Urban form, land use, and cover change and their impact on carbon emissions in the Monterrey Metropolitan area, Mexico

Urban Climate ◽  
2021 ◽  
Vol 39 ◽  
pp. 100947
Author(s):  
Alejandro Carpio ◽  
Roberto Ponce-Lopez ◽  
Diego Fabián Lozano-García
2018 ◽  
Vol 9 (2) ◽  
pp. 113-126 ◽  
Author(s):  
Kala Seetharam Sridhar

This article understands, from an empirical perspective, the determinants of carbon emissions, using internationally comparable data, and cross-national regressions for India and China. Next, it explores the relationship between urban land use regulations and carbon emissions in India’s cities. Urbanization has no impact on carbon emissions per capita or per unit of geographical area. Electricity consumption in China and electricity produced from coal in India have a positive effect on carbon emissions. GDP per capita has a positive effect in India and not so in China, but per capita GDP squared has a negative impact on emissions in both the countries. Does this imply that urbanization should be ignored in the two countries? The answer is no, because a city’s urban form, to which policy contributes, is correlated with carbon emissions. More suburbanized cities which sprawl more also emit more carbon. India’s land use regulations relating to building height restrictions are conservative, hence Indian cities sprawl, which lead to carbon emissions. Hence, the focus of urban policy has to be on the development of compact cities. The article concludes with caveats of the data.


2018 ◽  
Vol 10 (6) ◽  
pp. 1860 ◽  
Author(s):  
Armando Jiménez ◽  
Fernando Vilchez ◽  
Oyolsi González ◽  
Susana Flores

2021 ◽  
Vol 286 ◽  
pp. 112228
Author(s):  
Manan Bhan ◽  
Simone Gingrich ◽  
Nicolas Roux ◽  
Julia Le Noë ◽  
Thomas Kastner ◽  
...  

2011 ◽  
Vol 31 (2) ◽  
pp. 687-699 ◽  
Author(s):  
Adélia N. Nunes ◽  
António C. de Almeida ◽  
Celeste O.A. Coelho

2021 ◽  
Vol 13 (5) ◽  
pp. 974
Author(s):  
Lorena Alves Santos ◽  
Karine Ferreira ◽  
Michelle Picoli ◽  
Gilberto Camara ◽  
Raul Zurita-Milla ◽  
...  

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.


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