Related Experiment Video
Updated: Jan 17, 2026

Measuring Carbon Content in Airway Macrophages Exposed to Carbon-Containing Particulate Matters
Published on: July 12, 2024
Fiscal pressure, carbon emissions and economic growth: Evidence from China
1School of Finance and Economics, Fuzhou Technology and Business University, No. 1 Xueyuan Road, Geling Town, Yongtai County, Fuzhou City, Fujian Province, 350715, China.
Abstract:
Against the backdrop of rising global public debt and the escalating climate crisis, this study addresses the complex challenge Chinese local governments face in balancing economic growth and carbon reduction under fiscal pressure. While previous research often examined the relationships between fiscal pressure, carbon emissions, and economic growth in isolated pairs, this study fills a critical gap by analyzing their interactions within a unified framework using a panel vector autoregression (PVAR) model on data from 30 Chinese provinces from 2008 to 2021. The findings indicate that, in the short term, fiscal pressure reduces emissions by curbing high-carbon industries but also constrains economic growth. In the long run, fiscal imbalances worsen due to environmental governance costs and debt accumulation. Additionally, resource-based regions exhibit weaker fiscal-environmental resilience, highlighting the urgency of economic structural transformation. These insights provide critical evidence for formulating policies that balance short-term growth with long-term sustainability, highlighting the need for a stable policy framework that integrates carbon controls into fiscal incentives and strengthens market-based tools such as carbon pricing and green finance.
Related Concept Videos
The Carbon Cycle
Phase Diagrams
Population Growth
Global Climate Change
Exponential Equations for Modeling Growth
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:

