An integrated social-ecological indicator for low-carbon transition: evidence from the Beijing-Tianjin-Hebei, China

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators Pub Date : 2025-05-10 DOI:10.1016/j.ecolind.2025.113553

Feng Yan , Minli Wang , Jing Shan , Yanrui Ding , Liyao Dong , Yuwen Zhang , Huicong Zhang , Hao Xu , Jiao Pang , Yaheng Chen

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Abstract

Excessive carbon emissions in the Beijing-Tianjin-Hebei region have severely hindered regional sustainable economic development. Investigating grid-scale carbon sources and sinks incorporating social systems is of great significance for advancing China’s dual-carbon goals. This study proposes a novel productivity indicator, Net Social-ecological system Productivity (NSP), which integrates human society and natural ecosystems, and clarifies its spatiotemporal distribution patterns and multivariate driving mechanisms. The results show that: (1) From 2002 to 2020, the NSP in the study area exhibited a significant negative growth rate of −26.188 g C·(m²·a)⁻¹. The annual mean NSP displayed a spatial pattern of being lower in the east and higher in the west, as well as lower in the south and higher in the north, with low-value areas concentrated in urban zones of metropolitan regions. (2) The spatial distribution of NSP followed distinct clustering patterns. The Beijing-Tianjin area consistently formed a low-low cluster, while over half of the study area (50.4 %) showed a declining trend in NSP, particularly in counties such as Daxing, Dingzhou, Anping, and Feixiang. (3) Night light intensity, GDP density, and population density were the primary drivers of NSP (Q > 0.5). The interaction between night light intensity and population density had the strongest explanatory power (Q > 0.7). NSP was higher (indicating stronger carbon sequestration capacity) when night light intensity ranged from 0 to 250 lm/m² and population density ranged from 0 to 188 persons/km². (4) The spatial correlations between NSP and explanatory variables varied significantly. NSP was positively correlated with average annual temperature and solar radiation but negatively correlated with secondary industry GDP and land surface temperature (LST). The findings provide valuable insights for policymakers in the Beijing-Tianjin-Hebei region to formulate targeted carbon reduction strategies and promote low-carbon economic development.

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低碳转型的综合社会生态指标：来自京津冀的证据

京津冀地区碳排放过高严重阻碍了区域经济的可持续发展。研究并网规模的碳源和碳汇，结合社会系统，对推进中国的“双碳”目标具有重要意义。本文提出了整合人类社会与自然生态系统的生产力指标——净社会生态系统生产力（NSP），并阐明了其时空分布格局和多元驱动机制。结果表明：(1)2002 ~ 2020年，研究区NSP呈显著负增长，为- 26.188 g C·（m2·a）−1。年平均NSP呈东低西高、南低北高的空间格局，低值区集中在大都市区。(2) NSP的空间分布具有明显的聚类特征。京津冀地区持续形成低-低集群，超过半数（50.4%）的研究区NSP呈下降趋势，大兴、定州、安平、肥翔等县尤甚。(3)夜间光照强度、GDP密度和人口密度是NSP (Q >；0.5)。夜间光照强度与种群密度之间的相互作用具有最强的解释力(Q >；0.7)。当夜间光照强度为0 ~ 250 lm/m2，种群密度为0 ~ 188人/km2时，NSP较高，表明固碳能力较强。(4) NSP与各解释变量的空间相关性存在显著差异。NSP与年平均气温和太阳辐射呈正相关，与第二产业GDP和地表温度呈负相关。研究结果为京津冀地区决策者制定有针对性的碳减排战略，促进低碳经济发展提供了有价值的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Ecological Indicators 环境科学-环境科学

CiteScore

11.80

自引率

8.70%

发文量

1163

审稿时长

78 days

期刊介绍： The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published. • All aspects of ecological and environmental indicators and indices. • New indicators, and new approaches and methods for indicator development, testing and use. • Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources. • Analysis and research of resource, system- and scale-specific indicators. • Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs. • How research indicators can be transformed into direct application for management purposes. • Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators. • Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.