Multi-dimensional well-being associated with economic dependence on ecosystem services in deltaic social-ecological systems of Bangladesh

Abstract

AbstractWhile the benefits humans gain from ecosystem functions and processes are critical in natural resource-dependent societies with persistent poverty, ecosystem services as a pathway out of poverty remain an elusive goal, contingent on the ecosystem and mediated by social processes. Here, we investigate three emerging dimensions of the ecosystem service-poverty relationship: economic contribution of provisioning ecosystem services to the household livelihood mix, social-ecological systems producing different bundles of ecosystem services and material wealth versus reported life satisfaction. We analyse these relationships in Bangladesh, using data from a bespoke 1586-household survey, stratified by seven social-ecological systems in the delta coastal region. We create poverty lines to ensure comparability with traditional poverty measures that overlook environmental factors and subjective measurements of well-being. We find that any contribution of ecosystem service-based income to the livelihood mix decreases the likelihood of the incidence of poverty, and of individuals reporting dissatisfaction. We find no relationship between the incidence of material poverty and the specific social-ecological systems, from agriculture to fishery-dominated systems. However, the probability of the household head being dissatisfied was significantly associated with social-ecological system. Individuals living in areas dominated by export-oriented shrimp aquaculture reported lower levels of life satisfaction as an element of their perceived well-being. These results highlight the need for social policy on poverty that accounts for the diversity of outcomes across social-ecological systems, including subjective as well as material dimensions of well-being. National poverty reduction that degrades ecosystem services can have negative implications for the subjective well-being of local populations.