There is a clear pattern on the role of reduced inequality in positively affecting environmental and economic trajectories. That is one major finding of a new paper by sustainability scientists Datu Buyung Agusdinata and Rimjhim Aggarwal. The paper, published July 15, 2020, in the journal Environment, Development and Sustainability, is titled Economic growth, inequality, and environment nexus: using data mining techniques to unravel archetypes of development trajectories.
The paper spawned out of a seed grant the authors received from the PLuS Alliance on conceptualizing and implementing the United Nations Sustainable Development Goals (SDGs) as an integrated system, in contrast to the general practice of analyzing these goals in isolation. In this paper the authors focus on the interactions between economic growth (SDG 8), reduced inequalities (SDG 10), and climate action (SDG 13), the goals which underpin the economic, social, and environmental dimensions of sustainable development. Examining the interactions between these goals contributes towards understanding some of the complexities and nuances of the UN Agenda.
Using data from 70 countries, Agusdinata, Aggarwal and co-author Xiaosu Ding of Purdue University applied data mining techniques to identify twelve archetypes of development pathways, each of which shows a different pattern of interactions among these goals, thus highlighting the diversity in development experience across the world and lessons it may offer in shaping future policies.
The abstract follows.
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.