A multivariate statistical input–output model for analyzing water-carbon nexus system from multiple perspectives - Jing-Jin-Ji region. (15th March 2022)

• Record Type: Journal Article
• Authors: Wang, P.P.; Li, Y.P.; Huang, G.H.; Wang, S.G.
• Is Part Of: Applied energy. Volume 310(2022)
• Publication Date: 2022-03-15
• Subjects: Combined effects -- Input–output analysis -- Jing-Jin-Ji region -- Multivariate statistical analysis -- Multiple perspectives -- Water-carbon nexus; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118560
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A multivariate statistical input–output model is developed. The model is used for analyzing the water-carbon nexus system in Jing-Jin-Ji region. The water consumption and CO2 emission are evaluated. Different industrial technology upgrade policies are simulated. Decision support to water-carbon management can be provided. Abstract: Water scarcity and carbon dioxide (CO2 ) emission continue to be challenges faced by decision makers in urban and regional scales. In this study, a multivariate statistical input–output (MSIO) model is developed for analyzing water-carbon nexus system, through incorporating techniques of input–output analysis (IOA) and multivariate statistical analysis (MSA) into a general framework. MSIO is able to: (i) recognize the complicated characteristics of multi-element, multi-sector and multi-factor in water-carbon nexus system from network and statistical perspectives; (ii) simulate different technology-upgrade policies on key transmission sectors that are the middle nodes of supply chain paths; (iii) quantify the individual and interactive effects of sectors on water-carbon variations. MSIO is applied to analyzing water-carbon nexus system in Jing-Jin-Ji region (China). Major findings are: (i) for the region in 2030, agriculture, service and food industries would be typical water consumers (accounting for 35.0%, 22.8% and 10.8%); metal, service, and electricity and heat industries would be typical CO2 emitters (accounting for 24.1%, 22.0% and 19.7%); (ii) CO2 reduction policy could aim at the sectors of cluster 1 (i.e. energy production, manufacturing, construction and service industries); policy oriented toward water resource could aim at the sectors of cluster 2 (i.e. agriculture, food and textile industries); (iii) technology-upgrade policy on Beijing's electricity and heat industry would have significant performance in water-carbon reductions, indicating that this sector is highly dependent on upstream industry and intra-regional trade supply; (iv) the synergy of Hebei's heavy industry and Beijing's electricity and heat industry would perform best in water-carbon management (i.e. water-consumption intensity and CO2 -emission intensity would decrease by 3.3% and 15.3%, respectively), suggesting that it is crucial to improve the production capacity and output efficiency of these sectors from the perspective of the middle of the supply chain.