Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150525
E. Elwakil, M. Hegab
Trenchless technologies provide the technology necessary to install underground conduits without open excavation. Microtunneling is a multifaceted non-channel digging method. This technology has the capability to reduce environmental impacts. Therefore, the risk assessment of this process will ease the improvements and modeling of the forthcoming projects of microtunneling. A real data set of thirty-five microtunneling projects was collected through a questionnaire to obtain the agent's and agents' mass which is directed to experts. A risk level prediction model has been developed using the Clustering Analysis technique, Regression Analysis and the Analytical Hierarchy Process (AHP) to consider the effect of the qualitative factors involved in the microtunneling process. The model has been validated, which shows satisfactory results with 84.79% Average Validity Percent.
{"title":"Expert-based Risk Level Assessment Model for Microtunneling Projects","authors":"E. Elwakil, M. Hegab","doi":"10.1109/SusTech47890.2020.9150525","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150525","url":null,"abstract":"Trenchless technologies provide the technology necessary to install underground conduits without open excavation. Microtunneling is a multifaceted non-channel digging method. This technology has the capability to reduce environmental impacts. Therefore, the risk assessment of this process will ease the improvements and modeling of the forthcoming projects of microtunneling. A real data set of thirty-five microtunneling projects was collected through a questionnaire to obtain the agent's and agents' mass which is directed to experts. A risk level prediction model has been developed using the Clustering Analysis technique, Regression Analysis and the Analytical Hierarchy Process (AHP) to consider the effect of the qualitative factors involved in the microtunneling process. The model has been validated, which shows satisfactory results with 84.79% Average Validity Percent.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125121792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150485
M. Aboelmaged
This paper integrates habits into the Theory of Planned Behavior and the Technology Acceptance Model to predict waste electrical and electronic equipment (WEEE) or the e-waste recycling intention among young adults. This study adopts the structural equation modelling (SEM) technique, using the partial least squares (PLS) approach as a multivariate statistical method to analyze the survey data. The findings show that the integrated model has good explanatory power and confirms its robustness in predicting the e-waste recycling intention among young adults. The role of habits and perceived usefulness are demonstrated as strong predictors of young adults' e-waste recycling intention, as well as their attitudes. The paper concludes with several social and practical implications that can foster e-waste recycling initiatives in the UAE.
{"title":"Acceptance of E-waste Recycling Among Young Adults: An Empirical Study","authors":"M. Aboelmaged","doi":"10.1109/SusTech47890.2020.9150485","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150485","url":null,"abstract":"This paper integrates habits into the Theory of Planned Behavior and the Technology Acceptance Model to predict waste electrical and electronic equipment (WEEE) or the e-waste recycling intention among young adults. This study adopts the structural equation modelling (SEM) technique, using the partial least squares (PLS) approach as a multivariate statistical method to analyze the survey data. The findings show that the integrated model has good explanatory power and confirms its robustness in predicting the e-waste recycling intention among young adults. The role of habits and perceived usefulness are demonstrated as strong predictors of young adults' e-waste recycling intention, as well as their attitudes. The paper concludes with several social and practical implications that can foster e-waste recycling initiatives in the UAE.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"46 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116439107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/sustech47890.2020.9150506
C. Cheung, S. Kuppannagari, R. Kannan, V. Prasanna
Residential rooftop photovoltaics are commonly installed with energy storage in the form of batteries for the purpose of storing excess energy generated for use when extra energy is needed. Recently, there has been a lot of interest in improving the observability of behind-the-meter solar for better grid optimization strategies. However, existing algorithms only consider standalone photovoltaic systems not connected to a battery. Presence of battery changes the load pattern recorded from the households, thus these algorithms are not able to perform disaggregation accurately when batteries are present. We propose an improved disaggregation method for behind-the-meter solar that takes into account the charging and discharging activity of batteries by including the hidden-battery model. We compare our methods with a state-of-the-art temperature based load model for disaggregation and evaluate the methodologies on simulated data based on inputs from a real life dataset based in Austin, Texas. Our results show that hidden-battery model reduces mean absolute error by 28% on an existing method on aggregated customer data.
{"title":"Disaggregation of Behind-the-Meter Solar Generation in Presence of Energy Storage Resources","authors":"C. Cheung, S. Kuppannagari, R. Kannan, V. Prasanna","doi":"10.1109/sustech47890.2020.9150506","DOIUrl":"https://doi.org/10.1109/sustech47890.2020.9150506","url":null,"abstract":"Residential rooftop photovoltaics are commonly installed with energy storage in the form of batteries for the purpose of storing excess energy generated for use when extra energy is needed. Recently, there has been a lot of interest in improving the observability of behind-the-meter solar for better grid optimization strategies. However, existing algorithms only consider standalone photovoltaic systems not connected to a battery. Presence of battery changes the load pattern recorded from the households, thus these algorithms are not able to perform disaggregation accurately when batteries are present. We propose an improved disaggregation method for behind-the-meter solar that takes into account the charging and discharging activity of batteries by including the hidden-battery model. We compare our methods with a state-of-the-art temperature based load model for disaggregation and evaluate the methodologies on simulated data based on inputs from a real life dataset based in Austin, Texas. Our results show that hidden-battery model reduces mean absolute error by 28% on an existing method on aggregated customer data.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150492
Daniel Marques da Silva Chaves, Letícia Lagni Dagnese, M. A. Martins
To identify opportunities for the increase of sustainability driven performance, this paper proposes a mathematical modeling based on indicators, to measure the score of sustainability of underground grid conversion in Vila Olimpia, São Paulo. Based on the literature review, a micro-level indicator system has been proposed, focusing on project-level sustainability assessment that includes a specific approach for underground infrastructure, balanced by the three pillars of sustainability and integrating circular economy indicators. The main goal is to support decision making based on data driven solutions for sustainability assessment in the construction site. The results present consistent information in order to obtain success considering critical factors for sustainable development.
{"title":"Sustainability Driven Performance Evaluation of Underground Smart Grid Conversion","authors":"Daniel Marques da Silva Chaves, Letícia Lagni Dagnese, M. A. Martins","doi":"10.1109/SusTech47890.2020.9150492","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150492","url":null,"abstract":"To identify opportunities for the increase of sustainability driven performance, this paper proposes a mathematical modeling based on indicators, to measure the score of sustainability of underground grid conversion in Vila Olimpia, São Paulo. Based on the literature review, a micro-level indicator system has been proposed, focusing on project-level sustainability assessment that includes a specific approach for underground infrastructure, balanced by the three pillars of sustainability and integrating circular economy indicators. The main goal is to support decision making based on data driven solutions for sustainability assessment in the construction site. The results present consistent information in order to obtain success considering critical factors for sustainable development.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117334483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150501
Jane Namaganda-Kiyimba, J. Mutale
More than 1.5 billion people in the world today do not have access to electricity. In developing countries, most of these people live in rural and remote areas far from the main grid necessitating establishment of off-grid electrical systems such as solar PV microgrids. Establishment of these off-grid systems requires accurate prediction of the load so as to have a sustainable system that can meet the electrical needs of the community fully. An evaluation of the efficacy of the current methods for load estimation has been carried out in this research. Informed by this evaluation, an improved approach is proposed that yields more accurate estimates of the electrical demand for off-grid systems in rural areas. Load characteristics that take account of the influence of gender as well as changes in customer habits on estimated load are investigated using the Markov Chain process. MATLAB software is used to generate the load profiles. The results show that average electricity consumption in a similar village that is already electrified can be used as a sound basis to estimate the consumption in a village without electricity. Moreover it has found that the effect of gender on estimated demand is critical in microgrid design.
{"title":"Gender Considerations in Load Estimation for Rural Electrification","authors":"Jane Namaganda-Kiyimba, J. Mutale","doi":"10.1109/SusTech47890.2020.9150501","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150501","url":null,"abstract":"More than 1.5 billion people in the world today do not have access to electricity. In developing countries, most of these people live in rural and remote areas far from the main grid necessitating establishment of off-grid electrical systems such as solar PV microgrids. Establishment of these off-grid systems requires accurate prediction of the load so as to have a sustainable system that can meet the electrical needs of the community fully. An evaluation of the efficacy of the current methods for load estimation has been carried out in this research. Informed by this evaluation, an improved approach is proposed that yields more accurate estimates of the electrical demand for off-grid systems in rural areas. Load characteristics that take account of the influence of gender as well as changes in customer habits on estimated load are investigated using the Markov Chain process. MATLAB software is used to generate the load profiles. The results show that average electricity consumption in a similar village that is already electrified can be used as a sound basis to estimate the consumption in a village without electricity. Moreover it has found that the effect of gender on estimated demand is critical in microgrid design.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121921918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150523
Octavio Lasso-Lopez, Cecilia González-Espinoza, C. Lozoya, Alberto Venzor-Mendoza, Alfredo Dávila-Villalobos, Carlos Royo-Noble
Building operations are responsible for a large amount of energy consumption therefore the development of energy-saving solutions for buildings represents a relevant challenge with a major impact on our global community. This paper presents the design and implementation of an IoT (Internet of Things) prototype for monitoring energy consumption which was developed by a group of undergraduate students to reinforce their disciplinary and cross-disciplinary competencies by applying the challenge-based learning model. This educative approach allows the students to provide a solution for a relevant problem by using modern technology and applying adequate methodologies. The developed prototype has been installed in the mechatronic laboratory building and has been evaluated by the users for six months. Obtained results show that the prototype has provided benefits by identifying energy management issues in the laboratory also from the academic perspective students that participated in the challenge had superior performance in comparison with the students that previously used the traditional learning model.
{"title":"Implementing an IoT Energy Monitoring System Using the Challenge-based Learning Model","authors":"Octavio Lasso-Lopez, Cecilia González-Espinoza, C. Lozoya, Alberto Venzor-Mendoza, Alfredo Dávila-Villalobos, Carlos Royo-Noble","doi":"10.1109/SusTech47890.2020.9150523","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150523","url":null,"abstract":"Building operations are responsible for a large amount of energy consumption therefore the development of energy-saving solutions for buildings represents a relevant challenge with a major impact on our global community. This paper presents the design and implementation of an IoT (Internet of Things) prototype for monitoring energy consumption which was developed by a group of undergraduate students to reinforce their disciplinary and cross-disciplinary competencies by applying the challenge-based learning model. This educative approach allows the students to provide a solution for a relevant problem by using modern technology and applying adequate methodologies. The developed prototype has been installed in the mechatronic laboratory building and has been evaluated by the users for six months. Obtained results show that the prototype has provided benefits by identifying energy management issues in the laboratory also from the academic perspective students that participated in the challenge had superior performance in comparison with the students that previously used the traditional learning model.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122177143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150489
H. Alghamdi
The digital age brought along with it the idea that most business offices would go completely paperless and hard copies of records would become a thing of the past. While most businesses have definitely incorporated more technology into their offices and work processes, approximately 95% of all businesses still continue to store some of their documents on paper. With advancements in technology have come continued pressure for businesses to utilize new, innovative technologies to reduce their carbon footprint and impact on the environment. Organizations need to realize and understand how their business printing and office habits affect not only their bottom line but also the environment. This paper studies the two primary printing technologies to evaluate the impact it has on the environment as well as on the business's electricity costs. Selecting the correct type of printer can save the environment approximately 97.7% from CO2. Respectively, wise printing technology choices can save 94.5% on a business's electricity consumptiOon and expenses.
{"title":"Effect of Printing Technology to Electricity and Environment","authors":"H. Alghamdi","doi":"10.1109/SusTech47890.2020.9150489","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150489","url":null,"abstract":"The digital age brought along with it the idea that most business offices would go completely paperless and hard copies of records would become a thing of the past. While most businesses have definitely incorporated more technology into their offices and work processes, approximately 95% of all businesses still continue to store some of their documents on paper. With advancements in technology have come continued pressure for businesses to utilize new, innovative technologies to reduce their carbon footprint and impact on the environment. Organizations need to realize and understand how their business printing and office habits affect not only their bottom line but also the environment. This paper studies the two primary printing technologies to evaluate the impact it has on the environment as well as on the business's electricity costs. Selecting the correct type of printer can save the environment approximately 97.7% from CO2. Respectively, wise printing technology choices can save 94.5% on a business's electricity consumptiOon and expenses.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130605316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150482
Michael Balestrieri, A. James, Matthew Kedis, F. M. Gonzales
A major factor in the enablement of sustainable renewable energy is ensuring utilities are prepared to safely and reliably operate a grid of the future. Bi-directional power flow coupled with uncontrollable DERs requires that utility system operators and planners redesign their operation and load planning processes. Southern California Edison partnered with a third-party company to enhance and demonstrate a software application with the ability to visualize disaggregated masked load, PV generation, and flexible DERs within segmented sections of a distribution circuit. SCE successfully created higher-fidelity power system models for a 66/12kV substation and its associated feeders with 12,012 customers using customer meter and estimated solar from a 3rd party advanced algorithm. PV penetration for this substation varies from 0.66-65%, where PV penetration is defined as the ratio of total peak solar power to peak load apparent power. The application leverages disparate data to provide planners and system operators a more granular estimate and new views of what is happening on the circuit beyond the substation feeder-head. This paper describes the methodology, data sources, validation approach, integration and visualization results and lessons learned from SCE's perspective.
{"title":"Mitigation of Grid Susceptibility Caused by Behind-the-Meter Solar Generation","authors":"Michael Balestrieri, A. James, Matthew Kedis, F. M. Gonzales","doi":"10.1109/SusTech47890.2020.9150482","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150482","url":null,"abstract":"A major factor in the enablement of sustainable renewable energy is ensuring utilities are prepared to safely and reliably operate a grid of the future. Bi-directional power flow coupled with uncontrollable DERs requires that utility system operators and planners redesign their operation and load planning processes. Southern California Edison partnered with a third-party company to enhance and demonstrate a software application with the ability to visualize disaggregated masked load, PV generation, and flexible DERs within segmented sections of a distribution circuit. SCE successfully created higher-fidelity power system models for a 66/12kV substation and its associated feeders with 12,012 customers using customer meter and estimated solar from a 3rd party advanced algorithm. PV penetration for this substation varies from 0.66-65%, where PV penetration is defined as the ratio of total peak solar power to peak load apparent power. The application leverages disparate data to provide planners and system operators a more granular estimate and new views of what is happening on the circuit beyond the substation feeder-head. This paper describes the methodology, data sources, validation approach, integration and visualization results and lessons learned from SCE's perspective.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130988773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/SusTech47890.2020.9150494
C. Monjardin, F. A. Uy, F. J. Tan, Russel C. Carpio, Kevin Christian P. Javate, John Patrick Laquindanum
The study evaluates the performance of Artificial Neuro Fuzzy Interference System (ANFIS) and its applicability to rainfall runoff modelling considering Philippine setting specifically applied to Imus river basin located in Cavite. Rainfall-runoff modelling consists of complex approach and is a well-known to be a challenging field in hydrologic and hydraulic engineering. It can be suggested to use ANFIS approach in rainfall runoff modelling based on the results obtained for the selected basin. The input it requires to develop the model is less complex than of existing methods which uses a number of parameters. The structure of the runoff model was developed using MATLAB R2018a and it utilizes a Sugeno-Takagi Fuzzy Interference System to apply the ANFIS. To illustrate the flexibility of the approach a single input and dual input variable fuzzy model was calibrated and validated. On the basis of statistical parameter calculation using NSE, PBIAS, and RSR, the capability of the approach being used in rainfall runoff modelling is highly applicable. Calibrated rainfall-runoff model was developed using the Artificial Neuro Fuzzy and can readily be used for simulation. This model could be used for the simulation of flooding extent and can help government agencies to properly design the drainages and other flood control structures.
{"title":"Application of Artificial Neuro-Fuzzy Interference System in Rainfall-Runoff Modelling at Imus River, Cavite","authors":"C. Monjardin, F. A. Uy, F. J. Tan, Russel C. Carpio, Kevin Christian P. Javate, John Patrick Laquindanum","doi":"10.1109/SusTech47890.2020.9150494","DOIUrl":"https://doi.org/10.1109/SusTech47890.2020.9150494","url":null,"abstract":"The study evaluates the performance of Artificial Neuro Fuzzy Interference System (ANFIS) and its applicability to rainfall runoff modelling considering Philippine setting specifically applied to Imus river basin located in Cavite. Rainfall-runoff modelling consists of complex approach and is a well-known to be a challenging field in hydrologic and hydraulic engineering. It can be suggested to use ANFIS approach in rainfall runoff modelling based on the results obtained for the selected basin. The input it requires to develop the model is less complex than of existing methods which uses a number of parameters. The structure of the runoff model was developed using MATLAB R2018a and it utilizes a Sugeno-Takagi Fuzzy Interference System to apply the ANFIS. To illustrate the flexibility of the approach a single input and dual input variable fuzzy model was calibrated and validated. On the basis of statistical parameter calculation using NSE, PBIAS, and RSR, the capability of the approach being used in rainfall runoff modelling is highly applicable. Calibrated rainfall-runoff model was developed using the Artificial Neuro Fuzzy and can readily be used for simulation. This model could be used for the simulation of flooding extent and can help government agencies to properly design the drainages and other flood control structures.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126331318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/sustech47890.2020.9150510
Suraj Sheth, L. Bettencourt
The Community Human Development Index (CHDI)is a multidimensional measure designed to assess human achievement across scales at the state, county, city, and neighborhood levels to advance the localization of the United Nations Sustainable Development Goals (UNSDGs). Currently, one of the most widely used metrics is the Human Development Index (HDI), a composite index of income, education, and life expectancy calculated at a national level. However, national averages obscure substantial disparities between states, cities and smaller neighborhoods in large urban areas. The CHDI model is designed to gain a better understanding of heterogeneities in human development within cities and across communities on a subnational scale. As a case study, we calculated the CHDI for nearly every census tract in the state of Illinois in the United States. The analysis revealed significant disparities in local levels of development within Illinois. It also highlighted differences between communities within cities, including the city of Chicago. These results demonstrate that multidimensional indices created to track sustainable development at the national level can be disaggregated, calculated, analyzed and compared across scales. The CHDI model can be utilized by researchers and policymakers to identify, visualize and address local inequalities, and design and implement evidence based customized solutions at the community level.
{"title":"The Community Human Development Index (CHDI): Localizing Sustainable Development Goals Across Scales","authors":"Suraj Sheth, L. Bettencourt","doi":"10.1109/sustech47890.2020.9150510","DOIUrl":"https://doi.org/10.1109/sustech47890.2020.9150510","url":null,"abstract":"The Community Human Development Index (CHDI)is a multidimensional measure designed to assess human achievement across scales at the state, county, city, and neighborhood levels to advance the localization of the United Nations Sustainable Development Goals (UNSDGs). Currently, one of the most widely used metrics is the Human Development Index (HDI), a composite index of income, education, and life expectancy calculated at a national level. However, national averages obscure substantial disparities between states, cities and smaller neighborhoods in large urban areas. The CHDI model is designed to gain a better understanding of heterogeneities in human development within cities and across communities on a subnational scale. As a case study, we calculated the CHDI for nearly every census tract in the state of Illinois in the United States. The analysis revealed significant disparities in local levels of development within Illinois. It also highlighted differences between communities within cities, including the city of Chicago. These results demonstrate that multidimensional indices created to track sustainable development at the national level can be disaggregated, calculated, analyzed and compared across scales. The CHDI model can be utilized by researchers and policymakers to identify, visualize and address local inequalities, and design and implement evidence based customized solutions at the community level.","PeriodicalId":184112,"journal":{"name":"2020 IEEE Conference on Technologies for Sustainability (SusTech)","volume":"411 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133382389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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