Carbon neutral and techno-economic analysis for sewage treatment plants

Zhang, Qunli; Yang, Yihang; Zhang, Xinchao; Liu, Fang; Wang, Gang

doi:10.1016/j.eti.2022.102302

Cited by 23 publications

(4 citation statements)

References 30 publications

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“…Controlling the difference in urban economic development levels can effectively feedback the change in sewage disposal demand brought about by the agglomeration of economic activities, which is also one of the fundamental variables of the IPAT model [ 46 ]. (c) Technological factors ( TECH )—the technological progress variable, included because it is considered one of the developmental factors with wide-ranging effects [ 7 ]. In this paper, two variables were selected as inputs: the proportion of science and technology expenditure in GDP ( TCHR ) and the proportion of education expenditure in GDP ( EDUR ).…”

Section: Methodsmentioning

confidence: 99%

“…Countries have made efforts in terms of many related aspects. One category of literature focuses on technical means, such as improving the treatment process of sewage treatment plants, which is a crucial way to improve the efficiency of sewage treatment from scientific and technological perspectives [ 7 ]. Li et al [ 8 ] examined the recent development and prospects of drainage services departments by evaluating how different forms of renewable energy can be harnessed.…”

Section: Introductionmentioning

confidence: 99%

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Can Public–Private Partnership Wastewater Treatment Projects Help Reduce Urban Sewage Disposal? Empirical Evidence from 267 Cities in China

Hou

2022

IJERPH

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Human activities have placed enormous pressure on the world’s water resources. To improve the efficiency of water supply and wastewater treatment, public–private partnerships (PPPs) are widely used for sewage treatment. However, an academic question remains about whether PPP sewage treatment projects (PPPSTs) help reduce urban sewage disposal when responsibilities shift from the public sector to the private sector. This study used panel data of 267 prefecture-level cities in China from 2009 to 2020 to construct a difference-in-difference (DID) model based on the counterfactual framework to answer this question empirically. The model results significantly support the effect of PPPSTs on sewage disposal reduction. Furthermore, these results passed the parallel trend test and the placebo test, and the results were still achieved when the quadratic term of the core variable was introduced, indicating that the model is reliable. In addition, the moderating effect models were used to expand the analysis. That is, the regressions were derived by multiplying the relevant extended variables and the core independent variables. This analysis indicates that the operation mode of PPPST and the characteristics of national demonstration play an essential role in reducing the amount of urban sewage disposal. However, the effect of fiscal decentralization is not apparent. These conclusions were also confirmed in the model using the investment scale of PPPSTs. Therefore, paying attention to the formation of PPPST contracts and adopting a practical supervision system is of great significance for improving the effect of sewage disposal reduction.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can Public–Private Partnership Wastewater Treatment Projects Help Reduce Urban Sewage Disposal? Empirical Evidence from 267 Cities in China

Hou

2022

IJERPH

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“…Human activities (e.g., population growth, and industrial production) produce large volumes of sewage. STPs are an important industrial GHG production source, which contribute direct emissions through the treatment process or indirect emissions by consuming much electricity. The discharged sewage also inputs organic matter into rivers and other water bodies .…”

Section: Human Activity and Climate Change In Ghg Emissions Modelingmentioning

confidence: 99%

Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale

Deng,

Hu,

2023

ACS EST Water

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Water environments (e.g., oceans, lakes, and rivers) are important carbon sinks and sources and contribute to the carbon cycle of the earth's ecosystem. Machine learning provides a potential solution for recognizing greenhouse gas (GHG) emissions from water based on big data analysis. Data-driven machine learning can comprehensively recognize the key environmental drivers that affect GHG emissions. However, several urgent issues should be addressed to guarantee machine learning recognition of GHG emissions from water. For example, matching in situ data and databases is the greatest challenge in conducting large-scale machine learning research. It is imperative to unify in situ data collection methods and improve database quality (e.g., spatiotemporal matching and high resolution). Quantifying the contributions of human activity and climate change to GHG emissions from water is urgently needed to resolve future challenges. Beyond providing recognition and prediction, machine learning, due to its interpretability, can optimize the carbon cycle model; thus, empirical formulas for machine learning deserve attention. Overall, machine learning can manage large-scale and complicated data for climate change regarding GHG emissions from water.

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“…With the improvement of water quality standards in recent years, energy consumption and sludge production in the wastewater treatment industry has been increasing, along with its carbon emissions. [1]Wastewater treatment plants (WWTPs) have gradually become one of the main sources of greenhouse gases, according to statistics, 0.3% of China's total annual electricity consumption is used for wastewater treatment plants, releasing approximately 11.4 billion t CO 2 equivalent. As it is difficult to offset the carbon emissions caused by the necessary energy consumption during the wastewater treatment process, one feasible way to complete the overall carbon neutrality of the wastewater treatment industry is to recycle the resources contained in the sludge.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Carbon Emissions in Different Sludge Treatment Pathways - a Case Study of Jiaxing, China

C.³

et al. 2023

E3S Web of Conf.

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Sludge management strategy is essential to future carbon reduction in wastewater plants since the treatment and disposal of sludge contribute significantly to the carbon emissions. Using the method proposed by intergovernmental Panel on Climate Change(IPCC), the three sludge treatment paths of a typical wastewater treatment plant in Jiaxing were investigated. The result showed that net carbon emission is negative at -588kgCO2/tDS thanks to R3 which is characterized by centrifugal dehydrator, coal blending incineration with combined heat and power generation and cement substitute by slag is the rout with lowest carbon missions. The carbon emission of R1 is the highest at 699.87 kgCO2/tDS because of the chemical reagents. Process unit with larger carbon emissions was identified to be dewatering in which chemical reagent contributed the most. The main carbon offsets was the combined heat and power cogeneration which should be widely advocated.

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Carbon neutral and techno-economic analysis for sewage treatment plants

Cited by 23 publications

References 30 publications

Can Public–Private Partnership Wastewater Treatment Projects Help Reduce Urban Sewage Disposal? Empirical Evidence from 267 Cities in China

Can Public–Private Partnership Wastewater Treatment Projects Help Reduce Urban Sewage Disposal? Empirical Evidence from 267 Cities in China

Machine Learning Provides Opportunities to Recognize Greenhouse Gas Emissions from Water at a Large Scale

Comparison of Carbon Emissions in Different Sludge Treatment Pathways - a Case Study of Jiaxing, China

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