Integrating Local Behavioural Feedbacks to Identify Sustainable Pathways for Natural Resource Management
Natural resource management (NRM) is receiving significant attention from the individual, stakeholders and scholars on a local to global scale. In this regard, Integrated Assessment Models (IAMs) have significantly advanced in the last few decades, and they are widely considered a valuable tool at a regional and global scale for resource management and planning. However, IAMs suffer from the issue of scale and, therefore, have limitations to capture behavioural feedbacks generated at the individual level. This typically leads to policy resistance by the stakeholders to large scale interventions. IAMs suffer from two major limitations; the lack of behavioural models and the lack of interface from micro to macro. In particular, we focus on interventions that enhance resource use efficiency for the purpose of resource conservation. Local behavioural feedbacks from consumers result in increased demand, leading to a reduction in the expected gain from the intervention, commonly known as the rebound effect. Moreover, in extreme cases, this rebound can even result in a net increase in resource consumption (known as paradox). In this thesis, we effectively incorporate micro-level phenomena into macro-level policy frameworks of resource conservation to identify sustainable pathways for large scale adoption of efficient technologies using the irrigation efficient paradox as an expository application.
To understand these limitations, the existing IAM framework has been adopted for real-world case studies for the Indus river basin. In the first case study, we have explored the implication and trade-offs of adopting demand-side (smart irrigation) and supply-side (installation of dams) policies for water conservation in the Indus river basin (IRB). The analysis suggests that if the Indus countries (Including Pakistan, India, Afghanistan, and China) adopt a demand-based approach and irrigate their land completely using smart technologies, surface and groundwater withdrawals are indeed reduced. However, this reduction comes with a 33% increase in total expenditures, an increase in consumption across water and energy sectors, and higher withdrawals from fossil groundwater reserves. On the other hand, if the countries were to balance their investments between smart and hydropower technologies, it would not only reduce the increment in expenditure to 28% but would also conserve irrigation water while avoiding the increased multi-sectoral consumption and environmental degradation. Thus, balancing investments between smart irrigation and hydropower projects can significantly reduce the economic and environmental costs of multi-sector water conservation in the IRB.
In another case study, we have explored the pathways for meeting the Sustainable Development Goals (SDGs) in IRB with or without cross-country cooperation. We have found that Indus countries need to increase investments to US$10 billion per year to meet the SDGs. On the other hand, these costs would shrink to US$2 billion per year, with economic gains for all, if countries pursued more collaborative policies. After understanding the limitations of IAM with these case studies, we have addressed them with the following approaches.
To address the first limitation (the lack of behavioural models), we have developed a dynamical local behavioural model based on the rent-seeking behaviour of the farmers to capture the irrigation efficiency paradox (IEP). Our model combines a simple mass-balance description of the water flows (hydrological) with the rent-seeking behaviour of consumers (social). Through the socio-hydrological model, we find that the paradox in basins with lower evaporation and higher recharge is more pronounced, the policy implications of which are in contrast to the common narrative that seeks to reduce evaporation and increase recharge. We also use our findings along with global data sets to identify regions that are most susceptible to the IEP. We argue that much caution must be practiced while introducing efficient irrigation technologies in the identified regions so as to avoid paradoxical effects to as much extent as possible.
To address the second limitation (the lack of interface from micro to macro), the farmer’s local behavioural model has been transformed to capture the generalized behaviour of the consumer and then integrated with the IAMs. We present a conceptual interface between both models that is not only intuitive but also harmonizes the different spatial and temporal scales of the models. As a result, we observed that rebounds from one resource percolate into other resources due to overlaps in the consumption processes. Finally, we show how the central planner may restrict rebounds by appropriately adjusting the resource allocations to individual processes.
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