Technology, Institutions, and Organizational Performance in Public Health and Climate Change Contexts

Abstract

Understanding the drivers of organizational performance in public health and climate settings can enable policymakers to make better – less costly and more effective – decisions. Detailed, high-quality data on the performance of organizations is essential to inform sound public policy, and understanding how to lower CO₂ emissions at least cost is paramount to achieving global climate goals. This thesis explores the drivers of organization and system performance through three chapters that span the healthcare and steel industries, focusing on data quality and CO₂ emissions as the performance metrics of interest.

In the second chapter, I examine how ownership and institutional pressures influence data disclosure practices. This chapter focuses on healthcare providers during the COVID-19 pandemic and discusses how organizations may buffer themselves from reporting requirements to maintain both legitimacy and autonomy. Drawing from institutional theory, I use profit orientation to hypothesize how differing organizational priorities may mediate the conflicting institutional pressures to disclose data. In the face of conflicting pressure, I expect that for-profit organizations will respond more strongly to financial concerns, while their not-for-profit counterparts may be more receptive to reputational concerns. I also argue that not-for-profit organizations may be subject to higher levels of monitoring of non-financial outcomes, increasing the reputational risk of exposing potential symbolic compliance and increasing the likelihood of substantive compliance with reporting mandates. I focus on completeness and accuracy as measures of data quality and use applied microeconomic methods to find that for-profit and not-for-profit long-term care facilities are largely compliant with reporting requirements but that for-profit facilities submit less accurate data than their not-for-profit counterparts. These results suggest that organizational objectives may influence the quality of self-reported data and underscore the importance of designing incentives that align with organizational priorities to promote both completeness and accuracy, thereby reducing the risk of symbolic compliance with data disclosure mandates.

In the third chapter, I shift to industrial decarbonization and investigate how granular operational performance data can define the technological frontier for today’s iron and steelmaking technologies. Using plant-level and furnace-level data to examine the range of operational possibilities, I reveal significant variation in inputs and, thus, CO₂ emissions intensity for the global blast furnace fleet. I provide insights into the emissions envelope for blast furnaces and estimate that global CO₂ emissions could have been reduced by 2.8% – and up to 9.8% – if each furnace operated at its historical or national minimum CO₂ emissions intensity. These findings highlight the limitations of static emissions factors and emphasize the importance of using disaggregated data that captures the operational realities of industrial processes. Policies incentivizing existing technologies to operate at their minimum CO₂ emissions intensity may result in marginal but meaningful emissions reductions, especially in the near- to medium-term.

In the fourth chapter, I investigate how energy prices, electricity price volatility, and CO₂ prices influence the relative financial and CO₂ emissions performance of three different low-CO₂ iron and steelmaking technologies. Using material and energy balances, I construct different direct reduction (DR) plant archetypes using natural gas, hydrogen, or a combination to reduce iron ore. This chapter focuses on relative plant performance in terms of costs and CO₂ emissions in response to exogenous inputs, identifying patterns and drivers rather than projecting future trends or establishing correlations. By modeling a full range of operational scenarios, I highlight the emissions envelope these technologies may achieve and provide insight into how policy can help narrow this future. Although the reductant-flexible Hybrid-DR plant can minimize costs by switching between fuels in response to hourly or seasonal electricity price fluctuations, I find that it is never the least-cost option when electricity prices are fixed. Additionally, the choice of reductant preheater constrains the Hybrid-DR plant’s ability to decarbonize effectively, as the economic inefficiencies of burning hydrogen to preheat hydrogen result in a higher CO₂ footprint even when it is the least cost option. The results suggest that mechanisms that increase producer certainty – whether through hedging strategies to fix electricity prices at their mean or policies to increase CO₂ prices – reduce the cost competitiveness of the Hybrid-DR plant compared to less flexible deep decarbonization alternatives.