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Research data keyboard_double_arrow_right Dataset 2022Publisher:Science Data Bank Authors: Shuai ZHANG;Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Mendeley Authors: Kouton, J (via Mendeley Data);This file contains the data and the STATA estimation code to replicate the results in the article entitled: "Information Communication Technology development and energy demand in African countries".
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 United StatesPublisher:U.S. Geological Survey Authors: Finn, Thomas M;doi: 10.5066/p9sgagsu
This data release contains the boundaries of assessment units and input data for the assessment of undiscovered oil and gas resources in the Mowry formation of the Wind River Basin Province in Wyoming. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5066/p9sgagsu&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:SciELO journals Authors: Tahrir Jaber (10471122);ABSTRACT Context: reflecting the call being made by the United Nations to solve our current climate challenges and reduce companies’ CO2 emissions, there is a strong need for large corporations to not only employ the terminology of sustainable transitions, but to implement strategies and select new alternative sustainable solutions. Objective: this study fills a gap in the literature by developing and validating a model that helps researchers understand the factors that enable a large corporation undergoing a sustainable transition to select its new sustainable practices. The developed model used theories of sustainability transition and institutional theory with three pillars (regulative, normative, and cognitive) in order to help understand the nature of the company’s innovation selection criteria. Method: survey-based research was carried out among an oil and gas company’s employees, and structural equation modeling was used to test the model fit, validate the survey, and test the hypotheses. Results: the results showed that normative and regulative pillars play the main role in selecting renewable energy activities as a first step toward the company’s sustainable future. Conclusion: the findings provide researchers with a valuable model for understanding the main criteria for selecting new sustainable projects in established companies.
figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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more_vert figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; Crippa, Monica; Döbbeling, Niklas; Forster, Piers; Guizzardi, Diego; Olivier, Jos; Pongratz, Julia; Reisinger, Andy; Rigby, Matthew; Peters, Glen; Saunois, Marielle; Smith, Steven J.; Solazzo, Efisio; Tian, Hanqin;Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.
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visibility 3Kvisibility views 3,130 download downloads 1,221 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1476Publisher:Thammasat University Authors: Rabeya Basri;The study examines the linear and nonlinear relationships between per capita carbon dioxide emission, per capita real GDP, energy consumption, financial development, foreign direct investment, trade openness, urbanization, agriculture, and industry sectors as potential determining factors of CO2 emissions in the perspective of Bangladesh all through 44 years, starting from 1974. The study considers the CO2 emissions from the selected South Asian countries over the period from 1978 and 2018. The study uses three cointegration approaches. First, we employ linear cointegration method and find that crucial determining factors of CO2 emissions in Bangladesh are real GDP per capita, energy consumption, and urbanization. Then, we apply the nonlinear cointegration method and find that energy consumption and FDI have asymmetric impacts on carbon release in the long run. While energy consumption, financial development, and FDI have asymmetric influence in the short run. Finally, we apply a panel cointegration test to compare Bangladesh with other South Asian countries in terms of CO2 emissions. The estimated results disclose that the vital contributing factors of CO2 emissions in selected South Asian countries are real GDP, energy consumption, financial development, and urbanization. Our results show that energy consumption, financial development, and urbanization upturn CO2 emissions, while trade openness lowers emissions. We also claim that our results are consistent with the EKC hypothesis for both in Bangladesh and selected South Asian countries. The three cointegration estimation findings disclose that urbanization will deteriorate environmental worth in Bangladesh and selected South Asian countries in the long run.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.14457/tu.the.2019.945&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 07 Dec 2022Publisher:Dryad Shao, Junjiong; Zhou, Xuhui; van Groenigen, Kees; Zhou, Guiyao; Zhou, Huimin; Zhou, Lingyan; Lu, Meng; Xia, Jianyang; Jiang, Lin; Hungate, Bruce; Luo, Yiqi; He, Fangliang; Thakur, Madhav;Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.
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visibility 14visibility views 14 download downloads 1 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2014Embargo end date: 01 Aug 2014Publisher:Interdisciplinary Earth Data Alliance (IEDA) Brantley, Susan; Duffy, Collin; White, Tim; Dere, Ashlee; McKay, Larry;doi: 10.1594/ieda/100473
Weather stations deployed across the Critical Zone Observatory (CZO) Shale Transect, including sites in New York, Virginia, Tennessee, Alabama and Puerto Rico, provide continuous measurements of climatic conditions influencing shale weathering. Measurements are recorded every two hours and include precipitation, air temperature, relative humidity, solar radiation, wind speed, soil temperature, soil moisture and soil electrical conductivity. Ashlee Dere, (2014), Rates and mechanisms of shale weathering across a latitudinal climosequence, Ph.D. Dissertation, The Pennsylvania State University
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/ieda/100473&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Mendeley Authors: Dutton, C (via Mendeley Data);The Mara River basin in East Africa is a trans-boundary river basin that highlights many of the development and conservation challenges in East Africa. The Mara River flows from its headwaters in the Mau Forest of Kenya through the northern portion of the Serengeti-Mara Ecosystem and into Lake Victoria in Tanzania, where it forms part of the headwaters of the Nile River basin. Changes in land use and landcover in the basin have raised concerns about the quality and quantity of water in the Mara River. We analyzed sediment cores from the Mara Wetland (near the river’s outlet into Lake Victoria) to evaluate how sedimentation rates and sources have changed historically, through a period marked by major changes in human and livestock population densities, land use, and disease epidemics (rinderpest). We collected sediment cores in August 2015 along a transect through the Mara Wetland from the upstream reaches to Lake Victoria. Slices of sediment cores collected from four distinct regions of the wetland were age-calibrated using radiocarbon and lead-210 dating methods. Core slices were then analyzed for sediment sources using a sediment fingerprinting approach, nitrogen and carbon stable isotope signatures, and mercury. Our results suggest that ecological conditions in the Mara River basin were fairly stable over paleoecological time scales (2000-1000 years before present), but there has been a period of rapid change in the basin over the last 250 years, particularly since the 1960s, likely due to anthropogenic factors. We also observed that downstream effects of landcover and land use change can be exacerbated by increasing occurrence of extreme rainfall events in the region. The Mara Wetland likely plays an important role in mitigating the impact of those factors on Lake Victoria. This work was the result of a partnership between Yale University, the Cary Institute of Ecosystem Studies, WWF-UK, WWF-Kenya and WWF-Tanzania.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1993Publisher:ICPSR - Interuniversity Consortium for Political and Social Research Authors: United States Department Of Commerce. Bureau Of The Census;This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in post-secondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to help link individuals to the core files. A topical module was not created for Wave I. The Wave II Topical Module (Part 17) covers recipiency, employment, work disability, education and training, marital status, migration, and fertility histories along with household relationships. The Wave III Topical Module (Part 19) includes data on work schedules, child care, child support agreements, support for nonhousehold members, functional limitations and disability, and utilization of health care services. Data from the Wave IV Topical Module (Part 21) include assets and liabilities, retirement expectations and pension plan coverage, and real estate property and vehicles. The Wave V Topical Module (Part 23) provides data on educational financing and enrollment. The Wave VI Topical Module (Part 25) covers time spent outside the work force, child care, child support agreements, support for nonhousehold members, functional limitations and disability, and utilization of health care services. Data in the Wave VII Topical Module (Part 27) cover selected financial assets, medical expenses and work disability, and real estate, shelter costs, dependent care, and vehicles. Wave VIII Topical Module (Part 29) includes data on annual income and retirement accounts, taxes, and school enrollment and financing. Part 33 of this study is the Wave V Topical Module Research File, an unedited version of Part 23. This research file has not been edited nor imputed but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. Datasets: DS0: Study-Level Files DS1: Wave I Rectangular Data DS2: Data Dictionary for Wave I Rectangular File DS3: Wave II Rectangular Data DS4: Data Dictionary for Wave II Rectangular File DS5: Wave III Rectangular Data DS6: Data Dictionary for Wave III Rectangular File DS7: Wave IV Core Microdata File DS8: Data Dictionary for Wave IV Core Microdata File DS9: Wave V Core Microdata File DS10: Data Dictionary for Wave V Core Microdata File DS11: Wave VI Core Microdata File DS12: Data Dictionary for Wave VI Core Microdata File DS13: Wave VII Core Microdata File DS14: Data Dictionary for Wave VII Core Microdata File DS15: Wave VIII Core Microdata File DS16: Data Dictionary for Wave VIII Core Microdata File DS17: Wave II Topical Module Microdata File DS18: Data Dictionary for Wave II Topical Module Microdata File DS19: Wave III Topical Module Microdata File DS20: Data Dictionary for Wave III Topical Module Microdata File DS21: Wave IV Topical Module Microdata File DS22: Data Dictionary for Wave IV Topical Module Microdata File DS23: Wave V Topical Module Microdata File DS24: Data Dictionary for Wave V Topical Module Microdata File DS25: Wave VI Topical Module Microdata File DS26: Data Dictionary for Wave VI Topical Module Microdata File DS27: Wave VII Topical Module Microdata File DS28: Data Dictionary for Wave VII Topical Module Microdata File DS29: Wave VIII Topical Module Microdata File DS30: Data Dictionary for Wave VIII Topical Module Microdata File DS31: User Notes DS32: User Guide DS33: Wave V Topical Module Research File A multistage stratified sampling design was used. One-fourth of the sample households were interviewed each month, and households were reinterviewed at four-month intervals. All persons at least 15 years old who were present as household members at the time of the first interview were included for the entire study, except those who joined the military, were institutionalized for the entire study period, or moved from the United States. Original household members who moved during the study period were followed to their new residences and interviewed there. New persons moving into households of members of the original sample also were included in the survey, but were not followed if they left the household of an original sample person. Beginning with the 1990 Panel, the file structure of SIPP has changed. The unit of observation is one record for each person for each month, rather than one record per person. Also, topical modules are provided separately from core files.The codebooks are provided by ICPSR as a Portable Document Format (PDF) files and the data dictionaries are provided as ASCII text files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site. Resident population of the United States, excluding persons living in institutions and military barracks.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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Research data keyboard_double_arrow_right Dataset 2022Publisher:Science Data Bank Authors: Shuai ZHANG;Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China. Climate trends during maize growing period and their impacts on spring maize yield in North China was investigated. This dataset contains: 1) information of stations in cultivation region for spring maize in North China; 2) Trend in temperature and its effect on yield in cultivation region for spring maize in North China; 3) Trend in radiation and its effect on yield in cultivation region for spring maize in North China; 4) Trend in precipitation and its effect on yield in cultivation region for spring maize in North China.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.57760/sciencedb.06747&type=result"></script>'); --> </script>
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more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Mendeley Authors: Kouton, J (via Mendeley Data);This file contains the data and the STATA estimation code to replicate the results in the article entitled: "Information Communication Technology development and energy demand in African countries".
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/zvxzrkp6d8.1&type=result"></script>'); --> </script>
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more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.17632/zvxzrkp6d8.1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021 United StatesPublisher:U.S. Geological Survey Authors: Finn, Thomas M;doi: 10.5066/p9sgagsu
This data release contains the boundaries of assessment units and input data for the assessment of undiscovered oil and gas resources in the Mowry formation of the Wind River Basin Province in Wyoming. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. The only exceptions to this are Assessment Units that border the Federal-State water boundary. In these cases, the Federal-State water boundary forms part of the Assessment Unit boundary. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5066/p9sgagsu&type=result"></script>'); --> </script>
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more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5066/p9sgagsu&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:SciELO journals Authors: Tahrir Jaber (10471122);ABSTRACT Context: reflecting the call being made by the United Nations to solve our current climate challenges and reduce companies’ CO2 emissions, there is a strong need for large corporations to not only employ the terminology of sustainable transitions, but to implement strategies and select new alternative sustainable solutions. Objective: this study fills a gap in the literature by developing and validating a model that helps researchers understand the factors that enable a large corporation undergoing a sustainable transition to select its new sustainable practices. The developed model used theories of sustainability transition and institutional theory with three pillars (regulative, normative, and cognitive) in order to help understand the nature of the company’s innovation selection criteria. Method: survey-based research was carried out among an oil and gas company’s employees, and structural equation modeling was used to test the model fit, validate the survey, and test the hypotheses. Results: the results showed that normative and regulative pillars play the main role in selecting renewable energy activities as a first step toward the company’s sustainable future. Conclusion: the findings provide researchers with a valuable model for understanding the main criteria for selecting new sustainable projects in established companies.
figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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more_vert figshare arrow_drop_down Smithsonian figshareDataset . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.14321376&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2021Publisher:Zenodo Minx, Jan C.; Lamb, William F.; Andrew, Robbie M.; Canadell, Josep G.; Crippa, Monica; Döbbeling, Niklas; Forster, Piers; Guizzardi, Diego; Olivier, Jos; Pongratz, Julia; Reisinger, Andy; Rigby, Matthew; Peters, Glen; Saunois, Marielle; Smith, Steven J.; Solazzo, Efisio; Tian, Hanqin;Comprehensive and reliable information on anthropogenic sources of greenhouse gas emissions is required to track progress towards keeping warming well below 2°C as agreed upon in the Paris Agreement. Here we provide a dataset on anthropogenic GHG emissions 1970-2019 with a broad country and sector coverage. We build the dataset from recent releases from the “Emissions Database for Global Atmospheric Research” (EDGAR) for CO2 emissions from fossil fuel combustion and industry (FFI), CH4 emissions, N2O emissions, and fluorinated gases and use a well-established fast-track method to extend this dataset from 2018 to 2019. We complement this with information on net CO2 emissions from land use, land-use change and forestry (LULUCF) from three available bookkeeping models.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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visibility 3Kvisibility views 3,130 download downloads 1,221 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5281/zenodo.5548333&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1476Publisher:Thammasat University Authors: Rabeya Basri;The study examines the linear and nonlinear relationships between per capita carbon dioxide emission, per capita real GDP, energy consumption, financial development, foreign direct investment, trade openness, urbanization, agriculture, and industry sectors as potential determining factors of CO2 emissions in the perspective of Bangladesh all through 44 years, starting from 1974. The study considers the CO2 emissions from the selected South Asian countries over the period from 1978 and 2018. The study uses three cointegration approaches. First, we employ linear cointegration method and find that crucial determining factors of CO2 emissions in Bangladesh are real GDP per capita, energy consumption, and urbanization. Then, we apply the nonlinear cointegration method and find that energy consumption and FDI have asymmetric impacts on carbon release in the long run. While energy consumption, financial development, and FDI have asymmetric influence in the short run. Finally, we apply a panel cointegration test to compare Bangladesh with other South Asian countries in terms of CO2 emissions. The estimated results disclose that the vital contributing factors of CO2 emissions in selected South Asian countries are real GDP, energy consumption, financial development, and urbanization. Our results show that energy consumption, financial development, and urbanization upturn CO2 emissions, while trade openness lowers emissions. We also claim that our results are consistent with the EKC hypothesis for both in Bangladesh and selected South Asian countries. The three cointegration estimation findings disclose that urbanization will deteriorate environmental worth in Bangladesh and selected South Asian countries in the long run.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.14457/tu.the.2019.945&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2022Embargo end date: 07 Dec 2022Publisher:Dryad Shao, Junjiong; Zhou, Xuhui; van Groenigen, Kees; Zhou, Guiyao; Zhou, Huimin; Zhou, Lingyan; Lu, Meng; Xia, Jianyang; Jiang, Lin; Hungate, Bruce; Luo, Yiqi; He, Fangliang; Thakur, Madhav;Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.
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visibility 14visibility views 14 download downloads 1 Powered bymore_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.5061/dryad.gtht76hms&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2014Embargo end date: 01 Aug 2014Publisher:Interdisciplinary Earth Data Alliance (IEDA) Brantley, Susan; Duffy, Collin; White, Tim; Dere, Ashlee; McKay, Larry;doi: 10.1594/ieda/100473
Weather stations deployed across the Critical Zone Observatory (CZO) Shale Transect, including sites in New York, Virginia, Tennessee, Alabama and Puerto Rico, provide continuous measurements of climatic conditions influencing shale weathering. Measurements are recorded every two hours and include precipitation, air temperature, relative humidity, solar radiation, wind speed, soil temperature, soil moisture and soil electrical conductivity. Ashlee Dere, (2014), Rates and mechanisms of shale weathering across a latitudinal climosequence, Ph.D. Dissertation, The Pennsylvania State University
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1594/ieda/100473&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 2019Publisher:Mendeley Authors: Dutton, C (via Mendeley Data);The Mara River basin in East Africa is a trans-boundary river basin that highlights many of the development and conservation challenges in East Africa. The Mara River flows from its headwaters in the Mau Forest of Kenya through the northern portion of the Serengeti-Mara Ecosystem and into Lake Victoria in Tanzania, where it forms part of the headwaters of the Nile River basin. Changes in land use and landcover in the basin have raised concerns about the quality and quantity of water in the Mara River. We analyzed sediment cores from the Mara Wetland (near the river’s outlet into Lake Victoria) to evaluate how sedimentation rates and sources have changed historically, through a period marked by major changes in human and livestock population densities, land use, and disease epidemics (rinderpest). We collected sediment cores in August 2015 along a transect through the Mara Wetland from the upstream reaches to Lake Victoria. Slices of sediment cores collected from four distinct regions of the wetland were age-calibrated using radiocarbon and lead-210 dating methods. Core slices were then analyzed for sediment sources using a sediment fingerprinting approach, nitrogen and carbon stable isotope signatures, and mercury. Our results suggest that ecological conditions in the Mara River basin were fairly stable over paleoecological time scales (2000-1000 years before present), but there has been a period of rapid change in the basin over the last 250 years, particularly since the 1960s, likely due to anthropogenic factors. We also observed that downstream effects of landcover and land use change can be exacerbated by increasing occurrence of extreme rainfall events in the region. The Mara Wetland likely plays an important role in mitigating the impact of those factors on Lake Victoria. This work was the result of a partnership between Yale University, the Cary Institute of Ecosystem Studies, WWF-UK, WWF-Kenya and WWF-Tanzania.
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You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
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For further information contact us at helpdesk@openaire.euResearch data keyboard_double_arrow_right Dataset 1993Publisher:ICPSR - Interuniversity Consortium for Political and Social Research Authors: United States Department Of Commerce. Bureau Of The Census;This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals. There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, participation in various cash and noncash benefit programs, attendance in post-secondary schools, private health insurance coverage, public or subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to help link individuals to the core files. A topical module was not created for Wave I. The Wave II Topical Module (Part 17) covers recipiency, employment, work disability, education and training, marital status, migration, and fertility histories along with household relationships. The Wave III Topical Module (Part 19) includes data on work schedules, child care, child support agreements, support for nonhousehold members, functional limitations and disability, and utilization of health care services. Data from the Wave IV Topical Module (Part 21) include assets and liabilities, retirement expectations and pension plan coverage, and real estate property and vehicles. The Wave V Topical Module (Part 23) provides data on educational financing and enrollment. The Wave VI Topical Module (Part 25) covers time spent outside the work force, child care, child support agreements, support for nonhousehold members, functional limitations and disability, and utilization of health care services. Data in the Wave VII Topical Module (Part 27) cover selected financial assets, medical expenses and work disability, and real estate, shelter costs, dependent care, and vehicles. Wave VIII Topical Module (Part 29) includes data on annual income and retirement accounts, taxes, and school enrollment and financing. Part 33 of this study is the Wave V Topical Module Research File, an unedited version of Part 23. This research file has not been edited nor imputed but has been topcoded or bottomcoded and recoded if necessary by the Census Bureau to avoid disclosure of individual respondents' identities. Datasets: DS0: Study-Level Files DS1: Wave I Rectangular Data DS2: Data Dictionary for Wave I Rectangular File DS3: Wave II Rectangular Data DS4: Data Dictionary for Wave II Rectangular File DS5: Wave III Rectangular Data DS6: Data Dictionary for Wave III Rectangular File DS7: Wave IV Core Microdata File DS8: Data Dictionary for Wave IV Core Microdata File DS9: Wave V Core Microdata File DS10: Data Dictionary for Wave V Core Microdata File DS11: Wave VI Core Microdata File DS12: Data Dictionary for Wave VI Core Microdata File DS13: Wave VII Core Microdata File DS14: Data Dictionary for Wave VII Core Microdata File DS15: Wave VIII Core Microdata File DS16: Data Dictionary for Wave VIII Core Microdata File DS17: Wave II Topical Module Microdata File DS18: Data Dictionary for Wave II Topical Module Microdata File DS19: Wave III Topical Module Microdata File DS20: Data Dictionary for Wave III Topical Module Microdata File DS21: Wave IV Topical Module Microdata File DS22: Data Dictionary for Wave IV Topical Module Microdata File DS23: Wave V Topical Module Microdata File DS24: Data Dictionary for Wave V Topical Module Microdata File DS25: Wave VI Topical Module Microdata File DS26: Data Dictionary for Wave VI Topical Module Microdata File DS27: Wave VII Topical Module Microdata File DS28: Data Dictionary for Wave VII Topical Module Microdata File DS29: Wave VIII Topical Module Microdata File DS30: Data Dictionary for Wave VIII Topical Module Microdata File DS31: User Notes DS32: User Guide DS33: Wave V Topical Module Research File A multistage stratified sampling design was used. One-fourth of the sample households were interviewed each month, and households were reinterviewed at four-month intervals. All persons at least 15 years old who were present as household members at the time of the first interview were included for the entire study, except those who joined the military, were institutionalized for the entire study period, or moved from the United States. Original household members who moved during the study period were followed to their new residences and interviewed there. New persons moving into households of members of the original sample also were included in the survey, but were not followed if they left the household of an original sample person. Beginning with the 1990 Panel, the file structure of SIPP has changed. The unit of observation is one record for each person for each month, rather than one record per person. Also, topical modules are provided separately from core files.The codebooks are provided by ICPSR as a Portable Document Format (PDF) files and the data dictionaries are provided as ASCII text files. The PDF file format was developed by Adobe Systems Incorporated and can be accessed using PDF reader software, such as the Adobe Acrobat Reader. Information on how to obtain a copy of the Acrobat Reader is provided on the ICPSR Web site. Resident population of the United States, excluding persons living in institutions and military barracks.
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You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3886/icpsr09722&type=result"></script>'); --> </script>
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