Jing Cao, Mun S. Ho, and Wenhao Hu. 2020. “
Analyzing carbon price policies using a general equilibrium model with household energy demand functions.” In Measuring Economic Growth and Productivity: Foundations, KLEMS Production Models, and Extensions,
edited by Barbara Fraumeni. Cambridge, MA: Academic Press.
Publisher's VersionAbstract
Multi-sector general equilibrium models are used to simulate the effects of environmental policies on industry output and consumption at disaggregated levels. The specification of household demand in such models often use simpler forms such as CES or Linear Expenditure Systems since there are few estimates of more flexible systems. We estimate a 2-stage translog utility function that explicitly accounts for detailed energy expenditures to allow us to capture the price and income effects more accurately than these simpler forms. We incorporate this into a China growth model to simulate the effects of a carbon price to achieve the government targets for the Climate Change (Paris) agreements.
Richard Goettle, Mun S. Ho, and Peter Wilcoxen. 2020. “
Emissions accounting and carbon tax incidence in CGE models: bottom-up versus top-down.” In Measuring Economic Growth and Productivity: Foundations, KLEMS Production Models, and Extensions,
edited by Fraumeni, B, 1st ed. Cambridge, MA: Academic Press.
Publisher's VersionAbstractMulti-sector general equilibrium models are the work-horses used to analyze the impact of carbon prices in climate policy discussions. Such models often have distinct industries to represent coal, liquid fuels, and gas production where the output over time is represented by quantity and price indexes. The industries that buy these fuels, however, do not use a common homogenous quantity (e.g., steam coal vs. metallurgical coal) and have distinct purchasing price indexes. In accounting for energy use or CO2 emissions, modelers choose to attach coefficients either bottom-up to a sector specific input index or top-down to an average output index and this choice has a direct bearing on the incidence of carbon taxation. We discuss how different accounting methods for the differences in prices can have a large effect on the simulated impact of carbon prices. We emphasize the importance for modelers to be explicit about their methods.
Chenghe Guan and Peter Rowe. 2020. “
Multi-criteria locational analysis for retail development in small towns.” In The Geography of Mobility, Wellbeing and Development: Understanding China’s Transformations through Big Data, 1st ed., Pp. 220. London: Routledge.
Publisher's VersionAbstract
Big data is increasingly regarded as a new approach for understanding urban informatics and complex systems. Today, there is unprecedented data availability, with detailed remote-sensed data on the built environment and rich mineable web-based sources in the form of social media, web mapping, information services and other sources of unstructured "big data".
This book brings together a group of international contributors to consider the geographical implications of mobility, wellbeing and development within and across Chinese cities through location-based big data perspectives. The degree of urban sprawl, productive density and vibrancy can be reflected from location-based social media big data. The challenge is to identify, map and model these relationships to develop cities at different places in the urban hierarchical system that are more sustainable. This edited book aims to tackle these issues through two inter-related geographical scales: inter-city level and intra-city level.
The text is designed for graduate courses in planning, geography, public policy and administration, and for international researchers who are involved in urban and regional economics and economic geography.