Economic growth, inequality, and environment nexus: using data mining techniques to unravel archetypes of development trajectories

Datu Buyung Agusdinata (), Rimjhim Aggarwal () and Xiaosu Ding ()
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Datu Buyung Agusdinata: Arizona State University
Rimjhim Aggarwal: Arizona State University
Xiaosu Ding: Purdue University

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2021, vol. 23, issue 4, No 70, 6234-6258

Abstract: Abstract Implementation of sustainable development goals (SDGs) requires evidence-based analyses of the interactions between the different goals to design coherent policies. In this paper, we focus on the interactions between economic growth (SDG 8), reduced inequalities (SDG 10), and climate action (SDG 13). Some previous studies have found an inverted U-shaped relationship between income per capita and inequality, and a similar relationship between income per capita and environmental degradation. Despite their weak theoretical and empirical bases, these hypothesized relationships have gained popularity and are assumed to be universally true. Given differences in underlying contextual conditions across countries, the assumption of universal applicability of these curves for policy prescriptions can be potentially misleading. Advances in data analytics offer novel ways to probe deeper into these complex interactions. Using data from 70 countries, representing 72% of the world population and 89% of the global gross domestic product (GDP), we apply a nonparametric classification tree technique to identify clusters of countries that share similar development pathways in the pre-recession (1980–2008) and post-recession (2009–2014) period. The main outcome of interest is the change in per capita CO2 emissions (post-recession). We examine how it varies with trajectories of GDP growth, GDP growth variability, Gini index, carbon intensity, and CO2 emissions (pre-recession). Our study identifies twelve country clusters with three categories of emission trajectories: decreasing (four clusters), stabilizing (three clusters), and increasing (five clusters). Through the application of data mining tools, the study helps unravel the complexity of factors underlying development pathways and contributes toward informed policy decisions.

Keywords: Sustainable Development Goals (SDGs); Development trajectories; Archetypes; Clustering; Classification tree; Environmental Kuznets curve (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10668-020-00870-3

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