Related Experiment Video
Updated: May 20, 2025
09:05
Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites
Published on: June 24, 2019
7.8K
Quantifying the nonlinear relationships between environmental policy components and share energy from renewable
Yixin Chang1, Long Zhou2, Sihong Li2
1School of Business, Macau University of Science and Technology, Macau SAR, China.
Journal of Environmental Management
|March 26, 2025
Summary
Environmental policies significantly impact renewable energy. A study found carbon taxes and R&D spending are key drivers, while policy combinations create synergistic or antagonistic effects for renewable energy transitions.
Area of Science:
- Environmental Policy Analysis
- Renewable Energy Economics
- Econometrics
Background:
- Understanding the complex interplay between environmental policies and renewable energy adoption is crucial for effective climate action.
- Existing research often overlooks the nonlinear relationships and interaction effects of various policy components.
Purpose of the Study:
- To analyze the nonlinear relationships between 13 environmental policy components and the share of renewable energy in 19 OECD countries (2004-2020).
- To identify the most influential policy components and their interaction effects on renewable energy transitions.
Main Methods:
- Utilized Random Forest models to explore relationships between policy components and renewable energy share.
- Conducted a comprehensive analysis of market-based, non-market-based, and technology support policies.
Main Results:
- Identified nonlinear relationships and varying impacts of policy components.
- Carbon dioxide tax, low-carbon R&D expenditure, and particulate matter emission limits were most influential.
- Synergistic effects observed between carbon dioxide tax and solar energy support; antagonistic effects between carbon dioxide tax and particulate matter emission limits.
Conclusions:
- Quantified relative policy importance, threshold effects, and interactions to bridge knowledge gaps in renewable energy transitions.
- Highlighted the need for coordinated policy mixes, considering synergistic and antagonistic effects, to achieve renewable energy targets effectively.
Related Concept Videos
01:11Calculating and Interpreting the Linear Correlation Coefficient
5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
01:25Multiple Regression
2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
01:11Regression Analysis
5.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.5K
01:17Energy and Power Signals
223
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
223
01:07Residual Plots
4.5K
A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
When the residual values are plotted against the variable x, it is called a residual...
When the residual values are plotted against the variable x, it is called a residual...
4.5K
01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
34
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
34