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Related Concept Videos

Sampling Plans01:23

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Related Experiment Video

Updated: Sep 22, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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How will Chinese cities reduce their carbon emissions? Evidence from spatial differences.

Junna Yan1, Zhonghua Zhang2, Mingli Chen1

  • 1Business School, Tianjin University of Finance and Economics, Tianjin, 300222, People's Republic of China.

Environmental Science and Pollution Research International
|May 24, 2022
PubMed
Summary

China's urban regions significantly increase spatial differences in carbon dioxide (CO2) emissions. Per capita demand and sectoral energy intensity drive these disparities, highlighting the need for tailored urban climate policies.

Keywords:
CO2 emissionChinaGhosh input–output modelLeontief input–output modelMunicipalitiesSpatial structural decomposition analysis

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Area of Science:

  • Environmental Science
  • Economics
  • Urban Studies

Background:

  • Urban regions are major carbon emitters in China.
  • Addressing urban CO2 emissions is critical for China's 2030 climate goals.
  • Spatial differences in urban emissions and their drivers are understudied.

Purpose of the Study:

  • To investigate the driving factors behind spatial differences in CO2 emissions among Chinese municipalities.
  • To develop more effective emission abatement strategies for Chinese cities.

Main Methods:

  • Improved environmental Leontief and Ghosh input-output models.
  • Structural decomposition analysis.
  • Comparative analysis of CO2 emissions across four major municipalities.

Main Results:

  • Significant and increasing spatial differences in CO2 emissions were observed among the municipalities.
  • Per capita final demand (demand-side) and sectoral energy intensity (supply-side) were key drivers.
  • Structural factors played an increasingly critical role in emission disparities.

Conclusions:

  • Tailored, city-specific climate policies are necessary.
  • Policies should consider both demand- and supply-side factors, with an emphasis on structural elements.
  • Findings support "common but distinct" policy approaches for urban climate mitigation.