Research Article: Explaining long-term outcome trajectories in social–ecological systems

Date Published: April 15, 2019

Publisher: Public Library of Science

Author(s): Pushpendra Rana, Daniel C. Miller, Arun Jyoti Nath.

http://doi.org/10.1371/journal.pone.0215230

Abstract

Improved knowledge of long-term social and environmental trends and their drivers in coupled human and natural systems is needed to guide nature and society along a more sustainable trajectory. Here we combine common property theory and experimental impact evaluation methods to develop an approach for analyzing long-term outcome trajectories in social–ecological systems (SESs). We constructed robust counterfactual scenarios for observed vegetation outcome trajectories in the Indian Himalaya using synthetic control matching. This approach enabled us to quantify the contribution of a set of biophysical and socioeconomic factors in shaping observed outcomes. Results show the relative importance of baseline vegetation condition, governance, and demographic change in predicting long-term ecological outcomes. More generally, the findings suggest the broad potential utility of our approach to analyze long-term outcome trajectories, target new policy interventions, and assess the impacts of policies on sustainability goals in SESs across the globe.

Partial Text

Ensuring environmental sustainability while improving human well-being in the context of climate change is an increasingly critical global challenge [1,2]. To address this challenge policymakers and scholars are developing a range of new approaches to promote and assess sustainability in coupled human and natural systems [3,4]. One promising way forward is to analyze potentially favorable long-term trends and their drivers to help guide nature–society interactions toward more sustainable trajectories [5,6]. Retrospective research using rigorous ex-post analyses can help prioritize regulatory and other governance actions to further sustainability goals [7,8] and may help establish appropriate counterfactual reference scenarios to measure the performance of climate mitigation and other environmental policies against their stated goals [9]. However, understanding social and ecological sustainability requires a long time horizon, adequate data, and methods that allow outcomes to be tracked and assessed.

Our approach for analyzing key factors predictive of long-term trends in ecological outcomes in SESs includes four steps, as described in Fig 1. The approach is based on a SES framework, which we then operationalize using a literature-based theory of change (Figure A and Tables A-C in S1 File) and publicly available data from Kangra District, Himachal Pradesh, India (Table B in S1 File). The study area (Fig 2) includes 202 Forest Management Regions (FMRs) in Kangra district. To develop a representative set of structural counterfactuals necessary to identify key factors shaping observed long-term vegetation growth trajectories in the study area (Step 3 in Fig 1) we drew a random sample of 30 out of the 202 FMRs in Kangra.

This study has shown that our approach, based on the synthetic control method in an SES framework, can help identify a set of predicting factors that have higher relative importance in explaining observed vegetation trajectories in SESs. We found a suite of socioeconomic, forest governance and biophysical factors that have shaped long-term ecological outcomes in Kangra district of Himachal Pradesh. Our results also showed considerable heterogeneity in vegetation outcomes and predictors based on level of NDVI increase in FMRs.

 

Source:

http://doi.org/10.1371/journal.pone.0215230

 

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