Global Consensus Research Articles

Identifying vulnerable marine ecosystems from imagery in the Uruguayan continental shelf

Scientific surveys employing remotely operated vehicles (ROVs) provide valuable imagery for identifying Vulnerable Marine Ecosystems (VMEs). However, there is presently no established framework for distinguishing VMEs from these images. While existing guidelines effectively address certain habitat types, such as scleractinian reefs, dense octocoral gardens, and chemosynthetic ecosystems, achieving consensus becomes more challenging for other habitats. Our investigation centered on two key localized rocky outcrops on the Uruguayan shelf, conducting ROV dives to explore these environments. This study aims to evaluate initial global consensus guidelines for VME identification from imagery and discuss the suitability of these criteria, and report the existence of Hard Bottom Soft Coral Gardens and Sponge associations in the inner Uruguayan shelf. Recognizing these habitats is of utmost importance for the Uruguayan government, which has expressed the intent to safeguard ecologically representative and well-connected systems crucial for marine biodiversity.

Efficient decentralized optimization for edge-enabled smart manufacturing: A federated learning-based framework

The volume of industrial data of smart manufacturing is growing rapidly. Edge computing has emerged as an advanced technique that provides scalable resources for Industrial Internet of Things (IIoT) devices to analyze industrial data and enhance productivity. However, real-time and privacy-protecting data services are crucial for comprehensive decision-making, particularly utilizing machine learning in edge-enabled smart manufacturing. Therefore, how to optimize efficiently the data processing has become a critical concern for manufacturers seeking to upgrade the overall manufacturing operations. In this paper, we propose a decentralized federated learning-based framework to ensure the processing of industrial data in a low-latency and secure manner. Our solution designs the combination between edge nodes and IIoT devices as a global consensus problem with equilibrium constraints. Moreover, we adopt the distributed Alternating Direction Method of Multipliers (ADMM) algorithm to optimize the proposed data processing model, considering its advantages of decomposability and parallelism. To flexibly handle various types of industrial data, such as low-rank and high-dimensional data, we present two types of inexact ADMM algorithms to provide efficient model training services, respectively. Furthermore, an integrated optimization flow is designed for data processing in edge-enabled smart manufacturing. Finally, using industrial datasets from a thermal power plant for steam prediction case, we show that the presented inexact algorithms can decrease the response time by up to 17.2% and 58% respectively compared to other existing algorithms, respectively, while achieving the comparable level of statistical accuracy.

Predictive performance of corneal and lid margin sensitivity for dry eye disease: An investigator-masked, prospective, prognostic accuracy study

PurposeTo evaluate the prognostic ability of non-contact esthesiometry corneal and lid margin sensitivity measurements in detecting symptoms and signs of dry eye disease, as defined by the global consensus TFOS DEWS II criteria. MethodsA total of 87 community residents (58 females; mean ± SD age, 53 ± 16 years) were recruited in an investigator-masked, prospective, prognostic accuracy study. Dry eye symptomology, tear film parameters, and ocular surface characteristics were evaluated in a single clinical session, and non-contact esthesiometry corneal and lid margin sensitivity measurements performed by an independent masked assessor. ResultsOverall, 49 (56%) participants fulfilled the TFOS DEWS II criteria for dry eye disease, while 57 (66%) exhibited clinical symptoms, and 67 (77%) had positive signs. The prognostic abilities of corneal and lid margin sensitivity measurements were significantly greater than chance for the detection dry eye signs (both p ≤ 0.03), but not for symptoms or overall disease diagnosis (all p > 0.10). The Youden-optimal prognostic cut-offs for corneal and lid margin sensitivity thresholds were both ≥0.8 mbar for the detection of clinical dry eye signs. Lid margin sensitivity demonstrated marginally higher predictive performance than corneal sensitivity (C-statistic, 0.688 versus 0.658), and was significantly correlated with tear film stability, corneal, conjunctival and lid wiper staining (all p < 0.05). ConclusionsCorneal and lid margin sensitivity demonstrated moderate prognostic utility for detecting clinical dry eye signs. Future research is warranted to investigate the utility of incorporating non-contact esthesiometry in the workup for dry eye disease and neurotrophic keratopathy.

An Empirical Analysis of the Impact of Carbon Disclosure on Firms' Market Value in a "Dual Carbon" Context

With the acceleration of global warming, climate change and environmental issues have become a hotspot of global concern. At present, it has become a global consensus to vigorously develop a green economy and achieve "carbon neutrality and carbon peak". In this context, as an important part of the national economy, listed companies should pay more attention to carbon emission and information disclosure to help achieve the goals of "carbon peak" and "carbon neutral". In this paper, we use a multiple linear regression model to explore the specific mechanism of the impact of carbon emission disclosure on enterprise market value. In constructing the model, we consider that factors such as asset size, operating profit margin, and cash flow ratio may affect the market value of enterprises, so we add control variables in the model and use t-test, F-test, and analysis of covariance to verify whether there is a significant difference between different groups. Descriptive statistics and Pearson's correlation coefficient were also carried out during data processing in order to observe whether there is a correlation between carbon emissions and company market value. By analysing the results of regression fitting, we find that under the background of "dual-carbon", the active disclosure of carbon emissions by enterprises can help enterprises to improve their transparency, thus enhancing the trust and recognition of consumers and investors, and thus positively affecting the market value of the enterprises, and the impact of the active disclosure of carbon emissions by enterprises with high carbon emissions is even more significant on their market value. market value is even more significant. This implies that companies should actively participate in emission reduction actions and demonstrate their action on environmental protection by proactively disclosing carbon emission information in their annual reports or CSR reports.

Preventing poaching in marine protected areas: A crime script analysis to inform interventions

Ubiquitous poaching poses a persistent challenge to marine protected areas (MPAs) management. The global consensus to protect 30% of the ocean by 2030 has heightened the importance of effective MPA management. However, the law enforcement capacity for open-access common pool issues is frequently constrained. There remains an ongoing imperative to expand the knowledge on how to combat poaching. Drawing from environmental criminology approaches, this paper illustrates crime scripts of documented poaching cases in Taiwan's MPAs over the past decade to detail crime commission process and develops prevention strategies. We identify that poachers were often involved shore-based diving, shoreline handpicking and poaching by boats during clear summer evenings in easily accessible MPAs. To prevent marine poaching, suggested key interventions include managing access and exists, controlling fishing equipment, rationally deploying patrolling capacity and natural surveillance measures, creating traceable seafood supply chains, and increasing public vigilance and awareness. Environmental criminology approaches make poaching predictable and enable the precise allocation of law enforcement resources at a certain level. To further improve effectiveness of prevention strategies, we highlight the need to periodically review crime scripts and associated policies when new cases are identified. In addition, incorporating techniques from the best practices in MPA management into prevention strategies can also enhance the effectiveness of environmental criminology approaches in MPA poaching. This paper can inform the development of interventions and enhancing the management of MPAs toward global objectives.

BCLNet: Bilateral Consensus Learning for Two-View Correspondence Pruning

Correspondence pruning aims to establish reliable correspondences between two related images and recover relative camera motion. Existing approaches often employ a progressive strategy to handle the local and global contexts, with a prominent emphasis on transitioning from local to global, resulting in the neglect of interactions between different contexts. To tackle this issue, we propose a parallel context learning strategy that involves acquiring bilateral consensus for the two-view correspondence pruning task. In our approach, we design a distinctive self-attention block to capture global context and parallel process it with the established local context learning module, which enables us to simultaneously capture both local and global consensuses. By combining these local and global consensuses, we derive the required bilateral consensus. We also design a recalibration block, reducing the influence of erroneous consensus information and enhancing the robustness of the model. The culmination of our efforts is the Bilateral Consensus Learning Network (BCLNet), which efficiently estimates camera pose and identifies inliers (true correspondences). Extensive experiments results demonstrate that our network not only surpasses state-of-the-art methods on benchmark datasets but also showcases robust generalization abilities across various feature extraction techniques. Noteworthily, BCLNet obtains significant improvement gains over the second best method on unknown outdoor dataset, and obviously accelerates model training speed.

BeliefFlow: A Framework for Logic-Based Belief Diffusion via Iterated Belief Change

This paper presents BeliefFlow, a novel framework for representing how logical beliefs spread among interacting agents within a network. In a Belief Flow Network (BFN), agents communicate asynchronously. The agents' beliefs are represented using epistemic states, which encompass their current beliefs and conditional beliefs guiding future changes. When communication occurs between two connected agents, the receiving agent changes its epistemic state using an improvement operator, a well-known type of rational iterated belief change operator that generalizes belief revision operators. We show that BFNs satisfy appealing properties, leading to two significant outcomes. First, in any BFN with strong network connectivity, the beliefs of all agents converge towards a global consensus. Second, within any BFN, we show that it is possible to compute an optimal strategy for influencing the global beliefs. This strategy, which involves controlling the beliefs of a least number of agents through bribery, can be identified from the topology of the network and can be computed in polynomial time.

Graph Context Transformation Learning for Progressive Correspondence Pruning

Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progressive correspondence pruning. Specifically, we design the Graph Context Enhance Transformer which first generates the graph network and then transforms it into multi-branch graph contexts. Moreover, it employs self-attention and cross-attention to magnify characteristics of each graph context for emphasizing the unique as well as shared essential information. To further apply the recalibrated graph contexts to the global domain, we propose the Graph Context Guidance Transformer. This module adopts a confident-based sampling strategy to temporarily screen high-confidence vertices for guiding accurate classification by searching global consensus between screened vertices and remaining ones. The extensive experimental results on outlier removal and relative pose estimation clearly demonstrate the superior performance of GCT-Net compared to state-of-the-art methods across outdoor and indoor datasets.

Dataset on the effects of psychological care on depression and suicide ideation in underrepresented children

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and “left-behind” and “single-parenting” children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system.

The Constraints to Effective Implementing of Illegal Logging Prohibition Regulation in Nigeria

Since the adoption of the Non-Legally Binding Authoritative Statement of Principles for Global Consensus on the Management, Conservation and Sustainable Development of all types of Forest at the 1992 Earth Summit in Rio de Janeiro, the issue of combating deforestation remains one of the topical issues relating to climate change discussions globally. To combat deforestation, countries go beyond international conventions to institutionalize domestic frameworks to regulate illegal timber logging. However, the implementation of regulatory policies has been ineffective in countries like Nigeria. Situated within this problem, this study identified some domestic constraints affecting the effectiveness of timber logging regulation in Nigeria. To this end, data was collected from respondents using structured questionnaires. The data were analysed using the Pearson Product Moment Correlation Coefficient (PPMC) at a 0.05 level of significance. Constraints identified are corruption, insufficient legal framework, lack of coordination in regulation enforcement, inadequate staff, and the state monopoly of forest ownership.

O127: Global DELPHI Consensus Study on anticoagulation for splanchnic vein thrombosis in acute pancreatitis

Abstract Introduction Splanchnic vein thrombosis (SpVT) occurs in 16.6%-22.6% of acute pancreatitis cases, influenced by pancreatitis severity and imaging techniques. Serious sequelae include hepatic and bowel failure, while management with anticoagulation remains controversial amidst perceived bleeding risks. This study's aim, driven by heterogeneity in literature, utilized DELPHI methodology to discern expert consensus on SpVT treatment, advocating for standardized research approaches. Methods Using purposive, non-probability sampling and DELPHI methodology, n=173 global experts in SpVT-related acute pancreatitis were engaged from international networks. From April 2022-2023, a three-round Delphi process was implemented to completion. n=26 statements were posed, spanning diagnosis, prognosis, management, and outcomes, with feedback via a 4-point Likert scale. By Round 3, n=54/173 participants remained. Results The mean acute pancreatitis caseload per expert per year was 68.0, 72.6 and 73.0 across successive DELPHI rounds. Regarding SpVT anatomical location, there was strong consensus favouring anticoagulation for portal vein (90.7%) and SMV thrombosis (92.6%), but no consensus to treat splenic vein thrombosis (48.2%). 83.7% advocated anticoagulation for complete thrombus occlusion and 70.9% for partial occlusion. Expert views on anticoagulation duration were dichotomous, with 40.7% endorsing 3 or 6 months. In DELPHI Round 3, 74.1% rejected radiological resolution as a definitive treatment endpoint. Conclusion Grading of recommendations for SpVT treatment is precluded by heterogeneity in the evidence base. This inaugural DELPHI study underscores the salient considerations for future research. Overwhelming consensus stratified treatment for SpVT localization and degree of thrombus occlusion. 94.5% of experts agree on the feasibility of RCT to investigate treatment of SpVT.

Insulin Use During Gestational and Pre-existing Diabetes in Pregnancy: A Systematic Review of Study Design.

Insulin is the first-line pharmacologic therapy for women with diabetes in pregnancy. However, conducting well-designed randomized clinical trials (RCTs) and achieving recommended glycemic targets remains a challenge for this unique population. This systematic literature review (SLR) aimed to understand the evidence for insulin use in pregnancy and the outcome metrics most often used to characterize its effect on glycemic, maternal and fetal outcomes in gestational diabetes mellitus (GDM) and in pregnant women with diabetes. An SLR was conducted using electronic databases in Medline, EMBASE via Ovid platform, evidence-based medicine reviews (2010-2020) and conference proceedings (2018-2019). Studies were included if they assessed the effect of insulin treatment on glycemic, maternal or fetal outcomes in women with diabetes in pregnancy. Studies on any type of diabetes other than gestational or pre-existing diabetes as well as non-human studies were excluded. In women diagnosed with GDM or pre-existing diabetes, most studies compared treatment of insulin with metformin (n = 35) followed by diet along with lifestyle intervention (n = 24) and glibenclamide (n = 12). Most studies reporting on glycemic outcomes compared insulin with metformin (n = 22) and glibenclamide (n = 4). Fasting blood glucose was the most reported clinical outcome of interest. Among the studies reporting maternal outcomes, method of delivery and delivery complications were most commonly reported. Large for gestational age, stillbirth and perinatal mortality were the most common fetal outcomes reported. This SLR included a total of 108 clinical trials and observational studies with diverse populations and treatment arms. Outcomes varied across the studies, and a lack of consistent outcome measures to manage diabetes in pregnant women was observed. This elucidates a need for global consensus on study design and standardized clinical, maternal and fetal outcomes metrics.

Extreme high temperatures and corporate low-carbon actions

Greenhouse gas emissions are considered the culprit of extreme high temperatures, and low-carbon transformation has become a global consensus. The low-carbon action by enterprises is an inevitable choice if they are to adapt to economic and social needs. Green innovation is an important measure for enterprises to respond to the challenges of low-carbon transformation. This study mainly discusses the causal relationship between extreme high temperatures and green innovation of enterprises. Using China's industrial enterprise database, we construct county-annual panel data from 2000 to 2013. Results show that extreme high temperatures improve the level of green innovation of enterprises. After a series of robustness tests, the results remain. Mechanism analysis shows that extreme high temperatures increase the environmental concerns of the government, the public, and society, thus strengthening government regulation, public participation, and social supervision as well as encouraging enterprises to carry out green innovation. Our heterogeneous analysis shows that in the eastern region with a high level of legal system and economic development, it is easier to promote green innovation for enterprises. In addition, large-scale enterprises are more willing to carry out green innovation after experiencing a heat wave.

RESOLVING A REALISTIC MIDDLE GROUND TOWARDS THE SUSTAINABILITY TRANSITION IN THE GLOBAL POLITICAL AND ECONOMIC LANDSCAPE: A COMPREHENSIVE REVIEW ON GREEN CAPITALISM

In 2019, a climatic wake-up call was made by the IPCC as the world’s temperature is projected to increase by 4-6°C. With the systematically ingrained capitalist imperatives in the political and economic landscape, the notion of “green capitalism” was introduced in the justification of limitless economic growth with less energy consumption than the world can sustainably produce (Schweickart, 2010). Prior research has identified heterogenous stances on green capitalism. However, sustainable transition research has neither clarified the realistic middle ground that it should pursue, nor has it determined whether a pro- or anti-capitalistic stance should be taken in the first place (Feola, 2020). Through a literature review, the analysis of heterogeneous stances on green capitalism based on its debate scopes reveals the need to exploit capitalist imperatives to a sustainably appropriate extent rather than forcing it to produce a midway standpoint of the “Net Zero” global consensus. The findings suggest the need for a potential reconceptualization of capitalism in the political and economic landscape as a tool of green revolution toward sustainable transition, providing a realistic middle ground.

RESOLVING A REALISTIC MIDDLE GROUND TOWARDS THE SUSTAINABILITY TRANSITION IN THE GLOBAL POLITICAL AND ECONOMIC LANDSCAPE: A COMPREHENSIVE REVIEW ON GREEN CAPITALISM

In 2019, a climatic wake-up call was made by the IPCC as the world’s temperature is projected to increase by 4-6°C. With the systematically ingrained capitalist imperatives in the political and economic landscape, the notion of “green capitalism” was introduced in the justification of limitless economic growth with less energy consumption than the world can sustainably produce (Schweickart, 2010). Prior research has identified heterogenous stances on green capitalism. However, sustainable transition research has neither clarified the realistic middle ground that it should pursue, nor has it determined whether a pro- or anti-capitalistic stance should be taken in the first place (Feola, 2020). Through a literature review, the analysis of heterogeneous stances on green capitalism based on its debate scopes reveals the need to exploit capitalist imperatives to a sustainably appropriate extent rather than forcing it to produce a midway standpoint of the “Net Zero” global consensus. The findings suggest the need for a potential reconceptualization of capitalism in the political and economic landscape as a tool of green revolution toward sustainable transition, providing a realistic middle ground.

Distributed time-varying optimization with coupled constraints: Application in UAV swarm predefined-time cooperative consensus

This paper addresses the global optimal consensus problem with time-varying objective functions and state-coupled inequality constraints. By integrating the predefined-time consensus and prediction-correction schemes, a distributed optimization protocol is proposed for the cooperative interception scenarios involving the unmanned aerial vehicle (UAV) swarm. Specifically, the δ-exact penalty method is utilized to eliminate the coupled constraints. A prediction-correction term with the penalty function is then established to track the trajectory of the time-varying optimal solution. Additionally, an edge-based predefined-time consensus term is introduced to facilitate the state consensus among UAVs. Under the undirected and connected topology, the proposed protocol ensures the predefined-time state consensus and asymptotically minimizes the sum of individual convex objective functions. Furthermore, the protocol is extended to other scenarios involving switching topology, directed detail-balanced topology, and external disturbances. Numerical simulations illustrate that the UAV swarm can form the predesigned interception formation with a predefined-time frame and effectively track the target swarm.

Integrative research of Mediterranean climate regions: a global call to action

SummaryMediterranean climate regions (hereafter ‘Med-regions’) cover small areas globally but harbour a wide diversity of ecosystems that are under serious threat due to current global climate change. Despite intensive research and conservation efforts in freshwater, marine and terrestrial ecosystems to prevent the consequences of habitat fragmentation, overfishing and changes in fire regimes and heatwaves, there is still no global consensus on how to conserve and manage Med-region ecosystems. We define integrative research as an interdisciplinary approach with common concepts and metrics enabling a better understanding of the general mechanisms underlying ecosystems. Building upon existing literature that promotes integrative research in Med-region ecosystems, we advocate for a more integrative approach that takes into consideration cross-ecosystem generalities and common resilience mechanisms. We propose examples of potential studies for implementing such integrative research, including: (1) integrative research across ecosystems; (2) integrative research across Med-regions; and (3) integrative research across political boundaries. We then offer recommendations for promoting integrative studies, including leveraging biodiversity data, establishing working groups in research syntheses and testing theoretical frameworks. Promoting integrative research in Med-regions would enable and facilitate the roadmap towards a better understanding of Med-region ecosystems’ responses to natural and anthropogenic disturbances and the identification of ecological baselines. We seek to foster future research synergies to identify tipping points across ecosystems and aid in determining desired future conditions in Med-regions globally.

Computed tomographic angiography measures of coronary plaque in clinical trials: opportunities and considerations to accelerate drug translation.

Atherosclerotic coronary artery disease (CAD) is the causal pathological process driving most major adverse cardiovascular events (MACE) worldwide. The complex development of atherosclerosis manifests as intimal plaque which occurs in the presence or absence of traditional risk factors. There are numerous effective medications for modifying CAD but new pharmacologic therapies require increasingly large and expensive cardiovascular outcome trials to assess their potential impact on MACE and to obtain regulatory approval. For many disease areas, nearly a half of drugs are approved by the U.S. Food & Drug Administration based on beneficial effects on surrogate endpoints. For cardiovascular disease, only low-density lipoprotein cholesterol and blood pressure are approved as surrogates for cardiovascular disease. Valid surrogates of CAD are urgently needed to facilitate robust evaluation of novel, beneficial treatments and inspire investment. Fortunately, advances in non-invasive imaging offer new opportunity for accelerating CAD drug development. Coronary computed tomography angiography (CCTA) is the most advanced candidate, with the ability to measure accurately and reproducibly characterize the underlying causal disease itself. Indeed, favourable changes in plaque burden have been shown to be associated with improved outcomes, and CCTA may have a unique role as an effective surrogate endpoint for therapies that are designed to improve CAD outcomes. CCTA also has the potential to de-risk clinical endpoint-based trials both financially and by enrichment of participants at higher likelihood of MACE. Furthermore, total non-calcified, and high-risk plaque volume, and their change over time, provide a causally linked measure of coronary artery disease which is inextricably linked to MACE, and represents a robust surrogate imaging biomarker with potential to be endorsed by regulatory authorities. Global consensus on specific imaging endpoints and protocols for optimal clinical trial design is essential as we work towards a rigorous, sustainable and staged pathway for new CAD therapies.

Surgical management, including the role of transplantation, for intrahepatic and peri-hilar cholangiocarcinoma

Intrahepatic and peri-hilar cholangiocarcinoma are life threatening disease with poor outcomes despite optimal treatment currently available (5-year overall survival following resection 20-35%, and <10% cured at 10-years post resection). The insidious onset makes diagnosis difficult, the majority do not have a resection option and the high recurrence rate post-resection suggests that occult metastatic disease is frequently present. Advances in perioperative management, such as ipsilateral portal vein (and hepatic vein) embolisation methods to increase the future liver remnant volume, genomic profiling, and (neo)adjuvant therapies demonstrate great potential in improving outcomes. However multiple areas of controversy exist. Surgical resection rate and outcomes vary between centres with no global consensus on how ‘resectable’ disease is defined – molecular profiling and genomic analysis could potentially identify patients unlikely to benefit from resection or likely to benefit from targeted therapies. FDG-PET scanning has also improved the ability to detect metastatic disease preoperatively and avoid futile resection. However tumours frequently invade major vasculo-biliary structures, with resection and reconstruction associated with significant morbidity and mortality even in specialist centres. Liver transplantation has been investigated for very selected patients for the last decade and yet the selection algorithm, surgical approach and both value of both neoadjuvant and adjuvant therapies remain to be clarified. In this review, we discuss the contemporary management of intrahepatic and peri-hilar cholangiocarcinoma.

Carbon Footprint Drivers in China’s Municipal Wastewater Treatment Plants and Mitigation Opportunities through Electricity and Chemical Efficiency

Reducing greenhouse gas (GHG) emissions to address climate change is a global consensus, and municipal wastewater treatment plants (MWWTPs) should lead the way in low-carbon sustainable development. However, achieving effluent discharge standards often requires considerable energy and chemical consumption during operation, resulting in significant carbon footprints. In this study, GHG emissions are systematically accounted for, and the driving factors of carbon footprint growth in China’s MWWTPs are explored. In 2020, a total of 41.9 million tons (Mt) of carbon dioxide equivalent (CO2-eq) were released by the sector, with nearly two-thirds being indirect emissions resulting from energy and material usage. The intensity of electricity, carbon source, and phosphorus removing agent consumption increasingly influence carbon footprint growth over time. Through statistical inference, benchmarks for electricity and chemical consumption intensity are established across all MWWTPs under various operational conditions, and the potential for mitigation through more efficient energy and material utilization is calculated. The results suggest that many MWWTPs offer significant opportunities for emission reduction. Consequently, empirical decarbonization measures, including intelligent device control, optimization of aeration equipment, energy recovery initiatives, and other enhancements to improve operational and carbon efficiency, are recommended.