p < 0.01”