Spatiotemporal evolution and influence mechanism of the carbon footprint of energy consumption at county level in the Yellow River Basin

Mei Song; Liyan Zhang; Yan Gao; Enxu Li

doi:10.1016/j.scitotenv.2023.163710

Spatiotemporal evolution and influence mechanism of the carbon footprint of energy consumption at county level in the Yellow River Basin

Sci Total Environ. 2023 Jul 20:883:163710. doi: 10.1016/j.scitotenv.2023.163710. Epub 2023 Apr 25.

Authors

Mei Song¹, Liyan Zhang², Yan Gao³, Enxu Li⁴

Affiliations

¹ School of Management, China University of Mining and Technology-Beijing, Beijing 100083, China; Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Sanhe 065201, China. Electronic address: songmei6361@cumtb.edu.cn.
² School of Management, China University of Mining and Technology-Beijing, Beijing 100083, China. Electronic address: sheila0117@163.com.
³ School of Business, Hebei University of Economics and Business, Shijiazhuang 050061, China.
⁴ Institute of Information Photonics and Optical Communication, Beijing University of Posts and Telecommunications, Beijing 100876, China.

PMID: 37105471
DOI: 10.1016/j.scitotenv.2023.163710

Abstract

Implementing emission reduction policies at county level is important to realize high-quality development in the Yellow River Basin and achieve national "carbon peaking" and "carbon neutrality" goals. Based on remote-sensing data of night light, net primary productivity, and land use, the present study utilized the light‑carbon conversion and carbon footprint measurement models to quantify the carbon footprint of energy consumption. An exploratory spatiotemporal data analysis method was implemented to analyze the spatiotemporal evolution path. Panel quantile regression and spatiotemporal transition-nested models were used to reveal the influence mechanism of the spatiotemporal evolution of the carbon footprint. The following results were obtained. (1) The carbon footprint of counties increased from 2001 to 2020. Counties with high‑carbon footprint diffused around the "one center and two axes". Carbon-deficit counties exhibited a diffused trend towards the west. In 2020, 506 counties exhibited carbon deficits, and the carbon balance of the ecosystem was severely unbalanced. (2) The carbon footprint showed evident path dependence and Matthew effect. The high‑carbon footprint lock-in area comprising 177 counties is a challenging zone for governance. The 86 counties that exhibit carbon footprint changes are the key zones to drive the carbon footprint changes in the Basin. The change direction of the county's carbon footprint type, with evident spatial correlation characteristics, is in accordance with adjacent counties. (3) The carbon footprint spatiotemporal transition types and influence mechanisms in counties exhibited significant differences, with the coexistence of low-carbon footprint driving, low-carbon footprint restriction, high-carbon footprint driving and high-carbon footprint restriction modes. As the influence mechanisms of different modes and the paths to achieve "dual carbon" goals are different, the governance of different modes should focus on optimizing and strengthening restriction factors or controlling and improving of driving factors.

Keywords: Carbon neutrality; County carbon footprint; Evolutionary path; Light-carbon conversion; Panel quantile regression; Remote-sensing data.