1,720,991 research outputs found
DATA-DRIVEN DECISION INTELLIGENCE MODEL TO SUPPORT VALUE-ORIENTED MANAGEMENT IN THE GOVERNMENT INSTITUTES
No full textThis dissertation focuses on devising a framework to prepare government institutions for implementing Artificial Intelligence towards value-oriented management. It explores the factors influencing AI readiness in government institutions, focusing specifically on two critical areas: digital transformation (and its components) and data management (and its components). The study seeks to uncover the elements essential for preparing government bodies for AI implementation through an in-depth examination of the factors in these two areas. The objective of the study is to determine the requirements of AI Readiness for government institutions, proposing a framework to assess the digital transformation maturity level within these institutions to prepare the IT environment for AI, investigate the importance of data management, and assess the maturity level of data management in government organization to know and monitor progress and achieve an organizational level which is reflected on AI index for country government. The dissertation contributes to the body of research knowledge in several ways. It promotes research efforts to enhance the organizational performance of government institutes, as the focus of most research in this domain has been on corporate and business organizations. Firstly, the dissertation introduces a hybrid model for assessing the digital transformation maturity of government organizations. While existing maturity assessment models primarily cater to corporate organizations and their financial gains, this hybrid model, developed using the Analytic Hierarchy Process (AHP) and evaluated by Subject Matter Experts (SMEs), is tailored to the unique needs of government institutions. It enables them to assess their digital transformation progress and align their efforts with the value embedded in their vision and mission. Secondly, the dissertation provides an empirical study on evaluating a government organization's capabilities in Data management to drive business insights and decision-making. Finally, by addressing the specific requirements of both two areas Digital transformation (DT) and Data management (DM) we found that the AI readiness is strongly affected by both of these areas. Then we deploy decision intelligence modeling for AI readiness in government institutions, this research provides valuable insights for achieving value-oriented management in these organizations
Carbon footprint of construction industry: A global review and supply chain analysis
Full text linkThis paper conducts a global review and a macro-level supply chain analysis focusing on carbon footprint of construction industry worldwide for the period between 2009 and 2020 using the Scopus database. A total of 1833 journal articles are revealed with focus on carbon footprint in the field of construction in general, of which only 115 (6% of the total) studies have a macro-level analysis of the construction sector, providing a more holistic overview of the construction sector from various aspects. These macro-level studies were reviewed and classified based on journal, country, year, method, scope of analysis, type of construction, and period. The findings showed that approximately 60% of these studies focus on the Chinese construction industry and the majority of studies analyzed national-level (75%) and city-level (18%) carbon footprints of construction. On the contrary, global-level analysis has a lower share, which accounted for only 6% of reviewed articles. The review showed that more than 20% of studies use the input-output analysis as the main methodological approach to quantify macro-level carbon emission from construction sector, which is followed by the process-based life cycle assessment with 10% share, where more bottom-up approaches are employed. There are only a handful of articles found in the literature using a hybrid life cycle assessment and global multiregional input-output analysis for carbon footprint accounting of construction. Furthermore, there is also no study found in the literature, which presented a comprehensive regional and global supply chain analysis of construction carbon footprints. The results revealed that the largest portion of carbon emissions stem from the regional and global supply chains of the construction industries. The authors concluded that carbon reduction policies should not only consider the limited regional impacts; however, it must take into account the role of indirect, complex and interconnected global supply chains of construction industries
Global carbon accounting tool (GCAT) for manufacturing supply chains:the world model
No full textThis research presents a global carbon accounting tool (GCAT) developed by the Sustainable Systems & Solutions Lab (S3-Lab) at Istanbul Sehir University, Turkey. This tool is able to capture the regional and global carbon footprints of manufacturing supply chains for world's 40 major economies. The GCAT will enable users to conduct carbon footprint analysis for the specific industry of selected country in 5 steps (http://s3-lab.sehir.edu.tr/gcat.html). The European food and drink industry will be chosen as a case study to conduct supply chain-based carbon footprint analyses including production versus consumption based analysis, sector breakdown for supply chain components, scope-based analysis and impact-by-country analysis for supply chain carbon emissions
Scope-Based Carbon Footprint Analysis Of U.S. Residential And Commercial Buildings: An Input-Output Hybrid Life Cycle Assessment Approach
No full textAnalyzing building related carbon emissions remains as one of the most increasing interests in sustainability research. While majority of carbon footprint studies addressing buildings differ in system boundaries, scopes, GHGs and methodology selected, the increasing number of carbon footprint reporting in response to legal and business demand paved the way for worldwide acceptance and adoption of the Greenhouse Gas Protocol (GHG Protocol) set by the World Resources Institute (WRI) and World Business Council for Sustainable Development (WBCSD). Current research is an important attempt to quantify the carbon footprint of the U.S. residential and commercial buildings in accordance with carbon accounting standards and Scopes set by WRI, in which all possible indirect emissions are also considered. Emissions through the construction, use, and disposal phases were calculated for the benchmark year 2002 by using a comprehensive hybrid economic input-output life cycle analysis. The results indicate that emissions from direct purchases of electricity (Scope 2) with 48% have the highest carbon footprint in the U.S. buildings. Indirect emissions (Scope 3) with 32% are greater than direct emissions (Scope 1) with 20.4%. Commuting is the most influential activity among the Scope 3 emissions with more than 10% of the carbon footprint of the U.S. buildings overall. Construction supply chain is another important contributor to the U.S. building\u27s carbon footprint with 6% share. Use phase emissions are found to be the highest with 91% of the total emissions through all of the life cycle phases of the U.S. buildings. © 2013 Elsevier Ltd
Water and carbon footprint reduction potential of renewable energy in the United States: A policy analysis using system dynamics
No full textRenewable energy has gained popularity as an alternative to fossil fuels, which regularly emit large amounts of Greenhouse Gases and consume/withdraw large amounts of water, but renewable energy market penetration is still limited while fossil fuels are still the U.S.‘s dominant power source. This is due to resistance in the market, or in this case, the failure of renewable energy policies to achieve long-term environmental sustainability due to neglected external factors (economic, societal, etc.). No available literature analyzes potential sources and/or effects of this policy resistance, so this research investigates the underlying mechanisms in the renewable energy generation market by utilizing a system dynamics model. A two-alternative Generalized Bass Model was developed to simulate the renewable energy market (specifically with respect to solar PV and wind energy), including the environmental, societal, and economic concerns associated with each of the alternatives evaluated in this study, so as to identify and address possible causes of policy resistance and its subsequent effects on environmental impacts (esp. GHG emissions and water withdrawal rates). Based on this model, three separate policy areas (solar PV investments, wind power investments, and the elimination of fossil fuel subsidies) and various combinations thereof were proposed and tested within the context of the model. Based on the results of this study, it is highly recommended to invest as generously as possible into multiple renewable energy industries, reduce fossil fuel subsidies (in turn freeing up funding for renewable energy investments), and seek further advancement in renewable energy technologies (e.g. enhancing the useable lifetimes of wind turbines). A balanced policy have potential to increase the share of renewable's up to roughly 40% in the U.S. by 2050, as well as 17% and 32% GHG and water withdrawal reduction potential by 2050
Towards Greening The U.S. Residential Building Stock.A System Dynamics Approach
No full textEnergy consumption in residential buildings is one of the major sources of greenhouse gas (GHG) emissions in the U.S. Most of the efforts to minimize these emissions contemplate on construction of new high performance green buildings rather than retrofitting the existing residential building stock, which has the greatest emission reduction potential. In this paper, rapidly increasing GHG emissions trend associated with the U.S. residential building stock is addressed. The objective is to reduce or stabilize the increasing GHG emissions trend as a result of sprawling residential building stock across the country. System Dynamics (SD) is utilized to study the mid and long term impacts of green building related policies on the GHG emissions stock. SD model is built based on stock and flow diagram, which is derived from causal loop diagram that consists of 12 endogenous and 2 exogenous variables and causal relationships. Three important action areas are considered for policy making, namely.high performance green building construction, building retrofitting, and net zero building construction. From the three policy fields, a total of 19 policy strategies (7 single and 12 hybrid) is developed and the impacts of the policies on GHG emissions trend are experimented until 2050. Among the proposed policies, retrofitting-focused policies are found to be more effective on stabilizing the GHG emissions trend compared to the policies related to the construction of new net zero and high performance green buildings. On the other hand, hybrid implementation of policies from the three policy fields provided the greatest reduction in the GHG emissions trend. One of the most important outcomes of this study is that focusing on increasing the construction rate of net zero or high performance green buildings alone does not help with stabilizing/reducing the GHG emissions trend unless the retrofitting of existing residential building stock is seriously considered as a strict policy along with green building policies. Analysis results also revealed that the residential green building movement itself is found to be far from being the driver policy in stabilizing the rapidly increasing GHG emissions trend in the long run. © 2014 Elsevier Ltd
Investigating Carbon Footprint Reduction Potential Of Public Transportation In United States: A System Dynamics Approach
No full textAs part of sustainable urban planning, the potential of public transportation modes has been studied to reduce CO2 emissions and energy consumption and to increase roadway safety. Increasing public transportation ridership shares compared to drive alone transportation mode would therefore be a giant step toward more environmentally friendly and stress-free cities, and so this study aims to propose possible public transportation policies to be adopted by policy makers or urban planners. Although public transportation is one of the sub-sections of urban planning, it has a wide variety of aspects that affect local societies and the environment, so a system dynamics approach is used to model and simulate the most realistic and practical CO2 mitigation scenarios for U.S. cities by adopting public transportation policies for future years. Based on historical data and applicable model validation processes, the behavior of the U.S. commuters’ transportation mode choice and the potential of transit transportation to mitigate CO2 emissions are both forecasted for future years up to 2050 under several possible policy scenarios. The results indicate that, in order to decrease fuel consumption and CO2 emission trends, marginal and ambitious scenarios should be implemented. For instance, increasing public transportation ridership by 9% has the potential to reduce CO2 emissions by 766,000 tonnes annually in 2050, whereas a 25% increase in ridership could potentially reduce cumulative CO2 emissions by 61.3 million tonnes
Uncertainty-embedded dynamic life cycle sustainability assessment framework:an ex-ante perspective on the impacts of alternative vehicle options
No full textAlternative vehicle technologies have a great potential to minimize the transportation-related environmental impacts, reduce the reliance of the U.S. on imported petroleum, and increase energy security. However, they introduce new uncertainties related to their environmental, economic, and social impacts and certain challenges for widespread adoption. In this study, a novel method, uncertainty-embedded dynamic life cycle sustainability assessment framework, is developed to address both methodological challenges and uncertainties in transportation sustainability research. The proposed approach provides a more comprehensive, system-based sustainability assessment framework by capturing the dynamic relations among the parameters within the U.S. transportation system as a whole with respect to its environmental, social, and economic impacts. Using multivariate uncertainty analysis, likelihood of the impact reduction potentials of different vehicle types, as well as the behavioral limits of the sustainability potentials of each vehicle type are analyzed. Seven sustainability impact categories are dynamically quantified for four different vehicle types (internal combustion, hybrid, plug-in hybrid, and battery electric vehicles) from 2015 to 2050. Although impacts of electric vehicles have the largest uncertainty, they are expected (90% confidence) to be the best alternative in long-term for reducing human health impacts and air pollution from transportation. While results based on deterministic (average) values indicate that electric vehicles have greater potential of reducing greenhouse gas emissions, plug-in hybrid vehicles have the largest potential according to the results with 90% confidence interval
Integrating Triple Bottom Line Input-Output Analysis Into Life Cycle Sustainability Assessment Framework: The Case For Us Buildings
No full textPurpose: With the increasing concerns related to integration of social and economic dimensions of the sustainability into life cycle assessment (LCA), traditional LCA approach has been transformed into a new concept, which is called as life cycle sustainability assessment (LCSA). This study aims to contribute the existing LCSA framework by integrating several social and economic indicators to demonstrate the usefulness of input-output modeling on quantifying sustainability impacts. Additionally, inclusion of all indirect supply chain-related impacts provides an economy-wide analysis and a macro-level LCSA. Current research also aims to identify and outline economic, social, and environmental impacts, termed as triple bottom line (TBL), of the US residential and commercial buildings encompassing building construction, operation, and disposal phases. Methods: To achieve this goal, TBL economic input-output based hybrid LCA model is utilized for assessing building sustainability of the US residential and commercial buildings. Residential buildings include single and multi-family structures, while medical buildings, hospitals, special care buildings, office buildings, including financial buildings, multi-merchandise shopping, beverage and food establishments, warehouses, and other commercial structures are classified as commercial buildings according to the US Department of Commerce. In this analysis, 16 macro-level sustainability assessment indicators were chosen and divided into three main categories, namely environmental, social, and economic indicators. Results and discussion: Analysis results revealed that construction phase, electricity use, and commuting played a crucial role in much of the sustainability impact categories. The electricity use was the most dominant component of the environmental impacts with more than 50 % of greenhouse gas emissions and energy consumption through all life cycle stages of the US buildings. In addition, construction phase has the largest share in income category with 60 % of the total income generated through residential building\u27s life cycle. Residential buildings have higher shares in all of the sustainability impact categories due to their relatively higher economic activity and different supply chain characteristics. Conclusions: This paper is an important attempt toward integrating the TBL perspective into LCSA framework. Policymakers can benefit from such approach and quantify macro-level environmental, economic, and social impacts of their policy implications simultaneously. Another important outcome of this study is that focusing only environmental impacts may misguide decision-makers and compromise social and economic benefits while trying to reduce environmental impacts. Hence, instead of focusing on environmental impacts only, this study filled the gap about analyzing sustainability impacts of buildings from a holistic perspective. © 2014 Springer-Verlag
The assessment and integration of material footprint in national energy development plans
No full textIn this research, Multi Region Input-Output (MRIO) model is used for to investigate the nexus between electricity production from various renewable and non-renewable energy sources and their material consumption in Turkey and UK, enabling a global trade-based analysis for material footprint accounting. Three national electricity production scenarios such as Business-as-Usual, Official Plan, and Renewable Energy Development Plan were analyzed to help policy makers to estimate the consequences of energy investment scenarios on resource footprint based on 19 minerals from 12 different sources of electricity production. The Autoregressive Integrated Moving Average (ARIMA) forecast method is used to analyze the scenarios until 2050. The study revealed that electricity generation using coal is the most material-intensive energy source. Electricity production by coal in Turkey is expected to be responsible for 83.7% of metallic mineral and 80.3% of nonmetallic mineral consumption by 2050. In Turkey, coal, hydro and wind have been identified as the critical sources for electricity production under business-as-usual scenario, which are anticipated to constitute 72% of the total minerals consumption in 2050. For each kWh of electricity is produced by each energy source in Turkey, coal, natural gas, and oil together cause 81% of the total mineral consumption. However, in UK, 84.6% of metallic mineral and 81.4% of nonmetallic mineral consumption will be due to electricity production from coal and natural gas combined while coal alone will constitute to about 41% of the nonmetallic mineral consumption in 2050. Also, the nonmetallic mineral consumption by electricity production from coal and natural gas in UK will be 95.5% by 2050 under all three scenarios. The findings of this research can help identifying the critical minerals and energy resources to propose most optimum energy mix and eventually, to reduce dependency on the critical material consumption.Abstract iv
Öz v
Acknowledgments vii
List of Figures ix
List of Tables x
Abbreviations xi
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 Methodology 5
2.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.1.1 MRIO Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.1.2 Mathematical formulation of MRIO . . . . . . . . . . . . . . . . . 8
2.1.3 ARIMA forecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.4 ARIMA Validation and Goodness of Fit . . . . . . . . . . . . . . . 12
2.1.5 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.6 Scenarios Construction and Visualization . . . . . . . . . . . . . . 14
2.1.6.1 Business As Usual scenario (BAU) . . . . . . . . . . . . . 16
2.1.6.2 The Official Plan scenario (OP) . . . . . . . . . . . . . . 18
2.1.6.3 The Go green plan scenario . . . . . . . . . . . . . . . . . 19
3 Results and Discussions 22 3.1 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 Discussion and Conclusions 32 4.1 Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
Bibliography 3
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