2.1 Composite variables definitions
The Yale University Centre and other institutions like the Network of Columbia University composed the ESI and EPI under the SDGs in evaluating countries' progress in ecosystem vitality (Long et al., 2023); Wolf et al., 2022). Environmental sustainability has been defined and applied in the literature under the MDGs and SDGs (Mamat et al., 2016; Wolf et al., 2022). Literature has variously defined environmental sustainability in terms of animals' welfare (Place 2018), clean drinking water, (Palit 2017), resources and services (Khan et al., 2021), greenhouse gas mitigation, climate change and renewable energy (Ghosh, Westhoff and Debnath, 2019; Nathaniel et al.2021; Razzaq et al.2022), and health and organic agriculture (Chandra et al., 2021). The literature suggests various statistical methods had been used, including principal component analysis to ascertain accurate weights to composed EPI and ESI (Bell & Mores, 2008; Mamat et al., 2016). In line with the MDGs and SDGs frameworks, ESI in this study may be defined as the environment's ability to prevent natural resource depletion and maintain ecological balance (Becker, 2005). In line with (Bell & Mores, 2008; Mamat et a., 2016), the authors computed ESI by applying five datasets from WDI, which are categorized under the SDGs 14 &15 to compose a single index as ESI, see Appendix A in supplementary file.
Also, SDG 7: ensure affordable, reliable and modern energy (SDG 7.1), an increasing portion of renewable energy (RE) (SDG 7.2), and doubling the improvement of energy efficiency while reducing intensity (SDG7.3). Also, other indicators are that member countries should stimulate access to energy technology and investment in clean energy (SDG7.4) and further expand energy services for all (SDG7.5) enhance global sustainable economic development by 2030 (Awogbemi & Kallon, 2023). So, a sustainable energy indicator under the SDGs is a measurement that seeks to ensure an adequate clean supply of energy, enhance energy affordability in consumption and reduce potential and actual environmental damage for both current and future generations. The five specific indicators mentioned above represent sustainable energy measure. These indicators can be seen in Appendix A in supplementary file for SEI. In contribution to the literature, the authors use the SDG7 five indicators to compute SEI consistent with the literature (Nakthong & Kubaha, 2019; Ligus & Peternek, 2021) to examine the ten selected countries.
Furthermore, land use, forestry and agricultural sector events are significant anthrophonic factors that adversely affect environmental sustainability and climate change. That is, agriculture, land use together with the forestry sector emissions contribute over 18% of the global GHG from the three main sources i.e. methane, (NH4) and nitrous oxide (N2O) and some levels of CO2, which are predominately impacting global warming (Mathews et al., 2018). Sustainable agriculture measurement objective is an important policy strategy for meeting sustainable development and the environment under the SDGs framework. However, literature including (Leahy et al., 2020; Lynch et al., 2021; Leahy et al., 2022) suggests that the agricultural sector emissions have been increasing in geometrical measure following a great demand for food and agricultural products in manufacturing industries, and this has negatively impacted on climate change and environmental sustainability in many countries. Literature suggests that there is a current need to have a single internationally acceptable set of measurement indexes and datasets to track progress of SDGs in environmental sustainability in agriculture (Azim et al., 2022). Consistent with the literature (Talukder et al., 2020; Azim et al., 2022), we apply the following indicators (land under cereal production in hectares, agricultural land, cereal yield kilogram per hectare and agriculture, forestry, and finishing, value added per work) to compute SAI indicator in environmental sustainability analysis, see Appendix A in supplementary file.
Moreover, water and sanitation sustainability indicators were proposed initially by Seghezzo (2009) and later applied by other researchers Iribarnegaray (2012). This indicator has been variously applied in determining countries’ quality of water and sanitation development2 (Kayser et al., 2019; Pereira & Marques, 2021). According to (Fisher, Cavill and Reed, 2017; Dickin et al., 2021; Pereira & Marques, 2021), water and sanitation sustainability (SDG6) is a critical policy targeting dimension in achieving other strategic SDGs such as 1, 5, &10, most notably for women and girls in the developing countries. In order to examine ESI, the authors included the SDG6 indicators to construct WASSI as an explanatory variable in the econometric model consistent with the literature (Pereira & Marques, 2021; Dickin et al., 2021), as shown in Appendix A in supplementary file.
Similarly, literature shows that the CDI was initially proposed in 1996, and the United Nations Centre for Human Settlements (2001) measures the development level of cities. The application of the CDI usually includes some critical socioeconomic and environmental indicators, including (infrastructure such as clean water, electricity access, wastewater treatment, health, education, and city product based on the gross domestic product) for calculation (Bell & Morse, 2008). The mentioned socioeconomic and environmental components under the CDI are categorized under the following SDGs 3 (good health and well-being), SDG 4 (quality education), SDG 8 (promote inclusive and sustainable economic growth, employment and decent work for all) and SDG 11 (building inclusive, safe, resilient, sustainable cities and human settlements). The primary motivation of the study is to examine the environmental sustainability of the countries, and the researchers included these indicators in computing the CDI as an essential dependent and explanatory variable in the study. That is, the inclusion of the CDI variable supports the authors in establishing the relationship between the goal of environmental sustainability and the cities' (countries') development in the wake of sustainable development (Opoku et al., 2022). The authors here, therefore, applied both urban and rural indicators such as (access to electricity, people using at least basic drinking water services, people using at least essential sanitation services, people using at least basic sanitation services, gross national income per capita; educational attainment; health expenditure per capita and total labor force) to compute the CDI variable for the econometric analysis.
What more, according to Bell and Morse (2008), the ecological or carbon footprint indicator (CFPI) was developed by Wackernagel and Rees in 1997, and it is based on quantitative land and water capacity to sustain living things on earth. Carbon footprint or ecological footprint (Nathaniel et al., 2021; Razzaq et al., 2022) is the measurement of carbon dioxide or emissions (CO2) and the total greenhouse gases (GHG) present in the atmosphere, usually measured in terms of CO2 equivalent (Chen, Zhang, and Han, 2021; Opoku et al., 2022). Studies have shown that GHG has contributed over 75% to global warming or climate change consequences (Cai et al., 2019; Rebolledo-Leiva, 2019: Abudu et al., 2022). Literature shows a positive relationship between carbon footprint and climate change challenges and negatively affects environmental and economic factors (Yang & Meng, 2019; Chen, Zhang, and Han, 2021). This study, therefore, computes CFPI based on literature as another variable indicator in explaining ESI in developing countries (Opoku et al., 2022). The study uses SDG13 indicator variables (i.e.PM2.5 air pollution and CO2 emissions, see Appendix A in supplementary file) to compute the CFPI.
2.2 Hypothesis development
Literature suggests that sustainable development indicators still need to be in their fuzzy concepts stage: no available research theory, modelling, and unified data are used to evaluate the current SDGs indicators (Warchold et al.2022). That is, from the definition in the 1980s, MDGs application in 2000–2015, and broad implementation under the SDGs from 2015: theory development is through practice (Longyu et al., 2019). To effectively examine the research questions, the authors established hypotheses subject to the literature and the SDGs concept. The established hypotheses are interconnected under the SDGs to further show the relationship between the dependent and independent variables in answering the research questions. Therefore, in determining the environmental sustainability indicator (ESI) as the dependent variable, we consistently with the literature postulate that H1a-4a: sustainable agriculture indicator (SAI), sustainable energy indicator (SEI), city development indicator (CDI), and water and sanitation sustainability indicator (WASSI) should all be positively related with the dependent variable. Also, H5a: carbon footprint indicator (CFPI) should be negatively related to the dependent variable, environmental sustainability. Additionally, the authors hypothesized the following in response to the second research question. Literature suggests that the demand for sustainable energy, particularly renewable energy, will contribute positively to sustainable agriculture toward 2030 (Chel and Kaushik, 2011; Gorjian et al., 2021; 2022; Chopra et al., 2022). Therefore, the authors hypothesized that H1b: CFPI would negatively affect SAI. Furthermore, H2b-H5b: SEI, CDI, WASSI &ESI would positively impact SAI. Lastly, we adopted the Environmental Kuznets Curve (EKC) hypothesis, which hypothesized that higher economic countries would turn to improve environmental quality relative to low economic countries Beyene and Kotosz, (2020) to examine the third research question.
[2] Water Water and sanitation development index: https://epi.yale.edu/epi-results/2020/component/h2o.