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Given the increasing occurrence of high-impact low-probability (HILP) contingencies in existing power systems, strengthening the resilience of these systems has become of paramount importance. Enhancing the resilience of power systems is not solely a technical issue but also a socio-economic and policy concern. Therefore, improving the performance of power systems greatly relies on the guidance provided by energy policies. While the decarbonization response, supported by these policies to mitigate climate change, influences the adoption of energy technologies, its impact on the resilience of the system remains uncertain. To uncover the interactions between technologies, policies, and economics concerning power systems resilience, this study focuses on constructing resilience-oriented networked microgrid systems. It develops a two-stage stochastic programming model by integrating a method for selecting power outage scenarios identified by users, in the presence of emissions policies. The results confirm the contributions of integrated systems in enhancing resilience, but they also reveal that low-carbon emissions policies play an inhibiting role by increasing the financial costs associated with resilience planning and operations. Nevertheless, a 30% emissions reduction threshold can still be achieved from the integrated network, facilitating the dual benefits of maximizing emissions reduction and minimizing the burden of emissions taxes. The study's contributions are threefold: firstly, it incorporates techno-economic incentives and regulations simultaneously; secondly, it quantifies the unintended consequences of policies on resilience; and thirdly, it provides constructive guidance for future energy policymaking, particularly in maintaining system resilience. 

Climate change mitigation measures are often projected to reduce anthropogenic carbon dioxide concentrations. Yet, it seems there is ample evidence suggesting that we have a limited understanding of the impacts of these measures and their combinations. For example, the Inflation Reduction Act (IRA) enacted in the U.S. in 2022 contains significant provisions, such as the electric vehicle (EV) tax credits, to reduce CO2 emissions. However, the impact of such provisions is not fully understood across the U.S., particularly in the context of their interactions with other macroeconomic systems. In this paper, we employ an Integrated Assessment Model (IAM), the Global Change Assessment Model (GCAM), to estimate the future CO2 emissions in the U.S. GCAM is equipped to comprehensively characterize the interactions among different systems, e.g., energy, water, land use, and transportation. Thus, the use of GCAM-USA that has U.S. state-level resolution allows the projection of the impacts and consequences of major provisions in the IRA, i.e., EV tax credits and clean energy incentives. To compare the performance of these incentives and credits, a policy effectiveness index is used to evaluate the strength of the relationship between the achieved total CO2 emissions and the overarching emission reduction costs. Our results show that the EV tax credits as stipulated in the IRA can only marginally reduce carbon emissions across the U.S. In fact, it may lead to negative impacts in some states. However, simultaneously combining the incentives and tax credits improves performance and outcomes better than the sum of the individual effects of the policies. This demonstrates that the whole is greater than the sum of the parts in this decarbonization approach. Our findings provide insights for policymakers with a recommendation that combining EV tax credits with clean energy incentives magnifies the intended impact of emission reduction. 

There is a general consensus about the performance of photovoltaic plants particularly on their efficiency benefits. However, it is not clear to what extents such efficiencies correlate with the efficient frontier of performance when such plants are evaluated under varying geospatial environmental factors and over intertemporal periods. This study carries out a performance benchmarking exercise on photovoltaic power stations. It employs a non-parametric modelling technique in the form of Data Envelopment Analysis to evaluate the performance over time of three photovoltaic power plants within an electric utility. It presents an optimization modelling approach for performance benchmarking over time under situations where there are a limited number of decision-making units. Specifically, the study introduces a multi-period modeling approach which employs real data and captures actual variabilities in environmental factors that influence the output of photovoltaic power plants over time. In comparing the deterministic approach often employed in the extant literature with the multi-period model, the results reveal that the deterministic model overestimates the efficiency values and underestimates the output targets relative to a unit operating on the efficient frontier. The study further employs non-parametric statistical techniques and post-hoc tests to validate the findings. 

This study employs a Data Envelopment Analysis (DEA) modeling technique to investigate the efficiency and productivity of renewable energy (RE) adoption across technologically diverse electricity-generating utilities. By employing metrics capturing policy effects, the study evaluates the RE adoption efficiency and productivity using a dynamic DEA model and the Malmquist DEA technique. First, the findings reveal that RE adoption is not significantly different across regional electricity markets. Second, the study revealed that RE adoption increased over the last three years. The total mean productivity change over the entire study period showed a mean improvement of 4.8%. 

While extant research explores the impact of Electric Vehicle (EV) incentives on EV market shares, less is known about how such policies and other socioeconomic factors interact that ultimately affect the goal of transportation emission reductions. The study summarized herein employed a sample of 510 state-year CO 2 emissions data sets in the transportation sector spanning a decade (2010-2019) in a multiple linear regression model. Going beyond earlier studies, we find that, while a higher number of EV incentives would significantly contribute to transportation emission reductions, this effect could be dampened by population growth. In addition, we find that, while higher electricity prices may weaken the effectiveness of EV incentives, a high count of EV incentives is more effective in reducing CO 2 emissions than a low count of EV incentives when the electricity price is low. This finding implies that having multiple EV incentives can be effective in reducing transportation carbon emissions even in the face of rising prices of electricity. The study also examines the effectiveness of promoting the density of charging stations and alternative fuel incentives in advancing carbon emission reductions. 

Dam removal is gaining both support and resistance in different communities and political circles in the Pacific Northwest of the United States; given its sensitive environmental and economic consequences. The Columbia River Basin (CRB) offers a unique opportunity to examine to what extent the replacement of hydroelectric dams affects reliability and adequacy of the power system given long-standing proposals to remove the four Lower Snake River dams to improve the survival of the endangered salmon species. Key results show that replacing the four dams leads to an inadequate energy supply necessitating the need for more capacity to satisfy requirements. Although the four dams have higher nameplate capacity, they provide a much lower effective capacity. Thus, the debate about removing dams should be an opportunity for CRB managers to consider investment options in new ecosystem services and energy solutions that maintain adequate performance. 

This article investigates the relationship between firms’ carbon intensity, carbon management practices, and their financial performance. The extant literature on firms’ financial performance and their environmental performance has mostly considered a single dimension of firms’ environmental performance leading to restricted, and often, mixed outcomes. With panel data collected on financial statements and climate change related activities from 136 corporate firms in the U.S. between 2011 and 2018, this article integrates a process dimension based on an environmental management score with an outcome dimension represented by firms’ carbon emissions intensity. A regression model is employed to investigate the relationships between corporate environmental performance and corporate financial performance. We find evidence of a nonlinear relationship between corporate firms’ environmental performance and financial performance across both high and low-carbon intensive sectors. Specifically, we find that for firms in the high-carbon intensive sector, a U-shaped relationship exists between firms’ corporate environmental performance outcome dimension and their financial performance while for the low-carbon intensive sector, the converse is the case. The results show that the interaction between the outcome dimension of environmental performance and financial performance is moderated by the process dimension of environmental performance for firms in the low- and high-carbon intensive sectors.