Comprehensive Environmental Assessment Index of Ecological Footprint

In the 1980s, environmental sustainability concerns were emphasized, and concurrently with introducing this value, the manufacturing process’s efficiency has garnered increasing attention. Färe et al. (1989) argued that inputs in the production process generate both desired (as measured by GDP) and undesired output (as pollution). Later, Hu and Wang (2006) proposed the TFEE concept and argued that a proper combination of energy with other production factors such as labor and capital might produce output. Therefore, while analyzing the environmental efficiency of a process, we must consider both desirable and undesired inputs and outputs. Energy consumption is inseparable from production and economic activity (Kraft and Kraft 1976), establishing a connection between economic growth and energy consumption (Tiba and Omri 2017).

Given that fossil fuels play the most significant role in delivering the required energy for the industrial process, the destruction of the environment and the release of vast quantities of carbon, wastewater, and waste gas as undesired outputs are inevitable (Wu et al. 2016; Xu et al. 2020). In addition to natural resources, economic activities utilize mineral resources, water, forest, and land resources (Y. Li et al. 2019). Rapid economic expansion stimulates the use of these natural resources in a way that jeopardizes ecological sustainability (Todaro and Smith 2011). Environmental contamination may be a consequence of the overuse of natural resources.

Numerous indexes and methods are raised to scout sustainable development status worldwide. Environmental efficiency is one of the most important indexes in determining sustainable development (Song et al. 2013). Many researchers have focused on limiting energy consumption and carbon dioxide emissions to achieve a high level of environmental quality. In traditional industrialization and economic development, more attention has been paid to the environment to prevent environmental degradation. In addition, Wackernagel and Rees (1997) and Wackernagel et al. (2019) defined Ecological Footprint (EF) as an indicator of sustainability and measured environmental quality that shows the bio-capacity necessary for an economic system to sustain economic activity (Sbia et al. 2014). Then, there is a lengthy debate regarding the selection of ecological footprint as an indicator of environmental sustainability. (Wiedmann and Barrett 2010) discussed EF as a headline indicator for sustainability decision-making. (Kissinger and Haim 2008) used EF indicator to compare the ecological sustainability of Israel as a case study and declared that EF provides a valuable indicator of human ecological sustainability. Templet 2000 compared ecological footprint to the net primary productivity (NPP) across 95 countries and concluded that EF method is useful because it disaggregates the footprint into its component and can provide a reasonable level of detail for the policy maker.. EF reflects human dependence on the ecological system, so it can serve as a viable indicator for determining environmental quality (Bani Yami et al. 2021; Galli 2015). In recent years, the ecological footprint developed by Wackernagel and Rees (1997) has been recognized as the measure of environmental deterioration since it considers agriculture, fishing grounds, grazing land, carbon footprint, forestland, and developed land. In addition to CO₂ emissions, the monitoring of air, water, and land quality has become vital due to the undeniable influence of economic activities on these ecological indices (Wackernage and Rees 1997).

Theoretically, several drivers of environmental deterioration have been identified by scientists, including human activity, energy consumption, industrialization, urbanization, and growing populations (Khezri et al. 2021; Sadorsky 2014; Shahbaz et al. 2013). In general, agriculture, industry, fishing, and international commerce degrade water quality, alter the terrain, and have a detrimental effect on the atmosphere (Vitousek et al. 2018). According to Niewöhner et al. (2016), up to 50 percent of the land surface has been transformed by human activities, and CO₂ emissions from human activities have steadily risen over the last several decades. Li et al. (2019) argue that economic activities must consume natural resources like minerals, forests, water, and land. Sharma et al. (2021) argue that economic activities exacerbate deforestation, exacerbate the use of pesticides, and amplify urbanization. They strengthen industrialization, mining, disappearing coastal areas, and building dams. They have all destroyed land, air, and water quality and the alteration of the ecological system. Economic expansion is a structural transformation that increases each country’s industrial activity and energy consumption. Therefore, growth is closely tied to energy consumption, which is closely tied to pollution levels. Grossman and Krueger (1991) introduced the EKC hypothesis, which asserts the economic growth-environmental degradation nexus and demonstrates that economic activities lead to an environmental deficit in the initial phase of economic growth. When a threshold of high economic growth is reached, the ecological destruction caused by economic activities subsides. The validity of this hypothesis has been assessed through various empirical studies. Some scholars have proved this hypothesis, and some have rejected it. However, all theorists agree on one crucial point: economic activity and human activities cause environmental damage (Majeed et al. 2021). In this context, Stern (1998) and Stokey (1998) proposed three channels via which economic development may affect environmental quality: (a) scale effects, (b) technique effects, and (c) composition impacts. These three criteria might influence the real environmental effects of economic development. In addition, the factor endowment hypothesis asserts that countries with a high level of capital endowments that make an effort to expand capital-intensive industries and absorb foreign direct investments will experience an increase in pollution-intensive industries, resulting in a decline in environmental efficiency (Zheng et al. 2015).

Usman et al. (2020) found that economic expansion has a detrimental influence on the ecological footprints of Africa and Europe. In addition, economic liberalization has been proven to have negative environmental repercussions in Africa and the United States. In addition, they noted that foreign direct investment and primary energy use contribute to Africa and Asia’s ecological footprints. Yang et al. (2021) analyzed the effects of industrialization, economic growth, and globalization on the ecological footprint. They determined that industrialization and economic expansion are largely responsible for pollution levels. Turnbull et al. (2018) and Clausen and York (2008) demonstrate that economic growth is connected with declines in aquatic biodiversity, agri-food system degradation, and an increase in the fishing footprint. Song et al. (2013), Clark and Longo (2019), and Solarin et al. (2021) provided empirical evidence of the detrimental consequences of economic activities on environmental efficiency.

Energy is one of the essential production components in theories of economic development and the direction of expansion, which hinders energy consumption that consequently plays a vital role in environment-economic growth literature (Bölük and Mert 2014; Omri and kahouli 2014). Theoretically, energy consumption and demand for fossil fuel increases at the initial level of economic growth (Destek and Sinha 2020). The usage of energy results in CO₂ and SO₂ emissions that affect environmental quality. Almost all economic sectors, including the transportation sector, agriculture, mining, industry, and service sector, are energy-intensive, and many developing countries have lax regulations against polluting activities, which transforms these countries into pollution havens and exacerbates ecological footprints (Danish 2018; Mamkhezri et al. 2022; Sarkodie and Strezov 2019). To preserve environmental quality, enterprises must implement less harmful technology and renewable energy as economic development and environmental consciousness rise.

Generally, the energy consumption- environmental quality nexus analysis is based on two main streams of theories. The first emphasizes efficiency improvements in energy consumption to reduce toxic pollutants and control environmental demolitions, hence cutting fossil fuel energy subsidies is proposed to control energy consumption (Shahzad et al. 2021). Other literature confirms that economic complexity, good and services production structures, and economy of scale, play a predominant role in the effect of energy consumption on ecological footprints (Danish 2018; Khezri et al. 2022; Neagu and Teodoru 2019; Ozcan et al. 2019; Ulucak and Lin 2017). The early investigation on the effect of energy consumption on ecological footprint revealed that non-renewable energy usage had negative effects on ecological footprint. However, Abbasi and Abbasi (2000) and Saidur et al. (2011) argue that all types of energy (renewable/non-renewable) lead to environmental impacts, and renewable energy is not the universal cure. In this regard, Saidi and Omri (2020) and Nathaniel and Iheonu (2019) suggest that burning biomass materials generated by using renewable energy pollutes water and land. According to Shahzad et al. (2021), the increased consumption of diverse energies such as oil, coal, gas, electricity, and water is the primary cause of carbon emissions and ecological footprint in the United States. The United States’ heavy reliance on fossil fuels threatens the environment, global warming, public health, etc (Ahmed et al. 2020; Alola et al. 2019; Destek and Sinha 2020; Karimi et al. 2022; Zafar et al. 2019).

Reviewing the tremendous literature that has been done about measuring environmental performance, the DEA models have been widely applied in environmental performance assessments. These models consider labor, capital, and energy consumption as the main inputs in countries’ economic activities, leading to gross domestic product and undesirable outputs as ecological damages in the production process. There is a large bulk of empirical literature using DEA models in assessing environmental performance in OECD countries, as Xue et al. (2022), Wang et al. (2022), Fidanoski et al. (2021), Sun et al. (Iram et al. 2020), Iram et al. (Iram et al. 2020), Gavurova et al. (2018), Simsek (2014), Hoang and Alauddin (2012) among others. Therefore, we shall describe the use of DEA models in assessing environmental performance below.

The two primary aims of this study are as follows: First, we aim to evaluate the environmental efficiency of OECD countries based on different undesirable outputs, including ecological footprint, detailed categories, and CO₂ emissions. The Slack-Based Measurement DEA (SBM) model is utilized to accomplish this goal, which is thoroughly presented in sections Data Envelopment Analysis (DEA) and Slack-Based Measurement-DEA models. Second, we intend to decompose the environmental efficiency scores to analyze each country’s ecological performance more precisely. In section Key Performance Indicators (KPI), the method for measuring these KPIs is described. The flowchart depicted in Fig. 1 summarizes the methodology of this paper.

Data Envelopment Analysis (DEA) is a potent nonparametric technique for analyzing the relative performance of a group of comparable decision-making units (DMUs) with various inputs and outputs. Based on Kopp’s (1981) boundary notion, Charnes et al. (1978) introduced the CCR model as the first DEA framework. The CCR model assumes constant returns to scale (CRS) and assesses the DMUs’ technical efficiency. Banker et al. (1984) extended the CRS premise of the previous model. They created the BCC model, which assumes variable returns to scale (VRS) and assesses the pure technical efficiency of the DMUs.

Suppose a set of n DMUs denoted by (j = 1, .. ,n), and each DMU has m inputs and s outputs. x_ij and y_rj represent the i^th input and r^th output of DMU_j, respectively. The CCR model can be formulated as a linear programming model represented in Eq. (1):Where the optimal value of the model (\({\boldsymbol{\theta}}^*_{k}\)) reflects the relative efficiency of the DMU, and it ranges between 0 and 1. The frontier’s efficient DMUs have a score of 1 for efficiency. Efficiency ratings of less than 1 show the gap between an inefficient DMU and an efficient frontier. λ_j represents the vector of weights assigned to inputs and outputs. The BCC model based on the variable return to scale (VRS) assumption can be formulated by adding a convexity constraint (\(\mathop {\sum}\nolimits_{j = 1}^n {\lambda _j = 1}\)) to the above CCR model.

The basic DEA models illustrated in the previous section, CCR, and BCC, are known as radial models, which are not accurate enough in practice and suffer some drawbacks. First, radial models adjust all inputs or outputs by the same proportion to make DMUs efficient, while in the real world, inputs (or outputs) don’t necessarily behave in a proportional way (Muhammad et al. 2018). Second, they fail to consider the slack of indicators, and when there is a non-zero slack of inputs or outputs, the radial DEA models overestimate the efficiency of DMUs (Jiang et al. 2021). Tone et al. (2020) created a non-radial slack-based measurement DEA (SBM-DEA) model that dealt with slacks directly and disregarded the assumption of proportional changes in inputs and outputs to solve these disadvantages. Although Tone’s SBM-DEA model exhibited more discriminating power than conventional radial DEA models, it did not take into account undesirable outcomes. Chang et al. (2013) enhanced the SBM-DEA model for the first time by including undesirable output directly into the goal function and restrictions in Eq. (2):Where x_ik, \(y_{rk}^g\) and \(y_{rk}^b\) depict inputs, desirable and undesirable outputs of DMU_k. Slack variables are denoted by \(s_i^ -\), \(s_r^b\) and \(s_r^g\), showing the excess in inputs and undesirable outputs, and shortfall in desirable outputs, respectively. The optimal value of the model, θ^*, refers to the overall efficiency of the considered DMU, which is also called environmental efficiency and ranges between 0 to 1. A DMU is environmentally efficient if θ^* = 1, and all the slack variables are equal to zero. In contrast, an inefficient DMU has an efficiency score of less than one and at least one non-zero slack variable with a positive value. Inefficient DMUs may be improved by reducing the surplus of inputs and undesired outputs or increasing the lack of desirable outputs.

Energy efficiency and Carbon efficiency were two of the most widely used KPIs in the DEA context (Choi et al. 2012; Iftikhar et al. 2018; Park et al. 2016). Energy efficiency was first produced by Hu and Wang (2006) based on the “ratio of target to real” concept and later developed by Zhou et al. (2006) by incorporating undesirable outputs in energy efficiency analysis. We utilize Chang et al. (2013)‘s method suggested under the SBM-DEA model for calculating KPIs. This paper proposes ecological footprint efficiency as a substitute for carbon efficiency. Besides, five more KPI measures are developed based on the different ecological footprint categories to evaluate the carbon footprint efficiency, cropland efficiency, fishing grounds efficiency, forest area efficiency, and grazing land efficiency performances. The KPIs can be estimated as follows:Where the actual values of energy (E_k), ecological footprint (EF_k), carbon footprint (CF_k), cropland (CE_k), fishing grounds (FG_k), forest area (FA_k), and grazing land (GL_k) for DMU_k are deducted from corresponding slacks to measure the target value of each variable, and then KPIs are calculated through the ratio of the target to the actual values.

This research examines the environmental performance of 27 OECD nations (Fig. 2) using panel data gathered from 2000 to 2017 for a total of eighteen years. The SBM-DEA model is implemented with energy consumption, labor force, and net capital stock as inputs, Gross Domestic Product (GDP) as a desirable output, and CO₂ emissions and ecological footprint (EF) (with its various categories including carbon footprint, cropland, fishing grounds, forest area, and grazing land) as undesirable outputs. As discussed in the introduction, the ecological footprint is a more comprehensive metric for evaluating the impact of human activities on the natural environment (Ahmad et al. 2020). It is a much better option than CO₂ emissions for assessing environmental quality (Nathaniel and Khan 2020). In this respect, we evaluate and compare environmental efficiency ratings based on CO₂ emissions and Ecological Footprint undesirable outputs. In addition, we utilize each Ecological Footprint category, such as carbon footprint, fishing grounds, cropland, forest area, and grazing land, as independent negative aspects to study the environmental performance of OECD countries in depth. The data on the EF is extracted from the databases of the global footprint network (www.​footprintnetwork​.​org), and the rest data is retrieved from the OECD data (www.​data.​oecd.​or). Table 1 presents the summary of descriptive statistics of the considered variables. Figure 2 illustrates the distribution and aggregate input and output data trends in the OECD countries from 2010 to 2017.

As shown in Fig. 2, the United States utilizes the highest proportion of inputs, including energy consumption, net capital stock, and labor force, with respective shares of 45 percent, 39 percent, and 31 percent. This country generates the highest proportion of both desirable and undesirable outputs, including GDP (38 percent), Ecological Footprint (42 percent), and CO₂ emissions (47 percent). Japan ranks second on the input and output variables list, with an average share of 11 percent, after the United States. Luxembourg has the lowest labor force and Ecological Footprint among the countries with the lowest share. Estonia has the lowest net capital stock, energy consumption, and GDP among the countries with the lowest share.

Considering the aggregate time-series trends of variables in Fig. 2, while net capital stock and labor force rose by 96% and 11%, respectively, total energy consumption remained unchanged throughout the study period. GDP increased by almost 78% from 2000 to 2017 for OECD countries, but CO₂ emissions and Ecological Footprint decreased somewhat.

Moreover, a Pearson correlation test has been performed to calculate the correlation coefficients among the input and output variables. The results are reported in Table 2. Considering the correlation matrix, net capital stock, energy consumption, and labor force significantly positively correlate with GDP and Ecological Footprint.

Particularly, there is a very strong correlation (0.99) between net capital stock and labor force with the output GDP that emphasizes the importance of net capital stock and labor force as inputs of the DEA model. In addition, energy consumption is highly correlated with Ecological Footprint, showing that the more energy is consumed, the more environmental impact is made. Consequently, the positive correlation between inputs and outputs meets the isotonicity property of the DEA model, which stipulates that as inputs increase, outputs tend to rise as well, making this formulation suitable for statistical efficiency analysis.