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Does geography matter? A study of technological learning and innovation in the U.S. Solar photovoltaic balance-of-systems (PV BOS) industry
Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. Different learning channels (i.e., learning from upstream actors, local network, global codified knowledge, etc.) coexist in firms’ technological innovation processes. Although firms are exposed to globalization and convenient communication and transportation tools, researchers still argue about the role of geography in technology innovation. Geographic proximity can facilitate knowledge spillover and network-building. Localized learning and the significance of geography indicate the importance of local knowledge in innovative activities. Local knowledge tends to be more closely aligned with local firms’ technological demand and easier to understand and absorb, because they are created and applied in the same context. However, following Schumpeter, innovation researchers believe that diverse knowledge, especially knowledge from external industries and geographically distant places, is a valuable source for innovation, especially disruptive innovation. Therefore, firms must balance different learning channels, especially between localized learning and global learning. However, it is still unclear about the extent to which localized learning is important for technology innovations and which factors influence the degree of localized learning. Meanwhile, most studies in the area of localized learning usually take no account of the international dimension and the interdependent of knowledge at different geographic scales. To address this research gap, this paper empirically investigated firms’ learning and innovation processes in relation to the geography. The aim is to answer the following three questions: 1) how does geographic distance influence knowledge spillover; 2) how technological characteristics influence knowledge acquisition choices as regards local vs. non-local knowledge? and 3) how do local and non-local knowledge affect the quality of innovation? The subject of our empirical inquiry is photovoltaic balance-of-systems (PV BOS) components. This choice is primarily motivated by two factors: (1) large-scale deployment of distributed solar PV technologies is widely considered to be an important piece in addressing the environmental impacts of the electricity sector, making PV an important technology to study; furthermore, there is a general consensus that the biggest barrier for a wider deployment of PV is the high price of non-module components, and (2) technological aspects of non-module components of PV systems make local conditions (climate, regulation, learning networks, etc.) salient, thus affording a rich empirical setting to explore the geography-innovation-economy interactions at the local level. We find that the localization effect of technological learning in the solar PV BOS industry is at state level, the number of citations-based linkages decreases with distance, and we also find that international knowledge significantly contributes to the quality of patents, which suggests that state border might be a barrier of current knowledge spillover, and only focusing on local innovation networks is not sufficient for promoting PV BOS innovation.
The gut mycobiota of rural and urban individuals is shaped by geography
Understanding the structure and drivers of gut microbiota remains a major ecological endeavour. Recent studies have shown that several factors including diet, lifestyle and geography may substantially shape the human gut microbiota. However, most of these studies have focused on the more abundant bacterial component and comparatively less is known regarding fungi in the human gut. This knowledge deficit is especially true for rural and urban African populations. Therefore, we assessed the structure and drivers of rural and urban gut mycobiota. Results Our participants (n=100) were balanced by geography and sex. The mycobiota of these geographically separated cohorts was characterized using amplicon analysis of the Internal Transcribed Spacer (ITS) gene. We further assessed biomarker species specific to rural and urban cohorts. In addition to phyla which have been shown to be ubiquitous constituents of gut microbiota, Pichia were key constituents of the mycobiota. We found that several factors including geographic location and lifestyle factors such as the smoking status were major drivers of gut mycobiota. Linear discriminant and the linear discriminant analysis effect size analysis revealed several distinct urban and rural biomarkers. Conclusions Together, our analysis reveals distinct community structure in urban and rural South African individuals. Geography and lifestyle related factors were shown to be key drivers of rural and urban gut microbiota.
The Geography of Green Power (2017 Data)
Green power refers to the voluntary purchase of renewable electricity by retail electricity customers, as opposed to renewable energy procurement to comply with mandates imposed by law or regulation. In 2017, over five million customers procured about 113 million megawatt-hours (MWh) of green power in the United States, which represents about 27% of all U.S. renewable energy sales, excluding large hydropower. In this fact sheet, we use available data to illustrate the geography of green power demand (in terms of number of customers) and supply (in terms of MWh of generation) by state.
Influence of graphite geography on the yield of mechanically exfoliated few-layer graphene
Some of graphene's exciting properties that inspired countless studies over the last two decades are exclusive to defect-free, few-layer graphene (FLG, ≤4 layers) produced by mechanical exfoliation. Despite graphite exfoliation having been thoroughly studied, the influence of the graphite source on the successful exfoliation of FLG remains unexplored. Here, in this work, we examine the physicochemical properties and exfoliation performance of various graphite types (e.g., natural crystalline flake, natural vein, and synthetic) from around the globe (e.g., Alaska, Tanzania, China, etc.). We first established a normalization method for the facile comparison of FLG (Φ) yields between separately exfoliated graphites. Using this approach, we find that the relative yield of FLG ranges from 0% to 22% across graphite types, with regular natural crystalline flakes (NCF-R) performing the best overall (Φ NCF-R = 16% ± 5%). FLG yield was also found to depend on source geography, evidenced by Φ NCF-R ranging from 8% to 22% for NCF-R sourced from Alabama and Canada, respectively. We employed machine learning and Pearson correlation analysis to determine the graphite characteristics that govern FLG yield. Graphite source-dependent properties such as graphite surface area and mineral impurities, including aluminum and calcium, were found to be critically important. Our findings highlight the importance of the graphite source when trying to maximize the yield of mechanically exfoliated FLG and represent an important step toward producing higher quantities of defect-free FLG for graphene-based research.
Influence of Geography on Prostate Cancer Treatment
Several definitive treatment options are available for prostate cancer, but geographic access to those options is not uniform. We created maps illustrating provider practice patterns relation to patients and assessed the influence of distance to treatment receipt.
Review of environmental metrics used across multiple sectors and geographies to evaluate the effects of hydropower development
In this paper, as U.S. hydropower development continues to expand, a thorough inventory of potentially relevant environmental metrics will provide a comprehensive and transparent starting point for stakeholders seeking to evaluate the sustainability and environmental effects of this anticipated expansion. To address this need, we conducted a literature review to extract and analyze a swath of environmental metrics related to previous hydropower projects. Our examination of several U.S. Federal Energy Regulatory Commission licensing documents, several certification documents prepared by the Low Impact Hydropower Institute and the International Hydropower Association, and a set of peer-reviewed journal articles led to creation of a database of > 3000 current environmental metrics related to hydropower projects in 231 locations worldwide. Information recorded for each environmental metric was used to map the comprehensiveness and consistency of environmental measurements gathered across primary categories, dam life cycle stages, spatial scales and locations. We found that a majority of environmental metrics were related to water quantity and water quality, a large number of metrics were related to biota and biodiversity, and relatively few metrics were related to the categories of geomorphology, land cover, connectivity and fragmentation, and infrastructure and design. However, the categories of environmental metrics represented in each type of literature varied significantly. Forty-five subcategories of environmental metrics emerged from our analysis, thereby suggesting a potential envelope of environmental measurements that might be used to improve efficiency in evaluating the potential environmental effects of new U.S. hydropower projects.
The geographies, typologies, and trends of community-based organizations for solar energy in the United States
Community-based organizations (CBOs)1 play an important role in developing solar energy in low- and moderate-income (LMI) communities. This article shares the perspectives of CBO leaders in LMI communities, identifies and addresses solar information gaps, and provides recommendations State Energy Agencies and other government leaders can use to better involve CBOs in solar program initiatives. Using semi-structured interviews and focus groups with CBOs from across the United States (US) we develop typologies of CBO structure and function, and determine the primary motivations, challenges, opportunities, and communication barriers CBOs face. We explore the correlation between key typologies such as tenure, staff capacity, population served, organizational structure, and region with the organizational activities performed by solar-related CBOs. CBOs operate in disparate regional political and economic ecologies. Our findings suggest strategies for states to support their engagement in solar-related endeavors, particularly with respect to the dissemination of the Bipartisan Infrastructure Law, Inflation Reduction Act, and the EPA Greenhouse Gas Reduction Fund: Solar for All. Developing policies that encourage CBOs to enter the solar training and installation domains, alongside targeted grants and capacity-building initiatives can help maximize community benefits. Furthermore, states can additionally contribute to the positive trajectory and collaboration between state agencies and CBOs in advancing solar energy adoption by fostering a supportive environment.
The Geography of Employment Outcomes for Radiation Oncology Graduates in 2019
The geographic distribution of practicing radiation oncologists is of concern for multiple stakeholders within the field. Employment outcomes of graduating residents can affect that distribution, and they are of major concern to current residents. Data investigating employment outcomes of recent graduates are sparse. We aimed to analyze the employment outcomes of the radiation oncology residency class of 2019.
Landscape Age as a Major Control on the Geography of Soil Weathering
Global-scale models of rock-derived nutrient availability often assume that physical erosion drives soils toward an approximate “steady state” over geologic timescales. By definition, steady-state models do not represent landscape age—that is, the time elapsed since soil formation is initiated by major erosional or depositional events. We hypothesize that this steady-state assumption has large consequences on estimates of soil fertility because landscape age can mediate the retention of mobile elements in soil, particularly in low-relief landscapes and humid climates. We quantified the effect of landscape age on soil fertility by estimating Na retention in soils across the United States and explicitly resolving landscape age in regions that experienced significant deposition or glacial retreat after the Last Glacial Maximum (LGM). We then used a simple one-compartment model to simulate soil formation and weathering, comparing predictions that incorporated landscape age with those based on the steady-state assumption. We found that soils formed in LGM deposits in low-relief, humid settings generally retain 10 times more Na than soils formed outside of LGM deposits. Furthermore, the model that accounted for landscape age outperformed a steady-state model across the United States and increased globally averaged estimates of Na retention by 17%. These results reinforce the idea that landscape age is a major control on weathering and should not be ignored in simulations of nutrient cycling.
The role of geography in the complex diffusion of innovations
The urban–rural divide is increasing in modern societies calling for geographical extensions of social influence modelling. Improved understanding of innovation diffusion across locations and through social connections can provide us with new insights into the spread of information, technological progress and economic development. In this work, we analyze the spatial adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and uncover empirical features about the spatial adoption in social networks. During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million friendship ties of 3 million users. We find that the number of adopters as a function of town population follows a scaling law that reveals a strongly concentrated early adoption in large towns and a less concentrated late adoption. We also discover a strengthening distance decay of spread over the life-cycle indicating high fraction of distant diffusion in early stages but the dominance of local diffusion in late stages. The spreading process is modelled within the Bass diffusion framework that enables us to compare the differential equation version with an agent-based version of the model run on the empirical network. Although both model versions can capture the macro trend of adoption, they have limited capacity to describe the observed trends of urban scaling and distance decay. We find, however that incorporating adoption thresholds, defined by the fraction of social connections that adopt a technology before the individual adopts, improves the network model fit to the urban scaling of early adopters. Controlling for the threshold distribution enables us to eliminate the bias induced by local network structure on predicting local adoption peaks. Finally, we show that geographical features such as distance from the innovation origin and town size influence prediction of adoption peak at local scales in all model specifications.
Controlled-Environment Agriculture and the Geography of Food, Energy and Water Resilience in the United States
USDA guidelines call for an increase in fruit and vegetable consumption in U.S. households to promote healthier diets. Access to fresh fruits and vegetables are especially lacking in food deserts and food swamps (characterized by a prevalence of food that is highly processed and lacking in nutritional value). Adoption of the recommended healthy diet, which more than doubles the consumption of fruits and vegetables, would have a wide variety of health benefits and would reduce the land footprint of U.S diets. However, adopting a healthy diet would increase phosphate and nitrogen impacts associated with fertilizers and pesticides and would significantly increase freshwater consumption and energy use because of the resource intensity of field cultivation of fruits and vegetables. The majority of these crops are grown in just a few states, including California and Arizona, which are increasingly impacted by climate change. In addition, rural communities are rapidly becoming food deserts, while food produced in these areas is transported long distances to market. In recent years, highly intensified controlled environment (CE) agricultural systems, (i.e., vertical farming) have been developed to provide fresh food closer to consumers. Fruits and vegetables, many of which are amenable to CE culture, occupy a uniquely impactful segment of the food supply-chain, including their value in improving nutrition for vulnerable communities. The objective of this work is to elucidate the location dependency of the energy and water impacts of adoption of distributed CE farming for an important portion of the food system.
Coastal songbird condition on migratory stopover varies by geographical location and bird age
Abstract The period of migration can pose significant energetic challenges as birds attempt to reach their destinations. Suitable stopover habitat is, therefore, important to the success of migrating individuals, especially as they move along major migration corridors and geographic features, like coastlines. In this study, we used metrics of individual body condition (i.e. fat score, size-corrected body mass, and refueling rate) of fall migrants as they moved across the Gulf of Maine region, a complex coastal landscape. We investigated the extent to which these body condition indices varied by stopover site geography (island vs. mainland) and how these spatial patterns varied with species-specific characteristics such as migratory distance, foraging guild, and age. Geography was an important factor explaining variation in all 3 condition indices, and age explained additional variance in 2 of the 3. In general, individuals captured on islands exhibited significantly lower energetic condition than individuals on the mainland, and this pattern held true across all migratory strategies and foraging guilds. Immature individuals had, on average, lower energy reserves, with less fat and lower size-corrected mass than adults among all stopover sites. We also found that at all sites, size-corrected body mass, on average, significantly increased over the capture day, providing evidence that both island and mainland sites provided energetically beneficial stopover habitat. Our finding that birds offshore are in lower body condition at capture than those along the coast is suggestive that birds use mainland sites for longer stopover bouts than island sites, or that poorer condition birds reorient to land during over-water movements disproportionately use off-shore islands as their initial landing area. Decreases in either island or mainland stopover site availability or quality may affect individual fitness, with population-level consequences, but through different ways.
The impact of agricultural trade approaches on global economic modeling
Future socioeconomic and climate scenarios have been explored using integrated assessment models (IAMs) to understand interactions between human development and global environmental change in the long run. However, differences in trade modeling approaches are an important source of uncertainty in the assessments, particularly for regional projections. Here, we explore the critical role of trade modeling in assessing the potential future of global agroeconomics and terrestrial carbon emissions with a well-established IAM, the Global Change Assessment Model (GCAM). We update the crop trade modeling framework in GCAM from a Heckscher-Ohlin-Vanek (HOV) structure with integrated world markets (IWM) to a newly developed logit-based Armington approach with segmented regional markets (SRM). The updates make it possible to study the sensitivity of model projections of future agroeconomics and terrestrial carbon emissions to assumptions of the state and magnitude of global market integration. Our results demonstrate that assuming full global market integration, represented by homogeneous product modeling, neglecting economic geography, and excluding margins and tariffs, could lead to lower cropland use (i.e., by 115 million hectares globally) and terrestrial carbon fluxes (i.e., by 25%) by the end of the century. However, the results are highly heterogeneous across regions with more pronounced regional trade responses driven by global market integration. Our study highlights the critical role of trade modeling around product differentiation, economic geography, and regional trade parameterization in global economic or integrated assessment modeling. The results also imply that further reconciliations in trade model approaches could improve the convergence of regional results among models in model intercomparison studies.
Stable Machine‐Learning Parameterization of Subgrid Processes in a Comprehensive Atmospheric Model Learned From Embedded Convection‐Permitting Simulations
Modern climate projections often suffer from inadequate spatial and temporal resolution due to computational limitations, resulting in inaccurate representations of sub-grid processes. A promising technique to address this is the multiscale modeling framework (MMF), which embeds a kilometer-resolution cloud-resolving model (CRM) within each atmospheric column of a host climate model to replace traditional convection and cloud parameterizations. Machine learning offers a unique opportunity to make MMF more accessible by emulating the embedded CRM and reducing its substantial computational cost. Although many studies have demonstrated proof-of-concept success of achieving stable hybrid simulations, it remains a challenge to achieve near operational-level success with real geography and comprehensive variable emulation that includes, for example, explicit cloud condensate coupling. In this study, we present a stable hybrid model capable of integrating for at least 5 years with near operational-level complexity, including coarse-grid geography, seasonality, explicit cloud condensate and wind predictions, and land coupling. Our model demonstrates skillful online performance, achieving a 5-year zonal mean tropospheric temperature bias within 2 K, water vapor bias within 1 g/kg, and a precipitation root mean square error of 0.96 mm/day. Key factors contributing to our online performance include an expressive U-Net architecture and physical thermodynamic constraints for microphysics. With microphysical constraints mitigating unrealistic cloud formation, our work is the first to demonstrate realistic multi-year cloud condensate climatology under the MMF framework. Despite these advances, online diagnostics reveal persistent biases in certain regions, highlighting the need for innovative strategies to further optimize online performance.
A Decadal Hybrid GCM Simulation Using Deep‐Learning‐Based Cloud and Convection Parameterization Generalized to a Warm Climate
A critical challenge for machine‐learning (ML) parameterization in global climate models (GCMs) is to achieve stable, accurate simulations under climates not seen during training. Previous studies have demonstrated promising offline performance and year‐long online stability in aquaplanet simulations but have encountered difficulties in real geography and under climate warming. Here we report that a GCM with real geography configuration using neural‐network‐based cloud and convection parameterization, trained exclusively with present‐day climate data, successfully performs a stable, decade‐long simulation of a warm climate with +4 K sea surface temperature (SST). The neural network (NN) is based on Han et al. (2023, https://doi.org/10.1029/2022ms003508 ) with additional inputs. The simulation captures the global precipitation distribution, surface temperatures, vertical atmospheric structures, and extreme precipitation very well, closely matching simulations from both the superparameterized CAM (SPCAM) and the conventional CAM5 in the warm climate without accuracy degradation compared to those in the baseline climate. Moreover, it produces a climate response to +4 K SST in atmospheric thermodynamic states and circulations similar to those from SPCAM and CAM5. Prognostic ablation tests on NN input variables show that the NN without convective memory as input suffers from numerical instability, and the NN without considering radiative variables and land fraction as input, or with reduced training samples produce less accurate results. To our knowledge, this is the first time an ML parameterization successfully achieves online extrapolation to a warm climate without using additional warm‐climate data for training. It demonstrates the potential of ML‐driven parameterizations for credible long‐term climate projections.
Detecting Important Drivers of Gridded Population Modeling With Machine Learning
High-resolution population datasets have been lever-aged across a broad swath of domains, such as climate change, public policy, humanitarian aid, and rescue operations, among others. Machine learning methods were adopted to generate high-resolution or gridded population estimates by using various geospatial input features such as buildings, roads, and nighttime lights. In this study, we evaluate the importance of population features using Random Forest models across three levels of analysis, utilizing permutation measures. Our research aims to address key questions to enhance our understanding of high-resolution population modeling, such as: Are certain features globally (10 countries collectively) more important than others? Do optimal features vary by country? Within each country, do feature importance differ across administrative units? What similarities exist in feature importance at the global, country, and administrative unit levels? To answer these questions, we leverage the Kneedle algorithm to automate the selection of optimum features. We find that there are patterns displayed by features across spatial boundaries, evidenced by the same feature being the most important indicator of population across 7 of the 10 countries modeled. Our findings indicate that while important features may vary across geographies, certain features consistently hold greater importance than others agnostic of geography.
Building Stock Segmentation Cluster Development: Technical Reference Document
The building stock in the United States (U.S.) varies significantly as a function of several macro variables such as: climate, building type, vintage, and density. These variables change across the U.S. and can also significantly impact energy usage of the individual buildings and overall stock. For example, the square foot density and building type varies by several orders of magnitude from Manhattan to the eastern plains of Colorado. The diversity in energy use of the building stock of different areas of the U.S. is significant, and as a result, analyses that require localized results need to consider the relevant geography and the current makeup of the building stock. This document discusses the development and implementation of a stock clustering algorithm that produces a technically rigorous, consistent, and repeatable collection of geographies which are used as the basis for localized analysis. This framework considers the impact of built environment density, diversity, and climate in creating groupings of counties that create a far more nuanced analysis framework than national averages. Clustering of counties together represents a similarity of building characteristics and climate zone.