Exploring the effect of urban sprawl on carbon dioxide emissions: An urban sprawl model analysis from remotely sensed nighttime light data

2022 ◽  
Vol 93 ◽  
pp. 106731
Author(s):  
Yizhen Wu ◽  
Chuanlong Li ◽  
Kaifang Shi ◽  
Shirao Liu ◽  
Zhijian Chang
Keyword(s):  
Carbon Dioxide ◽  
Urban Sprawl ◽  
Carbon Dioxide Emissions ◽  
Remotely Sensed ◽  
Model Analysis ◽  
Nighttime Light Data ◽  
Nighttime Light

scholarly journals Global effect of urban sprawl, industrialization, trade and economic development on carbon dioxide emissions

2020 ◽  
Vol 15 (3) ◽  
pp. 034049 ◽  
Author(s):  
Samuel Asumadu Sarkodie ◽  
Phebe Asantewaa Owusu ◽  
Thomas Leirvik
Keyword(s):  
Carbon Dioxide ◽  
Economic Development ◽  
Urban Sprawl ◽  
Carbon Dioxide Emissions ◽  
Global Effect

scholarly journals A Simple Method to Improve Estimates of County-Level Economics in China Using Nighttime Light Data and GDP Growth Rate

2019 ◽  
Vol 8 (9) ◽  
pp. 419 ◽  
Author(s):  
Xiaole Ji ◽  
Xinze Li ◽  
Yaqian He ◽  
Xiaolong Liu
Keyword(s):  
Growth Rate ◽  
Remotely Sensed ◽  
Gdp Growth ◽  
County Level ◽  
Remotely Sensed Data ◽  
Simple Method ◽  
Gdp Growth Rate ◽  
Nighttime Light Data ◽  
Economic Statistics ◽  
Nighttime Light

County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions.

scholarly journals A New Fusion Approach for Extracting Urban Built-up Areas from Multisource Remotely Sensed Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2516 ◽  
Author(s):  
Xiaolong Ma ◽  
Chengming Li ◽  
Xiaohua Tong ◽  
Sicong Liu
Keyword(s):  
Vegetation Index ◽  
Sample Selection ◽  
Remotely Sensed ◽  
High Accuracy ◽  
Support Vector ◽  
Remotely Sensed Data ◽  
Decision Level ◽  
Nighttime Light Data ◽  
Nighttime Light ◽  
Fusion Approach

Recent advances in the fusion technology of remotely sensed data have led to an increased availability of extracted urban information from multiple spatial resolutions and multi-temporal acquisitions. Despite the existing extraction methods, there remains the challenging task of fully exploiting the characteristics of multisource remote sensing data, each of which has its own advantages. In this paper, a new fusion approach for accurately extracting urban built-up areas based on the use of multisource remotely sensed data, i.e., the DMSP-OLS nighttime light data, the MODIS land cover product (MCD12Q1) and Landsat 7 ETM+ images, was proposed. The proposed method mainly consists of two components: (1) the multi-level data fusion, including the initial sample selection, unified pixel resolution and feature weighted calculation at the feature level, as well as pixel attribution determination at decision level; and (2) the optimized sample selection with multi-factor constraints, which indicates that an iterative optimization with the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BSI), along with the sample training of the support vector machine (SVM) and the extraction of urban built-up areas, produces results with high credibility. Nine Chinese provincial capitals along the Silk Road Economic Belt, such as Chengdu, Chongqing, Kunming, Xining, and Nanning, were selected to test the proposed method with data from 2001 to 2010. Compared with the results obtained by the traditional threshold dichotomy and the improved neighborhood focal statistics (NFS) method, the following could be concluded. (1) The proposed approach achieved high accuracy and eliminated natural elements to a great extent while obtaining extraction results very consistent to those of the more precise improved NFS approach at a fine scale. The average overall accuracy (OA) and average Kappa values of the extracted urban built-up areas were 95% and 0.83, respectively. (2) The proposed method not only identified the characteristics of the urban built-up area from the nighttime light data and other daylight images at the feature level but also optimized the samples of the urban built-up area category at the decision level, making it possible to provide valuable information for urban planning, construction, and management with high accuracy.

Investigating the effect of carbon leakage on the environmental Kuznets curve using luminosity data

2017 ◽  
Vol 22 (6) ◽  
pp. 747-770
Author(s):  
Arne Steinkraus
Keyword(s):  
Carbon Dioxide Emissions ◽  
Environmental Kuznets Curve ◽  
Important Variable ◽  
Carbon Leakage ◽  
Kuznets Curve ◽  
Nighttime Light Data ◽  
The World ◽  
Nighttime Light ◽  
Global Coordination ◽  
The Relationship

AbstractThis paper studies the effect of carbon leakage on the environmental Kuznets curve (EKC) using satellite nighttime light data. It shows that nighttime lighting is an important variable for estimating carbon dioxide emissions that is superior to other existing indicators and covers all countries in the world, finding evidence of an inverted-U shaped relationship between light and, thus, greenhouse gas emissions and income, with a turning point at approximately US$50,000. However, the relationship is primarily driven by changes in the structure of international trade, implying strong carbon leakage effects. Consequently, environmental regulations that become operative in only one part of the world may fail without global coordination.

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