|Title||Estimating China’s Urban Energy Demand and CO2 Emissions: A Bottom-up Modeling Perspective|
|Publication Type||Conference Paper|
|Year of Publication||2016|
|Authors||Nina Khanna, David Fridley, Lynn K Price, Nan Zhou, Stephanie Ohshita|
|Conference Name||2016 ACEEE Summer Study on Energy Efficiency in Buildings|
|Conference Location||Pacific Grove, CA|
China is experiencing unprecedented urbanization with the urban share of population expected to grow to nearly 80% by 2050. Chinese urban residents consume nearly 1.6 times as much commercial energy as rural residents, and account for an even larger share of energy and carbon dioxide (CO2) emissions embodied in urban infrastructure and goods. As a result, cities can play an increasingly important role in helping China meet its future energy and CO2 intensity reduction targets. While some individual cities have conducted energy and greenhouse gas emission inventories, China lacks estimates of aggregate urban energy consumption and CO2 emissions that take into consideration detailed sectoral drivers, fuel mixes, and end-uses specific to urban areas.This paper describes the results of a bottom-up, energy end-use modeling methodology for estimating China’s urban energy demand and CO2 emissions for four key demand sectors. We present a detailed modeling framework that characterizes residential and commercial building end-uses in Chinese cities, differentiates between intra-city and inter-city transport attributable to urban residents, and evaluates the urban share of industrial production activity. Scenario analysis is also used to quantify the urban energy and CO2 emissions reduction potential within each sector. We find that the Chinese industrial sector alone accounts for 56% of urban primary energy demand and 62% of urban CO2 emissions in 2010 and holds the greatest mitigation potential — a characteristic unique to Chinese cities. Maximum deployment of commercially-available, cost-effective technologies across all four sectors can also help Chinese urban CO2 emissions peak earlier.