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Evaluating India’s climate targets: the implications of economy-wide and sector specific policies

MIT Joint Program research assistant Arun Singh, a master’s degree student in the Institute for Data, Systems and Society’s Technology and Policy Program (TPP), has analyzed climate policy options for India by building and applying a model of the Indian economy with detailed representation of the electricity sector.

Developed with his advisors, MIT Sloan School of Management Assistant Professor Valerie Karplus and MIT Joint Program Principal Research Scientist Niven Winchester, the model enables researchers to gauge the cost-effectiveness and efficiency of different technology and policy choices designed to transition India to a low-carbon energy system. Singh used the model to assess the economic, energy, and emissions impacts of implementing India’s Nationally Determined Contribution (NDC) to the Paris Agreement — which aims to reduce carbon dioxide emissions intensity by 33 to 35 percent from 2005 levels and increase non-fossil based electric power to about 40 percent of installed capacity by 2030.

Singh determined that compared to a no-policy scenario in 2030, the average cost per unit of emissions reduced is lowest under a carbon dioxide (CO2) pricing regime. Adding a renewable portfolio standard (RPS) to simulate electricity capacity targets increases the cost by more than ten times. Projected electricity demand in 2030 decreases by 8% under the CO2 price, while introducing an RPS further suppresses electricity demand. Importantly, a reduction in the costs of wind and solar power induced by favorable policies may result in cost convergence across instruments, paving the way for more aggressive decarbonization policies in the future.

Over the past three years, J-WAFS seed funding has catalyzed a diverse portfolio of MIT research relevant to water and food—spanning fundamental science, engineering and technology, supply chains, big data, business models, development efforts, economics, urban design and infrastructure, and more.  This fall, just as we were distributing our fourth call for proposals, our very first round of seed grant projects—beginning in 2015—came to a close.  As these inaugural projects end and others begin, we'd like to take a moment to highlight and celebrate the achievements the J-WAFS-funded MIT fac

J-WAFS' inaugural seed grant projects come to a close

When global oil prices declined dramatically in 2014 and 2015, leading energy analysts expected that oil production in the United States—consisting primarily of “tight oil” extracted from rock formations by means of massive hydraulic fracturing—would likewise decrease due to relatively high production costs. Despite prospects for a negative return on investment, however, U.S. tight oil production continued almost unabated. Perplexed by this development, a team of researchers sought to better understand the relationship between oil prices and production volumes.

Charting the future of oil and gas in a decarbonizing world
Toward a consistent modeling framework to assess multi-sectoral climate impacts

How might climate change affect the acidification of the world’s oceans or air quality in China and india in the coming decades, and what climate policies could be effective in minimizing such impacts? To answer such questions, decision-makers routinely rely on science-based projections of physical and economic impacts of climate change on selected regions and economic sectors. But the projections they obtain may not be as reliable or useful as they appear: today’s gold standard for climate impact assessments—model intercomparison projects (MIPs)—fall short in many ways.

MIPs, which use detailed climate and impact models to assess environmental and economic effects of different climate-change scenarios, require international coordination among multiple research groups, and use a rigid modeling structure with a fixed set of climate-change scenarios. This highly dispersed, inflexible modeling approach makes it difficult to produce consistent and timely climate impact assessments under changing economic and environmental policies. In addition, MIPs focus on a single economic sector at a time and do not represent feedbacks among sectors, thus degrading their ability to produce accurate projections of climate impacts and meaningful comparisons of those impacts across multiple sectors.

To overcome these drawbacks, researchers at the MIT Joint Program on the Science and Policy of Global Change propose an alternative method that only a handful of other groups are now pursuing: a self-consistent modeling framework to assess climate impacts across multiple regions and sectors. They describe the Joint Program’s implementation of this method and provide illustrative examples in a new study published in Nature Communications.

Tight Oil Market Dynamics: Benchmarks, Breakeven Points, and Inelasticities

When global oil prices declined dramatically in 2014 and 2015, leading energy analysts expected that oil production in the United States—consisting primarily of “tight oil” extracted from rock formations by means of massive hydraulic fracturing—would likewise decrease due to relatively high production costs. Despite prospects for a negative return on investment, however, U.S. tight oil production continued almost unabated. Perplexed by this development, a team of researchers sought to better understand the relationship between oil prices and production volumes. In particular, they aimed to pinpoint those factors that determine the “breakeven” points of tight oil production projects—essentially the oil price points at which revenue from sales equals the cost of production.

Though energy industry analysts have widely used breakeven costs to predict how oil producers will respond to changing market conditions and to assess the economic viability of proposed oil and gas development projects, they have routinely defined them imprecisely and inconsistently. This has resulted in predictions of limited utility and reliability. To enable more robust predictions, the researchers, who work for Schlumberger-Doll Research, the MIT Joint Program on the Science and Policy of Global Change, the Atlantic Council, the King Abdullah Petroleum Studies and Research Center, and the Columbia University School of International and Public Affairs, have developed a systematic method to understand the costs of oil production and how they change with time and circumstances. Applying this method, they have proposed a set of standard definitions for breakeven points at different stages of the oil production cycle.

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