Abstract
The aim of this study is to analyze the circular interconnections between recycling, renewable energy and economic development. A multi-equation system is employed, where recycling and renewable energy (among other growth-inducing factors) are assumed to be important for promoting sustainable development (as provided by the Human Development Index), showing that both are mostly driven by technology and human capital skills. The system of simultaneous equations is estimated in static and dynamic form through 3sls, exploring panel data for a set of 28 OECD countries over the period 2000–2016, capturing in this way important linkages between the levels of economic development, renewable energy consumption and recycling rates. The empirical evidence shows that the circular process is characterized by cumulative linkages with feedback effects, where recycling and renewable energy are important policy factors for generating sustainable economic development with less climate deterioration. This result supports the idea of circular interconnections between economic development and green policies, through renewables and recycling, generating a self-sustained development without environmental deterioration.
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Data source: United Nations Development Program—Human Development Reports (April 2018)
Data source: Eurostat database (April 2018). Note: 2000 for HR and 2016 for IE—values not available
Data source: Eurostat database (April 2018)
Data source: Eurostat database (April 2018)
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Notes
For more details on the impacts of renewable energy on the development level, see Soukiazis et al. (2019).
As noted by the report of the New Economic Foundation (NEF 2008), there are four approaches to the measurement of well-being. The first one is developing an extended set of indicators to measure physical or socioeconomic factors. The second approach combines into a single index several measures as is the case of the HDI. The third approach is subjective and is based on self-reporting, like the Happy Planet Index. The last approach converts all given components into a monetary unit, such as the Index for Sustainable Economic Welfare. Therefore the choice of this paper was to use the HDI rather than the other options, as it is a single index (and not a system of indices), is based on objective measures and does not transform the index into monetary units which can be questionable, and it is easily available for a large set of countries.
The United Nations Development Program created the Human Development Index, which measures the quality of life in different countries on a scale from zero to one. This index, apart from measurements such as the gross national product, considers also other factors like health and education, when evaluating a country's progress.
The reasoning behind this is: (1) more advanced countries have higher financial capacity to invest in renewable energy production, (2) countries with higher stages of development realize more fully the necessity to reduce environmental degradation and take measures to this direction, (3) more advanced countries have higher levels of education and better understand the benefits from the use of renewable energy, and finally, more advanced countries spend more on R&D in this domain, and have higher human skills to develop renewable energy strategies for the sake of the environmental protection.
The 3sls is a GLS estimation approach applied to a system of simultaneous equations, estimating jointly all equations in a unique model expressed in blocks. For instance, the dependent variable is a vector of four blocks, each containing the observations of the dependent variable in each equation. The variance–covariance matrix of the errors is determined by using the residuals obtained from the 2sls estimation approach. The list of the instruments used is given at the end of each table.
The panel data used has the cross section bigger than the time dimension (N > T), which makes the panel stationarity tests unreliable as they usually require that N should be smaller relative to T, which is not the case. For details see Baltagi (2008).
The partial adjustment mechanism assumes that actual variation of the dependent variable is a fraction of the desired variation given, in the case of first equation, as (HDIt-HDIt−1) = δ(HDIt* − HDIt-1), where HDI* is the optimal or desirable level of the development index, and δ the adjustment coefficient (0 < δ<1). Value of δ close to zero indicates a slow adjustment path and value close to one indicates a rapid adjustment process.
This dynamic specification expresses better the circular interrelations between the core variables of the system.
According to the partial adjustment mechanism, the long-run effect of the explanatory variable is given dividing the short-run effect (0.119) by the coefficient of adjustment (1 − 0.442). In this case: 0.119/0.558 = 0.231.
From Model (2), the long-run effect of the recycling rate on the development index is (0.0125/0.558) = 0.022-point increase.
The long-run effect in this model is obtained by summing up the short-run effects and dividing by one minus the coefficient of the lagged dependent variable, that is: (−0.367 + 0.411)/(1 − 0.884) = 0.379.
The energy dependence variable is also statistically significant in the static Model (1) at the 10% level only, but we do not consider this case since coefficients are not efficient due to autocorrelation problem.
∂lnCO2/HDI = 1.053 − 2 * 0.00611HDI = 0→HDI = 86.17. We recall that HDI is an index that takes values from 0 to 100.
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Cerqueira, P.A., Soukiazis, E. & Proença, S. Assessing the linkages between recycling, renewable energy and sustainable development: evidence from the OECD countries. Environ Dev Sustain 23, 9766–9791 (2021). https://doi.org/10.1007/s10668-020-00780-4
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DOI: https://doi.org/10.1007/s10668-020-00780-4