Structural Path Analysis (SPA) and the Hypothetical Extraction Method (HEM) are both established methods for studying CO emissions. However, their combined application to investigate emission linkages in specific sectors, such as construction, is relatively novel. This research integrates SPA and HEM to explore the CO emissions linkages within the construction sectors of China and the United States, providing a comprehensive understanding of how these emissions are interlinked. The findings show that construction sector of Untied States and China is the largest production-based CO emissions of construction sector in the world, but the consumption-based emissions of construction sector in China contributes 29.81% of total CO emissions, compared to 5.63% in the U.S. This suggests that the carbon footprint of the construction sector is a significant consideration, irrespective of whether it is assessed from the standpoint of production or consumption dynamics. Meanwhile, the development of construction sector has driven the electricity, gas, steam, and air conditioning supply sectors to emit a large amount of CO emission in both countries. By analyzing the differences in the main emission linkages and pathways of the construction sectors between China and the U.S., this study provides insights for reducing CO emissions in the construction sector and assists policymakers in developing future strategies.
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http://dx.doi.org/10.1038/s41598-024-77679-x | DOI Listing |
Heliyon
January 2025
Interdisciplinary Research Center for Construction and Building Materials, Research Institute, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Urbanization and population growth in India have quickened, leading to an annual generation of around 62 million tonnes of municipal solid waste (MSW). Improper management of organic waste presents a major environmental problem due to air and water pollution, soil contamination and greenhouse gas production. This research aims to develop refuse-derived fuel (RDF) as a viable option, converting waste into a high-calorific energy carrier for industrial use.
View Article and Find Full Text PDFChem Sci
December 2024
College of Materials Science and Engineering, Fuzhou University New Campus 350108 China
Atomically precise metal nanoclusters (NCs) have recently been recognized as an emerging sector of metal nanomaterials but suffer from light-induced poor stability, giving rise to the detrimental self-transformation into metal nanocrystals (NYs), losing the photosensitization effect and ultimately retarding their widespread applications in photoredox catalysis. Are metal NCs definitely superior to metal NYs in heterogeneous photocatalysis in terms of structural merits? To unlock this mystery, herein, we conceptually demonstrate how to rationally manipulate the instability of metal NCs to construct high-efficiency artificial photosystems and examine how the metal NYs self-transformed from metal NCs influence charge transfer in photoredox selective organic transformation. To our surprise, the results indicate that the Schottky-type electron-trapping ability of Au NYs surpasses the photosensitization effect of glutathione (GSH)-protected Au clusters [Au(GSH) NCs] in mediating charge separation and enhancing photoactivities towards selective photoreduction of aromatic nitro compounds to amino derivatives and photocatalytic oxidation of aromatic alcohols to aldehydes under visible light irradiation.
View Article and Find Full Text PDFInt J Phytoremediation
January 2025
Department of Land Management, Faculty of Agriculture, Universiti Putra Malaysia, Serdang, Malaysia.
The increasing demand for sustainable, robust, and cost-efficient arsenic (As) treatment techniques strengthens the implementation of new constructed wetland (CW) designs like aerated CWs in the agricultural sector. The aim was to assess and contrast the influence of various aeration rates on As elimination in subsurface flow CW utilizing plants for treating As-polluted sand. This study consisted of an experiment with 16 subsurface flow CW, operating at different As concentrations of 0, 5, 22, and 39 mg kg and aeration rates of 0, 0.
View Article and Find Full Text PDFWater Res
January 2025
State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China. Electronic address:
Both mechanical models and machine learning-based models are widely utilized for real-time dynamic control; however, their implementation in the water sector often incurs significant data and computational costs. To address these challenges, this study introduces an innovative feature extraction method designed to enhance the cost-effectiveness of dynamic control in wastewater treatment plants. The proposed method extracts dynamic features from time-series data of key substrate variables to construct a data-driven model and develop real-time control strategies.
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