Urban Density and Climate Change: A STIRPAT Analysis Using City-Level Data

By Brantley Liddle. 2013

126 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment, Transportation

View Paper: https://www.semanticscholar.org/paper/5c86b73f50a7628fa4b2fb715acd96aa7820feb6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Avik Sinha
2. Muhammad Shahbaz
3. Syed Jawad Hussain Shahzad
4. Daniel Balsalobre
5. Rajesh K Sharma
6. Xunpeng Shi
7. Pradeep Kautish
8. Xiaoling Zhang

In Renewable Energy, Sustainability and the Environment

1. Avik Sinha
2. Muhammad Shahbaz
3. Syed Jawad Hussain Shahzad
4. Daniel Balsalobre
5. Rajesh K Sharma
6. Xiaoling Zhang
7. Pradeep Kautish
8. Xunpeng Shi

In Transportation

1. Xiaoling Zhang
2. Shuai Shao
3. Xunpeng Shi
4. Yongping Sun
5. Zhi Li
6. Muhammad Shahbaz
7. Brantley Liddle

Citations in the corpus

• The Influencing Factors of CO2 Emissions and the Role of Biomass Energy Consumption: Statistical Experience from G-7 Countries (2018, background): “[97], Liddle [96], Iwata and Okada [74], Hou et al.”
• Does renewable energy consumption reduce ecological footprint? Evidence from eight developing countries of Asia (2020, background): “Further, studies in the past have quite often considered population growth rate (Pebley, 1998), population size (O’Neill et al., 2001), density (Sharma et al., 2020a), and age structure to determine pollution intensity in a region (Liddle, 2013).”
• Structural transformation of manufacturing, natural resource dependence, and carbon emissions reduction: Evidence of a threshold effect from China (2019, methodology): “Previous work often makes improvements based on (2) in order to carry out empirical studies ( Poumanyvong and Kaneko, 2010; Liddle, 2013).”
• Impact of population, age structure, and urbanization on carbon emissions/energy consumption: evidence from macro-level, cross-country analyses (2013, unknown): “The methods used by Liddle (2011, 2013a) account for endogeneity among variables implicitly via error correction/cointegration modeling.”
• What are the carbon emissions elasticities for income and population? Bridging STIRPAT and EKC via robust heterogeneous panel estimates (2015, background): “…1 in O’Neill et al. 2012); this phenomenon is true even for studies considering different dependent variables (e.g., fuelwood consumption by Knight and Rosa 2012), or different units/scales of analysis (e.g., US county-level data in Roberts 2011; international city-based data in Liddle 2013c).”
• Urbanization and income inequality in Sub-Saharan Africa (2019, unknown): “Because cities tend to produce a disproportionate amount of a country’s GDP (Liddle, 2013), and because this relative importance is stronger for less wealthy nations, cities tend to attract rural migrants (Liddle, 2013, 2017).”