The Dynamic Effects of the Fossil Energy Price Shocks on Industrial PPI: Evidence from 40 Sub-Industries in China

Using international and Chinese prices of oil, coal, and natural gas, together with the Producer Price Index (PPI) of 40 industrial sectors in China from 2014 to 2026, this study constructs a tail-risk network including fossil energy markets and China’s industrial system using the TENET model. Then we analyze the transmission and dynamic evolution of extreme price risks from fossil energy markets to different Chinese industries. Empirical analysis reveals that the tail-risk spillovers from fossil energy markets to China’s PPI are significantly asymmetric. Specifically, (1) the intensity of tail-risk spillovers from energy markets to China’s industrial system is significantly greater than that in the opposite direction. Moreover, downside-risk spillovers from energy markets to the industrial system are stronger than upside-risk spillovers. (2) Natural gas is the most key node through which fossil energy price shocks are transmitted to China’s industrial system. (3) Within China’s industrial system, a few key sectors, such as Petroleum and Nuclear Fuel and Ferrous Metal Smelting & Rolling, play important roles in transmitting fossil energy price shocks throughout the Chinese industrial system. (4) Following the COVID-19 pandemic and the Russia–Ukraine conflict, spillovers from fossil energy markets to China’s PPI increased, although the core structure of risk transmission remained largely unchanged. In the downside-risk network, only the centrality of Coal Mining and Washing increased markedly after the COVID-19 pandemic, whereas Ferrous Metal Mining became more central following the Russia–Ukraine conflict.

Authors

Institutions

Publication Details

Journal
Sustainability
Published
2026-09-25
DOI
https://doi.org/10.3390/su18199844
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

The Dynamic Effects of the Fossil Energy Price Shocks on Industrial PPI: Evidence from 40 Sub-Industries in China

Xin Liao, Lingying Pan, Zheyu Wang
Sustainability
Market Dynamics and Volatility
article

The Dynamic Effects of the Fossil Energy Price Shocks on Industrial PPI: Evidence from 40 Sub-Industries in China

Xin Liao, Lingying Pan, Zheyu Wang
article en

Abstract

Using international and Chinese prices of oil, coal, and natural gas, together with the Producer Price Index (PPI) of 40 industrial sectors in China from 2014 to 2026, this study constructs a tail-risk network including fossil energy markets and China’s industrial system using the TENET model. Then we analyze the transmission and dynamic evolution of extreme price risks from fossil energy markets to different Chinese industries. Empirical analysis reveals that the tail-risk spillovers from fossil energy markets to China’s PPI are significantly asymmetric. Specifically, (1) the intensity of tail-risk spillovers from energy markets to China’s industrial system is significantly greater than that in the opposite direction. Moreover, downside-risk spillovers from energy markets to the industrial system are stronger than upside-risk spillovers. (2) Natural gas is the most key node through which fossil energy price shocks are transmitted to China’s industrial system. (3) Within China’s industrial system, a few key sectors, such as Petroleum and Nuclear Fuel and Ferrous Metal Smelting & Rolling, play important roles in transmitting fossil energy price shocks throughout the Chinese industrial system. (4) Following the COVID-19 pandemic and the Russia–Ukraine conflict, spillovers from fossil energy markets to China’s PPI increased, although the core structure of risk transmission remained largely unchanged. In the downside-risk network, only the centrality of Coal Mining and Washing increased markedly after the COVID-19 pandemic, whereas Ferrous Metal Mining became more central following the Russia–Ukraine conflict.

SustainabilityVol. 18(19)
University of Shanghai for Science and Technology (CN)
Openalex Percentile: Top 5%
Market Dynamics and Volatility
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.