Climate Policy

Persistent and bioaccumulative toxins like mercury pose unique challenges for environmental governance. The complexity of their movement through coupled social, technological, and natural systems can make it difficult to trace their path from emissions to wider impacts, as emissions and impacts can be separated both in time and space. This separation can make it difficult to assess whether different management and policy proposals will effectively reduce negative impacts. Focusing primarily on mercury, this dissertation explores how we can use interdisciplinary tools and approaches—from atmospheric modelling to community engaged research—to better trace this path from policy to human impacts, in support of environmental decision-making at multiple levels of governance. Combining simulation modelling, statistical, and qualitative approaches, it considers three aspects of the path from policy to impacts: how policy translates into emissions changes, how emissions changes translate into changes in environmental concentrations and fluxes, and finally how these environmental concentrations and fluxes impact the well-being of human communities. Taken together, the three studies highlight the need to take into account how social, technical, and natural systems interact, as well as the uncertainty, variability, and pluralism that exist within them, in our efforts to manage these toxic pollutants.

In the first study, I investigate the social and technical factors that affect the domestic implementation of a global environmental treaty (the United Nations Minamata Convention on Mercury) in major emitter countries in Asia, and their potential implications for emissions and global transport using a scenario-based modelling approach. I project that the benefit of avoided emissions and deposition over Asia are large, even when considering a scenario where the Convention allows large flexibility in implementation. These benefits are primarily driven by India, where even modest improvements in mercury capture are projected to result in large emissions decreases given future economic growth. I also find that climate change policies that promote the transitioning away from fossil may be as effective as strict end-of-pipe pollution control approaches for mitigating mercury emissions.

In the second study, driven by interests from community research partners in the Great Lakes region—an area vulnerable to mercury pollution—I use chemical transport modelling experiments to explore the conditions under which regional and global policy change can be statistically detected by wet deposition monitoring networks. I find that, given the magnitude of expected emissions decreases, detecting policy-related decreases in wet deposition in the Great Lakes region on the decadal scale will be challenging as the magnitude of noise—in particular interannual meteorological variability—can exceed this signal. These results suggest that these variabilities need to be better quantified and taken into account in both the design of policies for effectiveness and evaluation of policy compliance.

In the third study, I investigate the role that university-community partnerships can play in the long-term management of persistent pollutants through an empirical case study of the Superfund Research Program, which has recently required that grantees engage communities impacted by the hazardous substances that they study. I argue that community engagement in practice often supports a community building function—engagement operates as a space where knowledge about pollutants and shared identities of being impacted by these pollutants can be co-produced. Because persistent pollutants can implicate new people across time and space, often in ways that are difficult for those affected to discern, I suggest that supporting the constitution of what I call communities of concern is a critical way that university-based researchers can support the long-term management of persistent pollutants. I propose a conceptual framework to characterize and assess the functions that academic partners can perform in supporting the constitution of communities of concern around persistent pollutants. Further, I call attention to the institutional conditions that can enable this work to continue within academic contexts.

Sustaining rapid economic growth and satisfying increasing energy demand while limiting greenhouse gas (GHG) emissions is a central challenge in India. Proposed policy solutions should be evaluated according to their impacts on the energy system and the economy to identify efficient policies. I have developed an energy-economic model for India that provides a comprehensive foundation for analyzing energy technologies and policies. This novel model based on a general equilibrium approach simulates the Indian economy, with detailed inter-sectoral linkages, and facilitates an understanding of economy-wide impacts of policies. The model allows for analysis of tradeoffs among different technology and policy choices in terms of their costs and efficiency in GHG emissions reduction.

While comprehensive carbon pricing is arguably the most economically efficient measure for emissions reduction, political considerations often favor technology-specific choices. Support for renewable energy factors prominently in India’s climate change mitigation strategy. To study the impact of policies that promote renewable energy, the model represents renewable electricity in detail. Impact of incentives and scale factors are also incorporated in projecting renewables expansion. I simulate India’s Nationally Determined Contributions (NDCs) to the Paris Agreement and compare their effectiveness, benchmarking them against the theoretical least-cost alternative of broad-based carbon pricing. Specifically, India’s NDCs include targets on non-fossil electricity capacity expansion and CO2 emissions intensity of GDP (GoI 2015a). This work provides valuable quantitative insights on the impact of these policy measures, and fills a critical knowledge gap in the design and implementation of effective climate policies in India.

My findings suggest that compared to a reference case of no policy constraint, the average cost of reducing a tonne of CO2 is lowest in a scenario with an emissions intensity target implemented via CO2 pricing, and more than 43 times higher in the pure non-fossil electricity target scenaio. Further, emissions intensity targets result in a 6.3% drop in total electricity demand, as the cost of fossil fuel based electricity increases. As CO2 emitting electricity sources become more expensive, non-fossil sources - particularly solar and wind - increase in the mix. Enforcing non-fossil electricity capacity targets leads to an additional 15.6% drop in total electricity demand as average electricity prices increase to account for a higher share of costlier non-fossil electricity. Non-fossil electricity capacity targets also result in leakage of emissions to non-electricty energy sectors. The magnitude of differences among these results depends on wind and solar electricity costs. Cheaper costs of wind and solar power lead to lower welfare losses and electricity demand levels that are comparable across scenarios.

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