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We examine unequal outcomes in the implementation of India's national rural electrification program in Uttar Pradesh. We ask two questions: (1) to what extent did Dalits, the lowest group in India's caste hierarchy, receive less attention when the state electrified rural communities? (2) Was BSP, the state's Dalit party, able to reduce this inequality? Using data from a hundred thousand villages, we provide robust evidence for unequal outcomes. Villages inhabited solely by Dalits were 20 percentage points less likely to be covered by the program than villages without any Dalits. Moreover, a regression discontinuity analysis shows that the electoral success of BSP failed to reduce such differences. These results highlight the magnitude and persistence of caste inequality in the implementation of democratic public policy, despite political representation.

The purpose of this study, Controversial Facilities in Japan, 1955 – 1995, is to understand the factors which lead decision-makers and authorities in Japan to select localities as host communities for often-unwanted and controversial facilities such as nuclear power plants, dams, and airports. Such projects regularly cause Not In My Back Yard, or NIMBY, responses from local residents around the world. <br /><br /> The dataset contains observations on approximately 500 Japanese cities, towns, and villages covering the period from 1955 through 1995. Data was collected through archival research, interviews with anti-facility activists and officials, and surveys of relevant government offices throughout Japan.<br /><br /> Variables assessed include the number of siting attempts and successes in the locality, the town’s location in Japan by prefecture and by political district code alongside batte ries of information on demographic, socioeconomic, and political factors. Demographic information includes sex ratios in the locality over time along with percentage of elderly in the population. Socioeconomic status was examined through measures of primary, secondary, and tertiary sector workforces over time along with variables on the coastal, mid-range, and deep sea fishing cooperatives (where applicable). Political variables include district magnitude, presence or absence of a prime minister from locally elected representatives, number of long-term Liberal Democratic Party (LDP) representatives, and the number of members of the town council and their political party. Additional political variables include the numbers and percentage of representatives from all major political parties in the national legislature, political party of the mayor, and measures of over-time support from the area for the long-dominant Liberal Democratic Party. The dataset contains publicly-available information on compensation provided to communities along with information on eminent domain use. Subject: STANDARD DEPOSIT TERMS 1.0 Type: DATAPASS:TERMS:STANDARD:1.0 Notes: This study was deposited under the of the Data-PASS standard deposit terms. A copy of the usage agreement is included in the file section of this study.;

The purpose of this study was to investigate the influence of increasing sulfate concentrations on chromium removal, to evaluate the effect of the presence of Cr(VI) on sulfate removal by Streptomyces sp. MC1 and to analyze the differential protein expression profile in the presence of this metal for the identification of proteins repressed or overexpressed. In the presence of Cr(VI) but in the absence of sulfate ions, bacterial growth was negligible, showing the Cr(VI) toxicity for this bacterium. However, the sulfate presence stimulated bacterium growth and Cr(VI) removal, regardless of its concentrations. Streptomyces sp. MC1 showed ability to remove chromium and sulfate simultaneously. Also, the sulfate presence favored the decrease of total chromium concentration from supernatants reaching a decrease of 50% at 48 h. In presence of chromium, seven proteins were down‐expressed and showed homology to proteins involved in protein biosynthesis, energy production and free radicals detoxification while two proteins involved in oxidation‐reduction processes identified as dihydrolipoamide dehydrogenase and S‐adenosyl‐l‐methionine synthase were overexpressed.

(1) As on-the-ground projects come into existence and continue to expand to adapt to climate change and empower women, it is important to understand their location within Transnational Advocacy Networks (TANs). Using Bhungroo technology, we conduct case-study research to assess the potential of TANs to increase the scope and scale of local projects as well as the ability of similar and emerging projects to create sustainable social and environmental change at local levels; (2) Using the theoretical and cross-disciplinary contributions of Keck and Sikkink and Appiah, our methodology focuses on analyzing interviews and earned media hits data from the UNFCCC Momentum for Change; (3) We find that while TANs may help increase the scale and scope of climate change projects, increasing their ability to effectively reach more people and areas is not completely certain, based on this case study; (4) We conclude by proposing ways women’s political participation may be enhanced by similar projects.

Effective access control techniques are in demand, as electronically assisted healthcare services require the patient’s sensitive health records. In emergency situations, where the patient’s well-being is jeopardized, different healthcare actors associated with emergency cases should be granted permission to access Electronic Health Records (EHRs) of patients. The research objective of our study is to develop machine learning techniques based on patients’ time sequential health metrics and integrate them with an Attribute Based Access Control (ABAC) mechanism. We propose an ABAC mechanism that can yield access to sensitive EHRs systems by applying prognostic context handlers where contextual information, is used to identify emergency conditions and permit access to medical records. Specifically, we use patients’ recent health history to predict the health metrics for the next two hours by leveraging Long Short Term Memory (LSTM) Neural Networks (NNs). These predicted health metrics values are evaluated by our personalized fuzzy context handlers, to predict the criticality of patients’ status. The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient’s well-being. Integrating this predictive mechanism with personalized context handlers proved to be a robust tool to enhance the performance of the access control mechanism to modern EHRs System.

Agricultural adaptation to climate change is critical for ensuring future food security. Social capital is important for climate change adaptation, but institutions and social networks at multiple scales (e.g., household, community, and institution) have been overlooked in studying agricultural climate change adaptation. We combine data from 13 sites in 11 low-income countries in East Africa, West Africa, and South Asia to explore how multiple scales of social capital relate to household food security outcomes among smallholder farmers. Using social network theory, we define three community organizational social network types (fragmented defined by lack of coordination, brokered defined as having a strong central actor, or shared defined by high coordination) and examine household social capital through group memberships. We find community and household social capital are positively related, with higher household group membership more likely in brokered and shared networks. Household group membership is associated with more than a 10% reduction in average months of food insecurity, an effect moderated by community social network type. In communities with fragmented and shared organizational networks, additional household group memberships is associated with consistent decreases in food insecurity, in some cases up to two months; whereas in brokered networks, reductions in food insecurity are only associated with membership in credit groups. These effects are confirmed by hierarchical random effects models, which control for demographic factors. This suggests that multiple scales of social capital—both within and outside the household—are correlated with household food security. This social capital may both be bridging (across groups) and bonding (within groups) with different implications for how social capital structure affects food security. Efforts to improve food security could recognize the potential for both household and community level social networks and collaboration, which further research can capture by analyzing multiple scales of social capital data.

Understanding the system of connections between societal contexts and policy outcomes in municipal governments provides important insights into how community sustainability happens, and why it happens differently in various communities. A growing body of research in recent years has focused on understanding the socio-economic characteristics of communities and cities that are recognized as policy leaders in sustainability. In this paper, we expand the focus beyond the leaders in sustainability as we apply a selection of socio-demographic influences of community sustainability to a large sample of U.S. communities using community classification analytics to identity a range of community types and levels of engagement with sustainability. Our typology presents an integrated and comprehensive perspective on the structure of community sustainability in the United States, highlighting key points of comparison between human capital factors such as population size and density, affluence, home ownership, and adoption of sustainability policy. The analysis provides new insights not only about community leaders in sustainability, but also communities with the civic and social capacity to do more, and the challenges that may inhibit sustainability efforts in others.

Purpose This study aims to consider environmental sustainability, a global challenge under the preview of sustainable development goals, highlighting the significance of knowledge economy in attaining sustainable aggregate demand behavior globally. For this purpose, 155 countries that have data available from 1995 to 2021 were selected. The purpose of selecting these countries is to test the global responsibility of the knowledge economy to attain environmental sustainability. Design/methodology/approach Results are estimated with the help of panel quantile regression. The empirical existence of aggregate demand-based environmental Kuznets curve (EKC) was tested using non-linear tests. Moreover, principal component analysis has been incorporated to construct the knowledge economy index. Findings U-shaped aggregate demand-based EKC at global level is validated. However, environmental deterioration increases with an additional escalation after US$497.945m in aggregate demand. As a determinant, the knowledge economy is reducing CO2 emissions. The knowledge economy has played a significant role in global responsibility, shifting the EKC downward and extending the CO2 reduction phase for every selected country. Further, urbanization, energy intensity, financial development and trade openness significantly deteriorate the environmental quality. Originality/value This study contains the empirical existence of aggregate demand-based EKC. The role of the knowledge economy is examined through an index which is calculated by using four pillars of the knowledge economy (technology, innovations, education and institutions). This study is based on a combined panel of all the countries for which the data was available.