**2. Literature Review**

This section presents a brief review of the literature on using DEA to estimate ecoefficiency at the country level. Bianchi et al. [16] use DEA and metafrontier analysis to measure eco-efficiency in 282 European regions for the period 2006 to 2014. For inputs, they use the employment rate and domestic material consumption per capita. The output variable is GDP per capita. They find evidence of an upward trend in eco-efficiency across European regions, although there is no evidence that regions are converging to similar levels of eco-efficiency. Halkos and Tzeremes [17] use DEA to calculate environmental efficiency for 17 OECD countries over the period 1980 to 2002. The main focus of their research is to test whether a Kuznet's-like hypothesis exists between environmental efficiency and income. The capital stock and labor are used as inputs to the DEA model, GDP is the desirable output, and sulfur emissions is the undesirable output. They do not

find evidence of such a relationship. Hsieh et al. [18] use DEA to estimate the energy and environmental efficiency of 29 EU countries for the period 2006 to 2013. In their DEA analysis, labor, capital, and energy consumption are inputs. GDP is the desirable output and greenhouse gas emissions and sulfur oxide emission are undesirable outputs. About half of the countries have room for environmental performance improvements. Environmental performance is higher in the latter part of the sample period. Somewhat surprising in this study is that Great Britain, Germany, France, and Italy have relatively low environmental efficiency scores due to their greenhouse gas emissions and SO<sup>2</sup> emissions. Iftikhar et al. [19] use slacks-based (SBM) DEA to estimate energy and CO<sup>2</sup> emissions efficiency for 26 major countries for the years 2013 and 2014. The inputs are capital, labor, and energy consumption, while the desirable and undesirable outputs are GDP and carbon dioxide emissions, respectively. Larger countries with raw material intense production, and weak carbon laws are the least efficient. In particular, China, India, and Russia have much room for improvement in eco-efficiency. Lacko and Hajduova [20] study environmental efficiency among 26 EU countries covering the years 2008 to 2016. CO<sup>2</sup> per capita, methane per capita, and nitrous oxide per capita are the inputs and the output is GDP per capita. Eastern European countries tend to have low environmental efficiency and England and Sweden have high environmental efficiency. Climate change and socioeconomic factors are important drivers of environmental efficiency. Lozowicka [7] uses SBM DEA to analyze ecological efficiency and MPI in selected EU member states for the years 2005, 2010, and 2015. The input variables include the share of non-renewable energy, the percentage of the population not connected to wastewater treatment systems, the non-forested land ratio, and the unprotected area relative to the area of the country. The output variables include biochemical oxygen demand, the balance of nutrients, index of clean energy, and population exposed to PM2.5 air pollution. Northern Europe states have the highest ecoefficiency, while Central and Eastern Europe states have the least. Marti and Puertas [21] study the efficiency of the ecological footprint and biocapacity of 45 African countries. They use a variable returns DEA model with ecological footprint and population as the inputs and GDP as the output. Countries are divided into two groups. One group has a biocapacity surplus while the other has a deficit. Among the deficit countries, Gambia, South Africa, Swaziland, Mauritius, and Nigeria are efficient. Angola, Gabon, and Guinea-Bissau are surplus countries with high efficiency. Moutinho and Madaleno [22] use DEA to study eco-efficiency for 27 European Union (EU) countries over the period 2008 to 2018. They use a two-step estimation approach where in the first step, eco-efficiency scores are estimated, and in the second step, a fractional regression is used to estimate the impact of pollutants per area on eco-efficiency. The output variable is the ratio of GDP per capita to greenhouse gas emissions per area. The input variables are capital per capita, labor per capita, energy use per area, electricity use per area, and a temperature variable. From the second step regression, increases in CO2/area and CH4/area decrease eco-efficiency. Moutinho et al. [4] use constant returns to scale (CRS) and variable returns to scale (VRS) DEA to study environmental efficiency for 26 European countries. The DEA input variables include labor productivity, capital productivity, and non-fossil fuel energy share. The output variable is GDP per greenhouse gas emissions. The shares of renewable energy and non-renewable energy sources are important factors explaining differences in country-level environmental efficiency. Moutinho et al. [5] use CRS and VRS DEA and MPI to study eco-efficiency in 16 Latin American countries for the time period 1994 to 2013. The input variables include energy use, population density, labor productivity, renewable energy consumption share, and capital productivity. The output variable is the ratio of GDP to CO<sup>2</sup> emissions. For most countries, the degree of technical efficiency is lower than the degree of technological efficiency, indicating that some of the overall inefficiency is due to producing below the production frontier. Sarkhosh-Sara et al. [23] use network DEA to measure the sustainability of three groups of countries (high, middle, and low income). In total, 97 developed and developing countries are studied for the year 2011. The first stage DEA uses labor, capital, and energy as inputs. GDP is the desirable output,

and CO<sup>2</sup> emissions is the undesirable output. For the second stage of the network analysis, GDP and population are used as inputs and income class is used as the output variable. Countries with high and low incomes perform well in the sustainable production stage but are weak performers in the sustainable distribution stage. Middle-income countries rank low on sustainable production but are strong performance in the sustainable distribution stage. Tsai et al. [24] use DEA-based meta frontier analysis to compare environmental efficiency between 37 European and 36 Asian countries. The input variables include the labor force, energy consumption, and government expenditures. The desirable output is GDP, and the undesirable output is CO<sup>2</sup> emissions. Mean meta-efficiency tends to be higher in European countries. Twum et al. [3] use DEA to calculate environmental efficiency for three Asia-Pacific regions. The desirable output is GDP and the undesirable output is CO<sup>2</sup> emissions. The input variables are the share of renewable energy and total patent applications. They find that East Asia is highly efficient, while South East Asia is the least efficient. They find evidence of an inverted U-shaped relationship between environmental efficiency and technological innovation. Wang et al. [1] use slacks-based DEA and MPI to investigate eco-efficiency for 17 European countries for the years 2013 to 2017. The desirable output variable is GDP per capita and the undesirable output is CO<sup>2</sup> emissions per capita. The input variables are energy consumption per capita, labor productivity, share of renewable energy consumption, and capital formation productivity. Nine of the 17 countries were found to have an eco-efficiency score of 1. As a group, the countries lacked eco-efficiency over the period 2013 to 2017. The lack of eco-efficiency comes mostly from a lack of technological progress.

In summary, while there is literature studying eco-efficiency at the country level for various groups of countries, there is no study that explicitly focuses on G18 eco-efficiency and how G18 eco-efficiency will evolve into the future.
