Related Experiment Video
Updated: Dec 16, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
Published on: January 20, 2023
A study of environmental regulation, technological innovation, and energy consumption in China based on spatial
Huan Zhou1, Shaojian Qu1,2,3, Zhong Wu4
1Business School, University of Shanghai for Science and Technology, Shanghai, People's Republic of China.
Abstract:
Under different technological innovation modes, regional energy consumption may have spatial heterogeneity. Spatial heterogeneity complicates the nexus between environmental regulation and energy consumption. Traditional spatial homogeneity analysis is hard to describe the nonlinear nexus between them. Based on the data of 30 provinces in China from 2007 to 2017, this paper employs the spatial econometric method and the nonlinear econometric method to investigate the spatial effects and nonlinearity of energy consumption, respectively. The results display that under the current level of economic development, per capita energy consumption has a significant spatial spillover effect. Environmental regulation promotes regional per capita energy consumption in the short term. On the contrary, the technological effect of environmental regulation has significantly reduced Chinese per capita energy consumption. Therefore, energy policy should be tailored to local conditions, and policymakers can strengthen the environmental regulatory system and encourage enterprises to implement green technology innovation.
Related Concept Videos
Mechanistic Models: Compartment Models in Individual and Population Analysis
Econometric Views (EViews)
Regression Analysis
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:
Manipulation and Analysis
Statistical Methods for Analyzing Epidemiological Data
Environmental Applications of Microorganisms

