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Abstract The field of sustainability science has grown significantly over the past two decades in terms of both conceptual development and empirical research. Systems-focused analysis is critical to building generalizable knowledge in the field, yet much relevant research does not take a systems view. Systems-oriented analytical frameworks can help researchers conceptualize and analyze sustainability-relevant systems, but existing frameworks may lack access or utility outside a particular research tradition. In this article, we outline the human–technical–environmental (HTE) framework, which provides analysts from different disciplinary backgrounds and fields of study a common way to advance systems-focused research on sustainability issues. We detail a step-by-step guide for the application of the HTE framework through a matrix-based approach for identifying system components, studying interactions among system components, and examining interventions targeting components and/or their interactions for the purpose of advancing sustainability. We demonstrate the applicability of the HTE framework and the matrix-based approach through an analysis of an empirical case of coal-fired power plants and mercury pollution, which is relevant to large-scale sustainability transitions. Based on this analysis, we identify specific insights related to the applicability of upstream and downstream leverage points, connections between energy markets and the use of pollution control technologies, and the importance of institutions fitting both biophysical dynamics and socioeconomic and political dynamics. Further application of the HTE framework and the identification of insights can help develop systems-oriented analysis, and inform societal efforts to advance sustainability, as well as contribute to the formulation of empirically grounded middle-range theories related to sustainability systems and sustainability transitions. We conclude with a discussion of areas for further development and application of the HTE framework.
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Evolution of system connectivity to support food production in the Indus Basin in Pakistan
Sustainability challenges related to food production arise from multiple nature-society interactions occurring over long time periods. Traditional methods of quantitative analysis do not represent long-term changes in the networks of system components, including institutions and knowledge that affect system behavior. Here, we develop an approach to study system structure and evolution by combining a qualitative framework that represents sustainability-relevant human, technological, and environmental components, and their interactions, mediated by knowledge and institutions, with network modeling that enables quantitative metrics. We use this approach to examine the water and food system in the Punjab province of the Indus River Basin in Pakistan, exploring how food production has been sustained, despite high population growth, periodic floods, and frequent political and economic disruptions. Using network models of five periods spanning 75 y (1947 to 2022), we examine how quantitative metrics of network structure relate to observed sustainability-relevant outcomes and how potential interventions in the system affect these quantitative metrics. We find that the persistent centrality of some and evolving centrality of other key nodes, coupled with the increasing number and length of pathways connecting them, are associated with sustaining food production in the system over time. Our assessment of potential interventions shows that regulating groundwater pumping and phasing out fossil fuels alters network pathways, and helps identify potential vulnerabilities for future food production.
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- Award ID(s):
- 1924148
- PAR ID:
- 10536297
- Publisher / Repository:
- Proceedings of the National Academy of Sciences
- Date Published:
- Journal Name:
- Proceedings of the National Academy of Sciences
- Volume:
- 121
- Issue:
- 18
- ISSN:
- 0027-8424
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1073/pnas.2215682121
