ElectricityNetwork
Electricity trade among power grids leads to difficulties in measuring environmental impacts of purchased electricity. In this project we use a network-based approach to fully capture the direct and indirect environmental impacts across the whole inter-connected electricity trade network induced by local electricity consumption. This new method can help organizations to evaluate their impacts on the environment (e.g., carbon footprint) more accurately.
Qu, S.; Liang, S.; Xu, M.* CO2 emissions embodied in interprovincial electricity transmissions in China. Environmental Science & Technology 2017, 51 (18), 10893-10902.
Qu, S.; Wang, H.-X.; Liang, S.; Shapiro, A. M.; Suh, S.; Sheldon, S.; Zik, O.; Fang, H.; Xu, M.* A Quasi-Input-Output model to improve the estimation of emission factors for purchased electricity from interconnected grids. Applied Energy 2017, 200, 249-259.
Ji, L.; Jia, X.-P.; Chiu, A. S. F.; Xu, M.* Global electricity trade network: structures and implications. PLOS ONE 2016, 11 (8), e0160869.
Ji, L.; Liang, S.; Qu, S.; Zhang, Y.-X.; Xu, M.*; Jia, X.-P.; Jia, Y.-T.; Niu, D.-X.; Yuan, J.-H.; Hou, Y.; Wang, H.-K.; Chiu, A. S. F.; Hu, X.-J. Greenhouse gas emission factors of purchased electricity from interconnected grids. Applied Energy, accepted. DOI: 10.1016/j.apenergy.2015.10.065.
"Big Data" for Human Mobility
This is a spin-off of the DOE CERC-CVC project, aiming to understand human mobility dynamics and implications to transportation sustainability using "big data" and data science methods. In particular, we use large-scale, real-time taxi trajectory data for large cities (e.g., ~20,000 vehicles for 30 days) to characterize mobility dynamics at the individual level. Based on this characterization of individual mobility dynamics, we evaluate environmental implications of large-scale deployment of electric vehicles, optimize the siting of public charging infrastructure for electric vehicles, and assess the environmental benefits of ride-sharing.
Cai, H.; Rao, R.; Xu, M. Modeling electric taxis’ charging behavior using real-world data. International Journal of Sustainable Transportation, in press.
Cai, H.*; Zhan, X.-W.; Zhu, J.; Jia, X.-P.; Chiu, A. S. F.; Xu, M.* Understanding taxi travel patterns. Physica A: Statistical Mechanics and Its Applications 2016, 457, 590-597.
Shahraki, N.; Cai, H.; Turkay, M.; Xu, M. Optimal locations of electric public charging stations using real world vehicle travel patterns. Transportation Research Part D: Transport and Environment 2015, 41, 165-176.
Xu, M.*; Cai, H.; Liang, S. Big data and industrial ecology. Journal of Industrial Ecology 2015, 19 (2), 205-210.
Cai, H.; Jia, X.-P.; Chiu, A. S. F.; Hu, X.-J.; Xu, M.* Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet. Transportation Research Part D: Transport and Environment 2014, 33, 39-46.
Cai, H.; Xu, M.* Greenhouse gas implications of fleet electrification based on Big Data-informed individual travel patterns. Environmental Science & Technology 2013, 47 (16), 9035-9043.
WaterRiskNetwork
Much work has been done to evaluate environmental impacts of international trade. Little attention has been paid to the other side of the coin, implications of the environmental challenges to the trade system. Funded by the National Science Foundation, this project addresses this intellectual gap using water scarcity risk as a case study. The project develops a probabilistic network analysis framework integrates methods from multiple fields, including input-output analysis, risk analysis, and network analysis. The integration of these methods is a unique contribution to research in sustainability implications of international trade. It allows evaluating cascading impacts of local water scarcity to upstream and downstream supply chains for each industry in each country of the global trade network. Multiple factors contributing to water scarcity risk are considered simultaneously, including the dependence of industries on local water resources, severeness of water scarcity across countries and river basins, and interconnectedness of the global trade network. This project focuses on industries in countries as the unit of analysis, improving the resolution of relevant, limited studies in the literature focusing only on countries.
LCI Network
A quality life cycle assessment (LCA) depends on the availability and quality of life cycle inventory (LCI) data. LCA practitioners have been increasingly relying on dedicated LCI databases which provide LCI data for common unit processes of a wide range of products. Despite the convenience, these LCI databases still depend on time-consuming, expensive empirical data collection. Supported by a NSF CAREER grant, this project aims to transform the current practice of developing LCI databases into a faster, less expensive process that still generates reliable LCI data, by developing a computational and data-driven framework for estimating missing LCI data solely based on limited known LCI data, without relying on additional empirical data. It will (1) create a framework for modeling and analyzing LCI networks, (2) develop computational models for estimating missing LCI data, and (3) apply these models to evaluate LCI data quality and predict LCI data for emerging technologies.
Building upon the transformative promise seen from link prediction techniques in network science that enable the prediction of missing information of a network based on limited observations, this research develops computational and data-driven approaches as complementary alternatives for compiling LCI databases.
Urban FEW Nexus
With more than half of the world’s population living in urban areas, the efficient provision of food, energy, and water (FEW) has become a pressing challenge for urban sustainability. This challenge comes from not only the increasing demand for FEW resources due to increased urban population, but also the complex interdependence of the urban FEW systems. Policy and technological solutions addressing the urban FEW challenge needs to be assessed through the lens of FEW nexus to (1) identify co-benefits that a single policy or technology can simultaneously improve the efficiency of multiple systems, and (2) avoid unintended consequences that desired changes in one system lead to undesired changes in other systems.
This project aims to develop an integrated systems modeling framework understanding urban FEW nexus to promote the efficient utilization of FEW resources. Through a case study in the Detroit city-region, we will demonstrate the integrated systems modeling framework by (1) characterizing the urban FEW flow networks, (2) examining the structure of the integrated network of FEW flow networks, and (3) developing and evaluating policy and technology scenarios with stakeholder inputs to identify co-benefit opportunities without unintended consequences.
IO Network
Input-output (IO) models are widely used in environmental studies to understand the relationship between production-related environmental pressures (e.g. greenhouse gas emissions) and consumption at the economy scale. However, little attention in the IO literature has been paid to study the structure of an economy as a complete, integrated system. Just as we cannot comprehend the dynamics of ecosystems by studying separated food chains or the behavior of cells by micro-scoping isolated biochemical pathways, we cannot fully understand the structure and its relation to the dynamics of an economy only by investigating isolated, separated interdependencies between sectors. A more “holistic” perspective is required. Modern “network analysis” offers an ideal framework to pursue such study using IO models.
Liang, S.; Qi, Z.-L.; Qu, S.; Zhu, J.; Chiu, A. S. F.; Jia, X.-P.; Xu, M.* Scaling of global input-output networks. Physica A: Statistical Mechanics and Its Applications 2016, 452, 311-319.
Liang, S.; Qu, S.; Xu, M.* Betweenness-based method to identify critical transmission sectors for supply chain environmental pressure mitigation. Environmental Science & Technology 2016, 50 (3), 1330-1337.
Liang, S.; Feng, Y.; Xu, M.* Structure of the global virtual carbon network revealing important sectors and communities. Journal of Industrial Ecology 2015, 19 (2), 307-320.
Xu, M.*; Allenby, B. R.; Crittenden, J. C. Interconnectedness and resilience of the U.S. economy. Advances in Complex Systems 2011, 14 (5), 649-672.
ABLCA
Life cycle assessment (LCA) quantifies the environmental impacts of a product or process in an effort to improve its environmental sustainability. LCA is easiest to implement on established industrial systems with available data; however, the systems that may benefit most from an LCA are emerging, uncertain, and difficult to quantify with traditional LCA methods. Funded by the National Science Foundation, this project develops a spatially-explicit agent-based LCA (ABLCA) framework to improve the standard LCA modeling technique by overcoming the issues involved with analyzing emerging technologies with dynamic and evolving supply chains. This improved LCA modeling framework is also applied to the U.S. switchgrass bioenergy system (biofuels and biomass electricity) to examine the supply chain dynamics and evolution and evaluate the associated life cycle environmental impacts.
Bichraoui-Draper, N.; Xu, M.*; Miller, S. A.; Guillaume, B. Agent-based life cycle assessment for switchgrass-based bioenergy systems. Resources, Conservation & Recycling 2015, 103, 171-178.
Choudhary, S.; Liang, S.; Cai, H.; Keoleian, G. A.; Miller, S. A.; Kelly, J.; Xu, M.* Reference and functional unit can change bioenergy pathway choices. The International Journal of Life Cycle Assessment 2014, 19 (4), 796-805.
CERC-CVC
As part of the US-China Clean Energy Research Center on Clean Vehicles (CERC-CVC) funded by the Department of Energy, this project assesses the feasibility of alternative vehicle fueling including biofuels and electricity for meeting significant penetration of clean vehicles. This project evaluates the impact on water and land resources in the US due to large-scale deployment of clean vehicles.
Pontau, P.; Hou, Y.; Cai, H.; Zhen, Y.; Jia, X.-P.; Chiu, A. S. F.; Xu, M. Assessing land-use impacts by clean vehicle systems. Resources, Conservation & Recycling 2015, 95, 112-119.
Cai, H.; Hu, X.-J.; Xu, M.* Impact of emerging clean vehicle system on water stress. Applied Energy 2013, 111, 644-651.
Prof. Xu receives the inaugural Nanova/CAPEES Frontier Research Award
