By Ramya Natarajan, Senior Research Engineer

Developing countries like India struggle to balance their developmental goals with their climate targets. For instance, while ‘housing for all’ is a basic developmental goal, achieving it has large implications on material demand and subsequent carbon emissions. Similarly, ‘mobility’ is a fundamental human need but the transport sector significantly contributes to global warming and air pollution. At CSTEP, we felt that there is a need to analyse sectoral and inter-sectoral interventions that could help India achieve its developmental goals in a sustainable manner. Additionally, we wanted to ensure that the analyses were accessible to policymakers, and therefore, developed a user-friendly model that visualised various developmental trajectories for India up to 2050. The objective of the model was to estimate, in a bottom-up manner, the material and energy demands and the consequent emissions involved in achieving India’s developmental goals.

Many long-term energy models study economic constraints but few look at physical constraints like land, water, and materials directly (i.e., in a non-economic manner). Considering that we were attempting a bottom-up estimation, we decided that it would be fruitful to structure our model to include water, land, and materials-based feedback on sectoral growth (driven by India’s goals). We also wanted to understand the trade-offs and co-benefits between the goals themselves as well as with sectors and resources. Thus, we began developing the ‘Sustainable Alternative Futures for India’ (SAFARI) model, using system dynamics to understand how India should plan its development pathway in a sustainable manner. We selected developmental goals based on national priorities, in line with the Sustainable Development Goals (SDGs), and their bearing on energy and material demand.

SAFARI would help understand the trade-offs and co-benefits between the goals themselves as well as with sectors and resources. Image Credit: Udita Palit, CSTEP

Untangling the complexity

So, what insights can this model add to policy? SAFARI can answer questions such as: Given the water and arable land conditions and population growth, would India be food-secure in, say, 2030? What kind of policy or technology interventions in agriculture may help achieve it sustainably? At what sanction rates could India meet its dynamic housing shortage? How would the demand for cement and other materials change as we construct more hospitals, schools, and houses in our efforts to achieve a desired quality of life? What are viable alternative materials for India to reduce the overall emissions from construction? Are electric vehicles enough or do we need more aggressive efforts to mitigate transport sector emissions? Would an increase in biofuel production affect food security? How could capacity addition in industries be planned to meet India’s growing needs? To what extent can our energy demands be met via fossil-free sources?

We believe that in long-term forecasting, understanding the dynamic behaviour of the system and its responses to various interventions is crucial. For instance, according to SAFARI, there is likely to be a foodgrains shortage from 2030 onwards, predominantly due to water scarcity. The model allows users to try out intervention scenarios of their choice and visualise the impacts on foodgrain gap, other demands, as well as emissions. We find that a dietary shift towards coarse cereals (away from water-intensive rice) reduces the gap in foodgrain supply but not completely to zero. This is because coarse cereals have a lower yield and therefore, require more land and fertilisers for cultivation. Understandably, a dietary shift combined with yield-improving interventions (like micro-irrigation) could bridge the gap in supply. In terms of emissions, increased cultivation of coarse cereals leads to an increase in greenhouse gas emissions from fertilisers (N2O from application and CO2 from production) but decreased rice-methane emissions. How would such a shift in cropping pattern affect farmers and their incomes? If it would help relieve the strain on water resources and thereby, achieve food security, what type of policies could we develop to make the shift profitable for farmers? What are the other implications? The ultimate purpose of SAFARI is to provide an understanding of such benefits and trade-offs in sustainably achieving our developmental goals.

The Approach

When it comes to ensuring quality, the focus for such models should be on validating the approach and logic behind each equation, rather than the exact projected value for a particular parameter. It is not only impossible to establish the ‘truthfulness’ of an estimate for thirty years into the future (at least, not until we figure out time travel), but it also ends up being a rabbit hole. While this is not an unusual point of view, it is often forgotten, and the ‘validation of results’ bug derails researchers from understanding complex system behaviours. SAFARI is, after all, meant to be all about the journey or safar!

We followed three main strategies to ensure that our approach and logic — the foundation of our model — were robust. First, we consulted with experts across various sectors to inspect key modelling structures and interdependencies between variables. Second, we chose 2011 as our base year and compared our model behaviour up to 2018 with real data. This kind of back-casting helped us refine and validate the modelling logic. Third, we performed ‘extreme conditions tests’ to check if our model provides reasonable outputs under those conditions.

SAFARI is our first attempt at understanding the interlinkages and interdependencies in select sectors of the economy, and we hope that it will be a useful guiding tool in policymaking. Insights from and the approach followed in SAFARI Version 1, currently under review, will be uploaded on soon.

Developing innovative technology options for a sustainable, secure and inclusive society.