The Climate Futures Project

Comparing India's energy, economic growth, and emissions futures through the lens of climate policy models

Most modelling studies use different climate policy models and varying input assumptions, which leads to varying and occasionally conflicting results. Recognising the uncertainties in future pathways, here we compare the results of various studies along several parameters. The aim of doing this is to illustrate the range of possibilities explored, the potential implications of these divergences, and the common trends and storylines that emerge across models.

ROBUSTNESS OF MODELLING EXERCISE AS PER THE
CLIMATE FUTURES PROJECT ASSESSMENT FRAMEWORK

An explanation of relative performance of models along 5 assessment parameters

In general, the robustness of any modelling exercise rests upon five parameters: transparency and validity of inputs, appropriateness of model choice, quality of scenario construction process, treatment of uncertainties, and validation of outputs. Under current practices and data limitations, not all modelling setups perform equally along all five parameters; they demonstrate different strengths and shortcomings.
PROJECTED EMISSIONS, GDP GROWTH, AND
PRIMARY ENERGY DEMAND

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Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

What can we observe from this graph?

This graph shows the emissions (not including carbon dioxide removal) in the projection end year against the average GDP growth rate from base year to end year for each scenario covered, with the size of the bubble denoting the primary energy demand in the end year.

IEA’s exploratory projections indicate annual emissions of between approximately 1.5986927 to 4 GtCO2 in 2040 at annual GDP growth rates of 5-6% across various scenarios. Emissions under the STEPS and IVC scenarios aren’t significantly higher than present, and emissions under SDS appear to be broadly in line with mid-century decarbonisation efforts. CEEW projections are built on higher assumed average growth rates over much longer time horizons, and their residual emissions are higher than IEA’s SDS and TERI-Shell projections. CEEW’s 2070 net-zero scenario has high residual emissions despite the lowest primary energy demand, relative to the other studies. TERI-Shell has the lowest assumed growth rate and the lowest residual emissions, despite the highest primary energy demand.

What can we infer from this graph?

This graph provides the projected scales of decarbonisation (without accounting for carbon dioxide removal) at the respective growth rates. The final energy demand represents the extent to which demand growth and demand reduction strategies could contribute to emission reductions. These projections also show that significant decarbonisation is feasible under the growth rates, low-carbon policies, and technological-feasibility assumptions of each of these models.
FINAL ENERGY DEMAND VS. PER CAPITA GDP,
BY SECTOR

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Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

What can we observe from this graph?

This graph shows the current (2019) and projected ‘final energy demand’ along the vertical axis, and the corresponding projections of GDP growth rate along the horizontal axis. The points are classified by sectors, indicated in the 4 panels: Buildings, Industry, Transport, and Total. Unfortunately, only the IEA study presented the data in this form, and comparable data was not available from the other studies.

Building energy demand is relatively constant across IEA scenarios, and does not increase materially above current estimates, indicating successful demand side measures offsetting the increase in building stock and the swapping of newer energy sources for traditional biomass. Transport energy demand increases marginally. However, increases are seen in industrial and therefore total energy demand.

What can we infer from this graph?

According to the IEA (2021) study, industry and transport sectors are projected to contribute the most to increased energy demand, while the building sector is expected to contribute the least. In terms of policy design, this indicates a strong role for policies related to demand reduction and energy efficiency in the building sector, and a smaller projected scope for such policies in the industry, transport sector.

Data was unavailable in a comparable format across the other studies analysed, which limited our ability to corroborate these inferences. Since sectoral demand projections are crucial for designing the aforementioned policies, we recommend that results across future studies be similarly disaggregated.

INSTALLED ELECTRICITY CAPACITY (GW)
IN END-YEAR

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Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

What can we observe from this graph?

This graph shows the electricity capacity (in GWs) along the vertical axis, and study-scenario instances along the horizontal axis. The bar graphs indicate the total capacity in each scenario, and the colours within represent different generation technologies.

Installed electricity capacity increases from appr. 1500 GW in the 2040 scenarios to, and further to appr. 7000 GW in the 2070 scenarios. Much of the increase in capacity is driven by solar and wind capacity, with declines seen in coal capacity over time. We also observe that while all three studies concur regarding the key role of solar and wind, there is far less consensus regarding technologies which can viably provide flexible or firm sources of low-carbon power to balance out the intermittency of renewable energy. While IEA highlights the role of batteries, TERI-Shell is hopeful of hydrogen-based technologies, and CEEW cautiously considers roles for carbon capture and storage, or hydrogen across various scenarios.

What can we infer from this graph?

The primary technologies for decarbonisation of the energy sector are expected to be solar and wind, their shares increasing substantially with time. Uncertainties in the projections increase with time, and arise primarily from the unknown technical and economic viability of carbon capture and storage (CCS) and hydrogen-based technologies. Consensus across the three studies indicates a strong role for policies to strengthen the solar and wind technologies and related manufacturing, while addressing related issues of justice and equity as employment and revenue sources shift. Uncertainty regarding emerging technologies indicates a more nuanced role – hedging risks and leveraging opportunities – across policies related to innovation, research, development, and even possibly large-scale deployment.
ELECTRICITY GENERATION BY FUEL SOURCE
IN END-YEAR

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Scelerisque ullamcorper. Metus pede lorem erat magnis quis morbi sagittis auctor vitae dui, id dolor. Hac elementum ridiculus justo pulvinar maecenas pulvinar. Nibh ad faucibus, porta molestie iaculis facilisi at nec fringilla proin quam volutpat congue amet platea suspendisse, conubia parturient.

What can we observe from this graph?

This graph highlights the total electricity generation in the projected end year for each scenario, stacked by the generation source.

In IEA’s 2040 scenarios, electricity generation ranges from 2,980 TWh to 3,146 TWh, and in TERI-Shell’s 2050 scenario, 8800 TWh (less than 6000 TWh in 2040, not shown). about twice as high as the IEA scenarios. Interestingly, although about half the generation in the IEA scenarios comes from wind and solar, these two constitute nearly 90% of total generation in the TERI-Shell scenario. . Nuclear appears to play a larger role in IEA’s 2040 scenarios than in TERI-Shell’s 2050. Unfortunately, comparable values were not available in the CEEW study.

What can we infer from this graph?

We make two broad inferences from this graph – one based on the total electricity demand, and the other on the technology portfolio contributing to the generation. Although growth rates are similar between the two studies, total electricity demand seems to differ widely. This is most likely due to stronger assumptions of electrification of end uses in the TERI-Shell scenarios, whereas demand reduction strategies play an important role in the IEA scenarios, indicating the extent of possible contributions of various decarbonisation strategies in reducing demand. The differences in technology portfolios across the two studies is largely influenced by the underlying cost projections of these technologies; again we see solar and wind playing the largest roles, with little consensus on the remaining technologies.
FUTURE DIRECTIONS FOR CLIMATE POLICY MODELLING
FUTURE DIRECTIONS FOR CLIMATE POLICY MODELLING

Despite their insights about the magnitude of transformations required, models say little about whether the transformations are actually feasible. Transformations of technological systems also need socio-economic reorganisations, involving decisions across a wide range of actors with diverging interests, resources and capabilities, and their interactions. Similarly, innovation processes are characterised by complex, hard-to-predict, emergent non-linear dynamics. Finally, mainstream economic systems have led to both increasing inequities and climate change, with feedback effects between them. However, such characteristics are rarely accounted for in mainstream emissions-economy or energy systems models, which present aggregate, simple representations of governance and economic systems.

Given these challenges, and building upon our analysis, we suggest two broad avenues for improvements to modelling going forward.

In the short term, there is a need for modelling exercises to be more transparent about modelling structures and input assumptions. The role of uncertainties – in technology costs, economic structures, resource constraints, among others – should be discussed more robustly. Modelling studies should undergo rigorous review and should clearly explain how their outputs may best be considered. This will also require a more collaborative engagement among the modelling community.

In the longer term, the underlying models themselves should be upgraded to reflect circumstances that are unique to developing country contexts, including the role of the informal economy. Additionally, they should be able to reflect equity and distributional impacts, for instance through integration with micro- and geospatial models. This will help better inform a just transition and will yield lessons for subnational policymaking.