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High quality energy systems information is a crucial input to energy systems research, modeling, and decision-making. Unfortunately, actionable information about energy systems is often of limited availability, incomplete, or only accessible for a substantial fee or through a non-disclosure agreement. Recently, remotely sensed data (e.g., satellite imagery, aerial photography) have emerged as a potentially rich source of energy systems information. However, the use of these data is frequently challenged by its sheer volume and complexity, precluding manual analysis. Recent breakthroughs in machine learning have enabled automated and rapid extraction of useful information from remotely sensed data, facilitating large-scale acquisition of critical energy system variables. Here we present a systematic review of the literature on this emerging topic, providing an in-depth survey and review of papers published within the past two decades. We first taxonomize the existing literature into ten major areas, spanning the energy value chain. Within each research area, we distill and critically discuss major features that are relevant to energy researchers, including, for example, key challenges regarding the accessibility and reliability of the methods. We then synthesize our findings to identify limitations and trends in the literature as a whole, and discuss opportunities for innovation. These include the opportunity to extend the methods beyond electricity to broader energy systems and wider geographic areas; and the ability to expand the use of these methods in research and decision making as satellite data become cheaper and easier to access. We also find that there are persistent challenges: limited standardization and rigor of performance assessments; limited sharing of code, which would improve replicability; and a limited consideration of the ethics and privacy of data., Comment: This is only an Arxived version. For actual publication please refer to https://doi.org/10.1016/j.apenergy.2022.119876

The geographic dimension of wind power is an important facet of energy transition. We conduct a national-scale spatial analysis of onshore wind deployment, quantifying its cumulative footprint (46,915 km2) and uncovering dynamic land requirements and occupancy patterns with implications for emerging electricity systems that rely on high wind penetrations. We reveal that national declines (68%) in capacity density are driven by evolution of turbine technology and regional deployment patterns. However, counter to a prevailing perception that land area requirements are increasing, we show that concurrent performance gains have stabilized energy production on a per-footprint basis. Surprisingly, turbines are commonly embedded within the built environment, with 90% located within 2 km of a structure. Moreover, evidence of regional clustering of wind plants highlight additional growth risks. Continued development on disturbed land, such as cropland — which have supported a 50.5% of historical deployment — could facilitate multiple uses while potentially alleviating ecological concerns.

The expansion of wind power poses distinct and varied geographic challenges to a sustainable energy transition. However, current knowledge of its land use impacts and synergies is limited by reliance on static characterizations that overlook the role of turbine technology and plant design in mediating interactions with the environment. Here, we investigate how wind technology development and innovation have shaped landscape interactions with social and ecological systems within the United States and contribute to evolving land area requirements. This work assesses trends in key land use facets of wind power using a holistic set of metrics to establish an evidence base that researchers, technology designers, land use managers, and policymakers can use in envisioning how future wind-intensive energy systems may be jointly optimized for clean energy, social, and environmental objectives. Since 2000, we find dynamic land occupancy patterns and regional trends that are driven by advancing technology and geographic factors. Though most historical U.S. wind deployment has been confined to the temperate grassland biome in the nation’s interior, regional expansion has implicated diverse land use and cover types. A large percentage of the typical wind plant footprint (∼96% to \,$?> > 99%) is not directly impacted by permanent physical infrastructure, allowing for multiple uses in the spaces between turbines. Surprisingly, turbines are commonly close to built structures. Moreover, rangeland and cropland have supported 93.4% of deployment, highlighting potential synergies with agricultural lands. Despite broadly decreasing capacity densities, offsetting technology improvements have stabilized power densities. Land use intensity, defined as the ratio of direct land usage to lifetime power generation of wind facilities, has also trended downwards. Although continued deployment on disturbed lands, and in close proximity to existing wind facilities and other infrastructure, could minimize the extent of impacts, ambitious decarbonization trajectories may predispose particular biomes to cumulative effects and risks from regional wind power saturation. Increased land-use and sustainability feedback in technology and plant design will be critical to sustainable management of wind power.

Children from disadvantaged backgrounds often experience high levels of traumatic stress, however, little is known about their experiences and the responses of their teachers following disasters. The aim of this study was to examine, from the perspective of teachers, the impact of a critical community-wide traumatic event on student and staff wellbeing, and student learning and teaching practices at a specialist school for disadvantaged and displaced youth in Australia. Eight school staff were interviewed, including administrative, teaching, and support personnel, with their responses interpreted using Interpretative Phenomenological Analysis. Results focused largely on the impact of the event and the resultant relocation of the school on staff and student health, reduced opportunities for learning, changes to teaching and student engagement, and the strengths and limitations of the trauma-informed approach of the school. Implications for teacher education and school trauma-informed models are discussed.

Mounting interest in ambitious clean energy goals is exposing critical gaps in our understanding of onshore wind power potential. Conventional approaches to evaluating wind power technical potential at the national scale rely on coarse geographic representations of land area requirements for wind power. These methods overlook sizable spatial variation in real-world capacity densities (i.e., nameplate power capacity per unit area) and assume that potential installation densities are uniform across space. Here, we propose a data-driven approach to overcome persistent challenges in characterizing localized deployment potentials over broad extents. We use machine learning to develop predictive relationships between observed capacity densities and geospatial variables. The model is validated against a comprehensive data set of United States (U.S.) wind facilities and subjected to interrogation techniques to reveal that key explanatory features behind geographic variation of capacity density are related to wind resource as well as urban accessibility and forest cover. We demonstrate application of the model by producing a high-resolution (2 km × 2 km) national map of capacity density for use in technical potential assessments for the United States. Our findings illustrate that this methodology offers meaningful improvements in the characterization of spatial aspects of technical potential, which are increasingly critical to draw reliable and actionable planning and research insights from renewable energy scenarios.

As clean energy ambitions have expanded, critically evaluating renewable energy supply has become increasingly important to the energy research community and stakeholders. This study examines the onshore wind resource potential for the conterminous United States and its sensitivity to siting constraints and turbine technology innovation. We compile localized regulatory information and use high-resolution data to present multiple siting regimes covering relatively constrained to unconstrained potentials. Our efforts reveal high sensitivity to these variables and sizable uncertainty in the overall wind energy resource potential. Specifically, we find that siting constraints may shift the total capacity available to commercial wind energy by 2.3–15.1 TW. Furthermore, our results illustrate that technology advancement could require larger setbacks from buildings and infrastructure, reducing the total available capacity potential by 20% relative to estimates using current technology, but that this reduction is largely offset by increased generation such that the net effect on generation is 1%. The observed sensitivity to and uncertainty resulting from the variables we analyze suggest there is value in continued study and development of increasingly sophisticated approaches to characterizing wind resource potential.

Global threats to freshwater resources are prompting widespread concern about their management and implications for well-being. In recent decades, hydrologic ecosystem services (HES) have emerged as an innovative concept to evaluate freshwater resources, providing opportunity for researchers to engage in decision-relevant science. We conducted a systematic review of studies published within the last decade, documenting approaches for mapping and quantifying HES and classifying the decision context. To gauge the relevance of HES science, we evaluated 49 case studies using multiple criteria for credibility, legitimacy, and saliency. We found compelling evidence that much of the variability in the quantification of HES can be explained by research motivations and scoping, reflecting the decision-oriented framing of the ecosystem services concept. Our review highlights key knowledge gaps in the state of the science including the need to articulate beneficiaries and to make connections to policy and ma...

“Environmental flows” is a research discipline that emphasizes freshwater allocation in rivers to sustain desired ecological conditions and human well-being. The basis for environmental flow requirements has traditionally relied on hydrological and ecological data. Contemporary methods focus on detailed hydro-ecological relationships within river ecosystems; however, there is currently no structured approach to systematically incorporate socially relevant data into the environmental flows discipline. To address this limitation we developed a flexible framework that applies a social-ecological systems approach to account for multiple flow-related objectives that reflect both biophysical sustainability and societal preferences. First, we conceptualize the freshwater social-ecological system as a hierarchy of human and environmental domains. Then, we recommend stepwise procedures to assess flow-related vulnerabilities of important system attributes, address their feedbacks, and translate these assessments to a common classification for comparative analyses that guide holistic flow management decisions.

The reactions of [Pt2(μ-S)2(PPh3)4] towards a range of palladium(II) complexes containing organometallic ligands (cyclopalladated N-donor ligands, η3-allyl, phenyl) have been explored, leading to the formation of a series of cationic, trinuclear sulfido-bridged aggregates containing {Pt2PdS2} cores. [Pt2(μ-S)2(PPh3)4] also reacts with the platinum(II) hydride complex trans-[PtHCl(PPh3)2] giving the adduct [Pt2(μ-S)2(PPh3)4PtH(PPh3)]+. X-ray crystal structure determinations on the complexes [Pt2(μ-S)2(PPh3)4PdPh(PPh3)]PF6 and [Pt2(μ-S)2(PPh3)4PtH(PPh3)]PF6 are reported, and show the expected bis μ3-sulfido aggregates with three square-planar metal centres.

In this chapter, we examine how climate change will likely affect areas of the Great Northern Landscape Conservation Cooperative (Great Northern LCC), but rather than using a fine-filter approach that focuses on a particular species, as has been done in many of the other chapters (e.g., chaps. 9, 10, and 12), we have applied a coarse-filter approach with which we consider our conservation targets to be broader levels of biodiversity. A coarse-filter approach focuses not on an individual species but, rather, on the community that supports a species (Noss 1987) or even on the physical environments as “arenas” of biological activity (Hunter, Jacobson, and Webb 1988). More recently, coarse-filter conservation has been interpreted in a climate change context, in which coarse-filter strategies seek to conserve sites that are minimally affected by climate change (Tingley, Darling, and Wilcove 2014).

Key to understanding the implications of climate and land use change on biodiversity and natural resources is to incorporate the physiographic platform on which changes in ecological systems unfold. Here, we advance a detailed classification and high-resolution map of physiography, built by combining landforms and lithology (soil parent material) at multiple spatial scales. We used only relatively static abiotic variables (i.e., excluded climatic and biotic factors) to prevent confounding current ecological patterns and processes with enduring landscape features, and to make the physiographic classification more interpretable for climate adaptation planning. We generated novel spatial databases for 15 landform and 269 physiographic types across the conterminous United States of America. We examined their potential use by natural resource managers by placing them within a contemporary climate change adaptation framework, and found our physiographic databases could play key roles in four of seven general adaptation strategies. We also calculated correlations with common empirical measures of biodiversity to examine the degree to which the physiographic setting explains various aspects of current biodiversity patterns. Additionally, we evaluated the relationship between landform diversity and measures of climate change to explore how changes may unfold across a geophysical template. We found landform types are particularly sensitive to spatial scale, and so we recommend using high-resolution datasets when possible, as well as generating metrics using multiple neighborhood sizes to both minimize and characterize potential unknown biases. We illustrate how our work can inform current strategies for climate change adaptation. The analytical framework and classification of landforms and parent material are easily extendable to other geographies and may be used to promote climate change adaptation in other settings.