Mo Li

Dr. Mo Li

orcid

Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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3 May 2022 in Scientific Data
Verified
Journal Article
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Wesley IngwersenWesley Ingwersen
Wesley Ingwersen
MLMo Li
Mo Li
BYBen Young
Ben Young
JVJorge Vendries
Jorge Vendries
CBCatherine Birney
Catherine Birney
USEEIO v2.0 is an environmental-economic model of US goods and services that can be used for life cycle assessment, footprinting, national prioritization, and related applications. This paper describes the development of the model and accompanies the release of a full model dataset as well as various supporting datasets of national environmental totals by US industry. Novel methodological elements since USEEIO v1 models include waste sector disaggregation, final demand vectors for US consumption and production, a domestic form of the model that can be used to separate domestic and foreign impacts, and price adjustment matrices for converting outputs to purchaser price and in various US dollar years. Improvements in modeling national totals of industry and environmental flows are described. The model is validated through reproduction of national totals from input data sources and through analysis of changes from the most recent complete USEEIO model that can be explained based on data updates or method changes. The model datasets can all be reproduced with open source software packages.

1 June 2020 in Resources, Conservation and Recycling
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Journal Article
Closed access
DMDavid E. Meyer
David E. Meyer
MLMo Li
Mo Li
Wesley IngwersenWesley Ingwersen
Wesley Ingwersen

19 September 2017 in Environmental Science & Technology
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Journal Article
Closed access
MLMo Li
Mo Li
TSTimothy M. Smith
Timothy M. Smith
Yi YangYi Yang
Yi Yang
EWElizabeth Wilson
Elizabeth Wilson

15 October 2015 in Technology Analysis & Strategic Management
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Journal Article
Closed access
MLMo Li
Mo Li
BNBang Nguyen
Bang Nguyen
XYXiaoyu Yu
Xiaoyu Yu
The study provides a game theoretic exploration of firms’ decisions on whether to compete or collaborate in the generation and adoption of a sequence of new technologies. Different from models proposed by previous studies, which concentrates on process innovation and a two-strategy set (innovation or do nothing), the present game theory model emphasises product innovation and a three-strategy set (innovation, collaboration, or do nothing). The study makes three contributions. The proposed game theory model extends current understanding of the impacts of collaboration possibilities and collaboration cost in a dynamic game theory. Further, the model clarifies the impact of transaction costs on the outcome. Finally, the study finds that the relationship between collaboration costs is not univariate, but depends on the market type and various market characteristics, such as technology gap, technology level, the product substitution index, transaction costs, and the discount rate of price sensitiveness.

23 December 2022 in International Regional Science Review
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Journal Article
Closed access
MLMo Li
Mo Li
JFJoão Pedro Ferreira
João Pedro Ferreira
CCChrista D. Court
Christa D. Court
DMDavid Meyer
David Meyer
MLMengming Li
Mengming Li
Wesley IngwersenWesley Ingwersen
Wesley Ingwersen
Subnational input–output (IO) tables capture industry- and region-specific production, consumption, and trade of commodities and serve as a common basis for regional and multi-regional economic impact analysis. However, subnational IO tables are not made available by national statistical offices, especially in the United States (US), nor have they been estimated with transparent methods for reproducibility or updated regularly for public availability. In this article, we describe a robust StateIO modeling framework to develop state and two-region IO models for all 50 states in the US using national IO tables and state industry and trade data from reliable public sources such as the US Bureau of Economic Analysis. We develop 2012-2017 state IO models and two-region IO models at the BEA summary level. The two regions are state of interest and rest of the US. All models are validated by a series of rigorous checks to ensure the results are balanced at state and national levels. We then use these models to calculate a 2012–2017 time series of macro economic indicators and highlight results for 11 states that have distinct economies with respect to size, geography, and industry structure. We also compare selected indicators to state IO models created by popular licensed and open-source software. Our StateIO modeling framework is consolidated in an open-source R package, stateior, to ensure transparency and reproducibility. Our StateIO models are US-focused, which may not be transferrable to international accounts, and form the economic base of state versions of the US environmentally-extended IO models.

28 April 2022 in Applied Sciences
Verified
Journal Article
Open access
PDF file
MLMo Li
Mo Li
Wesley IngwersenWesley Ingwersen
Wesley Ingwersen
BYBen Young
Ben Young
JVJorge Vendries
Jorge Vendries
CBCatherine Birney
Catherine Birney
useeior is an open-source R package that builds USEEIO models, a family of environmentally-extended input–output models of US goods and services used for life cycle assessment, environmental footprint estimation, and related applications. USEEIO models have gained a wide user base since their initial release in 2017, but users were often challenged to prepare required input data and undergo a complicated model building approach. To address these challenges, useeior was created. In useeior, economic and environmental data are conveniently retrievable for immediate use. Users can build models simply from given or user-specified model configuration and optional hybridization specifications. The assembly of economic and environmental data and matrix calculations are automatically performed. Users can export model results to desired formats. useeior is a core component of the USEEIO modeling framework. It improves transparency, efficiency, and flexibility in building USEEIO models, and was used to deliver the recent USEEIO model.