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Returning monarch butterflies (Danaus plexippus) to sustainable levels of abundance will require an array of contributors to protect and restore habitat over broad areas. Due to the diversity and scale of land managed by electric power companies across the monarch range, plus an additional 32 million hectares needed for new solar arrays by 2050 to meet renewable energy goals, the industry may have potential to contribute to monarch conservation. However, it is challenging to clearly understand an individual company's potential for monarch conservation because of the scale and distribution of their specific land assets (ranging from 4,800 to 240,000 hectares in this study alone), the complexity of monarch science, and the lack of a science-based approach for evaluating large land assets for monarch habitat. With monarchs potentially being protected under the United States Endangered Species Act in the future and thereby limiting land management approaches, there is interest from electric power companies to understand how their lands relate to monarchs. In collaboration with companies, we developed a GIS-based model to identify company landholdings that contain high-quality monarch habitat and applied the model to specific landholdings of eight power companies in the United States. We then facilitated discussions with company teams to balance conservation goals, corporate risk, and social opinion. This paper describes non-confidential results for developing a national GIS-based monarch habitat model and applying it to electric power companies who are considering monarch conservation while simultaneously transitioning to a new clean energy future. The model and applied experience may be useful for other organizations working across large landscapes to manage monarchs. [ABSTRACT FROM AUTHOR]

While the US electric power sector transitioning towards a low-carbon system, the escalated adoption of renewable energy has paradoxically increased water consumption. Consequently, it is imperative to engineer a power generation matrix that mitigates CO 2 emissions and minimizes water utilization. The primary endeavor should be to meticulously identify the variables affecting water consumption changes, especially highlighting and addressing the inherent tension between water conservation and carbon reduction. Employing temporal and spatial Logarithmic Mean Divisia Index methodologies, this study meticulously investigated the factors driving water consumption changes across state-level electricity production sectors in the US from 2010 to 2018. The analysis revealed that the generation mix structure was a predominant factor in escalating water consumption, exerting a more substantial influence than total power generation. Moreover, while the water consumption coefficient initially mitigates the impact of other factors, its effectiveness diminishes over time. Utilizing spatial Logarithmic Mean Divisia Index techniques, this paper further delineated state-level disparities, illuminating the heterogeneous nature of water consumption across regions. Projections up to 2050 suggest that water consumption reductions could be realized even amidst a highly intense scenario. The study's robustness and reliability were further augmented by employing an adaptive Logarithmic Mean Divisia Index. • Temporal-spatial LMDI reveals significant shifts in the US power sector water consumption. • Water consumption coefficient, fuel structure, and generation identified as key drivers. • Scenario analysis forecasts significant water savings potential by 2050. • Findings highlight the necessity for integrated water-energy transition policies. • Study proposes targeted strategies for a sustainable energy-water nexus. [ABSTRACT FROM AUTHOR]

Investing in renewable energy is of utmost importance, especially in the context of addressing climate change. However, while numerous studies have explored the role of renewable energy in achieving carbon neutrality, there is a noticeable gap in the literature regarding how uncertainties affect the impact of renewable energy on electric power CO2. To bridge this gap, the present study employs recently developed nonparametric techniques, namely multivariate quantile-on-quantile regression (MQQR) and time-varying quantile causality (TVQC), to investigate the relationship between renewable energy and electric power CO2 in the presence of uncertainties. The study utilizes monthly data spanning from January 1988 to May 2023. The bivariate results reveal that renewable energy and low uncertainties contribute to improving ecological quality by reducing electric power CO2. Furthermore, the multivariate quantile-on-quantile regression results highlight the substantial influence of low uncertainties on the impact of renewable energy in lowering electric power CO2. Additionally, the TVQC analysis demonstrates that both renewable energy and uncertainties possess predictive power regarding electric power sector CO2. The study discoveries showcase that the influence of renewable energy on electric power CO2 is subject to external moderation. The suggested policy framework in this study is structured to assist the United States in accomplishing the goals outlined in SDG 7. [ABSTRACT FROM AUTHOR]