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Related Concept Videos

Quantitative Analysis01:12

Quantitative Analysis

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Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
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Regression Analysis01:11

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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Turnover Number and Catalytic Efficiency01:19

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The turnover number of an enzyme is the maximum number of substrate molecules it can transform per unit time. Turnover numbers for most enzymes range from 1 to 1000 molecules per second. Catalase has the known highest turnover number, capable of converting up to 2.8×106 molecules of hydrogen peroxide into water and oxygen per second. Lysozyme has the lowest known turnover number of half a molecule per second.
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Econometric Views (EViews)01:29

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Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
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Factors Affecting Activity Coefficient01:17

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The extended Debye-Hückel equation indicates that the activity coefficient of an ion in an aqueous solution at 25°C depends on three partially interdependent properties: the ionic strength of the solution, the charge of the ion, and the ion size. 
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Quantifying and Rejecting Outliers: The Grubbs Test01:02

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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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How does technological innovation mitigate CO2 emissions in OECD countries? Heterogeneous analysis using panel

Cheng Cheng1, Xiaohang Ren2, Kangyin Dong3

  • 1School of Management Science & Engineering, Shanxi University of Finance & Economics, Shanxi, 030006, China.

Journal of Environmental Management
|December 28, 2020
PubMed
Summary

Technological innovation reduces carbon dioxide (CO2) emissions in OECD countries. However, this effect varies significantly across different economic levels and shows asymmetry, impacting economic growth and renewable energy adoption.

Keywords:
CO(2) emissionsInfluencing mechanismOECD countriesPanel quantile regressionTechnological innovation¬Patents

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Area of Science:

  • Environmental Economics
  • Technological Innovation Studies
  • Climate Change Mitigation

Background:

  • Rising carbon dioxide (CO2) emissions pose a significant threat to global climate stability.
  • Technological innovation is often proposed as a key strategy for mitigating CO2 emissions.
  • Understanding the nuanced effects of innovation on emissions within developed economies is crucial.

Purpose of the Study:

  • To investigate the direct impact of technological innovation on CO2 emissions in OECD countries.
  • To analyze the moderating role of technological innovation on the relationship between economic growth, renewable energy, and CO2 emissions.
  • To examine the heterogeneity and asymmetry of these effects across different quantiles.

Main Methods:

  • Utilized a balanced panel dataset for 35 OECD countries from 1996-2015.
  • Employed panel quantile regression to analyze direct and moderating effects.
  • Measured technological innovation by patent development.

Main Results:

  • Technological innovation significantly reduces CO2 emissions.
  • The impact of innovation on CO2 emissions is heterogeneous and asymmetric across quantiles.
  • Technological innovation moderates the effects of economic growth and renewable energy on CO2 emissions, with heterogeneous effects.

Conclusions:

  • Technological innovation is a vital tool for CO2 emission reduction in OECD countries.
  • Policy implications must consider the heterogeneous and asymmetric nature of innovation's impact.
  • Further research should explore these nuanced effects in diverse economic contexts.